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Chapter 2: Land–Climate interactions

Coordinating Lead Authors

• Gensuo Jia (China)
• Elena Shevliakova (United States)

Lead Authors

• Paulo Artaxo (Brazil)
• Nathalie De Noblet-Ducoudré (France)
• Richard Houghton (United States)
• Joanna House (United Kingdom)
• Kaoru Kitajima (Japan)
• Christopher Lennard (South Africa)
• Alexander Popp (Germany)
• Andrey Sirin (Russia)
• Raman Sukumar (India)
• Louis Verchot (Colombia, United States)

Contributing Authors

• William Anderegg (United States)
• Edward Armstrong (United Kingdom)
• Ana Bastos (Portugal, Germany)
• Terje Koren Bernsten (Norway)
• Peng Cai (China)
• Katherine Calvin (United States)
• Francesco Cherubini (Norway, Italy)
• Sarah Connors (France, United Kingdom)
• Annette Cowie (Australia)
• Edouard Davin (Switzerland, France)
• Cecile De Klein (New Zealand)
• Giacomo Grassi (Italy)
• Rafiq Hamdi (Belgium)
• Florian Humpenöder (Germany)
• David Kanter (United States)
• Gerhard Krinner (France)
• Sonali McDermid (India, United States)
• Devaraju Narayanappa (India, France)
• Josep Peñuelas (Spain)
• Prajal Pradhan (Germany, Nepal)
• Benjamin Quesada (Colombia)
• Stephanie Roe (Philippines, United States)
• Robert A. Rohde (United States)
• Martijn Slot (Panama)
• Rolf Sommer (Germany)
• Moa Sporre (Norway)
• Benjamin Sulman (United States)
• Alasdair Sykes (United Kingdom)
• Phil Williamson (United Kingdom)
• Yuyu Zhou (China)

Review Editors

• Pierre Bernier (Canada)
• Jhan Carlo Espinoza (Peru)
• Sergey Semenov (Russia)

Chapter Scientist

• Xiyan Xu (China)

This chapter should be cited as:

Jia, G., E. Shevliakova, P. Artaxo, N. De Noblet-Ducoudré, R. Houghton, J. House, K. Kitajima, C. Lennard, A. Popp, A. Sirin, R. Sukumar, L. Verchot, 2019: Land–climate interactions. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D.C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M, Belkacemi, J. Malley, (eds.)]. https://doi.org/10.1017/9781009157988.004

Executive Summary

Land and climate interact in complex ways through changes in forcing and multiple biophysical and biogeochemical feedbacks across different spatial and temporal scales. This chapter assesses climate impacts on land and land impacts on climate, the human contributions to these changes, as well as land-based adaptation and mitigation response options to combat projected climate changes.

Implications of climate change, variability and extremes for land systems

It is certain that globally averaged land surface air temperature (LSAT) has risen faster than the global mean surface temperature (i.e., combined LSAT and sea surface temperature) from the preindustrial period (1850–1900) to the present day (1999–2018). According to the single longest and most extensive dataset, from 1850–1900 to 2006–2015 mean land surface air temperature has increased by 1.53°C ( very likely range from 1.38°C to 1.68°C) while global mean surface temperature has increased by 0.87°C ( likely range from 0.75°C to 0.99°C). For the 1880–2018 period, when four independently produced datasets exist, the LSAT increase was 1.41°C (1.31–1.51°C), where the range represents the spread in the datasets’ median estimates. Analyses of paleo records, historical observations, model simulations and underlying physical principles are all in agreement that LSATs are increasing at a higher rate than SST as a result of differences in evaporation, land–climate feedbacks and changes in the aerosol forcing over land ( very high confidence ). For the 2000–2016 period, the land-to-ocean warming ratio (about 1.6) is in close agreement between different observational records and the CMIP5 climate model simulations (the likely range of 1.54–1.81). {2.2.1}

Anthropogenic warming has resulted in shifts of climate zones, primarily as an increase in dry climates and decrease of polar climates ( high confidence ). Ongoing warming is projected to result in new, hot climates in tropical regions and to shift climate zones poleward in the mid- to high latitudes and upward in regions of higher elevation ( high confidence ). Ecosystems in these regions will become increasingly exposed to temperature and rainfall extremes beyonwd the climate regimes they are currently adapted to ( high confidence ), which can alter their structure, composition and functioning. Additionally, high-latitude warming is projected to accelerate permafrost thawing and increase disturbance in boreal forests through abiotic (e.g., drought, fire) and biotic (e.g., pests, disease) agents ( high confidence ). {2.2.1, 2.2.2, 2.5.3}

Globally, greening trends (trends of increased photosynthetic activity in vegetation) have increased over the last 2–3 decades by 22–33%, particularly over China, India, many parts of Europe, central North America, southeast Brazil and southeast Australia ( high confidence ). This results from a combination of direct (i.e., land use and management, forest conservation and expansion) and indirect factors (i.e., CO ₂ fertilisation, extended growing season, global warming, nitrogen deposition, increase of diffuse radiation) linked to human activities ( high confidence ). Browning trends (trends of decreasing photosynthetic activity) are projected in many regions where increases in drought and heatwaves are projected in a warmer climate. There is low confidenc e in the projections of global greening and browning trends. {2.2.4, Cross- Chapter Box 4 in this chapter}

The frequency and intensity of some extreme weather and climate events have increased as a consequence of global warming and will continue to increase under medium and high emission scenarios ( high confidence ). Recent heat-related events, for example, heatwaves, have been made more frequent or intense due to anthropogenic greenhouse gas (GHG) emissions in most land regions and the frequency and intensity of drought has increased in Amazonia, north-eastern Brazil, the Mediterranean, Patagonia, most of Africa and north-eastern China ( medium confidence ). Heatwaves are projected to increase in frequency, intensity and duration in most parts of the world ( high confidence ) and drought frequency and intensity is projected to increase in some regions that are already drought prone, predominantly in the Mediterranean, central Europe, the southern Amazon and southern Africa ( medium confidence ). These changes will impact ecosystems, food security and land processes including GHG fluxes ( high confidence ). {2.2.5}

Climate change is playing an increasing role in determining wildfire regimes alongside human activity ( medium confidence ), with future climate variability expected to enhance the risk and severity of wildfires in many biomes such as tropical rainforests ( high confidence ). Fire weather seasons have lengthened globally between 1979 and 2013 ( low confidence ). Global land area burned has declined in recent decades, mainly due to less burning in grasslands and savannahs ( high confidence ). While drought remains the dominant driver of fire emissions, there has recently been increased fire activity in some tropical and temperate regions during normal to wetter than average years due to warmer temperatures that increase vegetation flammability ( medium confidence ). The boreal zone is also experiencing larger and more frequent fires, and this may increase under a warmer climate ( medium confidence ). {Cross-Chapter Box 4 in this chapter}

Terrestrial greenhouse gas fluxes on unmanaged and managed lands

Agriculture, forestry and other land use (AFOLU) is a significant net source of GHG emissions ( high confidence ), contributing to about 23% of anthropogenic emissions of carbon dioxide (CO ₂ ), methane (CH ₄ ) and nitrous oxide (N ₂ O) combined as CO ₂ equivalents in 2007–2016 ( medium confidence ). AFOLU results in both emissions and removals of CO ₂ , CH ₄ and N ₂ O to and from the atmosphere ( high confidence ). These fluxes are affected simultaneously by natural and human drivers, making it difficult to separate natural from anthropogenic fluxes ( very high confidence ). {2.3}

The total net land-atmosphere flux of CO ₂ on both managed and unmanaged lands very likely provided a global net removal from 2007 to 2016 according to models (-6.0 ± 3.7 CO ₂ yr ^–1 ,  likely range). This net removal is comprised of two major components: (i) modelled net anthropogenic emissions from AFOLU are 5.2 ± 2.6 GtCO ₂ yr ^–1 (likely range) driven by land cover change, including deforestation and afforestation/reforestation, and wood harvesting (accounting for about 13% of total net anthropogenic emissions of CO ₂ ) ( medium confidence) , and (ii) modelled net removals due to non-anthropogenic processes are 11.2 ± 2.6 GtCO ₂ yr ^–1 (likely range) on managed and unmanaged lands, driven by environmental changes such as increasing CO ₂ , nitrogen deposition and changes in climate (accounting for a removal of 29% of the CO ₂ emitted from all anthropogenic activities (fossil fuel, industry and AFOLU) ( medium confidence ). {2.3.1}

Global models and national GHG inventories use different methods to estimate anthropogenic CO ₂ emissions and removals for the land sector. Consideration of differences in methods can enhance understanding of land sector net emission such as under the Paris Agreement’s global stocktake ( medium confidence ). Both models and inventories produce estimates that are in close agreement for land-use change involving forest (e.g., deforestation, afforestation), and differ for managed forest. Global models consider as managed forest those lands that were subject to harvest whereas, consistent with IPCC guidelines, national GHG inventories define managed forest more broadly. On this larger area, inventories can also consider the natural response of land to human-induced environmental changes as anthropogenic, while the global model approach {Table SPM.1} treats this response as part of the non-anthropogenic sink. For illustration, from 2005 to 2014, the sum of the national GHG inventories net emission estimates is 0.1 ± 1.0 GtCO ₂ yr ^–1 , while the mean of two global bookkeeping models is 5.1 ± 2.6 GtCO ₂ yr ^–1 (likely range).

The gross emissions from AFOLU (one-third of total global emissions) are more indicative of mitigation potential of reduced deforestation than the global net emissions (13% of total global emissions), which include compensating deforestation and afforestation fluxes ( high confidence ). The net flux of CO ₂ from AFOLU is composed of two opposing gross fluxes: (i) gross emissions (20 GtCO ₂ yr ^–1 ) from deforestation, cultivation of soils and oxidation of wood products, and (ii) gross removals (–14 GtCO ₂ yr ^–1 ), largely from forest growth following wood harvest and agricultural abandonment ( medium confidence ). {2.3.1}

Land is a net source of CH ₄ , accounting for 44% of anthropogenic CH ₄ emissions for the 2006–2017 period ( medium confidence ). The pause in the rise of atmospheric CH ₄ concentrations between 2000 and 2006 and the subsequent renewed increase appear to be partially associated with land use and land use change. The recent depletion trend of the 13C isotope in the atmosphere indicates that higher biogenic sources explain part of the current CH ₄ increase and that biogenic sources make up a larger proportion of the source mix than they did before 2000 ( high confidence ). In agreement with the findings of AR5, tropical wetlands and peatlands continue to be important drivers of inter- annual variability and current CH ₄ concentration increases ( medium evidence, high agreement ). Ruminants and the expansion of rice cultivation are also important contributors to the current trend ( medium evidence, high agreement ). There is significant and ongoing accumulation of CH ₄ in the atmosphere ( very high confidence ). {2.3.2}

AFOLU is the main anthropogenic source of N ₂ O primarily due to nitrogen application to soils ( high confidence ). In croplands, the main driver of N ₂ O emissions is a lack of synchronisation between crop nitrogen demand and soil nitrogen supply, with approximately 50% of the nitrogen applied to agricultural land not taken up by the crop. Cropland soils emit over 3 MtN ₂ O-N yr ^–1 ( medium confidence ). Because the response of N ₂ O emissions to fertiliser application rates is non-linear, in regions of the world where low nitrogen application rates dominate, such as sub-Saharan Africa and parts of Eastern Europe, increases in nitrogen fertiliser use would generate relatively small increases in agricultural N ₂ O emissions. Decreases in application rates in regions where application rates are high and exceed crop demand for parts of the growing season will have very large effects on emissions reductions ( medium evidence, high agreement ). {2.3.3}

While managed pastures make up only one-quarter of grazing lands, they contributed more than three-quarters of N ₂ O emissions from grazing lands between 1961 and 2014 with rapid recent increases of nitrogen inputs resulting in disproportionate growth in emissions from these lands ( medium confidence ). Grazing lands (pastures and rangelands) are responsible for more than one-third of total anthropogenic N ₂ O emissions or more than one-half of agricultural emissions ( high confidence ). Emissions are largely from North America, Europe, East Asia, and South Asia, but hotspots are shifting from Europe to southern Asia ( medium confidence ). {2.3.3}

Increased emissions from vegetation and soils due to climate change in the future are expected to counteract potential sinks due to CO ₂ fertilisation ( low confidence ). Responses of vegetation and soil organic carbon (SOC) to rising atmospheric CO ₂ concentration and climate change are not well constrained by observations (medium confidence). Nutrient (e.g., nitrogen, phosphorus) availability can limit future plant growth and carbon storage under rising CO ₂ ( high confidence ). However, new evidence suggests that ecosystem adaptation through plant-microbe symbioses could alleviate some nitrogen limitation ( medium evidence, high agreement ). Warming of soils and increased litter inputs will accelerate carbon losses through microbial respiration ( high confidence ). Thawing of high latitude/altitude permafrost will increase rates of SOC loss and change the balance between CO ₂ and CH ₄ emissions( medium confidence ).Thebalancebetweenincreased respiration in warmer climates and carbon uptake from enhanced plant growth is a key uncertainty for the size of the future land carbon sink ( medium confidence ). {2.3.1, 2.7.2, Box 2.3}

Biophysical and biogeochemical land forcing and feedbacks to the climate system

Changes in land conditions from human use or climate change in turn affect regional and global climate ( high confidence ). On the global scale, this is driven by changes in emissions or removals of CO ₂ , CH ₄ and N ₂ O by land (biogeochemical effects) and by changes in the surface albedo ( very high confidence ). Any local land changes that redistribute energy and water vapour between the land and the atmosphere influence regional climate (biophysical effects; high confidence ). However, there is no confidence in whether such biophysical effects influence global climate. {2.1, 2.3, 2.5.1, 2.5.2}

Changes in land conditions modulate the likelihood, intensity and duration of many extreme events including heatwaves ( high confidence ) and heavy precipitation events ( medium confidence ). Dry soil conditions favour or strengthen summer heatwave conditions through reduced evapotranspiration and increased sensible heat. By contrast wet soil conditions, for example from irrigation or crop management practices that maintain a cover crop all year round, can dampen extreme warm events through increased evapotranspiration and reduced sensible heat. Droughts can be intensified by poor land management. Urbanisation increases extreme rainfall events over or downwind of cities ( medium confidence ). {2.5.1, 2.5.2, 2.5.3}

Historical changes in anthropogenic land cover have resulted in a mean annual global warming of surface air from biogeochemical effects ( very high confidence ), dampened by a cooling from biophysical effects ( medium confidence ). Biogeochemical warming results from increased emissions of GHGs by land, with model-based estimates of +0.20 ± 0.05°C (global climate models) and +0.24 ± 0.12°C – dynamic global vegetation models (DGVMs) as well as an observation-based estimate of +0.25 ± 0.10°C. A net biophysical cooling of –0.10 ± 0.14°C has been derived from global climate models in response to the increased surface albedo and decreased turbulent heat fluxes, but it is smaller than the warming effect from land-based emissions. However, when both biogeochemical and biophysical effects are accounted for within the same global climate model, the models do not agree on the sign of the net change in mean annual surface air temperature. {2.3, 2.5.1, Box 2.1}

The future projected changes in anthropogenic land cover that have been examined for AR5 would result in a biogeochemical warming and a biophysical cooling whose magnitudes depend on the scenario ( high confidence ). Biogeochemical warming has been projected for RCP8.5 by both global climate models (+0.20 ± 0.15°C) and DGVMs (+0.28 ± 0.11°C) ( high confidence ). A global biophysical cooling of 0.10 ± 0.14°C is estimated from global climate models and is projected to dampen the land-based warming ( low confidence ). For RCP4.5, the biogeochemical warming estimated from global climate models (+0.12 ± 0.17°C) is stronger than the warming estimated by DGVMs (+0.01 ± 0.04°C) but based on limited evidence, as is the biophysical cooling (–0.10 ± 0.21°C). {2.5.2}

Regional climate change can be dampened or enhanced by changes in local land cover and land use ( high confidence ) but this depends on the location and the season ( high confidence ). In boreal regions, for example, where projected climate change will migrate the treeline northward, increase the growing season length and thaw permafrost, regional winter warming will be enhanced by decreased surface albedo and snow, whereas warming will be dampened during the growing season due to larger evapotranspiration ( high confidence ). In the tropics, wherever climate change will increase rainfall, vegetation growth and associated increase in evapotranspiration will result in a dampening effect on regional warming ( medium confidence ). {2.5.2, 2.5.3}

According to model-based studies, changes in local land cover or available water from irrigation will affect climate in regions as far as few hundreds of kilometres downwind ( high confidence ). The local redistribution of water and energy following the changes on land affect the horizontal and vertical gradients of temperature, pressure and moisture, thus altering regional winds and consequently moisture and temperature advection and convection and subsequently, precipitation. {2.5.2, 2.5.4, Cross-Chapter Box 4}

Future increases in both climate change and urbanisation will enhance warming in cities and their surroundings (urban heat island), especially during heatwaves ( high confidence ). Urban and peri-urban agriculture, and more generally urban greening, can contribute to mitigation ( medium confidence ) as well as to adaptation ( high confidence ), with co-benefits for food security and reduced soil-water-air pollution. {Cross-Chapter Box 4}

Regional climate is strongly affected by natural land aerosols ( medium confidence ) (e.g., mineral dust, black, brown and organic carbon), but there is low confidence in historical trends, inter-annual and decadal variability and future changes. Forest cover affects climate through emissions of biogenic volatile organic compounds (BVOC) and aerosols ( low confidence ). The decrease in the emissions of BVOC resulting from the historical conversion of forests to cropland has resulted in a positive radiative forcing through direct and indirect aerosol effects, a negative radiative forcing through the reduction in the atmospheric lifetime of methane and it has contributed to increased ozone concentrations in different regions ( low confidence ). {2.4, 2.5}

Consequences for the climate system of land-based adaptation and mitigation options, including carbon dioxide removal (negative emissions)

About one-quarter of the 2030 mitigation pledged by countries in their initial nationally determined contributions (NDCs) under the Paris Agreement is expected to come from land- based mitigation options ( medium confidence ). Most of the NDCs submitted by countries include land-based mitigation, although many lack details. Several refer explicitly to reduced deforestation and forest sinks, while a few include soil carbon sequestration, agricultural management and bioenergy. Full implementation of NDCs (submitted by February 2016) is expected to result in net removals of 0.4–1.3 GtCO ₂ yr ^–1 in 2030 compared to the net flux in 2010, where the range represents low to high mitigation ambition in pledges, not uncertainty in estimates ( medium confidence ). {2.6.3}

Several mitigation response options have technical potential for >3 GtCO ₂ -eq yr ^–1 by 2050 through reduced emissions and Carbon Dioxide Removal (CDR) ( high confidence ), some of which compete for land and other resources, while others may reduce the demand for land ( high confidence ). Estimates of the technical potential of individual response options are not necessarily additive. The largest potential for reducing AFOLU emissions are through reduced deforestation and forest degradation (0.4–5.8 GtCO ₂ -eq yr ^–1 ) ( high confidence ), a shift towards plant- based diets (0.7–8.0 GtCO ₂ -eq yr ^–1 ) ( high confidence ) and reduced food and agricultural waste (0.8–4.5 GtCO ₂ -eq yr ^–1 ) ( high confidence ). Agriculture measures combined could mitigate 0.3–3.4 GtCO ₂ -eq yr ^–1 ( medium confidence ). The options with largest potential for CDR are afforestation/reforestation (0.5–10.1 GtCO ₂ -eq yr ^–1 ) ( medium confidence ), soil carbon sequestration in croplands and grasslands (0.4–8.6 GtCO ₂ -eq yr ^–1 ) (high confidence) and Bioenergy with Carbon Capture and Storage (BECCS) (0.4–11.3 GtCO ₂ -eq yr ^–1 ) ( medium confidence ). While some estimates include sustainability and cost considerations, most do not include socio-economic barriers, the impacts of future climate change or non-GHG climate forcings. {2.6.1}

Response options intended to mitigate global warming will also affect the climate locally and regionally through biophysical effects ( high confidence ). Expansion of forest area, for example, typically removes CO ₂ from the atmosphere and thus dampens global warming (biogeochemical effect, high confidence ), but the biophysical effects can dampen or enhance regional warming depending on location, season and time of day. During the growing season, afforestation generally brings cooler days from increased evapotranspiration, and warmer nights ( high confidence ). During the dormant season, forests are warmer than any other land cover, especially in snow-covered areas where forest cover reduces albedo ( high confidence ). At the global level, the temperature effects of boreal afforestation/reforestation run counter to GHG effects, while in the tropics they enhance GHG effects. In addition, trees locally dampen the amplitude of heat extremes ( medium confidence ). {2.5.2, 2.5.4, 2.7, Cross-Chapter Box 4}

Mitigation response options related to land use are a key element of most modelled scenarios that provide strong mitigation, alongside emissions reduction in other sectors ( high confidence ). More stringent climate targets rely more heavily on land-based mitigation options, in particular, CDR ( high confidence ). Across a range of scenarios in 2100, CDR is delivered by both afforestation (median values of –1.3, –1.7 and –2.4 GtCO ₂ yr ^–1 for scenarios RCP4.5, RCP2.6 and RCP1.9 respectively) and bioenergy with carbon capture and storage (BECCS) (–6.5, –11 and –14.9 GtCO ₂ yr ^–1 respectively). Emissions of CH ₄ and N ₂ O are reduced through improved agricultural and livestock management as well as dietary shifts away from emission-intensive livestock products by 133.2, 108.4 and 73.5 MtCH ₄ yr ^–1 ; and 7.4, 6.1 and 4.5 MtN ₂ O yr ^–1 for the same set of scenarios in 2100 ( high confidence ). High levels of bioenergy crop production can result in increased N ₂ O emissions due to fertiliser use. The Integrated Assessment Models that produce these scenarios mostly neglect the biophysical effects of land-use on global and regional warming. {2.5, 2.6.2}

Large-scale implementation of mitigation response options that limit warming to 1.5°C or 2°C would require conversion of large areas of land for afforestation/reforestation and bioenergy crops, which could lead to short-term carbon losses ( high confidence ). The change of global forest area in mitigation pathways ranges from about –0.2 to +7.2 Mkm2 between 2010 and 2100 (median values across a range of models and scenarios: RCP4.5, RCP2.6, RCP1.9), and the land demand for bioenergy crops ranges from about 3.2 to 6.6 Mkm2 in 2100 ( high confidence ). Large- scale land-based CDR is associated with multiple feasibility and sustainability constraints (Chapters 6 and 7). In high carbon lands such as forests and peatlands, the carbon benefits of land protection are greater in the short-term than converting land to bioenergy crops for BECCS, which can take several harvest cycles to ‘pay-back’ the carbon emitted during conversion (carbon-debt), from decades to over a century ( medium confidence ). {2.6.2, Chapters 6, 7}

It is possible to achieve climate change targets with low need for land-demanding CDR such as BECCS, but such scenarios rely more on rapidly reduced emissions or CDR from forests, agriculture and other sectors. Terrestrial CDR has the technical potential to balance emissions that are difficult to eliminate with current technologies (including food production). Scenarios that achieve climate change targets with less need for terrestrial CDR rely on agricultural demand-side changes (diet change, waste reduction), and changes in agricultural production such as agricultural intensification. Such pathways that minimise land use for bioenergy and BECCS are characterised by rapid and early reduction of GHG emissions in all sectors, as well as earlier CDR in through afforestation. In contrast, delayed mitigation action would increase reliance on land-based CDR ( high confidence ). {2.6.2}

2.1 Introduction: Land–climate interactions

This chapter assesses the literature on two-way interactions between climate and land, with focus on scientific findings published since AR5 and some aspects of the land–climate interactions that were not assessed in previous IPCC reports. Previous IPCC assessments recognised that climate affects land cover and land surface processes, which in turn affect climate. However, previous assessments mostly focused on the contribution of land to global climate change via its role in emitting and absorbing greenhouse gases (GHGs) and short-lived climate forcers (SLCFs), or via implications of changes in surface reflective properties (i.e., albedo) for solar radiation absorbed by the surface. This chapter examines scientific advances in understanding the interactive changes of climate and land, including impacts of climate change, variability and extremes on managed and unmanaged lands. It assesses climate forcing of land changes from direct (e.g., land use change and land management) and indirect (e.g., increasing atmospheric CO ₂ concentration and nitrogen deposition) effects at local, regional and global scales.

2.1.1 Recap of previous IPCC and other relevant reports as baselines

The evidence that land cover matters for the climate system have long been known, especially from early paleoclimate modelling studies and impacts of human-induced deforestation at the margin of deserts (de Noblet et al. 1996 ¹ ; Kageyama et al. 2004 ² ). The understanding of how land use activities impact climate has been put forward by the pioneering work of Charney (1975) ³ who examined the role of overgrazing-induced desertification on the Sahelian climate.

Since then there have been many modelling studies that reported impacts of idealised or simplified land cover changes on weather patterns (e.g., Pielke et al. 2011 ⁴ ). The number of studies dealing with such issues has increased significantly over the past 10 years, with more studies that address realistic past or projected land changes. However, very few studies have addressed the impacts of land cover changes on climate as very few land surface models embedded within climate models (whether global or regional), include a representation of land management. Observation-based evidence of land-induced climate impacts emerged even more recently (e.g., Alkama and Cescatti 2016 ⁵ ; Bright et al. 2017 ⁶ ; Lee et al. 2011 ⁷ ; Li et al. 2015 ⁸ ; Duveiller et al. 2018 ⁹ ; Forzieri et al. 2017 ¹⁰ ) and the literature is therefore limited.

In previous IPCC reports, the interactions between climate change and land were covered separately by three working groups. AR5 WGI assessed the role of land use change in radiative forcing, land-based GHGs source and sink, and water cycle changes that focused on changes of evapotranspiration, snow and ice, runoff and humidity. AR5 WGII examined impacts of climate change on land, including terrestrial and freshwater ecosystems, managed ecosystems, and cities and settlements. AR5 WGIII assessed land-based climate change mitigation goals and pathways related to the agriculture, forestry and other land use (AFOLU). Here, this chapter assesses land–climate interactions from all three working groups. It also builds on previous special reports such as the Special Report on Global Warming of 1.5°C (SR15). It links to the IPCC Guidelines on National Greenhouse Gas Inventories in the land sector. Importantly, this chapter assesses knowledge that has never been reported in any of those previous reports. Finally, the chapter also tries to reconcile the possible inconsistencies across the various IPCC reports.

Land-based water cycle changes

AR5 reported an increase in global evapotranspiration from the early 1980s to 2000s, but a constraint on further increases from low soil moisture availability. Rising CO ₂ concentration limits stomatal opening and thus also reduces transpiration, a component of evapotranspiration. Increasing aerosol levels, declining surface wind speeds and declining levels of solar radiation reaching the ground are additional regional causes of the decrease in evapotranspiration.

Land area precipitation change

Averaged over the mid-latitude land areas of the northern hemisphere, precipitation has increased since 1901 (medium confidence before 1951 and high confidence thereafter). For other latitudes, area-averaged long-term positive or negative trends have low confidence. There are likely more land regions where the number of heavy precipitation events has increased than where it has decreased. Extreme precipitation events over most of the mid- latitude land masses and over wet tropical regions will very likely become more intense and more frequent (IPCC 2013a ¹¹ ).

Land-based GHGs

AR5 reported that annual net CO ₂ emissions from anthropogenic land use change were 0.9 [0.1–1.7] GtC yr ^–1 on average during 2002–2011 (medium confidence). From 1750–2011, CO ₂ emissions from fossil fuel combustion have released an estimated 375 [345–405] GtC to the atmosphere, while deforestation and other land use change have released an estimated 180 [100–260] GtC. Of these cumulative anthropogenic CO ₂ emissions, 240 [230–250] GtC have accumulated in the atmosphere, 155 [125–185] GtC have been taken up by the ocean and 160 [70–250] GtC have accumulated in terrestrial ecosystems (i.e., the cumulative residual land sink) (Ciais et al. 2013a ¹² ). Updated assessment and knowledge gaps are covered in Section 2.3.

Future terrestrial carbon source/sink

AR5 projected with high confidence that tropical ecosystems will uptake less carbon and with medium confidence that at high latitudes, land carbon sink will increase in a warmer climate. Thawing permafrost in the high latitudes is potentially a large carbon source in warmer climate conditions, however the magnitude of CO ₂ and CH ₄ emissions due to permafrost thawing is still uncertain. The SR15 further indicates that constraining warming to 1.5°C would prevent the melting of an estimated permafrost area of 2 million km ² over the next centuries compared to 2°C. Updates to these assessments are found in Section 2.3.

Land use change altered albedo

AR5 stated with high confidence that anthropogenic land use change has increased the land surface albedo, which has led to a RF of –0.15 ± 0.10 W m ^–2 . However, it also underlined that the sources of the large spread across independent estimates were caused by differences in assumptions for the albedo of natural and managed surfaces and for the fraction of land use change before 1750. Generally, our understanding of albedo changes from land use change has been enhanced from AR4 to AR5, with a narrower range of estimates and a higher confidence level. The radiative forcing from changes in albedo induced by land use changes was estimated in AR5 at–0.15 W m ^–2 (–0.25 to about –0.05), with medium confidence in AR5 (Myhre et al. 2013 ¹³ ). This was an improvement over AR4 in which it was estimated at –0.2 W m ^–2 (–0.4 to about 0), with low to medium confidence (Forster et al. 2007 ¹⁴ ). Section 2.5 shows that albedo is not the only source of biophysical land-based climate forcing to be considered.

Hydrological feedback to climate

Land use changes also affect surface temperatures through non- radiative processes, and particularly through the hydrological cycle. These processes are less well known and are difficult to quantify but tend to offset the impact of albedo changes. As a consequence, there is low agreement on the sign of the net change in global mean temperature as a result of land use change (Hartmann et al. 2013a ¹⁵ ). An updated assessment on these points is covered in Sections 2.5 and 2.2.

Climate-related extremes on land

AR5 reported that impacts from recent climate-related extremes reveal significant vulnerability and exposure of some ecosystems to current climate variability. Impacts of such climate-related extremes include alteration of ecosystems, disruption of food production and water supply, damage to infrastructure and settlements, morbidity and mortality, and consequences for mental health and human well- being (Burkett et al. 2014 ¹⁶ ). The SR15 further indicates that limiting global warming to 1.5°C limits the risks of increases in heavy precipitation events in several regions ( high confidence ). In urban areas, climate change is projected to increase risks for people, assets, economies and ecosystems ( very high confidence ). These risks are amplified for those lacking essential infrastructure and services or living in exposed areas. An updated assessment and a knowledge gap for this chapter are covered in Section 2.2 and Cross-Chapter Box 4.

Land-based climate change adaptation and mitigation

AR5 reported that adaptation and mitigation choices in the near- term will affect the risks related to climate change throughout the 21st century (Burkett et al. 2014 ¹⁷ ). AFOLU are responsible for about 10–12 GtCO ₂ eq yr ^–1 anthropogenic greenhouse gas emissions, mainly from deforestation and agricultural production. Global CO ₂ emissions from forestry and other land use have declined since AR4, largely due to increased afforestation. The SR15 further indicates that afforestation and bioenergy with carbon capture and storage (BECCS) are important land-based carbon dioxide removal (CDR) options. It also states that land use and land-use change emerge as a critical feature of virtually all mitigation pathways that seek to limit global warming to 1.5oC. The Climate Change 2014 Synthesis Report concluded that co-benefits and adverse side effects of mitigation could affect achievement of other objectives, such as those related to human health, food security, biodiversity, local environmental quality, energy access, livelihoods and equitable sustainable development. Updated assessment and knowledge gaps are covered in Section 2.6 and Chapter 7.

Overall, sustainable land management is largely constrained by climate change and extremes, but also puts bounds on the capacity of land to effectively adapt to climate change and mitigate its impacts. Scientific knowledge has advanced on how to optimise our adaptation and mitigation efforts while coordinating sustainable land management across sectors and stakeholders. Details are assessed in subsequent sections.

2.1.2 Introduction to the chapter structure

This chapter assesses the consequences of changes in land cover and functioning, resulting from both land use and climate change, to global and regional climates. The chapter starts with an assessment of the historical and projected responses of land processes to climate change and extremes (Section 2.2). Subsequently, the chapter assesses historical and future changes in terrestrial GHG fluxes (Section 2.3) as well as non-GHG fluxes and precursors of SLCFs (Section 2.4). Section 2.5 focuses on how historical and future changes in land use and land cover influence climate change/variability through biophysical and biogeochemical forcing and feedbacks, how specific land management affects climate, and how, in turn, climate- induced land changes feed back to climate. Section 2.6 assesses the consequences of land-based adaptation and mitigation options for the climate system in GHG and non-GHG exchanges. Sections 2.3 and 2.6 address implications of the Paris Agreement for land– climate interactions, and the scientific evidence base for ongoing negotiations around the Paris rulebook, the global stocktake and credibility in measuring, reporting and verifying the climate impacts of anthropogenic activities on land. This chapter also examines how land use and management practices may affect climate change through biophysical feedbacks and radiative forcing (Section 2.5), and assesses policy-relevant projected land use changes and sustainable land management for mitigation and adaptation (Section 2.6). Finally, the chapter concludes with a brief assessment of advances in the understanding of the ecological and biogeochemical processes underlying land–climate interactions (Section 2.7).

The chapter includes three chapter boxes providing general overview of (i) processes underlying land–climate interactions (Box 2.1), (ii) methodological approaches for estimating anthropogenic land carbon fluxes from national to global scales (Box 2.2), and (iii) CO ₂ fertilisation and enhanced terrestrial uptake of carbon (Box 2.3). In addition, this chapter includes two cross-chapter boxes on climate change and fire (Cross-Chapter Box 3), and on urbanisation and climate change (Cross-Chapter Box 4).

In summary, the chapter assesses scientific understanding related to (i) how a changing climate affects terrestrial ecosystems, including those on managed lands, (ii) how land affects climate through biophysical and biogeochemical feedbacks, and (iii) how land use or cover change and land management play an important and complex role in the climate system. This chapter also pays special attention to advances in understanding cross-scale interactions, emerging issues, heterogeneity and teleconnections.

B 2.1 Processes underlying land–climate interactions

Land continuously interacts with the atmosphere through exchanges of, for instance, GHGs (e.g., CO ₂ , CH ₄ , N ₂ O), water, energy or precursors of short lived-climate forcers (e.g., biogenic volatile organic compounds, dust, black carbon). The terrestrial biosphere also interacts with oceans through processes such as the influx of freshwater, nutrients, carbon and particles. These interactions affect where and when rain falls and thus irrigation needs for crops, frequency and intensity of heatwaves, and air quality. They are modified by global and regional climate change, decadal, inter-annual and seasonal climatic variations, and weather extremes, as well as human actions on land (e.g., crop and forest management, afforestation and deforestation). This in turn affects atmospheric composition, surface temperature, hydrological cycle and thus local, regional and global climate. This box introduces some of the fundamental land processes governing biophysical and biogeochemical effects and feedbacks to the climate (Box 2.1, Figure 1).

Box-2.1-Figure-1

The structure and functioning of managed and unmanaged ecosystems that affect local, regional and global climate. Land surface characteristics such as albedo and emissivity determine the amount of solar and long-wave radiation absorbed by land and reflected or emitted to the atmosphere. Surface roughness influences turbulent exchanges of momentum, energy, water and biogeochemical tracers. Land […]

The structure and functioning of managed and unmanaged ecosystems that affect local, regional and global climate. Land surface characteristics such as albedo and emissivity determine the amount of solar and long-wave radiation absorbed by land and reflected or emitted to the atmosphere. Surface roughness influences turbulent exchanges of momentum, energy, water and biogeochemical tracers. Land ecosystems modulate the atmospheric composition through emissions and removals of many GHGs and precursors of SLCFs, including biogenic volatile organic compounds (BVOCs) and mineral dust.
Atmospheric aerosols formed from these precursors affect regional climate by altering the amounts of precipitation and radiation reaching land surfaces through their role in clouds physics.

‘Biophysical interactions’ are exchanges of water and energy between the land and the atmosphere (Section 2.5). Land warms up from absorbing solar and long-wave radiation; it cools down through transfers of sensible heat (via conduction and convection) and latent heat (energy associated with water evapotranspiration) to the atmosphere and through long-wave radiation emission from the land surface (Box 2.1, Figure 1). These interactions between the land and the atmosphere depend on land surface characteristics, including reflectivity of shortwave radiation (albedo), emissivity of long wave radiation by vegetation and soils, surface roughness and soil water access by vegetation, which depends on both soil characteristics and amounts of roots. Over seasonal, inter-annual and decadal timescales, these characteristics vary among different land cover and land-use types and are affected by both natural processes and land management (Anderson et al. 2011 ¹⁸ ). A dense vegetation with high leaf area index, like forests, may absorb more energy than nearby herbaceous vegetation partly due to differences in surface albedo (especially when snow is on the ground). However, denser vegetation also sends more energy back to the atmosphere in the form of evapotranspiration (Bonan, 2008 ¹⁹ ; Burakowski et al., 2018 ²⁰ ; Ellison et al., 2017 ²¹ ) (Section 2.5.2) and this contributes to changes in atmospheric water vapour content, and subsequently to changes in rainfall.

Particularly in extra-tropical regions, these characteristics exhibit strong seasonal patterns with the development and senescence of the vegetation (e.g., leaf colour change and drop). For example, in deciduous forests, seasonal growth increases albedo by 20–50% from the spring minima to growing season maxima, followed by rapid decrease during leaf fall, whereas in grasslands, spring greening causes albedo decreases and only increases with vegetation browning (Hollinger et al. 2010 ²² ). The seasonal patterns of sensible and latent heat fluxes are also driven by the cycle of leaf development and senescence in temperate deciduous forests: sensible heat fluxes peak in spring and autumn and latent heat fluxes peak in mid-summer (Moore et al. 1996 ²³ ; Richardson et al. 2013 ²⁴ ).

Exchanges of GHGs between the land and the atmosphere are referred to as ‘biogeochemical interactions’ (Section 2.3), which are driven mainly by the balance between photosynthesis and respiration by plants, and by the decomposition of soil organic matter by microbes. The conversion of atmospheric carbon dioxide into organic compounds by plant photosynthesis, known as terrestrial net primary productivity, is the source of plant growth, food for human and other organisms, and soil organic carbon. Due to strong seasonal patterns of growth, northern hemisphere terrestrial ecosystems are largely responsible for the seasonal variations in global atmospheric CO ₂ concentrations. In addition to CO ₂ , soils emit methane (CH ₄ ) and nitrous oxide (N ₂ O) (Section 2.3). Soil temperature and moisture strongly affect microbial activities and resulting fluxes of these three GHGs.

Much like fossil fuel emissions, GHG emissions from anthropogenic land cover change and land management are ‘forcers’ on the climate system. Other land-based changes to climate are described as ‘feedbacks’ to the climate system – a process by which climate change influences some property of land, which in turn diminishes (negative feedback) or amplifies (positive feedback) climate change. Examples of feedbacks include the changes in the strength of land carbon sinks or sources, soil moisture and plant phenology (Section 2.5.3).

Incorporating these land–climate processes into climate projections allows for increased understanding of the land’s response to climate change (Section 2.2), and to better quantify the potential of land-based response options for climate change mitigation (Section 2.6). However, to date Earth system models (ESMs) incorporate some combined biophysical and biogeochemical processes only to limited extent and many relevant processes about how plants and soils interactively respond to climate changes are still to be included (Section 2.7). And even within this class of models, the spread in ESM projections is large, in part because of their varying ability to represent land–climate processes (Hoffman et al. 2014 ²⁵ ). Significant progress in understanding of these processes has nevertheless been made since AR5.

2.2 The effect of climate variability and change on land

2.2.1 Overview of climate impacts on land

2.2.1.1 Climate drivers of land form and function

Energy is redistributed from the warm equator to the colder poles through large-scale atmospheric and oceanic processes driving the Earth’s weather and climate (Oort and Peixóto 1983 ²⁶ ; Carissimo et al. 1985 ²⁷ ; Yang et al. 2015a ²⁸ ). Subsequently, a number of global climate zones have been classified ranging from large-scale primary climate zones (tropical, sub-tropical, temperate, sub-polar, polar) to much higher-resolution, regional climate zones (e.g., the Köppen-Geiger classification, Kottek et al. 2006 ²⁹ ). Biomes are adapted to regional climates (Figure 2.1) and may shift as climate, land surface characteristics (e.g., geomorphology, hydrology), CO ₂ fertilisation and fire interact. These biomes and the processes therein are subject to modes of natural variability in the ocean-atmosphere system that result in regionally wetter/dryer or hotter/cooler periods having temporal scales from weeks to months (e.g., Southern Annular Mode), months to seasons (e.g., Madden-Julian Oscillation), years (e.g., El Niño Southern Oscillation) and decades (e.g., Pacific Decadal Oscillation). Furthermore, climate and weather extremes (such as drought, heatwaves, very heavy rainfall, strong winds), whose frequency, intensity and duration are often a function of large-scale modes of variability, impact ecosystems at various space and timescales.

It is very likely that changes to natural climate variability as a result of global warming has and will continue to impact terrestrial ecosystems with subsequent impacts on land processes (Hulme et al. 1999 ³⁰ ; Parmesan and Yohe 2003 ³¹ ; Di Lorenzo et al. 2008 ³² ; Kløve et al. 2014 ³³ ; Berg et al. 2015 ³⁴ ; Lemordant et al. 2016 ³⁵ ; Pecl et al. 2017 ³⁶ ). This chapter assesses climate variability and change, particularly extreme weather and climate, in the context of desertification, land degradation, food security and terrestrial ecosystems more generally. This section does specifically assess the impacts of climate variability and climate change on desertification, land degradation and food security as these impacts are assessed respectively in Chapters 3, 4 and 5. This chapter begins with an assessment of observed warming on land.

Figure 2.1

Worldwide Bioclimatic Classification System, 1996–2018.Source: Rivas-Martinez et al. (2011). Online at www.globalbioclimatics.org

Worldwide Bioclimatic Classification System, 1996–2018.Source: Rivas-Martinez et al. (2011). Online at www.globalbioclimatics.org

2.2.1.2 Changes in global land surface air temperature

Based on analysis of several global and regional land surface air temperature (LSAT) datasets, AR5 concluded that the global LSAT had increased over the instrumental period of record, with the warming rate approximately double that reported over the oceans since 1979 and that ‘it is certain that globally averaged LSAT has risen since the late 19th century and that this warming has been particularly marked since the 1970s’. Warming found in the global land datasets is also in a broad agreement with station observations (Hartmann et al. 2013a ³⁷ ).

Since AR5, LSAT datasets have been improved and extended. The National Center for Environmental Information, which is a part of the US National Oceanic and Atmospheric Administration (NOAA), developed a new, fourth version of the Global Historical Climatology Network monthly dataset (GHCNm, v4). The dataset provides an expanded set of station temperature records with more than 25,000 total monthly temperature stations compared to 7200 in versions v2 and v3 (Menne et al. 2018 ³⁸ ). Goddard Institute for Space Studies, which is a part of the US National Aeronautics and Space Administration, (NASA/ GISS), provides estimate of land and ocean temperature anomalies (GISTEMP). The GISTEMP land temperature anomalies are based upon primarily NOAA/GHCN version 3 dataset (Lawrimore et al. 2011 ³⁹ ) and account for urban effects through nightlight adjustments (Hansen et al. 2010 ⁴⁰ ). The Climatic Research Unit (CRU) of the University of East Anglia, UK (CRUTEM) dataset, now version CRUTEM4.6,

incorporates additional stations (Jones et al. 2012 ⁴¹ ). Finally, the Berkeley Earth Surface Temperature (BEST) dataset provides LSAT from 1750 to present based on almost 46,000 time series and has the longest temporal coverage of the four datasets (Rohde et al. 2013 ⁴² ). This dataset was derived with methods distinct from those used for development of the NOAA GHCNm, NASA/GISS GISTEMP and the University of East Anglia CRUTEM datasets.

Table 2.1

Increases in land surface air temperature (LSAT) from preindustrial period and the late 19th century to present day.

Time period BEST CRUTEM4.6 GHCNm, v4 GISTEMP From 1850–1900 to 2006–2015 1.53
1.38–1.68
(95% confidence) 1.32* From 1850–1900 to 1999–2018 1.52
1.39–1.66
(95% confidence) 1.31 NA NA From 1881–1900 to 1999–2018 1.51
1.40–1.63
(95% confidence) 1.31 1.37 1.45

Dataset of LSAT increase (°C)

\* CRUTEM4.6 LSAT increase is computed from 1856–1900 average.

According to the available observations in the four datasets, the globally averaged LSAT increased by 1.44°C from the preindustrial period (1850–1900) to the present day (1999–2018).The warming from the late 19th century (1881–1900) to the present day (1999–2018) was 1.41°C (1.31°C–1.51°C) (Table 2.1). The 1.31°C–1.51°C range represents the spread in median estimates from the four available land datasets and does not reflect uncertainty in data coverage or methods used. Based on the BEST dataset (the longest dataset with the most extensive land coverage) the total observed increase in LSAT between the average of the 1850–1900 period and the 2006– 2015 period was 1.53°C, (1.38–1.68°C; 95% confidence), while the GMST increase for the same period was 0.87°C (0.75–0.99°C; 90% confidence) (IPCC, 2018: Summary for policymakers, Allen et al. 2018 ⁴³ ).

The extended and improved land datasets reaffirmed the AR5 conclusion that it is certain that globally averaged LSAT has risen since the preindustrial period and that this warming has been particularly marked since the 1970s (Figure 2.2).

Recent analyses of LSAT and sea surface temperature (SST) observations, as well as analyses of climate model simulations, have refined our understanding of underlying mechanisms responsible for a faster rate of warming over land than over oceans. Analyses of paleo records, historical observations, model simulations and underlying physical principles are all in agreement that the land is warming faster than the oceans as a result of differences in evaporation, land–climate feedbacks (Section 2.5) and changes in the aerosol forcing over land ( very high confidence ) (Braconnot et al. 2012 ⁴⁴ ; Joshi et al. 2013 ⁴⁵ ; Sejas et al. 2014 ⁴⁶ ; Byrne and O’Gorman 2013 ⁴⁷ , 2015 ⁴⁸ ; Wallace and Joshi 2018 ⁴⁹ ; Allen et al. 2019 ⁵⁰ ). There is also high confidence that difference in land and ocean heat capacity is not the primary reason for faster land than ocean warming. For the recent period, the land-to-ocean warming ratio is in close agreement between different observational records (about 1.6) and the CMIP5 climate model simulations (the likely range of 1.54°C to 1.81°C). Earlier studies analysing slab ocean models (models in which it is assumed that the deep ocean has equilibrated) produced a higher land temperature increases than sea surface temperature (Manabe et al. 1991 ⁵¹ ; Sutton et al. 2007 ⁵² ).

It is certain that globally averaged LSAT has risen faster than GMST from the preindustrial period (1850–1900) to the present day (1999–2018). This is because the warming rate of the land compared to the ocean is substantially higher over the historical period (by approximately 60%) and because the Earth’s surface is approximately one-third land and two-thirds ocean. This enhanced land warming impacts land processes with implications for desertification (Section 2.2.2 and Chapter 3), food security (Section 2.2.3 and Chapter 5), terrestrial ecosystems (Section 2.2.4), and GHG and non-GHG fluxes between the land and climate (Sections 2.3 and 2.4). Future changes in land characteristics through adaptation and mitigation processes and associated land–climate feedbacks can dampen warming in some regions and enhance warming in others (Section 2.5).

Figure 2.2

Evolution of land surface air temperature (LSAT) and global mean surface temperature (GMST) over the period of instrumental observations. The brown line shows annual mean LSAT in the BEST, CRUTEM4.6, GHCNmv4 and GISTEMP datasets, expressed as departures from global average LSAT in 1850–1900, with the brown line thickness indicating inter-dataset range. The blue line shows […]

Evolution of land surface air temperature (LSAT) and global mean surface temperature (GMST) over the period of instrumental observations. The brown line shows annual mean LSAT in the BEST, CRUTEM4.6, GHCNmv4 and GISTEMP datasets, expressed as departures from global average LSAT in 1850–1900, with the brown line thickness indicating inter-dataset range. The blue line shows annual mean GMST in the HadCRUT4, NOAAGlobal Temp, GISTEMP and Cowtan&Way datasets (monthly values of which were reported in the Special Report on Global Warming of 1.5ºC; Allen et al. 2018 ⁵³ ).

2.2.2 Climate-driven changes in aridity

Desertification is defined and discussed at length in Chapter 3 and is a function of both human activity and climate variability and change. There are uncertainties in distinguishing between historical climate- caused aridification and desertification and future projections of aridity as different measurement methods of aridity do not agree on historical or projected changes (Sections 3.1.1 and 3.2.1). However, warming trends over drylands are twice the global average (Lickley and Solomon 2018 ⁵⁴ ) and some temperate drylands are projected to convert to subtropical drylands as a result of an increased drought frequency causing reduced soil moisture availability in the growing season (Engelbrecht et al. 2015 ⁵⁵ ; Schlaepfer et al. 2017 ⁵⁶ ). We therefore assess with medium confidence that a warming climate will result in regional increases in the spatial extent of drylands under mid- and high emission scenarios and that these regions will warm faster than the global average warming rate.

2.2.3 The influence of climate change on food security

Food security and the various components thereof are addressed in depth in Chapter 5. Climate variables relevant to food security and food systems are predominantly temperature and precipitation-related, but also include integrated metrics that combine these and other variables (e.g., solar radiation, wind, humidity) and extreme weather and climate events including storm surge (Section 5.2.1). The impact of climate change through changes in these variables is projected to negatively impact all aspects of food security (food availability, access, utilisation and stability), leading to complex impacts on global food security ( high confidence ) (Chapter 5, Table 5.1).

Climate change will have regionally distributed impacts, even under aggressive mitigation scenarios (Howden et al. 2007 ⁵⁷ ; Rosenzweig et al. 2013 ⁵⁸ ; Challinor et al. 2014 ⁵⁹ ; Parry et al. 2005 ⁶⁰ ; Lobell and Tebaldi 2014 ⁶¹ ; Wheeler and Von Braun 2013 ⁶² ). For example, in the northern hemisphere the northward expansion of warmer temperatures in the middle and higher latitudes will lengthen the growing season (Gregory and Marshall 2012 ⁶³ ; Yang et al. 2015b ⁶⁴ ) which may benefit crop productivity (Parry et al. 2004 ⁶⁵ ; Rosenzweig et al., 2014 ⁶⁶ ; Deryng et al. 2016 ⁶⁷ ). However, continued rising temperatures are expected to impact global wheat yields by about 4–6% reductions for every degree of temperature rise (Liu et al. 2016a ⁶⁸ ; Asseng et al. 2015 ⁶⁹ ) and across both mid- and low latitude regions, rising temperatures are also expected to be a constraining factor for maize productivity by the end of the century (Bassu et al. 2014 ⁷⁰ ; Zhao et al. 2017 ⁷¹ ). Although there has been a general reduction in frost occurrence during winter and spring, and a lengthening of the frost free season in response to growing concentrations of GHGs (Fischer and Knutti 2014 ⁷² ; Wypych et al. 2017 ⁷³ ), there are regions where the frost season length has increased, for example, in southern Australia (Crimp et al. 2016 ⁷⁴ ). Despite the general reduced frost season length, late spring frosts may increase risk of damage to warming induced precocious vegetation growth and flowering (Meier et al. 2018 ⁷⁵ ). Observed and projected warmer minimum temperatures have, and will continue to, reduce the number of winter chill units required by temperate fruit and nut trees (Luedeling 2012 ⁷⁶ ). Crop yields are impacted negatively by increases of seasonal rainfall variability in the tropics, sub-tropics, water-limited and high elevation environments, while drought severity and growing season temperatures also have a negative impact on crop yield (Nelson et al. 2009 ⁷⁷ ; Schlenker and Lobell 2010 ⁷⁸ ; Müller et al. 2017 ⁷⁹ ; Parry et al. 2004 ⁸⁰ ; Wheeler and Von Braun 2013 ⁸¹ ; Challinor et al. 2014 ⁸² ).

Changes in extreme weather and climate (Section 2.2.5) have negative impacts on food security through regional reductions of crop yields. A recent study shows that 18–43% of the explained yield variance of four crops (maize, soybeans, rice and spring wheat) is attributable to extremes of temperature and rainfall, depending on the crop type (Vogel et al. 2019 ⁸³ ). Climate shocks, particularly severe drought impact low-income small-holder producers disproportionately (Vermeulen et al. 2012 ⁸⁴ ; Rivera Ferre 2014 ⁸⁵ ). Extremes also compromise critical food supply chain infrastructure, making transport of and access to harvested food more difficult (Brown et al. 2015 ⁸⁶ ; Fanzo et al. 2018 ⁸⁷ ). There is high confidence that the impacts of enhanced climate extremes, together with non-climate factors such as nutrient limitation, soil health and competitive plant species, generally outweighs the regionally positive impacts of warming (Lobell et al. 2011 ⁸⁸ ; Leakey et al. 2012 ⁸⁹ ; Porter et al. 2014 ⁹⁰ ; Gray et al. 2016 ⁹¹ ; Pugh et al. 2016 ⁹² ; Wheeler and Von Braun 2013 ⁹³ ; Beer 2018 ⁹⁴ ).

2.2.4 Climate-driven changes in terrestrial ecosystems

Previously, the IPCC AR5 reported high confidence that the Earth’s biota composition and ecosystem processes have been strongly affected by past changes in global climate and that the magnitudes of projected changes for the 21st century under high warming scenarios (for example, RCP8.5) are higher than those under historic climate change (Settele et al. 2014 ⁹⁵ ). There is high confidence that as a

result of climate changes over recent decades many plant and animal species have experienced range size and location changes, altered abundances and shifts in seasonal activities (Urban 2015 ⁹⁶ ; Ernakovich et al. 2014 ⁹⁷ ; Elsen and Tingley 2015 ⁹⁸ ; Hatfield and Prueger 2015 ⁹⁹ ; Savage and Vellend 2015 ¹⁰⁰ ; Yin et al. 2016 ¹⁰¹ ; Pecl et al. 2017 ¹⁰² ; Gonsamo et al. 2017 ¹⁰³ ; Fadrique et al. 2018 ¹⁰⁴ ; Laurance et al. 2018 ¹⁰⁵ ). There is high confidence that climate zones have already shifted in many parts of the world, primarily as an increase of dry, arid climates accompanied by a decrease of polar climates (Chan and Wu 2015 ¹⁰⁶ ; Chen and Chen 2013 ¹⁰⁷ ; Spinoni et al. 2015b ¹⁰⁸ ). Regional climate zones shifts have been observed over the Asian monsoon region (Son and Bae 2015 ¹⁰⁹ ), Europe (Jylhä et al. 2010 ¹¹⁰ ), China (Yin et al. 2019 ¹¹¹ ), Pakistan (Adnan et al. 2017 ¹¹² ), the Alps (Rubel et al. 2017 ¹¹³ ) and north-eastern Brazil, southern Argentina, the Sahel, Zambia and Zimbabwe, the Mediterranean area, Alaska, Canada and north-eastern Russia (Spinoni et al. 2015b ¹¹⁴ ).

There is high confidence that bioclimates zones will further shift as the climate warms (Williams et al. 2007 ¹¹⁵ ; Rubel and Kottek 2010 ¹¹⁶ ; Garcia et al. 2016 ¹¹⁷ ; Mahony et al. 2017 ¹¹⁸ ; Law et al. 2018 ¹²⁰ ). There is also high confidence that novel, unprecedented climates (climate conditions with no analogue in the observational record) will emerge, particularly in the tropics (Williams and Jackson 2007 ¹²¹ ; Colwell et al. 2008a ¹²² ; Mora et al. 2013 ¹²³ , 2014 ¹²⁴ ; Hawkins et al. 2014 ¹²⁶ ; Mahony et al. 2017 ¹²⁷ ; Maule et al. 2017 ¹²⁸ ). It is very likely that terrestrial ecosystems and land processes will be exposed to disturbances beyond the range of current natural variability as a result of global warming, even under low- to medium-range warming scenarios, and that these disturbances will alter the structure, composition and functioning of the system (Settele et al. 2014 ¹²⁹ ; Gauthier et al. 2015; Seddon et al. 2016 ¹³⁰ ).

In a warming climate, many species will be unable to track their climate niche as it moves, especially those in extensive flat landscapes with low dispersal capacity and in the tropics whose thermal optimum is already near current temperature (Diffenbaugh and Field 2013 ¹³¹ ; Warszawski et al. 2013 ¹³² ). Range expansion in higher latitudes and elevations as a result of warming often, but not exclusively, occurs in abandoned lands (Harsch et al. 2009 ¹³³ ; Landhäusser et al. 2010 ¹³⁴ ; Gottfried et al. 2012 ¹³⁵ ; Boisvert-Marsh et al. 2014 ¹³⁶ ; Bryn and Potthoff 2018 ¹³⁷ ; Rumpf et al. 2018 ¹³⁸ ; Buitenwerf et al. 2018 ¹³⁹ ; Steinbauer et al. 2018 ¹⁴⁰ ). This expansion typically favours thermophilic species at the expense of cold adapted species as the climate becomes suitable for lower latitude/altitude species (Rumpf et al. 2018 ¹⁴¹ ). In temperate drylands, however, range expansion can be countered by intense and frequent drought conditions which result in accelerated rates of taxonomic change and spatial heterogeneity in an ecotone (Tietjen et al. 2017 ¹⁴² ).

Since the advent of satellite observation platforms, a global increase in vegetation photosynthetic activity (i.e., greening) as evidenced through remotely sensed indices such as leaf area index (LAI) and normalised difference vegetation index (NDVI). Three satellite-based leaf area index records (GIMMS3g, GLASS and GLOMAP) imply increased growing season LAI (greening) over 25–50% and browning over less than 4% of the global vegetated area, resulting in greening trend of 0.068 ± 0.045 m ² m ⁻² yr ⁻¹ over 1982–2009 (Zhu et al. 2016 ¹⁴³ ). Greening has been observed in southern Amazonia, southern Australia, the Sahel and central Africa, India, eastern China and the northern extratropical latitudes (Myneni et al. 1997 ¹⁴⁴ ; de Jong et al. 2012 ¹⁴⁵ ; Los 2013 ¹⁴⁶ ; Piao et al. 2015 ¹⁴⁷ ; Mao et al. 2016 ¹⁴⁸ ; Zhu et al. 2016 ¹⁴⁹ ; Carlson et al. 2017 ¹⁵⁰ ; Forzieri et al. 2017 ¹⁵¹ ; Pan et al. 2018 ¹⁵² ; Chen et al. 2019 ¹⁵³ ). Greening has been attributed to direct factors, namely human land use management and indirect factors such as CO ₂ fertilisation, climate change, and nitrogen deposition (Donohue et al. 2013 ¹⁵⁴ ; Keenan et al. 2016 ¹⁵⁵ ; Zhu et al. 2016 ¹⁵⁶ ). Indirect factors have been used to explain most greening trends primarily through CO ₂ fertilisation in the tropics and through an extended growing season and increased growing season temperatures as a result of climate change in the high latitudes (Fensholt et al. 2012 ¹⁵⁷ ; Zhu et al. 2016 ¹⁵⁸ ). The extension of the growing season in high latitudes has occurred together with an earlier spring greenup (the time at which plants begin to produce leaves in northern mid- and high-latitude ecosystems) (Goetz et al. 2015 ¹⁵⁹ ; Xu et al. 2016a ¹⁶⁰ , 2018 ¹⁶¹ ) with subsequent earlier spring carbon uptake (2.3 days per decade) and gross primary productivity (GPP) (Pulliainen et al. 2017 ¹⁶² ). The role of direct factors of greening are being increasingly investigated and a recent study has attributed over a third of observed global greening between 2000 and 2017 to direct factors, namely afforestation and croplands, in China and India (Chen et al. 2019 ¹⁶³ ).

It should be noted that measured greening is a product of satellite- derived radiance data and, as such, does not provide information on ecosystem health indicators such as species composition and richness, homeostasis, absence of disease, vigour, system resilience and the different components of ecosystems (Jørgensen et al. 2016 ¹⁶⁴ ). For example, a regional greening attributable to croplands expansion or intensification might occur at the expense of ecosystem biodiversity.

Within the global greening trend are also detected regional decreases in vegetation photosynthetic activity (i.e., browning) in northern Eurasia, the southwestern USA, boreal forests in North America, inner Asia and the Congo Basin, largely as a result of intensified drought stress. Since the late 1990s rates and extents of browning have exceeded those of greening in some regions, the collective result of which has been a slowdown of the global greening rate (de Jong et al. 2012 ¹⁶⁵ ; Pan et al. 2018 ¹⁶⁶ ). Within these long-term trends, inter-annual variability of regional greening and browning is attributable to regional climate variability, responses to extremes such as drought, disease and insect infestation and large- scale tele-connective controls such as ENSO and the Atlantic Multi- decadal Organization (Verbyla 2008 ¹⁶⁷ ; Revadekar et al. 2012 ¹⁶⁸ ; Epstein et al. 2018 ¹⁶⁹ ; Zhao et al. 2018 ¹⁷⁰ ).

Projected increases in drought conditions in many regions suggest long-term global vegetation greening trends are at risk of reversal to browning in a warmer climate (de Jong et al. 2012 ¹⁷¹ ; Pan et al. 2018 ¹⁷² ; Pausas and Millán 2018 ¹⁷³ ). On the other hand, in higher latitudes vegetation productivity is projected to increase as a result of higher atmospheric CO ₂ concentrations and longer growing periods as a result of warming (Ito et al. 2016) (Section 2.3 and Box 2.3). Additionally, climate-driven transitions of ecosystems, particularly range changes, can take years to decades for the equilibrium state to be realised and the rates of these ‘committed ecosystem changes’ (Jones et al. 2009 ¹⁷⁴ ) vary between low and high latitudes (Jones et al. 2010 ¹⁷⁵ ). Furthermore, as direct factors are poorly integrated into Earth systems models (ESMs) uncertainties in projected trends of greening and browning are further compounded (Buitenwerf et al. 2018 ¹⁷⁶ ; Chen et al. 2019 ¹⁷⁷ ). Therefore, there is low confidence in the projection of global greening and browning trends.

Increased atmospheric CO ₂ concentrations have both direct and indirect effects on terrestrial ecosystems (Sections 2.2.2 and 2.2.3, and Box 2.3). The direct effect is primarily through increased vegetation photosynthetic activity as described above. Indirect effects include decreased evapotranspiration that may offset the projected impact of drought in some water-stressed plants through improved water use efficiency in temperate regions, suggesting that some rain-fed cropping systems and grasslands will benefit from elevated atmospheric CO ₂ concentrations (Roy et al. 2016 ¹⁷⁸ ; Milly and Dunne 2016 ¹⁷⁹ ; Swann et al. 2016 ¹⁸⁰ ; Chang et al. 2017 ¹⁸¹ ; Zhu et al. 2017 ¹⁸² ). In tropical regions, increased flowering activity is associated primarily with increasing atmospheric CO ₂ , suggesting that a long- term increase in flowering activity may persist in some vegetation, particularly mid-story trees and tropical shrubs, and may enhance reproduction levels until limited by nutrient availability or climate factors such as drought frequency, rising temperatures, and reduced insolation (Pau et al. 2018 ¹⁸³ ).

2.2.5 Climate extremes and their impact on land functioning

Extreme weather events are generally defined as the upper or lower statistical tails of the observed range of values of climate variables or climate indicators (e.g., temperature/rainfall or drought/aridity indices respectively). Previous IPCC reports have reported with high confidence on the increase of many types of observed extreme temperature events (Seneviratne et al. 2012 ¹⁸³ ; Hartmann et al. 2013b ¹⁸⁴ ; Hoegh-Guldberg et al. 2018 ¹⁸⁵ ). However, as a result of observational constraints, increases in precipitation extremes are less confident , except in observations-rich regions with dense, long-lived station networks, such as Europe and North America, where there have been likely increases in the frequency or intensity of heavy rainfall.

Extreme events occur across a wide range of time and space scales (Figure 2.3) and may include individual, relatively short-lived weather events (e.g., extreme thunderstorms storms) or a combination or accumulation of non-extreme events (Colwell et al. 2008b ¹⁸⁶ ; Handmer et al. 2012 ¹⁸⁷ ), for example, moderate rainfall in a saturated catchment having the flood peak at mean high tide (Leonard et al. 2014 ¹⁸⁸ ). Combinatory processes leading to a significant impact are referred to as a compound event and are a function of the nature and number of physical climate and land variables, biological agents such as pests and disease, the range of spatial and temporal scales, the strength of dependence between processes and the perspective of the stakeholder who defines the impact (Leonard et al. 2014 ¹⁸⁹ ; Millar and Stephenson 2015 ¹⁹⁰ ). Currently, there is low confidence in the impact of compound events on land as the multi-disciplinary approaches needed to address the problem are few (Zscheischler et al. 2018 ¹⁹¹ ) and the rarity of compound extreme climatic events renders the analysis of impacts difficult.

Figure 2.3

Spatial and temporal scales of typical extreme weather and climate events and the biological systems they impact (shaded grey).Individuals, populations and ecosystems within these space-time ranges respond to relevant climate stressors. Orange (blue) labels indicate an increase (decrease) in the frequency or intensity of the event, with bold font reflecting confidence in the change. Non-bold […]

Spatial and temporal scales of typical extreme weather and climate events and the biological systems they impact (shaded grey).Individuals, populations and ecosystems within these space-time ranges respond to relevant climate stressors. Orange (blue) labels indicate an increase (decrease) in the frequency or intensity of the event, with bold font reflecting confidence in the change. Non-bold black labels indicate low confidence in observed changes in frequency or intensity of these events. Each event type indicated in the figure is likely to affect biological systems at all temporal and spatial scales located to the left and below the specific event position in the figure. From Ummenhofer and Meehl (2017 ¹⁹² ).

2.2.5.1 Changes in extreme temperatures, heatwaves and drought

It is very likely that most land areas have experienced a decrease in the number of cold days and nights, and an increase in the number of warm days and unusually hot nights (Orlowsky and Seneviratne 2012 ¹⁹³ ; Seneviratne et al. 2012 ¹⁹⁴ ; Mishra et al. 2015 ¹⁹⁵ ; Ye et al. 2018 ¹⁹⁶ ). Although there is no consensus definition of heatwaves, as some heatwave indices have relative thresholds and others absolute thresholds, trends between indices of the same type show that recent heat-related events have been made more frequent or more intense due to anthropogenic GHG emissions in most land regions (Lewis and Karoly 2013 ¹⁹⁷ ; Smith et al. 2013b ¹⁹⁸ ; Scherer and Diffenbaugh 2014 ¹⁹⁹ ; Fischer and Knutti 2015 ²⁰⁰ ; Ceccherini et al. 2016 ²⁰¹ ; King et al. 2016 ²⁰² ; Bador et al. 2016 ²⁰³ ; Stott et al. 2016 ²⁰⁴ ; King 2017 ²⁰⁵ ; Hoegh-Guldberg et al. 2018 ²⁰⁶ ). Globally, 50–80% of the land fraction is projected to experience significantly more intense hot extremes than historically recorded (Fischer and Knutti 2014 ²⁰⁷ ; Diffenbaugh et al. 2015 ²⁰⁸ ; Seneviratne et al. 2016 ²⁰⁹ ). There is high confidence that heatwaves will increase in frequency, intensity and duration into the 21st century (Russo et al. 2016 ²¹⁰ ; Ceccherini et al. 2017 ²¹¹ ; Herrera-Estrada and Sheffield 2017 ²¹² ) and under high emission scenarios, heatwaves by the end of the century may become extremely long (more than 60 consecutive days) and frequent (once every two years) in Europe, North America, South America, Africa, Indonesia, the Middle East, South and Southeast Asia and Australia (Rusticucci 2012 ²¹³ ; Cowan et al. 2014 ²¹⁴ ; Russo et al. 2014 ²¹⁵ ; Scherer and Diffenbaugh 2014 ²¹⁶ ; Pal and Eltahir 2016 ²¹⁷ ; Rusticucci et al. 2016 ²¹⁸ ; Schär 2016 ²¹⁹ ; Teng et al. 2016 ²²⁰ ; Dosio 2017 ²²¹ ; Mora et al. 2017 ²²² ; Dosio et al. 2018 ²²³ ; Lehner et al. 2018 ²²⁴ ; Lhotka et al. 2018 ²²⁵ ; Lopez et al. 2018 ²²⁶ ; Tabari and Willems 2018 ²²⁷ ). Furthermore, unusual heatwave conditions today will occur regularly by 2040 under the RCP 8.5 scenario (Russo et al. 2016 ²²⁸ ). The intensity of heat events may be modulated by land cover and soil characteristics (Miralles et al. 2014 ²²⁹ ; Lemordant et al. 2016 ²³⁰ ; Ramarao et al. 2016 ²³¹ ). Where temperature increase results in decreased soil moisture, latent heat flux is reduced while sensible heat fluxes are increased, allowing surface air temperature to rise further. However, this feedback may be diminished if the land surface is irrigated through enhanced evapotranspiration (Mueller et al. 2015 ²³² ; Siebert et al. 2017 ²³³ ) (Section 2.5.2.2).

Drought (IPCC 2013c ²³⁴ ), including megadroughts of the last century, for example, the Dustbowl drought (Hegerl et al. 2018 ²³⁵ ) (Chapter 5), is a normal component of climate variability (Hoerling et al. 2010; Dai 2011 ²³⁶ ) and may be seasonal, multi-year (Van Dijk et al. 2013 ²³⁷ ) or multi-decadal (Hulme 2001 ²³⁸ ) with increasing degrees of impact on regional activities. This inter-annual variability is controlled particularity through remote sea surface temperature (SST) forcings, such as the Inter-decadal Pacific Oscillation (IPO) and the Atlantic Multi-decadal Oscillation (AMO), El Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), that cause drought as a result of reduced rainfall (Kelley et al. 2015 ²³⁹ ; Dai 2011 ²⁴⁰ ; Hoell et al. 2017 ²⁴¹ ; Espinoza et al. 2018 ²⁴² ). In some cases however, large scale SST modes do not fully explain the severity of drought some recent event attribution studies have identified a climate change fingerprint in several regional droughts, for example, the western Amazon (Erfanian et al. 2017 ²⁴³ ), southern Africa (Funk et al. 2018 ²⁴⁴ ; Yuan et al. 2018 ²⁴⁵ ), southern Europe and the Mediterranean including North Africa (Kelley et al. 2015 ²⁴⁶ ; Wilcox et al. 2018 ²⁴⁷ ), parts of North America (Williams et al. 2015 ²⁴⁸ ; Mote et al. 2016 ²⁴⁹ ), Russia (Otto et al. 2012 ²⁵⁰ ), India (Ramarao et al. 2015 ²⁵¹ ) and Australia (Lewis and Karoly 2013 ²⁵² ).

Long-term global trends in drought are difficult to determine because of this natural variability, potential deficiencies in drought indices (especially in how evapotranspiration is treated) and the quality and availability of precipitation data (Sheffield et al. 2012 ²⁵³ ; Dai 2013 ²⁵⁴ ; Trenberth et al. 2014 ²⁵⁵ ; Nicholls and Seneviratne 2015 ²⁵⁶ ; Mukherjee et al. 2018 ²⁵⁷ ). However, regional trends in frequency and intensity of drought are evident in several parts of the world, particularly in low latitude land areas, such as the Mediterranean, North Africa and the Middle East (Vicente-Serrano et al. 2014 ²⁵⁸ ; Spinoni et al. 2015a ²⁵⁹ ; Dai and Zhao 2017 ²⁶⁰ ; Páscoa et al. 2017 ²⁶¹ ), many regions of sub-Saharan Africa (Masih et al. 2014 ²⁶² ; Dai and Zhao 2017 ²⁶³ ), central China (Wang et al. 2017e ²⁶⁴ ), the southern Amazon (Fu et al. 2013 ²⁶⁵ ; Espinoza et al. 2018 ²⁶⁶ ), India (Ramarao et al. 2016 ²⁶⁷ ), east and south Asia, parts of North America and eastern Australia (Dai and Zhao 2017 ²⁶⁸ ). A recent analysis of 4500 meteorological droughts globally found increased drought frequency over the East Coast of the USA, Amazonia and north-eastern Brazil, Patagonia, the Mediterranean region, most of Africa and north-eastern China with decreased drought frequency over northern Argentina, Uruguay and northern Europe (Spinoni et al. 2019 ²⁶⁹ ). The study also found drought intensity has become more severe over north-western USA, parts of Patagonia and southern Chile, the Sahel, the Congo River basin, southern Europe, north-eastern China, and south-eastern Australia, whereas the eastern USA, south-eastern Brazil, northern Europe, and central- northern Australia experienced less severe droughts. In addition to the IPCC SR15 assessment of medium confidence in increased drying over the Mediterranean region (Hoegh-Guldberg et al. 2018 ²⁷⁰ ), it is further assessed with medium confidence that frequency and intensity of droughts in Amazonia, north-eastern Brazil, Patagonia, most of Africa, and north-eastern China has increased.

There is low confidence in how large-scale modes of variability will respond to a warming climate (Deser et al. 2012 ²⁷¹ ; Liu 2012 ²⁷² ; Christensen et al. 2013 ²⁷³ ; Hegerl et al. 2015 ²⁷⁴ ; Newman et al. 2016 ²⁷⁵ ). Although, there is evidence for an increased frequency of extreme ENSO events, such as the 1997/98 El Niño and 1988/89 La Niña (Cai et al. 2014a ²⁷⁶ , 2015 ²⁷⁷ ) and extreme positive phases of the IOD (Christensen et al. 2013 ²⁷⁸ ; Cai et al. 2014b ²⁷⁹ ). However, the assessment by the SR15 was retained on an increased regional drought risk ( medium confidence ), specifically over the Mediterranean and South Africa at both 1.5°C and 2°C warming levels compared to present day, with drought risk at 2°C being significantly higher than at 1.5°C (Hoegh-Guldberg et al. 2018 ²⁸⁰ ).

2.2.5.2 Impacts of heat extremes and drought on land

There is high confidence that heat extremes such as unusually hot nights, extremely high daytime temperatures, heatwaves and drought are damaging to crop production (Chapter 5). Extreme heat events impact a wide variety of tree functions including reduced photosynthesis, increased photooxidative stress, leaves abscise, a decreased growth rate of remaining leaves and decreased growth of the whole tree (Teskey et al. 2015 ²⁸¹ ). Although trees are more resilient to heat stress than grasslands (Teuling et al. 2010 ²⁸² ), it has been observed that different types of forest (e.g., needleleaf vs broadleaf) respond differently to drought and heatwaves (Babst et al. 2012 ²⁸³ ). For example, in the Turkish Anatolian forests net primary productivity (NPP) generally decreased during drought and heatwave events between 2000 and 2010 but in a few other regions, NPP of needle leaf forests increased (Erşahin et al. 2016 ²⁸⁴ ). However, forests may become less resilient to heat stress in future due to the long recovery period required to replace lost biomass and the projected increased frequency of heat and drought events (Frank et al. 2015a ²⁸⁵ ; McDowell and Allen 2015 ²⁸⁶ ; Johnstone et al. 2016 ²⁸⁷ ; Stevens-Rumann et al. 2018 ²⁸⁸ ). Additionally, widespread regional tree mortality may be triggered directly by drought and heat stress (including warm winters) and exacerbated by insect outbreak and fire (Neuvonen et al. 1999 ²⁸⁹ ; Breshears et al. 2005 ²⁹⁰ ; Berg et al. 2006 ²⁹¹ ; Soja et al. 2007 ²⁹² ; Kurz et al. 2008 ²⁹³ ; Allen et al. 2010 ²⁹⁴ ).

Gross primary production (GPP) and soil respiration form the first and second largest carbon fluxes from terrestrial ecosystems to the atmosphere in the global carbon cycle (Beer et al. 2010 ²⁹⁵ ; Bond- Lamberty and Thomson 2010 ²⁹⁶ ). Heat extremes impact the carbon cycle through altering these and change ecosystem-atmosphere CO ₂ fluxes and the ecosystem carbon balance. Compound heat and drought events result in a stronger carbon sink reduction compared to single-factor extremes as GPP is strongly reduced and ecosystem respiration less so (Reichstein et al. 2013 ²⁹⁷ ; Von Buttlar et al. 2018 ²⁹⁸ ). In forest biomes, however, GPP may increase temporarily as a result of increased insolation and photosynthetic activity as was seen during the 2015–2016 ENSO related drought over Amazonia (Zhu et al. 2018 ²⁹⁹ ). Longer extreme events (heatwave or drought or both) result in a greater reduction in carbon sequestration and may also reverse long-term carbon sinks (Ciais et al. 2005 ³⁰⁰ ; Phillips et al. 2009 ³⁰¹ ; Wolf et al. 2016b ³⁰² ; Ummenhofer and Meehl 2017 ³⁰³ ; Von Buttlar et al. 2018 ³⁰⁴ ; Reichstein et al. 2013 ³⁰⁵ ). Furthermore, extreme heat events may impact the carbon cycle beyond the lifetime of the event. These lagged effects can slow down or accelerate the carbon cycle: it will slow down if reduced vegetation productivity and/or widespread mortality after an extreme drought are not compensated by regeneration, or speed up if productive tree and shrub seedlings cause rapid regrowth after windthrow or fire (Frank et al. 2015a ³⁰⁶ ). Although some ecosystems may demonstrate resilience to a single heat climate stressor like drought (e.g., forests), compound effects of, for example, deforestation, fire and drought, potentially can result in changes to regional precipitation patterns and river discharge, losses of carbon storage and a transition to a disturbance-dominated regime (Davidson et al. 2012 ³⁰⁷ ). Additionally, adaptation to seasonal drought may be overwhelmed by multi-year drought and their legacy effects (Brando et al. 2008 ³⁰⁸ ; da Costa et al. 2010 ³⁰⁹ ).

Under medium- and high-emission scenarios, global warming will exacerbate heat stress, thereby amplifying deficits in soil moisture and runoff despite uncertain precipitation changes (Ficklin and Novick 2017 ³¹⁰ ; Berg and Sheffield 2018 ³¹¹ ; Cook et al. 2018 ³¹² ; Dai et al. 2018 ³¹³ ; Engelbrecht et al. 2015 ³¹⁴ ; Ramarao et al. 2015 ³¹⁵ ; Grillakis 2019 ³¹⁶ ). This will  increase the rate of drying causing drought to set in quicker, become more intense and widespread, last longer and could result in an increased global aridity (Dai 2011 ³¹⁷ ; Prudhomme et al. 2014 ³¹⁸ ).

The projected changes in the frequency and intensity of extreme temperatures and drought is expected to result in decreased carbon sequestration by ecosystems and degradation of ecosystems health and loss of resilience (Trumbore et al. 2015 ³¹⁹ ). Also affected are many aspects of land functioning and type including agricultural productivity (Lesk et al. 2016 ³²⁰ ), hydrology (Mosley 2015 ³²¹ ; Van Loon and Laaha 2015 ³²² ), vegetation productivity and distribution (Xu et al. 2011 ³²³ ; Zhou et al. 2014 ³²⁴ ), carbon fluxes and stocks, and other biogeochemical cycles (Frank et al. 2015b ³²⁵ ; Doughty et al. 2015 ³²⁶ ; Schlesinger et al. 2016 ³²⁷ ). Carbon stocks are particularly vulnerable to extreme events due to their large carbon pools and fluxes, potentially large lagged impacts and long recovery times to regain lost stocks (Frank et al. 2015a ³²⁸ ) (Section 2.2).

2.2.5.3 Changes in heavy precipitation

A large number of extreme rainfall events have been documented over the past decades (Coumou and Rahmstorf 2012 ³²⁹ ; Seneviratne et al. 2012 ³³⁰ ; Trenberth 2012 ³³¹ ; Westra et al. 2013 ³³² ; Espinoza et al. 2014 ³³³ ; Guhathakurta et al. 2017 ³³⁴ ; Taylor et al. 2017 ³³⁵ ; Thompson et al. 2017 ³³⁶ ; Zilli et al. 2017 ³³⁷ ). The observed shift in the trend distribution of precipitation extremes is more distinct than for annual mean precipitation and the global land fraction experiencing more intense precipitation events is larger than expected from internal variability (Fischer and Knutti 2014 ³³⁸ ; Espinoza et al. 2018 ³³⁹ ; Fischer et al. 2013 ³⁴⁰ ). As a result of global warming, the number of record-breaking rainfall events globally has increased significantly by 12% during the period 1981–2010 compared to those expected due to natural multi-decadal climate variability (Lehmann et al. 2015 ³⁴¹ ). The IPCC SR15 reports robust increases in observed precipitation extremes for annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day) (Hoegh-Guldberg et al. 2018 ³⁴² ; Schleussner et al. 2017 ³⁴³ ). A number of extreme rainfall events have been attributed to human influence (Min et al. 2011 ³⁴⁴ ; Pall et al. 2011 ³⁴⁵ ; Sippel and Otto 2014 ³⁴⁶ ; Trenberth et al. 2015 ³⁴⁷ ; Krishnan et al. 2016 ³⁴⁸ ) and the largest fraction of anthropogenic influence is evident in the most rare and extreme events (Fischer and Knutti 2014 ³⁴⁹ ).

A warming climate is expected to intensify the hydrological cycle as a warmer climate facilitates more water vapour in the atmosphere, as approximated by the Clausius-Clapeyron (C-C) relationship, with subsequent effects on regional extreme precipitation events (Christensen and Christensen 2003 ³⁵⁰ ; Pall et al. 2007 ³⁵¹ ; Berg et al. 2013 ³⁵² ; Wu et al. 2013 ³⁵³ ; Guhathakurta et al. 2017 ³⁵⁴ ; Thompson et al. 2017 ³⁵⁵ ; Taylor et al. 2017 ³⁵⁶ ; Zilli et al. 2017 ³⁵⁷ ; Manola et al. 2018 ³⁵⁸ ). Furthermore, changes to the dynamics of the atmosphere amplify or weaken future precipitation extremes at the regional scale (O’Gorman 2015 ³⁵⁹ ; Pfahl et al. 2017 ³⁶⁰ ). Continued anthropogenic warming is very likely to increase the frequency and intensity of extreme rainfall in many regions of the globe (Seneviratne et al. 2012 ³⁶¹ ; Mohan and Rajeevan 2017 ³⁶² ; Prein et al. 2017 ³⁶³ ; Stott et al. 2016 ³⁶⁴ ) although many general circulation models (GCMs) underestimate observed increased trends in heavy precipitation suggesting a substantially stronger intensification of future heavy rainfall than the multi-model mean (Borodina et al. 2017 ³⁶⁵ ; Min et al. 2011 ³⁶⁶ ). Furthermore, the response of extreme convective precipitation to warming remains uncertain because GCMs and regional climate models (RCMs) are unable to explicitly simulate sub-grid scale processes such as convection, the hydrological cycle and surface fluxes and have to rely on parameterisation schemes for this (Crétat et al. 2012 ³⁶⁷ ; Rossow et al. 2013 ³⁶⁸ ; Wehner 2013 ³⁶⁹ ; Kooperman et al. 2014 ³⁷⁰ ; O’Gorman 2015 ³⁷¹ ; Larsen et al. 2016 ³⁷² ; Chawla et al. 2018 ³⁷³ ; Kooperman et al. 2018 ³⁷⁴ ; Maher et al. 2018 ³⁷⁵ ; Rowell and Chadwick 2018 ³⁷⁶ ). High-resolution RCMs that explicitly resolve convection have a better representation of extreme precipitation but are dependent on the GCM to capture the large scale environment in which the extreme event may occur (Ban et al. 2015 ³⁷⁷ ; Prein et al. 2015 ³⁷⁸ ; Kendon et al. 2017 ³⁷⁹ ). Inter- annual variability of precipitation extremes in the convective tropics are not well captured by global models (Allan and Liu 2018 ³⁸⁰ ).

There is low confidence in the detection of long-term observed and projected seasonal and daily trends of extreme snowfall. The narrow rain–snow transition temperature range at which extreme snowfall can occur is relatively insensitive to climate warming and subsequent large interdecadal variability (Kunkel et al. 2013 ³⁸¹ ; O’Gorman 2014 ³⁸² , 2015 ³⁸³ ).

2.2.5.4 Impacts of precipitation extremes on different land cover types

More intense rainfall leads to water redistribution between surface and ground water in catchments as water storage in the soil decreases (green water) and runoff and reservoir inflow increases (blue water) (Liu and Yang 2010 ³⁸⁴ ; Eekhout et al. 2018 ³⁸⁵ ). This results in increased surface flooding and soil erosion, increased plant water stress and reduced water security, which in terms of agriculture means an increased dependency on irrigation and reservoir storage (Nainggolan et al. 2012 ³⁸⁶ ; Favis-Mortlock and Mullen 2011 ³⁸⁷ ; García- Ruiz et al. 2011 ³⁸⁸ ; Li and Fang 2016 ³⁸⁹ ; Chagas and Chaffe 2018 ³⁹⁰ ). As there is high confidence of a positive correlation between global warming and future flood risk, land cover and processes are likely to be negatively impacted, particularly near rivers and in floodplains (Kundzewicz et al. 2014 ³⁹¹ ; Alfieri et al. 2016 ³⁹² ; Winsemius et al. 2016 ³⁹³ ; Arnell and Gosling 2016 ³⁹⁴ ; Alfieri et al. 2017 ³⁹⁵ ; Wobus et al. 2017 ³⁹⁶ ).

In agricultural systems, heavy precipitation and inundation can delay planting, increase soil compaction and cause crop losses through anoxia and root diseases (Posthumus et al. 2009 ³⁹⁷ ). In tropical regions, flooding associated with tropical cyclones can lead to crop failure from both rainfall and storm surge. In some cases, flooding can affect yield more than drought, particularly in tropical regions (e.g., India) and in some mid/high latitude regions such as China and central and northern Europe (Zampieri et al. 2017 ³⁹⁸ ). Waterlogging of croplands and soil erosion also negatively affect farm operations and block important transport routes (Vogel and Meyer 2018 ³⁹⁹ ; Kundzewicz and Germany 2012 ⁴⁰⁰ ). Flooding can be beneficial in drylands if the floodwaters infiltrate and recharge alluvial aquifers along ephemeral river pathways, extending water availability into dry seasons and drought years, and supporting riparian systems and human communities (Kundzewicz and Germany 2012; Guan et al. 2015 ⁴⁰¹ ). Globally, the impact of rainfall extremes on agriculture is less than that of temperature extremes and drought, although in some regions and for some crops, extreme precipitation explains a greater component of yield variability, for example, of maize in the Midwestern USA and southern Africa (Ray et al. 2015 ⁴⁰² ; Lesk et al. 2016 ⁴⁰³ ; Vogel et al. 2019 ⁴⁰⁴ ).

Although many soils on floodplains regularly suffer from inundation, the increases in the magnitude of flood events mean that new areas with no recent history of flooding are now becoming severely affected (Yellen et al. 2014 ⁴⁰⁵ ). Surface flooding and associated soil saturation often results in decreased soil quality through nutrient loss, reduced plant productivity, stimulated microbial growth and microbial community composition, negatively impacted soil redox and increased GHG emissions (Bossio and Scow 1998 ⁴⁰⁶ ; Niu et al. 2014 ⁴⁰⁷ ; Barnes et al. 2018 ⁴⁰⁸ ; Sánchez-Rodríguez et al. 2019 ⁴⁰⁹ ). The impact of flooding on soil quality is influenced by management systems that may mitigate or exacerbate the impact. Although soils tend to recover quickly after floodwater removal, the impact of repeated extreme flood events over longer timescales on soil quality and function is unclear (Sánchez-Rodríguez et al. 2017 ⁴¹⁰ ).

Flooding in ecosystems may be detrimental through erosion or permanent habitat loss, or beneficial, as a flood pulse brings nutrients to downstream regions (Kundzewicz et al. 2014 ⁴¹¹ ). Riparian forests can be damaged through flooding; however, increased flooding may also be of benefit to forests where upstream water demand has lowered stream flow, but this is difficult to assess and the effect of flooding on forests is not well studied (Kramer et al. 2008 ⁴¹² ; Pawson et al. 2013 ⁴¹³ ). Forests may mitigate flooding, however flood mitigation potential is limited by soil saturation and rainfall intensity (Pilaš et al. 2011 ⁴¹⁴ ; Ellison et al. 2017 ⁴¹⁵ ). Some grassland species under heavy rainfall and soil saturated conditions responded negatively with decreased reproductive biomass and germination rates (Gellesch et al. 2017 ⁴¹⁶ ), however overall productivity in grasslands remains constant in response to heavy rainfall (Grant et al. 2014 ⁴¹⁷ ).

Extreme rainfall alters responses of soil CO ₂ fluxes and CO ₂ uptake by plants within ecosystems, and therefore result in changes in ecosystem carbon cycling (Fay et al. 2008 ⁴¹⁸ ; Frank et al. 2015a ⁴¹⁹ ). Extreme rainfall and flooding limits oxygen in soil which may suppress the activities of soil microbes and plant roots and lower soil respiration, therefore lowering carbon cycling (Knapp et al. 2008 ⁴²⁰ ; Rich and Watt 2013 ⁴²¹ ; Philben et al. 2015 ⁴²² ). However, the impact of extreme rainfall on carbon fluxes in different biomes differs. For example, extreme rainfall in mesic biomes reduces soil CO ₂ flux to the atmosphere and GPP whereas in xeric biomes the opposite is true, largely as a result of increased soil water availability (Knapp and Smith 2001 ⁴²³ ; Heisler and Knapp 2008 ⁴²⁴ ; Heisler-White et al. 2009 ⁴²⁵ ; Zeppel et al. 2014 ⁴²⁶ ; Xu and Wang 2016 ⁴²⁷ ; Liu et al. 2017b ⁴²⁸ ; Connor and Hawkes 2018 ⁴²⁹ ).

As shown above GHG fluxes between the land and atmosphere are affected by climate. The next section assesses these fluxes in greater detail and the potential for land as a carbon sink.

CCB3 Fire and climate change

Raman Sukumar (India), Almut Arneth (Germany), Werner Kurz (Canada), Andrey Sirin (Russian Federation), Louis Verchot (Colombia/The United States of America)

Fires have been a natural part of Earth’s geological past and its biological evolution since at least the late Silurian, about 400 million years ago (Scott 2000 ⁴³⁰ ). Presently, roughly 3% of the Earth’s land surface burns annually which affects both energy and matter exchanges between the land and atmosphere (Stanne et al. 2009 ⁴³¹ ). Climate is a major determinant of fire regimes through its control of fire weather, as well as through its interaction with vegetation productivity (fuel availability) and structure (fuel distribution and flammability) (Archibald et al. 2013 ⁴³² ) at the global (Krawchuk and Moritz 2011 ⁴³³ ), regional (Pausas and Paula 2012 ⁴³⁴ ) and local (Mondal and Sukumar 2016 ⁴³⁵ ) landscape scales. Presently, humans are the main cause of fire ignition with lightning playing a lesser role globally (Bowman et al. 2017 ⁴³⁶ ; Harris et al. 2016 ⁴³⁷ ), although the latter factor has been predominantly responsible for large fires in regions such as the North American boreal forests (Veraverbeke et al. 2017 ⁴³⁸ ). Humans also influence fires by actively extinguishing them, reducing spread and managing fuels.

Historical trends and drivers in land area burnt

While precipitation has been the major influence on wildfire regimes in pre-Industrial times, human activities have become the dominant drivers since then. There was less biomass burning during the 20th century than at any time during the past two millennia as inferred from charcoal sedimentary records (Doerr and Santín 2016 ⁴³⁹ ), though there has been an increase in the most recent decades (Marlon et al. 2016 ⁴⁴⁰ ). Trends in land area burnt have varied regionally (Giglio et al. 2013 ⁴⁴¹ ). Northern hemisphere Africa has experienced a fire decrease of 1.7 Mha yr ^–1 (–1.4% yr ^–1 ) since 2000, while southern hemisphere Africa saw an increase of 2.3 Mha yr ^–1 (+1.8% yr ^–1 ) during the same period. Southeast Asia witnessed a small increase of 0.2 Mha yr ^–1 (+2.5% yr ^–1 ) since 1997, while Australia experienced a sharp decrease of about 5.5 Mha yr ^–1 (–10.7% yr ^–1 ) during 2001–2011, followed by an upsurge in 2011 that exceeded the annual area burned in the previous 14 years. A recent analysis using the Global Fire Emissions Database v.4 (GFED4s) that includes small fires concluded that the net reduction in land area burnt globally during 1998–2015 was –24.3 ± 8.8% (–1.35 ± 0.49% yr ^–1 ) (Andela et al. 2017 ⁴⁴² ). However, from the point of fire emissions it is important to consider the land cover types which have experienced changes in area burned; in this instance, most of the declines have come from grasslands, savannas and other non-forest land cover types (Andela et al. 2017 ⁴⁴³ ). Significant increases in forest area burned (with higher fuel consumption per unit area) have been recorded in western and boreal North America (Abatzoglou and Williams 2016 ⁴⁴⁴ ; Ansmann et al. 2018 ⁴⁴⁵ ) and in boreal Siberia (Ponomarev et al. 2016 ⁴⁴⁶ ) in recent times. The 2017 and 2018 fires in British Columbia, Canada, were the largest ever recorded since the 1950s with 1.2 Mha and 1.4 Mha of forest burnt, respectively (Hanes et al. 2018 ⁴⁴⁷ ) and smoke from these fires reaching the stratosphere over central Europe (Ansmann et al. 2018 ⁴⁴⁸ ).

Climate variability and extreme climatic events such as severe drought, especially those associated with the El Niño Southern Oscillation (ENSO), play a major role in fire upsurges, as in equatorial Asia (Huijnen et al. 2016 ⁴⁴⁹ ). Fire emissions in tropical forests increased by 133% on average during and following six El Niño years compared to six La Niña years during 1997–2016, due to reductions in precipitation and terrestrial water storage (Chen et al. 2017 ⁴⁵⁰ ). The expansion of agriculture and deforestation in the humid tropics has also made these regions more vulnerable to drought-driven fires (Davidson et al. 2012 ⁴⁵¹ ; Brando et al. 2014 ⁴⁵² ). Even when deforestation rates were overall declining, as in the Brazilian Amazon during 2003–2015, the incidence of fire increased by 36% during the drought of 2015 (Aragão et al. 2018 ⁴⁵³ ).

GHG emissions from fires

Emissions from wildfires and biomass burning are a significant source of GHGs (CO ₂ , CH ₄ , N ₂ O), carbon monoxide (CO), carbonaceous aerosols, and an array of other gases including non-methane volatile organic compounds (NMVOC) (Akagi et al. 2011 ⁴⁵⁴ ; Van Der Werf et al. 2010 ⁴⁵⁵ ). GFED4s has updated fire-related carbon emission estimates biome-wise (regionally and globally), using higher resolution input data gridded at 0.25 ̊, a new burned area dataset with small fires, improved fire emission factors (Akagi et al. 2011 ⁴⁵⁶ ; Urbanski 2014 ⁴⁵⁷ ) and better fire severity characterisation of boreal forests (van der Werf et al. 2017 ⁴⁵⁸ ). The estimates for the period 1997–2016 are 2.2 GtC yr ^–1 , being highest in the 1997 El Nino (3.0 GtC yr ^–1 ) and lowest in 2013 (1.8 GtC yr ^–1 ). Furthermore, fire emissions during 1997–2016 were dominated by savanna (65.3%), followed by tropical forest (15.1%), boreal forest (7.4%), temperate forest (2.3%), peatland (3.7%) and agricultural waste burning (6.3%) (van der Werf et al. 2017 ⁴⁵⁹ ).

Fires not only transfer carbon from land to the atmosphere but also between different terrestrial pools: from live to dead biomass to soil, including partially charred biomass, charcoal and soot constituting 0.12–0.39 GtC yr ^–1 or 0.2–0.6% of annual terrestrial NPP (Doerr and Santín 2016 ⁴⁶⁰ ). Carbon from the atmosphere is sequestered back into regrowing vegetation at rates specific to the type of vegetation and other environmental variables (Loehman et al. 2014 ⁴⁶¹ ). Fire emissions are thus not necessarily a net source of carbon into the atmosphere, as post-fire recovery of vegetation can sequester a roughly equivalent amount back into biomass over a time period of one to a few years (in grasslands and agricultural lands) to decades (in forests) (Landry and Matthews 2016 ⁴⁶² ). Fires from deforestation (for land use change) and on peatlands (which store more carbon than terrestrial vegetation) obviously are a net source of carbon from the land to the atmosphere (Turetsky et al. 2014 ⁴⁶³ ); these types of fires were estimated to emit 0.4 GtC yr ^–1 in recent decades (van der Werf et al. 2017 ⁴⁶⁴ ). Peatland fires dominated by smouldering combustion under low temperatures and high moisture conditions can burn for long periods (Turetsky et al. 2014 ⁴⁶⁵ ).

Fires, land degradation/desertification and land-atmosphere exchanges

Flammable ecosystems are generally adapted to their specific fire regimes (Bond et al. 2005 ⁴⁶⁶ ). A fire regime shift alters vegetation and soil properties in complex ways, both in the short- and the long-term, with consequences for carbon stock changes, albedo, fire-atmosphere-vegetation feedbacks and the ultimate biological capacity of the burnt land (Bond et al. 2004 ⁴⁶⁷ ; Bremer and Ham 1999 ⁴⁶⁸ ; MacDermott et al. 2016 ⁴⁶⁹ ; Tepley et al. 2018 ⁴⁷⁰ ; Moody et al. 2013 ⁴⁷¹ ; Veraverbeke et al. 2012 ⁴⁷² ) A fire-driven shift in vegetation from a forested state to an alternative stable state such as a grassland (Fletcher et al. 2014 ⁴⁷³ ; Moritz 2015 ⁴⁷⁴ ) with much less carbon stock is a distinct possibility. Fires cause soil erosion through action of wind and water (Moody et al. 2013 ⁴⁷⁵ ) thus resulting in land degradation (Chapter 4) and eventually desertification (Chapter 3). Fires also affect carbon exchange between land and atmosphere through ozone (which retards photosynthesis) and aerosol (which slightly increases diffuse radiation) emissions. The net effect from fire on global GPP during 2002–2011 is estimated to be –0.86 ± 0.74 GtC yr ^–1 (Yue and Unger 2018 ⁴⁷⁶ ).

Fires under future climate change

Temperature increase and precipitation decline would be the major driver of fire regimes under future climates as evapotranspiration increases and soil moisture decreases (Pechony and Shindell 2010 ⁴⁷⁷ ; Aldersley et al. 2011 ⁴⁷⁸ ; Abatzoglou and Williams 2016 ⁴⁷⁹ ; Fernandes et al. 2017 ⁴⁸⁰ ). The risk of wildfires in future could be expected to change, increasing significantly in North America, South America, central Asia, southern Europe, southern Africa and Australia (Liu et al. 2010 ⁴⁸¹ ). There is emerging evidence that recent regional surges in wildland fires are being driven by changing weather extremes, thereby signalling geographical shifts in fire proneness (Jolly et al. 2015 ⁴⁸² ). Fire weather season has already lengthened by 18.7% globally between 1979 and 2013, with statistically significant increases across 25.3% but decreases only across 10.7% of Earth’s land surface covered with vegetation. Even sharper changes have been observed during the second half of this period (Jolly et al. 2015 ⁴⁸³ ). Correspondingly, the global area experiencing long fire weather seasons (defined as experiencing a fire weather season greater than one standard deviation (SD) from the mean global value) has increased by 3.1% per annum or 108.1% during 1979–2013. Fire frequencies under 2050 conditions are projected to increase by approximately 27% globally, relative to the 2000 levels, with changes in future fire meteorology playing the most important role in enhancing global wildfires, followed by land cover changes, lightning activities and land use, while changes in population density exhibit the opposite effects (Huang et al. 2014 ⁴⁸⁴ ).

However, climate is only one driver of a complex set of environmental, ecological and human factors in influencing fire regimes (Bowman et al. 2011 ⁴⁸⁵ ). While these factors lead to complex projections of future burnt area and fire emissions (Knorr et al. 2016a ⁴⁸⁶ , b ⁴⁸⁷ ), human exposure to wildland fires could still increase due to population expansion into areas already under high risk of fires (Knorr et al. 2016a ⁴⁸⁸ , b ⁴⁸⁹ ). There are still major challenges in projecting future fire regimes and how climate, vegetation and socio/economic factors will interact (Hantson et al. 2016 ⁴⁹⁰ ; Harris et al. 2016 ⁴⁹¹ ). There is also need for integrating various fire management strategies, such as fuel-reduction treatments in natural and planted forests, with other environmental and societal considerations to achieve the goals of carbon emissions reductions, maintain water quality, biodiversity conservation and human safety (Moritz et al. 2014 ⁴⁹² ; Gharun et al. 2017 ⁴⁹³ ).

Cross-Chapter Box 3, Figure 1

The probability of low-fire regions becoming fire prone (positive values), or of fire-prone areas changing to a low-fire state (negative values) between 1971–2000 and 2071–2100 based on eight-Earth system model (ESM) ensembles, two Shared Socio-economic Pathways (SSPs) and two Representative Concentration Pathways (RCPs). Light grey: areas where at least one ensemble simulation predicts a positive […]

The probability of low-fire regions becoming fire prone (positive values), or of fire-prone areas changing to a low-fire state (negative values) between 1971–2000 and 2071–2100 based on eight-Earth system model (ESM) ensembles, two Shared Socio-economic Pathways (SSPs) and two Representative Concentration Pathways (RCPs). Light grey: areas where at least one ensemble simulation predicts a positive and one a negative change (lack of agreement). Dark grey: area with >50% past or future cropland. Fire-prone areas are defined as having a fire frequency of >0.01 yr-1, (a) RCP4.5 emissions with SSP3 demographics, and (b) RCP8.5 emissions with SSP5 demographics (Knorr et al. 2016a).

In summary, climate change is playing an increasing role in determining wildfire regimes alongside human activity ( medium confidence ), with future climate variability expected to enhance the risk and severity of wildfires in many biomes, such as tropical rainforests ( high confidence ). Fire weather seasons have lengthened globally between 1979 and 2013 ( low confidence ). Global land area burned has declined in recent decades, mainly due to less burning in grasslands and savannas ( high confidence ). While drought remains the dominant driver of fire emissions, there has recently been increased fire activity in some tropical and temperate regions during normal to wetter-than-average years due to warmer temperatures that increase vegetation flammability ( medium confidence ). The boreal zone is also experiencing larger and more frequent fires, and this may increase under a warmer climate ( medium confidence ).

2.3 Greenhouse gas fluxes between land and atmosphere

Land is simultaneously a source and sink for several GHGs. Moreover, both natural and anthropogenic processes determine fluxes of GHGs, making it difficult to separate ‘anthropogenic’ and ‘non-anthropogenic’ emissions and removals. A meeting report by the IPCC (2010 ⁴⁹⁵ ) divided the processes responsible for fluxes from land into three categories: (i) the direct effects of anthropogenic activity due to changing land cover and land management, (ii) the indirect effects of anthropogenic environmental change, such as climate change, carbon dioxide (CO ₂ ) fertilisation, nitrogen deposition, and (iii) natural climate variability and natural disturbances (e.g., wildfires, windrow, disease). The meeting report (IPCC 2010) noted that it was impossible with any direct observation to separate direct anthropogenic effects from non-anthropogenic (indirect and natural) effects in the land sector.

As a result, different approaches and methods for estimating the anthropogenic fluxes have been developed by different communities to suit their individual purposes, tools and data availability.

The major GHGs exchanged between land and the atmosphere discussed in this chapter are CO ₂ (Section 2.3.1), methane (CH ₄ ) (Section 2.3.2) and nitrous oxide (N2O) (Section 2.3.3). We estimate the total emissions from AFOLU to be responsible for approximately 23% of global anthropogenic GHG emissions over the period 2007–2016 (Smith et al. 2013a ⁴⁹⁶ ; Ciais et al. 2013a ⁴⁹⁷ ) (Table 2.2). The estimate is similar to that reported in AR5 (high confidence), with slightly more than half these emissions coming as non-CO ₂ GHGs from agriculture. Emissions from AFOLU have remained relatively constant since AR4, although their relative contribution to anthropogenic emissions has decreased due to increases in emissions from the energy sector.

Table 2.2

Net anthropogenic emissions due to Agriculture, Forestry, and other Land Use (AFOLU) and non-AFOLU (average for 2007–2016)

Positive value represents emissions; negative value represents removals.

1. Estimates are only given until 2016 as this is the latest date when data are available for all gases.
2. Net anthropogenic flux of CO ₂ due to land cover change such as deforestation and afforestation, and land management including wood harvest and regrowth, as well as peatland burning, based on two bookkeeping models as used in the Global Carbon Budget and for AR5. Agricultural soil carbon stock change under the same land use is not considered in these models.
3. Estimates show the mean and assessed uncertainty of two databases, FAOSTAT and USEPA 2012.
4. Total non-AFOLU emissions were calculated as the sum of total CO ₂ -eq emissions values for energy, industrial sources, waste and other emissions with data from the Global Carbon Project for CO ₂ , including international aviation and shipping and from the PRIMAP database for CH ₄ and N ₂ O averaged over 2007–2014 only as that was the period for which data were available.
5. The natural response of land to human-induced environmental changes is the response of vegetation and soils to environmental changes such as increasing atmospheric CO ₂ concentration, nitrogen deposition, and climate change. The estimate shown represents the average from Dynamic Global Vegetation Models.
6. All values expressed in units of CO ₂ -eq are based on AR5 100-year Global Warming Potential (GWP) values without climate-carbon feedbacks (N ₂ O = 265; CH ₄ = 28). Note that the GWP has been used across fossil fuel and biogenic sources of methane. If a higher GWP for fossil fuel CH ₄ (30 per AR5), then total anthropogenic CH ₄ emissions expressed in CO ₂ -eq would be 2% greater.

Figure 2.4

Net and gross fluxes of CO2 from land (annual averages for 2008–2017).Left: The total net flux of CO2 between land and atmosphere (grey) is shown with its two component fluxes, (i) net AFOLU emissions (blue), and (ii) the net land sink (brown), due to indirect environmental effects and natural effects on managed and unmanaged lands. […]

Net and gross fluxes of CO ₂ from land (annual averages for 2008–2017).Left: The total net flux of CO ₂ between land and atmosphere (grey) is shown with its two component fluxes, (i) net AFOLU emissions (blue), and (ii) the net land sink (brown), due to indirect environmental effects and natural effects on managed and unmanaged lands. Middle: The gross emissions and removals contributing to the net AFOLU flux. Right: The gross emissions and removals contributing to the land sink.

2.3.1 Carbon dioxide

This section is divided into four sub-sections (Figure 2.4): (i) the total net flux of CO ₂ between land and atmosphere, (ii) the contributions of AFOLU fluxes and the non-AFOLU land sink to that total net CO ₂ flux, (iii) the gross emissions and removals comprising the net AFOLU flux, and (iv) the gross emissions and removals comprising the land sink. Emissions to the atmosphere are positive; removals from the atmosphere are negative.

Figure 2.4

Net and gross fluxes of CO2 from land (annual averages for 2008–2017).Left: The total net flux of CO2 between land and atmosphere (grey) is shown with its two component fluxes, (i) net AFOLU emissions (blue), and (ii) the net land sink (brown), due to indirect environmental effects and natural effects on managed and unmanaged lands. […]

Net and gross fluxes of CO ₂ from land (annual averages for 2008–2017).Left: The total net flux of CO ₂ between land and atmosphere (grey) is shown with its two component fluxes, (i) net AFOLU emissions (blue), and (ii) the net land sink (brown), due to indirect environmental effects and natural effects on managed and unmanaged lands. Middle: The gross emissions and removals contributing to the net AFOLU flux. Right: The gross emissions and removals contributing to the land sink.

2.3.1.1 The total net flux of CO2 between land and atmosphere

The net effects of all anthropogenic and non-anthropogenic processes on managed and unmanaged land result in a net removal of CO ₂ from the atmosphere ( high confidence ). This total net land-atmosphere removal (defined here as the total net land flux ) is estimated to have averaged 6.0 ± 2.0 GtCO ₂ yr ^–1 ( likely range ) from 2007–2016 (Table 2.3). The estimate is determined from summing the AFOLU and non-AFOLU fluxes due to transient climate change, CO ₂ fertilisation and nitrogen deposition calculated by models in the global carbon budget (Le Quéré et al. 2018 ⁴⁹⁸ ), and is consistent with inverse modelling techniques based on atmospheric CO ₂ concentrations and air transport (range: 5.1–8.8 GtCO ₂ yr ^–1 ) (Peylin et al. 2013 ⁴⁹⁹ ; Van Der Laan-Luijkx et al. 2017 ⁵⁰⁰ ; Saeki and Patra 2017 ⁵⁰¹ ; Le Quéré et al. 2018 ⁵⁰² ) (see Box 2.2 for methods). A recent inverse analysis, considering carbon transport in rivers and oceans, found a net flux of CO ₂ for land within this range, but a lower source from southern lands and a lower sink in northern lands (Resplandy et al. 2018 ⁵⁰³ ).

The net removal of CO ₂ by land has generally increased over the last 60 years in proportion to total emissions of CO ₂ ( high confidence ). Although land has been a net sink for CO ₂ since around the middle of last century, it was a net source to the atmosphere before that time, primarily as a result of emissions from AFOLU (Le Quéré et al. 2018 ⁵⁰⁴ ).

2.3.1.2 Separation of the total net land flux into AFOLU fluxes and the land sink

The total net flux of carbon between land and the atmosphere can be divided into fluxes due to direct human activities (i.e., AFOLU) and fluxes due to indirect anthropogenic and natural effects (i.e., the land sink) (Table 2.3). These two components are less certain than their sums, the total net flux of CO ₂ between atmosphere and land. The land sink, estimated with DGVMs, is least certain (Figure 2.5).

Fluxes attributed to AFOLU

The modelled AFOLU flux was a net emission of 5.2 ± 2.6 GtCO ₂ yr ^–1 ( likely range ) for 2007–2016, approximately 13% of total anthropogenic CO ₂ emissions (Le Quéré et al. 2018 ⁵⁰⁵ ) (Table 2.3). This net flux was due to direct anthropogenic activities, predominately tropical deforestation, but also afforestation/reforestation, and fluxes due to forest management (e.g., wood harvest) and other types of land management, as well as peatland drainage and burning. The AFOLU flux is the mean of two estimates from bookkeeping models (Hansis et al. 2015 ⁵⁰⁶ ; Houghton and Nassikas 2017 ⁵⁰⁷ ), and this estimated mean is consistent with the mean obtained from an assemblage of DGVMs (Le Quéré et al. 2018 ⁵⁰⁸ ) (Box 2.2 and Figure 2.5), although not all individual DGMVs include the same types of land use. Net CO ₂ emissions from AFOLU have been relatively constant since 1900. AFOLU emissions were the dominant anthropogenic emissions until around the middle of the last century when fossil fuel emissions became dominant (Le Quéré et al. 2018 ⁵⁰⁹ ). AFOLU activities have resulted in emissions of CO ₂ over recent decades ( robust evidence, high agreement ) although there is a wide range of estimates from different methods and approaches (Smith et al. 2014 ⁵¹⁰ ; Houghton et al. 2012 ⁵¹¹ ; Gasser and Ciais 2013 ⁵¹² ; Pongratz et al. 2014 ⁵¹³ ; Tubiello et al. 2015 ⁵¹⁴ ; Grassi et al. 2018 ⁵¹⁵ ) (Box 2.2, Figure 2.5 and Figure 2.7).

DGVMs and one bookkeeping model (Hansis et al. 2015 ⁵¹⁶ ) used spatially explicit, harmonised land-use change data (LUH2) (Hurtt et al. 2017 ⁵¹⁷ ) based on HYDE 3.2. The HYDE data, in turn, are based on changes in the areas of croplands and pastures. In contrast, the Houghton bookkeeping approach (Houghton and Nassikas 2017 ⁵¹⁸ ) used primarily changes in forest area from the FAO Forest Resource Assessment (FAO 2015 ⁵¹⁹ ) and FAOSTAT to determine changes in land use. To the extent that forests are cleared for land uses other than crops and pastures, estimates from Houghton and Nassikas (2017 ⁵²⁰ , 2018 ⁵²¹ ) are higher than estimates from DGMVs. In addition, both bookkeeping models (Hansis et al. 2015 ⁵²² ; Houghton and Nassikas 2017 ⁵²³ ) included estimates of carbon emissions in Southeast Asia from peat burning from GFED4s (Randerson et al. 2015 ⁵²⁴ ) and from peat drainage (Hooijer et al. 2010 ⁵²⁵ ).

Satellite-based estimates of CO ₂ emissions from losses of tropical forests during 2000–2010 corroborate the modelled emissions but are quite variable; 4.8 GtCO ₂ yr ^–1 (Tyukavina et al. 2015 ⁵²⁶ ), 3.0 GtCO ₂ yr ^–1 (Harris et al. 2015 ⁵²⁷ ), 3.2 GtCO ₂ yr ^–1 (Achard et al. 2014 ⁵²⁸ ) and 1.6 GtCO ₂ yr ^–1 (Baccini et al. 2017 ⁵²⁹ ). Differences in estimates can be explained to a large extent by the different approaches used. For example, the analysis by Tyukavina et al. (2015 ⁵³⁰ ) led to a higher estimate because they used a finer spatial resolution. Three of the estimates considered losses in forest area and ignored degradation and regrowth of forests. Baccini et al. (2017 ⁵³¹ ) in contrast, included both losses and gains in forest area and losses and gains of carbon within forests (i.e., forest degradation and growth). The four remote sensing studies cited above also reported committed emissions; in essence, all of the carbon lost from deforestation was assumed to be released to the atmosphere in the year of deforestation. In reality, only some of the carbon in trees is released immediately to the atmosphere at the time of deforestation. The unburned portion is transferred to woody debris and wood products. Both bookkeeping models and DGVMs account for the delayed emissions in growth and decomposition. Finally, the satellite-based estimates do not include changes in soil carbon.

In addition to differences in land-cover data sets between models and satellites, there are many other methodological reasons for differences (Houghton et al. 2012 ⁵³² ; Gasser and Ciais 2013 ⁵³³ ; Pongratz et al. 2014 ⁵³⁴ ; Tubiello et al. 2015 ⁵³⁵ ) (Box 2.2). There are different definitions of land-cover type, including forest (e.g., FAO uses a tree cover threshold for forests of 10%, Tyukavina et al. (2017 ⁵³⁶ ) used 25%), different estimates of biomass and soil carbon density (MgC ha–1), different approaches to tracking emissions through time (legacy effects) and different types of activity included (e.g., forest harvest, peatland drainage and fires). Most DGVMS only recently (since AR5) included forest management processes, such as tree harvesting and land clearing for shifting cultivation, leading to larger estimates of CO ₂ emissions than when these processes are not considered (Arneth et al. 2017 ⁵³⁷ ; Erb et al. 2018 ⁵³⁸ ). Grazing management has likewise been found to have large effects (Sanderman et al. 2017 ⁵³⁹ ), and is not included in most DGVMs (Pugh et al. 2015 ⁵⁴⁰ ; Pongratz et al., 2018 ⁵⁴¹ ).

Nationally reported greenhouse gas inventories versus global model estimates

There are large differences globally between estimates of net anthropogenic land-atmosphere fluxes of CO ₂ from national GHGIs and from global models, and the same is true in many regions (Figure 2.5). Fluxes reported to the UNFCCC through country GHGIs were noted as about 4.3 GtCO ₂ yr ^–1 lower (Grassi et al. 2018 ⁵⁴² ) than estimates from the bookkeeping model (Houghton et al. 2012) used in the carbon budget for AR5 (Ciais et al. 2013a ⁵⁴³ ). The anthropogenic emissions of CO ₂ from AFOLU reported in countries’ GHG inventories were 0.1 ± 1.0 GtCO ₂ yr ^–1 globally during 2005–2014 (Grassi et al. 2018 ⁵⁴⁴ ) much lower than emission estimates from the two global bookkeeping models of 5.1 ± 2.6 GtCO ₂ yr ^–1 ( likely range ) over the same time period (Le Quéré et al. 2018 ⁵⁴⁵ ). Transparency and comparability in estimates can support measuring, reporting and verifying GHG fluxes under the UNFCCC, and also the global stocktake, which will assess globally the progress towards achieving the long-term goals of the Paris Agreement. These differences can be reconciled largely by taking account of the different approaches to defining ‘anthropogenic’ in terms of different areas of land and treatment of indirect environmental change (Grassi et al. 2018 ⁵⁴⁶ ).

To date there has been one study that quantitatively reconciles the global model estimates with GHGIs (Grassi et al. 2018 ⁵⁴⁷ ). The separation of anthropogenic from non-anthropogenic effects is impossible with direct observation (IPCC 2010 ⁵⁴⁸ ). The different approaches of models and GHGIs to estimating anthropogenic emissions and removals are shown in (Figure 2.6). The difficulty is that indirect effects of environmental changes (e.g., climate change and rising atmospheric CO ₂ ) affect both manged and unmanaged lands, and some approaches treat these as anthropogenic while others do not. Bookkeeping models (e.g., Houghton and Nassikas 2017 ⁵⁴⁹ ) attempt to estimate the fluxes of CO ₂ driven by direct anthropogenic effects alone. DGVMs model the indirect environmental effects of climate and CO ₂ . If the indirect effects happen on land experiencing anthropogenic land cover change or management (harvest and regrowth), DGVMs treat this as anthropogenic. Country GHGIs separately report fluxes due to land conversion (e.g., forests to croplands) and fluxes due to land management (e.g., forest land remaining forest land). The ‘managed land proxy’ is used as a pragmatic approach to estimate anthropogenic fluxes on managed lands, whereby countries define the areas they consider managed and include all of the emissions and removals that occur on those lands. Emissions and removals are caused simultaneously by direct, indirect and natural drivers and are captured in the reporting, which often relies on inventories.

Grassi et al. (2018 ⁵⁵⁰ ) demonstrated that estimates of CO ₂ emissions from global models and from nationally reported GHGIs were similar for deforestation and afforestation, but different for managed forests. Countries generally reported larger areas of managed forests than the models and the carbon removals by these managed forests were also larger. The flux due to indirect effects on managed lands was quantified using post-processing of results from DGVMs, looking at the indirect effects of CO ₂ and climate change on secondary forest areas. The derived DGVM indirect managed forest flux was found to account for most of the difference between the bookkeeping models and the inventories.

Regional differences

Figure 2.7 shows regional differences in emissions due to AFOLU. Recent increases in deforestation rates in some tropical countries have been partially balanced by increases in forest area in India, China, the USA and Europe (FAO-FRA 2015 ⁵⁵¹ ). The trend in emissions from AFOLU since the 1990s is uncertain because some data suggest a declining rate of deforestation (FAO-FRA 2015 ⁵⁵² ), while data from satellites suggest an increasing rate (Kim 2014 ⁵⁵³ ; Hansen et al. 2012 ⁵⁵⁴ ). The disagreement results in part from differences in the definition of forest and approaches to estimating deforestation. The FAO defines deforestation as the conversion of forest to another land use (FAO-FRA 2015 ⁵⁵⁵ ), while the measurement of forest loss by satellite may include wood harvests (forests remaining forests) and natural disturbances that are not directly caused by anthropogenic activity (e.g., forest mortality from droughts and fires). Trends in anthropogenic and natural disturbances may be in opposite directions. For example, recent drought-induced fires in the Amazon have increased the emissions from wildfires at the same time that emissions from anthropogenic deforestation have declined (Aragão et al. 2018 ⁵⁵⁶ ). Furthermore, there have been advances since AR5 in estimating the GHG effects of different types of forest management (e.g., Valade et al. 2017 ⁵⁵⁷ ). Overall, there is robust evidence and high agreement for a net loss of forest area and tree cover in the tropics and a net gain, mainly of secondary forests and sustainably managed forests, in the temperate and boreal zones (Chapter 1).

Processes responsible for the land sink

Just over half of total net anthropogenic CO ₂ emissions (AFOLU and fossil fuels) were taken up by oceanic and land sinks ( robust evidence, high agreement ) (Table 2.3). The land sink was referred to in AR5 as the ‘residual terrestrial flux’, as it was not estimated directly, but calculated by difference from the other directly estimated fluxes in the budget (Table 2.3). In the 2018 budget (Le Quéré et al. 2018 ⁵⁵⁸ ), the land sink term was instead estimated directly by DGVMs, leaving a budget imbalance of 2.2 GtCO ₂ yr ^–1 (sources overestimated or sinks underestimated). The budget imbalance may result from variations in oceanic uptake or from uncertainties in fossil fuel or AFOLU emissions, as well as from land processes not included in DGVMs.

The land sink is thought to be driven largely by the indirect effects of environmental change (e.g., climate change, increased atmospheric CO ₂ concentration, nitrogen deposition) on unmanaged and managed lands ( robust evidence, high agreement ). The land sink has generally increased since 1900 and was a net sink of 11.7 ± 3.7 GtCO ₂ yr ^–1 during the period 2008–2017 (Table 2.3), absorbing 29% of global anthropogenic emissions of CO ₂ . The land sink has slowed the rise in global land-surface air temperature by 0.09 ± 0.02°C since 1982 (medium confidence) (Zeng et al. 2017 ⁵⁵⁹ ).

The rate of CO ₂ removal by land accelerated from –0.026 ± 0.24 GtCO ₂ yr ^–1 during the warming period (1982–1998) to –0.436 ± 0.260 GtCO ₂ yr ^–1 during the warming hiatus (1998–2012). One explanation is that respiration rates were lower during the warming hiatus (Ballantyne et al. 2017 ⁵⁶⁰ ). However, the lower rate of growth in atmospheric CO ₂ during the warming hiatus may have resulted, not from lower rates of respiration, but from declining emissions from AFOLU (lower rates of tropical deforestation and increased forest growth in northern mid-latitudes) (Piao et al. 2018 ⁵⁶¹ ). Changes in the growth rate of atmospheric CO ₂ , by themselves, do not identify the processes responsible and the cause of the variation is uncertain.

While year-to-year variability in the indirect land sink is high in response to climate variability, DGVM fluxes are influenced far more on decadal timescales by CO ₂ fertilisation. A DGVM intercomparison (Sitch et al. 2015 ⁵⁶² ) for 1990–2009 found that CO ₂ fertilisation alone contributed a mean global removal of –10.54 ± 3.68 GtCO ₂ yr ^–1 (trend –0.444 ± 0.202 GtCO ₂ yr ^–1 ). Data from forest inventories around the world corroborate the modelled land sink (Pan et al. 2011). The geographic distribution of the non-AFOLU land sink is less certain. While it seems to be distributed globally, its distribution between the tropics and non-tropics is estimated to be between 1:1 (Pan et al. 2011 ⁵⁶³ ) and 1:2 (Houghton et al. 2018 ⁵⁶⁴ ).

As described in Box 2.3, rising CO ₂ concentrations have a fertilising effect on land, while climate has mixed effects; for example, rising temperature increases respiration rates and may enhance or reduce photosynthesis depending on location and season, while longer growing seasons might allow for higher carbon uptake. However, these processes are not included in DGVMs, which may account for at least some of the land sink. For example, a decline in the global area burned by fires each year (Andela et al. 2017 ⁵⁶⁵ ) accounts for an estimated net sink (and/or reduced emissions) of 0.5 GtCO ₂ yr ^–1 (limited evidence, medium agreement) (Arora and Melton 2018 ⁵⁶⁶ ).

Boreal forests represent an exception to this decline (Kelly et al. 2013 ⁵⁶⁷ ). The reduction in burning not only reduces emissions, but also allows more growth of recovering forests. There is also an estimated net carbon sink of about the same magnitude (0.5 GtCO ₂ yr ^–1 ) as a result of soil erosion from agricultural lands and redeposition in anaerobic environments where respiration is reduced (limited evidence, low agreement) (Wang et al. 2017d ⁵⁶⁸ ). A recent study attributes an increase in land carbon to a longer-term (1860–2005) aerosol-induced cooling (Zhang et al. 2019 ⁵⁶⁹ ). Recent evidence also suggests that DGVMs and ESMs underestimate the effects of drought on CO ₂ emissions (Humphrey et al. 2018 ⁵⁷⁰ ; Green et al. 2019 ⁵⁷¹ ; Kolus et al. 2019 ⁵⁷² ).

Table 2.3

Perturbation of the global carbon cycle caused by anthropogenic activities (GtCO ₂ yr–1)

Source: Le Quéré et al. (2018 ⁵⁷³ ).

CO ₂ flux (GtCO ₂ y ^–1 ), 10-year mean Emissions Fossil CO ₂ emissions 11.4 ± 0.7 17.2 ± 0.7 19.8 ± 1.1 23.1 ± 1.1 28.6 ± 1.5 34. ± 1.8 AFOLU net emissions 5.5 ± 2.6 4.4 ± 2.6 4.4 ± 2.6 5.1 ± 2.6 4.8 ± 2.6 5.5 ± 2.6 Partitioning Growth in atmosphere 6.2 ± 0.3 10.3 ± 0.3 12.5 ± 0.07 11.4 ± 0.07 14.7 ± 0.07 17.2 ± 0.07 Ocean sink 3.7 ± 1.8 4.8 ± 1.8 6.2 ± 1.8 7.3 ± 1.8 7.7 ± 1.8 8.8 ± 1.8 Land sink (non-AFOLU) 4.4 ± 1.8 7.7 ± 1.5 6.6 ± 2.2 8.8 ± 1.8 9.9 ± 2.6 11.7 ± 2.6 Budget imbalance 2.2 –1.1 –1.1 0.7 0.7 1.8 Total net land flux (AFOLU – land sink) +1.1 ± 3.2 –3.3 ± 3.0 –2.2 ± 3.4 –3.7 ± 2.2 –5.1 ± 3.2 –6.2 ± 3.7

1960–1969 1970–1979 1980–1989 1990–1999 2000–2009 2008–2017

Figure 2.5

Global net CO2 emissions due to AFOLU from different approaches (in GtCO2 yr–1).Brown line: the mean and individual estimates (brown shading) from two bookkeeping models (Houghton and Nassikas 2017; Hansis et al. 2015). Blue line: the mean from DGVMs run with the same driving data with the pale blue shading showing the ±1 standard deviation […]

Global net CO ₂ emissions due to AFOLU from different approaches (in GtCO ₂ yr ^–1 ).Brown line: the mean and individual estimates (brown shading) from two bookkeeping models (Houghton and Nassikas 2017 ⁵⁷⁴ ; Hansis et al. 2015 ⁵⁷⁵ ). Blue line: the mean from DGVMs run with the same driving data with the pale blue shading showing the ±1 standard deviation range. Yellow line: data downloaded from FAOSTAT website (Tubiello et al. 2013 ⁵⁷⁶ ); the dashed line is primarily forest-related emissions, while the solid yellow line also includes emissions from peat fires and peat draining. Orange line: Greenhouse Gas Inventories (GHGI) based on country reports to UNFCCC (Grassi et al. 2018 ⁵⁷⁷ ), data are shown only from 2005 because reporting in many developing countries became more consistent/reliable after this date. For more details on methods see Box 2.2.

Figure 2.6

Summary of the main conceptual differences between GHG Inventories and global models in considering what is the ‘anthropogenic land CO2 flux’. Adapted from Grassi et al. (2018), effects of key processes on the land flux as defined by IPCC (2010) including where these effects occur (in managed and/or unmanaged lands) and how these effects are […]

Summary of the main conceptual differences between GHG Inventories and global models in considering what is the ‘anthropogenic land CO ₂ flux’. Adapted from Grassi et al. (2018) ⁵⁷⁸ , effects of key processes on the land flux as defined by IPCC (2010) ⁵⁷⁹ including where these effects occur (in managed and/or unmanaged lands) and how these effects are captured in (a) bookkeeping models that do not explicitly model the effects of environmental change (although some is implicitly captured in data on carbon densities and growth and decay rates), (b) DGVMs that include the effects of environmental change on all lands, and run the models with and without land use change to diagnose ‘land use change’. The ‘land sink’ is then conceptually assumed to be a natural response of land to the anthropogenic perturbation of environmental change, DGVMs include the effects of inter-annual climate variability, and some include fires but no other natural disturbances, and (c) GHG Inventories reported by countries to the UNFCCC that report all fluxes in areas the countries define as ‘managed land’ but do not report unmanaged land. This is the CO ₂ flux due to Land Use Land Use Change and Forestry (LULUCF) which is a part of the overall AFOLU flux. The area of land considered as managed in the inventories is greater than that considered as subject to direct management activities (harvest and regrowth) in the models.

Figure 2.7

Regional trends in net anthropogenic land-atmosphere CO2 flux from a range of different approaches (in GtCO2 yr–1).Red symbols: bookkeeping models (hexagon: Houghton and Nassikas 2017; square: Hansis et al. 2015). Blue cross: the mean from DGMVs with the box showing the 1 standard deviation range. Green triangles: downloaded from FAOSTAT website; the open triangle is […]

Regional trends in net anthropogenic land-atmosphere CO ₂ flux from a range of different approaches (in GtCO ₂ yr ^–1 ).Red symbols: bookkeeping models (hexagon: Houghton and Nassikas 2017 ⁵⁸⁰ ; square: Hansis et al. 2015 ⁵⁸¹ ). Blue cross: the mean from DGMVs with the box showing the 1 standard deviation range. Green triangles: downloaded from FAOSTAT website; the open triangle is primarily forest-related emissions, while the closed triangle includes emission from peat fires and peat drainage. Yellow inverted triangle: GHGI LULUCF flux based on country reports to UNFCCC (Grassi et al. 2018 ⁵⁸² ). Data for developing countries are only shown for 2006–2015 because reporting in many developing countries became more consistent/reliable after 2005. For more details on methods see Box 2.2.

2.3.1.3 Gross emissions and removals contributing to AFOLU emissions

The modelled AFOLU flux of 5.5 ± 3.7 GtCO ₂ yr ^–1 over the period 2008–2017 represents a net value. It consists of both gross emissions of CO ₂ from deforestation, forest degradation and the oxidation of wood products, as well as gross removals of CO ₂ in forests and soils recovering from harvests and agricultural abandonment (Figure 2.4). The uncertainty of these gross fluxes is high because few studies report gross fluxes from AFOLU. Houghton and Nassikas (2017 ⁵⁸³ ) estimated gross emissions to be as high as 20.2 GtCO ₂ yr ^–1 ( limited evidence, low agreement ) (Figure 2.4), and even this may be an underestimate because the land-use change data used from FAOSTAT (Tubiello et al. 2013 ⁵⁸⁴ ) is itself a net of all changes within a country.

Gross emissions and removals of CO ₂ result from rotational uses of land, such as wood harvest and shifting cultivation, including regrowth. These gross fluxes are more informative for assessing the timing and potential for mitigation than estimates of net fluxes, because the gross fluxes include a more complete accounting of individual activities. Gross emissions from rotational land use in the tropics are approximately 37% of total CO ₂ emissions, rather than 14%, as suggested by net AFOLU emissions (Houghton and Nassikas 2018 ⁵⁸⁵ ). Further, if the forest is replanted or allowed to regrow, gross removals of nearly the same magnitude would be expected to continue for decades.

2.3.1.4 Gross emissions and removals contributing to the non-anthropogenic land sink

The net land sink averaged 11.2 ± 2.6 GtCO ₂ yr ^–1 (l ikely range ) over 2007–2016 (Table 2.3.2), but its gross components have not been estimated at the global level. There are many studies that suggest increasing emissions of carbon are due to indirect environmental effects and natural disturbance, for example, temperature-induced increases in respiration rates (Bond-Lamberty et al. 2018 ⁵⁸⁶ ), increased tree mortality (Brienen et al. 2015 ⁵⁸⁷ ; Berdanier and Clark 2016 ⁵⁸⁸ ; McDowell et al. 2018 ⁵⁸⁹ ) and thawing permafrost (Schuur et al. 2015 ⁵⁹⁰ ). The global carbon budget indicates that land and ocean sinks have increased over the last six decades in proportion to total CO ₂ emissions (Le Quéré et al. 2018 ⁵⁹¹ ) ( robust evidence, high agreement ). That means that any emissions must have been balanced by even larger removals (likely driven by CO ₂ fertilisation, climate change, nitrogen deposition, erosion and redeposition of soil carbon, a reduction in areas burned, aerosol-induced cooling and changes in natural disturbances) (Box 2.3).

Climate change is expected to impact terrestrial biogeochemical cycles via an array of complex feedback mechanisms that will act to either enhance or decrease future CO ₂ emissions from land. Because the gross emissions and removals from environmental changes are not constrained at present, the balance of future positive and negative feedbacks remains uncertain. Estimates from climate models in Coupled Model Intercomparison Project 5 (CMIP5) exhibit large differences for different carbon and nitrogen cycle feedbacks and how they change in a warming climate (Anav et al. 2013 ⁵⁹² ; Friedlingstein et al. 2006 ⁵⁹³ ; Friedlingstein, et al. 2014 ⁵⁹⁴ ). The differences are in large part due to the uncertainty regarding how primary productivity and soil respiration will respond to environmental changes, with many of the models not even agreeing on the sign of change. Furthermore, many models do not include a nitrogen cycle, which may limit the CO ₂ fertilisation effect in the future (Box 2.3). There is an increasing amount of observational data available and methods to constrain models (e.g., Cox et al. 2013 ⁵⁹⁵ ; Prentice, et al., 2015 ⁵⁹⁶ ) which can reduce uncertainty.

2.3.1.5 Potential impact of mitigation on atmospheric CO ₂ concentrations

If CO ₂ concentrations decline in the future as a result of low 2 emissions and large negative emissions, the global land and
ocean sinks are expected to weaken (or even reverse). The oceans
are expected to release CO ₂ back to the atmosphere when the concentration declines (Ciais et al. 2013a ⁵⁹⁷ ; Jones et al. 2016 ⁵⁹⁸ ).

This means that to maintain atmospheric CO ₂ and temperature at low levels, both the excess CO ₂ from the atmosphere and the CO ₂ progressively outgassed from the ocean and land sinks will need to be removed. This outgassing from the land and ocean sinks is called the ‘rebound effect’ of the global carbon cycle (Ciais et al. 2013a ⁵⁹⁹ ). It will reduce the effectiveness of negative emissions and increase the deployment level needed to achieve a climate stabilisation target (Jackson et al. 2017 ⁶⁰⁰ ; Jones et al. 2016 ⁶⁰¹ ) ( limited evidence, high agreement ).

2.3.2 Methane

2.3.2.1 Atmospheric trends

In 2017, the globally averaged atmospheric concentration of CH ₄ was 1850 ± 1 ppbv (Figure 2.8A). Systematic measurements of atmospheric CH ₄ concentrations began in the mid-1980s and trends show a steady increase between the mid-1980s and early- 1990s, slower growth thereafter until 1999, a period of no growth between 1999 and 2006, followed by a resumption of growth in 2007. The growth rates show very high inter-annual variability with a negative trend from the beginning of the measurement period until about 2006, followed by a rapid recovery and continued high inter- annual variability through 2017 (Figure 2.8B). The growth rate has been higher over the past 4 years ( high confidence ) (Nisbet et al. 2019 ⁶⁰² ). The trend in δ ¹³ C-CH ₄ prior to 2000 with less depleted ratios indicated that the increase in atmospheric concentrations was due to thermogenic (fossil) CH ₄ emissions; the reversal of this trend after 2007 indicates a shift to biogenic sources (Figure 2.8C).

Understanding the underlying causes of temporal variation in atmospheric CH ₄ concentrations is an active area of research. Several studies concluded that inter-annual variability of CH ₄ growth was driven by variations in natural emissions from wetlands (Rice et al. 2016 ⁶⁰³ ; Bousquet et al. 2006 ⁶⁰⁴ ; Bousquet et al. 2011 ⁶⁰⁵ ). These modelling efforts concluded that tropical wetlands were responsible for between 50 and 100% of the inter-annual fluctuations and the renewed growth in atmospheric concentrations after 2007. However, results were inconsistent for the magnitude and geographic distribution of the wetland sources between the models. Pison et al. (2013) ⁶⁰⁶ used two atmospheric inversion models and the ORCHIDEE model and found greater uncertainty in the role of wetlands in inter-annual variability between 1990 and 2009 and during the 1999–2006 pause. Poulter et al. (2017) ⁶⁰⁷ used several biogeochemical models and inventory-based wetland area data to show that wetland CH ₄ emissions increases in the boreal zone have been offset by decreases in the tropics, and concluded that wetlands have not contributed significantly to renewed atmospheric CH ₄ growth.

The models cited above assumed that atmospheric hydroxyl radical (OH) sink over the period analysed did not vary. OH reacts with CH ₄ as the first step toward oxidation to CO ₂ . In global CH ₄ budgets, the atmospheric OH sink has been difficult to quantify because its short lifetime (about 1 second) and its distribution is controlled by precursor species that have non-linear interactions (Taraborrelli et al., 2012 ⁶⁰⁸ ; Prather et al., 2017 ⁶⁰⁹ ). Understanding of the atmospheric OH sink has evolved recently. The development of credible time series of methyl chloroform (MCF: CH3CCl3) observations offered a way to understand temporal dynamics of OH abundance and applying this to global budgets further weakened the argument for the role of wetlands in determining temporal trends since 1990. Several authors used the MCF approach and concluded that changes in the atmospheric OH sink explained a large portion of the suppression in global CH ₄ concentrations relative to the pre-1999 trend (Turner et al. 2017 ⁶¹⁰ ; Rigby et al. 2013 ⁶¹¹ ; McNorton et al. 2016 ⁶¹² ). These studies could not reject the null hypothesis that OH has remained constant in recent decades and they did not suggest a mechanism for the inferred OH concentration changes (Nisbet et al. 2019 ⁶¹³ ). Nicely et al. (2018) ⁶¹⁴ used a mechanistic approach and demonstrated that variation in atmospheric OH was much lower than what MCF studies claimed that positive trends in OH due to the effects of water vapour, nitrogen oxides (NOx), tropospheric ozone and expansion of the tropical Hadley cells offsets the decrease in OH that is expected from increasing atmospheric CH ₄ concentrations.

The depletion of δ ¹³ C _atm beginning in 2009 could be due to changes in several sources. Decreased fire emissions combined with increased tropical wetland emissions compared to earlier years could explain the δ ¹³ C perturbations to atmospheric CH ₄ sources (Worden et al. 2017 ⁶¹⁵ ; Schaefer et al. 2016 ⁶¹⁶ ). However, because tropical wetland emissions are higher in the southern hemisphere, and the remote sensing observations show that CH ₄ emissions increases are largely in the north tropics (Bergamaschi et al. 2013 ⁶¹⁷ ; Melton et al. 2013 ⁶¹⁸ ; Houweling et al. 2014 ⁶¹⁹ ), an increased wetland source does not fit well with the southern hemisphere δ ¹³ C observations. New evidence shows that tropical wetland CH ₄ emissions are significantly underestimated, perhaps by a factor of 2, because estimates do not account for release by tree stems (Pangala et al. 2017 ⁶²⁰ ). Several authors have concluded that agriculture is a more probable source of increased emissions, particularly from rice and livestock in the tropics, which is consistent with inventory data (Wolf et al. 2017 ⁶²¹ ; Patra et al. 2016 ⁶²² ; Schaefer et al. 2016 ⁶²³ ).

The importance of fugitive emissions in the global atmospheric accumulation rate is growing ( medium evidence, high agreement ). The increased production of natural gas in the US from the mid 2000s is of particular interest because it coincides with renewed atmospheric CH ₄ growth (Rice et al. 2016 ⁶²⁴ ; Hausmann et al. 2015 ⁶²⁵ ). Reconciling increased fugitive emissions with increased isotopic depletion of atmospheric CH ₄ indicates that there are likely multiple changes in emissions and sinks that affect atmospheric accumulation ( medium confidence ).

With respect to atmospheric CH ₄ growth rates, we conclude that there is significant and ongoing accumulation of CH ₄ in the atmosphere ( very high confidence ). The reason for the pause in growth rates and subsequent renewed growth is at least partially associated with land use and land use change. Evidence that variation in the atmospheric OH sink plays a role in the year-to-year variation of the CH ₄ is accumulating, but results are contradictory ( medium evidence, low agreement ) and refining this evidence is constrained by lack of long-term isotopic measurements at remote sites, particularly in the tropics. Fugitive emissions likely contribute to the renewed growth after 2006 ( medium evidence, high agreement ). Additionally, the recent depletion trend of ¹³ C isotope in the atmosphere indicates that growth in biogenic sources explains part of the current growth and that biogenic sources make up a larger proportion of the source mix compared to the period before 1997 ( robust evidence, high agreement ). In agreement with the findings of AR5, we conclude that wetlands are important drivers of inter- annual variability and current growth rates ( medium evidence, high agreement ). Ruminants and the expansion of rice cultivation are also important contributors to the current growth trend ( medium evidence, high agreement ).

Figure 2.8

Globally averaged atmospheric CH4 mixing ratios (Frame A) and instantaneous rates of change (Frame B) and C isotope/variation (Frame C). Data sources: NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends_ch4); Dlugokencky et al. (1994) and Schaefer et al. (2016).

Globally averaged atmospheric CH ₄ mixing ratios (Frame A) and instantaneous rates of change (Frame B) and C isotope/variation (Frame C). Data sources: NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends_ch4); Dlugokencky et al. (1994) ⁶²⁶ and Schaefer et al. (2016) ⁶²⁷ .

2.3.2.2 Land use effects

Agricultural emissions are predominantly from enteric fermentation and rice, with manure management and waste burning contributing
small amounts (Figure 2.9). Since 2000, livestock production has been responsible for 33% of total global emissions and 66% of agricultural emissions (EDGAR 4.3.2 database, May 2018; USEPA 2012 ⁶²⁸ ; Tubiello et al. 2014 ⁶²⁹ ; Janssens-Maenhout et al. 2017b ⁶³⁰ ). Asia has the largest livestock emissions (37%) and emissions in the region have been growing by around 2% per year over the same period.

North America is responsible for 26% and emissions are stable; Europe is responsible for around 8% of emissions, and these are decreasing slightly (<1% per year). Africa is responsible for 14%, but emissions are growing fastest in this region at around 2.5% y ^–1 . In Latin America and the Caribbean, livestock emissions are decreasing at around 1.6% per year and the region makes up 16% of emissions.

Rice emissions are responsible for about 24% of agricultural emissions and 89% of these are from Asia. Rice emissions are increasing by 0.9% per year in that region. These trends are predicted to continue through 2030 (USEPA 2013 ⁶³¹ ).

Upland soils are a net sink of atmospheric CH ₄ , but soils both produce and consume the gas. On the global scale, climatic zone, soil texture and land cover have an important effect on CH ₄ uptake in upland soils (Tate 2015 ⁶³² ; Yu et al. 2017 ⁶³³ ; Dutaur and Verchot 2007 ⁶³⁴ ). Boreal soils take up less than temperate or tropical soils, coarse textured soils take up more CH ₄ than medium and fine textured soils, and forests take up more than other ecosystems. Low levels of nitrogen fertilisation or atmospheric deposition can affect the soil microbial community and stimulate soil CH ₄ uptake in nitrogen-limited soils, while higher fertilisation rates decrease uptake (Edwards et al. 2005 ⁶³⁵ ; Zhuang et al., 2013 ⁶³⁶ ). Globally, nitrogen fertilisation on agricultural lands may have suppressed CH ₄ oxidation by as much as 26 Tg between 1998 and 2004 ( low confidence, low agreement ) (Zhuang et al., 2013 ⁶³⁷ ). The effect of nitrogen additions is cumulative and repeated fertilisation events have progressively greater suppression effects ( robust evidence, high agreement ) (Tate 2015 ⁶³⁸ ). Other factors such as higher temperatures, increased atmospheric concentrations and changes in rainfall patterns stimulate soil CH ₄ consumption in unfertilised ecosystems. Several studies (Yu et al. 2017 ⁶³⁹ ; Xu et al. 2016 ⁶⁴⁰ ; Curry 2009 ⁶⁴¹ ) have shown that globally, uptake has been increasing during the second half of the 20th century and is expected to continue to increase by as much as 1 Tg in the 21st century, particularly in forests and grasslands ( medium evidence, high agreement ).

Northern peatlands (40–70°N) are a significant source of atmospheric CH ₄ , emitting about 48 TgCH ₄ , or about 10% of the total emissions to the atmosphere (Zhuang et al. 2006 ⁶⁴² ; Wuebbles and Hayhoe 2002 ⁶⁴³ ). CH ₄ emissions from natural northern peatlands are highly variable, with the highest rate from fens ( medium evidence, high agreement ). Peatland management and restoration alters the exchange of CH ₄ with the atmosphere ( medium evidence, high agreement ). Management of peat soils typically converts them from CH ₄ sources to sinks (Augustin et al. 2011 ⁶⁴⁴ ; Strack and Waddington 2008 ⁶⁴⁵ ; Abdalla et al. 2016 ⁶⁴⁶ ) ( robust evidence, high agreement ). While restoration decreases CO ₂ emissions (Section 4.9.4), CH ₄ emissions often increase relative to the drained conditions ( robust evidence, high agreement ) (Osterloh et al. 2018 ⁶⁴⁷ ; Christen et al. 2016 ⁶⁴⁸ ; Koskinen et al. 2016 ⁶⁴⁹ ; Tuittila et al. 2000 ⁶⁵⁰ ; Vanselow-Algan et al. 2015 ⁶⁵¹ ; Abdalla et al. 2016 ⁶⁵² ). Drained peatlands are usually considered to be negligible methane sources, but they emit CH ₄ under wet weather conditions and from drainage ditches (Drösler et al. 2013 ⁶⁵³ ; Sirin et al. 2012 ⁶⁵⁴ ). While ditches cover only a small percentage of the drained area, emissions can be sufficiently high that drained peatlands emit comparable CH ₄ as undrained ones ( medium evidence, medium agreement ) (Sirin et al. 2012 ⁶⁵⁵ ; Wilson et al. 2016 ⁶⁵⁶ ).

Because of the large uncertainty in the tropical peatland area, estimates of the global flux are highly uncertain. A meta-analysis of the effect of conversion of primary forest to rice production showed that emissions increased by a factor of four ( limited evidence, high agreement ) (Hergoualc’h and Verchot, 2012 ⁶⁵⁷ ). For land uses that required drainage, emissions decreased by a factor of three ( limited evidence, high agreement ).There are no representative measurements of emissions from drainage ditches in tropical peatlands.

Figure 2.9

Average agricultural CH4 emissions estimates from 1990. Sub-sectorial agricultural emissions are based on the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al. 2017a); FAOSTAT (Tubiello et al. 2013); and National GHGI data (Grassi et al. 2018). GHGI data are aggregate values for the sector. Note that EDGAR data are complete only through […]

Average agricultural CH ₄ emissions estimates from 1990. Sub-sectorial agricultural emissions are based on the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al. 2017a ⁶⁵⁸ ); FAOSTAT (Tubiello et al. 2013 ⁶⁵⁹ ); and National GHGI data (Grassi et al. 2018 ⁶⁶⁰ ). GHGI data are aggregate values for the sector. Note that EDGAR data are complete only through 2012; the data in the right-hand panel represent the three years 2010–2012 and are presented for comparison.

2.3.3 Nitrous oxide

2.3.3.1 Atmospheric trends

The atmospheric abundance of N ₂ O has increased since 1750, from a pre-industrial concentration of 270 ppbv to 330 ppbv in 2017 (high agreement, robust evidence) (US National Oceanographic and Atmospheric Agency, Earth Systems Research Laboratory)

(Figure 2.10). The rate of increase has also increased, from approximately 0.15 ppbv yr ^–1 100 years ago, to 0.85 ppbv yr ^–1 over the period 2001–2015 (Wells et al. 2018 ⁶⁶¹ ). Atmospheric N ₂ O isotopic composition (14/15N) was relatively constant during the pre-industrial period (Prokopiou et al. 2018 ⁶⁶² ) and shows a decrease in the δ15N as the N ₂ O mixing ratio in the atmosphere has increased between 1940 and 2005. This recent decrease indicates that terrestrial sources are the primary driver of increasing trends and marine sources contribute around 25% (Snider et al. 2015 ⁶⁶³ ). Microbial denitrification and nitrification processes are responsible for more than 80% of total global N ₂ O emissions, which includes natural soils, agriculture and oceans, with the remainder coming from non-biological sources such as biomass burning and fossil-fuel combustion (Fowler et al. 2015 ⁶⁶⁴ ). The isotopic trend also indicates a shift from denitrification to nitrification as the primary source of N ₂ O as a result of the use of synthetic nitrogen fertiliser (high evidence, high agreement) (Park et al. 2012 ⁶⁶⁵ ; Toyoda et al. 2013 ⁶⁶⁶ ; Snider et al. 2015 ⁶⁶⁷ ; Prokopiou et al. 2018 ⁶⁶⁸ ).

The three independent sources of N ₂ O emissions estimates from agriculture at global, regional and national levels are: USEPA, EDGAR and FAOSTAT (USEPA 2013 ⁶⁶⁹ ; Tubiello et al. 2015 ⁶⁷⁰ ; Janssens-Maenhout et al. 2017a ⁶⁷¹ ). EDGAR and FAOSTAT have temporal resolution beyond 2005 and these databases compare well with national inventory data (Figure 2.10). USEPA has historical estimates through 2005 and projections thereafter. The independent data use IPCC methods, with Tier 1 emission factors and national reporting of activity data. Tier 2 approaches are also available based on top-down and bottom-up approaches. Recent estimates using inversion modelling and process models estimate total annual global N ₂ O emissions of 16.1–18.7 (bottom-up) and 15.9–17.7 TgN (top-down), demonstrating relatively close agreement (Thompson et al. 2014 ⁶⁷² ). Agriculture is the largest source and has increased with extensification and intensification. Recent modelling estimates of terrestrial sources show a higher emissions range that is slightly more constrained than what was reported in AR5: approximately 9 (7–11) TgN ₂ O-N yr ^–1 (Saikawa et al. 2014 ⁶⁷³ ; Tian et al. 2016 ⁶⁷⁴ ) compared to 6.6 (3.3–9.0) TgN ₂ O-N yr ^–1 (Ciais et al. 2013a ⁶⁷⁵ ). Estimates of marine N ₂ O emissions are between 2.5 and 4.6 TgN ₂ O-N yr ^–1 (Buitenhuis et al., 2018 ⁶⁷⁶ ; Saikawa et al., 2014 ⁶⁷⁷ ).

To conclude, N ₂ O is continuing to accumulate in the atmosphere at an increasingly higher rate (very high confidence), driven primarily by increases in manure production and synthetic nitrogen fertiliser use from the mid-20th century onwards (high confidence). Findings since AR5 have constrained regional and global estimates of annual N ₂ O emissions and improved our understanding of the spatio-temporal dynamics of N ₂ O emissions, including soil rewetting and freeze-thaw cycles which are important determinants of total annual emission fluxes in some regions (medium confidence).

Figure 2.10

Globally averaged atmospheric N2O mixing ratios since 1984. Data source: NOAA/ESRL Global Monitoring Division (www.esrl.noaa.gov/gmd/hats/combined/N2O.html).

Globally averaged atmospheric N ₂ O mixing ratios since 1984. Data source: NOAA/ESRL Global Monitoring Division ( www.esrl.noaa.gov/gmd/hats/combined/N ₂ O.html ).

Figure 2.11

Average agricultural N2O emissions estimates from 1990. Sub-sectorial agricultural emissions are based on the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al. 2017a); FAOSTAT (Tubiello et al. 2013) and National GHGI data (Grassi et al. 2018). GHGI data are aggregate values for the sector. Note that EDGAR data are complete only through […]

Average agricultural N ₂ O emissions estimates from 1990. Sub-sectorial agricultural emissions are based on the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2 ²¹²⁸ ; Janssens-Maenhout et al. 2017a ²¹²⁹ ); FAOSTAT (Tubiello et al. 2013 ²¹³⁰ ) and National GHGI data (Grassi et al. 2018 ²¹³¹ ). GHGI data are aggregate values for the sector. Note that EDGAR data are complete only through 2012; the EDGAR data in the right-hand panel represent the three years 2010–2012 and are presented for comparison.

2.3.3.2 Land use effects

Agriculture is responsible for approximately two-thirds of N ₂ O emissions ( robust evidence, high agreement ) (Janssens-Maenhout et al. 2017b ⁶⁷⁸ ). Total emissions from this sector are the sum of direct and indirect emissions. Direct emissions from soils are the result of mineral fertiliser and manure application, manure management, deposition of crop residues, cultivation of organic soils and inorganic nitrogen inputs through biological nitrogen fixation. Indirect emissions come from increased warming, enrichment of downstream water bodies from runoff, and downwind nitrogen deposition on soils. The main driver of N ₂ O emissions in croplands is a lack of synchronisation between crop nitrogen demand and soil nitrogen supply, with approximately 50% of nitrogen applied to agricultural land not taken up by the crop (Zhang et al. 2017 ⁶⁷⁹ ). Cropland soils emit over 3 TgN ₂ O-N yr ^–1 ( medium evidence, high agreement ) (Janssens-Maenhout et al. 2017b ⁶⁸⁰ ; Saikawa et al. 2014 ⁶⁸¹ ). Regional inverse modelling studies show larger tropical emissions than the inventory approaches and they show increases in N ₂ O emissions from the agricultural sector in South Asia, Central America, and South America (Saikawa et al. 2014 ⁶⁸² ; Wells et al. 2018 ⁶⁸³ ).

Emissions of N ₂ O from pasturelands and rangelands have increased by as much as 80% since 1960 due to increased manure production and deposition ( robust evidence, high agreement ) (de Klein et al. 2014 ⁶⁸⁴ ; Tian et al. 2018 ⁶⁸⁵ ; Chadwick et al. 2018 ⁶⁸⁶ ; Dangal et al. 2019 ⁶⁸⁷ ; Cardenas et al. 2019 ⁶⁸⁹ ). Studies consistently report that pasturelands and rangelands are responsible for around half of the total agricultural N ₂ O emissions (Davidson 2009 ⁶⁹⁰ ; Oenema et al. 2014 ⁶⁹¹ ; Dangal et al. 2019 ⁶⁹² ). An analysis by Dangal et al. (2019) shows that, while managed pastures make up around one-quarter of the global grazing lands, they contribute 86% of the net global N ₂ O emissions from grasslands and that more than half of these emissions are related to direct deposition of livestock excreta on soils.

Many studies calculate N ₂ O emissions from a linear relationship between nitrogen application rates and N ₂ O emissions. New studies are increasingly finding nonlinear relationships, which means that N ₂ O emissions per hectare are lower than the Tier 1 EFs (IPCC 2003 ⁶⁹³ ) at low nitrogen application rates, and higher at high nitrogen application rates ( robust evidence, high agreement ) (Shcherbak et al. 2014 ⁶⁹⁴ ; van Lent et al. 2015 ⁶⁹⁵ ; Satria 2017 ⁶⁹⁶ ). This not only has implications for how agricultural N ₂ O emissions are estimated in national and regional inventories, which now often use a linear relationship between nitrogen applied and N ₂ O emissions, it also means that in regions of the world where low nitrogen application rates dominate, increases in nitrogen fertiliser use would generate relatively small increases in agricultural N ₂ O emissions. Decreases in application rates in regions where application rates are high and exceed crop demand for parts of the growing season are likely to have very large effects on emissions reductions ( medium evidence, high agreement ).

Deforestation and other forms of land-use change alter soil N ₂ O emissions. Typically, N ₂ O emissions increase following conversion of native forests and grasslands to pastures or croplands (McDaniel et al. 2019 ⁶⁹⁷ ; van Lent et al. 2015 ⁶⁹⁸ ). This increase lasts from a few years to a decade or more, but there is a trend toward decreased N ₂ O emissions with time following land use change and, ultimately, lower N ₂ O emissions than had been occurring under native vegetation, in the absence of fertilisation ( medium evidence, high agreement ) (Meurer et al. 2016 ²¹³² ; van Lent et al. 2015 ⁶⁹⁹ ) (Figure 2.12). Conversion of native vegetation to fertilised systems typically leads to increased N ₂ O emissions over time, with the rate of emission often being a function of nitrogen fertilisation rates, however, this response can be moderated by soil characteristics and water availability ( medium evidence, high agreement ) (van Lent et al. 2015 ⁷⁰⁰ ; Meurer et al. 2016 ⁷⁰¹ ). Restoration of agroecosystems to natural vegetation, over the period of one to two decades does not lead to recovery of N ₂ O emissions to the levels of the original vegetation (McDaniel et al. 2019 ⁷⁰² ). To conclude, findings since AR5 increasingly highlight the limits of linear N ₂ O emission factors, particularly from field to regional scales, with emissions rising nonlinearly at high nitrogen application rates ( high confidence ). Emissions from unfertilised systems often increase and then decline over time with typically lower emissions than was the case under native vegetation ( high confidence ).

While soil emissions are the predominant source of N ₂ O in agriculture, other sources are important (or their importance is only just emerging). Biomass burning is responsible for approximately 0.7 TgN ₂ O-N yr ^–1 (0.5–1.7 TgN ₂ O-N yr ^–1 ) or 11% of total gross anthropogenic emissions due to the release of N ₂ O from the oxidation of organic nitrogen in biomass (UNEP 2013 ⁷⁰³ ). This source includes crop residue burning, forest fires, household cook stoves and prescribed savannah, pasture and cropland burning. Aquaculture is currently not accounted for in most assessments or compilations. While it is currently responsible for less than 0.1 TgN ₂ O-N yr ^–1 , it is one of the fastest growing sources of anthropogenic N ₂ O emissions (Williams and Crutzen 2010 ⁷⁰⁴ ; Bouwman et al. 2013 ⁷⁰⁵ ) ( limited evidence, high agreement ). Finally, increased nitrogen deposition from terrestrial sources is leading to greater indirect N ₂ O emissions, particularly since 1980 ( moderate evidence, high agreement ) (Tian et al. 2018 ⁷⁰⁶ , 2016 ²¹³³ ). In marine systems, deposition is estimated to have increased the oceanic N ₂ O source by 0.2 TgN ₂ O-N yr ^–1 or 3% of total gross anthropogenic emissions (Suntharalingam et al. 2012 ⁷⁰⁷ ).

Figure 2.12

Effect of time since conversion on N2O fluxes in unfertilised (orange circles) and fertilised (blue circles) tropical croplands (left frame) and in unfertilised tropical pastures (right frame). Average N2O flux and 95% confidence intervals are given for upland forests (orange inverted triangle) and low canopy forests (blue inverted triangle), for comparison. The solid lines represent […]

Effect of time since conversion on N ₂ O fluxes in unfertilised (orange circles) and fertilised (blue circles) tropical croplands (left frame) and in unfertilised tropical pastures (right frame). Average N ₂ O flux and 95% confidence intervals are given for upland forests (orange inverted triangle) and low canopy forests (blue inverted triangle), for comparison. The solid lines represent the trends for unfertilised and fertilised cases. Data source: van Lent et al. (2015) ⁷⁰⁸ .

B2.2 Methodologies for estimating national to global scale anthropogenic land carbon fluxes

Bookkeeping/accounting models calculate changes in biomass and soils that result from changes in land activity using data on biomass density and rates of growth/decomposition, typically from ground-based inventory data collection (field measurements of carbon in trees and soils) (Houghton et al. 2012 ⁷⁰⁹ ; Hansis et al. 2015 ⁷¹⁰ ; Houghton and Nassikas 2017 ⁷¹¹ ). The approach includes only those changes directly caused by major categories of land-use change and management. The models do not explicitly include the indirect effects to changing environmental conditions, although some effects are implicit in the biomass, growth rates and decay rates used. Thus, the models may overestimate past fluxes. The bookkeeping models include fluxes from peatland burning based on GFED estimates (Randerson et al. 2015 ⁷¹² ).

DGVMs simulate ecological processes, such as photosynthesis, respiration, allocation, growth, decomposition etc., driven by environmental conditions (climate variability, climate change, CO ₂ , nitrogen concentrations). Models vary with respect to the processes included, with many since AR5 now including forest management, fire, nitrogen and other management (Sitch et al. 2005 ⁷¹³ ; Le Quéré et al. 2018 ⁷¹⁴ ). Models are forced with increasing atmospheric CO ₂ and changing climate, and run with and without ‘land use change’ (land cover and forest harvest) to differentiate the anthropogenic effects from the indirect effects of climate and CO ₂ : the ‘land sink’. Thus, indirect effects are explicitly included. This approach also includes a ‘lost atmospheric sink capacity’, or the carbon uptake due to environmental effects on forests that does not happen once the forests are removed (Pongratz et al. 2010 ⁷¹⁵ ).

Integrated assessment models (IAMs) use storylines to construct alternative future scenarios of GHG emissions and atmospheric concentrations within a global socio-economic framework, including projections of AFOLU based on assumptions of, for example, crop yields, population growth and bioenergy use (Cross-Chapter Box 1 and Chapter 1). Some models include simplified DGVMs, which may include climate and CO ₂ effects, while others use AFOLU emissions from other sources.

ESMs couple DGVMs, surface hydrology, and energy exchange models with atmosphere, ocean, and sea ice models, enabling exploration of feedbacks between climate change and the carbon cycle (e.g., warming effects increase soil and plant respiration and lead to higher atsmpheric CO ₂ concentrations, which in turn promote plant growth) (Friedlingstein et al. 2014 ⁷¹⁶ ). They sometimes include numerical experiments with and without land-use change to diagnose the anthropogenic AFOLU flux (Lawrence et al. 2016 ⁷¹⁷ ).

Satellite data can be used as a proxy for plant activity (e.g., greenness) and to map land cover, vegetation fires and biomass density. Algorithms, models and independent data are used to calculate fluxes of CO ₂ from satellite data, although calculating the net carbon flux is difficult because of the lack of information on the respiratory flux. Some active satellite sensors (LiDAR) are able to measure three-dimensional structure in woody vegetation, which is closely related to biomass density (Zarin et al. 2016 ⁷¹⁸ ; Baccini et al. 2012 ⁷¹⁹ ; Saatchi et al. 2011 ⁷²⁰ ). Together with land-cover change data, these estimates of biomass density can be used to provide observational-based estimates of fluxes due to changes in forest area (e.g., Tyukavina et al. (2015) ⁷²¹ , Harris et al. (2015) ⁷²² and Baccini et al. (2012) ⁷²³ or degradation (Baccini et al. 2017 ⁷²⁴ )). Satellite estimates of biomass vary considerably (Mitchard et al. 2013 ⁷²⁵ ; Saatchi et al. 2015 ⁷²⁶ ; Avitabile et al. 2016 ⁷²⁷ ): data are available only for recent decades, methods generally assume that all losses of carbon are immediately released to the atmosphere and changes in soil carbon are generally ignored. The approach implicitly includes indirect and natural disturbance effects as well as direct anthropogenic effects.

Atmospheric inversions use observations of atmospheric concentrations with a model of atmospheric transport, based on data for wind speed and direction, to calculate implied emissions (Gatti et al. 2014 ⁷²⁸ ; Liu et al. 2017a ⁷²⁹ ; van der Laan-Luijkx et al. 2017 ⁷³⁰ ). Since AR5, there has been an increase in availability of concentration data from flux tower networks and satellites, enabling better global coverage at finer spatial scales and some national estimates (e.g., in the UK inverse techniques are used together with national GHG inventories). A combination of concentrations of different gases and isotopes enables the separation of fossil, ocean and land fluxes. However, inversions give only the net flux of CO ₂ from land; they cannot separate natural and anthropogenic fluxes.

Micrometeorological flux measurements data on CO ₂ concentrations and air movements recorded on instrumented towers enable the calculation of CO ₂ flux at the ecosystem scale. Global and regional Flux Networks (FluxNet (global), AsiaFlux, Ameriflux (North America), ICOS (EU), NEON (USA), and others) contribute to a global flux database, which is used to verify the results of modelling, inventory and remote sensing studies.

FAOSTAT has produced country level estimates of GHG emissions (Tubiello et al. 2013 ⁷³¹ ) from agriculture (1961–2016) and land use (1990–2016) using a globally consistent methodological approach based largely on IPCC Tier 1 methods of the 2006 IPCC Guidelines (FAO 2015 ⁷³² ). FAO emissions estimates were used as one of the three database inputs into the AR5 WGIII AFOLU chapter. Non-CO ₂ emissions from agriculture are estimated directly from national statistics of activity data reported by countries to FAO. CO ₂ emissions from land use and land-use change are computed mostly at Tier 1, albeit at fine geospatial scales to capture effects from peatland degradation and biomass fires (Rossi et al. 2016 ⁷³³ ). Emissions from forest land and deforestation are based on the IPCC carbon stock change method, thus constituting a Tier 3 estimate relying on country statistics of carbon stocks and forest area collected through the FAO FRA. The carbon flux is estimated assuming instantaneous emissions in the year of forest area loss and changes in carbon stocks within extant forests, but does not distinguish ‘managed’ and ‘unmanaged’ forest areas, albeit it treats separately emissions from primary, secondary and planted forest (Federici et al. 2015 ⁷³⁴ ).

Country Reporting of GHG Inventories (GHGIs) : All parties to the UNFCCC are required to report national GHGIs of anthropogenic emissions and removals. Reporting requirements are differentiated between developed and developing countries. Because of the difficulty of separating direct anthropogenic fluxes from indirect or natural fluxes, the IPCC (2003) ⁷³⁵ adopted the ‘managed land’ concept as a proxy to facilitate GHGI reporting. All GHG fluxes on ‘managed land’ are defined as anthropogenic, with each country applying their own definition of ‘managed land’ (i.e., ‘where human interventions and practices have been applied to perform production, ecological or social functions’ (IPCC 2006) ⁷³⁶ ). Fluxes may be determined on the basis of changes in carbon stocks (e.g., from forest inventories) or by activity data (e.g., area of land cover change management activity multiplied by emission factors or with modelled fluxes). Depending on the specific methods used, GHGIs include all direct anthropogenic effects and may include the indirect anthropogenic effects of environmental change (generally sinks) and natural effects (Section 2.3.1.2). GHG fluxes from ‘unmanaged land’ are not reported in GHGIs because they are assumed to be non-anthropogenic. The reported estimates may then be filtered through agreed ‘accounting rules’ (i.e., what countries actually count towards their mitigation targets (Cowie et al. 2007 ⁷³⁷ ; Lee and Sanz 2017 ⁷³⁸ ). The accounting aims to better quantify the additional mitigation actions by, for example, factoring out the impact of natural disturbances and forest age-related dynamics (Canadell et al. 2007 ⁷³⁹ ; Grassi et al. 2018 ⁷⁴⁰ ).

B 2.3 CO ₂ fertilisation and enhanced terrestrial uptake of carbon

All DGVMs and ESMs represent the CO ₂ fertilisation effect (Le Quéré et al. 2017 ⁷⁴¹ ; Hoffman et al. 2014 ⁷⁴² ). There is high confidence that elevated CO ₂ results in increased short-term CO ₂ uptake per unit leaf area (Swann et al. 2016 ⁷⁴³ ; Field et al. 1995 ⁷⁴⁴ ; Donohue et al. 2013 ⁷⁴⁵ ), however, whether this increased CO ₂ uptake at the leaf level translates into increased growth for the whole plant differs among plant species and environments, because growth is constrained by whole-plant resource allocation and nutrient limitation (e.g., nitrogen, phosphorus, potassium and soil water and light limitations (Körner 2006 ⁷⁴⁶ ; Peñuelas et al. 2017 ⁷⁴⁷ ; Friend et al. 2014a ⁷⁴⁸ )). Interactions between plants and soil microbes further modulate the degree of nutrient limitation on CO ₂ fertilisation (Terrer et al. 2017 ⁷⁴⁹ ).

At the ecosystems level, enhanced CO ₂ uptake at decadal or longer timescales depends on changes in plant community composition and ecosystem respiration, as well disturbance and natural plant mortality (De Kauwe et al. ⁷⁵⁰ , 2016; Farrior et al., 2015 ⁷⁵¹ ; Keenan et al., 2017 ⁷⁵² ; Sulman et al, 2019 ⁷⁵³ ). The results of free-air carbon dioxide enrichment (FACE) experiments over two decades are highly variable because of these factors (Norby et al. 2010 ⁷⁵⁴ ; Körner 2015 ⁷⁵⁵ ; Feng et al. 2015 ⁷⁵⁶ ; Paschalis et al. 2017 ⁷⁵⁷ ; Terrer et al. 2017 ⁷⁵⁸ ; Du et al. 2019 ⁷⁵⁹ ). Under higher atmospheric CO ₂ concentrations, the ratio of CO ₂ uptake to water loss (water use efficiency (WUE)), increases and enhances drought tolerance of plants ( high confidence ) (Berry et al., 2010 ⁷⁶⁰ ; Ainsworth and Rogers 2007 ⁷⁶¹ ).

Long-term CO ₂ and water vapour flux measurements show that WUE in temperate and boreal forests of the northern hemisphere has increased more than predicted by photosynthetic theory and models over the past two decades ( high confidence ) (Keenan et al. 2013 ⁷⁶² ; Laguë and Swann 2016 ⁷⁶³ ). New theories have emerged on how CO ₂ uptake by trees is related to water loss and to the risk of damaging xylem (water conducting tissues) in the trunk and branches (Wolf et al. 2016a ⁷⁶⁴ ; Anderegg et al. 2018a ⁷⁶⁵ ). Tree ring studies of stable carbon and oxygen isotopes also detected increased WUE in recent decades (Battipaglia et al. 2013 ⁷⁶⁶ ; Silva and Anand 2013 ⁷⁶⁷ ; van der Sleen et al. 2014 ⁷⁶⁸ ). Yet, tree ring studies often fail to show acceleration of tree growth rates in support of CO ₂ fertilisation, even when they show increased WUE (van der Sleen et al. 2014 ⁷⁶⁹ ). The International Tree Ring Data Bank (ITRDB) indicated that only about 20% of the sites in the database showed increasing trends in tree growth that cannot be explained by climate variability, nitrogen deposition, elevation or latitude. Thus there is l imited evidence ( low agreement ) among observations of enhanced tree growth due to CO ₂ fertilisation of forests during the 20th century (Gedalof and Berg 2010 ⁷⁷⁰ ).

In grasslands, although it is possible for CO ₂ fertilisation to alleviate the impacts of drought and heat stress on net carbon uptake (Roy et al. 2016 ⁷⁷¹ ), there is low confidence about its projected magnitude. Because of its effect on water use efficiency, CO ₂ fertilisation is expected to be pronounced in semi-arid habitats; and because of different metabolic pathways, C3 plants are expected to be more sensitive to elevated CO ₂ concentrations than C4 grasses (Donohue et al. 2013 ⁷⁷² ; Morgan et al. 2011 ⁷⁷³ ; Derner et al. 2003 ⁷⁷⁴ ). Neither of these expectations was observed over a 12-year study of elevated CO ₂ in a grassland system: enhanced growth was not observed during dry summers and growth of C4 grasses was unexpectedly stimulated, while growth of C3 grasses was not (Reich et al. 2014 ⁷⁷⁵ , 2018 ⁷⁷⁶ ).

There is medium confidence that CO ₂ fertilisation effects have increased water use efficiency in crops and thus reduced agricultural water use per unit of crop produced (Deryng et al. 2016 ⁷⁷⁷ ; Nazemi and Wheater 2015 ⁷⁷⁸ ; Elliott et al. 2014 ⁷⁷⁹ ). This effect could lead to near-term continued greening of agricultural areas. However, current assessments of these effects are based on limited observations, mostly from the temperate zone (Deryng et al. 2016 ⁷⁸⁰ ).

One line of evidence for CO ₂ fertilisation is the increasing land sink (‘the residual land sink’ in AR5) over the last 50 years as the atmospheric CO ₂ concentration has increased (Los 2013 ⁷⁸¹ ; Sitch et al. 2015 ⁷⁸² ; Campbell et al. 2017 ⁷⁸³ ; Keenan and Riley 2018 ⁷⁸⁴ ). A combined analysis of atmospheric inverse analyses, ecosystem models and forest inventory data concluded that 60% of the recent terrestrial carbon sink can be directly attributed to increasing atmospheric CO ₂ (Schimel et al. 2015 ⁷⁸⁵ ). A global analysis using a ‘reconstructed vegetation index’ (RVI) for the period 1901–2006 from MODIS satellite-derived normalised vegetation difference index (NDVI) showed that CO ₂ fertilisation contributed at least 40% of the observed increase in the land carbon sink (Los 2013 ⁷⁸⁶ ). Without CO ₂ fertilisation, ESMs are unable to simulate the increasing land sink and the observed atmospheric CO ₂ concentration growth rate since the middle of the 20th century (Shevliakova et al. 2013 ⁷⁸⁷ ). There are other mechanisms that could explain enhanced land carbon uptake such as increased regional forest and shrub cover (Chen et al. 2019 ⁷⁸⁸ ) (Cross-Chapter Box 2 and Chapter 1), and, at higher latitudes, increasing temperatures and longer growing seasons (Zhu et al. 2016 ⁷⁸⁹ ).

In summary, there is low confidence about the magnitude of the CO ₂ effect and other factors that may explain at least a portion of the land sink (e.g., nitrogen deposition, increased growing season, reduced burning, erosion and re-deposition or organic sediments, aerosol-induced cooling). Increases in atmospheric CO ₂ result in increased water use efficiency and increase leaf-level photosynthesis ( high confidence ). The extent to which CO ₂ fertilisation results in plant- or ecosystem-level carbon accumulation is highly variable and affected by other environmental constraints ( high confidence ). Even in ecosystems where CO ₂ fertilisation has been detected in recent decades, those effects are found to weaken as a result of physiological acclimation, soil nutrient limitation and other constraints on growth (Friend et al., 2014 ⁷⁹⁰ ; Körner, 2006 ⁷⁹¹ ; Peñuelas et al., 2017 ⁷⁹² ).

2.4 Emissions and impacts of short-lived climate forcers (SLCF) from land

While the rising atmospheric concentration of GHGs is the largest driver of anthropogenic changes in climate, the levels of short-lived climate forcers (SLCF) can significantly modulate regional climate by altering radiation exchanges and hydrological cycle and impact ecosystems ( high confidence ) (Boucher et al. 2013 ⁷⁹³ ; Rogelj et al. 2014 ⁷⁹⁴ ; Kok et al. 2018 ⁷⁹⁵ ). This section assesses the current state of knowledge with respect to past and future emissions of the three major SLCFs and their precursors: mineral dust, carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) and BVOCs. This section also reports on implications of changes in their emissions for climate. Aerosols particles with diameters between about 0.010 μm to about 20 μm are recognised as SLCFs, a term that refers to their short atmospheric lifetime (a few days). BVOCs are important precursors of ozone and OC, both important climate forcing agents with short atmospheric lifetimes.

While the AR5 did not assess land aerosols emissions in depth, their findings stated that although progress in quantifying regional emissions of anthropogenic and natural land aerosols has been made, considerable uncertainty still remains about their historical trends, their inter-annual and decadal variability and about any changes in the future (Calvo et al. 2013 ⁷⁹⁶ ; Klimont et al. 2017 ⁷⁹⁷ ). Some new and improved understanding of processes controlling emissions and atmospheric processing has been developed since AR5, for example, a better understanding of the climatic role of BC as well as the understanding of the role of BVOCs in formation of secondary organic aerosols (SOA).

Depending on the chemical composition and size, aerosols can absorb or scatter sunlight and thus directly affect the amount of absorbed and scattered radiation (Fuzzi et al. 2015 ⁷⁹⁸ ; Nousiainen 2011 ⁷⁹⁹ ; de Sá et al. 2019 ⁸⁰⁰ ) Aerosols affect cloud formation and development, and thus can also influence precipitation patterns and amounts (Suni et al. 2015 ⁸⁰¹ ). In addition, deposition of aerosols – especially BC – on snow and ice surfaces can reduce albedo and increase warming as a self-reinforcing feedback. Aerosols deposition also changes biogeochemical cycling in critical terrestrial ecosystems, with deposition of nutrients such as nitrogen and phosphorus (Andreae et al. 2002 ⁸⁰² ). Primary land aerosols are emitted directly into the atmosphere due to natural or anthropogenic processes and include mineral aerosols (or dust), volcanic dust, soot from combustion, organic aerosols from industry, vehicles or biomass burning, bioaerosols from forested regions and others. SOAs are particulates that are formed in the atmosphere by the gas-to-particles conversion processes from gaseous precursors, such as BVOCs, and account for a large fraction of fine mode (particles less than 2.5μm) aerosol mass (Hodzic et al. 2016 ⁸⁰³ ; Manish et al. 2017 ⁸⁰⁴ ). Land use change can affect the climate through changed emissions of SLCFs such as aerosols, ozone precursors and methane.

Aerosols from air pollution will decline in the coming years as a means for improving urban and regional air, but their removal will lead to additional warming (Boucher et al. 2013 ⁸⁰⁵ ), with important regional variability, and partially offsetting projected mitigation effects for two to three decades in 1.5°C consistent pathways ( high confidence ) (IPCC 2018 ⁸⁰⁶ ). It is important to emphasise that changes in emissions can either be due to external forcing or through a feedback in the climate system (Box 2.1). For instance, enhanced dust emissions due to reduced vegetation could be a forcing if overgrazing is the cause of larger dust emission, or a feedback if dryer climate is the cause. This distinction is important in terms of mitigation measures to be implemented.

2.4.1 Mineral dust

One of the most abundant atmospheric aerosols emitted into the atmosphere is mineral dust, a ‘natural’ aerosol that is produced by wind strong enough to initiate the emissions process of sandblasting. Mineral dust is preferentially emitted from dry and unvegetated soils in topographic depressions where deep layers of alluvium have been accumulated (Prospero et al. 2002 ⁸⁰⁷ ). Dust is also emitted from disturbed soils by human activities, with a 25% contribution to global emissions based on a satellite-based estimate (Ginoux et al. 2012 ⁸⁰⁸ ).

Dust is then transported over long distances across continents and oceans. The dust cycle, which consists of mineral dust emission, transport, deposition and stabilisation, has multiple interactions with many climate processes and biogeochemical cycles.

2.4.1.1 Mineral dust as a short-lived climate forcer from land

Depending on the dust mineralogy, mixing state and size, dust particles can absorb or scatter shortwave and longwave radiation. Dust particles serve as cloud condensation nuclei and ice nuclei. They can influence the microphysical properties of clouds, their lifetime and precipitation rate (Kok et al. 2018 ⁸⁰⁹ ). New and improved understanding of processes controlling emissions and transport of dust, its regional patterns and variability, as well as its chemical composition, has been developed since AR5.

While satellites remain the primary source of information to locate dust sources and atmospheric burden, in-situ data remains critical to constrain optical and mineralogical properties of the dust (DiBiagio et al. 2017 ⁸¹⁰ ; Rocha-Lima et al. 2018 ⁸¹¹ ). Dust particles are composed of minerals, including iron oxides which strongly absorb shortwave radiation and provide nutrients for marine ecosystems. Another mineral such as feldspar is an efficient ice nuclei (Harrison et al. 2016 ⁸¹² ). Dust mineralogy varies depending on the native soils, so global databases were developed to characterise the mineralogical composition of soils for use in weather and climate models (Journet et al. 2014 ⁸¹³ ; Perlwitz et al. 2015 ⁸¹⁴ ). New field campaigns, as well as new analyses of observations from prior campaigns, have produced insights into the role of dust in western Africa in climate system, such as long-ranged transport of dust across the Atlantic (Groß et al. 2015 ⁸¹⁵ ) and the characterisation of aerosol particles and their ability to act as ice and cloud condensation nuclei (Price et al. 2018 ⁸¹⁶ ). Size distribution at emission is another key parameter controlling dust interactions with radiation. Most models now use the parametrisation of Kok (2011) ⁸¹⁷ based on the theory of brittle material. It was shown that most models underestimate the size of the global dust cycle (Kok 2011 ⁸¹⁸ ).

Characterisation of spatial and temporal distribution of dust emissions is essential for weather prediction and climate projections ( high confidence ). Although there is a growing confidence in characterising the seasonality and peak of dust emissions (i.e., spring–summer (Wang et al. 2015 ⁸¹⁹ )) and how the meteorological and soil conditions control dust sources, an understanding of long-term future dust dynamics, inter-annual dust variability and how they will affect future climate still requires substantial work. Dust is also important at high latitude, where it has an impact on snow-covered surface albedo and weather (Bullard et al. 2016 ⁸²⁰ ).

2.4.1.2 Effects of past climate change on dust emissions and feedbacks

A limited number of model-based studies found that dust emissions have increased significantly since the late 19th century: by 25% from the preindustrial period to the present day (e.g., from 729 Tg yr ^–1 to 912 Tg yr ^–1 ) with about 50% of the increase driven by climate change and about 40% driven by land use cover change, such as conversion of natural land to agriculture ( low confidence ) (Stanelle et al. 2014 ⁸²¹ ). These changes resulted in a clear sky radiative forcing at the top of the atmosphere of –0.14 W m ^–2 (Stanelle et al. 2014 ⁸²² ). The authors found that, in North Africa, most dust is of natural origin, with a recent 15% increase in dust emissions attributed to climate change. In North America two-thirds of dust emissions take place on agricultural lands and both climate change and land-use change jointly drive the increase; between the pre-industrial period and the present day, the overall effect of changes in dust was –0.14 W m ^–2 cooling of clear sky net radiative forcing on top of the atmosphere, with –0.05 W m ^–2 from land use and –0.083 W m ^–2 from changes in climate.

The comparison of observations for vertically integrated mass of atmospheric dust per unit area (i.e., dust mass path (DMP)) obtained from the remotely sensed data and the DMP from CMIP5 models reveal that the model-simulate range of DMP was much lower than the estimates (Evan et al. 2014 ⁸²³ ). ESMs typically do not reproduce inter- annual and longer timescales variability seen in observations (Evan et al. 2016 ⁸²⁴ ). Analyses of the CMIP5 models (Evan 2018 ⁸²⁵ ; Evan et al. 2014 ⁸²⁶ ) reveal that all climate models systematically underestimate dust emissions, the amount of dust in the atmosphere and its inter- annual variability ( medium confidence ).

One commonly suggested reason for the lack of dust variability in climate models is the models’ inability to simulate the effects of land surface changes on dust emission (Stanelle et al. 2014 ⁸²⁷ ). Models that account for changes in land surface show more agreement with the satellite observations both in terms of aerosol optical depth and DMP (Kok et al. 2014 ⁸²⁸ ). New prognostic dust emissions models are now able to account for both changes in surface winds and vegetation characteristics (e.g., leaf area index and stem area index) and soil water, ice and snow cover (Evans et al. 2016 ⁸²⁹ ). As a result, new modelling studies (e.g., Evans et al. 2016 ⁸³⁰ ) indicate that, in regions where soil and vegetation respond strongly to ENSO events, such as in Australia, inclusion of dynamic vegetation characteristics into dust emission parameterisations improves comparisons between the modelled and observed relationship with long-term climate variability (e.g., ENSO) and dust levels (Evans et al. 2016 ⁸³¹ ). Thus, there has been progress in incorporating the effects of vegetation, soil moisture, surface wind and vegetation on dust emission source functions, but the number of studies demonstrating such improvement remains small ( limited evidence, medium agreement ).

2.4.1.3 Future changes of dust emissions

There is no agreement about the direction of future changes in dust emissions. Atmospheric dust loading is projected to increase over the southern edge of the Sahara in association with surface wind and precipitation changes (Pu and Ginoux, 2018 ⁸³² ), while Evan et al. (2016) ⁸³³ project a decline in African dust emissions. Dust optical depth (DOD) is also projected to increase over the central Arabian peninsula in all seasons, and to decrease over northern China from March-April-May to September-October-November (Pu and Ginoux 2018 ⁸³⁴ ). Climate models project rising drought risks over the south-western and central US in the 21st century. The projected drier regions largely overlay the major dust sources in the US. However, whether dust activity in the US will increase in the future is not clear, due to the large uncertainty in dust modelling (Pu and Ginoux 2017 ⁸³⁵ ). Future trends of dust emissions will depend on changes in precipitation patterns and atmospheric circulation ( limited evidence, high agreement ). However, implication of changes in human activities, including mitigation (e.g., bioenergy production) and adaption (e.g., irrigation) are not characterised in the current literature.

2.4.2 Carbonaceous aerosols

Carbonaceous aerosols are one of the most abundant components of aerosol particles in continental areas of the atmosphere and a key land–atmosphere component (Contini et al. 2018 ⁸³⁶ ). They can make up to 60–80% of PM2.5 (particulate matter with size less than 2.5 μm) in urban and remote atmospheres (Tsigaridis et al. 2014 ⁸³⁷ ; Kulmala et al. 2011 ⁸³⁸ ). It comprises an organic fraction (OC) and a refractory light-absorbing component, generally referred to as elemental carbon (EC), from which BC is the optically active absorption component of EC (Gilardoni et al. 2011 ⁸³⁹ ; Bond et al. 2013 ⁸⁴⁰ ).

2.4.2.1 Carbonaceous aerosol precursors of short-lived climate forcers from land

OC is a major component of aerosol mass concentration, and it originates from different anthropogenic (combustion processes) and natural (natural biogenic emissions) sources (Robinson et al. 2007 ⁸⁴¹ ). A large fraction of OC in the atmosphere has a secondary origin, as it can be formed in the atmosphere through condensation to the aerosol phase of low vapour pressure gaseous compounds emitted as primary pollutants or formed in the atmosphere. This component is SOA (Hodzic et al. 2016 ⁸⁴² ). A third component of the optically active aerosols is the so-called brown carbon (BrC), an organic material that shows enhanced solar radiation absorption at short wavelengths (Wang et al. 2016b ⁸⁴³ ; Laskin et al. 2015 ⁸⁴⁴ ; Liu et al. 2016a ⁸⁴⁵ ; Bond et al. 2013 ⁸⁴⁶ ; Saturno et al. 2018 ⁸⁴⁷ ).

OC and EC have distinctly different optical properties, with OC being important for the scattering properties of aerosols and EC central for the absorption component (Rizzo et al. 2013 ⁸⁴⁸ ; Tsigaridis et al. 2014 ⁸⁴⁹ ; Fuzzi et al. 2015 ⁸⁵⁰ ). While OC is reflective and scatters solar radiation, it has a cooling effect on climate. On the other side, BC and BrC absorb solar radiation and they have a warming effect in the climate system (Bond et al. 2013 ⁸⁵¹ ).

OC is also characterised by a high solubility with a high fraction of water-soluble organic compounds (WSOC) and it is one of the main drivers of the oxidative potential of atmospheric particles. This makes particles loaded with oxidised OC an efficient cloud condensation nuclei (CCN) in most of the conditions (Pöhlker et al. 2016 ⁸⁵² ; Thalman et al. 2017 ⁸⁵³ ; Schmale et al. 2018 ⁸⁵⁴ ).

Biomass burning is a major global source of carbonaceous aerosols (Bowman et al. 2011 ⁸⁵⁵ ; Harrison et al. 2010 ⁸⁵⁶ ; Reddington et al. 2016 ⁸⁵⁷ ; Artaxo et al. 2013 ⁸⁵⁸ ). As knowledge of past fire dynamics improved through new satellite observations, new fire proxies’ datasets (Marlon et al. 2013 ⁸⁵⁹ ; van Marle et al. 2017a ⁸⁶⁰ ), process-based models (Hantson et al. 2016 ⁸⁶¹ ) and a new historic biomass burning emissions dataset starting in 1750 have been developed (van Marle et al. 2017b ⁸⁶² ) (Cross-Chapter Box 3 in this chapter). Revised versions of OC biomass burning emissions (van Marle et al. 2017b ⁸⁶³ ) show, in general, reduced trends compared to the emissions derived by Lamarque et al. (2010) ⁸⁶⁴ for CMIP5. CMIP6 global emissions pathways (Gidden et al. 2018 ⁸⁶⁵ ; Hoesly et al. 2018 ⁸⁶⁶ ) estimate global BC emissions in 2015 at 9.8 MtBC yr ^–1 , while global OC emissions are 35 MtOC yr ^–1 .

Land use change is critically important for carbonaceous aerosols, since biomass-burning emissions consist mostly of organic aerosol, and the undisturbed forest is also a large source of organic aerosols (Artaxo et al. 2013 ⁸⁶⁷ ). Additionally, urban aerosols are also mostly carbonaceous because of the source composition (traffic, combustion, industry, etc.) (Fuzzi et al. 2015 ⁸⁶⁸ ). Burning of fossil fuels, biomass- burning emissions and SOA from natural BVOC emissions are the main global sources of carbonaceous aerosols. Any change in each of these components directly influence the radiative forcing (Contini et al. 2018 ⁸⁶⁹ ; Boucher et al. 2013 ⁸⁷⁰ ; Bond et al. 2013 ⁸⁷¹ ).

One important component of carbonaceous aerosols is the primary biological aerosol particles (PBAP), also called bioaerosols, that correspond to a significant fraction of aerosols in forested areas (Fröhlich-Nowoisky et al. 2016 ⁸⁷² ; Pöschl and Shiraiwa 2015 ⁸⁷³ ). They are emitted directly by the vegetation as part of the biological processes (Huffman et al. 2012 ⁸⁷⁴ ). Airborne bacteria, fungal spores, pollen, archaea, algae and other bioparticles are essential for the reproduction and spread of organisms across various terrestrial ecosystems. They can serve as nuclei for cloud droplets, ice crystals and precipitation, thus influencing the hydrological cycle and climate (Whitehead et al. 2016 ⁸⁷⁵ ; Scott et al. 2015 ⁸⁷⁶ ; Pöschl et al. 2010 ⁸⁷⁷ ).

2.4.2.2 Effects of past climate change on carbonaceous aerosols emissions and feedbacks

Annual global emission estimates of BC range from 7.2–7.5 Tg yr ^–1 (using bottom-up inventories) (Bond et al. 2013 ⁸⁷⁸ ; Klimont et al. 2017 ⁸⁷⁹ ) up to 17.8 ± 5.6 Tg yr ^–1 (using a fully coupled climate-aerosol-urban model constrained by aerosol measurements) (Cohen and Wang 2014 ⁸⁸⁰ ), with considerably higher BC emissions for Eastern Europe, southern East Asia, and Southeast Asia, mostly due to higher anthropogenic BC emissions estimates. A significant source of BC, the net trend in global burned area from 2000–2012 was a modest decrease of 4.3 Mha yr ^–1 (–1.2% yr ^–1 ).

Carbonaceous aerosols are important in urban areas as well as pristine continental regions, since they can be responsible for 50–85% of PM2.5 (Contini et al. 2018 ⁸⁸¹ ; Klimont et al. 2017 ⁸⁸² ). In boreal and tropical forests, carbonaceous aerosols originate from BVOC oxidation (Section 2.4.3). The largest global source of BC aerosols is open burning of forests, savannah and agricultural lands with emissions of about 2700 Gg yr ^–1 in the year 2000 (Bond et al. 2013 ⁸⁸³ ).

ESMs most likely underestimate globally averaged EC emissions (Bond et al. 2013 ⁸⁸⁴ ; Cohen and Wang 2014 ⁸⁸⁵ ), although recent emission inventories have included an upwards adjustment in these numbers (Hoesly et al. 2018 ⁸⁸⁶ ). Vertical EC profiles have also been shown to be poorly constrained (Samset et al. 2014 ⁸⁸⁷ ), with a general tendency of too much EC at high altitudes. Models differ strongly in the magnitude and importance of the coating-enhancement of ambient EC absorption (Boucher et al. 2016 ⁸⁸⁸ ; Gustafsson and Ramanathan 2016 ⁸⁸⁹ ) in their estimated lifetime of these particles, as well as in dry and wet removal efficiency ( limited evidence, medium agreement ) (Mahmood et al. 2016 ⁸⁹⁰ ).

The equilibrium in emissions and concentrations between the scattering properties of organic aerosol versus the absorption component of BC is a key ingredient in the future climatic projections of aerosol effects ( limited evidence, high agreement ). The uncertainties in net climate forcing from BC-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both BC and co-emitted OC. A strong positive forcing of about 1.1 wm ^–2 was calculated by Bond et al. (2013), but this forcing is balanced by a negative forcing of –1.45 wm ^–2 , and shows clearly a need to work on the co-emission issue for carbonaceous aerosols. The forcing will also depend on the aerosol-cloud interactions, where carbonaceous aerosol can be coated and change their CCN capability. It is difficult to estimate the changes in any of these components in a future climate, but this will strongly influence the radiative forcing ( high confidence ) (Contini et al. 2018 ⁸⁹¹ ; Boucher et al. 2013 ⁸⁹² ; Bond et al. 2013 ⁸⁹³ ).

De Coninck et al. (2018) ⁸⁹⁴ reported studies estimating a lower global temperature effect from BC mitigation (e.g., Samset et al. 2014 ⁸⁹⁵ ; Boucher et al. 2016 ⁸⁹⁶ ), although commonly used models do not capture properly observed effects of BC and co-emissions on climate (e.g., Bond et al. 2013 ⁸⁹⁷ ). Regionally, the warming effects can be substantially larger, for example, in the Arctic (Sand et al. 2015 ⁸⁹⁸ ) and high mountain regions near industrialised areas or areas with heavy biomass-burning impacts ( high confidence ) (Ming et al. 2013 ⁸⁹⁹ ).

2.4.2.3 Future changes of carbonaceous aerosol emissions

Due to the short atmospheric lifetime of carbonaceous aerosols in the atmosphere, of the order of a few days, most studies dealing with the future concentration levels have a regional character (Cholakian et al. 2018 ⁹⁰⁰ ; Fiore et al. 2012 ⁹⁰¹ ). The studies agree that the uncertainties in changes in emissions of aerosols and their precursors are generally higher than those connected to climate change itself. Confidence in future changes in carbonaceous aerosol concentration projections is limited by the reliability of natural and anthropogenic emissions (including wildfires, largely caused by human activity) of primary aerosol as well as that of the precursors. The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate impacts of aerosols and chemically reactive gases (Lamarque et al. 2013 ⁹⁰² ). These simulations calculated future responses to SLCF emissions for the RCP scenarios in terms of concentration changes and radiative forcing. Carbonaceous aerosol emissions are expected to increase in the near future due to possible increases in open biomass-burning emissions (from forest, savannah and agricultural fires), and increase in SOA from oxidation of BVOCs ( medium confidence ) (Tsigaridis et al. 2014 ⁹⁰³ ; van Marle et al. 2017b ⁹⁰⁴ ; Giglio et al. 2013 ⁹⁰⁵ ).

More robust knowledge has been produced since the conclusions reported in AR5 (Boucher et al. 2013 ⁹⁰⁶ ) and all lines of evidence now agree on a small effect on carbonaceous aerosol global burden due to climate change ( medium confidence ). The regional effects, however, are predicted to be much higher (Westervelt et al. 2015 ⁹⁰⁷ ). With respect to possible changes in the chemical composition of PM as a result of future climate change, only a few sparse data are available in the literature and the results are, as yet, inconclusive. The co-benefits of reducing aerosol emissions due to air quality issues will play an important role in future carbonaceous aerosol emissions ( high confidence ) (Gonçalves et al. 2018 ⁹⁰⁸ ; Shindell et al. 2017 ⁹⁰⁹ ).

2.4.3 Biogenic volatile organic compounds

BVOCs are emitted in large amounts by forests (Guenther et al. 2012 ⁹¹⁰ ). They include isoprene, terpenes, alkanes, alkenes, alcohols, esters, carbonyls and acids (Peñuelas and Staudt 2010 ⁹¹¹ ; Guenther et al. 1995 ⁹¹² , 2012 ⁹¹³ ). Their emissions represent a carbon loss to the ecosystem, which can be up to 10% of the carbon fixed by photosynthesis under stressful conditions (Bracho-Nunez et al. 2011 ⁹¹⁴ ). The global average emission for vegetated surfaces is 0.7g C m ^–2 yr ^–1 but can exceed 100 g C m ^–2 yr ^–1 in some tropical ecosystems (Peñuelas and Llusià 2003 ⁹¹⁵ ).

2.4.3.1 BVOC precursors of short-lived climate forcers from land

BVOCs are rapidly oxidised in the atmosphere to form less volatile compounds that can condense and form SOA. In boreal and tropical forests, carbonaceous aerosols originate from BVOC oxidation, of which isoprene and terpenes are the most important precursors (Claeys et al. 2004 ⁹¹⁶ ; Hu et al. 2015 ⁹¹⁷ ; De Sá et al. 2017 ⁹¹⁸ ; de Sá et al. 2018 ⁹¹⁹ ; Liu et al. 2016b ⁹²⁰ ). See the following sub-section for more detail.

BVOCs are the most important precursors of SOA. The transformation process of BVOCs affects the aerosol size distribution both by contributing to new particle formation and to the growth of larger pre-existing particles. SOA affects the scattering of radiation by the particles themselves (direct aerosol effect), but also changes the amount of CCN and the lifetime and optical properties of clouds (indirect aerosol effect).

High amounts of SOA are observed over forest areas, in particular in boreal and tropical regions where they have been found to mostly originate from BVOC emissions (Manish et al. 2017 ⁹²¹ ). In particular, isoprene epoxydiol-derived SOA (IEPOX-SOA) is being identified in recent studies in North America and Amazonian forest as a major component in the oxidation of isoprene (Allan et al. 2014 ⁹²² ; Schulz et al. 2018 ⁹²³ ; De Sá et al. 2017 ⁹²⁴ ). In tropical regions, BVOCs can be convected up to the upper atmosphere, where their volatility is reduced and where they become SOA. In some cases those particles are transported back to the lower atmosphere (Schulz et al. 2018 ⁹²⁵ ; Wang et al. 2016a ⁹²⁶ ; Andreae et al. 2018 ⁹²⁷ ). In the upper troposphere in the Amazon, SOA are important CCN and are responsible for the vigorous hydrological cycle (Pöhlker et al. 2018 ⁹²⁸ ). This strong link between BVOC emissions by plants and the hydrological cycle has been discussed in a number of studies (Fuentes et al. 2000 ⁹²⁹ ; Schmale et al. 2018 ⁹³⁰ ; Pöhlker et al. 2018 ⁹³¹ , 2016 ⁹³² ).

Changing BVOC emissions also affect the oxidant concentrations in the atmosphere. Their impact on the concentration of ozone depends on the NOx concentrations. In polluted regions, high BVOC emissions lead to increased production of ozone, followed by the formation of more OH and a reduction in the methane lifetime. In more pristine regions (NOx-limited), increasing BVOC emissions instead lead to decreasing OH and ozone concentrations, resulting in a longer methane lifetime. The net effect of BVOCs then can change over time if NOx emissions are changing.

BVOCs’ possible climate effects have received little attention because it was thought that their short lifetime would preclude them from having any significant direct influence on climate (Unger 2014a ⁹³³ ; Sporre et al. 2019 ⁹³⁴ ). Higher temperatures and increased CO ₂ concentrations are (separately) expected to increase the emissions of BVOCs (Jardine et al. 2011 ⁹³⁵ , 2015 ⁹³⁶ ; Fuentes et al. 2016 ⁹³⁷ ). This has been proposed to initiate negative climate feedback mechanisms through increased formation of SOA (Arneth et al. 2010 ⁹³⁸ ; Kulmala 2004 ⁹³⁹ ; Unger et al. 2017 ⁹⁴⁰ ). More SOA can make clouds more reflective, which can provide a cooling effect. Furthermore, the increase in SOA formation has also been proposed to lead to increased aerosol scattering, resulting in an increase in diffuse radiation. This could boost GPP and further increase BVOC emissions (Kulmala et al. 2014 ⁹⁴¹ ; Cirino et al. 2014 ⁹⁴² ; Sena et al. 2016 ⁹⁴³ ; Schafer et al. 2002 ⁹⁴⁴ ; Ometto et al. 2005 ⁹⁴⁵ ; Oliveira et al. 2007 ⁹⁴⁶ ). This important feedback is starting to emerge (Sporre et al. 2019 ⁹⁴⁷ ; Kulmala 2004 ⁹⁴⁸ ; Arneth et al. 2017 ⁹⁴⁹ ). However, there is evidence that this influence might be significant at different spatial scales, from local to global, through aerosol formation and through direct and indirect greenhouse effects (l imited evidence, medium agreement ). Most tropical forest BVOCs are primarily emitted from tree foliage, but soil microbes can also be a major source of some compounds including sesquiterpenes (Bourtsoukidis et al. 2018 ⁹⁵⁰ ).

2.4.3.2 Historical changes of BVOCs and contribution to climate change

Climate warming over the past 30 years, together with the longer growing season experienced in boreal and temperate environments, have increased BVOC global emissions since the preindustrial times ( limited evidence, medium agreement ) (Peñuelas 2009 ⁹⁵¹ ; Sanderson et al. 2003 ⁹⁵² ; Pacifico et al. 2012 ⁹⁵³ ). This was opposed by lower BVOC emissions caused by the historical conversion of natural vegetation and forests to cropland ( limited evidence, medium agreement ) (Unger 2013 ⁹⁵⁴ , 2014a ⁹⁵⁵ ; Fu and Liao 2014 ⁹⁵⁶ ). The consequences of historical anthropogenic land cover change were a decrease in the global formation of SOA (–13%) (Scott et al. 2017 ⁹⁵⁷ ) and tropospheric burden (–13%) (Heald and Geddes 2016 ⁹⁵⁸ ). This has resulted in a positive radiative forcing (and thus warming) from 1850–2000 of 0.017 W m ^–2 (Heald and Geddes 2016 ⁹⁵⁹ ), 0.025 W m ^–2 (Scott et al. 2017 ⁹⁶⁰ ) and 0.09 W m ^–2 (Unger 2014b ⁹⁶¹ ) through the direct aerosol effect. In present-day conditions, global SOA production from all sources spans between 13 and 121 Tg yr ^–1 (Tsigaridis et al. 2014 ⁹⁶² ). The indirect aerosol effect (change in cloud condensation nuclei), resulting from land use induced changes in BVOC emissions, adds an additional positive radiative forcing of 0.008 W m ^–2 (Scott et al. 2017 ⁹⁶³ ). More studies with different model setups are needed to fully assess this indirect aerosol effect associated with land use change from the preindustrial to present. CMIP6 global emissions pathways (Hoesly et al. 2018 ⁹⁶⁴ ; Gidden et al. 2018 ⁹⁶⁵ ) estimates global VOCs emissions in 2015 at 230 MtVOC yr ^–1 . They also estimated that, from 2000–2015, emissions were up from 200–230 MtVOC yr ^–1 .

There is ( limited evidence, medium agreement ) that historical changes in BVOC emissions have also impacted on tropospheric ozone. At most surface locations where land use has changed, the NOx concentrations are sufficiently high for the decrease in BVOC emissions to lead to decreasing ozone concentrations (Scott et al. 2017 ⁹⁶⁶ ). However, in more pristine regions (with low NOx concentrations), the imposed conversion to agriculture has increased ozone through decreased BVOC emissions and their subsequent decrease in OH (Scott et al. 2017 ⁹⁶⁷ ; Heald and Geddes 2016 ⁹⁶⁸ ). In parallel, the enhanced soil NOx emissions from agricultural land can increase the ozone concentrations in NOx limited regions (Heald and Geddes 2016 ⁹⁶⁹ ).

Another impact of the historical decrease in BVOC emissions is the reduction in the atmospheric lifetime of methane ( limited evidence, medium agreement ), which results in a negative radiative forcing that ranges from –0.007 W m ^–2 (Scott et al. 2017 ⁹⁷⁰ ) to –0.07 W m ^–2 (Unger 2014b ⁹⁷¹ ). However, knowledge of the degree that BVOC emissions impact on oxidant concentrations, in particular OH (and thus methane concentrations), is still limited and therefore these numbers are very uncertain (Heald and Spracklen 2015 ⁹⁷² ; Scott et al. 2017 ⁹⁷³ ). The effect of land use change on BVOC emissions are highly heterogeneous (Rosenkranz et al. 2015 ⁹⁷⁴ ) and though the global values of forcing described above are small, the local or regional values can be higher, and even of opposite sign, than the global values.

2.4.3.3 Future changes of BVOCs

Studies suggest that increasing temperature will change BVOC emissions through change in species composition and rate of BVOC production. A further 2°C–3°C rise in the mean global temperature could increase BVOC global emissions by an additional 30–45% (Peñuelas and Llusià 2003 ⁹⁷⁵ ). In two modelling studies, the impact on climate from rising BVOC emissions was found to become even larger with decreasing anthropogenic aerosol emissions (Kulmala et al. 2013 ⁹⁷⁶ ; Sporre et al. 2019 ⁹⁷⁷ ). A negative feedback on temperature, arising from the BVOC-induced increase in the first indirect aerosol effect, has been estimated by two studies to be in the order of –0.01 W m ^–2 K (Scott et al. 2018b ⁹⁷⁸ ; Paasonen et al. 2013 ⁹⁷⁹ ). Enhanced aerosol scattering from increasing BVOC emissions has been estimated to contribute to a global gain in BVOC emissions of 7% (Rap et al. 2018 ⁹⁸⁰ ). In a warming planet, BVOC emissions are expected to increase but magnitude of this increase is unknown and will depend on future land use change, in addition to climate ( limited evidence, medium agreement ).

There is a very limited number of studies investigating the climate impacts of BVOCs using future land use scenarios (Ashworth et al. 2012 ⁹⁸¹ ; Pacifico et al. 2012 ⁹⁸² ). Scott et al. (2018a) ⁹⁸³ found that a future deforestation according to the land use scenario in RCP8.5 leads to a 4% decrease in BVOC emissions at the end of the century. This resulted in a direct aerosol forcing of +0.006 W m ^–2 (decreased reflection by particles in the atmosphere) and a first indirect aerosol forcing of –0.001 W m ^–2 (change in the amount of CCN). Studies not including future land use scenarios but investigating the climate feedbacks leading to increasing future BVOC emissions, have found a direct aerosol effect of –0.06 W m ^–2 (Sporre et al. 2019 ⁹⁸⁴ ) and an indirect aerosol effect of –0.45 W m ^–2 (Makkonen et al. 2012 ⁹⁸⁵ ; Sporre et al. 2019 ²¹³⁴ ). The stronger aerosol effects from the feedback compared to the land use are, at least partly, explained by a much larger change in the BVOC emissions.

A positive climate feedback could happen in a future scenario with increasing BVOC emissions, where higher ozone and methane concentrations could lead to an enhanced warming which could further increase BVOC emissions (Arneth et al. 2010 ⁹⁸⁶ ). This possible feedback is mediated by NOx levels. One recent study including dynamic vegetation, land use change, CO ₂ and climate change found no increase and even a slight decrease in global BVOC emissions at the end of the century (Hantson et al. 2017 ⁹⁸⁷ ). There is a lack of understanding concerning the processes governing the BVOC emissions, the oxidation processes in the atmosphere, the role of the BVOC oxidation products in new particle formation and particle growth, as well as general uncertainties in aerosol–cloud interactions. There is a need for continued research into these processes, but the current knowledge indicates that changing BVOC emissions need to be taken into consideration when assessing the future climate and how land use will affect it. In summary, the magnitude and sign of net effect of BVOC emissions on the radiation budget and surface temperature is highly uncertain.

2.5 Land impacts on climate and weather through biophysical and GHG effects

The focus of this section is summarised in Figure 2.13. We report on what we know regarding the influence land has on climate via biophysical and biogeochemical exchanges. Biogeochemical effects herein only refer to changes in net emissions of CO ₂ from land. The influence of land on atmospheric composition is discussed in Section 2.3.

All sections discuss impacts of land on global and regional climate, and climate extremes, whenever the information is available. Section 2.5.1 presents effects of historical and future land use scenarios, Section 2.5.2 is devoted to impacts of specific anthropogenic land uses such as forestation, deforestation, irrigation, crop and forest management, Section 2.5.3 focuses on how climate-driven land changes feedback on climate, and Section 2.5.4 puts forward the theory that land use changes in one region can affect another region.

Figure 2.13

Global, local and regional climate changes are the focus of this section. They are examined through changes in climate states (e.g., changes in air temperature and humidity, rainfall, radiation) as well as through changes in atmospheric dynamics (e.g., circulation patterns). Changes in land that influence climate are either climate- or human-driven. Dark-blue arrows and boxes […]

Global, local and regional climate changes are the focus of this section. They are examined through changes in climate states (e.g., changes in air temperature and humidity, rainfall, radiation) as well as through changes in atmospheric dynamics (e.g., circulation patterns). Changes in land that influence climate are either climate- or human-driven. Dark-blue arrows and boxes refer to what we consider imposed changes (forcings). Dark-grey arrows and boxes refer to responses of land to forcings (blue boxes and blue-outline box) and feedbacks on those initial forcings. Pale-grey and pale-blue arrows and boxes refer respectively to global and local/regional climate changes and their subsequent changes on land.

2.5.1 Impacts of historical and future anthropogenic land cover changes

The studies reported below focus essentially on modelling experiments, as there is no direct observation of how historical land use changes have affected the atmospheric dynamics and physics at the global and regional scales. Moreover, the climate modelling experiments only assess the impacts of anthropogenic land cover changes (e.g., deforestation, urbanisation) and neglect the effects of changes in land management (e.g., irrigation, use of fertilisers, choice of species varieties among managed forests or crops). Because of this restricted accounting for land use changes, we will use the term ‘land cover changes’ in Sections 2.5.1.1 and 2.5.1.2.

Each section starts by describing changes at the global scale and regional scale, and ends with what we know about the impacts of those scenarios on extreme weather events, whenever the information is available.

2.5.1.1 Impacts of global historical land cover changes on climate

At the global level

The contribution of anthropogenic land cover changes to the net global warming throughout the 20th century has been derived from few model-based estimates that account simultaneously for biogeochemical and biophysical effects of land on climate (Table 2.4). The simulated net change in mean global annual surface air temperature, averaged over all the simulations, is a small warming of 0.078 ± 0.093°C, ranging from small cooling simulated by two models (–0.05°C and –0.02°C respectively in Brovkin et al. (2004) ⁹⁸⁸ and Simmons and Matthews (2016) ⁹⁸⁹ , to larger warming simulated by three models (>+0.14°C; Shevliakova et al. 2013 ⁹⁹⁰ ; Pongratz et al. 2010 ⁹⁹¹ ; Matthews et al. 2004 ⁹⁹² ). When starting from the Holocene period, He et al. (2014) ⁹⁹³ estimated an even larger net warming effect of anthropogenic land cover changes (+0.72°C).

This net small warming signal results from the competing effects of biophysical cooling ( medium confidence ) and biogeochemical warming ( very high confidence ) (Figure 2.14 ¹ ). The global biophysical cooling alone has been estimated by a larger range of climate models and is –0.10 ± 0.14°C; it ranges from –0.57°C to +0.06°C (e.g., Zhang et al. 2013a ⁹⁹⁴ ; Hua and Chen 2013 ⁹⁹⁵ ; Jones et al. 2013b ⁹⁹⁶ ; Simmons and Matthews 2016 ⁹⁹⁷ ) (Table A2.1). This cooling is essentially dominated by increases in surface albedo: historical land cover changes have generally led to a dominant brightening of land as discussed in AR5 (Myhre et al. 2013 ⁹⁹⁸ ). Reduced incoming longwave radiation at the surface from reduced evapotranspiration and thus less water vapour in the atmosphere has also been reported as a potential contributor to this cooling (Claussen et al. 2001 ⁹⁹⁹ ). The cooling is, however, dampened by decreases in turbulent fluxes, leading to decreased loss of heat and water vapour from the land through convective processes. Those non-radiative processes are well-known to often oppose the albedo- induced surface temperature changes (e.g., Davin and de Noblet- Ducoudre (2010) ¹⁰⁰⁰ , Boisier et al. (2012) ¹⁰⁰¹ ).

Historical land cover changes have contributed to the increase in atmospheric CO ₂ content (Section 2.3) and thus to global warming (biogeochemical effect, very high confidence ). The global mean biogeochemical warming has been calculated from observation- based estimates (+0.25 ± 0.10°C) (e.g., Li et al. (2017a) ¹⁰⁰² , Avitabile et al. (2016) ¹⁰⁰³ , Carvalhais et al. (2014) ¹⁰⁰⁴ , Le Quéré et al. (2015) ¹⁰⁰⁵ ), or estimated from DGVMs (+0.24 ± 0.12°C) (Peng et al. 2017 ¹⁰⁰⁶ ; Arneth et al. 2017 ¹⁰⁰⁷ ; Pugh et al. 2015 ¹⁰⁰⁸ ; Hansis et al. 2015 ¹⁰⁰⁹ ) and global climate models (+0.20 ± 0.05°C) (Pongratz et al. 2010 ¹⁰¹⁰ ; Brovkin et al. 2004 ¹⁰¹¹ ; Matthews et al. 2004 ¹⁰¹² ; Simmons and Matthews 2016 ¹⁰¹³ ).

The magnitude of these simulated biogeochemical effects may, however, be underestimated as they do not account for a number of processes such as land management, nitrogen/phosphorus cycles, changes in the emissions of CH ₄ , N ₂ O and non-GHG emissions from land (Ward et al. 2014 ¹⁰¹⁴ ; Arneth et al. 2017 ¹⁰¹⁵ ; Cleveland et al. 2015 ¹⁰¹⁶ ; Pongratz et al. 2018 ¹⁰¹⁷ ). Two studies have accounted for those compounds and found a global net positive radiative forcing in response to historical anthropogenic land cover changes, indicating a net surface warming (Mahowald et al. 2017 ¹⁰¹⁸ ; Ward et al. 2014 ¹⁰¹⁹ ). However, first the estimated biophysical radiative forcing in those studies only accounts for changes in albedo and not for changes in turbulent fluxes. Secondly, the combined estimates also depend on other several key modelling estimates such as climate sensitivity, CO ₂ fertilisation caused by land use emissions, possible synergistic effects, validity of radiative forcing concept for land forcing. The comparison with the other above-mentioned modelling studies is thus difficult.

In addition, most of those estimates do not account for the evolution of natural vegetation in unmanaged areas, while observations and numerical studies have reported a greening of the land in boreal regions resulting from both extended growing season and poleward migration of tree lines (Lloyd et al. 2003 ¹⁰²⁰ ; Lucht et al. 1995 ¹⁰²¹ ; Section 2.2). This greening enhances global warming via a reduction of surface albedo (winter darkening of the land through the snow- albedo feedbacks; e.g., Forzieri et al. 2017 ¹⁰²² ). At the same time, cooling occurs due to increased evapotranspiration during the growing season, along with enhanced photosynthesis, in essence, increased CO ₂ sink (Qian et al. 2010 ¹⁰²³ ). When feedbacks from the poleward migration of treeline are accounted for, together with the biophysical effects of historical anthropogenic land cover change, the biophysical annual cooling (about –0.20°C to –0.22°C on land, –0.06°C globally) is significantly dampened by the warming (about +0.13°C) resulting from the movements of natural vegetation (Strengers et al. 2010 ¹⁰²⁴ ). Accounting simultaneously for both anthropogenic and natural land cover changes reduces the cooling impacts of historical land cover change in this specific study.

At the regional level

The global and annual estimates reported above mask out very contrasted regional and seasonal differences. Biogeochemical effects of anthropogenic land cover change on temperature follow the spatial patterns of GHG-driven climate change with stronger warming over land than ocean, and stronger warming in northern high latitudes than in the tropics and equatorial regions (Arctic amplification). Biophysical effects on the contrary are much stronger where land cover has been modified than in their surroundings (see Section 2.5.4 for a discussion on non-local effects). Very contrasted regional temperature changes can thus result, depending on whether biophysical processes dampen or exacerbate biogeochemical impacts.

Figure 2.15 compares, for seven climate models, the biophysical effects of historical anthropogenic land cover change in North America and Eurasia (essentially cooling) to the regional warming resulting from the increased atmospheric CO ₂ content since pre- industrial times (De Noblet-Ducoudré et al. 2012 ¹⁰²⁵ ; comparing 1973–2002 to 1871–1900). It shows a dominant biophysical cooling effect of changes in land cover, at all seasons, as large as the regional footprint of anthropogenic global warming. Averaged over all agricultural areas of the world (Pongratz et al. 2010 ¹⁰²⁶ ) reported a 20th century biophysical cooling of –0.10°C, and Strengers et al. (2010) ¹⁰²⁷ reported a land induced cooling as large as –1.5°C in western Russia and eastern China between 1871 and 2007. There is thus medium confidence that anthropogenic land cover change has dampened warming in many regions of the world over the historical period.

Very few studies have explored the effects of historical land cover changes on seasonal climate. There is, however, evidence that the seasonal magnitude and sign of those effects at the regional level are strongly related to soil-moisture/evapotranspiration and snow regimes, particularly in temperate and boreal latitudes (Teuling et al. 2010 ¹⁰²⁸ ; Pitman and de Noblet-Ducoudré 2012 ¹⁰²⁹ ; Alkama and Cescatti 2016 ¹⁰³⁰ ). Quesada et al. (2017a) ¹⁰³¹ showed that atmospheric circulation changes can be significantly strengthened in winter for tropical and temperate regions. However, the lack of studies underlines the need for a more systematic assessment of seasonal, regional and other- than-mean-temperature metrics in the future.

Effects on extremes

The effect of historical deforestation on extreme temperature trends is intertwined with the effect of other climate forcings, thus making it difficult to quantify based on observations. Based on results from four climate models, the impact of historical anthropogenic land cover change on temperature and precipitation extremes was found to be locally as important as changes arising from increases in atmospheric CO ₂ and sea-surface temperatures, but with a lack of model agreement on the sign of changes (Pitman et al. 2012 ¹⁰³² ). In some regions, the impact of land cover change masks or amplifies the effect of increased CO ₂ on extremes (Avila et al. 2012 ¹⁰³³ ; Christidis et al. 2013 ¹⁰³⁴ ). Using an observational constraint for the local biophysical effect of land cover change applied to a set of CMIP5 climate models, Lejeune et al. (2018) ¹⁰³⁵ found that historical deforestation increased extreme hot temperatures in northern mid-latitudes. The results also indicate a stronger impact on the warmest temperatures compared to mean temperatures. Findell et al. (2017) ¹⁰³⁶ reached similar conclusions, although using only a single climate model. Importantly, the climate models involved in these three studies did not consider the effect of management changes, which have been shown to be important, as discussed in Section 2.5.2.

Based on the studies discussed above, there is limited evidence but high agreement that land cover change affects local temperature extremes more than mean values. Observational studies assessing the role of land cover on temperature extremes are still very limited (Zaitchik et al. 2006 ¹⁰³⁷ ; Renaud and Rebetez 2008 ¹⁰³⁸ ), but suggest that trees dampen seasonal and diurnal temperature variations at all latitudes, and even more so in temperate regions compared to short vegetation (Chen et al. 2018 ¹⁰³⁹ ; Duveiller et al. 2018 ¹⁰⁴⁰ ; Li et al. 2015a ¹⁰⁴¹ ; Lee et al. 2011 ¹⁰⁴² ). Furthermore, trees also locally dampen the amplitude of heat extremes (Renaud and Rebetez 2008 ¹⁰⁴³ ; Zaitchik et al. 2006 ¹⁰⁴⁴ ) although this result depends on the forest type, coniferous trees providing less cooling effect than broadleaf trees (Renaud et al. 2011 ¹⁰⁴⁵ ; Renaud and Rebetez 2008 ¹⁰⁴⁶ ).

Table 2.4

Change in mean global annual surface air temperature resulting from anthropogenic land cover change over the historical period.

This historical period varies from one simulation to another (middle column).

Reference of the study Time period Mean global annual change in surface air temperature (°C) Simmons and Matthews (2016) 1750–2000 –0.02 Shevliakova et al. (2013) 1861–2005 +0.17 Pongratz et al. (2010) 1900–2000 +0.14 Matthews et al. (2004) 1700–2000 +0.15 Brovkin et al. (2004) 1850–2000 –0.05 Mean ± standard deviation 0.078 ± 0.093

Figure 2.14

Changes in mean global annual surface air temperature (ºC) in response to historical and future anthropogenic land cover changes as estimated from a range of studies. See Table A2.1 in the Appendix for detailed information. Temperature changes resulting from biophysical processes (e.g., changes in physical land surface characteristics such as albedo, evapotranspiration and roughness length) […]

Changes in mean global annual surface air temperature (ºC) in response to historical and future anthropogenic land cover changes as estimated from a range of studies. See Table A2.1 in the Appendix for detailed information. Temperature changes resulting from biophysical processes (e.g., changes in physical land surface characteristics such as albedo, evapotranspiration and roughness length) are illustrated using blue symbols and temperature changes resulting from biogeochemical processes (e.g., changes in atmospheric CO ₂ composition) use red symbols. Future changes are shown for three distinct scenarios: RCP8.5, RCP4.5 and RCP2.6. The markers ‘filled circle’, ‘filled cross’ and ‘filled triangle down’ represent estimates from global climate models, DGVMs and observations respectively. When the number of estimates is sufficiently large, box plots are overlaid; they show the ensemble minimum, first quartile (25th percentile), median, third quartile (75th percentile), and the ensemble maximum. Scatter points beyond the box plot are the outliers. Details about how temperature change is estimated from DGVMs and observations is provided in the Appendix. Numbers on the right-hand side give the mean and the range of simulated mean global annual warming from various climate models

Figure 2.15

Simulated changes in mean surface air temperature (ºC) between the pre-industrial period (1870–1900) and the present-day (1972–2002) for all seasons and for (A) North America and (B) Eurasia.Source: De Noblet-Ducoudré et al. (2012). Brown boxes are the changes simulated in response to increased atmospheric GHG content between both time periods and subsequent changes in sea-surface […]

Simulated changes in mean surface air temperature (ºC) between the pre-industrial period (1870–1900) and the present-day (1972–2002) for all seasons and for (A) North America and (B) Eurasia.Source: De Noblet-Ducoudré et al. (2012) ¹⁰⁴⁷ . Brown boxes are the changes simulated in response to increased atmospheric GHG content between both time periods and subsequent changes in sea-surface temperature and sea-ice extent (SST/CO ₂ ). The CO ₂ changes accounted for include emissions from all sources, including land use. Blue boxes are the changes simulated in response to the biophysical effects of historical land cover changes. The box-and-whisker plots have been drawn using results from seven climate models and ensembles of 10 simulations per model and time period. The bottom and top of each grey box are the 25th and 75th percentiles, and the horizontal line within each box is the 50th percentile (the median). The whiskers (straight lines) indicate the ensemble maximum and minimum values. Seasons are respectively December-January-February (DJF), March-April-May (MAM), June-July-August (JJA) and September-October-November (SON). North America and Eurasia are extended regions where land-use changes are the largest between the two time periods considered (their contours can be found in Figure 1 of De Noblet-Ducoudré et al. (2012).

2.5.1.2 Impacts of future global land cover changes on climate

At the global level

The most extreme CMIP5 emissions scenario, RCP8.5, has received the most attention in the literature with respect to how projected future anthropogenic land use land cover changes (Hurtt et al. 2011 ¹⁰⁴⁸ ) will affect the highest levels of global warming.

Seven model-based studies have examined both the biophysical and biogeochemical effects of anthropogenic changes in land cover, as projected in RCP8.5, on future climate change (Simmons and Matthews 2016 ¹⁰⁴⁹ ; Davies-Barnard et al. 2014 ¹⁰⁵⁰ ; Boysen et al. 2014 ¹⁰⁵¹ ) (Table 2.5). They all agree on a biogeochemical warming, ranging from +0.04°C to +0.35°C, in response to land cover change. Two models predict an additional biophysical warming, while the others agree on a biophysical cooling that dampens (or overrules) the biogeochemical warming. Using a wider range of global

climate models, the biogeochemical warming ( high confidence ) is +0.20 ± 0.15°C whereas it is +0.28 ± 0.11°C when estimated from DGVMs (Pugh et al. 2015 ¹⁰⁵² ; Stocker et al. 2014 ¹⁰⁵³ ). This biogeochemical warming is compensated for by a biophysical cooling ( medium confidence ) of –0.10 ± 0.14°C (Quesada et al. 2017a ¹⁰⁵⁴ ; Davies-Barnard et al. 2015 ¹⁰⁵⁵ ; Boysen et al. 2014 ¹⁰⁵⁶ ). The estimates of temperature changes resulting from anthropogenic land cover changes alone remain very small compared to the projected mean warming of +3.7°C by the end of the 21st century (ranging from 2.6°C–4.8°C depending on the model and compared to 1986–2005; Figure 2.14).

Two other projected land cover change scenarios have been examined (RCP4.5 and RCP2.6; Table 2.5; Figure 2.14) but only one climate modelling experiment has been carried out for each, to estimate the biophysical impacts on climate of those changes (Davies-Barnard et al. 2015 ¹⁰⁵⁷ ). For RCP2.6, ESMs and DGVMs agree on a systematic biogeochemical warming resulting from the imposed land cover changes, ranging from +0.03 to +0.28°C (Brovkin et al. 2013 ¹⁰⁵⁸ ), which is significant compared to the projected mean climate warming of +1°C by the end of the 21st century (ranging from 0.3°C–1.7°C depending on the models, compared to 1986–2005). A very small amount of biophysical cooling is expected from the one estimate. For RCP4.5, biophysical warming is expected from only one estimate, and results from a projected large forestation in the temperate and high latitudes. There is no agreement on the sign of the biogeochemical effect: there are as many studies predicting cooling as warming, whichever the method to compute those effects (ESMs or DGVMs).

Previous scenarios – Special Report on Emission Scenarios (SRES) results of climate studies using those scenarios were reported in AR4 – displayed larger land use changes than the more recent ones (RCP,AR5). There is low confidence from some of those previous scenarios (SRES A2 and B1) of a small warming effect (+0.2 to +0.3°C) of anthropogenic land cover change on mean global climate, this being dominated by the release of CO ₂ in the atmosphere from land conversions (Sitch et al. 2005 ¹⁰⁵⁹ ). This additional warming remains quite small when compared to the one resulting from the combined anthropogenic influences (+1.7°C for SRES B1 and +2.7°C for SRES A2). A global biophysical cooling of –0.14°C is estimated in response to the extreme land cover change projected in SRES A2, a value that far exceeds the impacts of historical land use changes (–0.05°C) calculated using the same climate model (Davin et al. 2007 ¹⁰⁶⁰ ). The authors derived a biophysical climatic sensitivity to land use change of about –0.3°C W.m ^–2 for their model, whereas a warming of about 1°C W.m ^–2 is obtained in response to changes in atmospheric CO ₂ concentration.

Those studies generally do not report on changes in atmospheric variables other than surface air temperature, thereby limiting our ability to assess the effects of anthropogenic land cover changes on regional climate (Sitch et al. 2005 ¹⁰⁶¹ ). However, small reductions reported in rainfall via changes in biophysical properties of the land, following the massive tropical deforestation in SRES A2 (+0.5 and +0.25 mm day ^–1 respectively in the Amazon and Central Africa). They also report opposite changes – that is, increased rainfall of about 0.25 mm day ^–1 across the entire tropics and subtropics, triggered by biogeochemical effects of this same deforestation.

At the regional level

In regions that will undergo land cover changes, dampening of the future anthropogenic warming can be as large as –26% while enhancement is always smaller than 9% within RCP8.5 by the end of the 21st century (Boysen et al. 2014 ¹⁰⁶² ). Voldoire (2006) ¹⁰⁶³ shows that, by 2050, and following the SRES B2 scenario, the contribution of land cover changes to the total temperature change can be as large as 15% in many boreal regions, and as large as 40% in south-western tropical Africa. Feddema et al. (2005) ¹⁰⁶⁴ simulate large decreases in the diurnal temperature range in the future (2050 and 2100 in SRES B1 and A2) following tropical deforestation in both scenarios. In the Amazon, for example, the diurnal temperature range is lowered by 2.5°C due to increases in minimum temperature, while little change is obtained for the maximum value.

There is thus medium evidence that future anthropogenic land cover change will have a significant effect on regional temperature via biophysical effects in many regions of the world. There is, however, no agreement on whether warming will be dampened or enhanced, and there is no agreement on the sign of the contribution across regions.

There are very few studies that go beyond analysing the changes in mean surface air temperature. Some studies attempted to look at global changes in rainfall and found no significant influence of future land cover changes (Brovkin et al. 2013 ¹⁰⁶⁵ ; Sitch et al. 2005 ¹⁰⁶⁶ ; Feddema et al. 2005 ¹⁰⁶⁷ ). Quesada et al. (2017a ¹⁰⁶⁸ , b ¹⁰⁶⁹ ) however carried out a systematic multi-model analysis of the response of a number of atmospheric, radiative and hydrological variables (e.g., rainfall, sea level pressure, geopotential height, wind speed, soil-moisture, turbulent heat fluxes, shortwave and longwave radiation, cloudiness) to RCP8.5 land cover scenario. In particular, they found a significant reduction of rainfall in six out of eight monsoon regions studied (Figure 2.16) of about 1.9–3% (which means more than –0.5 mm day ^–1 in some areas) in response to future anthropogenic land cover changes. Including those changes in global climate models reduces the projected increase in rainfall by about 9–41% in those same regions, when all anthropogenic forcings are accounted for (30% in the global monsoon region as defined by Wang and Ding (2008 ¹⁰⁷⁰ )). In addition, they found a shortening of the monsoon season of one to four days. They conclude that the projected future increase in monsoon rains may be overestimated by those models that do not yet include biophysical effects of land cover changes. Overall, the regional hydrological cycle was found to be substantially reduced and wind speed significantly strengthened in response to regional deforestation within the tropics, with magnitude comparable to projected changes with all forcings (Quesada et al. 2017b ¹⁰⁷¹ ).

Effects on extremes

Results from a set of climate models have shown that the impact of future anthropogenic land cover change on extreme temperatures can be of similar magnitude as the changes arising from half a degree global mean annual surface temperature change (Hirsch et al. 2018 ¹⁰⁷² ). However, this study also found a lack of agreement between models with respect to the magnitude and sign of changes, thus making land cover change a factor of uncertainty in future climate projections.

Table 2.5

Change in mean global annual surface air temperature resulting from anthropogenic land cover changes projected for the future, according to three different scenarios: RCP8.5, RCP4.5 and RCP2.6.

Temperature changes resulting from biophysical and biogeochemical effects of land cover change are examined.

Figure 2.16

Changes in monsoon rainfall in RCP8.5 scenario resulting from projected changes in anthropogenic land cover, in eight monsoon regions (%, blue bars). Differences are calculated between the end of the 21st century (2071–2100) and the end of the 20th century (1976–2005), and the percent change is calculated with reference to 1976–2005. Grey bars refer to […]

Changes in monsoon rainfall in RCP8.5 scenario resulting from projected changes in anthropogenic land cover, in eight monsoon regions (%, blue bars). Differences are calculated between the end of the 21st century (2071–2100) and the end of the 20th century (1976–2005), and the percent change is calculated with reference to 1976–2005. Grey bars refer to the relative contribution of land-cover changes (in %) to future rainfall projections: it is the ratio between the change in rainfall responding to land cover changes and the one responding to all anthropogenic changes (Quesada et al. 2017b ¹⁰⁷³ ). Negative values mean that changes in land cover have an opposite effect (dampening) on rainfall compared to the effects of all anthropogenic changes. Monsoon regions have been defined following Yim et al. (2014) ¹⁰⁷⁴ . The changes have been simulated by five climate models (Brovkin et al. 2013) ¹⁰⁷⁵ . Results are shown for December-January-February for southern hemisphere regions, and for June-July-August for northern hemisphere regions. Statistical significance is given by green tick marks and circles: one, two and three blue tick marks are displayed for the regions where at least 80% of the climate models have regional changes significant at the 66th, 75th and 80th confidence level, respectively; green circles are added when the regional values are also significant at 90th confidence level. Note that future land cover change impacts on South American monsoon are neither significant nor robust among models, along with very small future projected changes in South American monsoon rainfall.

2.5.2 Impacts of specific land use changes

2.5.2.1 Impacts of deforestation and forestation

Deforestation or forestation ² , wherever it occurs, triggers simultaneously warming and cooling of the surface and of the atmosphere via changes in its various characteristics (Pitman 2003 ¹⁰⁷⁶ ; Strengers et al. 2010 ¹⁰⁷⁷ ; Bonan 2008 ¹⁰⁷⁸ ). Following deforestation, warming results from (i) the release of  CO ₂ and other GHGs in the atmosphere (biogeochemical impact) and subsequent increase in incoming infrared radiation at surface (greenhouse effect), (ii) a decrease in the total loss of energy through turbulent fluxes (latent and sensible heat fluxes) resulting from reduced surface roughness, (iii) an increased incoming solar radiation following reduced cloudiness that often (but not always) accompanies the decreased total evapotranspiration. Cooling occurs in response to (iv) increased surface albedo that reduces the amount of absorbed solar radiation, (v) reduced incoming infrared radiation triggered by the decreased evapotranspiration and subsequent decrease in atmospheric water vapour. Points ii–v are referred to as biophysical effects. Deforestation and forestation also alter rainfall and winds (horizontal as well as vertical, as will be further discussed below).

The literature that discusses the effects of forestation on climate is more limited than for deforestation, but they reveal a similar climatic response with opposite sign, as further discussed below. For each latitudinal band (tropical, temperate and boreal) we look at how very large-scale deforestation or forestation impacts on the global mean climate, followed by an examination of the large-scale changes in the specific latitudinal band, and finally more regionally focused analysis. Large-scale idealised deforestation or forestation experiments are often carried out with global or regional climate models as they allow us to understand and measure how sensitive climate is to very large changes

in land cover (similar to the instant doubling of CO ₂ in climate models to calculate the climatic sensitivity to GHGs). Details of the model-based studies discussed below can be found in Table A2.2 in the Appendix.

Global and regional impacts of deforestation/forestation in tropical regions

A pan-tropical deforestation would lead to the net release of CO ₂ from land, and thus to mean global annual warming, with model-based estimates of biogeochemical effects ranging from +0.19 to +1.06°C, with a mean value of +0.53 ± 0.32°C (Ganopolski et al. 2001 ¹⁰⁷⁹ ; Snyder et al. 2004 ¹⁰⁸⁰ ; Devaraju et al. 2015a ¹⁰⁸¹ ; Longobardi et al. 2016 ¹⁰⁸² ; Perugini et al. 2017 ¹⁰⁸³ ). There is, however, no agreement between models on the magnitude and sign of the biophysical effect of such changes at the global scale (the range spans from –0.5°C to +0.7°C with a mean value of +0.1 ± 0.27°C) (e.g., Devaraju et al. (2015b) ¹⁰⁸⁴ , Snyder (2010) ¹⁰⁸⁵ , Longobardi et al. (2016a) ¹⁰⁸⁶ ) (Figure 2.17). This is the result of many compensation effects in action: increased surface albedo following deforestation, decreased atmospheric water vapour content due to less tropical evapotranspiration, and decreased loss of energy from tropical land in the form of latent and sensible heat fluxes.

There is, however, high confidence that such large land cover change would lead to a mean biophysical warming when averaged over the deforested land. A mean warming of +0.61 ± 0.48°C is found over the entire tropics. On the other hand, biophysical regional cooling and global warming is expected from forestation (Wang et al. 2014b ¹⁰⁸⁷ ; Bathiany et al. 2010 ¹⁰⁸⁸ ).

Large-scale deforestation (whether pan-tropical or imposed at the sub-continent level, e.g., the Amazon) results in significant mean rainfall decrease (Lawrence and Vandecar 2015 ¹⁰⁸⁹ ; Lejeune et al. 2015 ¹⁰⁹⁰ ; Perugini et al. 2017 ¹⁰⁹¹ ). In their review, Perugini et al. (2017) ¹⁰⁹² reported an average simulated decrease of –288 ± 75 mm yr ^–1 (95% confidence interval). Inversely large-scale forestation increases tropical rainfall by 41 ± 21 mm yr ^–1 . The magnitude of the change in precipitation strongly depends on the type of land cover conversion. For instance, conversion of tropical forest to bare soil causes larger reductions in regional precipitation than conversion to pasture (respectively –470 ± 60 mm yr ^–1 and –220 ± 100 mm yr ^–1 ). Biogeochemical effects in response to pan-tropical deforestation, particularly CO ₂ release, are generally not taken into account in those studies, but could intensify the hydrological cycle and thus precipitation (Kendra Gotangco Castillo and Gurney 2013 ¹⁰⁹³ ).

Specific model-based deforestation studies have been carried out for Africa (Hagos et al. 2014 ¹⁰⁹⁴ ; Boone et al. 2016 ¹⁰⁹⁵ ; Xue et al. 2016 ¹⁰⁹⁶ ; Nogherotto et al. 2013 ¹⁰⁹⁷ ; Hartley et al. 2016 ¹⁰⁹⁸ ; Klein et al. 2017 ¹⁰⁹⁹ ; Abiodun et al. 2012 ¹¹⁰⁰ ), southern America (Butt et al. 2011 ¹¹⁰¹ ; Wu et al. 2017 ¹¹⁰² ; Spracklen and Garcia-Carreras 2015 ¹¹⁰³ ; Lejeune et al. 2015) and Southeast Asia (Ma et al. 2013b ¹¹⁰⁴ ; Werth and Avissar 2005 ¹¹⁰⁵ ; Mabuchi et al. 2005 ¹¹⁰⁶ ; Tölle et al. 2017 ¹¹⁰⁷ ). All found decreases in evapotranspiration following deforestation ( high agreement ), resulting in surface warming, despite the competing effect from increased surface albedo ( high agreement ). Changes in thermal gradients between deforested and adjacent regions, between land and ocean, affect horizontal surface winds ( high agreement ) and thus modify the areas where rain falls, as discussed in Section 2.5.4. An increase in the land-sea thermal contrast has been found in many studies as surface friction is reduced by deforestation, thus increasing the monsoon flow in Africa and South America (Wu et al. 2017 ¹¹⁰⁸ ).

Observation-based estimates all agree that deforestation increases local land-surface and ambient air temperatures in the tropics, while forestation has the reverse effect ( very high confidence ) (Prevedello et al. 2019 ¹¹⁰⁹ ; Schultz et al. 2017 ¹¹¹⁰ ; Li et al. 2015b ¹¹¹¹ ; Alkama and Cescatti 2016 ¹¹¹² ). There is very high confidence that forests are cooler than any shorter vegetation (crops, grasses, bare soil) during daytime due to larger transpiration rates, and there is high confidence that the amplitude of the diurnal cycle is smaller in the presence of forests.

Large-scale forestation scenarios of West Africa (Abiodun et al. 2012 ¹¹¹³ ), eastern China (Ma et al. 2013a ¹¹¹⁴ ) or the Saharan and Australian deserts (Ornstein et al. 2009 ¹¹¹⁵ ; Kemena et al. 2017 ¹¹¹⁶ ) all concluded that regional surface cooling is simulated wherever trees are grown (–2.5°C in the Sahel, –1°C in the savanna area of West Africa, up to –8°C in the western Sahara and –1.21°C over land in eastern China) while cooling of the ambient air is smaller (–0.16°C). In the case of savanna forestation, this decrease entirely compensates the GHG-induced future warming (+1°C following the SRESA1B scenario).WestAfrican countries thus have the potential to reduce, or even totally cancel in some places, the GHG-induced warming in the deforested regions (Abiodun et al. 2012 ¹¹¹⁷ ). However, this is compensated by enhanced warming in adjacent countries (non-local effect).

Global and regional impacts of deforestation/forestation in temperate regions.

As for the tropics, model-based experiments show that large- scale temperate deforestation would induce a small mean global annual warming through the net release of CO ₂ into the atmosphere (ranging from +0.10 to +0.40°C with a mean value of +0.20 ± 0.13°C) (Figure 2.17), whereas there is less agreement on the sign of the mean global annual temperature change resulting from biophysical processes: estimates range from –0.5°C to +0.18°C with a mean value of –0.13 ± 0.22°C. There is also very low agreement on the mean annual temperature change in the temperate zone (–0.4 ± 0.62°C; Phillips et al. 2007 ¹¹¹⁸ ; Snyder et al. 2004 ¹¹¹⁹ ; Longobardi et al. 2016a ¹¹²⁰ ; Devaraju et al. 2015a ¹¹²¹ , 2018 ¹¹²² ). There is medium agreement on a global and latitudinal biophysical warming in response to forestation (Laguë and Swann 2016 ¹¹²³ ; Swann et al. 2012 ¹¹²⁴ ; Gibbard et al. 2005 ¹¹²⁵ ; Wang et al. 2014b ¹¹²⁶ ) (Figure 2.17), but this is based on a smaller number of studies.

Figure 2.17

Changes in mean annual surface air temperature (ºC) in response to idealised large-scale deforestation (circles) or forestation (crosses).Estimated from a range of studies (see Table A2.2 in the Appendix for detailed information and references to the studies). Temperature changes resulting from biophysical processes (e.g., changes in physical land surface characteristics such as albedo, evapotranspiration, and […]

Changes in mean annual surface air temperature (ºC) in response to idealised large-scale deforestation (circles) or forestation (crosses).Estimated from a range of studies (see Table A2.2 in the Appendix for detailed information and references to the studies). Temperature changes resulting from biophysical processes (e.g., changes in physical land surface characteristics such as albedo, evapotranspiration, and roughness length) are illustrated using blue symbols and temperature changes resulting from biogeochemical processes (e.g., changes in atmospheric CO ₂ composition) use orange symbols. Small blue and orange circles and crosses are model-based estimates of changes in temperature averaged globally. Large circles are estimates averaged only over the latitudinal band where deforestation is imposed.

The lack of agreement at the annual scale among the climate models is, however, masking rising agreement regarding seasonal impacts of deforestation at those latitudes. There is high agreement that temperate deforestation leads to summer warming and winter cooling (Bright et al. 2017 ¹¹²⁷ ; Zhao and Jackson 2014 ¹¹²⁸ ; Gálos et al. 2011 ¹¹²⁹ , 2013 ¹¹³⁰ ; Wickham etal.2013 ¹¹³¹ ;Ahlswede and Thomas 2017 ¹¹³² ; Anderson-Teixeira et al. 2012 ¹¹³³ ; Anderson et al. 2011 ¹¹³⁴ ; Chen et al. 2012 ¹¹³⁵ ; Strandberg and Kjellström 2018 ¹¹³⁶ ). The winter cooling is driven by the increased surface albedo, amplified by the snow-albedo feedback. In some models, and when deforestation is simulated for very large areas, the cooling is further amplified by high latitude changes in sea-ice and snow extent (polar amplification). Summer warming occurs because the latent and sensible heat fluxes that take energy out of the surface diminish with the smaller roughness length and lower evapotranspiration efficiency of low vegetation, as compared to tree canopies (Davin and de Noblet-Ducoudre 2010 ¹¹³⁷ ; Anav et al. 2010 ¹¹³⁸ ). Conversely, there is high agreement that forestation in North America or in Europe cools surface climate during summer time, especially in regions where water availability can support large evapotranspiration rates. In temperate regions with water deficits, the simulated change in evapotranspiration following forestation will be insignificant, while the decreased surface albedo will favour surface warming.

Observation-based estimates confirm the existence of a seasonal pattern of response to deforestation, with colder winters any time there is snow on the ground and in any place where soils are brighter than the trees, and warmer summers (Schultz et al. 2017 ¹¹³⁹ ; Wickham et al. 2014 ¹¹⁴⁰ ; Juang et al. 2007 ¹¹⁴¹ ; Tang et al. 2018 ¹¹⁴² ; Peng et al. 2014 ¹¹⁴³ ; Zhang et al. 2014b ¹¹⁴⁴ ; Prevedello et al. 2019 ¹¹⁴⁵ ; Li et al. 2015b ¹¹⁴⁶ ; Alkama and Cescatti 2016 ¹¹⁴⁷ ). In contrast, forestation induces cooler summers wherever trees have access to sufficient soil moisture to transpire. The magnitude of the cooling depends on the wetness of the area of concern (Wickham et al. 2013) as well as on the original and targeted species and varieties implicated in the vegetation conversion (Peng et al. 2014 ¹¹⁴⁸ ; Juang et al. 2007 ¹¹⁴⁹ ).

There is also high confidence from observation-based estimates that mean annual daytime temperatures are warmer following deforestation, while night-time temperatures are cooler (Schultz et al. 2017 ¹¹⁵⁰ ; Wickham et al. 2014 ¹¹⁵¹ ; Juang et al. 2007 ¹¹⁵² ; Tang et al. 2018 ¹¹⁵³ ; Prevedello et al. 2019 ¹¹⁵⁴ ; Peng et al. 2014 ¹¹⁵⁵ ; Zhang et al. 2014b ¹¹⁵⁶ ; Li et al. 2015b ¹¹⁵⁷ ; Alkama and Cescatti 2016 ¹¹⁵⁸ ). Deforestation then increases the amplitude of diurnal temperature variations while forestation reduces it ( high confidence ). Two main reasons have been put forward to explain why nights are warmer in forested areas: their larger capacity to store heat and the existence of a nocturnal temperature inversion bringing warmer air from the higher atmospheric levels down to the surface.

In addition to those seasonal and diurnal fluctuations, Lejeune et al. (2018) ¹¹⁵⁹ found systematic warming of the hottest summer days following historical deforestation in the northern mid-latitudes, and this echoes Strandberg and Kjellström (2018) ¹¹⁶⁰ who argue that the August 2003 and July 2010 heatwaves could have been largely mitigated if Europe had been largely forested.

In a combined modelling of large-scale forestation of western Europe and climate change scenario (SRES A2), Gálos et al. (2013) ¹¹⁶¹ found relatively small dampening potential of additional forest on ambient air temperature at the end of the 21st century when compared to the beginning (the cooling resulting from land cover changes is –0.5°C whereas the GHG-induced warming exceeds 2.5°C). Influence on rainfall was, however, much larger and significant. Projected annual rainfall decreases following warming were cancelled in Germany and significantly reduced in both France and Ukraine through forestation. In addition, forestation decreased the number of warming-induced dry days but increased the number of extreme precipitation events.

The net impact of forestation, combining both biophysical and biogeochemical effects, has been tested in the warmer world predicted by RCP 8.5 scenario (Sonntag et al. 2016 ¹¹⁶² , 2018 ¹¹⁶³ ). The cooling effect from the addition of 8 Mkm2 of forests following the land use RCP 4.5 scenario was too small (–0.27°C annually) to dampen the RCP 8.5 warming. However, it reached about –1°C in some temperate regions and –2.5°C in boreal ones. This is accompanied by a reduction in the number of extremely warm days.

Global and regional impacts of deforestation/forestation in boreal regions

Consistent with what we have previously discussed for temperate and tropical regions, large-scale boreal deforestation induces a biogeochemical warming of +0.11 ± 0.09°C (Figure 2.17). But contrary to those other latitudinal bands, the biophysical effect is a consistent cooling across all models (–0.55 ± 0.29°C when averaged globally). It is also significantly larger than the biogeochemical warming (e.g., Dass et al. (2013) ¹¹⁶⁴ , Longobardi et al. (2016a) ¹¹⁶⁵ , Devaraju et al. (2015a) ¹¹⁶⁶ , Bathiany et al. (2010) ¹¹⁶⁷ , Devaraju et al. (2018) ¹¹⁶⁸ ) and is driven by the increased albedo, enhanced by the snow-albedo feedback as well as by an increase in sea-ice extent in the Arctic. Over boreal lands, the cooling is as large as –1.8 ± 1.2°C. However, this means that annual cooling masks a seasonal contrast, as discussed in Strandberg and Kjellström (2018) ¹¹⁶⁹ and Gao et al. (2014) ¹¹⁷⁰ : during summer time, following the removal of forest, the decreased evapotranspiration results in a significant summer warming that outweighs the effect of an increased albedo effect.

The same observation-based estimates (as discussed in the previous subsection) show similar patterns for the temperate latitudes: seasonal and daily contrasts. Schultz et al. (2017) ¹¹⁷¹ , however, found that mean annual night-time changes are as large as daytime ones in those regions (mean annual nocturnal cooling of –1.4 ± 0.10°C, balanced by mean annual daytime warming of 1.4 ± 0.04°C). This contrasts with both temperate and tropical regions where daytime changes are always larger than the night-time ones.

Arora and Montenegro (2011) ¹¹⁷² combined large-scale forestation and climate change scenario (SRES A2): forestation of either 50% or 100% of the total agricultural area was gradually prescribed between years 2011 and 2060 everywhere. In addition, boreal, temperate and tropical forestation have been tested separately. Both biophysical and biogeochemical effects were accounted for. The net simulated impact of forestation was a cooling varying from –0.04°C to –0.45°C, depending on the location and magnitude of the additional forest cover. It was, however, quite marginal compared to the large global warming resulting from anthropogenic GHG emissions (+3°C at the end of the 21st century). In their experiment, forestation in boreal regions led to biophysical warming and biogeochemical cooling that compensated each other, whereas forestation in the tropics led to both biophysical and biogeochemical cooling. The authors concluded that tropical forestation is three times more effective at cooling down climate than boreal or temperate forestation.

Conclusion

In conclusion, planting trees will always result in capturing more atmospheric CO ₂ , and thus will mean annual cooling of the globe ( very high confidence ). At the regional level, however, the magnitude and sign of the local temperature change depends on (i) where forestation occurs, (ii) its magnitude, (iii) the level of warming under which the land cover change is applied, and (iv) the land conversion type. This is because the background climatic conditions (e.g., precipitation and snow regimes, mean annual temperature) within which the land cover changes occur vary across regions (Pitman et al. 2011 ¹¹⁷³ ; Montenegro et al. 2009 ¹¹⁷⁴ ; Juang et al. 2007 ¹¹⁷⁵ ; Wickham et al. 2014 ¹¹⁷⁶ ; Hagos et al. 2014 ¹¹⁷⁷ ; Voldoire 2006 ¹¹⁷⁸ ; Feddema et al. 2005 ¹¹⁷⁹ ; Strandberg and Kjellström 2018 ¹¹⁸⁰ ). In addition, there is high confidence that estimates of the influence of any land cover or land use change on surface temperature from the sole consideration of the albedo and the CO ₂ effects is incorrect as changes in turbulent fluxes (i.e., latent and sensible heat fluxes) are large contributors to local temperature change (Bright et al. 2017 ¹¹⁸¹ ).

There is high confidence that, in boreal and temperate latitudes, the presence of forest cools temperature in warmer locations and seasons (provided that the soil is not dry), whereas it warms temperature in colder locations and seasons (provided the soil is brighter than the trees or covered with snow). In the humid tropics, forestation increases evapotranspiration year-round and thus decreases temperature ( high confidence ). In tropical areas with a strong seasonality of rainfall, forestation will also increase evapotranspiration year-round, unless the soil becomes too dry. In all regions there is medium confidence that the diurnal temperature range decreases with increasing forest cover, with potentially reduced extreme values of temperature.

Although there is not enough literature yet that rigorously compares both biophysical and biogeochemical effects of realistic scenarios of forestation, there is high confidence that, at the local scale (that is where the forest change occurs), biophysical effects on surface temperature are far more important than the effects resulting from the changes in emitted CO ₂ .

What is lacking in the literature today is an estimate of the impacts that natural disturbances in forests will have on local climates and on the build-up of atmospheric CO ₂ (O’Halloran et al. 2012 ¹¹⁸² ), illustrated with many examples that changes in albedo following disturbances can result in radiative forcing changes opposite to, and as large as, the ones resulting from the associated changes in the net release of CO ₂ by land. The resulting climate effects depend on the duration of the perturbation and of the following recovery of vegetation.

2.5.2.2 Impacts of changes in land management

There have been little changes in net cropland area over the past 50 years (at the global scale) compared to continuous changes in land management (Erb et al. 2017 ¹¹⁸³ ). Similarly, in Europe, change in forest management has resulted in a very significant anthropogenic land change. Management affects water, energy and GHG fluxes exchanged between the land and the atmosphere, and thus affects temperature and rainfall, sometimes to the same extent as changes in land cover do (as discussed in Luyssaert et al. (2014) ¹¹⁸⁴ ).

The effects of irrigation, which is a practice that has been substantially studied, including one attempt to manage solar radiation via increases in cropland albedo (geoengineering the land) are assessed, along with a discussion of recent findings on the effects of forest management on local climate, although there is not enough literature yet on this topic to carry out a thorough assessment. The effects of urbanisation on climate are assessed in a specific cross-chapter box within this chapter (Cross-Chapter Box 4 in this chapter).

There are a number of other practices that exist whose importance for climate mitigation has been examined (some are reported in Section 2.6 and Chapter 6). There is, however, not enough literature available for assessing their biophysical effect on climate. Few papers are generally found per agricultural practice, for example, Jeong et al. (2014b) ¹¹⁸⁵ for double cropping, Bagley et al. (2017) ¹¹⁸⁶ for the timing of the growing season and Erb et al. (2017) ¹¹⁸⁷ for a review of 10 management practices.

Similarly, there are very few studies that have examined how choosing species varieties and harvesting strategies in forest management impacts on climate through biophysical effects, and how those effects compare to the consequences of the chosen strategies on the net CO ₂ sink of the managed forest. The modelling studies highlight the existence of competing effects, for example, between the capacity of certain species to store more carbon than others (thus inducing cooling) while at the same time reducing the total evapotranspiration loss and absorbing more solar radiation via lower albedo (thus inducing warming) (Naudts et al. 2016a ¹¹⁸⁸ ; Luyssaert et al. 2018 ¹¹⁸⁹ ).

Irrigation

There is substantial literature on the effects of irrigation on local, regional and global climate as this is a major land management issue. There is very high confidence that irrigation increases total evapotranspiration, increases the total amount of water vapour in the atmosphere and decreases mean surface daytime temperature within the irrigated area and during the time of irrigation (Bonfils and Lobell 2007 ¹¹⁹⁰ ; Alter et al. 2015 ¹¹⁹¹ ; Chen and Jeong 2018 ¹¹⁹² ; Christy et al. 2006 ¹¹⁹³ ; Im and Eltahir 2014 ¹¹⁹⁴ ; Im et al. 2014 ¹¹⁹⁵ ; Mueller et al. 2015 ¹¹⁹⁶ ). Decreases in maximum daytime temperature can locally be as large as –3°C to –8°C (Cook et al. 2015 ¹¹⁹⁷ ; Han and Yang 2013 ¹¹⁹⁸ ; Huber et al. 2014 ¹¹⁹⁹ ; Alter et al. 2015 ¹²⁰⁰ ; Im et al. 2014 ¹²⁰¹ ). Estimates of the contribution of irrigation to past historical trends in ambient air temperature vary between –0.07°C and –0.014°C/decade in northern China (Han and Yang 2013 ¹²⁰² ; Chen and Jeong 2018 ¹²⁰³ ) while being quite larger in California, USA (–0.14°C to –0.25°C/decade) (Bonfils and Lobell 2007 ¹²⁰⁴ ). Surface cooling results from increased energy being taken up from the land via larger evapotranspiration rates. In addition, there is growing evidence from modelling studies that such cooling can locally mitigate the effect of heatwaves (Thiery et al. 2017 ¹²⁰⁵ ; Mueller et al. 2015 ¹²⁰⁶ ).

There is no agreement on changes in night-time temperatures, as discussed in Chen and Jeong (2018) ¹²⁰⁷ who summarised the findings from observations in many regions of the world (India, China, North America and eastern Africa) (Figure 2.18). Where night-time warming is found (Chen and Jeong 2018 ¹²⁰⁸ ; Christy et al. 2006 ¹²⁰⁹ ), two explanations are put forward, (i) an increase in incoming longwave radiation in response to increased atmospheric water vapour content (greenhouse effect), and (ii) an increased storage of heat in the soil during daytime. Because of the larger heat capacity of moister soil, heat is then released to the atmosphere at night.

Figure 2.18

Global map of areas equipped for irrigation (colours), expressed as a percentage of total area, or irrigation fraction. Source: Siebert et al. (2013). Numbered boxes show regions where irrigation causes cooling (down arrow) of surface mean (Tmean), maximum (Tmax) or minimum (Tmin) temperature, or else no significant effect (right arrow) or where the effect is […]

Global map of areas equipped for irrigation (colours), expressed as a percentage of total area, or irrigation fraction. Source: Siebert et al. (2013) ¹²¹⁰ . Numbered boxes show regions where irrigation causes cooling (down arrow) of surface mean (Tmean), maximum (Tmax) or minimum (Tmin) temperature, or else no significant effect (right arrow) or where the effect is uncertain (question mark), based on observational studies as reviewed in Chen and Jeong (2018) ¹²¹¹ . Tmax refers to the warmest daily temperature while Tmin to the coldest one, which generally occurs at night (Alter et al. 2015 ¹²¹² ; Han and Yang 2013 ¹²¹³ ; Roy et al. 2007 ¹²¹⁴ ; Shi et al. 2013 ¹²¹⁵ ; Bonfils and Lobell 2007 ¹²¹⁶ ; Lobell et al. 2008 ¹²¹⁷ ; Lobell and Bonfils 2008 ¹²¹⁸ ; Christy et al. 2006 ¹²¹⁹ ; Mahmood et al. 2006 ¹²²⁰ ; Mueller et al. 2015 ¹²²¹ ).

There is robust evidence from modelling studies that implementing irrigation enhances rainfall, although there is very low confidence on where this increase occurs. When irrigation occurs in Sahelian Africa during the monsoon period, rainfall is decreased over irrigated areas ( high agreement ), increased in the southwest if the crops are located in western Africa (Alter et al. 2015 ¹²²² ) and increased in the east/northeast when crops are located further east in Sudan (Im and Eltahir 2014 ¹²²³ ; Im et al. 2014 ¹²²⁴ ) The cooler irrigated surfaces in the Sahel, because of their greater evapotranspiration, inhibit convection and create an anomalous descending motion over crops that suppresses rainfall but influences the circulation of monsoon winds. Irrigation in India occurs prior to the start of the monsoon season and the resulting land cooling decreases the land-sea temperature contrast. This can delay the onset of the Indian monsoon and decrease its intensity (Niyogi et al. 2010 ¹²²⁵ ; Guimberteau et al. 2012 ¹²²⁶ ). Results from a modelling study by De Vrese et al. (2016) ¹²²⁷ suggest that part of the excess rainfall triggered by Indian irrigation falls westward, in the horn of Africa. The theory behind those local and downwind changes in rainfall support the findings from the models, but we do not yet have sufficient literature to robustly assess the magnitude and exact location of the expected changes driven by irrigation.

Cropland albedo

Various methods have been proposed to increase surface albedo in cropland and thus reduce local surface temperature ( high confidence ): choose ‘brighter’ crop varieties (Ridgwell et al. 2009 ¹²²⁸ ; Crook et al. 2015 ¹²²⁹ ; Hirsch et al. 2017 ¹²³⁰ ; Singarayer et al. 2009 ¹²³¹ ; Singarayer and Davies-Barnard 2012 ¹²³² ), abandon tillage (Lobell et al. 2006 ¹²³³ ; Davin et al. 2014 ¹²³⁴ ), include cover crops into rotation in areas where soils are darker than vegetation (Carrer et al. 2018 ¹²³⁵ ; Kaye and Quemada 2017 ¹²³⁶ ) or use greenhouses (as in Campra et al. (2008) ¹²³⁷ ). See Seneviratne et al. (2018) ¹²³⁸ for a review.

Whatever the solution chosen, the induced reduction in absorbed solar radiation cools the land – more specifically during the hottest summer days ( low confidence ) (Davin et al. 2014 ¹²³⁹ ; Wilhelm et al. 2015 ¹²⁴⁰ ; Figure 2.19). Changes in temperature are essentially local and seasonal (limited to crop growth season) or sub-seasonal (when resulting from inclusion of cover crop or tillage suppression). Such management action on incoming solar radiation thus holds the potential to counteract warming in cultivated areas during crop growing season.

Figure 2.19

Change in summer (July–August) daily maximum temperature (ºC) resulting from increased surface albedo in unploughed versus ploughed land, in (A) southern, and (B) northern Europe, during the period 1986–2009. Changes are simulated for different quantiles of the daily maximum temperature distribution, where Q1 represents the coolest 1% and Q99 the warmest 1% of summer days. […]

Change in summer (July–August) daily maximum temperature (ºC) resulting from increased surface albedo in unploughed versus ploughed land, in (A) southern, and (B) northern Europe, during the period 1986–2009. Changes are simulated for different quantiles of the daily maximum temperature distribution, where Q1 represents the coolest 1% and Q99 the warmest 1% of summer days. Only grid cells with more than 60% of their area in cropland are included. The dashed bars represent the standard deviation calculated across all days and grid points. SE refers to southern Europe (below 45ºN) and NE to northern Europe (above 45ºN). (Davin et al., 2014)

Introducing cover crops into a rotation can also have a warming effect in areas where vegetation has a darker albedo than soil, or in winter during snow periods if the cover crops or their residues are tall enough to overtop the snow cover (Kaye and Quemada 2017 ¹²⁴¹ ; Lombardozzi et al. 2018 ¹²⁴² ). In addition, evapotranspiration greater than that of bare soil during this transitional period reduces soil temperature (Ceschia et al. 2017 ¹²⁴³ ). Such management strategy can have another substantial mitigation effect as it allows carbon to be stored in the soil and to reduce both direct and indirect N ₂ O emissions (Basche et al. 2014 ¹²⁴⁴ ; Kaye and Quemada 2017 ¹²⁴⁵ ), in particular if fertilisation of the subsequent crop is reduced (Constantin et al. 2010 ¹²⁴⁶ , 2011 ¹²⁴⁷ ). The use of cover crops thus substantially improves the GHG budget of croplands (Kaye and Quemada 2017 ¹²⁴⁸ ; Tribouillois et al. 2018 ¹²⁴⁹ ). More discussion on the role of management practices for mitigation can be found in Section 2.6 and Chapter 6.

Only a handful of modelling studies have looked at effects other than changes in atmospheric temperature in response to increased cropland albedo. Seneviratne et al. (2018) ¹²⁵⁰ have found significant changes in rainfall following an idealised increase in cropland albedo, especially within the Asian monsoon regions. The benefits of cooler temperature on production, resulting from increased albedo, is cancelled out by decreases in rainfall that are harmful for crop productivity. The rarity of a concomitant evaluation of albedo management impact on crop productivity prevents us from providing a robust assessment of this practice in terms of both climate mitigation and food security.

2.5.3 Amplifying/dampening climate changes via land responses

Section 2.1 and Box 2.1 illustrate the various ways through which land can affect the atmosphere and thereby climate and weather. Section 2.2 illustrates the many impacts that climate changes have on the functioning of land ecosystems. Section 2.3 discusses the effects that future climatic conditions have on the capacity of the land to absorb anthropogenic CO ₂ , which then controls the sign of the feedback to the initial global warming. Sections 2.5.1 and 2.5.2 show the effects of changes in anthropogenic land cover or land management on climate variables or processes. Therefore, land has the potential to dampen or amplify the GHG-induced global climate warming, or can be used as a tool to mitigate regional climatic consequences of global warming such as extreme weather events, in addition to increasing the capacity of land to absorb CO ₂ (Figure 2.20).

Land-to-climate feedbacks are difficult to assess with global or regional climate models, as both types of models generally omit a large number of processes. Among these are (i) the response of vegetation to climate change in terms of growth, productivity, and geographical distribution, (ii) the dynamics of major disturbances such as fires, (iii) the nutrients dynamics, and (iv) the dynamics and effects of short-lived chemical tracers such as biogenic volatile organic compounds (Section 2.4). Therefore, only those processes that are fully accounted for in climate models are considered here.

Figure 2.20

Schematics of the various ways land has been shown in the literature to either amplify or dampen the initial GHG-induced climatic change. Brown arrows and boxes represent the global scale and blue arrows and boxes represent the regional/local level. Grey arrows and boxes refer to what we consider herein as imposed changes – that is, […]

Schematics of the various ways land has been shown in the literature to either amplify or dampen the initial GHG-induced climatic change. Brown arrows and boxes represent the global scale and blue arrows and boxes represent the regional/local level. Grey arrows and boxes refer to what we consider herein as imposed changes – that is, the initial atmospheric GHG content as well as anthropogenic land cover change and land management. Dampening feedbacks are represented with dashed lines, amplifying ones with solid lines and feedbacks where the direction may be variable are represented using dotted lines. The feedbacks initiated by changes in snow and permafrost areas in boreal regions are discussed in Section 2.5.3.2, the ones initiated by changes in ecosystem distribution are discussed in Sections 2.5.3.1, 2.5.1 and 2.5.2, and the feedbacks related to changes in the land functioning are discussed in Sections 2.5.3.3 and 2.5.1, as well as in Sections 2.3and 2.5 (for changes in net CO ₂ fluxes). References supporting this figure can be found in each of those sections.

2.5.3.1 Effects of changes in land cover and productivity resulting from global warming

In boreal regions, the combined northward migration of the treeline and increased growing season length in response to increased temperatures in those regions (Section 2.2) will have positive feedbacks both on global and regional annual warming ( high confidence ) (Garnaud and Sushama 2015 ¹²⁵¹ ; Jeong et al. 2014a ¹²⁵² ; O’ishi and Abe-Ouchi 2009 ¹²⁵³ ; Port et al. 2012 ¹²⁵⁴ ; Strengers et al. 2010 ¹²⁵⁵ ). The warming resulting from the decreased surface albedo remains the dominant signal in all modelling studies at the annual timescale and during the snow season, while cooling is obtained during the growing season (Section 2.5.2.1 and Figure 2.21, right panel).

In the tropics, climate change will cause both greening and browning (Section 2.2). Where global warming provokes a decrease in rainfall, the induced decrease in biomass production leads to increased local warming ( high confidence ) (Port et al. 2012 ¹²⁵⁶ ; Wu et al. 2016 ¹²⁵⁷ ; Yu et al. 2016 ¹²⁵⁸ ). The reverse is true where warming generates increases in rainfall and thus greening. As an example, Port e tal. (2012) ¹²⁵⁹ simulated decreases in tree cover and shortened growing season in the Amazon, despite the CO ₂ fertilisation effects, in response to both future tropical warming and reduced precipitation (Figure 2.21, left panel). This browning of the land decreases both evapotranspiration and atmospheric humidity. The warming driven by the drop in evapotranspiration is enhanced via a decrease in cloudiness, increasing solar radiation, and is dampened by reduced water vapour greenhouse radiation.

There is very low confidence on how feedbacks affect rainfall in the tropics where vegetation changes may occur, as the sign of the change in precipitation depends on where the greening occurs and on the season (as discussed in Section 2.5.2). There is, however, high confidence that increased vegetation growth in the southern Sahel increases African monsoon rains (Yu et al. 2016 ¹²⁶⁰ ; Port et al. 2012 ¹²⁶¹ ; Wu et al. 2016 ¹²⁶³ ). Confidence on the direction of such feedbacks is also based on a significant number of paleoclimate studies that analysed how vegetation dynamics helped maintain a northward position of the African monsoon during the Holocene time period (9–6 kyr BP) (de Noblet-Ducoudré et al. 2000 ¹²⁶⁴ ; Rachmayani et al. 2015 ¹²⁶⁵ ).

Figure 2.21

Schematic illustration of the processes through which the effects of global warming in (a) the Amazon (blue arrows and boxes), and (b) boreal regions (grey arrows and boxes) feedback on the regional climate change. In boreal regions, the sign of the feedbacks depends on the season, although annually global warming is further enhanced in those […]

Schematic illustration of the processes through which the effects of global warming in (a) the Amazon (blue arrows and boxes), and (b) boreal regions (grey arrows and boxes) feedback on the regional climate change. In boreal regions, the sign of the feedbacks depends on the season, although annually global warming is further enhanced in those regions. Dashed lines illustrate negative feedbacks, while solid lines indicate positive feedbacks. References supporting this figure can be found in the text.

2.5.3.2 Feedbacks to climate from high-latitude land-surface changes

In high latitudes, snow albedo and permafrost carbon feedbacks are the most well-known and most important surface-related climate feedbacks because of their large-scale impacts.

In response to ongoing and projected decrease in seasonal snow cover (Derksen and Brown 2012 ¹²⁶⁶ ; Brutel-Vuilmet et al. 2013 ¹²⁶⁷ ) warming is, and will continue to be, enhanced in boreal regions ( high confidence ) (Brutel-Vuilmet et al. 2013 ¹²⁶⁸ ; Perket et al. 2014 ¹²⁶⁹ ; Thackeray and Fletcher 2015 ¹²⁷⁰ ; Mudryk et al. 2017 ¹²⁷¹ ). One reason for this is the large reflectivity (albedo) the snow exerts on shortwave radiative forcing: the all- sky global land snow shortwave radiative effect is evaluated to be around –2.5 ± 0.5 W m ^–2 (Flanner et al. 2011 ¹²⁷² ; Singh et al. 2015 ¹²⁷³ ). In the southern hemisphere, perennial snow on the Antarctic is the dominant contribution, while in the northern hemisphere, this is essentially attributable to seasonal snow, with a smaller contribution  from snow on glaciated areas. Another reason is the sensitivity of snow cover to temperature: Mudryk et al. (2017) ¹²⁷⁴ recently showed that, in the high latitudes, climate models tend to correctly represent this sensitivity, while in mid-latitude and alpine regions, the simulated snow cover sensitivity to temperature variations tends to be biased low. In total, the global snow albedo feedback is about 0.1 W m ^–2 K ^–1 , which amounts to about 7% of the strength of the globally dominant water vapour feedback (e.g., Thackeray and Fletcher (2015) ¹²⁷⁵ . While climate models do represent this feedback, a persistent spread in the modelled feedback strength has been noticed (Qu and Hall 2014 ¹²⁷⁶ ) and, on average, the simulated snow albedo feedback strength tends to be somewhat weaker than in reality ( medium confidence ) (Flanner et al. 2011 ¹²⁷⁷ ; Thackeray and Fletcher 2015 ¹²⁷⁸ ). Various reasons for the spread and biases of the simulated snow albedo feedback have been identified, notably inadequate representations of vegetation masking snow in forested areas (Loranty et al. 2014 ¹²⁷⁹ ; Wang et al. 2016c ¹²⁸⁰ ; Thackeray and Fletcher 2015 ¹²⁸¹ ).

The second most important potential feedback from land to climate relates to permafrost decay. There is high confidence that, following permafrost decay from a warming climate, the resulting emissions of CO ₂ and/or CH ₄ (caused by the decomposition of organic matter in previously frozen soil) will produce additional GHG- induced warming. There is, however, substantial uncertainty on the magnitude of this feedback, although recent years have seen large progress in its quantification. Lack of agreement results from several critical factors that carry large uncertainties. The most important are (i) the size of the permafrost carbon pool, (ii) its decomposability, (iii) the magnitude, timing and pathway of future high-latitude climate change, and (iv) the correct identification and model representation of the processes at play (Schuur et al. 2015 ¹²⁸² ). The most recent comprehensive estimates establish a total soil organic carbon storage in permafrost of about 1500 ± 200 PgC (Hugelius et al. 2014 ¹²⁸³ , 2013 ¹²⁸⁴ ; Olefeldt et al. 2016 ¹²⁸⁵ ), which is about 300 Pg C lower than previous estimates ( low confidence ). Important progress has been made in recent years at incorporating permafrost-related processes in complex ESMs (e.g., McGuire et al. (2018) ¹²⁸⁶ ), but representations of some critical processes such as thermokarst formation are still in their infancy (Schuur et al. 2015) ¹²⁸⁷ . Recent model-based estimates of future permafrost carbon release (Koven et al. 2015 ¹²⁸⁸ ; McGuire et al. 2018 ¹²⁸⁹ ) have converged on an important insight. Their results suggest that substantial net carbon release of the coupled vegetation-permafrost system will probably not occur before about 2100 because carbon uptake by increased vegetation growth will initially compensate for GHG releases from permafrost ( limited evidence, high agreement ).

2.5.3.3 Feedbacks related to changes in soil moisture resulting from global warming

There is medium evidence but high agreement that soil moisture conditions influence the frequency and magnitude of extremes such as drought and heatwaves. Observational evidence indicates that dry soil moisture conditions favour heatwaves, in particular in regions where evapotranspiration is limited by moisture availability (Mueller and Seneviratne 2012 ¹²⁹⁰ ; Quesada et al. 2012 ¹²⁹¹ ; Miralles et al. 2018 ¹²⁹² ; Geirinhas et al. 2018 ¹²⁹³ ; Miralles et al. 2014 ¹²⁹⁴ ; Chiang et al. 2018 ¹²⁹⁵ ; Dong and Crow 2019 ¹²⁹⁶ ; Hirschi et al. 2014 ¹²⁹⁷ ).

In future climate projections, soil moisture plays an important role in the projected amplification of extreme heatwaves and drought in many regions of the world ( medium confidence ) (Seneviratne et al. 2013 ¹²⁹⁸ ; Vogel et al. 2017 ¹²⁹⁹ ; Donat et al. 2018 ¹³⁰⁰ ; Miralles et al. 2018 ¹³⁰¹ ). In addition, the areas where soil moisture affects heat extremes will not be located exactly where they are today. Changes in rainfall, temperature, and thus in evapotranspiration, will induce changes in soil moisture and therefore where temperature and latent heat flux will be negatively coupled (Seneviratne et al. 2006 ¹³⁰² ; Fischer et al. 2012 ¹³⁰³ ). Quantitative estimates of the actual role of soil moisture feedbacks are, however, very uncertain due to the low confidence in projected soil moisture changes (IPCC 2013a ¹³⁰⁴ ), to weaknesses in the representation of soil moisture–atmosphere interactions in climate models (Sippel et al. 2017 ¹³⁰⁵ ; Ukkola et al. 2018 ¹³⁰⁶ ; Donat et al. 2018 ¹³⁰⁷ ; Miralles et al. 2018 ¹³⁰⁸ ) and to methodological uncertainties associated with the soil moisture prescription framework commonly used to disentangle the effect of soil moisture on changes in temperature extremes (Hauser et al. 2017 ¹³⁰⁹ ).

Where soil moisture is predicted to decrease in response to climate change in the subtropics and temperate latitudes, this drying could be enhanced by the existence of soil moisture feedbacks ( low confidence ) (Berg et al. 2016 ¹³¹⁰ ). The initial decrease in precipitation and increase in potential evapotranspiration and latent heat flux, in response to global climate change, leads to decreased soil moisture at those latitudes and can potentially amplify both. Such a feature is consistent with evidence that, in a warmer climate, land and atmosphere will be more strongly coupled via both the water and energy cycles (Dirmeyer et al. 2014 ¹³¹¹ ; Guo et al. 2006 ¹³¹² ). This increased sensitivity of atmospheric response to land perturbations implies that changes in land uses and cover are expected to have more impact on climate in the future than they do today.

Beyond temperature, it has been suggested that soil moisture feedbacks influence precipitation occurrence and intensity. But the importance, and even the sign of this feedback, is still largely uncertain and debated (Tuttle and Salvucci 2016 ¹³¹³ ; Yang et al. 2018 ¹³¹⁴ ; Froidevaux et al. 2014 ¹³¹⁵ ; Guillod et al. 2015 ¹³¹⁶ ).

2.5.4 Non-local and downwind effects resulting from changes in land cover

Changes in land cover or land management do not just have local consequences but also affect adjacent or more remote areas. Those non-local impacts may occur in three different ways.

1. Any action on land that affects photosynthesis and respiration has an impact on the atmospheric CO ₂ content as this GHG is well mixed in the atmosphere. In turn, this change affects the downwelling longwave radiation everywhere on the planet and contributes to global climate change. This is more thoroughly discussed in Section 2.6 where various land-based mitigation solutions are examined. Local land use changes thus have the potential to affect the global climate via changes in atmospheric CO ₂ .
2. Any change in land cover or land management may impact on local surface air temperature and moisture, and thus sea-level pressure. Thermal, moisture and surface pressure gradients between the area of change and neighbouring areas are then modified and affect the amount of heat, water vapour and pollutants flowing out (downwind) of the area (e.g., Ma et al. (2013b) ¹³¹⁷ , McLeod et al. (2017) ¹³¹⁸ , Abiodun et al. (2012) ¹³¹⁹ , Keys (2012) ¹³²⁰ ). Forests, for example, provide water vapour to the atmosphere which supports terrestrial precipitation downwind (Ellison et al. 2017 ¹³²¹ ; Layton and Ellison 2016 ¹³²² ; Spracklen et al. 2012 ¹³²³ , 2018 ¹³²⁴ ). Within a few days, water vapour can travel several hundred kilometres before being condensed into rain and potentially being transpired again (Makarieva et al. 2009 ¹³²⁵ ). This cascading moisture recycling (succession of evapotranspiration, water vapour transport and condensation-rainfall) has been observed in South America (Spracklen et al. 2018 ¹³²⁶ ; Zemp et al. 2014 ¹³²⁷ ; Staal et al. 2018 ¹³²⁸ ; Spracklen et al. 2012 ¹³²⁹ ). Deforestation can thus potentially decrease rainfall downwind, while combining ‘small- scale’ forestation and irrigation, which in the semi-arid region is susceptible to boost the precipitation-recycling mechanism with better vegetation growth downwind (Ellison et al. 2017 ¹³³⁰ ; Layton and Ellison, 2016 ¹³³¹ ) (Figure 2.22).
3. Many studies using global climate models have reported that the climatic changes resulting from changes in land are not limited to the lower part of the atmosphere, but can reach the upper levels via changes in large-scale ascent (convection) or descent (subsidence) of air. This coupling to the upper atmosphere triggers perturbations in large-scale atmospheric transport (of heat, energy and water) and subsequent changes in temperature and rainfall in regions located quite far away from the original perturbation (Laguë and Swann 2016 ¹³³² ; Feddema et al. 2005 ¹³³³ , Badger and Dirmeyer 2016 ¹³³⁴ ; Garcia 2016 ¹³³⁵ ; Stark 2015 ¹³³⁶ ; Devaraju 2018 ¹³³⁷ ; Quesada et al. 2017a ¹³³⁸ ) (Figure 2.23).

De Vrese et al. (2016) ¹³³⁹ for example, using a global climate model, found that irrigation in India could affect regions as remote as eastern

Africa through changes in the atmospheric transport of water vapour. At the onset of boreal spring (February to March) evapotranspiration is already large over irrigated crops and the resulting excess moisture in the atmosphere is transported southwestward by low-level winds. This results in increases in precipitation as large as 1mm d–1 in the Horn of Africa. Such a finding implies that, if irrigation is to decrease in India, rainfall can decrease in eastern Africa where the consequences of drought are already disastrous.

Changes in sea-surface temperature have also been simulated in response to large-scale vegetation changes (Cowling et al. 2009 ¹³⁴⁰ ; Davin and de Noblet-Ducoudre 2010 ¹³⁴¹ ; Wang et al. 2014b ¹³⁴² , Notaro Liu 2007 ¹³⁴³ ). Most of those modelling studies have been carried out with land cover changes that are extremely large and often exaggerated with respect to reality. The existence of such teleconnections can thus be biased, as discussed in Lorenz et al. (2016) ¹³⁴⁴ .

In conclusion, there is high confidence that any action on land (for example, to dampen global warming effects), wherever they occur, will not only have effects on local climate but also generate atmospheric changes in neighbouring regions, and potentially as far as hundreds of kilometres downwind. More remote teleconnections, thousands of kilometres away from the initial perturbation, are impossible to observe and have only been reported by modelling studies using extreme land cover changes. There is very low confidence that detectable changes due to such long-range processes can occur.

Figure 2.22

Schematic illustration of how combined forestation and irrigation can influence downwind precipitation on mountainous areas, favour vegetation growth and feed back to the forested area via increased runoff. Showing Los Angeles, California (Layton and Ellison 2016). Areas of forest plantations and irrigation are located on the left panel, whereas consequent downwind effects and feedbacks are […]

Schematic illustration of how combined forestation and irrigation can influence downwind precipitation on mountainous areas, favour vegetation growth and feed back to the forested area via increased runoff. Showing Los Angeles, California (Layton and Ellison 2016 ¹³⁴⁵ ). Areas of forest plantations and irrigation are located on the left panel, whereas consequent downwind effects and feedbacks are illustrated in the middle and right panels.

Figure 2.23

Extra-tropical effects on precipitation due to deforestation in each of the three major tropical regions. Increasing (circles) and decreasing (triangles) precipitation result from complete deforestation of either Amazonia (red), Africa (yellow) or Southeast Asia (blue) as reviewed by Lawrence and Vandecar (2015). Boxes indicate the area where tropical forest was removed in each region. Numbers […]

Extra-tropical effects on precipitation due to deforestation in each of the three major tropical regions. Increasing (circles) and decreasing (triangles) precipitation result from complete deforestation of either Amazonia (red), Africa (yellow) or Southeast Asia (blue) as reviewed by Lawrence and Vandecar (2015) ¹³⁴⁶ . Boxes indicate the area where tropical forest was removed in each region. Numbers refer to the study the data were derived from (Avissar and Werth 2005 ¹³⁴⁷ ; Gedney and Valdes 2000 ¹³⁴⁸ ; Semazzi and Song 2001 ¹³⁴⁹ ; Werth 2002; Mabuchi et al. 2005 ¹³⁵⁰ ; Werth 2005 ¹³⁵¹ )

CCB4 Climate change and urbanisation

Nathalie de Noblet-Ducoudré (France), Peng Cai (China), Sarah Connors (France/United Kingdom), Martin Dallimer (United Kingdom), Jason Evans (Australia), Rafiq Hamdi (Belgium), Gensuo Jia (China), Kaoru Kitajima (Japan), Christopher Lennard (South Africa), Shuaib Lwasa (Uganda), Carlos Fernando Mena (Ecuador), Soojeong Myeong (Republic of Korea), Lennart Olsson (Sweden), Prajal Pradhan (Nepal/Germany), Lindsay Stringer (United Kingdom)

Cities extent, population, and expected growth

Despite only covering 0.4–0.9% of the global land surface (Esch et al. 2017 ¹³⁵² ; Zhou et al. 2015 ¹³⁵³ ), over half the world’s population live in towns and cities (United Nations, 2017 ¹³⁵⁴ ) generating around three-quarters of the global total carbon emissions from energy use (Creutzig et al. 2015b ¹³⁵⁵ ; Seto et al. 2014 ¹³⁵⁶ ). Urban food consumption is a large source of these anthropogenic GHG emissions (Goldstein et al. 2017 ¹³⁵⁷ ). In developed countries, per capita emissions are larger in small cities than bigger ones, while the opposite is found in developing countries (Gudipudi et al. 2019 ¹³⁵⁸ ). Climate change is expected to increase the energy demand of people living in urban areas (Santamouris et al. 2015 ¹³⁵⁹ ; Wenz et al. 2017 ¹³⁶⁰ ).

In addition to being a driver of emissions, urbanisation contributes to forest degradation, converts neighbouring agricultural, forested or otherwise undeveloped land to urban use, altering natural or semi-natural ecosystems both within and outside of urban areas (Du and Huang 2017 ¹³⁶¹ ). It has been identified as a major driver of land degradation, as illustrated in Chapters 3, 4 and 5. Highly productive lands are experiencing the highest rate of conversion to urbanised landscapes (Nizeyimana et al. 2001 ¹³⁶² ; Pandey et al. 2018 ¹³⁶³ ), affecting food security. Loss of agricultural land and increased pollution and waste are some of key challenges arising from urbanisation and urban growth (Chen 2007 ¹³⁶⁴ ). The proportion of urban population is predicted to reach about 70% by the middle of the century (United Nations 2017 ¹³⁶⁵ ) with growth especially taking place in the developing world (Angel et al. 2011 ¹³⁶⁶ ; Dahiya 2012 ¹³⁶⁷ ). Urban sprawl is projected to consume 1.8–2.4% and 5% of the current cultivated land by 2030 and 2050, respectively (Pradhan et al. 2014 ¹³⁶⁸ ; Bren d’Amour et al. 2016 ¹³⁶⁹ ), driven by both general population increase and immigration from rural areas (Adger et al. 2015 ¹³⁷⁰ ; Seto et al. 2011 ¹³⁷¹ ; Geddes et al. 2012 ¹³⁷³ ). New city dwellers in developing countries will require land for housing to be converted from non-urban to urban land (Barbero-Sierra et al. 2013 ¹³⁷³ ), indicating future degradation. These growing urban areas will experience direct and indirect climate change impacts, such as sea level rise and storm surges (Boettle et al. 2016 ¹³⁷⁴ ; Revi et al. 2014 ¹³⁷⁵ ), increasing soil salinity and landslides from precipitation extremes. Furthermore, poorly planned urbanisation can increase people’s risk to climate hazards as informal settlements and poorly built infrastructure are often the most exposed to hazards from fire, flooding and landslides (Adger et al. 2015 ¹³⁷⁶ ; Geddes et al. 2012 ¹³⁷⁷ ; Revi et al. 2014 ¹³⁷⁸ ). Currently, avoiding land degradation and maintaining/enhancing ecosystem services are rarely considered in planning processes (Kuang et al. 2017 ¹³⁷⁹ ).

Climate change, urban heat island and threats specific to urban populations

Cities alter the local atmospheric conditions as well as those of the surrounding areas (Wang et al. 2016b ¹³⁸⁰ ; Zhong et al. 2017 ¹³⁸¹ ). There is high confidence that urbanisation increases mean annual surface air temperature in cities and in their surroundings, with increases ranging from 0.19–2.60°C (Torres-Valcárcel et al. 2015 ¹³⁸² ; Li et al. 2018a ¹³⁸³ ; Doan et al. 2016 ¹³⁸⁴ ) (Cross-Chapter Box 4; Figure 1). This phenomenon is referred to as the urban heat island (UHI) effect (Oke et al. 2017 ¹³⁸⁵ ; Bader et al. 2018 ¹³⁸⁶ ). The magnitude and diurnal amplitude of the UHI varies from one city to another and depends on the local background climate (Wienert and Kuttler 2005 ¹³⁸⁷ ; Zhao et al. 2014 ¹³⁸⁸ ; Ward et al. 2016 ¹³⁸⁹ ). There is nevertheless high confidence that urbanisation affects night-time temperatures more substantially than daytime ones (Argüeso et al. 2014 ¹³⁹⁰ ; Alghamdi and Moore 2015 ¹³⁹¹ ; Alizadeh-Choobari et al. 2016 ¹³⁹² ; Fujibe, 2009 ¹³⁹³ ; Hausfather et al. 2013 ¹³⁹⁴ ; Liao et al. 2017 ¹³⁹⁵ ; Sachindra et al. 2016 ¹³⁹⁶ ; Camilloni and Barrucand 2012 ¹³⁹⁷ ; Wang et al. 2017a ¹³⁹⁸ ; Hamdi, 2010 ¹³⁹⁹ ; Arsiso et al. 2018 ¹⁴⁰⁰ ; Elagib 2011 ¹⁴⁰¹ ; Lokoshchenko 2017 ¹⁴⁰² ; Robaa 2013 ¹⁴⁰³ ). In addition, there is high confidence that the UHI effect makes heatwaves more intense in cities by 1.22–4°C, particularly at night (Li and Bou-Zeid 2013 ¹⁴⁰⁴ ; Li et al. 2017b ¹⁴⁰⁵ ; Hamdi et al. 2016 ¹⁴⁰⁶ ; Founda and Santamouris 2017 ¹⁴⁰⁷ ; Wang et al. 2017a ¹⁴⁰⁸ ). As there is a well-established relationship between extremely high temperatures and morbidity, mortality (Watts et al. 2015 ¹⁴⁰⁹ ) and labour productivity (Costa et al. 2016 ¹⁴¹⁰ ), an expected increase in extreme heat events with future climate change will worsen the conditions in cities.

Cross Chapter Box 4 Figure 1

Change in annual mean surface air temperature resulting from urbanisation (ºC). The colour and size of the circles refer to the magnitude of the change. (This map has been compiled using the following studies: Kim et al. (2016), Sun et al. (2016), Chen et al. (2016a), Founda et al. (2015), Rafael et al. (2017), Hinkel […]

Change in annual mean surface air temperature resulting from urbanisation (ºC). The colour and size of the circles refer to the magnitude of the change. (This map has been compiled using the following studies: Kim et al. (2016) ¹⁴¹¹ , Sun et al. (2016) ¹⁴¹² , Chen et al. (2016a) ¹⁴¹³ , Founda et al. (2015) ¹⁴¹⁴ , Rafael et al. (2017) ¹⁴¹⁵ , Hinkel and Nelson (2007) ¹⁴¹⁶ , Chrysanthou et al. (2014) ¹⁴¹⁷ , Dou et al. (2014) ¹⁴¹⁸ , Zhou et al. (2016) ¹⁴¹⁹ , (2017) ¹⁴²⁰ , Polydoros et al. (2018) ¹⁴²¹ , Li et al. (2018a) ¹⁴²² , Bader et al. (2018) ¹⁴²³ , Alizadeh-Choobari et al. (2016) ¹⁴²⁴ , Fujibe (2009) ¹⁴²⁵ , Lokoshchenko (2017) ¹⁴²⁶ , Torres-Valcárcel et al. (2015) ¹⁴²⁷ , Doan et al. (2016) ¹⁴²⁸ , Elagib (2011) ¹⁴²⁹ , Liao et al. (2017) ¹⁴³⁰ ).

Individual city case studies show that precipitation mean and extremes are increased over and downwind of urban areas, especially in the afternoon and early evening when convective rise of the atmosphere is the strongest ( medium confidence ). The case studies covered: different inland and coastal US cities (Haberlie et al. 2014 ¹⁴³¹ ; McLeod et al. 2017 ¹⁴³² ; Ganeshan and Murtugudde 2015 ¹⁴³³ ), Dutch coastal cities (Daniels et al. 2016 ¹⁴³⁴ ), Hamburg (Schlünzen et al. 2010 ¹⁴³⁵ ), Shanghai (Liang and Ding 2017 ¹⁴³⁶ ), Beijing (Dou et al. 2014 ¹⁴³⁷ ), and Jakarta and Kuala Lumpur (Lorenz et al. 2016 ¹⁴³⁸ ). Increased aerosol concentrations, however, can interrupt the precipitation formation process and thereby reduce heavy rainfall (Daniels et al. 2016 ¹⁴³⁹ ; Zhong et al. 2017 ¹⁴⁴⁰ ). Urban areas also experience altered water cycle in other aspects: the evaporative demand for plants in cities are increased by as much as 10% (Zipper et al. 2017 ¹⁴⁴¹ ), while the high proportion of paving in cities means that surface runoff of water is high (Hamdi et al. 2011 ¹⁴⁴² ; Pataki et al. 2011 ¹⁴⁴³ ). In addition, water retention is lower in degraded, sealed soils beneath urban surfaces compared to intact soils. Increased surface water runoff, especially when and where the rainfall intensity is likely to intensify (IPCC 2013a ¹⁴⁴⁴ ), leads to a greater likelihood of flooding in urban areas without implementation of adaptation measures (Shade and Kremer 2019 ¹⁴⁴⁵ ; Wang et al. 2013 ¹⁴⁴⁶ ; EPA 2015 ¹⁴⁴⁷ ).

Urbanisation alters the stock size of soil organic carbon (SOC) and its stability. The conversion of vegetated land to urban land results in a loss of carbon stored in plants, while stresses associated with the urban environment (e.g., heat, limited water availability, pollution) reduce plant growth and survival in cities (Xu et al. 2016b ¹⁴⁴⁸ ). Overall, carbon densities or stocks decrease from natural land areas to the urban core along the rural-urban gradient (Tao et al. 2015 ¹⁴⁴⁹ ; Zhang et al. 2015 ¹⁴⁵⁰ ). For example, the Seoul Forest Park, an urban park, shows a tenfold difference in SOC stocks across its land cover types (Bae and Ryu 2015 ¹⁴⁵¹ ). In Changchun in Northeast China, however, SOC density is higher in recreational forests within urban areas compared to a production forest (Zhang et al. 2015 ¹⁴⁵² ).

Urban air pollution as an environmental risk increases with climate change. Increased air temperatures can lead to reduced air quality by enhancing the formation of photochemical oxidants and increasing the concentration of air pollutants such as ozone, with corresponding threats to human health (Sharma et al. 2013 ¹⁴⁵³ ). The occurrence of bronchial asthma and allergic respiratory diseases is increasing worldwide, and urban residents are experiencing poor air quality conditions more frequently than rural residents (D’Amato et al. 2010 ¹⁴⁵⁴ ). Excess morbidity and mortality related to extremely poor air quality are found in many cities worldwide (Harlan and Ruddell 2011 ¹⁴⁵⁵ ). Some emissions that lead to reduced air quality are also contributors to climate change (Shindell et al. 2018 ¹⁴⁵⁶ ; de Coninck et al. 2018 ¹⁴⁵⁷ ).

Urban response options for climate change, desertification, land degradation and food security

Urban green infrastructure (UGI) has been proposed as a solution to mitigate climate change directly through carbon sequestration (Davies et al. 2011 ¹⁴⁵⁸ ; Edmondson et al. 2014 ¹⁴⁵⁹ ). However, compared to overall carbon emissions from cities, its mitigation effects are likely to be small ( medium confidence ). UGI nevertheless has an important role in adapting cities to climate change (Demuzere et al. 2014 ¹⁴⁶⁰ ; Sussams et al. 2015 ¹⁴⁶¹ ; Elmqvist et al. 2016 ¹⁴⁶² ; Gill et al. 2007 ¹⁴⁶³ ; Revi et al. 2014 ¹⁴⁶⁴ ). Adaptation through UGIs is achieved through, for example, (i) reduction in air temperature (Cavan et al. 2014 ¹⁴⁶⁵ ; Di Leo et al. 2016 ¹⁴⁶⁷ ; Feyisa et al. 2014 ¹⁴⁶⁸ ; Zölch et al. 2016 ¹⁴⁶⁹ ; Li et al. 2019 ¹⁴⁷⁰ ) which can help improve human health and comfort (e.g., Brown and Nicholls 2015 ¹⁴⁷¹ ; Klemm et al. 2015 ¹⁴⁷² ), (ii) reduction in the energy demands of buildings through the use of green roofs and walls (e.g., Coma et al. 2017 ¹⁴⁷³ ), and (iii) reduction in surface water runoff and flood risk (Zeleňáková et al. 2017 ¹⁴⁷⁴ ). Given that UGI necessarily involves the retention and management of non-sealed surfaces, co-benefits for land degradation will also be apparent (limited evidence, high agreement) (Murata and Kawai 2018 ¹⁴⁷⁵ ; Scalenghe and Marsan 2009 ¹⁴⁷⁶ ).

Urban agriculture is one aspect of UGI that has the potential to both meet some of the food needs of cities and reduce land degradation pressures in rural areas ( low confidence ) (e.g., Wilhelm and Smith (2018) ¹⁴⁷⁷ ). Urban agriculture has many forms, such as backyard gardening, allotments, plants on rooftops or balconies, urban-fringe/peri-urban agriculture, hydroponics, aquaponics, livestock grazing in open spaces and vertical farming (Gerster-Bentaya 2013 ¹⁴⁷⁸ ) (Section 5.6.5).

Consuming locally produced food and enhancing the efficiency of food processing and transportation can minimise food losses, contribute to food security and, in some circumstances, reduce GHG emissions (Brodt et al. 2013 ¹⁴⁷⁹ ; Michalský and Hooda 2015 ¹⁴⁸⁰ ; Tobarra et al. 2018 ¹⁴⁸¹ ) (Section 5.5.2.3). Furthermore, urban agriculture has the potential to counteract the separation of urban populations from food production. This separation is one driver of the transition towards more homogeneous, high-protein diets, which are associated with increased GHG emissions (Goldstein et al. 2017 ¹⁴⁸² ; Moragues-Faus and Marceau 2018 ¹⁴⁸³ ; Magarini and Calori 2015 ¹⁴⁸⁴ ). Barriers to the uptake of urban agriculture as a climate change mitigation option include the need for efficient distribution systems to ensure lowered carbon emissions (Newman et al. 2012 ¹⁴⁸⁵ ) and the concern that urban agriculture may harbour pathogenic diseases, or that its products be contaminated by soil or air pollution (Hamilton et al. 2014 ¹⁴⁸⁶ ; Ercilla-Montserrat et al. 2018 ¹⁴⁸⁷ ).

In summary

Climate change is already affecting the health and energy demand of large numbers of people living in urban areas ( high confidence ) (Section 2.2). Future changes to both climate and urbanisation will enhance warming in cities and their surroundings, especially during heatwaves ( high confidence ). Urban and peri-urban agriculture and, more generally, the implementation of urban green infrastructure, can contribute to climate change mitigation ( medium confidence ) as well as to adaptation ( high confidence ), including co-benefits for food security and reduced soil-water-air pollution.

2.6 Climate consequences of response options

Response options can affect climate mitigation and adaptation simultaneously, therefore this Special Report on Climate Change and Land (SRCCL) discusses land-based response options in an integrated way (Chapter 1). In this chapter, we assess response options that that have an effect on climate. A description of the full set of response options across the SRCCL can be found in Chapter 6, including the interplay between mitigation, adaptation, desertification, land degradation, food security and other co- benefits and trade-offs. Response options specific to desertification, degradation and food security are described in more detail in Chapters 3, 4 and 5.

Some response options lead to land use change and can compete with other land uses, including other response options, while others may free-up land that can be used for further mitigation/adaptation by reducing demand for land or products (e.g., agricultural intensification, diet shifts and reduction of waste) ( high confidence ).

Some response options result in a net removal of GHGs from the atmosphere and storage in living or dead organic material, or in geological stores (IPCC SR15 ¹⁴⁸⁸ ). Such options are frequently referred to in the literature as CO ₂ removal (CDR), greenhouse gas removal (GGR) or negative emissions technologies (NETs). CDR options are assessed alongside emissions reduction options. Although they have a land footprint, solar and wind farms are not are not assessed here as they affect GHG flux in the energy industrial sectors with minimal effect in the land sector, but the impact of solar farms on agricultural land competition is dealt with in Chapter 7.

A number of different types of scenario approach exist for estimating climate contribution of land-based response options (Cross-Chapter Box 1 and Chapter 1). Mitigation potentials have been estimated for single and sometimes multiple response options using stylised ‘bottom-up’ scenarios. Response options are not mutually exclusive (e.g., management of soil carbon and cropland management). Different options interact with each other; they may have additive effects or compete with each other for land or other resources and thus these potentials cannot necessarily be added up. The interplay between different land-based mitigation options, as well as with mitigation options in other sectors (such as energy or transport), in contributing to specific mitigation pathways has been assessed using IAMs (Section 2.7.2). These include interactions with wider socioeconomic conditions (Cross-Chapter Box 1 and Chapter 1) and other sustainability goals (Chapter 6).

2.6.1 Climate impacts of individual response options

Since AR5, there have been many new estimates of the climate impacts of single or multiple response options, summarised in Figure 2.24 and discussed in sub-sections below. Recently published syntheses of mitigation potential of land-based response options (e.g., Hawken (2017a) ¹⁴⁸⁹ , Smith et al. (2016b) ¹⁴⁹⁰ , Griscom et al. (2017) ¹⁴⁹¹ , Minx et al. (2018) ¹⁴⁹² , Fuss et al. (2018b) ¹⁴⁹³ , Nemet et al. (2018) ¹⁴⁹⁴ ) are also included in Figure 2.24. The wide range in mitigation estimates reflects differences in methodologies that may not be directly comparable, and estimates cannot be necessarily be added if they were calculated independently as they may be competing for land and other resources.

Some studies assess a ‘technical mitigation potential’ – the amount possible with current technologies. Some include resource constraints (e.g., limits to yields, limits to natural forest conversion) to assess a ‘sustainable potential’. Some assess an ‘economic potential’ mitigation at different carbon prices. Few include social and political constraints (e.g., behaviour change, enabling conditions) (Chapter 7), the biophysical climate effects (Section 2.5) or the impacts of future climate change (Section 2.3). Carbon stored in biomass and soils may be at risk of future climate change (Section 2.2), natural disturbances such as wildfire (Cross-Chapter Box 3 in this chapter) and future changes in land use or management changes that result in a net loss of carbon (Gren and Aklilu 2016 ¹⁴⁹⁵ ).

2.6.1.1 Land management in agriculture

Reducing non-CO ₂ emissions from agriculture through cropland nutrient management, enteric fermentation, manure management, rice cultivation and fertiliser production has a total mitigation potential of 0.30–3.38 GtCO ₂ -eq yr ^–1 ( medium confidence ) (combined sub-category measures in Figure 2.24, details below) with a further 0.25–6.78 GtCO ₂ -eq yr ^–1 from soil carbon management (Section 2.6.1.3). Other literature that looks at broader categories finds mitigation potential of 1.4–2.3 GtCO ₂ -eq yr ^–1 from improved cropland management (Smith et al. 2008 ¹⁴⁹⁶ , 2014 ¹⁴⁹⁷ ; Pradhan et al., 2013 ¹⁴⁹⁸ ); 1.4–1.8 GtCO ₂ -eq yr ^–1 from improved grazing land management (Conant et al. 2017 ¹⁴⁹⁹ ; Herrero et al. 2016 ¹⁵⁰⁰ ; Smith et al. 2008 ¹⁵⁰¹ , 2014 ¹⁵⁰² ) and 0.2–2.4 GtCO ₂ -eq yr ^–1 from improved livestock management (Smith et al. 2008 ¹⁵⁰³ , 2014 ¹⁵⁰⁴ ; Herrero et al. 2016 ¹⁵⁰⁵ , FAO 2007 ¹⁵⁰⁶ ). Detailed discussions of the mitigation potential of agricultural response options and their co-benefits are provided in Chapter 5 and Sections 5.5 and 5.6.

The three main measures to reduce enteric fermentation include improved animal diets (higher quality, more digestible livestock feed), supplements and additives (reduce methane by changing the microbiology of the rumen), and animal management and breeding (improve husbandry practices and genetics). Applying these measures can mitigate 0.12–1.18 GtCO ₂ -eq yr ^–1 ( medium confidence ) (Hristov et al. 2013 ¹⁵⁰⁷ ; Dickie et al. 2014 ¹⁵⁰⁸ ; Herrero et al. 2016 ¹⁵⁰⁹ ; Griscom et al. 2017 ¹⁵¹⁰ ). However, these measures may have limitations such as need of crop-based feed (Pradhan et al. 2013 ¹⁵¹¹ ) and associated ecological costs, toxicity and animal welfare issues related to food additives (Llonch et al. 2017 ¹⁵¹² ). Measures to manage manure include anaerobic digestion for energy use, composting as a nutrient source, reducing storage time and changing livestock diets, and have a potential of 0.01–0.26 GtCO ₂ -eq yr ^–1 (Herrero et al. 2016 ¹⁵¹³ ; Dickie et al. 2014 ¹⁵¹⁴ ).

On croplands, there is a mitigation potential of 0.03–0.71 GtCO ₂ -eq yr ^–1 for cropland nutrient management (fertiliser application) ( medium confidence ) (Griscom et al. 2017 ¹⁵¹⁵ ; Hawken 2017 ¹⁵¹⁶ ; Paustian et al. 2016 ¹⁵¹⁷ ; Dickie et al. 2014 ¹⁵¹⁸ ; Beach et al. 2015 ¹⁵¹⁹ ). Reducing emissions from rice production through improved water management (periodic draining of flooded fields to reduce methane emissions from anaerobic decomposition) and straw residue management (applying in dry conditions instead of on flooded fields and avoiding burning to reduce methane and N2O emissions) has the potential to mitigate up to 60% of emissions (Hussain et al. 2015 ¹⁵²⁰ ), or 0.08–0.87 GtCO ₂ -eq yr ^–1 ( medium confidence ) (Griscom et al. 2017 ¹⁵²¹ ; Hawken 2017 ¹⁵²² ; Paustian et al. 2016 ¹⁵²³ ; Hussain et al. 2015 ¹⁵²⁴ ; Dickie et al. 2014 ¹⁵²⁵ ; Beach et al. 2015 ¹⁵²⁶ ). Furthermore, sustainable intensification through the integration of crop and livestock systems can increase productivity, decrease emission intensity and act as a climate adaptation option (Section 5.5.1.4).

Agroforestry is a land management system that combines woody biomass (e.g., trees or shrubs) with crops and/or livestock). The mitigation potential from agroforestry ranges between 0.08–5.7 GtCO ₂ yr ^–1 , ( medium confidence ) (Griscom et al. 2017 ¹⁵²⁷ ; Dickie et al. 2014 ¹⁵²⁸ ; Zomer et al. 2016 ¹⁵²⁹ ; Hawken 2017 ¹⁵³⁰ ). The high estimate is from an optimum scenario combing four agroforestry solutions (silvopasture, tree intercropping, multistrata agroforestry and tropical staple trees) of Hawken (2017a) ¹⁵³¹ . Zomer et al. (2016) ¹⁵³² reported that the trees in agroforestry landscapes had increased carbon stock by 7.33 GtCO ₂ between 2000 and 2010, or 0.7 GtCO ₂ yr ^–1 (Section 5.5.1.3).

Figure 2.24

Mitigation potential of response options in 2020–2050, measured in GtCO2-eq yr–1, adapted from Roe et al. (2017).Mitigation potentials reflect the full range of low to high estimates from studies published after 2010, differentiated according to technical (possible with current technologies), economic (possible given economic constraints) and sustainable potential (technical or economic potential constrained by sustainability […]

Mitigation potential of response options in 2020–2050, measured in GtCO ₂ -eq yr ^–1 , adapted from Roe et al. (2017).Mitigation potentials reflect the full range of low to high estimates from studies published after 2010, differentiated according to technical (possible with current technologies), economic (possible given economic constraints) and sustainable potential (technical or economic potential constrained by sustainability considerations). Medians are calculated across all potentials in categories with more than four data points. We only include references that explicitly provide mitigation potential estimates in CO ₂ -eq yr ^–1 (or a similar derivative) by 2050. Not all options for land management potentials are additive, as some may compete for land. Estimates reflect a range of methodologies (including definitions, global warming potentials and time horizons) that may not be directly comparable or additive. Results from IAMs are shown to compare with single option ‘bottom-up’ estimates, in available categories from the 2ºC and 1.5ºC scenarios in the SSP Database (version 2.0). The models reflect land management changes, yet in some instances, can also reflect demand-side effects from carbon prices, so may not be defined exclusively as ‘supply-side’. References: 1) Griscom et al. (2017) ²¹³⁶ , 2) Hawken (2017) ²¹³⁷ , 3) Paustian et al. (2016) ²¹³⁸ , 4) Beach et al. (2016) ²¹³⁹ , 5) Dickie et al. (2014) ²¹⁴⁰ , 6) Herrero et al. (2013) ²¹⁴¹ , 7) Herrero et al. (2016) ²¹⁴² , 8) Hussain et al. (2015) ²¹⁴³ , 9) Hristov, et al. (2013) ²¹⁴⁴ , 10) Zhang et al. (2013) ²¹⁴⁵ , 11) Houghton and Nassikas (2018) ²¹⁴⁶ , 12) Busch and Engelmann (2017) ²¹⁴⁷ , 13) Baccini et al. (2017) ²¹⁴⁸ , 14) Zarin et al. (2016) ²¹⁴⁹ , 15) Houghton, et al. (2015) ²¹⁵⁰ , 16) Federici et al. (2015) ²¹⁵¹ , 17) Carter et al. (2015) ²¹⁵² , 18) Smith et al. (2013) ²¹⁵³ , 19) Pearson et al. (2017) ²¹⁵⁴ , 20) Hooijer et al. (2010) ²¹⁵⁵ , 21) Howard (2017) ²¹⁵⁶ , 22) Pendleton et al. (2012) ²¹⁵⁷ , 23) Fuss et al. (2018) ²¹⁵⁸ , 24) Dooley and Kartha (2018) ²¹⁵⁹ , 25) Kreidenweis et al. (2016) ²¹⁶⁰ , 26) Yan et al. (2017) ²¹⁶¹ , 27) Sonntag et al. (2016) ²¹⁶² , 28) Lenton (2014) ²¹⁶³ , 29) McLaren (2012) ²¹⁶⁴ , 30) Lenton (2010) ²¹⁶⁵ , 31) Sasaki et al. (2016) ²¹⁶⁶ , 32) Sasaki et al. (2012) ²¹⁶⁷ , 33) Zomer et al. (2016) ²¹⁶⁸ , 34) Couwenberg et al. (2010) ²¹⁶⁹ , 35) Conant et al. (2017) ²¹⁷⁰ , 36) Sanderman et al. (2017) ²¹⁷¹ , 37) Frank et al. (2017) ²¹⁷² , 38) Henderson et al. (2015) ²¹⁷³ , 39) Sommer and Bossio (2014) ²¹⁷⁴ , 40. Lal (2010) ²¹⁷⁵ , 41. Zomer et al. (2017) ²¹⁷⁶ , 42. Smith et al. (2016) ²¹⁷⁷ , 43) Poeplau and Don (2015) ²¹⁷⁸ , 44. Powlson et al. (2014) ²¹⁷⁹ , 45. Powell and Lenton (2012) ²¹⁸⁰ , 46) Woolf et al. (2010) ²¹⁸¹ , 47) Roberts et al. (2010) ²¹⁸² , 48. Pratt and Moran (2010) ²¹⁸³ , 49. Turner et al. (2018) ²¹⁸⁴ , 50) Koornneef et al. (2012) ²¹⁸⁵ , 51) Bajželj et al. (2014) ²¹⁹² , 52) Springmann et al. (2016) ²¹⁸⁷ , 53) Tilman and Clark (2014) ²¹⁸⁸ , 54) Hedenus et al. (2014) ²¹⁸⁹ , 55) Miner (2010) ²¹⁹⁰ , 56) Bailis et al. (2015) ²¹⁹¹ .

2.6.1.2 Land management in forests

The mitigation potential for reducing and/or halting deforestation and degradation ranges from 0.4–5.8 GtCO ₂ yr ^–1 ( high confidence ) (Griscom et al. 2017 ¹⁵³³ ; Hawken 2017 ¹⁵³⁴ ; Busch and Engelmann 2017 ¹⁵³⁵ ; Baccini et al. 2017 ¹⁵³⁶ ; Zarin et al. 2016 ¹⁵³⁷ ; Federici et al. 2015 ¹⁵³⁸ ; Carter et al. 2015 ¹⁵³⁹ ; Houghton et al. 2015 ¹⁵⁴⁰ ; Smith et al. 2013a ¹⁵⁴¹ ; Houghton and Nassikas 2018 ¹⁵⁴² ). The higher figure represents a complete halting of land use conversion in forests and peatlands (i.e., assuming recent rates of carbon loss are saved each year). Separate estimates of degradation only range from 1.0–2.18 GtCO ₂ yr ^–1 . Reduced deforestation and forest degradation include conservation of existing carbon pools in vegetation and soil through protection in reserves, controlling disturbances such as fire and pest outbreaks, and changing management practices. Differences in estimates stem from varying land cover definitions, the time periods assessed and the carbon pools included (most higher estimates include belowground, dead wood, litter, soil and peat carbon). When deforestation and degradation are halted, it may take many decades to fully recover the biomass initially present in native ecosystems (Meli et al. 2017 ¹⁵⁴³ ) (Section 4.8.3).

Afforestation/reforestation (A/R) and forest restoration can increase carbon sequestration in both vegetation and soils by 0.5–10.1 GtCO ₂ yr ^–1 ( medium confidence ) (Fuss et al. 2018 ¹⁵⁴⁴ ; Griscom et al. 2017 ¹⁵⁴⁵ ; Hawken 2017 ¹⁵⁴⁶ ; Kreidenweis et al. 2016 ¹⁵⁴⁷ ; Li et al. 2016 ¹⁵⁴⁸ ; Huang et al. 2017 ¹⁵⁴⁹ ; Sonntag et al. 2016 ¹⁵⁵⁰ ; Lenton 2014 ¹⁵⁵¹ ; McLaren 2012 ¹⁵⁵² ; Lenton 2010 ¹⁵⁵³ ; Erb et al. 2018 ¹⁵⁵⁴ ; Dooley and Kartha 2018 ¹⁵⁵⁵ ; Yan et al. 2017 ¹⁵⁵⁶ ; Houghton et al. 2015 ¹⁵⁵⁷ ; Houghton and Nassikas 2018 ¹⁵⁵⁸ ). Afforestation is the conversion to forest of land that historically has not contained forests. Reforestation is the conversion to forest of land that has previously contained forests but that has been converted to some other use. Forest restoration refers to practices aimed at regaining ecological integrity in a deforested or degraded forest landscape. The lower estimate represents the lowest range from an ESM (Yan et al. 2017 ¹⁵⁵⁹ ) and of sustainable global negative emissions potential (Fuss et al. 2018 ¹⁵⁶⁰ ), and the higher estimate reforests all areas where forests are the native cover type, constrained by food security and biodiversity considerations (Griscom et al. 2017 ¹⁵⁶¹ ). It takes time for full carbon removal to be achieved as the forest grows. Removal occurs at faster rates in young- to medium-aged forests and declines thereafter such that older forest stands have smaller carbon removals but larger stocks, with net uptake of carbon slowing as forests reach maturity (Yao et al. 2018 ¹⁵⁶² ; Poorter et al. 2016 ¹⁵⁶³ ; Tang et al. 2014 ¹⁵⁶⁴ ). The land intensity of afforestation and reforestation has been estimated at 0.0029 km ² tC ^–1 yr ^–1 (Smith et al. 2016a ¹⁵⁶⁵ ). Boysen et al. (2017) ¹⁵⁶⁶ estimated that to sequester about 100 GtC by 2100 would require 13 Mkm ² of abandoned cropland and pastures (Section 4.8.3).

Forest management has the potential to mitigate 0.4–2.1 GtCO ₂ -eq yr ^–1 ( medium confidence ) (Sasaki et al. 2016 ¹⁵⁶⁷ ; Griscom et al. 2017 ¹⁵⁶⁸ ; Sasaki et al. 2012 ¹⁵⁶⁹ ). Forest management can alter productivity, turnover rates, harvest rates carbon in soil and carbon in wood products (Erb et al. 2017 ¹⁵⁷⁰ ; Campioli et al. 2015 ¹⁵⁷¹ ; Birdsey and Pan 2015 ¹⁵⁷² ; Erb et al. 2016 ¹⁵⁷³ ; Noormets et al. 2015 ¹⁵⁷⁴ ; Wäldchen et al. 2013 ¹⁵⁷⁵ ; Malhi et al. 2015 ¹⁵⁷⁶ ; Quesada et al. 2018 ¹⁵⁷⁷ ; Nabuurs et al. 2017 ¹⁵⁷⁸ ; Bosello et al. 2009 ¹⁵⁷⁹ ) (Section 4.8.4). Fertilisation may enhance productivity but would increase N ₂ O emissions. Preserving and enhancing carbon stocks in forests has immediate climate benefits but the sink can saturate and is vulnerable to future climate change (Seidl et al. 2017 ¹⁵⁸⁰ ). Wood can be harvested and used for bioenergy substituting for fossil fuels (with or without carbon capture and storage) (Section 2.6.1.5), for long- lived products such as timber (see below), to be buried as biochar (Section 2.6.1.1) or for use in the wider bioeconomy, enabling areas of land to be used continuously for mitigation. This leads to initial carbon loss and lower carbon stocks but with each harvest cycle, the carbon loss (debt) can be paid back and after a parity time, result in net savings (Laganière et al. 2017 ¹⁵⁸¹ ; Bernier and Paré 2013 ¹⁵⁸² ; Mitchell et al. 2012 ¹⁵⁸³ ; Haberl et al. 2012 ¹⁵⁸⁴ ; Haberl 2013 ¹⁵⁸⁵ ; Ter-Mikaelian et al. 2015 ¹⁵⁸⁶ ; Macintosh et al. 2015 ¹⁵⁸⁷ ). The trade-off between maximising forest carbon stocks and maximising substitution is highly dependent on the counterfactual assumption (no-use vs extrapolation of current management), initial forest conditions and site-specific contexts (such as regrowth rates and the displacement factors and efficiency of substitution), and relative differences in emissions released during extraction, transport and processing of the biomass- or fossil- based resources, as well as assumptions about emission associated with the product or energy source that is substituted (Grassi et al. 2018b ¹⁵⁸⁸ ; Nabuurs et al. 2017 ¹⁵⁸⁹ ; Pingoud et al. 2018 ¹⁵⁹⁰ ; Smyth et al. 2017a ¹⁵⁹¹ ; Luyssaert et al. 2018 ¹⁵⁹² ; Valade et al. 2017 ¹⁵⁹³ ; York 2012 ¹⁵⁹⁴ ; Ter-Mikaelian et al. 2014 ¹⁵⁹⁵ ; Naudts et al. 2016b ¹⁵⁹⁶ ; Mitchell et al. 2012 ¹⁵⁹⁷ ; Haberl et al. 2012 ¹⁵⁹⁸ ; Macintosh et al. 2015 ¹⁵⁹⁹ ; Laganière et al. 2017 ¹⁶⁰⁰ ; Haberl 2013 ¹⁶⁰¹ ). This leads to uncertainty about optimum mitigation strategies in managed forests, while high carbon ecosystems such as primary forests would have large initial carbon losses and long pay-back times, and thus protection of stocks would be more optimal (Lemprière et al. 2013 ¹⁶⁰² ; Kurz et al. 2016 ¹⁶⁰³ ; Keith et al. 2014 ¹⁶⁰⁴ ) (Section 4.8.4).

Global mitigation potential from increasing the demand of wood products to replace construction materials range from 0.25–1 GtCO ₂ -eq yr ^–1 ( medium confidence ) (McLaren 2012 ¹⁶⁰⁵ ; Miner 2010 ¹⁶⁰⁶ ), the uncertainty is determined in part by consideration of the factors described above, and is sensitive to the displacement factor, or the substitution benefit in CO ₂ , when wood is used instead of another material, which may vary in the future as other sectors reduce emissions (and may also vary due to market factors) (Sathre and O’Connor 2010 ¹⁶⁰⁷ ; Nabuurs et al. 2018 ¹⁶⁰⁸ ; Iordan et al. 2018 ¹⁶⁰⁹ ; Braun et al. 2016 ¹⁶¹⁰ ; Gustavsson et al. 2017 ¹⁶¹¹ ; Peñaloza et al. 2018 ¹⁶¹² ; Soimakallio et al. 2016 ¹⁶¹³ ; Grassi et al. 2018b ¹⁶¹⁴ ). Using harvested carbon in long-lived products (e.g., for construction) can represent a store that can sometimes be from decades to over a century, while the wood can also substitute for intensive building materials, avoiding emissions from the production of concrete and steel (Sathre and O’Connor 2010 ¹⁶¹⁵ ; Smyth et al. 2017b ¹⁶¹⁶ ; Nabuurs et al. 2007 ¹⁶¹⁷ ; Lemprière et al. 2013 ¹⁶¹⁸ ). The harvest of carbon and storage in products affects the net carbon balance of the forest sector, with the aim of sustainable forest management strategies being to optimise carbon stocks and use harvested products to generate sustained mitigation benefits (Nabuurs et al. 2007 ¹⁶¹⁹ ).

Biophysical effects of forest response options are variable depending on the location and scale of activity (Section 2.6). Reduced deforestation or afforestation in the tropics contributes to climate mitigation through both biogeochemical and biophysical effects. It also maintains rainfall recycling to some extent. In contrast, in higher latitude boreal areas, observational and modelling studies show that afforestation and reforestation lead to local and global warming effects, particularly in snow covered regions in the winter as the albedo is lower for forests than bare snow (Bathiany et al. 2010 ¹⁶²⁰ ; Dass et al. 2013 ¹⁶²¹ ; Devaraju et al. 2018 ¹⁶²² ; Ganopolski et al. 2001 ¹⁶²³ ; Snyder et al. 2004 ¹⁶²⁴ ; West et al. 2011 ¹⁶²⁵ ; Arora and Montenegro 2011 ¹⁶²⁶ ) (Section 2.6). Management, for example, thinning practices in forestry, could increase the albedo in regions where albedo decreases with age. The length of rotation cycles in forestry affects tree height and thus roughness, and through the removal of leaf mass harvest reduces evapotranspiration (Erb et al. 2017 ¹⁶²⁷ ), which could lead to increased fire susceptibility in the tropics. In temperate and boreal sites, biophysical forest management effects on surface temperature were shown to be of similar magnitude than changes in land cover (Luyssaert et al. 2014 ¹⁶²⁸ ). These biophysical effects could be of a magnitude to overcompensate biogeochemical effects, for example, the sink strength of regrowing forests after past depletions (Luyssaert et al. 2018 ¹⁶²⁹ ; Naudts et al. 2016b ¹⁶³⁰ ), but many parameters and assumptions on counterfactual influence the account (Anderson et al. 2011 ¹⁶³¹ ; Li et al. 2015b ¹⁶³² ; Bright et al. 2015 ¹⁶³³ ).

Forest cover also affects climate through reactive gases and aerosols, with limited evidence and medium agreement that the decrease in the emissions of BVOC resulting from the historical conversion of forests to cropland has resulted in a positive radiative forcing through direct and indirect aerosol effects. A negative radiative forcing through reduction in the atmospheric lifetime of CH ₄ has increased and decreased ozone concentrations in different regions (Section 2.4).

2.6.1.3 Land management of soils

The global mitigation potential for increasing soil organic matter stocks in mineral soils is estimated to be in the range of 0.4–8.64 GtCO ₂ yr ^–1 ( high confidence ), though the full literature range is wider with high uncertainty related to some practices (Fuss et al. 2018 ¹⁶³⁴ ; Sommer and Bossio 2014 ¹⁶³⁵ ; Lal 2010 ¹⁶³⁶ ; Lal et al. 2004 ¹⁶³⁷ ; Conant et al. 2017 ¹⁶³⁸ ; Dickie et al. 2014 ¹⁶³⁹ ; Frank et al. 2017a ¹⁶⁴⁰ ; Griscom et al. 2017 ¹⁶⁴¹ ; Herrero et al. 2015 ¹⁶⁴² , 2016 ¹⁶⁴³ ; McLaren 2012 ¹⁶⁴⁴ ; Paustian et al. 2016 ¹⁶⁴⁵ ; Poeplau and Don 2015 ¹⁶⁴⁶ ; Powlson et al. 2014 ¹⁶⁴⁷ ; Smith et al. 2016c ¹⁶⁴⁸ ; Zomer et al. 2017 ¹⁶⁴⁹ ). Some studies have separate potentials for soil carbon sequestration in croplands (0.25–6.78 GtCO ₂ yr ^–1 ) (Griscom et al. 2017 ¹⁶⁵⁰ ; Hawken 2017 ¹⁶⁵¹ ; Frank et al. 2017a ¹⁶⁵² ; Paustian et al. 2016 ¹⁶⁵³ ; Herrero et al. 2016 ¹⁶⁵⁴ ; Henderson et al. 2015 ¹⁶⁵⁵ ; Dickie et al. 2014 ¹⁶⁵⁶ ; Conant et al. 2017 ¹⁶⁵⁷ ; Lal 2010 ¹⁶⁵⁸ ) and soil carbon sequestration in grazing lands (0.13–2.56 GtCO ₂ yr ^–1 ) (Griscom et al. 2017 ¹⁶⁵⁹ ; Hawken 2017 ¹⁶⁶⁰ ; Frank et al. 2017a ¹⁶⁶¹ ; Paustian et al. 2016 ¹⁶⁶² ; Powlson et al. 2014 ¹⁶⁶³ ; McLaren 2012 ¹⁶⁶⁴ ; Zomer et al. 2017 ¹⁶⁶⁵ ; Smith et al. 2015 ¹⁶⁶⁶ ; Sommer and Bossio 2014 ¹⁶⁶⁷ ; Lal 2010 ¹⁶⁶⁸ ). The potential for soil carbon sequestration and storage varies considerably depending on prior and current land management approaches, soil type, resource availability, environmental conditions, microbial composition and nutrient availability among other factors (Hassink and Whitmore 1997 ¹⁶⁶⁹ ; Smith and Dukes 2013 ¹⁶⁷⁰ ; Palm et al. 2014 ¹⁶⁷¹ ; Lal 2013 ¹⁶⁷² ; Six et al. 2002 ¹⁶⁷³ ; Feng et al. 2013 ¹⁶⁷⁴ ). Soils are a finite carbon sink and sequestration rates may decline to negligible levels over as little as a couple of decades as soils reach carbon saturation (West et al. 2004 ¹⁶⁷⁵ ; Smith and Dukes 2013 ¹⁶⁷⁶ ). The sink is at risk of reversibility, in particular due to increased soil respiration under higher temperatures (Section 2.3).

Land management practices to increase carbon interact with agricultural and fire management practices (Cross-chapter Box 3 and Chapter 5) and include improved rotations with deeper rooting cultivars, addition of organic materials and agroforestry (Lal 2011 ¹⁶⁷⁷ ; Smith et al. 2008 ¹⁶⁷⁸ ; Lorenz and Pitman 2014 ¹⁶⁷⁹ ; Lal 2013 ¹⁶⁸⁰ ; Vermeulen et al. 2012 ¹⁶⁸¹ ; de Rouw et al. 2010 ¹⁶⁸² ). Adoption of green manure cover crops, while increasing cropping frequency or diversity, helps sequester SOC (Poeplau and Don 2015 ¹⁶⁸³ ; Mazzoncini et al. 2011 ¹⁶⁸⁴ ; Luo et al. 2010 ¹⁶⁸⁵ ). Studies of the long-term SOC sequestration potential of conservation agriculture (i.e., the simultaneous adoption of minimum tillage, (cover) crop residue retention and associated soil surface coverage, and crop rotations) include results that are both positive (Powlson et al. 2016 ¹⁶⁸⁶ ; Zhang et al. 2014 ¹⁶⁸⁷ ) and inconclusive (Cheesman et al. 2016 ¹⁶⁸⁸ ; Palm et al. 2014 ¹⁶⁸⁹ ; Govaerts et al. 2009 ¹⁶⁹⁰ ).

The efficacy of reduced and zero-till practices is highly context-specific; many studies demonstrate increased carbon storage (e.g., Paustian et al. (2000) ¹⁶⁹¹ , Six et al. (2004) ¹⁶⁹² , van Kessel et al. (2013) ¹⁶⁹³ ), while others show the opposite effect (Sisti et al. 2004 ¹⁶⁹⁴ ; Álvaro-Fuentes et al. 2008 ¹⁶⁹⁵ ; Christopher et al. 2009 ¹⁶⁹⁶ ). On the other hand, deep ploughing can contribute to SOC sequestration by burying soil organic matter in the subsoil where it decomposes slowly (Alcántara et al. 2016 ¹⁶⁹⁷ ). Meta- analyses (Haddaway et al. 2017 ¹⁶⁹⁸ ; Luo et al. 2010 ¹⁶⁹⁹ ; Meurer et al. 2018 ¹⁷⁰⁰ ) also show a mix of positive and negative responses, and the lack of robust comparisons of soils on an equivalent mass basis continues to be a problem for credible estimates (Wendt and Hauser 2013 ¹⁷⁰¹ ; Powlson et al. 2011 ¹⁷⁰² ; Powlson et al. 2014 ¹⁷⁰³ ).

Soil carbon management interacts with N ₂ O (Paustian et al. 2016 ¹⁷⁰⁴ ). For example, Li et al. (2005) ¹⁷⁰⁵ estimate that the management strategies required to increase carbon sequestration (reduced tillage, crop residue and manure recycling) would increase N ₂ O emissions significantly, offsetting 75–310% of the carbon sequestered in terms of CO ₂ equivalence, while other practices such as cover crops can reduce N ₂ O emissions (Kaye and Quemada 2017 ¹⁷⁰⁶ ).

The management of soil erosion could avoid a net emissions of 1.36–3.67 GtCO ₂ yr ^–1 and create a sink of 0.44–3.67 GtCO ₂ yr ^–1 ( low confidence ) (Jacinthe and Lal 2001 ¹⁷⁰⁷ ; Lal et al. 2004 ¹⁷⁰⁸ ; Stallard 1998 ¹⁷⁰⁹ ; Smith et al. 2001 ¹⁷¹⁰ ; Van Oost et al. 2007 ¹⁷¹¹ ). The overall impact of erosion control on mitigation is context-specific and uncertain at the global level and the final fate of eroded material is still debated (Hoffmann et al., 2013 ¹⁷¹² ).

Biochar is produced by thermal decomposition of biomass in the absence of oxygen (pyrolysis) into a stable, long-lived product like charcoal that is relatively resistant to decomposition (Lehmann et al. 2015 ¹⁷¹³ ) and which can stabilise organic matter when added to soil (Weng et al. 2017 ¹⁷¹⁴ ). Although charcoal has been used traditionally by many cultures as a soil amendment, ‘modern biochar’, produced in facilities that control emissions, is not widely used. The range of global potential of biochar is 0.03–6.6 GtCO ₂ -eq yr ^–1 by 2050, including energy substitution, with 0.03–4.9 GtCO ₂ yr ^–1 for CDR only ( medium confidence ) (Griscom et al. 2017 ¹⁷¹⁵ ; Hawken 2017 ¹⁷¹⁶ ; Paustian et al. 2016 ¹⁷¹⁷ ; Fuss et al. 2018 ¹⁷¹⁸ ; Lenton 2014 ¹⁷¹⁹ , 2010 ¹⁷²⁰ ; Powell and Lenton 2012 ¹⁷²¹ ; Woolf et al. 2010 ¹⁷²² ; Pratt and Moran 2010 ¹⁷²³ ; Smith 2016 ¹⁷²⁴ ; Roberts et al. 2010 ¹⁷²⁵ ). An analysis in which biomass supply constraints were applied to protect against food insecurity, loss of habitat and land degradation, estimated technical potential abatement of 3.7–6.6 GtCO ₂ -eq yr ^–1 (including 2.6–4.6 GtCO ₂ yr ^–1 carbon stabilisation) (Woolf et al. 2010 ¹⁷²⁶ ). Fuss et al. (2018) ¹⁷²⁷ propose a range of 0.5–2 GtCO ₂ -eq yr ^–1 as the sustainable potential for negative emissions through biochar. Griscom et al. (2017) ¹⁷²⁸ suggest a potential of 1.0 GtCO ₂ yr ^–1 based on available residues. Biochar can provide additional climate change mitigation benefits by decreasing N ₂ O emissions from soil and reducing nitrogen fertiliser requirements in agricultural soils (Borchard et al. 2019 ¹⁷²⁹ ). Application of biochar to cultivated soils can darken the surface and reduce its mitigation potential via decreases in surface albedo, but the magnitude of this effect depends on soil moisture content, biochar application method and type of land use ( low confidence ) (Verheijen et al. 2013 ¹⁷³⁰ ; Bozzi et al. 2015 ¹⁷³¹ ) (Section 4.9.5).

2.6.1.4 Land management in other ecosystems

Protection and restoration of wetlands, peatlands and coastal habitats reduces net carbon loss (primarily from sediment/soils) and provides continued or enhanced natural CO2 removal (Section 4.9.4). Reducing annual emissions from peatland conversion, draining and burning could mitigate 0.45–1.22 GtCO2-eq yr–1 up to 2050 (medium confidence) (Hooijer et al. 2010 ¹⁷³² ; Griscom et al. 2017 ¹⁷³³ ; Hawken 2017 ¹⁷³⁴ ) and peatland restoration 0.15–0.81 (low confidence) (Couwenberg et al. 2010 ¹⁷³⁵ ; Griscom et al. 2017 ¹⁷³⁶ ). The upper end from Griscom et al. (2017) ¹⁷³⁷ represents a maximum sustainable potential (accounting for biodiversity and food security safeguards) for rewetting and biomass enhancement. Wetland drainage and rewetting was included as a flux category under the second commitment period of the Kyoto Protocol, with significant management knowledge gained over the last decade (IPCC 2013b). However, there are high uncertainties as to carbon storage and flux rates, in particular the balance between CH4 sources and CO2 sinks (Spencer et al. 2016 ¹⁷³⁸ ). Peatlands are sensitive to climate change which may increase carbon uptake by vegetation and carbon emissions due to respiration, with the balance being regionally dependent (high confidence). There is low confidence about the future peatland sink globally. Some peatlands have been found to be resilient to climate change (Minayeva and Sirin 2012 ¹⁷³⁹ ), but the combination of land use change and climate change may make them vulnerable to fire (Sirin et al. 2011 ¹⁷⁴⁰ ). While models show mixed results for the future sink (Spahni et al. 2013 ¹⁷⁴¹ ; Chaudhary et al. 2017 ¹⁷⁴² ; Ise et al. 2008 ¹⁷⁴³ ), a study that used extensive historical data sets to project change under future warming scenarios found that the current global peatland sink could increase slightly until 2100 and decline thereafter (Gallego-Sala et al. 2018 ¹⁷⁴⁴ ).

Reducing the conversion of coastal wetlands (mangroves, seagrass and marshes) could reduce emissions by 0.11–2.25 GtCO2-eq yr–1 by 2050 (medium confidence) (Pendleton et al. 2012 ¹⁷⁴⁵ ; Griscom et al. 2017 ¹⁷⁴⁶ ; Howard et al. 2017 ¹⁷⁴⁷ ; Hawken 2017 ¹⁷⁴⁸ ). Mangrove restoration can mitigate the release of 0.07 GtCO2 yr–1 through rewetting (Crooks et al. 2011 ¹⁷⁴⁹ ) and take up 0.02–0.84 GtCO2 yr–1 from biomass and soil enhancement (medium confidence) (Griscom et al. 2017 ¹⁷⁵⁰ ). The ongoing benefits provided by mangroves as a natural carbon sink can be nationally-important for small island developing states (SIDS) and other countries with extensive coastlines, based on estimates of high carbon sequestration rates per unit area (McLeod et al. 2011 ¹⁷⁵¹ ; Duarte et al. 2013 ¹⁷⁵² ; Duarte 2017 ¹⁷⁵³ ; Taillardat et al. 2018 ¹⁷⁵⁴ ). There is only medium confidence in the effectiveness of enhanced carbon uptake using mangroves, due to the many uncertainties regarding the response of mangroves to future climate change (Jennerjahn et al. 2017 ¹⁷⁵⁵ ), dynamic changes in distributions (Kelleway et al. 2017 ¹⁷⁵⁶ ) and other local-scale factors affecting long-term sequestration and climatic benefits (e.g., methane release) (Dutta et al. 2017 ¹⁷⁵⁷ ). The climate mitigation potential of coastal vegetated habitats (mangrove forests, tidal marshes and seagrasses) is considered in Chapter 5 of the IPCC Special Report on the Ocean, Cryosphere and Climate Change (SROCC), in a wider ‘blue carbon’ context.

2.6.1.5 Bioenergy and bioenergy with carbon capture and storage

An introduction and overview of bioenergy and bioenergy with carbon capture and storage (BECCS) can be found in Cross-Chapter Boxes 7 and 12, and Chapters 6 and 7. CCS technologies are discussed in SR15. The discussion below refers to modern bioenergy only (e.g., liquid biofuels for transport and the use of solid biofuels in combined heat and power plants).

The mitigation potential of bioenergy coupled with CCS (i.e., BECCS), is estimated to be between 0.4 and 11.3 GtCO ₂ yr ^–1 ( medium confidence ) based on studies that directly estimate mitigation for BECCS (not bioenergy) in units of CO ₂ (not EJ) (McLaren 2012 ¹⁷⁵⁸ ; Lenton 2014 ¹⁷⁵⁹ ; Fuss et al. 2018 ¹⁷⁶⁰ ; Turner et al. 2018b ¹⁷⁶¹ ; Lenton 2010 ¹⁷⁶² ; Koornneef et al. 2012 ¹⁷⁶³ ; Powell and Lenton 2012 ¹⁷⁶⁴ ). SR15 reported a potential of 1–85 GtCO ₂ yr ^–1 which they noted could be narrowed to a range of 0.5–5 GtCO ₂ yr ^–1 when taking account of sustainability aims (Fuss et al. 2018 ¹⁷⁶⁵ ). The upper end of the SR15 range is considered as a theoretical potential. Previously, the IPCC Special Report on Renewable Energy Sources concluded the technical potential of biomass supply for energy (without BECCS) could reach 100–300 EJ yr–1 by 2050, which would be 2–15 GtCO ₂ yr ^–1 (using conversion factors 1 EJ = 0.02–0.05 GtCO ₂ yr ^–1 emission reduction, SR15). A range of recent studies including sustainability or economic constraints estimate that 50–244 EJ (1–12 GtCO ₂ yr ^–1 using the conversion factors above) of bioenergy could be produced on 0.1–13 Mkm ² of land (Fuss et al. 2018 ¹⁷⁶⁶ ; Chan and Wu 2015 ¹⁷⁶⁷ ; Schueler et al. 2016 ¹⁷⁶⁷ ; Wu et al. 2013 ¹⁷⁶⁸ ; Searle and Malins 2015 ¹⁷⁶⁹ ; Wu et al. 2019 ¹⁷⁷⁰ ; Heck et al. 2018 ¹⁷⁷¹ ; Fritz et al. 2013 ¹⁷⁷² ).

There is high confidence that the most important factors determining future biomass supply for energy are land availability and land productivity (Berndes et al. 2013 ¹⁷⁷³ ; Creutzig et al. 2015a ¹⁷⁷⁴ ; Woods et al. 2015 ¹⁷⁷⁵ ; Daioglou et al. 2019 ¹⁷⁷⁶ ). Estimates of marginal/degraded lands currently considered available for bioenergy range from 3.2–14.0 Mkm2, depending on the adopted sustainability criteria, land class definitions, soil conditions, land mapping method and environmental and economic considerations (Campbell et al. 2008 ¹⁷⁷⁸ ; Cai et al. 2011 ¹⁷⁷⁹ ; Lewis and Kelly 2014 ¹⁷⁸⁰ ).

Bioenergy production systems can lead to net emissions in the short term that can be ‘paid-back’ over time, with multiple harvest cycles and fossil fuel substitution, unlike fossil carbon emissions (Campbell et al. 2008 ¹⁷⁸¹ ; Cai et al. 2011 ¹⁷⁸² ; Lewis and Kelly 2014 ¹⁷⁸³ ; De Oliveira Bordonal et al. 2015 ¹⁷⁸⁴ ). Stabilising bioenergy crops in previous high carbon forestland or peatland results in high emissions of carbon that may take from decades to more than a century to be re-paid in terms of net CO ₂ emission savings from replacing fossil fuels, depending on previous forest carbon stock, bioenergy yields and displacement efficiency (Elshout et al. 2015 ¹⁷⁸⁵ ; Harper et al. 2018 ¹⁷⁸⁶ ; Daioglou et al. 2017 ¹⁷⁸⁷ ). In the case of bioenergy from managed forests, the magnitude and timing of the net mitigation benefits is controversial as it varies with differences due to local climate conditions, forest management practice, fossil fuel displacement efficiency and methodological approaches (Hudiburg et al. 2011 ¹⁷⁸⁸ ; Berndes et al. 2013 ¹⁷⁸⁹ ; Guest et al. 2013 ¹⁷⁹⁰ ; Lamers and Junginger 2013 ¹⁷⁹¹ ; Cherubini et al. 2016 ¹⁷⁹² ; Cintas et al. 2017 ¹⁷⁹³ ; Laurance et al. 2018 ¹⁷⁹⁴ ; Valade et al. 2018 ¹⁷⁹⁵ ; Baker et al. 2019 ¹⁷⁹⁶ ). Suitable bioenergy crops can be integrated in agricultural landscapes to reverse ecosystem carbon depletion (Creutzig et al. 2015a ¹⁷⁹⁷ ; Robertson et al. 2017 ¹⁷⁹⁸ ; Vaughan et al. 2018 ¹⁷⁹⁹ ; Daioglou et al. 2017 ¹⁸⁰⁰ ). Cultivation of short rotation woody crops and perennial grasses on degraded land or cropland previously used for annual crops typically accumulate carbon in soils due to their deep root systems (Don et al. 2012 ¹⁸⁰¹ ; Robertson et al. 2017 ¹⁸⁰² ). The use of residues and organic waste as bioenergy feedstock can mitigate land use change pressures associated with bioenergy deployment, but residues are limited and the removal of residues that would otherwise be left on the soil could lead soil degradation (Chum et al. 2011 ¹⁸⁰³ ; Liska et al. 2014 ¹⁸⁰⁴ ; Monforti et al. 2015 ¹⁸⁰⁵ ; Zhao et al. 2015 ¹⁸⁰⁶ ; Daioglou et al. 2016 ¹⁸⁰⁷ ).

The steps required to cultivate, harvest, transport, process and use biomass for energy generate emissions of GHGs and other climate pollutants (Chum et al. 2011 ¹⁸⁰⁸ ; Creutzig et al. 2015b ¹⁸⁰⁹ ; Staples et al. 2017 ¹⁸¹⁰ ; Daioglou et al. 2019 ¹⁸¹¹ ). Life-cycle GHG emissions of modern bioenergy alternatives are usually lower than those for fossil fuels ( robust evidence, medium agreement ) (Chum et al. 2011 ¹⁸¹² ; Creutzig et al. 2015b ¹⁸¹³ ). The magnitude of these emissions largely depends on location (e.g., soil quality, climate), prior land use, feedstock used (e.g., residues, dedicated crops, algae), land use practice (e.g., soil management, fertiliser use), biomass transport (e.g., distances and transport modes) and the bioenergy conversion pathway and product (e.g., wood pellets, ethanol). Use of conventional food and feed crops as a feedstock generally provides the highest bioenergy yields per hectare, but also causes more GHG emissions per unit energy compared to agriculture residues, biomass from managed forests and lignocellulosic crops such as short-rotation coppice and perennial grasses (Chum et al. 2011 ¹⁸¹⁴ ; Gerbrandt et al. 2016 ¹⁸¹⁵ ) due to the application of fertilisers and other inputs (Oates et al. 2016 ¹⁸¹⁶ ; Rowe et al. 2016 ¹⁸¹⁷ ; Lai et al. 2017 ¹⁸¹⁸ ; Robertson et al. 2017 ¹⁸¹⁹ ).

Bioenergy from dedicated crops are in some cases held responsible for GHG emissions resulting from indirect land use change (iLUC), that is the bioenergy activity may lead to displacement of agricultural or forest activities into other locations, driven by market-mediated effects. Other mitigation options may also cause iLUC. At a global level of analysis, indirect effects are not relevant because all land-use emissions are direct. iLUC emissions are potentially more significant for crop-based feedstocks such as corn, wheat and soybean, than for advanced biofuels from lignocellulosic materials (Chum et al. 2011 ¹⁸²⁰ ; Wicke et al. 2012 ¹⁸²¹ ; Valin et al. 2015 ¹⁸²² ; Ahlgren and Di Lucia 2014 ¹⁸²³ ). Estimates of emissions from iLUC are inherently uncertain, widely debated in the scientific community and are highly dependent on modelling assumptions, such as supply/demand elasticities, productivity estimates, incorporation or exclusion of emission credits for coproducts and scale of biofuel deployment (Rajagopal and Plevin 2013 ¹⁸²⁴ ; Finkbeiner 2014 ¹⁸²⁵ ; Kim et al. 2014 ¹⁸²⁶ ; Zilberman 2017 ¹⁸²⁷ ). In some cases, iLUC effects are estimated to result in emission reductions. For example, market-mediated effects of bioenergy in North America showed potential for increased carbon stocks by inducing conversion of pasture or marginal land to forestland (Cintas et al. 2017 ¹⁸²⁸ ; Duden et al. 2017 ¹⁸²⁹ ; Dale et al. 2017 ¹⁸³⁰ ; Baker et al. 2019 ¹⁸³¹ ). There is a wide range of variability in iLUC values for different types of biofuels, from –75–55 gCO ₂ MJ ^–1  (Ahlgren and Di Lucia 2014 ¹⁸³² ; Valin et al. 2015 ¹⁸³³ ; Plevin et al. 2015 ¹⁸³⁴ ; Taheripour and Tyner 2013 ¹⁸³⁵ ; Bento and Klotz 2014 ¹⁸³⁶ ). There is low confidence in attribution of emissions from iLUC to bioenergy.

Bioenergy deployment can have large biophysical effects on regional climate, with the direction and magnitude of the impact depending on the type of bioenergy crop, previous land use and seasonality ( limited evidence, medium agreement ). A study of two alternative future bioenergy scenarios using 15 Mkm2 of intensively used managed land or conversion of natural areas showed a nearly neutral effect on surface temperature at global levels (considering biophysical effects and CO ₂ and N ₂ O fluxes from land but not substitution effects), although there were significant seasonal and regional differences (Kicklighter et al. 2013 ¹⁸³⁷ ). Modelling studies on biofuels in the US found the switch from annual crops to perennial bioenergy plantations like Miscanthus could lead to regional cooling due to increases in evapotranspiration and albedo (Georgescu et al. 2011 ¹⁸³⁸ ; Harding et al. 2016 ¹⁸³⁹ ), with perennial bioenergy crop expansion over suitable abandoned and degraded farmlands causing near-surface cooling up to 5°C during the growing season (Wang et al. 2017b ¹⁸⁴⁰ ). Similarly, growing sugarcane on existing cropland in Brazil cools down the local surface during daytime conditions up to –1°C, but warmer conditions occur if sugar cane is deployed at the expense of natural vegetation (Brazilian Cerrado (Loarie et al. 2011 ¹⁸⁴¹ ). In general, bioenergy crops (as for all crops) induce a cooling of ambient air during the growing season, but after harvest the decrease in evapotranspiration can induce warming (Harding et al. 2016 ¹⁸⁴² ; Georgescu et al. 2013 ¹⁸⁴³ ; Wang et al. 2017b ¹⁸⁴⁴ ). Bioenergy crops were found to cause increased isoprene emissions in a scenario where 0.69 Mkm2 of oil palm for biodiesel in the tropics and 0.92 Mkm2 of short rotation coppice (SRC) in the mid-latitudes were planted, but effects on global climate were negligible (Ashworth et al. 2012 ¹⁸⁴⁵ ).

2.6.1.6 Enhanced weathering

Weathering is the natural process of rock decomposition via chemical and physical processes in which CO ₂ is removed from the atmosphere and converted to bicarbonates and/or carbonates (IPCC 2005 ¹⁸⁴⁶ ). Formation of calcium carbonates in the soil provides a permanent sink for mineralised organic carbon (Manning 2008 ¹⁸⁴⁷ ; Beerling et al. 2018 ¹⁸⁴⁸ ). Mineral weathering can be enhanced through grinding up rock material to increase the surface area, and distributing it over land to provide carbon removals of 0.5–4.0 GtCO ₂ yr ^–1 ( medium confidence ) (Beerling et al. 2018 ¹⁸⁴⁹ ; Lenton 2010 ¹⁸⁵⁰ ; Smith et al. 2016a ¹⁸⁵¹ ; Taylor et al. 2016 ¹⁸⁵² ). While the geochemical potential is quite large, agreement on the technical potential is low due to a variety of unknown parameters and limits, such as rates of mineral extraction, grinding, delivery and challenges with scaling and deployment.

2.6.1.7 Demand management in the food sector (diet change, waste reduction)

Demand-side management has the potential for climate change mitigation via reducing emissions from production, switching to consumption of less emission intensive commodities and making land available for CO ₂ removal (Section 5.5.2). Reducing food losses and waste increases the overall efficiency of food value chains (with less land and inputs needed) along the entire supply chain and has the potential to mitigate 0.8–4.5 GtCO ₂ -eq yr ^–1 ( high confidence ) (Bajželj et al. 2014 ¹⁸⁵³ ; Dickie et al 2014 ¹⁸⁵⁴ ; Hawken 2017 ¹⁸⁵⁵ ; Hiç et al. 2016 ¹⁸⁵⁶ ) (Section 5.5.2.5).

Shifting to diets that are lower in emissions-intensive foods like beef delivers a mitigation potential of 0.7–8.0 GtCO ₂ -eq yr ^–1 ( high confidence ) (Bajželj et al. 2014 ¹⁸⁵⁷ ; Dickie et al. 2014 ¹⁸⁵⁸ ; Herrero et al. 2016 ¹⁸⁵⁹ ; Hawken 2017 ¹⁸⁶⁰ ; Springmann et al. 2016 ¹⁸⁶¹ ; Tilman and Clark 2014 ¹⁸⁶² ; Hedenus et al. 2014 ¹⁸⁶³ ; Stehfest et al. 2009 ¹⁸⁶⁴ ) with most of the higher end estimates (>6 GtCO ₂ -eq yr ^–1 ) based on veganism, vegetarianism or very low ruminant meat consumption (Section 5.5.2). In addition to direct mitigation gains, decreasing meat consumption, primarily of ruminants, and reducing wastes further reduces water use, soil degradation, pressure on forests and land used for feed potentially freeing up land for mitigation (Tilman and Clark 2014 ¹⁸⁶⁵ ) (Chapters 5 and 6). Additionally, consumption of locally produced food, shortening the supply chain, can in some cases minimise food loss, contribute to food security and reduce GHG emissions associated with energy consumption and food loss (Section 5.5.2.6).

2.6.2 Integrated pathways for climate change mitigation

Land-based response options have the potential to interact, resulting in additive effects (e.g., climate co-benefits) or negating each other (e.g., through competition for land). They also interact with mitigation options in other sectors (such as energy or transport) and thus they need to be assessed collectively under different climate mitigation targets and in combination with other sustainability goals (Popp et al. 2017 ¹⁸⁶⁶ ; Obersteiner et al. 2016 ¹⁸⁶⁷ ; Humpenöder et al. 2018 ¹⁸⁶⁸ ). IAMs with distinctive land-use modules are the basis for the assessment of mitigation pathways as they combine insights from various disciplines in a single framework and cover the largest sources of anthropogenic GHG emissions from different sectors (see also SR15 Chapter 2 and Technical Annex for more details). IAMs consider a limited, but expanding, portfolio of land- based mitigation options. Furthermore, the inclusion and detail of a specific mitigation measure differs across IAMs and studies (see also SR15 and Chapter 6). For example, the IAM scenarios based on the shared socio-economic pathways (SSPs) (Riahi et al. 2017 ¹⁸⁶⁹ ) (Cross-Chapter Box 1 and Chapter 1) include possible trends in agriculture and land use for five different socioeconomic futures, but cover a limited set of land-based mitigation options: dietary changes, higher efficiency in food processing (especially in livestock production systems), reduction of food waste, increasing agricultural productivity, methane reductions in rice paddies, livestock and grazing management for reduced methane emissions from enteric fermentation, manure management, improvement of N-efficiency, 1st generation biofuels, reduced deforestation, afforestation, 2nd generation bioenergy crops and BECCS (Popp et al. 2017 ¹⁸⁷⁰ ). However, many ‘natural climate solutions’ (Griscom et al. 2017 ¹⁸⁷¹ ), such as forest management, rangeland management, soil carbon management or wetland management, are not included in most of these scenarios. In addition, most IAMs neglect the biophysical effects of land-use such as changes in albedo or evapotranspiration with few exceptions (Kreidenweis et al. 2016 ¹⁸⁷² ).

Mitigation pathways, based on IAMs, are typically designed to find the least cost pathway to achieve a pre-defined climate target (Riahi et al. 2017 ¹⁸⁷³ ). Such cost-optimal mitigation pathways, especially in RCP2.6 (broadly a 2°C target) and 1.9 scenarios (broadly a 1.5°C target), project GHG emissions to peak early in the 21st century, 2 strict GHG emission reduction afterwards and, depending on the climate target, net CDR from the atmosphere in the second half of the century (Chapter 2 of SR15; Tavoni et al. 2015 ¹⁸⁷⁴ ; Riahi et al. 2017 ¹⁸⁷⁵ ). In most of these pathways, land use is of great importance because of its mitigation potential as discussed in Section 2.7.1: these pathways are based on the assumptions that (i) large-scale afforestation and reforestation removes substantial amounts of CO ₂ from the atmosphere, (ii) biomass grown on cropland or from forestry residues can be used for energy generation or BECCS substituting fossil fuel emissions and generating CDR, and (iii) non-CO ₂ emissions from agricultural production can be reduced, even under improved agricultural management (Popp et al. 2017 ¹⁸⁷⁶ ; Rogelj et al. 2018a ¹⁸⁷⁷ ; Van Vuuren et al. 2018 ¹⁸⁷⁸ , Frank et al. 2018 ¹⁸⁷⁹ ).

From the IAM scenarios available to this assessment, a set of feasible mitigation pathways has been identified which is illustrative of the range of possible consequences on land use and GHG emissions (presented in this chapter) and sustainable development (Chapter 6). Thus, the IAM scenarios selected here vary due to underlying socio- economic and policy assumptions, the mitigation options considered, long-term climate goals, the level of inclusion of other sustainability goals (such as land and water restrictions for biodiversity conservation or food production) and the models by which they are generated.

In the baseline case without climate change mitigation, global CO ₂ emissions from land-use change decrease over time in most scenarios due to agricultural intensification and decreases in demand for agricultural commodities – some even turning negative by the end of the century due to abandonment of agricultural land and associated carbon uptake through vegetation regrowth. Median global CO ₂ emissions from land-use change across 5 SSPs and 5 IAMs decrease throughout the 21st century: 3, 1.9 and –0.7 GtCO ₂ yr ^–1 in 2030, 2050 and 2100 respectively (Figure 2.25). In contrast, CH ₄ and N ₂ O emissions from agricultural production remain rather constant throughout the 21st century (CH ₄ : 214, 231.7 and 209.1 MtCH ₄ yr ^–1 in 2030, 2050 and 2100 respectively; N ₂ O: 9.1, 10.1 and 10.3 MtN ₂ O yr ^–1 in 2030, 2050 and 2100 respectively).

Figure 2.25

Land-based global GHG emissions and removals in 2030, 2050 and 2100 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017), Rogelj et al. (2018), Riahi et al. (2017). Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. 2018; Rogelj et al. […]

Land-based global GHG emissions and removals in 2030, 2050 and 2100 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017) ²¹⁹³ , Rogelj et al. (2018) ²¹⁹⁴ , Riahi et al. (2017) ²¹⁹⁵ . Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. 2018 ²¹⁹⁶ ; Rogelj et al. 2018 ²¹⁹⁷ ). Boxplots (Tukey style) show median (horizontal line), interquartile range (IQR box) and the range of values within 1.5 × IQR at either end of the box (vertical lines) across 5 SSPs and across 5 IAMs. Outliers (red crosses) are values greater than 1.5 × IQR at either end of the box. The categories CO ₂ Land, CH ₄ Land and N ₂ O Land include GHG emissions from land-use change and agricultural land use (including emissions related to bioenergy production). In addition, the category CO ₂ Land includes negative emissions due to afforestation. BECCS reflects the CO ₂ emissions captured from bioenergy use and stored in geological deposits.

In the mitigation cases (RCP4.5, RCP2.6 and RCP1.9), most of the scenarios indicate strong reductions in CO ₂ emissions due to (i) reduced deforestation and (ii) carbon uptake due to afforestation. However, CO ₂ emissions from land use can occur in some mitigation scenarios as a result of weak land-use change regulation (Fujimori et al. 2017 ¹⁸⁸⁰ ; Calvin et al. 2017 ¹⁸⁸¹ ) or displacement effects into pasture land caused by high bioenergy production combined with forest protection only (Popp et al. 2014 ¹⁸⁸² ). The level of CO ₂ removal globally (median value across SSPs and IAMs) increases with the stringency of the climate target (RCP4.5, RCP2.6 and RCP1.9) for both afforestation (–1.3, –1.7 and –2.4 GtCO ₂ yr ^–1 in 2100) and BECCS (–6.5, –11 and –14.9 GtCO ₂ yr ^–1 in 2100) (Cross-Chapter Box 7 and Chapter 6). In the mitigation cases (RCP4.5, RCP2.6 and RCP1.9), CH ₄ and N ₂ O emissions are remarkably lower compared to the baseline case (CH4: 133.2, 108.4 and 73.5 MtCH ₄ yr ^–1 in 2100; N ₂ O: 7.4, 6.1 and 4.5 MtN ₂ O yr ^–1 in 2100; see previous paragraph for CH ₄ and N ₂ O emissions in the baseline case). The reductions in the mitigation cases are mainly due to improved agricultural management such as improved nitrogen fertiliser management, improved water management in rice production, improved manure management (by, for example, covering of storages or adoption of biogas plants), better herd management and better quality of livestock through breeding and improved feeding practices. In addition, dietary shifts away from emission-intensive livestock products also lead to decreased CH ₄ and N ₂ O emissions especially in RCP2.6 and RCP1.9 scenarios. However, high levels of bioenergy production can result in increased N ₂ O emissions due to nitrogen fertilisation of dedicated bioenergy crops.

Such high levels of CO ₂ removal through mitigation options that require land conversion (BECCS and afforestation) shape the land system dramatically (Figure 2.26). Across the different RCPs, SSPs and IAMs, median change of global forest area throughout the 21st century ranges from about –0.2 to +7.2 Mkm ² between 2010 and 2100, and agricultural land used for 2nd generation bioenergy crop production ranges from about 3.2–6.6 Mkm ² in 2100 (Popp et al. 2017 ¹⁸⁸³ ; Rogelj et al. 2018 ¹⁸⁸⁴ ). Land requirements for bioenergy and afforestation for a RCP1.9 scenario are higher than for a RCP2.6 scenario and especially a RCP4.5 mitigation scenario. As a consequence of the expansion of mainly land-demanding mitigation options, global pasture land is reduced in most mitigation scenarios much more strongly than compared to baseline scenarios (median reduction of 0, 2.6, 5.1 and 7.5 Mkm ² between 2010 and 2100 in baseline, RCP4.5, RCP2.6 and RCP1.9 respectively). In addition, cropland for food and feed production decreases with the stringency of the climate target (+1.2, +0.2, –1.8 and –4 Mkm ² in 2100 compared to 2010 in baseline, RCP4.5, RCP2.6 and RCP1.9 respectively). These reductions in agricultural land for food and feed production are facilitated by agricultural intensification on agricultural land and in livestock production systems (Popp et al. 2017 ¹⁸⁸⁵ ), but also by changes in consumption patterns (Fujimori et al. 2017 ¹⁸⁸⁶ ; Frank et al. 2017b ¹⁸⁸⁷ ).

The pace of projected land-use change over the coming decades in ambitious mitigation scenarios goes well beyond historical changes in some instances (Turner et al. (2018b) ²²⁰⁷ , see also SR15). This raises issues for societal acceptance, and distinct policy and governance for avoiding negative consequences for other sustainability goals will be required (Humpenöder et al. 2018 ¹⁸⁸⁸ ; Obersteiner et al. 2016 ¹⁸⁸⁹ ; Calvin et al. 2014 ¹⁸⁹⁰ ) (Chapters 6 and 7).

Different mitigation strategies can achieve the net emissions reductions that would be required to follow a pathway that limits global warming to 2°C or 1.5°C, with very different consequences on the land system.

Figure 2.27 shows six alternative pathways (archetypes) for achieving ambitious climate targets (RCP2.6 and RCP1.9), highlighting land- based strategies and GHG emissions. All pathways are assessed by different models but are all based on the SSP2 (Riahi et al. 2017 ¹⁸⁹¹ ), with all based on an RCP 1.9 mitigation pathway expect for Pathway 1, which is RCP2.6. All scenarios show land-based negative emissions, but the amount varies across pathways, as do the relative contributions of different land-based CDR options, such as afforestation/reforestation and BECCS.

Pathway 1 RCP2.6 ‘Portfolio’ (Fricko et al. 2017 ¹⁸⁹² ) shows a strong near-term decrease of CO ₂ emissions from land-use change, mainly due to reduced deforestation, as well as slightly decreasing N ₂ O and CH4 emissions after 2050 from agricultural production due to improved agricultural management and dietary shifts away from emissions-intensive livestock products. However, in contrast to CO ₂ emissions, which turn net-negative around 2050 due to afforestation/ reforestation, CH ₄ and N ₂ O emissions persist throughout the century due to difficulties of eliminating these residual emissions based on existing agricultural management methods (Stevanović et al. 2017 ¹⁸⁹³ ; Frank et al. 2017b ¹⁸⁹⁴ ). In addition to abating land related GHG emissions as well as increasing the terrestrial sink, this example also shows the importance of the land sector in providing biomass for BECCS and hence CDR in the energy sector. In this scenario, annual BECCS-based CDR is about three times higher than afforestation-based CDR in 2100 (–11.4 and –3.8 GtCO ₂ yr ^–1 respectively). Cumulative CDR throughout the century amounts to –395 GtCO ₂ for BECCS and –73 GtCO ₂ for afforestation. Based on these GHG dynamics, the land sector turns GHG emission neutral in 2100. However, accounting also for BECCS- based CDR taking place in the energy sector, but with biomass provided by the land sector, turns the land sector GHG emission neutral already in 2060, and significantly net-negative by the end of the century.

Pathway 2 RCP1.9 ‘Increased Ambition’ (Rogelj et al. 2018 ¹⁸⁹⁵ ) has dynamics of land-based GHG emissions and removals that are very similar to those in Pathway 1 (RCP2.6) but all GHG emission reductions as well as afforestation/reforestation and BECCS-based CDR start earlier in time at a higher rate of deployment. Cumulative CDR throughout the century amounts to –466 GtCO ₂ for BECCS and –117 GtCO ₂ for afforestation.

Pathway 3 RCP 1.9 ‘Only BECCS’, in contrast to Pathway 2, includes only BECCS-based CDR (Kriegler et al. 2017 ¹⁸⁹⁶ ). As a consequence, CO ₂ emissions are persistent much longer, predominantly from indirect land-use change due to large-scale bioenergy cropland expansion into non-protected natural areas (Popp et al. 2017 ¹⁸⁹⁷ ; Calvin et al. 2014 ¹⁸⁹⁸ ). While annual BECCS CDR rates in 2100 are similar to Pathways 1 and 2 (–15.9 GtCO ₂ yr ^–1 ), cumulative BECCS-based CDR throughout the century is much larger (–944 GtCO ₂ ).

Pathway 4 RCP1.9 ‘Early CDR’ (Bertram et al. 2018 ¹⁸⁹⁹ ) indicates that a significant reduction in the later century in the BECCS-related CDR as well as CDR in general can be achieved with earlier and mainly terrestrial CDR, starting in 2030. In this scenario, terrestrial CDR is based on afforestation but could also be supported by soil organic carbon sequestration (Paustian et al. 2016 ¹⁹⁰⁰ ) or other natural climate solutions, such as rangeland or forest management (Griscom et al. 2017 ¹⁹⁰¹ ). This scenario highlights the importance of the timing for CDR- based mitigation pathways (Obersteiner et al. 2016 ¹⁹⁰² ). As a result of near-term and mainly terrestrial CDR deployment, cumulative BECCS- based CDR throughout the century is limited to –300 GtCO ₂ , while cumulative afforestation-based CDR amounts to –428 GtCO ₂ .

In Pathway 5 RCP1.9 ‘Low residual emissions’ (van Vuuren et al. 2018 ¹⁹⁰³ ), land-based mitigation is driven by stringent enforcement of measures and technologies to reduce end-of-pipe non-CO ₂ emissions and by introduction of in-vitro (cultured) meat, reducing residual N ₂ O and CH ₄ emissions from agricultural production. In consequence, much lower amounts of CDR from afforestation and BECCS are needed with much later entry points to compensate for residual emissions. Cumulative CDR throughout the century amounts to –252 GtCO ₂ for BECCS and –128 GtCO ₂ for afforestation. Therefore, total cumulative land-based CDR in Pathway 5 is substantially lower compared to Pathways 2–4 (380 GtCO ₂ ).

Finally, Pathway 6 RCP1.9 ‘Low Energy’ (Grubler et al. 2018 ¹⁹⁰⁴ ), equivalent to Pathway LED in SR15, indicates the importance of other sectoral GHG emission reductions for the land sector. In this example, rapid and early reductions in energy demand and associated drops in energy- related CO ₂ emissions limit overshoot and decrease the requirements for negative emissions technologies, especially for land-demanding CDR, such as biomass production for BECCS and afforestation. While BECCS is not used at all in Pathway 6, cumulative CDR throughout the century for afforestation amounts to –124 GtCO ₂ .

Besides their consequences on mitigation pathways and land consequences, those archetypes can also affect multiple other sustainable development goals that provide both challenges and opportunities for climate action (Chapter 6).

Figure 2.26

Global change of major land cover types by 2030, 2050 and 2100 relative to 2010 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017), Rogelj et al. (2018), Riahi et al. (2017). Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. […]

Global change of major land cover types by 2030, 2050 and 2100 relative to 2010 for baseline, RCP4.5, RCP2.6 and RCP1.9 based on the SSP. Source: Popp et al. (2017) ²²⁰⁸ , Rogelj et al. (2018) ²²⁰⁹ , Riahi et al. (2017) ²²¹⁰ . Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Huppmann et al. 2018 ²²¹¹ ; Rogelj et al. 2018 ²²¹² ). Boxplots (Tukey style) show median (horizontal line), interquartile range IQR (box) and the range of values within 1.5 × IQR at either end of the box (vertical lines) across 5 SSPs and across 5 IAMs. Outliers (red crosses) are values greater than 1.5 × IQR at either end of the box. In 2010, total land cover at global scale was estimated 15–16 Mkm ² for cropland, 0–0.14 Mkm ² for bioenergy, 30–35 Mkm ² for pasture and 37–42 Mkm ² for forest, across the IAMs that reported SSP pathways (Popp et al. 2017 ²¹⁹⁸ ).

Figure 2.27

Evolution and breakdown of global land-based GHG emissions and removals under six alternative mitigation pathways.This figure illustrates the differences in timing and magnitude of land-based mitigation approaches including afforestation and BECCS. All pathways are based on different IAM realisations of SSP2. Pathway 1 is based on RCP 2.6, while all other pathways are based on […]

Evolution and breakdown of global land-based GHG emissions and removals under six alternative mitigation pathways.This figure illustrates the differences in timing and magnitude of land-based mitigation approaches including afforestation and BECCS. All pathways are based on different IAM realisations of SSP2. Pathway 1 is based on RCP 2.6, while all other pathways are based on RCP 1.9. Pathway 1: MESSAGE-GLOBIOM (Fricko et al. 2017 ²¹⁹⁹ ); Pathway 2: MESSAGE-GLOBIOM (Rogelj et al. 2018 ²²⁰⁰ ); Pathway 3: REMIND-MAgPIE (Kriegler et al. 2017 ²²⁰¹ ); Pathway 4: REMIND-MAgPIE (Bertram et al. 2018 ²²⁰² ); Pathway 5: IMAGE (van Vuuren et al. 2018 ²²⁰³ ); Pathway 6: MESSAGE-GLOBIOM (Grubler et al. 2018 ²²⁰⁴ ). Data is from an update of the IAMC Scenario Explorer developed for the SR15 (Rogelj et al. 2018 ²²⁰⁵ ). The categories CO ₂ Land, CH ₄ Land and N ₂ O Land include GHG emissions from land-use change and agricultural land use (including emissions related to bioenergy production). In addition, the category CO ₂ Land includes negative emissions due to afforestation. BECCS reflects the CO ₂ emissions captured from bioenergy use and stored in geological deposits. Solid lines show the net effect of all land based GHG emissions and removals (CO ₂ Land, CH ₄ Land, N ₂ O Land and BECCS), while dashed lines show the net effect excluding BECCS. CH ₄ and N ₂ O emissions are converted to CO ₂ -eq using GWP factors of 28 and 265 respectively.

2.6.3 The contribution of response options to the Paris Agreement

The previous sections indicated how land-based response options have the potential to contribute to the Paris Agreement, not only though reducing anthropogenic emissions but also for providing anthropogenic sinks that can contribute to “…a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century…” (Paris Agreement, Article 4). The balance applies globally, and relates only to GHGs, not aerosols (Section 2.4) or biophysical effects (Section 2.5).

The Paris Agreement includes an enhanced transparency framework to track countries’ progress towards achieving their individual targets (i.e., nationally determined contributions (NDCs)), and a global stocktake (every five years starting in 2023), to assess the countries’ collective progress towards the long-term goals of the Paris Agreement. The importance of robust and transparent definitions and methods (including the approach to separating anthropogenic from natural fluxes) (Fuglestvedt et al. 2018 ¹⁹¹⁰ ), and the needs for reconciling country GHG inventories and models (Grassi et al. 2018a ¹⁹¹¹ ), was highlighted in Section 2.3 in relation to estimating emissions. Issues around estimating mitigation is also key to transparency and credibility and is part of the Paris Rulebook.

The land sector is expected to deliver up to 25% of GHG mitigation pledged by countries by 2025–2030 in their NDCs, based on early assessments of ‘Intended’ NDCs submitted ahead of the Paris Agreement and updates immediately after ( low confidence ) (Grassi et al. 2017 ¹⁹¹² ; Forsell et al. 2016 ¹⁹¹³ ). While most NDCs submitted to date include commitments related to the land sector, they vary with how much information is given and the type of target, with more ambitious targets for developing countries often being ‘conditional’ on support and climate finance. Some do not specify the role of AFOLU but include it implicitly as part of economy-wide pledges (e.g., reducing total emission or emission intensity), a few mention multi-sectoral mitigation targets which include AFOLU in a fairly unspecified manner. Many NDCs include specific AFOLU response options, with most focused on the role of forests. A few included soil carbon sequestration or agricultural mitigation and a few explicitly mentioned bioenergy (e.g., Cambodia, Indonesia and Malaysia), but this could be implicitly included with reduced emissions in energy sectors through fuel substitution (see Cross-Chapter Box 7 and Chapter 6 for discussion on cross sector flux reporting). The countries indicating AFOLU mitigation most prominently were Brazil and Indonesia, followed by other countries focusing either on avoiding carbon emissions (e.g., Ethiopia, Gabon, Mexico, DRC, Guyana and Madagascar) or on promoting the sink through large afforestation programmes (e.g., China, India) (Grassi et al. 2017 ¹⁹¹⁴ ).

Figure 2.28

Global LULUCF net GHG flux for the historical period and future scenarios based on analyses of countries’ NDCs.The LULUCF historical data (blue solid line) reflect the following countries’ documents (in order of priority): (i) data submitted to UNFCCC (NDCs, 2015 GHG Inventories and recent National Communications ), (ii) other official countries’ documents, (iii) FAO-based datasets […]

Global LULUCF net GHG flux for the historical period and future scenarios based on analyses of countries’ NDCs.The LULUCF historical data (blue solid line) reflect the following countries’ documents (in order of priority): (i) data submitted to UNFCCC (NDCs ⁴ , 2015 GHG Inventories ⁵ and recent National Communications ^{6 7} ), (ii) other official countries’ documents, (iii) FAO-based datasets (i.e., FAO-FRA for forest (Tian et al. 2015)) as elaborated by (Federici et al. 2015), and (iv) FAOSTAT for non-forest land use emissions (FAO 2015). The four future scenarios reflect official countries’ information, mostly intended NDCs or updated NDCs available at the time of the analysis (Feb 2016), complemented by Biennial Update Reports ⁸ and National Communications, and show (i) the BAU scenario as defined by the country, (ii) the trend based on pre-NDC levels of activity (current policies in place in countries), and (iii) the unconditional NDC and conditional NDC scenarios. The shaded area indicates the full range of countries’ available projections (min-max), expressing the available countries’ information on uncertainties beyond the specific scenarios shown. The range of historical country datasets (dotted lines) reflects differences between alternative selections of country sources, in essence, GHG inventories for developed countries complemented by FAO-based datasets (upper range) or by data in National Communications (lower range) for developing countries.

Figure 2.28 shows the CO ₂ mitigation potential of NDCs compared to historical fluxes from LULUCF. ³ It shows future fluxes based on current policies in place and on country-stated Business As Usual (BAU) activities (these are different from current policies as many countries are already implementing polices that they do not include as part of their historical business-as-usual baseline) (Grassi et al. 2017). Under implementation of unconditional pledges, the net LULUCF flux in 2030 has been estimated to be a sink of –0.41 ± 0.68 GtCO ₂ yr ^–1 , which increases to –1.14 ± 0.48 GtCO ₂ yr ^–1 in 2030 with conditional activities. This compares to net LULUCF in 2010 calculated from the GHG Inventories of 0.01 ± 0.86 GtCO ₂ yr ^–1 (Grassi et al. 2017 ¹⁹¹⁵ ). Forsell et al. (2016) ¹⁹¹⁶ similarly find a reduction in 2030 compared to 2010 of 0.5 GtCO ₂ yr ^–1 (range: 0.2–0.8) by 2020 and 0.9 GtCO ₂ yr ^–1 (range: 0.5–1.3) by 2030 for unconditional and conditional cases.

The approach of countries to calculating the LULUCF contribution towards the NDC varies, with implications for comparability and transparency. For example, by following the different approaches used to include LULUCF in country NDCs, Grassi et al. (2017) ¹⁹¹⁷ found a three-fold difference in estimated mitigation: 1.2–1.9 GtCO ₂ -eq yr ^–1 when 2030 expected emissions are compared to 2005 emissions, 0.7–1.4 GtCO ₂ -eq yr ^–1 when 2030 emissions are compared to reference scenarios based on current policies or 2.3–3.0 GtCO ₂ -eq yr ^–1 when compared to BAU, and 3.0–3.8 GtCO ₂ -eq yr ^–1 when based on using each countries’ approach to calculation stated in the NDC (i.e., when based on a mix of country approaches, using either past years or BAU projections as reference).

In exploring the effectiveness of the NDCs, SR15 concluded “[e]stimates of global average temperature increase are 2.9°–3.4°C above preindustrial levels with a greater than 66% probability by 2100” (Roberts et al. 2006 ¹⁹¹⁸ ; Rogelj et al. 2016 ¹⁹¹⁹ ), under a full implementation of unconditional NDCs and a continuation of climate action similar to that of the NDCs. In order to achieve either the 1.5°C or 2°C pathways, this shortfall would imply the need for submission (and achievement) of more ambitious NDCs, and plan for a more rapid transformation of their national energy, industry, transport and land use sectors (Peters and Geden 2017 ¹⁹²⁰ ; Millar et al. 2017 ¹⁹²¹ ; Rogelj et al. 2016 ¹⁹²² ).

Response options relying on the use of land could provide around a third of the additional mitigation needed in the near term (2030) to close the gap between current policy trajectories based on NDCs and what is required to achieve a 2°C (>66% chance) or 1.5°C (50–66% chance) pathway according to the UNEP Emissions Gap Report (Roberts et al. 2006 ¹⁹²³ ). The report estimates annual reduction potentials in 2030 from agriculture at 3.0 (2.3–3.7) GtCO ₂ -eq yr ^–1 , a combination of ‘uncertain measures’ (biochar, peat-related emission reductions and demand-side management) at 3.7 (2.6–4.8) GtCO ₂ -eq yr ^–1 ; forests at 5.3 (4.1–6.5) GtCO ₂ -eq yr ^–1 , bioenergy at 0.9 GtCO ₂ -eq yr ^–1 and BECCS at 0.3 (0.2–0.4) GtCO ₂ -eq yr ^–1 (UNEP 2017 ¹⁹²⁴ ) (Table 4.1). These response options account for 35% of potential reduction (or 32% without bioenergy and BECCS) out of a total (all sector) potential of 38 (35–41) GtCO ₂ -eq yr ^–1 . The potentials estimated in the UNEP Emissions Gap Report are based on the technical potential of individual response options from literature including that presented in Section 2.1. CDR related to land use, while not a substitute for strong action in the energy sector, has the technical potential to balance unavoidable emissions that are difficult to eliminate with current technologies ( high confidence ), with early action avoiding deeper and more rapid action later ( very high confidence ) (Strefler et al. 2018 ¹⁹²⁵ ; Elmar et al. 2018 ¹⁹²⁶ ; SR15).

2.7 Plant and soil processes underlying land–climate interactions

Projecting future complex interactions between land and climate require ESMs. A growing number of studies suggested that many processes important for interactions between land and climate were missing in the CMIP5-class ESMs and that the DGVMs used tended to elevate CO ₂ emission and removals ( high confidence ) (Busch and Sage 2017 ¹⁹²⁷ ; Rogers et al. 2017 ¹⁹²⁸ ; Anderegg et al. 2016 ¹⁹²⁹ ; Tjoelker 2018 ¹⁹³⁰ ; Sulman et al. 2014 ¹⁹³¹ ; Wieder et al. 2018 ¹⁹³² ; Davidson et al. 2006a ¹⁹³³ ).

Ecosystem complexity stemming from the diversity of plants, animals and microbes, as well as their biological responses to gradual climate changes (e.g., adaptive migration) and disturbance events (e.g., extreme weather events, fire, pest outbreaks) (Section 2.2), are of potential importance. Of these processes, this section focuses on plant and soil processes as recent empirical work, including those explained in the following subsections, offers potential for improved model projections under warmer and CO ₂ -rich futures.

The magnitude of future uptake and release of CO ₂ and other GHGs by vegetation are among the greatest uncertainties (Ciais et al. 2013b ¹⁹³⁴ ). One reason for this uncertainty stems from the lack of understanding of the mechanisms responsible for plant responses to increasing temperatures. The short- and long-term projections of gross photosynthesis responses to changes in temperature, CO ₂ and nutrient availability vary greatly among the models (Busch and Sage 2017 ¹⁹³⁵ ; Rogers et al. 2017 ¹⁹³⁶ ). Net CO ₂ exchange requires estimation of autotrophic respiration, which is another source of uncertainty in ESM projections (Malhi et al. 2011 ¹⁹³⁷ ). The importance of plant acclimation of photosynthesis and respiration in understanding vegetation response to climate change is now widely recognised ( high confidence ) (Rogers et al., 2017 ¹⁹³⁸ ; Tan et al., 2017 ²²⁰⁶ ; Tjoelker, 2018 ¹⁹³⁹ ; Vanderwel et al., 2015 ¹⁹⁴⁰ ) (Section 2.7.1). Acclimation is broadly defined as the biochemical, physiological, morphological or developmental adjustments within the lifetime of organisms that result in improved performance under the new condition. Acclimation often operates over a time span of days to weeks, and can mitigate the negative effects of climate change on organismal growth and ecosystem functions (Tjoelker 2018 ¹⁹⁴¹ ).

Soil carbon and microbial processes, which interact with plant responses to climate, represent another large source of uncertainty in model projections ( medium confidence ) (Sections 2.7.2, 2.7.3 and 2.7.4). Given the wide range of uncertainty associated with SOC size estimates, CMIP5 models use a wide range of starting SOC stocks from 510–3040 GtC (Todd-Brown et al. 2013 ¹⁹⁴² ). Soil microbial respiration is estimated to release 40–70 GtC annually from the soil to the atmosphere globally (Hawkes et al. 2017 ¹⁹⁴³ ). Projections of changes in global SOC stocks during the 21st century by CMIP5 models also ranged widely, from a loss of 37 Gt to a gain of 146 Gt, with differences largely explained by initial SOC stocks, differing carbon input rates and different decomposition rates and temperature sensitivities (Todd-Brown et al. 2013 ¹⁹⁴⁴ ). With respect to land–climate interactions, the key processes affecting SOC stocks are warming (which is expected to accelerate SOC losses through microbial respiration) and acceleration of plant growth (which increases inputs of carbon to soils). However, complex mechanisms underlying SOC responses to moisture regimes, carbon addition, and warming drive considerable uncertainty in projections of future changes in SOC stocks (Sulman et al. 2014 ¹⁹⁴⁵ ; Singh et al. 2010 ¹⁹⁴⁶ ; Wieder et al. 2018 ¹⁹⁴⁷ ).

2.7.1 Temperature responses of plant and ecosystem production

Climate-change responses of net ecosystem production cannot be modelled by simple instantaneous response functions because of thermal acclimation responses of plants and soil microbes, as well as delayed responses arising from interactions between plants and the soil ( high confidence ) (Slot et al. 2014 ¹⁹⁴⁸ ; Rogers et al. 2017 ¹⁹⁴⁹ ; Tan et al. 2017 ¹⁹⁵⁰ ; Tjoelker 2018 ¹⁹⁵¹ ). Photosynthesis and respiration of component plant species exhibit different functional shapes among species (Slot et al. 2014 ¹⁹⁵² ), and carbon balance at the stand level is influenced by respiration of ecosystem biomass other than plants. Large uncertainty remains for thermal responses of bacteria and other soil organisms (Section 2.7.5). Bayesian statistical estimates of global photosynthesis and total ecosystem respirations suggest that they exhibit different responses to thermal anomalies during the last 35 years (Li et al. 2018b ¹⁹⁵³ ).

Thermal responses of plant respiration, which consumes approximately one half of GPP, have not been appropriately incorporated in most ESMs (Davidson et al., 2006 ¹⁹⁵⁴ ; Tjoelker, 2018 ¹⁹⁵⁵ ). Assumptions associated with respiration have been a major source of uncertainty for ESMs at the time of AR5. In most existing models, a simple assumption that respiration doubles with each 10°C increase of temperature (i.e., Q10 = 2) is adopted, ignoring acclimation. Even a small error stemming from this assumption can strongly influence estimated net carbon balance at large spatial scales of ecosystems and biomes over the time period of multiple decades (Smith and Dukes 2013 ¹⁹⁵⁶ ; Smith et al. 2016b ¹⁹⁵⁷ ). In order to estimate more appropriate thermal response curves of respiration, a global database including data from 899 plant species has been compiled (Atkin et al. 2015 ¹⁹⁵⁸ ), and respiration data from 231 plants species across seven biomes have been analysed (Heskel et al. 2016 ¹⁹⁵⁹ ). These empirical data on thermal responses of respiration demonstrate a globally convergent pattern (Huntingford et al. 2017 ¹⁹⁶⁰ ). According to a sensitivity analysis of a relatively small number of ESMs, a newly derived function of instantaneous responses of plant respiration to temperature (instead of a traditional exponential function of Q10 = 2) makes a significant difference in estimated autotrophic respiration especially in cold biomes (Heskel et al. 2016 ¹⁹⁶¹ ).

Acclimation results in reduced sensitivity of plant respiration with rising temperature, in essence, down regulation of warming-related increase in respiratory carbon emission ( high confidence ) (Atkin et al. 2015 ¹⁹⁶² ; Slot and Kitajima 2015 ¹⁹⁶³ ; Tjoelker 2018 ¹⁹⁶⁴ ). For example, experimental data from a tropical forest canopy show that temperature acclimation ameliorates the negative effects of rising temperature to leaf and plant carbon balance (Slot et al. 2014 ¹⁹⁶⁵ ). Analysis of CO ₂ flux data to quantify optimal temperature of net primary production of tropical forests also suggest acclimation potential for many tropical forests (Tan et al. 2017 ¹⁹⁶⁶ ). Comparisons of models with and without thermal acclimation of respiration show that acclimation can halve the increase of plant respiration with projected temperature increase by the end of 21st century (Vanderwel et al. 2015 ¹⁹⁶⁷ ).

It is typical that acclimation response to warming results in increases of the optimum temperature for photosynthesis and growth (Slot and Winter 2017 ¹⁹⁶⁸ ; Yamori et al. 2014 ¹⁹⁶⁹ ; Rogers et al. 2017 ¹⁹⁷⁰ ). Although such shift is a result of a complex interactions of biochemical, respiratory and stomatal regulation (Lloyd and Farquhar 2008 ¹⁹⁷¹ ), it can be approximated by a simple algorithm to address acclimation (Kattge et al. 2007 ¹⁹⁷² ). Mercado et al. (2018) ¹⁹⁷³ , using this approach, found that inclusion of biogeographical variation in photosynthetic temperature response was critically important for estimating future land surface carbon uptake. In the tropics, CO ₂ fertilisation effect (Box 2.3) is suggested to be more important for observed increases in carbon sink strength than increased leaf area index or a longer growing season (Zhu et al. 2016 ¹⁹⁷⁴ ). Acclimation responses of photosynthesis and growth to simultaneous changes of temperature and CO ₂ , as well as stress responses above the optimal temperature for photosynthesis, remain a major knowledge gap in modelling responses of plant productivity under future climate change (Rogers et al. 2017 ¹⁹⁷⁵ ).

2.7.2 Water transport through soil-plant-atmosphere continuum and drought mortality

How climate change, especially changes of precipitation patterns, influence water transport through the soil-plant-atmosphere continuum, is a key element in projecting the future of water vapour flux from land and cooling via latent heat flux ( high confidence ) (Sellers et al. 1996 ¹⁹⁷⁶ ; Bonan 2008 ¹⁹⁷⁷ ; Brodribb 2009 ¹⁹⁷⁸ ; Choat et al. 2012 ¹⁹⁷⁹ ; Sperry and Love 2015 ¹⁹⁸⁰ ; Novick et al. 2016 ¹⁹⁸¹ ; Sulman et al. 2016 ¹⁹⁸² ). Even without changes in leaf area per unit area of land, when plants close stomata in response to water shortage, dry atmosphere or soil moisture deficit, the stand-level fluxes of water (and associated latent heat flux) decrease (Seneviratne et al. 2018 ¹⁹⁸³ ). Closing stomata enhances drought survival at the cost of reduced photosynthetic production, while not closing stomata avoids loss of photosynthetic production at the cost of increased drought mortality (Sperry and Love 2015 ¹⁹⁸⁴ ). Hence, species-specific responses to drought, in terms of whether they close stomata or not, have short- and long-term consequences (Anderegg et al. 2018a ¹⁹⁸⁵ ; Buotte et al. 2019 ¹⁹⁸⁶ ). Increased drought-induced mortality of forest trees, often exacerbated by insect outbreak and fire (e.g., Breshears et al. (2005) ¹⁹⁸⁷ , Kurz et al. (2008) ¹⁹⁸⁸ , Allen et al. (2010) ¹⁹⁸⁹ ) (Section 2.2.4), have long-term impact on hydrological interactions between land and atmosphere (Anderegg et al. 2018b ¹⁹⁹⁰ ).

New models linking plant water transport with canopy gas exchange and energy fluxes are expected to improve projections of climate change impacts on forests and land-atmosphere interactions ( medium confidence ) (Bohrer et al., 2005 ¹⁹⁹¹ ; Anderegg et al., 2016 ¹⁹⁹² ; Sperry and Love, 2015 ¹⁹⁹³ ; Wolf et al., 2016 ¹⁹⁹⁴ ). Yet, there is much uncertainty in the ability of current vegetation and land surface models to adequately capture tree mortality and the response of forests to climate extremes like drought (Rogers et al. 2017 ¹⁹⁹⁵ ; Hartmann et al. 2018 ¹⁹⁹⁶ ). Most vegetation models use climate stress envelopes or vegetation carbon balance estimations to project climate-driven mortality and loss of forests (McDowell et al. 2011 ¹⁹⁹⁷ ); these may not adequately project biome shifts and impacts of disturbance in future climates. For example, a suite of vegetation models was compared to a field drought experiment in the Amazon on mature rainforest trees and all models performed poorly in projecting the timing and magnitude of biomass loss due to drought (Powell et al. 2013 ¹⁹⁹⁸ ). More recently, the loss of water transport due to embolism (disruption of xylem water continuity) (Sperry and Love 2015 ¹⁹⁹⁹ ), rather than carbon starvation (Rowland et al. 2015 ²⁰⁰⁰ ), is receiving attention as a key physiological process relevant for drought-induced tree mortality (Hartmann et al. 2018 ²⁰⁰¹ ). A key challenge to modelling efforts is to consider differences among plant species and vegetation types in their drought responses. One approach is to classify plant species to ‘functional types’ that exhibit similar responses to environmental variations (Anderegg et al. 2016 ²⁰⁰² ). Certain traits of species, such as tree height, is shown to be predictive of growth decline and mortality in response to drought (Xu et al. 2016a ²⁰⁰³ ). Similarly, tree rooting depth is positively related to mortality, contrary to expectation, during prolonged droughts in tropical dry forest (Chitra-Tarak et al. 2017 ²⁰⁰⁴ ).

2.7.3 Soil microbial effects on soil nutrient dynamics and plant responses to elevated CO2

Soil microbial processes influencing nutrient and carbon dynamics represent a large source of uncertainty in projecting land–climate interactions. For example, ESMs incorporating nitrogen and phosphorus limitations (but without considering the effects of mycorrhizae and rhizosphere priming) indicate that the simulated future carbon-uptake on land is reduced significantly when both nitrogen and phosphorus are limited as compared to only carbon- stimulation, by 63% (of 197 Pg C) under RCP2.6 and by 67% (of 425 Pg C) under RCP8.5 (Zhang et al. 2013c ²⁰⁰⁵ ). Mineral nutrient limitation progressively reduces the CO ₂ fertilisation effects on plant growth and productivity over time ( robust evidence, medium agreement ) (Norby et al. 2010 ²⁰⁰⁶ ; Sardans et al. 2012 ²⁰⁰⁷ ; Reich and Hobbie 2013 ²⁰⁰⁸ ; Feng et al. 2015 ²⁰⁰⁹ ; Terrer et al. 2017 ²⁰¹⁰ ). The rates at which nutrient limitation develops differ among studies and sites. A recent meta- analysis shows that experimental CO ₂ enrichment generally results in lower nitrogen and phosphorus concentrations in plant tissues (Du et al. 2019 ²⁰¹¹ ), and isotopic analysis also suggest a global trend of decreases in leaf nutrient concentration (Craine et al. 2018 ²⁰¹² ; Jonard et al. 2015 ²⁰¹³ ). However, reduced responses to elevated CO ₂ (eCO ₂ ) may not be a simple function of nitrogen dilution per se, as they result from complex interactions of ecosystem factors that influence nitrogen acquisition by plants (Liang et al. 2016 ²⁰¹⁴ ; Rutting 2017 ²⁰¹⁵ ; Du et al. 2019 ²⁰¹⁶ ).

Increasing numbers of case studies suggest that soil microbial processes, such as nitrogen mineralisation rates and symbiosis with plants, influence nutrient limitation on eCO ₂ effects on plant growth ( medium confidence ) (Drake et al. 2011 ²⁰¹⁷ ; Zak et al. 2011 ²⁰¹⁸ ; Hungate et al. 2013 ²⁰¹⁹ ; Talhelm et al. 2014 ²⁰²⁰ ; Du et al. 2019 ²⁰²¹ ). Rhizosphere priming effects (i.e., release of organic matters by roots to stimulate microbial activities) and mycorrhizal associations are proposed to explain why some sites are becoming nitrogen limited after a few years and others are sustaining growth through accelerated nitrogen uptake (limited evidence, medium agreement) (Phillips et al. 2011 ²⁰²² ; Terrer et al. 2017 ²⁰²³ ).

Model assessments that including rhizosphere priming effects and ectomycorrhizal symbiosis suggest that soil organic matter (SOM) cycling is accelerated through microbial symbiosis ( medium confidence ) (Elbert et al. 2012 ²⁰²⁴ ; Sulman et al. 2017 ²⁰²⁵ ; Orwin et al. 2011 ²⁰²⁶ ; Baskaran et al. 2017 ²⁰²⁷ ). Uncertainty exists in differences among ectomycorrhizal fungal species in their ability to decompose SOM (Pellitier and Zak 2018 ²⁰²⁸ ) and the capacity of ecosystems to sustain long-term growth with these positive symbiotic feedbacks is still under debate (Terrer et al. 2017 ²⁰²⁹ ). ESMs include only biological nitrogen cycles, even though a recent study suggests that bedrock weathering can be a significant source of nitrogen to plants (Houlton et al. 2018 ²⁰³⁰ ). In contrast, rock weathering is widely considered to be key for phosphorus availability, and tropical forests with highly weathered soils are considered to be limited by phosphorus availability rather than nitrogen availability (Reed et al. 2015 ²⁰³¹ ). Yet evidence from phosphorus fertilisation experiments is lacking (Schulte-Uebbing and de Vries 2018 ²⁰³² ) and phosphorus limitation of tropical tree growth may be strongly species-specific (Ellsworth et al. 2017 ²⁰³³ ; Turner et al. 2018a ²⁰³⁴ ). Limitation by availability of soil nutrients other than nitrogen and phosphorus has not been studied in the context of land–climate interactions, except potassium as a potentially limiting factor for terrestrial plant productivity in interaction with nitrogen, phosphorus and hydrology (Sardans and Peñuelas 2015 ²⁰³⁵ ; Zhao et al. 2017 ²⁰³⁶ ; Wright et al. 2018 ²⁰³⁷ ).

Anthropogenic alteration of global and regional nitrogen and phosphorus cycles, largely through use of chemical fertilisers and pollution, has major implications for future ecosystem attributes, including carbon storage, in natural and managed ecosystems ( high confidence ) (Peñuelas et al. 2013 ²⁰³⁸ , 2017 ²⁰³⁹ ; Wang et al. 2017c ²⁰⁴⁰ ; Schulte- Uebbing and de Vries 2018 ²⁰⁴¹ ; Yuan et al. 2018 ²⁰⁴² ). During 1997–2013, the contribution of nitrogen deposition to the global carbon sink has been estimated at 0.27 ± 0.13 GtC yr ^–1 , and the contribution of phosphorus deposition as 0.054 ± 0.10 GtC yr ^–1 ; these constitute about 9% and 2% of the total land carbon sink, respectively (Wang et al. 2017c ²⁰⁴³ ). Anthropogenic deposition of nitrogen enhances carbon sequestration by vegetation (Schulte-Uebbing and de Vries 2018 ²⁰⁴⁴ ), but this effect of nitrogen deposition on carbon sequestration may be offset by increased emission of GHGs such as N ₂ O and CH ₄ (Liu and Greaver 2009 ²⁰⁴⁵ ). Furthermore, nitrogen deposition may lead to imbalance of nitrogen vs phosphorus availability (Peñuelas et al. 2013 ²⁰⁴⁶ ), soil microbial activity and SOM decomposition (Janssens et al. 2010 ²⁰⁴⁷ ) and reduced ecosystem stability (Chen et al. 2016b ²⁰⁴⁸ ).

2.7.4 Vertical distribution of soil organic carbon

It has long been recognised that dynamics of soil organic carbon (SOC) represent a large source of uncertainties on biogeochemical interactions of land with atmosphere and climate as detailed below. Since AR5, there have been new understandings on SOC size, as well as on the microbial processes that influence SOM dynamics under climate change and LULCC. Three existing databases (SoilGrids, the Harmonized World Soil Data Base and Northern Circumpolar Soil Database) substantially differ in the estimated size of global SOC stock down to 1 m depth, varying between 2500 Pg to 3400 Pg with differences among databases largely attributable to carbon stored in permafrost (Joosten 2015 ²⁰⁴⁹ ; Köchy et al. 2015 ²⁰⁵⁰ ; Tifafi et al. 2018 ²⁰⁵¹ ). These values are four to eight times larger than the carbon stock associated with the terrestrial vegetation (Bond-Lamberty et al. 2018 ²⁰⁵² ). New estimates since AR5 show that much larger areas in the Amazon and Congo basins are peatlands (Gumbricht et al. 2017 ²⁰⁵³ ; Dargie et al. 2019 ²⁰⁵⁴ ).

Deep soil layers can contain much more carbon than previously assumed ( limited evidence, medium agreement ) (e.g., González- Jaramillo et al. (2016) ²⁰⁵⁵ ). Based on radiocarbon measurements, deep SOC can be very old, with residence times up to several thousand years (Rumpel and Kögel-Knabner 2011 ²⁰⁵⁶ ) or even several tens of thousands of years (Okuno and Nakamura 2003 ²⁰⁵⁷ ). Dynamics associated with such deeply buried carbon remain poorly studied and ignored by the models, and are not addressed in most of the studies assessed in this subsection. Deep soil carbon is thought to be stabilised by mineral interactions, but recent experiments suggest that CO ₂ release from deep soils can also be increased by warming, with a 4 ̊C warming enhancing annual soil respiration by 34–37% (Hicks Pries et al. 2017 ²⁰⁵⁸ ), or with the addition of fresh carbon (Fontaine et al. 2007 ²⁰⁵⁹ ). While erosion is not typically modelled as a carbon flux in ESMs, erosion and burial of carbon-containing sediments is likely a significant carbon transfer from land to ocean ( medium confidence ) (Berhe et al. 2007 ²⁰⁶⁰ ; Asefaw et al. 2008 ²⁰⁶¹ ; Wang et al. 2017e ²⁰⁶² ).

2.7.5 Soil carbon responses to warming and changes in soil moisture

Annually, 119 GtC is estimated to be emitted from the terrestrial ecosystem to the atmosphere, of which about 50% is attributed to soil microbial respiration (Auffret et al. 2016 ²⁰⁶³ ; Shao et al. 2013 ²⁰⁶⁴ ). It is yet not possible to make mechanistic and quantitative projections about how multiple environmental factors influence soil microbial respiration (Davidson et al. 2006a ²⁰⁶⁵ ; Dungait et al. 2012 ²⁰⁶⁶ ). Soil warming experiments show significant variability in temperature and moisture responses across biomes and climates; Crowther et al. (2016) ²⁰⁶⁷ found that warming-induced SOC loss is greater in regions with high initial carbon stocks, while an analysis of an expanded version of the same dataset did not support this conclusion (Gestel et al. 2018 ²⁰⁶⁸ ). Studies of SOC responses to warming over time have also shown complex responses. In a multi-decadal warming experiment, Melillo et al. (2017) ²⁰⁶⁹ found that soil respiration response to warming went through multiple phases of increasing and decreasing strength, which were related to changes in microbial communities and available substrates over time. Conant et al. (2011) ²⁰⁷⁰ and Knorr et al. (2005) ²⁰⁷¹ suggested that transient decomposition responses to warming could be explained by depletion of labile substrates, but that long-term SOC losses could be amplified by high temperature sensitivity of slowly decomposing SOC components. Overall, long-term SOC responses to warming remain uncertain (Davidson et al. 2006a ²⁰⁷² ; Dungait et al. 2012 ²⁰⁷³ ; Nishina et al. 2014 ²⁰⁷⁴ ; Tian et al. 2015 ²⁰⁷⁵ ).

It is widely known that soil moisture plays an important role in SOM decomposition by influencing microbial processes (e.g., Monard et al. (2012) ²⁰⁷⁶ , Moyano et al. (2013) ²⁰⁷⁷ , Yan et al. (2018) ²⁰⁷⁸ ), as confirmed by a recent global meta-analysis ( high confidence ) (Hawkes et al. 2017 ²⁰⁷⁹ ). A likely mechanism is that increased soil moisture lowers carbon mineralisation rates under anaerobic conditions, resulting in enhanced carbon stocks, but experimental analyses have shown that this effect may last for only 3–4 weeks after which iron reduction can actually accelerate the loss of previously protected OC by facilitating microbial access (Huang and Hall 2017 ²⁰⁸⁰ ).

Experimental studies of responses of microbial respiration to warming have found variable results (Luo et al. 2001 ²⁰⁸¹ ; Bradford et al. 2008 ²⁰⁸² ; Zhou et al. 2011 ²⁰⁸³ ; Carey et al. 2016 ²⁰⁸⁴ ; Teramoto et al. 2016 ²⁰⁸⁵ ). No acclimation was observed in carbon-rich calcareous temperate forest soils (Schindlbacher et al. 2015 ²⁰⁸⁶ ) and arctic soils (Hartley et al. 2008 ²⁰⁸⁷ ), and a variety of ecosystems from the Arctic to the Amazon indicated that microbes appear to enhance the temperature sensitivity of soil respiration in Arctic and boreal soils, thereby releasing even more carbon than currently projected (Karhu et al. 2014 ²⁰⁸⁸ ). In tropical forests, phosphorus limitation of microbial processes is a key factor influencing soil respiration (Camenzind et al. 2018 ²⁰⁸⁹ ). Temperature responses of symbiotic mycorrhizae differ widely among host plant species, without a clear pattern that may allow generalisation across plant species and vegetation types (Fahey et al. 2016 ²⁰⁹⁰ ).

Some new insights have been obtained since AR5 from investigations of improved mechanistic understanding of factors that regulate temperature responses of soil microbial respiration. Carbon use efficiency and soil nitrogen dynamics have large influence on SOC responses to warming ( high confidence ) (Allison et al. 2010 ²⁰⁹¹ ; Frey et al. 2013 ²⁰⁹² ; Wieder, William R., Bonan, Gordon B., Allison 2013 ²⁰⁹³ ; García-Palacios et al. 2015 ²⁰⁹⁴ ). More complex community interactions including competitive and trophic interactions could drive unexpected responses to SOC cycling to changes in temperature, moisture and carbon inputs (Crowther et al. 2015 ²⁰⁹⁵ ; Buchkowski et al. 2017 ²⁰⁹⁶ ).

Competition for nitrogen among bacteria and fungi could also suppress decomposition (Averill et al. 2014 ²⁰⁹⁷ ). Overall, the roles of soil microbial community and trophic dynamics in global SOC cycling remain very uncertain.

2.7.6 Soil carbon responses to changes in organic matter inputs by plants

While current ESM structures mean that increasing carbon inputs to soils drive corresponding increases in SOC stocks, long-term carbon addition experiments have found contradictory SOC responses. Some litter addition experiments have observed increased SOC accumulation (Lajtha et al. 2014b ²⁰⁹⁸ ; Liu et al. 2009 ²⁰⁹⁹ ), while others suggest insignificant SOC responses (Lajtha et al. 2014a ²¹⁰⁰ ; van Groenigen et al. 2014 ²¹⁰¹ ). Microbial dynamics are believed to have an important role in driving complex responses to carbon additions. The addition of fresh organic material can accelerate microbial growth and SOM decomposition via priming effects (Kuzyakov et al. 2014 ²¹⁰² ; Cheng et al. 2017 ²¹⁰³ ). SOM cycling is dominated by ‘hot spots’ including the rhizosphere as well as areas surrounding fresh detritus ( medium evidence, high agreement ) (Finzi et al. 2015 ²¹⁰⁴ ; Kuzyakov and Blagodatskaya 2015 ²¹⁰⁵ ). This complicates projections of SOC responses to increasing plant productivity as increasing carbon inputs could promote higher SOC storage, but these fresh carbon inputs could also deplete SOC stocks by promoting faster decomposition (Hopkins et al. 2014 ²¹⁰⁶ ; Guenet et al. 2018 ²¹⁰⁷ ; Sulman et al. 2014 ²¹⁰⁸ ). A meta-analysis by van Groenigen et al. (2014) ²¹⁰⁹ suggested that elevated CO ₂ accelerated SOC turnover rates across several biomes. These effects could be especially important in high-latitude regions where soils have high organic matter content and plant productivity is increasing (Hartley et al. 2012 ²¹¹⁰ ), but have also been observed in the tropics (Sayer et al. 2011 ²¹¹¹ ).

Along with biological decomposition, another source of uncertainty in projecting responses of SOC to climate change is stabilisation via interactions with mineral particles ( high confidence ) (Kögel- Knabner et al. 2008 ²¹¹² ; Kleber et al. 2011 ²¹¹³ ; Marschner et al. 2008 ²¹¹⁴ ; Schmidt 2011 ²¹¹⁵ ). Historically, conceptual models of SOC cycling have centred on the role of chemical recalcitrance: the hypothesis that long-lived components of SOC are formed from organic compounds that are inherently resistant to decomposition. Under the emerging new paradigm, stable SOC is primarily formed by the bonding of microbially-processed organic material to mineral particles, which limits the accessibility of organic material to microbial decomposers (Lützow et al. 2006 ²¹¹⁶ ; Keiluweit et al. 2015 ²¹¹⁷ ; Kallenbach et al. 2016 ²¹¹⁸ ; Kleber et al. 2011 ²¹¹⁹ ; Hopkins et al. 2014 ²¹²⁰ ). SOC in soil aggregates can be protected from microbial decomposition by being trapped in soil pores too small for microbes to access (Blanco-Canqui and Lal 2004 ²¹²¹ ; Six et al. 2004 ²¹²² ) or by oxygen limitation (Keiluweit et al. 2016 ²¹²³ ). Some new models are integrating these mineral protection processes into SOC cycling projections (Wang et al. 2017a ²¹²⁴ ; Sulman et al. 2014 ²¹²⁵ ; Riley et al. 2014 ²¹²⁶ ; Wieder et al. 2015 ²¹²⁷ ), although the sensitivity of mineral-associated organic matter to changes in temperature, moisture, fire (Box 2.1) and carbon inputs is highly uncertain. Improved quantitative understanding of soil ecosystem processes will be critically important for projection of future land–climate feedback interactions.

Footnotes

1. Estimates are only given until 2016 as this is the latest date when data are available for all gases.
2. Net anthropogenic flux of CO ₂ due to land cover change such as deforestation and afforestation, and land management including wood harvest and regrowth, as well as peatland burning, based on two bookkeeping models as used in the Global Carbon Budget and for AR5. Agricultural soil carbon stock change under the same land use is not considered in these models.
3. Estimates show the mean and assessed uncertainty of two databases, FAOSTAT and USEPA 2012
4. Total non-AFOLU emissions were calculated as the sum of total CO ₂ -eq emissions values for energy, industrial sources, waste and other emissions with data from the Global Carbon Project for CO ₂ , including international aviation and shipping and from the PRIMAP database for CH ₄ and N ₂ O averaged over 2007–2014 only as that was the period for which data were available.
5. The natural response of land to human-induced environmental changes is the response of vegetation and soils to environmental changes such as increasing atmospheric CO ₂ concentration, nitrogen deposition, and climate change. The estimate shown represents the average from Dynamic Global Vegetation Models.
6. All values expressed in units of CO ₂ -eq are based on AR5 100-year Global Warming Potential (GWP) values without climate-carbon feedbacks (N2O = 265; CH ₄ = 28). Note that the GWP has been used across fossil fuel and biogenic sources of methane. If a higher GWP for fossil fuel CH ₄ (30 per AR5), then total anthropogenic CH ₄ emissions expressed in CO ₂ -eq would be 2% greater.
7. The detailed list of all values used to construct this figure is provided in Table A2.1 in the Appendix at the end of the chapter.
8. The term “forestation” is used herein as this chapter does not distinguish between afforestation and reforestation. In model-based studies, simulations with and without trees are compared; in observation-based estimates, sites with and without trees are compared.
9. CO ₂ fluxes due to land use, land-use change and forestry, in essence, not including the part of AFOLU fluxes that are from agriculture.
10. UNFCCC. INDCs as communicated by Parties, www4.unfccc.int/submissions/indc/Submission%20Pages/submissions.aspx (UNFCCC, 2015).
11. UNFCCC. Greenhouse Gas Inventories, unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/8812.php (UNFCCC, 2015).
12. UNFCCC. National Communications Non-Annex 1, unfccc.int/nationalreports/non-annexinatcom/submittednatcom/items/653.php (UNFCCC, 2015).
13. UNFCCC. National Communications Annex 1, unfccc.int/nationalreports/annexinatcom/submittednatcom/items/7742.php (UNFCCC, 2015).
14. UNFCCC. Biennial Update Reports, unfccc.int/national_reports/non-annex_i_natcom/reporting_on_climate_change/items/8722.php (UNFCCC, 2015).

References

1. de Noblet, N.I. et al., 1996: Possible role of atmosphere-biosphere interactions in triggering the last glaciation. Geophys. Res. Lett., 23, 3191–3194, doi:10.1029/96GL03004.
2. Kageyama, M., S. Charbit, C. Ritz, M. Khodri, and G. Ramstein, 2004: Quantifying ice-sheet feedbacks during the last glacial inception. Geophys. Res. Lett., 31, 1–4, doi:10.1029/2004GL021339.
3. Charney, J.G., 1975: Dynamics of deserts and drought in the Sahel. Q.J.R. Meteorol. Soc., 101, 193–202, doi:10.1002/qj.49710142802.
4. Pielke, R.A. et al., 2011: Land use/land cover changes and climate: Modeling analysis and observational evidence. Wiley Interdiscip. Rev. Clim. Chang., 2, 828–850, doi:10.1002/wcc.144.
5. Alkama, R., and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/science.aac8083.
6. Bright, R. et al., 2017: Local temperature response to land cover and management change driven by non-radiative processes. Nat. Clim. Chang., 7, 296–302, doi:10.1038/nclimate3250.
7. Lee, X. et al., 2011: Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384–387, doi:10.1038/nature10588.
8. Li, D. et al., 2015a: Contrasting responses of urban and rural surface energy budgets to heat waves explain synergies between urban heat islands and heat waves. Environ. Res. Lett., 10, 54009, doi:10.1088/1748-9326/10/5/054009.
9. Duveiller, G., J. Hooker, and A. Cescatti, 2018: The mark of vegetation change on Earth’s surface energy balance. Nat. Commun., 9, 679, doi:10.1038/s41467-017-02810-8.
10. Forzieri, G., R. Alkama, D.G. Miralles, and A. Cescatti, 2017: Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science, 356, 1180–1184, doi:10.1126/science.aal1727.
11. IPCC, 2013a: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–29.
12. Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
13. Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 659–740.
15. Hartmann, D.L. Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, 2013a: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.
16. Burkett, V.R., A.G. Suarez, M. Bindi, C. Conde, R. Mukerji, M.J. Prather, A.L. St. Clair, and G.W. Yohe, 2014: Point of departure. Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 169–194.
17. Burkett, V.R., A.G. Suarez, M. Bindi, C. Conde, R. Mukerji, M.J. Prather, A.L. St. Clair, and G.W. Yohe, 2014: Point of departure. Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 169–194.
18. Anderson, R.G. et al., 2011: Biophysical considerations in forestry for climate protection. Front. Ecol. Environ., 9, 174–182, doi:10.1890/090179.
19. Bonan, G.B., 2008: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320, 1444–1449, doi:10.1126/science.1155121.
20. Burakowski, E. et al., 2018: The role of surface roughness, albedo, and Bowen ratio on ecosystem energy balance in the Eastern United States. Agric. For. Meteorol., 249, 367–376, doi:10.1016/j.agrformet.2017.11.030.
21. Ellison, D. et al., 2017: Trees, forests and water: Cool insights for a hot world. Glob. Environ. Chang., 43, 51–61, doi:10.1016/j.gloenvcha.2017.01.002.
22. Hollinger, D.Y. et al., 2010: Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration. Glob. Chang. Biol., 16, 696–710, doi:10.1111/j.1365-2486.2009.02028.x.
23. Moore, K.E. et al., 1996: Seasonal variation in radiative and turbulent exchange at a deciduous forest in Central Massachusetts. J. Appl. Meterology, 35, 122–134, doi:10.1175/1520-0450(1996)035<0122:SVIRAT>2.0.CO;2.
24. Richardson, A. et al., 2013: Climate change, phenology, and phenological controlof vegetatif feedbacks to the climate system. Agric. For, Meteorol, 169, 156–157, doi:10.1016/j.agrformet.2012.09.012.
25. Hoffman, F.M. et al., 2014: Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models. J. Geophys. Res. Biogeosciences, 119, 141–162, doi:10.1002/2013JG002381.
26. Oort, A.H., and J.P. Peixóto, 1983: Global angular momentum and energy balance requirements from observations. Adv. Geophys., 25, 355–490, doi:10.1016/S0065-2687(08)60177-6.
27. Carissimo, B.C., A.H. Oort, and T.H. Vonder Haar, 1985: Estimating the meridional energy transports in the atmosphere and ocean. J. Phys. Oceanogr., 15, 82–91, doi:10.1175/1520-0485(1985)015<0082:ETMETI>2.0.CO;2.
28. Yang, H., Q. Li, K. Wang, Y. Sun, and D. Sun, 2015a: Decomposing the meridional heat transport in the climate system. Clim. Dyn., 44, 2751–2768, doi:10.1007/s00382-014-2380-5.
29. Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel, 2006: World map of the Köppen-Geiger climate classification updated. Meteorol. Zeitschrift, 15, 259–263, doi:10.1127/0941-2948/2006/0130.
30. Hulme, M. et al., 1999: Relative impacts of human-induced climate change and natural climate variability. Nature, 397, 688–691, doi:10.1038/17789.
31. Parmesan, C. and G. Yohe, 2003: A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37–42, doi:10.1038/nature01286.
32. Di Lorenzo, E. et al., 2008: North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophys. Res. Lett., 35, L08607, doi:10.1029/2007GL032838.
33. Kløve, B. et al., 2014: Climate change impacts on groundwater and dependent ecosystems. J. Hydrol., 518, 250–266, doi:10.1016/j.jhydrol.2013.06.037.
34. Berg, A. et al., 2015: Interannual coupling between summertime surface temperature and precipitation over land: Processes and implications for climate change. J. Clim., 28, 1308–1328, doi:10.1175/JCLI-D-14-00324.1.
35. Lemordant, L., P. Gentine, M. Stefanon, P. Drobinski, and S. Fatichi, 2016: Modification of land-atmosphere interactions by CO2 effects: Implications for summer dryness and heat wave amplitude. Geophys. Res. Lett., 43, 10,240-10,248, doi:10.1002/2016GL069896.
36. Pecl, G.T. et al., 2017: Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355, eaai9214, doi:10.1126/science.aai9214.
37. Hartmann, D.L. Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, 2013a: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.
38. Menne, M.J., C.N. Williams, B.E. Gleason, J.J. Rennie, and J.H. Lawrimore, 2018: The global historical climatology network monthly temperature dataset, Version 4. J. Clim., 31, 9835–9854, doi:10.1175/JCLI-D-18-0094.1.
39. Lawrimore, J.H. et al. 2011: An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res. Atmos., 116, doi:10.1029/2011JD016187.
40. Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345.
41. Jones, P.D. et al., 2012: Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to 2010. J. Geophys. Res. Atmos., 117, doi:10.1029/2011JD017139.
42. Rohde, R. et al., 2013: Berkeley earth temperature averaging process. Geoinformatics Geostatistics An Overv., 1, 1–13, doi:10.4172/2327-4581.1000103.
43. Allen, M., O.P. Dube, W. Solecki, F. Aragón–Durand, W. Cramer, S. Humphreys, M. Kainuma, J. Kala, N. Mahowald, Y. Mulugetta, R. Perez, M. Wairiu, and K. Zickfeld, 2018: Framing and Context. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre- industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press.
44. Braconnot, P. et al., 2012: Evaluation of climate models using palaeoclimatic data. Nat. Clim. Chang., 2, 417–424, doi:10.1038/nclimate1456.
45. Joshi, M.M., F.H. Lambert, and M.J. Webb, 2013: An explanation for the difference between twentieth and twenty-first century land-sea warming ratio in climate models. Clim. Dyn., 41, 1853–1869, doi:10.1007/s00382-013-1664-5.
46. Sejas, S.A., O.S. Albert, M. Cai, and Y. Deng, 2014: Feedback attribution of the land-sea warming contrast in a global warming simulation of the NCAR CCSM4. Environ. Res. Lett., 9, 124005, doi:10.1088/1748-9326/9/12/124005.
47. Byrne, M.P. and P.A. O’Gorman, 2013: Land-ocean warming contrast over a wide range of climates: Convective quasi-equilibrium theory and idealized simulations. J. Clim., 26, 4000–4016, doi:10.1175/JCLI-D-12-00262.1.
48. Byrne, M.P., and P.A. O’Gorman, 2015: The response of precipitation minus evapotranspiration to climate warming: Why the “wet-get-wetter, dry- get-drier” scaling does not hold over land. J. Clim., 28, 8078–8092, doi:10.1175/JCLI-D-15-0369.1.
49. Wallace, C.J. and M. Joshi, 2018: Comparison of land-ocean warming ratios in updated observed records and CMIP5 climate models. Environ. Res. Lett., 13, doi:10.1088/1748-9326/aae46f.
50. Allen, R.J., T. Hassan, C.A. Randles, and H. Su, 2019: Enhanced land–sea warming contrast elevates aerosol pollution in a warmer world. Nat. Clim. Chang., 9, 300-305, doi:10.1038/s41558-019-0401-4.
51. Manabe, S., R.J. Stouffer, M.J. Spelman, and K. Bryan, 1991: Transient responses of a coupled ocean–atmosphere model to gradual changes of atmospheric CO2. Part I: Annual mean response. J. Clim., 4, 785–818, doi:10.1175/1520-0442(1991)004<0785:TROACO>2.0.CO;2.
52. Sutton, R.T., B. Dong, and J.M. Gregory, 2007: Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys. Res. Lett., 34, doi:10.1029/2006GL028164.
53. Allen, M., O.P. Dube, W. Solecki, F. Aragón–Durand, W. Cramer, S. Humphreys, M. Kainuma, J. Kala, N. Mahowald, Y. Mulugetta, R. Perez, M. Wairiu, and K. Zickfeld, 2018: Framing and Context. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre- industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press.
54. Lickley, M., and S. Solomon, 2018: Drivers, timing and some impacts of global aridity change. Environ. Res. Lett., 13, 104010, doi:10.1088/1748-9326/aae013.
55. Engelbrecht, F. et al., 2015: Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett., 10, 85004, doi:10.1088/1748-9326/10/8/085004.
56. Schlaepfer, D.R. et al., 2017: Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nat. Commun., 8, 14196, doi:10.1038/ncomms14196.
57. Howden, S.M. et al., 2007: Adapting agriculture to climate change. Proc. Natl. Acad. Sci., 104, 19691–19696, doi:10.1073/pnas.0701890104.
58. Rosenzweig, C. et al., 2013: The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agric. For. Meteorol., 170, 166–182, doi:10.1016/j.agrformet.2012.09.011.
59. Challinor, A.J. et al., 2014: A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang., 4, 287–291, doi:10.1038/nclimate2153.
60. Parry, M., C. Rosenzweig, and M. Livermore, 2005: Climate change, global food supply and risk of hunger. Philos. Trans. R. Soc. B Biol. Sci., 360, 2125–2138, doi:10.1098/rstb.2005.1751.
61. Lobell, D.B., and C. Tebaldi, 2014: Getting caught with our plants down: The risks of a global crop yield slowdown from climate trends in the next two decades.Environ.Res.Lett.,9,74003,doi:10.1088/1748-9326/9/7/074003.
62. Wheeler, T. and J. Von Braun, 2013: Climate change impacts on global food security. Science, 341, 508–513, doi:10.1126/science.1239402.
63. Gregory, P.J., and B. Marshall, 2012: Attribution of climate change: A methodology to estimate the potential contribution to increases in potato yield in Scotland since 1960. Glob. Chang. Biol., 18, 1372–1388, doi:10.1111/j.1365-2486.2011.02601.x.
64. Yang, X. et al., 2015b: Potential benefits of climate change for crop productivity in China. Agric. For. Meteorol., 208, 76–84, doi:10.1016/j.agrformet.2015.04.024.
65. Parry, M.L., C. Rosenzweig, A. Iglesias, M. Livermore, and G. Fischer, 2004: Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob. Environ. Chang., 14, 53–67, doi:10.1016/j.gloenvcha.2003.10.008.
66. Rosenzweig, C. et al., 2013: The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agric. For. Meteorol., 170, 166–182, doi:10.1016/j.agrformet.2012.09.011.
67. Deryng, D. et al., 2016b: Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat. Clim. Chang., 6, 786–790, doi:10.1038/nclimate2995.
68. Liu, B. et al., 2016a: Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Chang., 6, 1130–1136, doi:10.1038/nclimate3115.
69. Asseng, S. et al., 2015: Rising temperatures reduce global wheat production. Nat. Clim. Chang., 5, 143–147, doi:10.1038/nclimate2470.
70. Bassu, S. et al., 2014: How do various maize crop models vary in their responses to climate change factors? Glob. Chang. Biol., 20, 2301–2320, doi:10.1111/gcb.12520.
71. Zhao, C. et al., 2017: Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci., 114(35), 9326–9331, doi:10.1073/pnas.1701762114.
72. Fischer, E.M. and R. Knutti, 2014: Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys. Res. Lett., 41, 547–554, doi:10.1002/2013GL058499.
73. Wypych, A., Z. Ustrnul, A. Sulikowska, F.M. Chmielewski, and B. Bochenek, 2017: Spatial and temporal variability of the frost-free season in Central Europe and its circulation background. Int. J. Climatol., 37, 3340–3352, doi:10.1002/joc.4920.
74. Crimp, S.J. et al., 2016: Recent seasonal and long-term changes in southern Australian frost occurrence. Clim. Change, 139, 115–128, doi:10.1007/s10584-016-1763-5.
75. Meier, M., J. Fuhrer, and A. Holzkämper, 2018: Changing risk of spring frost damage in grapevines due to climate change? A case study in the Swiss Rhone Valley. Int. J. Biometeorol., 62, 991–1002, doi:10.1007/s00484-018-1501-y.
76. Luedeling, E., 2012: Climate change impacts on winter chill for temperate fruit and nut production: A review. Sci. Hortic. (Amsterdam)., 144, 218–229, doi:10.1016/j.scienta.2012.07.011.
77. Nelson, G.C., et al., 2009: Climate Change: Impact on Agriculture and Costs of Adaptation. Food Policy Report. International Food Policy Research Institute (IFPRI), Washington, DC, USA, doi:10.2499/0896295354.
78. Schlenker, W., and D.B. Lobell, 2010: Robust negative impacts of climate change on African agriculture. Environ. Res. Lett., 5, 14010, doi:10.1088/1748-9326/5/1/014010.
79. Müller, C. et al., 2017: An AgMIP framework for improved agricultural representation in integrated assessment models. Environ. Res. Lett., 12, 125003, doi:10.1088/1748-9326/aa8da6.
80. Parry, M.L., C. Rosenzweig, A. Iglesias, M. Livermore, and G. Fischer, 2004: Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob. Environ. Chang., 14, 53–67, doi:10.1016/j.gloenvcha.2003.10.008.
81. Wheeler, T. and J. Von Braun, 2013: Climate change impacts on global food security. Science, 341, 508–513, doi:10.1126/science.1239402.
82. Challinor, A.J. et al., 2014: A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang., 4, 287–291, doi:10.1038/nclimate2153.
83. Vogel, E. et al., 2019: The effects of climate extremes on global agricultural yields. Environ. Res. Lett., doi:10.1088/1748-9326/ab154b.
84. Vermeulen, S.J., B.M. Campbell, and J.S.I. Ingram, 2012a: Climate change and food systems. Annu. Rev. Environ. Resour., 37, 195–222, doi:10.1146/annurev-environ-020411-130608.
85. Rivera Ferre, M.G., 2014: Impacts of climate change on food availability: Distribution and exchange of food. In: Global Environmental Change [Freedman, B. (ed.)]. Springer Dordrecht, Netherlands, pp. 701–707.
86. Brown, M.E. et al., 2015: Climate Change, Global Food Security, and the U.S. Food System. US Global Change Research Program, Washington DC, USA, 146 pp.
87. Fanzo, J., C. Davis, R. McLaren, and J. Choufani, 2018: The effect of climate change across food systems: Implications for nutrition outcomes. Glob. Food Sec., 18, 12–19, doi:10.1016/j.gfs.2018.06.001.
88. Lobell, D.B., W. Schlenker, and J. Costa-Roberts, 2011: Climate trends and global crop production since 1980. Science, 333, 616–620, doi:10.1126/ 2 science.1204531.
89. Leakey, A.D.B., K.A. Bishop, and E.A. Ainsworth, 2012: A multi-biome gap in understanding of crop and ecosystem responses to elevated CO2. Curr. Opin. Plant Biol., 15, 228–236, doi:10.1016/j.pbi.2012.01.009.
90. Porter, J.R., L. Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, and M.I. Travasso, 2014: Food Security and Food Production Systems. Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485–533.
91. Gray, S.B. et al., 2016: Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean. Nat. Plants, 2, 16132, doi:10.1038/nplants.2016.132.
92. Pugh, T.A.M. et al., 2016: Climate analogues suggest limited potential for intensification of production on current croplands under climate change. Nat. Commun., 7, 1–8, doi:10.1038/ncomms12608.
93. Wheeler, T. and J. Von Braun, 2013: Climate change impacts on global food security. Science, 341, 508–513, doi:10.1126/science.1239402.
94. Beer, T., 2018: The impact of extreme weather events on food security. In: Climate Change, Extreme Events and Disaster Risk Reduction [S. Mal, R. Singh, and C. Huggel (eds.)]. Sustainable Development Goals Series. Springer, Cham, Switzerland, pp. 121–133.
95. Settele, J., R. Scholes, R. Betts, S. Bunn, P. Leadley, D. Nepstad, J.T. Overpeck, and M.A. Taboada 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–360.
96. Urban, M.C., 2015: Accelerating extinction risk from climate change. Science, 348, 571–573, doi:10.1126/science.aaa4984.
97. Ernakovich, J.G. et al., 2014: Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Chang. Biol., 20, 3256–3269, doi:10.1111/gcb.12568.
98. Elsen, P.R., and M.W. Tingley, 2015: Global mountain topography and the fate of montane species under climate change. Nat. Clim. Chang., 5, 772–776, doi:10.1038/nclimate2656.
99. Hatfield, J.L. and J.H. Prueger, 2015: Temperature extremes: Effect on plant growth and development. Weather Clim. Extrem., 10, 4–10, doi:10.1016/J.WACE.2015.08.001.
100. Savage, J. and M. Vellend, 2015: Elevational shifts, biotic homogenization and time lags in vegetation change during 40 years of climate warming. Ecography (Cop.)., 38, 546–555, doi:10.1111/ecog.01131.
101. Yin, Y., Q. Tang, L. Wang, and X. Liu, 2016: Risk and contributing factors of ecosystem shifts over naturally vegetated land under climate change in China. Sci. Rep., 6, 1–11, doi:10.1038/srep20905.
102. Pecl, G.T. et al., 2017: Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355, eaai9214, doi:10.1126/science.aai9214.
103. Gonsamo, A., J.M. Chen, Y.W. Ooi, C.J.M., and O.Y.W., 2017: Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems. Glob. Chang. Biol., 24, 2117–2128, doi:10.1111/gcb.14001.
104. Fadrique, B. et al., 2018: Widespread but heterogeneous responses of Andean forests to climate change. Nature, 564, 207–212, doi:10.1038/s41586-018-0715-9.
105. Laurance, S. et al., 2018: Compositional response of Amazon forests to climate change. Glob. Chang. Biol., 25, 39–56, doi:10.1111/gcb.14413.
106. Chan, D. and Q. Wu, 2015: Significant anthropogenic-induced changes of climate classes since 1950. Sci. Rep., 5, 13487, doi:10.1038/srep13487.
107. Chen, D. and H.W. Chen, 2013: Using the Köppen classification to quantify climate variation and change: An example for 1901–2010. Environ. Dev., 6, 69–79, doi:10.1016/j.envdev.2013.03.007.
108. Spinoni, J., J. Vogt, G. Naumann, H. Carrao, and P. Barbosa, 2015b: Towards identifying areas at climatological risk of desertification using the Köppen-Geiger classification and FAO aridity index. Int. J. Climatol., 35, 2210–2222, doi:10.1002/joc.4124.
109. Son, K.H. and D.H. Bae, 2015: Drought analysis according to shifting of climate zones to arid climate zone over Asia monsoon region. J. Hydrol., 529, 1021–1029, doi:10.1016/j.jhydrol.2015.09.010.
110. Jylhä, K. et al., 2010: Observed and projected future shifts of climatic zones in Europe and their use to visualize climate change information. Weather. Clim. Soc., 2, 148–167, doi:10.1175/2010wcas1010.1.
111. Yin, Y., D. Ma, and S. Wu, 2019: Enlargement of the semi-arid region in China from 1961 to 2010. Clim. Dyn., 52, 509–521, doi:10.1007/s00382-018-4139-x.
112. Adnan, S., K. Ullah, S. Gao, A.H. Khosa, and Z. Wang, 2017: Shifting of agro-climatic zones, their drought vulnerability, and precipitation and temperature trends in Pakistan. Int. J. Climatol., 37, 529–543, doi:10.1002/joc.5019.
113. Rubel, F., K. Brugger, K. Haslinger, and I. Auer, 2017: The climate of the European Alps: Shift of very high resolution Köppen-Geiger climate zones 1800–2100. Meteorol. Zeitschrift, 26, 115–125, doi:10.1127/metz/2016/0816.
114. Spinoni, J., J. Vogt, G. Naumann, H. Carrao, and P. Barbosa, 2015b: Towards identifying areas at climatological risk of desertification using the Köppen-Geiger classification and FAO aridity index. Int. J. Climatol., 35, 2210–2222, doi:10.1002/joc.4124.
115. Williams, J.W., S.T. Jackson, and J.E. Kutzbach, 2007: Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl. Acad. Sci., 104, 5738–5742, doi:10.1073/pnas.0606292104.
116. Rubel, F. and M. Kottek, 2010: Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Zeitschrift, 19, 135–141, doi:10.1127/0941-2948/2010/0430.
117. Garcia, R.A., M. Cabeza, R. Altwegg, and M.B. Araújo, 2016: Do projections from bioclimatic envelope models and climate change metrics match? Glob. Ecol. Biogeogr., 25, 65–74, doi:10.1111/geb.12386.
118. Mahony, C.R., A.J. Cannon, T. Wang, and S.N. Aitken, 2017: A closer look at novel climates: new methods and insights at continental to landscape scales. Glob. Chang. Biol., 23, 3934–3955, doi:10.1111/gcb.13645.
120. Law, D.J. et al., 2018: Bioclimatic envelopes for individual demographic events driven by extremes: Plant mortality from drought and warming. Int. J. Plant Sci., 180, 53–62, doi:10.1086/700702.
121. Williams, J.W. and S.T. Jackson, 2007: Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ., 5, 475–482, doi:10.1890/070037.
122. Colwell, R.K., G. Brehm, C.L. Cardelús, A.C. Gilman, and J.T. Longino, 2008a: Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science, 322, 258–261, doi:10.1126/science.1162547.
123. Mora, C. et al., 2013: The projected timing of climate departure from recent variability. Nature, 502, 183–187, doi:10.1038/nature12540.
124. Mora, C. et al., 2014: Mora et al. reply. Nature, 511, E5–E6, doi:10.1038/nature13524.
126. Hawkins, E. et al., 2014: Uncertainties in the timing of unprecedented climates. Nature, 511, E3–E5, doi:10.1038/nature13523.
127. Mahony, C.R., A.J. Cannon, T. Wang, and S.N. Aitken, 2017: A closer look at novel climates: new methods and insights at continental to landscape scales. Glob. Chang. Biol., 23, 3934–3955, doi:10.1111/gcb.13645.
128. Maule, C.F., T. Mendlik, and O.B. Christensen, 2017: The effect of the pathway to a two degrees warmer world on the regional temperature change of Europe. Clim. Serv., 7, 3–11, doi:10.1016/j.cliser.2016.07.002.
129. Settele, J., R. Scholes, R. Betts, S. Bunn, P. Leadley, D. Nepstad, J.T. Overpeck, and M.A. Taboada 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–360.
130. Seddon, A.W.R., M. Macias-Fauria, P.R. Long, D. Benz, and K.J. Willis, 2016: Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531, 229–232, doi:10.1038/nature16986.
131. Diffenbaugh, N.S., and C.B. Field, 2013: Changes in ecologically critical terrestrial climate conditions. Science, 341, 486–492, doi:10.1126/science.1237123.
132. Warszawski, L. et al., 2013: A multi-model analysis of risk of ecosystem shifts under climate change. Environ. Res. Lett., 8, 44018, doi:10.1088/1748-9326/8/4/044018.
133. Harsch, M.A., P.E. Hulme, M.S. McGlone, and R.P. Duncan, 2009: Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett., 12, 1040–1049, doi:10.1111/j.1461-0248.2009.01355.x.
134. Landhäusser, S.M., D. Deshaies, and V.J. Lieffers, 2010: Disturbance facilitates rapid range expansion of aspen into higher elevations of the Rocky Mountains under a warming climate. J. Biogeogr., 37, 68–76, doi:10.1111/j.1365-2699.2009.02182.x.
135. Gottfried, M. et al., 2012: Continent-wide response of mountain vegetation to climate change. Nat. Clim. Chang., 2, 111–115, doi:10.1038/nclimate1329.
136. Boisvert-Marsh, L., C. Périé, and S. de Blois, 2014: Shifting with climate? Evidence for recent changes in tree species distribution at high latitudes. Ecosphere, 5 (7), 1–33, doi:10.1890/ES14-00111.1.
137. Bryn, A. and K. Potthoff, 2018: Elevational treeline and forest line dynamics in Norwegian mountain areas – a review. Landsc. Ecol., 33, 1225–1245, doi:10.1007/s10980-018-0670-8.
138. Rumpf, S.B. et al., 2018: Range dynamics of mountain plants decrease with elevation. Proc. Natl. Acad. Sci., 115, 1848–1853, doi:10.1073/pnas.1713936115.
139. Buitenwerf, R., B. Sandel, S. Normand, A. Mimet, and J.-C. Svenning, 2018: Land surface greening suggests vigorous woody regrowth throughout European semi-natural vegetation. Glob. Chang. Biol., 24, 5789–5801, doi:10.1111/gcb.14451.
140. Steinbauer, M.J. et al., 2018: Accelerated increase in plant species richness on mountain summits is linked to warming. Nature, 556, 231–234, doi:10.1038/s41586-018-0005-6.
141. Rumpf, S.B. et al., 2018: Range dynamics of mountain plants decrease with elevation. Proc. Natl. Acad. Sci., 115, 1848–1853, doi:10.1073/pnas.1713936115.
142. Tietjen, B. et al., 2017: Climate change-induced vegetation shifts lead to more ecological droughts despite projected rainfall increases in many global temperate drylands. Glob. Chang. Biol., 23, 2743–2754, doi:10.1111/gcb.13598.
143. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.
144. Myneni, R.B., C.D. Keeling, C.J. Tucker, G. Asrar, and R.R. Nemani, 1997: Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698–702, doi:10.1038/386698a0.
145. de Jong, R., J. Verbesselt, M.E. Schaepman, and S. de Bruin, 2012: Trend changes in global greening and browning: Contribution of short-term trends to longer-term change. Glob. Chang. Biol., 18, 642–655, doi:10.1111/j.1365-2486.2011.02578.x.
146. Los, S.O., 2013: Analysis of trends in fused AVHRR and MODIS NDVI data for 1982-2006: Indication for a CO2 fertilization effect in global vegetation. Global Biogeochem. Cycles, 27, 318–330, doi:10.1002/gbc.20027.
147. Piao, S. et al., 2015: Detection and attribution of vegetation greening trend in China over the last 30 years. Glob. Chang. Biol., 21, 1601–1609, doi:10.1111/gcb.12795.
148. Mao, J. et al., 2016: Human-induced greening of the northern extratropical land surface. Nat. Clim. Chang., 6, 959–963, doi:10.1038/nclimate3056.
149. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.
150. Carlson, B.Z. et al., 2017: Observed long-term greening of alpine vegetation – A case study in the French Alps. Environ. Res. Lett., 12, 114006, doi:10.1088/1748-9326/aa84bd.
151. Forzieri, G., R. Alkama, D.G. Miralles, and A. Cescatti, 2017: Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science, 356, 1180–1184, doi:10.1126/science.aal1727.
152. Pan, N., X. Feng, B. Fu, S. Wang, F. Ji, and S. Pan, 2018: Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sens. Environ., 214, 59–72, doi:10.1016/j.rse.2018.05.018.
153. Chen, C. et al., 2019: China and India lead in greening of the world through land-use management. Nat. Sustain., 2, 122–129, doi:10.1038/s41893-019-0220-7.
154. Donohue, R.J., M.L. Roderick, T.R. McVicar, and G.D. Farquhar, 2013: Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett., 40, 3031–3035, doi:10.1002/grl.50563.
155. Keenan, T.F. et al., 2016: Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun., 7, doi:10.1038/ncomms13428.
156. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.
157. Fensholt, R. et al., 2012: Greenness in semi-arid areas across the globe 1981–2007 – an Earth Observing Satellite based analysis of trends and drivers. Remote Sens. Environ., 121, 144–158, doi:10.1016/j.rse.2012.01.017.
158. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.
159. Goetz, S.J. et al., 2015: Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation. Environ. Res. Lett., 10, 123001, doi:10.1088/1748-9326/10/12/123001.
160. Xu, C., H. Liu, A.P. Williams, Y. Yin, and X. Wu, 2016a: Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob. Chang. Biol., 22, 2852–2860, doi:10.1111/gcb.13224.
161. Xu, X., W.J. Riley, C.D. Koven, and G. Jia, 2018: Observed and simulated sensitivities of spring greenup to preseason climate in northern temperate and boreal regions. J. Geophys. Res. Biogeosciences, 123, 60–78, doi:10.1002/2017JG004117.
162. Pulliainen, J. et al., 2017: Early snowmelt significantly enhances boreal springtime carbon uptake. Proc. Natl. Acad. Sci., 114, 201707889, doi:10.1073/pnas.1707889114.
163. Chen, C. et al., 2019: China and India lead in greening of the world through land-use management. Nat. Sustain., 2, 122–129, doi:10.1038/s41893-019-0220-7.
164. Jørgensen, S.E., L. Xu, J. Marques, and F. Salas, 2016: Application of indicators for the assessment of ecosystem health. In: Handbook of Ecological Indicators for Assessment of Ecosystem Health [Jørgensen, S.E., L. Xu, and R. Costanza (eds.)]. CRC Press, Boca Raton, Florida Taylor & Francis Group, pp. 22–89.
165. de Jong, R., J. Verbesselt, M.E. Schaepman, and S. de Bruin, 2012: Trend changes in global greening and browning: Contribution of short-term trends to longer-term change. Glob. Chang. Biol., 18, 642–655, doi:10.1111/j.1365-2486.2011.02578.x.
166. Pan, N., X. Feng, B. Fu, S. Wang, F. Ji, and S. Pan, 2018: Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sens. Environ., 214, 59–72, doi:10.1016/j.rse.2018.05.018.
167. Verbyla, D., 2008: The greening and browning of Alaska based on 1982–2003 satellite data. Glob. Ecol. Biogeogr., 17, 547–555, doi:10.1111/j.1466-8238.2008.00396.x.
168. Revadekar, J.V., Y.K. Tiwari, and K.R. Kumar, 2012: Impact of climate variability on NDVI over the Indian region during 1981–2010. Int. J. Remote Sens., 33, 7132–7150, doi:10.1080/01431161.2012.697642.
169. Epstein, H. et al., 2018: Tundra Greeness. In: NOAA Arctic Report Card 2018, 46–52 pp https://www.arctic.noaa.gov/Report-Card .
170. Zhao, L., A. Dai, and B. Dong, 2018: Changes in global vegetation activity and its driving factors during 1982–2013. Agric. For. Meteorol., 249, 198–209, doi:10.1016/j.agrformet.2017.11.013.
171. de Jong, R., J. Verbesselt, M.E. Schaepman, and S. de Bruin, 2012: Trend changes in global greening and browning: Contribution of short-term trends to longer-term change. Glob. Chang. Biol., 18, 642–655, doi:10.1111/j.1365-2486.2011.02578.x.
172. Pan, N., X. Feng, B. Fu, S. Wang, F. Ji, and S. Pan, 2018: Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sens. Environ., 214, 59–72, doi:10.1016/j.rse.2018.05.018.
173. Pausas, J.G., and M.M. Millán, 2018: greening and browning in a climate change hotspot: The Mediterranean Basin. Bioscience, 69, 143–151, doi:10.1093/biosci/biy157.
174. Jones, C., J. Lowe, S. Liddicoat, and R. Betts, 2009: Committed terrestrial ecosystem changes due to climate change. Nat. Geosci., 2, 484–487, doi:10.1038/ngeo555.
175. Buitenwerf, R., B. Sandel, S. Normand, A. Mimet, and J.-C. Svenning, 2018: Land surface greening suggests vigorous woody regrowth throughout European semi-natural vegetation. Glob. Chang. Biol., 24, 5789–5801, doi:10.1111/gcb.14451.
176. Chen, C. et al., 2019: China and India lead in greening of the world through land-use management. Nat. Sustain., 2, 122–129, doi:10.1038/s41893-019-0220-7.
177. Milly, P.C.D., and K.A. Dunne, 2016: Potential evapotranspiration and continental drying. Nat. Clim. Chang., 6, 946–949, doi:10.1038/nclimate3046.
178. Roy, J. et al., 2016: Elevated CO2 maintains grassland net carbon uptake under a future heat and drought extreme. Proc. Natl. Acad. Sci., 113, 6224–6229, doi:10.1073/pnas.1524527113.
179. Milly, P.C.D., and K.A. Dunne, 2016: Potential evapotranspiration and continental drying. Nat. Clim. Chang., 6, 946–949, doi:10.1038/nclimate3046.
180. Swann, A.L.S., F.M. Hoffman, C.D. Koven, and J.T. Randerson, 2016: Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl. Acad. Sci., 113, 10019–10024, doi:10.1073/pnas.1604581113.
181. Chang, J. et al., 2017: Future productivity and phenology changes in European grasslands for different warming levels: Implications for grassland management and carbon balance. Carbon Balance Manag., 12, 11, doi:10.1186/s13021-017-0079-8.
182. Zhu, P. et al., 2017: Elevated atmospheric CO2 negatively impacts photosynthesis through radiative forcing and physiology-mediated climate feedback. Geophys. Res. Lett., 44, 1956–1963, doi:10.1002/2016GL071733.
183. Pau, S., D.K. Okamoto, O. Calderón, and S.J. Wright, 2018: Long-term increases in tropical flowering activity across growth forms in response to rising CO2 and climate change. Glob. Chang. Biol., 24, 2105–2116, doi:10.1111/gcb.14004.
184. Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, 2012: Changes in Climate Extremes and Their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109–230.
185. Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J.Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5°C of Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I.Gomis, E. Lonnoy, T.Maycock, M.Tignor, and T. Waterfield (eds.)]. In press, pp. 175–311.
186. Colwell, R.K., G. Brehm, C.L. Cardelús, A.C. Gilman, and J.T. Longino, 2008b: Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science, 322, 258–261, doi:10.1126/science.1162547.
187. Handmer, J., Y. Honda, Z.W. Kundzewicz, N. Arnell, G. Benito, J. Hatfield, I.F. Mohamed, P. Peduzzi, S. Wu, B. Sherstyukov, K. Takahashi, and Z. Yan, 2012: Changes in Impacts of Climate Extremes: Human Systems and Ecosystems. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 231–290.
188. Leonard, M. et al., 2014: A compound event framework for understanding extreme impacts. Wiley Interdiscip. Rev. Clim. Chang., 5, 113–128, doi:10.1002/wcc.252.
189. Leonard, M. et al., 2014: A compound event framework for understanding extreme impacts. Wiley Interdiscip. Rev. Clim. Chang., 5, 113–128, doi:10.1002/wcc.252.
190. Millar, C.I., and N.L. Stephenson, 2015: Temperate forest health in an era of emerging megadisturbance. Science, 349, 823–826, doi:10.1126/science.aaa9933.
192. Ummenhofer, C.C. and G.A. Meehl, 2017: Extreme weather and climate events with ecological relevance: A review. Philos. Trans. R. Soc. B Biol. Sci., 372, 20160135, doi:10.1098/rstb.2016.0135.
193. Orlowsky, B., and S.I. Seneviratne, 2012: Global changes in extreme events: Regional and seasonal dimension. Clim. Change, 110, 669–696, doi:10.1007/s10584-011-0122-9.
194. Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, 2012: Changes in Climate Extremes and Their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109–230.
195. Mishra, V., A.R. Ganguly, B. Nijssen, and D.P. Lettenmaier, 2015: Changes in observed climate extremes in global urban areas. Environ. Res. Lett., 10, 24005, doi:10.1088/1748-9326/10/2/024005.
196. Ye, J.S. et al., 2018: Which temperature and precipitation extremes best explain the variation of warm versus cold years and wet versus dry years? J. Clim., 31, 45–59, doi:10.1175/JCLI-D-17-0377.1.
197. Lewis, S.C., and D.J. Karoly, 2013: Anthropogenic contributions to Australia’s record summer temperatures of 2013. Geophys. Res. Lett., 40, 3708–3709, doi:10.1002/grl.50673.
198. Smith, T.T., B.F. Zaitchik, and J.M. Gohlke, 2013b: Heat waves in the United States: Definitions, patterns and trends. Clim. Change, 118, 811–825, doi:10.1007/s10584-012-0659-2.
199. Scherer, M. and N.S. Diffenbaugh, 2014: Transient twenty-first century changes in daily-scale temperature extremes in the United States. Clim. Dyn., 42, 1383–1404, doi:10.1007/s00382-013-1829-2.
200. Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Chang., 5, 560–564, doi:10.1038/nclimate2617.
201. Ceccherini, G., S. Russo, I. Ameztoy, C. Patricia Romero, and C. Carmona-Moreno, 2016: Magnitude and frequency of heat and cold waves in recent decades: The case of South America. Nat. Hazards Earth Syst. Sci., 16, 821–831, doi:10.5194/nhess-16-821-2016.
202. King, A.D. et al., 2016: Emergence of heat extremes attributable to anthropogenic influences. Geophys. Res. Lett., 43, 3438–3443, doi:10.1002/2015GL067448.
203. Bador, M., L. Terray, and J. Boé, 2016: Emergence of human influence on summer record-breaking temperatures over Europe. Geophys. Res. Lett., 43, 404–412, doi:10.1002/2015GL066560. http://doi.wiley.com/10.1002/2015GL066560 (Accessed October 28, 2018).
204. Stott, P.A. et al., 2016: Attribution of extreme weather and climate-related events. Wiley Interdiscip. Rev. Clim. Chang., 7, 23–41, doi:10.1002/wcc.380.
205. King, A.D., 2017: Attributing changing rates of temperature record breaking to anthropogenic influences. Earth’s Futur., 5, 1156–1168, doi:10.1002/2017EF000611.
206. Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J.Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5°C of Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I.Gomis, E. Lonnoy, T.Maycock, M.Tignor, and T. Waterfield (eds.)]. In press, pp. 175–311.
207. Fischer, E.M. and R. Knutti, 2014: Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys. Res. Lett., 41, 547–554, doi:10.1002/2013GL058499.
208. Diffenbaugh, N.S., D.L. Swain, and D. Touma, 2015: Anthropogenic warming has increased drought risk in California. Proc. Natl. Acad. Sci., 112, 3931–3936, doi:10.1073/pnas.1422385112.
209. Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529, 477–483, doi:10.1038/nature16542.
210. Russo, S., A.F. Marchese, J. Sillmann, and G. Imme, 2016: When will unusual heat waves become normal in a warming Africa? Environ. Res. Lett., 11, 54016, doi:10.1088/1748-9326/11/5/054016.
211. Ceccherini, G., S. Russo, I. Ameztoy, A. Francesco Marchese, and C. Carmona-Moreno, 2017: Heat waves in Africa 1981-2015, observations and reanalysis. Nat. Hazards Earth Syst. Sci., 17, 115–125, doi:10.5194/nhess-17-115-2017.
212. Herrera-Estrada, J.E., and J. Sheffield, 2017: Uncertainties in future projections of summer droughts and heat waves over the contiguous United States. J. Clim., 30, 6225–6246, doi:10.1175/JCLI-D-16-0491.1.
213. Rusticucci, M., 2012: Observed and simulated variability of extreme temperature events over South America. Atmos. Res., 106, 1–17, doi:10.1016/j.atmosres.2011.11.001.
214. Cowan, T. et al., 2014: More frequent, longer, and hotter heat waves for Australia in the twenty-first century. J. Clim., 27, 5851–5871, doi:10.1175/JCLI-D-14-00092.1.
215. Russo, S. et al., 2014: Magnitude of extreme heat waves in present climate and their projection in a warming world. J. Geophys. Res. Atmos., 119, 12500–12512, doi:10.1002/2014JD022098.
216. Scherer, M. and N.S. Diffenbaugh, 2014: Transient twenty-first century changes in daily-scale temperature extremes in the United States. Clim. Dyn., 42, 1383–1404, doi:10.1007/s00382-013-1829-2.
217. Pal, J.S. and E.A.B. Eltahir, 2016: Future temperature in southwest Asia projected to exceed a threshold for human adaptability. Nat. Clim. Chang., 6, 197–200, doi:10.1038/nclimate2833.
218. Rusticucci, M., J. Kyselý, G. Almeira, and O. Lhotka, 2016: Long-term variability of heat waves in Argentina and recurrence probability of the severe 2008 heat wave in Buenos Aires. Theor. Appl. Climatol., 124, 679–689, doi:10.1007/s00704-015-1445-7.
219. Schär, C., 2016: Climate extremes: The worst heat waves to come. Nat. Clim. Chang., 6, 128–129, doi:10.1038/nclimate2864.
220. Teng, H., G. Branstator, G.A. Meehl, and W.M. Washington, 2016: Projected intensification of subseasonal temperature variability and heat waves in the Great Plains. Geophys. Res. Lett., 43, 2165–2173, doi:10.1002/2015GL067574.
221. Dosio, A., 2017: Projection of temperature and heat waves for Africa with an ensemble of CORDEX Regional Climate Models. Clim. Dyn., 49, 493–519, doi:10.1007/s00382-016-3355-5.
222. Mora, C. et al., 2017: Global risk of deadly heat. Nat. Clim. Chang., 7, 501–506, doi:10.1038/nclimate3322.
223. Dosio, A., L. Mentaschi, E.M. Fischer, and K. Wyser, 2018: Extreme heat waves under 1.5°C and 2°C global warming. Environ. Res. Lett., 13, 54006, doi:10.1088/1748-9326/aab827.
224. Lehner, F., C. Deser, and B.M. Sanderson, 2018: Future risk of record-breaking summer temperatures and its mitigation. Clim. Change, 146, 363–375, doi:10.1007/s10584-016-1616-2.
225. Lhotka, O., J. Kyselý, and A. Farda, 2018: Climate change scenarios of heat waves in Central Europe and their uncertainties. Theor. Appl. Climatol., 131, 1043–1054, doi:10.1007/s00704-016-2031-3.
226. Lopez, H. et al., 2018: Early emergence of anthropogenically forced heat waves in the western United States and Great Lakes. Nat. Clim. Chang., 8, 414–420, doi:10.1038/s41558-018-0116-y.
227. Tabari, H. and P. Willems, 2018: More prolonged droughts by the end of the century in the Middle East. Environ. Res. Lett., 13, 104005, doi:10.1088/1748-9326/aae09c.
228. Russo, S., A.F. Marchese, J. Sillmann, and G. Imme, 2016: When will unusual heat waves become normal in a warming Africa? Environ. Res. Lett., 11, 54016, doi:10.1088/1748-9326/11/5/054016.
229. Miralles, D.G., A.J. Teuling, C.C. Van Heerwaarden, and J.V.G. De Arellano, 2014: Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci., 7, 345–349, doi:10.1038/ngeo2141.
230. Lemordant, L., P. Gentine, M. Stefanon, P. Drobinski, and S. Fatichi, 2016: Modification of land-atmosphere interactions by CO2 effects: Implications for summer dryness and heat wave amplitude. Geophys. Res. Lett., 43, 10,240-10,248, doi:10.1002/2016GL069896.
231. Ramarao, M.V.S., J. Sanjay, and R. Krishnan, 2016: Modulation of summer monsoon sub-seasonal surface air temperature over India by soil moisture-temperature coupling. Mausam, 67, 53–66.
232. Mueller, N.D., 2015: Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Chang., 6, 317–322, doi:10.1038/nclimate2825.
233. Siebert, S., H. Webber, G. Zhao, and F. Ewert, 2017: Heat stress is overestimated in climate impact studies for irrigated agriculture. Environ. Res. Lett., 12, 54023, doi:10.1088/1748-9326/aa702f.
234. IPCC, 2013c: Annex III: Glossary [Planton, S. (ed.)]. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1447–1466.
235. Hegerl, G.C., S. Brönnimann, A. Schurer, and T. Cowan, 2018: The early 20th century warming: Anomalies, causes, and consequences. Wiley Interdiscip. Rev. Clim. Chang., 9, e522, doi:10.1002/wcc.522.
236. Hoerling, M., J. Eischeid, and J. Perlwitz, 2010: Regional precipitation trends: Distinguishing natural variability from anthropogenic forcing. J. Clim., 23, 2131–2145, doi:10.1175/2009JCLI3420.1.
237. Van Dijk, A.I.J.M. et al., 2013: The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour. Res., 49, 1040–1057, doi:10.1002/wrcr.20123.
238. Hulme, M., 2001: Climatic perspectives on Sahelian desiccation: 1973–1998. Glob. Environ. Chang., 11, 19–29, doi:10.1016/S0959-3780(00)00042-X.
239. Kelley, C.P., S. Mohtadi, M.A. Cane, R. Seager, and Y. Kushnir, 2015: Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proc. Natl. Acad. Sci., 112, 3241–3246, doi:10.1073/pnas.1421533112.
240. Dai, A., 2011: Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Chang., 2, 45–65, doi:10.1002/wcc.81.
241. Hoell, A., M. Hoerling, J. Eischeid, X.W. Quan, and B. Liebmann, 2017: Reconciling theories for human and natural attribution of recent East Africa drying. J. Clim., 30, 1939–1957, doi:10.1175/JCLI-D-16-0558.1.
242. Espinoza, J.C., J. Ronchail, J.A. Marengo, and H. Segura, 2018: Contrasting North–South changes in Amazon wet-day and dry-day frequency and related atmospheric features (1981–2017). Climate Dynamics, 52, 5413–5430, doi:10.1007/s00382-018-4462-2.
243. Erfanian, A., G. Wang, and L. Fomenko, 2017: Unprecedented drought over tropical South America in 2016: Significantly under-predicted by tropical SST. Sci. Rep., 7, 5811, doi:10.1038/s41598-017-05373-2.
244. Funk, C. et al., 2018: Anthropogenic enhancement of moderate-to-strong El Niño events likely contributed to drought and poor harvests in southern Africa during 2016. Bull. Am. Meteorol. Soc., 99, S91–S96, doi:10.1175/BAMS-D-17-0112.1.
245. Yuan, X., L. Wang, and E. Wood, 2018: Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bull. Am. Meteorol. Soc., 99, S86–S90, doi:10.1175/BAMS-D-17-0077.1.
246. Kelley, C.P., S. Mohtadi, M.A. Cane, R. Seager, and Y. Kushnir, 2015: Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proc. Natl. Acad. Sci., 112, 3241–3246, doi:10.1073/pnas.1421533112.
247. Wilcox, L.J. et al., 2018: Multiple perspectives on the attribution of the extreme European summer of 2012 to climate change. Clim. Dyn., 50, 3537–3555, doi:10.1007/s00382-017-3822-7.
248. Williams, A.P. et al., 2015: Contribution of anthropogenic warming to California drought during 2012-2014. Geophys. Res. Lett., 42, 6819–6828, doi:10.1002/2015GL064924.
249. Mote, P.W. et al., 2016: Perspectives on the causes of exceptionally low 2015 snowpack in the western United States. Geophys. Res. Lett., 43, 10,980-10,988, doi:10.1002/2016GL069965.
250. Otto, F.E.L., N. Massey, G.J. Van Oldenborgh, R.G. Jones, and M.R. Allen, 2012: Reconciling two approaches to attribution of the 2010 Russian heat wave. Geophys. Res. Lett., 39, n/a-n/a, doi:10.1029/2011GL050422.
251. Ramarao, M.V.S., R. Krishnan, J. Sanjay, and T.P. Sabin, 2015: Understanding land surface response to changing South Asian monsoon in a warming climate. Earth Syst. Dyn., 6, 569–582, doi:10.5194/esd-6-569-2015.
252. Lewis, S.C., and D.J. Karoly, 2013: Anthropogenic contributions to Australia’s record summer temperatures of 2013. Geophys. Res. Lett., 40, 3708–3709, doi:10.1002/grl.50673.
253. Sheffield, J., E.F. Wood, and M.L. Roderick, 2012: Little change in global drought over the past 60 years. Nature, 491, 435–438, doi:10.1038/nature11575.
254. Dai, A., 2013: Increasing drought under global warming in observations and models. Nat. Clim. Chang., 3, 52–58, doi:10.1038/nclimate1633.
255. Trenberth, K.E. et al., 2014: Global warming and changes in drought. Nat. Clim. Chang., 4, 17–22, doi:10.1038/nclimate2067.
256. Nicholls, N., and S.I. Seneviratne, 2015: Comparing IPCC assessments: How do the AR4 and SREX assessments of changes in extremes differ? Clim. Change, 133, 7–21, doi:10.1007/s10584-013-0818-0.
257. Mukherjee, S., A. Mishra, and K.E. Trenberth, 2018: Climate change and drought: A perspective on drought indices. Curr. Clim. Chang. Reports, 4, 145–163, doi:10.1007/s40641-018-0098-x.
258. Vicente-Serrano, S.M. et al., 2014: Evidence of increasing drought severity caused by temperature rise in southern Europe. Environ. Res. Lett., 9, 44001, doi:10.1088/1748-9326/9/4/044001.
259. Spinoni, J., G. Naumann, J. Vogt, and P. Barbosa, 2015a: European drought climatologies and trends based on a multi-indicator approach. Glob. Planet. Change, 127, 50–57, doi:10.1016/J.GLOPLACHA.2015.01.012.
260. Dai, A., and T. Zhao, 2017: Uncertainties in historical changes and future projections of drought. Part I: Estimates of historical drought changes. Clim. Change, 144, 519–533, doi:10.1007/s10584-016-1705-2.
261. Páscoa, P., C.M. Gouveia, A. Russo, and R.M. Trigo, 2017: Drought trends in the Iberian Peninsula over the last 112 years. Adv. Meteorol., 2017, 1–13, doi:10.1155/2017/4653126.
262. Masih, I., S. Maskey, F.E.F. Mussá, and P. Trambauer, 2014: A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol. Earth Syst. Sci., 18, 3635–3649, doi:10.5194/hess-18-3635-2014.
263. Dai, A., and T. Zhao, 2017: Uncertainties in historical changes and future projections of drought. Part I: Estimates of historical drought changes. Clim. Change, 144, 519–533, doi:10.1007/s10584-016-1705-2.
264. Wang, Z. et al., 2017e: Does drought in China show a significant decreasing trend from 1961 to 2009? Sci. Total Environ., 579, 314–324, doi:10.1016/j.scitotenv.2016.11.098.
265. Fu, R. et al., 2013: Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl. Acad. Sci., 110, 18110–18115, doi:10.1073/pnas.1302584110.
266. Espinoza, J.C., J. Ronchail, J.A. Marengo, and H. Segura, 2018: Contrasting North–South changes in Amazon wet-day and dry-day frequency and related atmospheric features (1981–2017). Climate Dynamics, 52, 5413–5430, doi:10.1007/s00382-018-4462-2.
267. Ramarao, M.V.S., J. Sanjay, and R. Krishnan, 2016: Modulation of summer monsoon sub-seasonal surface air temperature over India by soil moisture-temperature coupling. Mausam, 67, 53–66.
268. Dai, A., and T. Zhao, 2017: Uncertainties in historical changes and future projections of drought. Part I: Estimates of historical drought changes. Clim. Change, 144, 519–533, doi:10.1007/s10584-016-1705-2.
269. Spinoni, J. et al., 2019: A new global database of meteorological drought events from 1951 to 2016. J. Hydrol. Reg. Stud., 22, 100593, doi:10.1016/j.ejrh.2019.100593.
270. Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J.Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5°C of Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I.Gomis, E. Lonnoy, T.Maycock, M.Tignor, and T. Waterfield (eds.)]. In press, pp. 175–311.
271. Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: 1. Uncertainty in climate change projections: The role of internal variability. Clim. Dyn., 38, 527–546, doi:10.1007/s00382-010-0977-x.
272. Liu, Z., 2012: Dynamics of interdecadal climate variability: A historical perspective. J. Clim., 25, 1963–1995, doi:10.1175/2011JCLI3980.1.
273. Christensen, J.H. et al., 2013: Climate phenomena and their relevance for future regional climate change. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
274. Hegerl, G.C. et al., 2015: Challenges in quantifying changes in the global water cycle. Bull. Am. Meteorol. Soc., 96, 1097–1115, doi:10.1175/BAMS-D-13-00212.1.
275. Newman, M. et al., 2016: The Pacific Decadal Oscillation, revisited. J. Clim., 29, 4399–4427, doi:10.1175/JCLI-D-15-0508.1.
276. Cai, W. et al., 2014a: Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Chang., 4, 111–116, doi:10.1038/nclimate2100.
277. Cai, W. et al., 2015: Increased frequency of extreme La Niña events under greenhouse warming. Nat. Clim. Chang., 5, 132–137, doi:10.1038/nclimate2492.
278. Christensen, J.H. et al., 2013: Climate phenomena and their relevance for future regional climate change. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
279. Cai, W. et al., 2014b: Increased frequency of extreme Indian ocean dipole events due to greenhouse warming. Nature, 510, 254–258, doi:10.1038/nature13327.
280. Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J.Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5°C of Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I.Gomis, E. Lonnoy, T.Maycock, M.Tignor, and T. Waterfield (eds.)]. In press, pp. 175–311.
281. Teskey, R. et al., 2015: Responses of tree species to heat waves and extreme heat events. Plant Cell Environ., 38, 1699–1712, doi:10.1111/pce.12417.
282. Teuling, A.J. et al., 2010: Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci., 3, 722–727, doi:10.1038/ngeo950.
283. Babst, F. et al., 2012: 500 years of regional forest growth variability and links to climatic extreme events in Europe. Environ. Res. Lett., 7, 45705, doi:10.1088/1748-9326/7/4/045705.
284. Erşahin, S., B.C. Bilgili, Ü. Dikmen, and I. Ercanli, 2016: Net primary productivity of Anatolian forests in relation to climate, 2000–2010. For. Sci., 62, 698–709, doi:10.5849/forsci.15-171.
285. Frank, D. et al., 2015a: Effects of climate extremes on the terrestrial carbon cycle: Concepts, processes and potential future impacts. Glob. Chang. Biol., 21, 2861–2880, doi:10.1111/gcb.12916.
286. McDowell, N.G., and C.D. Allen, 2015: Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Chang., 5, 669–672, doi:10.1038/nclimate2641.
287. Johnstone, J.F. et al., 2016: Changing disturbance regimes, ecological memory, and forest resilience. Front. Ecol. Environ., 14, 369–378, doi:10.1002/fee.1311.
288. Stevens-Rumann, C.S. et al., 2018: Evidence for declining forest resilience to wildfires under climate change. Ecol. Lett., 21, 243–252, doi:10.1111/ele.12889.
289. Neuvonen, S., P. Niemela, and T. Virtanen, 1999: Climatic change and insect outbreaks in boreal forests: The role of winter temperatures. Ecological Bulletins., 47, 63–67, http://www.jstor.org/stable/20113228 .
290. Breshears, D.D. et al., 2005: Regional vegetation die-off in response to global-change-type drought. Proc. Natl. Acad. Sci., 102, 15144–15148, doi:10.1073/pnas.0505734102.
291. Berg, E.E., J. David Henry, C.L. Fastie, A.D. De Volder, and S.M. Matsuoka, 2006: Spruce beetle outbreaks on the Kenai Peninsula, Alaska, and Kluane National Park and Reserve, Yukon Territory: Relationship to summer temperatures and regional differences in disturbance regimes. For. Ecol. Manage., 227, 219–232, doi:10.1016/j.foreco.2006.02.038.
292. Soja, A.J. et al., 2007: Climate-induced boreal forest change: Predictions versus current observations. Glob. Planet. Change, 56, 274–296, doi:10.1016/j.gloplacha.2006.07.028.
293. Kurz, W.A. et al., 2008: Mountain pine beetle and forest carbon feedback to climate change. Nature, 452, 987–990, doi:10.1038/nature06777.
294. Allen, C.D. et al., 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage., 259, 660–684, doi:10.1016/j.foreco.2009.09.001.
295. Beer, C. et al., 2010: Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329, 834–838, doi:10.1126/science.1184984.
296. Bond-Lamberty, B. and A. Thomson, 2010: Temperature-associated increases in the global soil respiration record. Nature, 464, 579–582, doi:10.1038/nature08930.
297. Reichstein, M. et al., 2013: Climate extremes and the carbon cycle. Nature, 500, 287–295, doi:10.1038/nature12350.
298. Von Buttlar, J. et al., 2018: Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: A systematic assessment across ecosystems and climate zones. Biogeosciences, 15, 1293–1318, doi:10.5194/bg-15-1293-2018.
299. Zhu, J., M. Zhang, Y. Zhang, X. Zeng, and X. Xiao, 2018: Response of tropical terrestrial gross primary production to the super El Niño event in 2015. J. Geophys. Res. Biogeosciences, 123, 3193–3203, doi:10.1029/2018JG004571.
300. Ciais, P. et al., 2005: Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529–533, doi:10.1038/nature03972.
301. Phillips, O.L. et al., 2009: Drought sensitivity of the Amazon Rainforest. Science, 323, 1344–1347, doi:10.1126/science.1164033.
302. Wolf, S. et al., 2016b: Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl. Acad. Sci., 113, 5880–5885, doi:10.1073/pnas.1519620113.
303. Ummenhofer, C.C. and G.A. Meehl, 2017: Extreme weather and climate events with ecological relevance: A review. Philos. Trans. R. Soc. B Biol. Sci., 372, 20160135, doi:10.1098/rstb.2016.0135.
304. Von Buttlar, J. et al., 2018: Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: A systematic assessment across ecosystems and climate zones. Biogeosciences, 15, 1293–1318, doi:10.5194/bg-15-1293-2018.
305. Reichstein, M. et al., 2013: Climate extremes and the carbon cycle. Nature, 500, 287–295, doi:10.1038/nature12350.
306. Frank, D. et al., 2015a: Effects of climate extremes on the terrestrial carbon cycle: Concepts, processes and potential future impacts. Glob. Chang. Biol., 21, 2861–2880, doi:10.1111/gcb.12916.
307. Davidson, E.A. et al., 2012: The Amazon basin in transition. Nature, 481, 321–328, doi:10.1038/nature10717.
308. Brando, P.M. et al., 2008: Drought effects on litterfall, wood production and belowground carbon cycling in an Amazon forest: results of a throughfall reduction experiment. Philos. Trans. R. Soc. B Biol. Sci., 363, 1839–1848, doi:10.1098/rstb.2007.0031.
309. da Costa, A.C.L. et al., 2010: Effect of 7 yr of experimental drought on vegetation dynamics and biomass storage of an eastern Amazonian rainforest. New Phytol., 187, 579–591, doi:10.1111/j.1469-8137.2010.03309.x.
310. Ficklin, D.L., and K.A. Novick, 2017: Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res., 122, 2061–2079, doi:10.1002/2016JD025855.
311. Berg, A. and J. Sheffield, 2018: Climate change and drought: The soil moisture perspective. Curr. Clim. Chang. Reports, 4, 180–191, doi:10.1007/s40641-018-0095-0.
312. Cook, B.I., J.S. Mankin, and K.J. Anchukaitis, 2018: Climate change and drought: From past to future. Curr. Clim. Chang. Reports, 4, 164–179, doi:10.1007/s40641-018-0093-2.
313. Dai, A., T. Zhao, and J. Chen, 2018: Climate change and drought: A precipitation and evaporation perspective. Curr. Clim. Chang. Reports, 4, 301–312, doi:10.1007/s40641-018-0101-6.
314. Engelbrecht, F. et al., 2015: Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett., 10, 85004, doi:10.1088/1748-9326/10/8/085004.
315. Ramarao, M.V.S., R. Krishnan, J. Sanjay, and T.P. Sabin, 2015: Understanding land surface response to changing South Asian monsoon in a warming climate. Earth Syst. Dyn., 6, 569–582, doi:10.5194/esd-6-569-2015.
316. Grillakis, M.G., 2019: Increase in severe and extreme soil moisture droughts for Europe under climate change. Sci. Total Environ., 660, 1245–1255, doi:10.1016/j.scitotenv.2019.01.001.
317. Dai, A., 2011: Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Chang., 2, 45–65, doi:10.1002/wcc.81.
318. Prudhomme, C. et al., 2014: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment. Proc. Natl. Acad. Sci., 111, 3262–3267, doi:10.1073/pnas.1222473110.
319. Trumbore, S., P. Brando, and H. Hartmann, 2015: Forest health and global change. Science, 349, 814–818, doi:10.1126/science.aac6759.
320. Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84. doi:10.1038/nature16467.
321. Mosley, L.M., 2015: Drought impacts on the water quality of freshwater systems; review and integration. Earth-Science Rev., 140, 203–214, doi:10.1016/j.earscirev.2014.11.010.
322. Van Loon, A.F., and G. Laaha, 2015: Hydrological drought severity explained by climate and catchment characteristics. J. Hydrol., 526, 3–14, doi:10.1016/j.jhydrol.2014.10.059.
323. Xu, L. et al., 2011: Widespread decline in greenness of Amazonian vegetation due to the 2010 drought. Geophys. Res. Lett., 38, L07402, doi:10.1029/2011GL046824.
324. Zhou, L. et al., 2014: Widespread decline of Congo rainforest greenness in the past decade. Nature, 508, 86–90, doi:10.1038/nature13265.
325. Frank, D. et al., 2015b: Effects of climate extremes on the terrestrial carbon cycle: Concepts, processes and potential future impacts. Glob. Chang. Biol., 21, 2861–2880, doi:10.1111/gcb.12916.
326. Doughty, C.E. et al., 2015: Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature, 519, 78–82, doi:10.1038/nature14213.
327. Schlesinger, W.H. et al., 2016: Forest biogeochemistry in response to drought. Glob. Chang. Biol., 22, 2318–2328, doi:10.1111/gcb.13105.
328. Frank, D. et al., 2015a: Effects of climate extremes on the terrestrial carbon cycle: Concepts, processes and potential future impacts. Glob. Chang. Biol., 21, 2861–2880, doi:10.1111/gcb.12916.
329. Coumou, D. and S. Rahmstorf, 2012: A decade of weather extremes. Nat. Clim. Chang., 2, 491–496, doi:10.1038/nclimate1452.
330. Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, 2012: Changes in Climate Extremes and Their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109–230.
331. Trenberth, K.E., 2012: Framing the way to relate climate extremes to climate change. Clim. Change, 115, 283–290, doi:10.1007/s10584-012-0441-5.
332. Westra, S., L.V. Alexander, and F.W. Zwiers, 2013: Global increasing trends in annual maximum daily precipitation. J. Clim., 26, 3904–3918, doi:10.1175/JCLI-D-12-00502.1.
333. Espinoza, J.C. et al., 2014: The extreme 2014 flood in south-western Amazon basin: The role of tropical-subtropical South Atlantic SST gradient. Environ. Res. Lett., 9, 124007, doi:10.1088/1748-9326/9/12/124007.
334. Guhathakurta, P., D.S. Pai, and M.N. Rajeevan, 2017: Variability and trends of extreme rainfall and rainstorms. In: Observed Climate Variability and Change over the Indian Region [Rajeevan, M. and S. Nayak (eds.)]. Springer, Singapore, pp. 37–49.
335. Taylor, C.M. et al., 2017: Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature, 544, 475–478, doi:10.1038/nature22069.
336. Thompson, V. et al., 2017: High risk of unprecedented UK rainfall in the current climate. Nat. Commun., 8, 107, doi:10.1038/s41467-017-00275-3.
337. Zilli, M.T., L.M.V. Carvalho, B. Liebmann, and M.A. Silva Dias, 2017: A comprehensive analysis of trends in extreme precipitation over southeastern coast of Brazil. Int. J. Climatol., 37, 2269–2279, doi:10.1002/joc.4840.
338. Fischer, E.M. and R. Knutti, 2014: Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys. Res. Lett., 41, 547–554, doi:10.1002/2013GL058499.
339. Espinoza, J.C., J. Ronchail, J.A. Marengo, and H. Segura, 2018: Contrasting North–South changes in Amazon wet-day and dry-day frequency and related atmospheric features (1981–2017). Climate Dynamics, 52, 5413–5430, doi:10.1007/s00382-018-4462-2.
340. Fischer, E.M., U. Beyerle, and R. Knutti, 2013: Robust spatially aggregated projections of climate extremes. Nat. Clim. Chang., 3, 1033–1038, doi:10.1038/nclimate2051.
341. Lehmann, J., D. Coumou, and K. Frieler, 2015: Increased record-breaking precipitation events under global warming. Clim. Change, 132, 501–515, doi:10.1007/s10584-015-1434-y.
342. Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J.Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5°C of Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I.Gomis, E. Lonnoy, T.Maycock, M.Tignor, and T. Waterfield (eds.)]. In press, pp. 175–311.
343. Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nat. Clim. Chang., 7, 460–462, doi:10.1038/nclimate3320.
344. Min, S.K., X. Zhang, F.W. Zwiers, and G.C. Hegerl, 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378–381, doi:10.1038/ nature09763.
345. Pall, P. et al., 2011: Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature, 470, 382–385, doi:10.1038/nature09762.
346. Sippel, S. and F.E.L. Otto, 2014: Beyond climatological extremes – Assessing how the odds of hydrometeorological extreme events in South-East Europe change in a warming climate. Clim. Change, 125, 381–398, doi:10.1007/s10584-014-1153-9.
347. Trenberth, K.E., J.T. Fasullo, and T.G. Shepherd, 2015: Attribution of climate extreme events. Nat. Clim. Chang., 5, 725–730, doi:10.1038/nclimate2657.
348. Krishnan, R. et al., 2016: Deciphering the desiccation trend of the South Asian monsoon hydroclimate in a warming world. Clim. Dyn., 47, 1007–1027, doi:10.1007/s00382-015-2886-5.
349. Fischer, E.M. and R. Knutti, 2014: Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys. Res. Lett., 41, 547–554, doi:10.1002/2013GL058499.
350. Christensen, J.H., and O.B. Christensen, 2003: Severe summertime flooding in Europe. Nature, 421, 805–806, doi:10.1038/421805a.
351. Pall, P., M.R. Allen, and D.A. Stone, 2007: Testing the Clausius-Clapeyron constraint on changes in extreme precipitation under CO2 warming. Clim. Dyn., 28, 351–363, doi:10.1007/s00382-006-0180-2.
352. Berg, P., C. Moseley, and J.O. Haerter, 2013: Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci., 6, 181–185, doi:10.1038/ngeo1731.
353. Wu, P., N. Christidis, and P. Stott, 2013: Anthropogenic impact on Earth’s hydrological cycle. Nat. Clim. Chang., 3, 807–810, doi:10.1038/nclimate1932.
354. Guhathakurta, P., D.S. Pai, and M.N. Rajeevan, 2017: Variability and trends of extreme rainfall and rainstorms. In: Observed Climate Variability and Change over the Indian Region [Rajeevan, M. and S. Nayak (eds.)]. Springer, Singapore, pp. 37–49.
355. Thompson, V. et al., 2017: High risk of unprecedented UK rainfall in the current climate. Nat. Commun., 8, 107, doi:10.1038/s41467-017-00275-3.
356. Taylor, C.M. et al., 2017: Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature, 544, 475–478, doi:10.1038/nature22069.
357. Zilli, M.T., L.M.V. Carvalho, B. Liebmann, and M.A. Silva Dias, 2017: A comprehensive analysis of trends in extreme precipitation over southeastern coast of Brazil. Int. J. Climatol., 37, 2269–2279, doi:10.1002/joc.4840.
358. Manola, I., B. Van Den Hurk, H. De Moel, and J.C.J.H. Aerts, 2018: Future extreme precipitation intensities based on a historic event. Hydrol. Earth Syst. Sci., 22, 3777–3788, doi:10.5194/hess-22-3777-2018.
359. O’Gorman, P.A., 2015: Precipitation extremes under climate change. Curr. Clim. Chang. Reports, 1, 49–59, doi:10.1007/s40641-015-0009-3.
360. Pfahl, S., P.A. O’Gorman, and E.M. Fischer, 2017: Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Clim. Chang., 7, 423–427, doi:10.1038/nclimate3287.
361. Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, 2012: Changes in Climate Extremes and Their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109–230.
362. Mohan, T.S., and M. Rajeevan, 2017: Past and future trends of hydroclimatic intensity over the Indian monsoon region. J. Geophys. Res. Atmos., 122, 896–909, doi:10.1002/2016JD025301.
363. Prein, A.F. et al., 2017: The future intensification of hourly precipitation extremes. Nat. Clim. Chang., 7, 48–52, doi:10.1038/nclimate3168.
364. Stott, P.A. et al., 2016: Attribution of extreme weather and climate-related events. Wiley Interdiscip. Rev. Clim. Chang., 7, 23–41, doi:10.1002/wcc.380.
365. Borodina, A., E.M. Fischer, and R. Knutti, 2017: Models are likely to underestimate increase in heavy rainfall in the extratropical regions with high rainfall intensity. Geophys. Res. Lett., 44, 7401–7409, doi:10.1002/2017GL074530.
366. Min, S.K., X. Zhang, F.W. Zwiers, and G.C. Hegerl, 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378–381, doi:10.1038/nature09763.
367. Crétat, J., B. Pohl, Y. Richard, and P. Drobinski, 2012: Uncertainties in simulating regional climate of Southern Africa: Sensitivity to physical parameterizations using WRF. Clim. Dyn., 38, 613–634, doi:10.1007/s00382-011-1055-8.
368. Rossow, W.B., A. Mekonnen, C. Pearl, and W. Goncalves, 2013: Tropical precipitation extremes. J. Clim., 26, 1457–1466, doi:10.1175/JCLI-D-11-00725.1. http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00725.1 (Accessed October 30, 2018).
369. Wehner, M.F., 2013: Very extreme seasonal precipitation in the NARCCAP ensemble: Model performance and projections. Clim. Dyn., 40, 59–80, doi:10.1007/s00382-012-1393-1.
370. Kooperman, G.J., M.S. Pritchard, and R.C.J. Somerville, 2014: The response of US summer rainfall to quadrupled CO2 climate change in conventional and superparameterized versions of the NCAR community atmosphere model. J. Adv. Model. Earth Syst., 6, 859–882, doi:10.1002/2014MS000306.
371. O’Gorman, P.A., 2015: Precipitation extremes under climate change. Curr. Clim. Chang. Reports, 1, 49–59, doi:10.1007/s40641-015-0009-3.
372. Larsen, M.A.D., J.H. Christensen, M. Drews, M.B. Butts, and J.C. Refsgaard, 2016: Local control on precipitation in a fully coupled climate-hydrology model. Sci. Rep., 6, 22927, doi:10.1038/srep22927.
373. Chawla, I., K.K. Osuri, P.P. Mujumdar, and D. Niyogi, 2018: Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrol. Earth Syst. Sci., 22, 1095–1117, doi:10.5194/hess-22-1095-2018.
374. Kooperman, G.J., M.S. Pritchard, T.A. O’Brien, and B.W. Timmermans, 2018: Rainfall from resolved rather than parameterized processes better represents the present-day and climate change response of moderate rates in the Community Atmosphere Model. J. Adv. Model. Earth Syst., 10, 971–988, doi:10.1002/2017MS001188.
375. Maher, P., G.K. Vallis, S.C. Sherwood, M.J. Webb, and P.G. Sansom, 2018: The impact of parameterized convection on climatological precipitation in atmospheric global climate models. Geophys. Res. Lett., 45, 3728–3736, doi:10.1002/2017GL076826.
376. Rowell, D.P. and R. Chadwick, 2018: Causes of the uncertainty in projections of tropical terrestrial rainfall change: East Africa. J. Clim., 31, 5977–5995, doi:10.1175/JCLI-D-17-0830.1.
377. Ban, N., J. Schmidli, and C. Schär, 2015: Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster? Geophys. Res. Lett., 42, 1165–1172, doi:10.1002/2014GL062588.
378. Prein, A.F. et al., 2015: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys., 53, 323–361, doi:10.1002/2014RG000475.
379. Kendon, E.J. et al., 2017: Do convection-permitting regional climate models improve projections of future precipitation change? Bull. Am. Meteorol. Soc., 98, 79–93, doi:10.1175/BAMS-D-15-0004.1.
380. Allan, R.P., and C. Liu, 2018: Evaluating large-scale variability and change in tropical rainfall and its extremes. In: Tropical Extremes: Natural Variability and Trends, [Venugopal, V., J. Sukhatme, R. Murtugudde, and R. Roca, (eds.)]. Elsevier, pp. 139–163.
381. Kunkel, K.E. et al., 2013: Monitoring and understanding trends in extreme storms: State of knowledge. Bull. Am. Meteorol. Soc., 94, 499–514, doi:10.1175/BAMS-D-11-00262.1.
382. O’Gorman, P.A., 2014: Contrasting responses of mean and extreme snowfall to climate change. Nature, 512, 416–418, doi:10.1038/nature13625.
383. O’Gorman, P.A., 2015: Precipitation extremes under climate change. Curr. Clim. Chang. Reports, 1, 49–59, doi:10.1007/s40641-015-0009-3.
384. Liu, J., and H. Yang, 2010: Spatially explicit assessment of global consumptive water uses in cropland: Green and blue water. J. Hydrol., 384, 187–197, doi:10.1016/j.jhydrol.2009.11.024.
385. Eekhout, J.P.C., J.E. Hunink, W. Terink, and J. de Vente, 2018: Why increased extreme precipitation under climate change negatively affects water security. Hydrol. Earth Syst. Sci. Discuss., 1–16, doi:10.5194/hess-2018-161.
386. Nainggolan, D. et al., 2012: Afforestation, agricultural abandonment and intensification: Competing trajectories in semi-arid Mediterranean agro-ecosystems. Agric. Ecosyst. Environ., 159, 90–104, doi:10.1016/j.agee.2012.06.023.
387. Favis-Mortlock, D., and D. Mullen, 2011: Soil erosion by water under future climate change. In: Soil Hydrology, Land Use and Agriculture: Measurement and Modelling [Shukla, M.K. (ed.)]. CABI, Wallingford, UK, 384–414 pp.
388. García-Ruiz, J.M., I.I. López-Moreno, S.M. Vicente-Serrano, T. Lasanta-Martínez, and S. Beguería, 2011: Mediterranean water resources in a global change scenario. Earth-Science Rev., 105, 121–139, doi:10.1016/j.earscirev.2011.01.006.
389. Li, Z. and H. Fang, 2016: Impacts of climate change on water erosion: A review. Earth-Science Rev., 163, 94–117, doi:10.1016/j.earscirev.2016.10.004.
390. Chagas, V.B.P. and P.L.B. Chaffe, 2018: The role of land cover in the propagation of rainfall into streamflow trends. Water Resour. Res., 54, 5986–6004, doi:10.1029/2018WR022947.
391. Kundzewicz, Z.W. et al., 2014: Flood risk and climate change: Global and regional perspectives. Hydrol. Sci. J., 59, 1–28, doi:10.1080/02626667.2013.857411.
392. Alfieri, L., L. Feyen, and G. Di Baldassarre, 2016: Increasing flood risk under climate change: A pan-European assessment of the benefits of four adaptation strategies. Clim. Change, 136, 507–521, doi:10.1007/s10584-016-1641-1.
393. Winsemius, H.C. et al., 2016: Global drivers of future river flood risk. Nat. Clim. Chang., 6, 381–385, doi:10.1038/nclimate2893.
394. Arnell, N.W., and S.N. Gosling, 2016: The impacts of climate change on river flood risk at the global scale. Clim. Change, 134, 387–401, doi:10.1007/s10584-014-1084-5.
395. Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth’s Futur., 5, 171–182, doi:10.1002/2016EF000485.
396. Wobus, C. et al., 2017: Climate change impacts on flood risk and asset damages within mapped 100-year floodplains of the contiguous United States. Nat. Hazards Earth Syst. Sci., 17, 2199–2211, doi:10.5194/nhess-17-2199-2017.
397. Posthumus, H. et al., 2009: Impacts of the summer 2007 floods on agriculture in England. J. Flood Risk Manag., 2, 182–189, doi:10.1111/j.1753-318X.2009.01031.x.
398. Zampieri, M., A. Ceglar, F. Dentener, and A. Toreti, 2017: Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ. Res. Lett., 12, 64008, doi:10.1088/1748-9326/aa723b.
399. Vogel, E. and R. Meyer, 2018: Climate change, climate extremes, and global food production—adaptation in the agricultural sector. Resilience, 31–49, doi:10.1016/B978-0-12-811891-7.00003-7.
401. Guan, K., B. Sultan, M. Biasutti, C. Baron, and D.B. Lobell, 2015: What aspects of future rainfall changes matter for crop yields in West Africa? Geophys. Res. Lett., 42, 8001–8010, doi:10.1002/2015GL063877.
402. Ray, D.K., J.S. Gerber, G.K. Macdonald, and P.C. West, 2015: Climate variation explains a third of global crop yield variability. Nat. Commun., 6, 5989, doi:10.1038/ncomms6989.
403. Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84. doi:10.1038/nature16467.
404. Vogel, E. et al., 2019: The effects of climate extremes on global agricultural yields. Environ. Res. Lett., doi:10.1088/1748-9326/ab154b.
405. Yellen, B. et al., 2014: Source, conveyance and fate of suspended sediments following Hurricane Irene. New England, USA. Geomorphology, 226, 124–134, doi:10.1016/j.geomorph.2014.07.028.
406. Bossio, D.A. and K.M. Scow, 1998: Impacts of carbon and flooding on soil microbial communities: Phospholipid fatty acid profiles and substrate utilization patterns. Microb. Ecol., 35, 265–278, doi:10.1007/s002489900082.
407. Niu, S. et al., 2014: Plant growth and mortality under climatic extremes: An overview. Environ. Exp. Bot., 98, 13–19, doi:10.1016/j.envexpbot.2013.10.004.
408. Barnes, C.J., C.J. van der Gast, N.P. Mcnamara, R. Rowe, and G.D. Bending, 2018: Extreme rainfall affects assembly of the root-associated fungal community. New Phytol., doi:10.1111/nph.14990.
409. Sánchez-Rodríguez, A.R., C. Nie, P.W. Hill, D.R. Chadwick, and D.L. Jones, 2019: Extreme flood events at higher temperatures exacerbate the loss of soil functionality and trace gas emissions in grassland. Soil Biol. Biochem., 130, 227–236, doi:10.1016/j.soilbio.2018.12.021.
410. Sánchez-Rodríguez, A.R., P.W. Hill, D.R. Chadwick, and D.L. Jones, 2017: Crop residues exacerbate the negative effects of extreme flooding on soil quality. Biol. Fertil. Soils, 53, 751–765, doi:10.1007/s00374-017-1214-0.
411. Kundzewicz, Z.W. et al., 2014: Flood risk and climate change: Global and regional perspectives. Hydrol. Sci. J., 59, 1–28, doi:10.1080/02626667.2013.857411.
412. Kramer, K., S.J. Vreugdenhil, and D.C. van der Werf, 2008: Effects of flooding on the recruitment, damage and mortality of riparian tree species: A field and simulation study on the Rhine floodplain. For. Ecol. Manage., 255, 3893–3903, doi:10.1016/j.foreco.2008.03.044.
413. Pawson, S.M. et al., 2013: Plantation forests, climate change and biodiversity. Biodivers. Conserv., 22, 1203–1227, doi:10.1007/s10531-013-0458-8.
414. Pilaš, I., K.-H. Feger, U. Vilhar, and A. Wahren, 2011: Multidimensionality of scales and approaches for forest–water interactions. In: Forest Management and the Water Cycle [Bredemeier, M., S. Cohen, D.L. Goldbold, E. Lode, V. Pichler, and P. Schleppi (eds.)]. Springer, Heidelberg, Germany, pp. 351–380.
415. Ellison, D. et al., 2017: Trees, forests and water: Cool insights for a hot world. Glob. Environ. Chang., 43, 51–61, doi:10.1016/j.gloenvcha.2017.01.002.
416. Gellesch, E., M.A.S. Arfin Khan, J. Kreyling, A. Jentsch, and C. Beierkuhnlein, 2017: Grassland experiments under climatic extremes: Reproductive fitness versus biomass. Environ. Exp. Bot., 144, 68–75, doi:10.1016/j.envexpbot.2017.10.007.
417. Grant, K., J. Kreyling, H. Heilmeier, C. Beierkuhnlein, and A. Jentsch, 2014: Extreme weather events and plant–plant interactions: Shifts between competition and facilitation among grassland species in the face of drought and heavy rainfall. Ecol. Res., 29, 991–1001, doi:10.1007/s11284-014-1187-5.
418. Fay, P.A., D.M. Kaufman, J.B. Nippert, J.D. Carlisle, and C.W. Harper, 2008: Changes in grassland ecosystem function due to extreme rainfall events: Implications for responses to climate change. Glob. Chang. Biol., 14, 1600–1608, doi:10.1111/j.1365-2486.2008.01605.x.
419. Frank, D. et al., 2015a: Effects of climate extremes on the terrestrial carbon cycle: Concepts, processes and potential future impacts. Glob. Chang. Biol., 21, 2861–2880, doi:10.1111/gcb.12916.
420. Knapp, A.K. et al., 2008: Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience, 58, 811–821, doi:10.1641/B580908.
421. Rich, S.M. and M. Watt, 2013: Soil conditions and cereal root system architecture: Review and considerations for linking Darwin and Weaver. J. Exp. Bot., 64, 1193–1208, doi:10.1093/jxb/ert043.
422. Philben, M. et al., 2015: Temperature, oxygen, and vegetation controls on decomposition in a James Bay peatland. Global Biogeochem. Cycles, 29, 729–743, doi:10.1002/2014GB004989.
423. Knapp, A.K., and M.D. Smith, 2001: Variation among biomes in temporal dynamics of aboveground primary production. Science, 291, 481–484, doi:10.1126/science.291.5503.481.
424. Heisler, J.L., and A.K. Knapp, 2008: Temporal coherence of aboveground net primary productivity in mesic grasslands. Ecography (Cop.)., 31, 408–416, doi:10.1111/j.0906-7590.2008.05351.x.
425. Heisler-White, J.L., J.M. Blair, E.F. Kelly, K. Harmoney, and A.K. Knapp, 2009: Contingent productivity responses to more extreme rainfall regimes across a grassland biome. Glob. Chang. Biol., 15, 2894–2904, doi:10.1111/j.1365-2486.2009.01961.x.
426. Zeppel, M.J.B., J.V. Wilks, and J.D. Lewis, 2014: Impacts of extreme precipitation and seasonal changes in precipitation on plants. Biogeosciences, 11, 3083–3093, doi:10.5194/bg-11-3083-2014.
427. Xu, H.-jie, and X.-P. Wang, 2016: Effects of altered precipitation regimes on plant productivity in the arid region of northern China. Ecol. Inform., 31, 137–146, doi:10.1016/j.ecoinf.2015.12.003.
428. Liu, W.J. et al., 2017b: Repackaging precipitation into fewer, larger storms reduces ecosystem exchanges of CO2 and H2O in a semiarid steppe. Agric. For. Meteorol., 247, 356–364, doi:10.1016/j.agrformet.2017.08.029.
429. Connor, E.W. and C.V. Hawkes, 2018: Effects of extreme changes in precipitation on the physiology of C4 grasses. Oecologia, 188, 355–365, doi:10.1007/s00442-018-4212-5.
430. Scott, A., 2000: The Pre-Quaternary history of fire. Palaeogeogr. Palaeoclimatol. Palaeoecol., 164, 281–329, doi:10.1016/S0031-0182(00)00192-9.
431. Stanne, T.M., L.L.E. Sjögren, S. Koussevitzky, and A.K. Clarke, 2009: Identification of new protein substrates for the chloroplast ATP-dependent Clp protease supports its constitutive role in Arabidopsis. Biochem. J., 417, 257–269, doi:10.1042/BJ20081146.
432. Archibald, S., C.E.R. Lehmann, J.L. Gómez-Dans, and R.A. Bradstock, 2013: Defining pyromes and global syndromes of fire regimes. Proc. Natl. Acad. Sci., 110, 6442–6447, doi:10.1073/pnas.1211466110.
433. Krawchuk, M.A., and M.A. Moritz, 2011: Constraints on global fire activity vary across a resource gradient. Ecology, 92, 121–132, doi:10.1890/09-1843.1.
434. Pausas, J.G., and S. Paula, 2012: Fuel shapes the fire-climate relationship: Evidence from Mediterranean ecosystems. Glob. Ecol. Biogeogr., 21, 1074–1082, doi:10.1111/j.1466-8238.2012.00769.x.
435. Mondal, N., and R. Sukumar, 2016: Fires in seasonally dry tropical forest: Testing the varying constraints hypothesis across a regional rainfall gradient. PLoS One, 11, e0159691.
436. Bowman, D.M.J.S. et al., 2017: Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol., 1, 0058, doi: 10.1038/s41559-016-0058.
437. Harris, R.M.B., T.A. Remenyi, G.J. Williamson, N.L. Bindoff, and D.M.J.S. Bowman, 2016: Climate–vegetation–fire interactions and feedbacks: Trivial detail or major barrier to projecting the future of the Earth system? Wiley Interdiscip. Rev. Clim. Chang., 7, 910–931, doi:10.1002/wcc.428.
438. Veraverbeke, S. et al., 2017: Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Chang., 7, 529, doi:10.1038/nclimate3329.
439. Doerr, S.H., and C. Santín, 2016: Global trends in wildfire and its impacts: Perceptions versus realities in a changing world. Philos. Trans. R. Soc. B Biol. Sci., 371, 20150345, doi:10.1098/rstb.2015.0345.
440. Marlon, J.R. et al., 2016: Reconstructions of biomass burning from sediment-charcoal records to improve data–model comparisons. Biogeosciences, 13, 3225–3244, doi:10.5194/bg-13-3225-2016.
441. Giglio, L., J.T. Randerson, and G.R. Van Der Werf, 2013: Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J. Geophys. Res. Biogeosciences, 118, 317–328, doi:10.1002/jgrg.20042.
442. Andela, N. et al., 2017: A human-driven decline in global burned area. Science, 356, 1356–1362, doi:10.1126/science.aal4108.
443. Andela, N. et al., 2017: A human-driven decline in global burned area. Science, 356, 1356–1362, doi:10.1126/science.aal4108.
444. Abatzoglou, J.T. and A.P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci., 113, 11770–11775, doi:10.1073/pnas.1607171113.
445. Ansmann, A. et al., 2018: Extreme levels of Canadian wildfire smoke in the stratosphere over central Europe on 21–22 August 2017. Atmos. Chem. Phys., 18, 11831–11845, doi:10.5194/acp-18-11831-2018.
446. Ponomarev, I.E., I.V. Kharuk, and J.K. Ranson, 2016: Wildfires dynamics in Siberian larch forests. For., 7, 125, doi:10.3390/f7060125.
447. Hanes, C.C. et al., 2018: Fire-regime changes in Canada over the last half century. Can. J. For. Res., 49, 256–269, doi:10.1139/cjfr-2018-0293.
448. Ansmann, A. et al., 2018: Extreme levels of Canadian wildfire smoke in the stratosphere over central Europe on 21–22 August 2017. Atmos. Chem. Phys., 18, 11831–11845, doi:10.5194/acp-18-11831-2018.
449. Huijnen, V. et al., 2016: Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep., 6, 26886, doi:10.1038/srep26886.
450. Chen, Y. et al., 2017: A pan-tropical cascade of fire driven by El Niño/Southern Oscillation. Nat. Clim. Chang., 7, 906–911, doi:10.1038/s41558-017-0014-8.
451. Davidson, E.A. et al., 2012: The Amazon basin in transition. Nature, 481, 321–328, doi:10.1038/nature10717.
452. Brando, P.M. et al., 2014: Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proc. Natl. Acad. Sci., 111, 6347–6352, doi:10.1073/pnas.1305499111.
453. Aragão, L.E.O.C. et al., 2018: 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun., 9, 536, doi:10.1038/s41467-017-02771-y.
454. Akagi, S.K. et al., 2011: Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos. Chem. Phys., 11, 4039–4072, doi:10.5194/acp-11-4039-2011.
455. Van Der Werf, G.R. et al., 2010: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys., 10, 11707–11735, doi:10.5194/acp-10-11707-2010.
456. Akagi, S.K. et al., 2011: Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos. Chem. Phys., 11, 4039–4072, doi:10.5194/acp-11-4039-2011.
457. Urbanski, S.P., 2014: Wildland fire emissions, carbon, and climate: Emission factors. For. Ecol. Manage., 317, 51–60, doi:10.1016/j.foreco.2013.05.045.
458. van der Werf, G.R. et al., 2017: Global fire emissions estimates during 1997–2015. Earth Syst. Sci. Data Discuss., 1–43, doi:10.5194/essd-2016-62.
459. van der Werf, G.R. et al., 2017: Global fire emissions estimates during 1997–2015. Earth Syst. Sci. Data Discuss., 1–43, doi:10.5194/essd-2016-62.
460. Doerr, S.H., and C. Santín, 2016: Global trends in wildfire and its impacts: Perceptions versus realities in a changing world. Philos. Trans. R. Soc. B Biol. Sci., 371, 20150345, doi:10.1098/rstb.2015.0345.
461. Loehman, R.A., E. Reinhardt, and K.L. Riley, 2014: Wildland fire emissions, carbon, and climate: Seeing the forest and the trees – A cross-scale assessment of wildfire and carbon dynamics in fire-prone, forested ecosystems. For. Ecol. Manage., 317, 9–19, doi:10.1016/j.foreco.2013.04.014.
462. Landry, J.S., and H.D. Matthews, 2016: Non-deforestation fire vs. fossil fuel combustion: The source of CO2 emissions affects the global carbon cycle and climate responses. Biogeosciences, 13, 2137–2149, doi:10.5194/bg-13-2137-2016.
463. Turetsky, M.R. et al., 2014: Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci., 8, 11. doi:10.1038/ngeo2325.
464. van der Werf, G.R. et al., 2017: Global fire emissions estimates during 1997–2015. Earth Syst. Sci. Data Discuss., 1–43, doi:10.5194/essd-2016-62.
465. Turetsky, M.R. et al., 2014: Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci., 8, 11. doi:10.1038/ngeo2325.
467. Bond, W.J., F.I. Woodward, G.F. Midgley, 2004: The global distribution of ecosystems in a world without fire. New Phytol., 165, 525–538, doi:10.1111/j.1469-8137.2004.01252.x.
468. Bremer, D.J., and J.M. Ham, 1999: Effect of spring burning on the surface energy balance in a tallgrass prairie. Agric. For. Meteorol., 97, 43–54, doi:10.1016/S0168-1923(99)00034-9.
469. MacDermott, H.J., R.J. Fensham, Q. Hua, and D.M.J.S. Bowman, 2016: Vegetation, fire and soil feedbacks of dynamic boundaries between rainforest, savanna and grassland. Austral Ecol., 42, 154–164, doi:10.1111/aec.12415.
470. Tepley, A.J. et al., 2018: Influences of fire–vegetation feedbacks and post-fire recovery rates on forest landscape vulnerability to altered fire regimes. J. Ecol., 106, 1925–1940, doi:10.1111/1365-2745.12950.
471. Moody, J.A., R.A. Shakesby, P.R. Robichaud, S.H. Cannon, and D.A. Martin, 2013: Current research issues related to post-wildfire runoff and erosion processes. Earth-Science Rev., 122, 10–37, doi:10.1016/j.earscirev.2013.03.004.
472. Veraverbeke, S. et al., 2012: Assessing post-fire vegetation recovery using red-near infrared vegetation indices: Accounting for background and vegetation variability. ISPRS J. Photogramm. Remote Sens., 68, 28–39, doi:10.1016/j.isprsjprs.2011.12.007.
473. Fletcher, M.-S., S.W. Wood, and S.G. Haberle, 2014: A fire-driven shift from forest to non-forest: evidence for alternative stable states? Ecology, 95, 2504–2513, doi:10.1890/12-1766.1.
474. Moritz, E.B. and D.D.A. and M.A., 2015: A minimal model of fire-vegetation feedbacks and disturbance stochasticity generates alternative stable states in grassland–shrubland–woodland systems. Environ. Res. Lett., 10, 034018, doi:10.1088/1748-9326/10/3/034018.
475. Moody, J.A., R.A. Shakesby, P.R. Robichaud, S.H. Cannon, and D.A. Martin, 2013: Current research issues related to post-wildfire runoff and erosion processes. Earth-Science Rev., 122, 10–37, doi:10.1016/j.earscirev.2013.03.004.
476. Yue, X. and N. Unger, 2018: Fire air pollution reduces global terrestrial productivity. Nat. Commun., 9, 5413, doi:10.1038/s41467-018-07921-4. https://doi.org/10.1038/s41467-018-07921-4 .
477. Pechony, O. and D.T. Shindell, 2010: Driving forces of global wildfires over the past millennium and the forthcoming century. Proc. Natl. Acad. Sci., 107, 19167–19170, doi:10.1073/pnas.1003669107.
478. Aldersley, A., S.J. Murray, and S.E. Cornell, 2011: Global and regional analysis of climate and human drivers of wildfire. Sci. Total Environ., 409, 3472–3481, doi:10.1016/j.scitotenv.2011.05.032.
479. Abatzoglou, J.T. and A.P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci., 113, 11770–11775, doi:10.1073/pnas.1607171113.
480. Fernandes, K. et al., 2017: Heightened fire probability in Indonesia in non-drought conditions: The effect of increasing temperatures. Environ. Res. Lett., 12, doi:10.1088/1748-9326/aa6884.
481. Liu, J., and H. Yang, 2010: Spatially explicit assessment of global consumptive water uses in cropland: Green and blue water. J. Hydrol., 384, 187–197, doi:10.1016/j.jhydrol.2009.11.024.
482. Jolly, W.M. et al., 2015: Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun., 6, 7537, doi:10.1038/ncomms8537.
483. Jolly, W.M. et al., 2015: Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun., 6, 7537, doi:10.1038/ncomms8537.
484. Huang, Y., S. Wu, and J.O. Kaplan, 2014: Sensitivity of global wildfire occurrences to various factors in thecontext of global change. Atmos. Environ., 121, 86–92, doi:10.1016/j.atmosenv.2015.06.002.
485. Bowman, D.M.J.S. et al., 2011: The human dimension of fire regimes on Earth. J. Biogeogr., 38, 2223–2236, doi:10.1111/j.1365-2699.2011.02595.x.
486. Knorr, W., A. Arneth, and L. Jiang, 2016a: Demographic controls of future global fire risk. Nat. Clim. Chang., 6, 781–785, doi:10.1038/nclimate2999.
487. Knorr, W., L. Jiang, and A. Arneth, 2016b: Climate, CO2 and human population impacts on global wildfire emissions. Biogeosciences, 13, 267–282, doi:10.5194/bg-13-267-2016.
488. Knorr, W., A. Arneth, and L. Jiang, 2016a: Demographic controls of future global fire risk. Nat. Clim. Chang., 6, 781–785, doi:10.1038/nclimate2999.
489. Knorr, W., L. Jiang, and A. Arneth, 2016b: Climate, CO2 and human population impacts on global wildfire emissions. Biogeosciences, 13, 267–282, doi:10.5194/bg-13-267-2016.
490. Hantson, S. et al., 2016: The status and challenge of global fire modelling. Biogeosciences, 13, 3359–3375, doi:10.5194/bg-13-3359-2016.
491. Harris, R.M.B., T.A. Remenyi, G.J. Williamson, N.L. Bindoff, and D.M.J.S. Bowman, 2016: Climate–vegetation–fire interactions and feedbacks: Trivial detail or major barrier to projecting the future of the Earth system? Wiley Interdiscip. Rev. Clim. Chang., 7, 910–931, doi:10.1002/wcc.428.
492. Moritz, M.A. et al., 2014: Learning to coexist with wildfire. Nature, 515, 58. doi:10.1038/nature13946.
493. Gharun, M., M. Possell, T.L. Bell, and M.A. Adams, 2017: Optimisation of fuel reduction burning regimes for carbon, water and vegetation outcomes. J. Environ. Manage., 203, 157–170, doi:10.1016/j.jenvman.2017.07.056.
494. Knorr, W., A. Arneth, and L. Jiang, 2016a: Demographic controls of future global fire risk. Nat. Clim. Chang., 6, 781–785, doi:10.1038/nclimate2999.
495. IPCC, 2010a: Revisiting the Use of Managed Land as a Proxy for Estimating National Anthropogenic Emissions and Removals [Eggleston H.S., N. Srivastava N., K. Tanabe, and J. Baasansuren (eds.)]. Meeting Report, 5–7 May, 2009, INPE, São José dos Campos, Brazil. IGES, Japan 2010, 56 pp.
496. Smith, P. et al., 2013a: How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Glob. Chang. Biol., 19, 2285–2302, doi:10.1111/gcb.12160.
497. Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
498. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
499. Peylin, P. et al., 2013: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions. Biogeosciences, 10, 6699–6720, doi:10.5194/bg-10-6699-2013.
500. van der Laan-Luijkx, I.T. et al., 2017: The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: Implementation and global carbon balance 2001–2015. Geosci. Model Dev., 10, 2785–2800, doi:10.5194/gmd-10-2785-2017.
501. Saeki, T., and P.K. Patra, 2017: Implications of overestimated anthropogenic CO2 emissions on East Asian and global land CO2 flux inversion. Geosci. Lett., 4, 9, doi:10.1186/s40562-017-0074-7.
502. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
503. Resplandy, L. et al., 2018: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nat. Geosci., 11, 504–509, doi:10.1038/s41561-018-0151-3.
504. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
505. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
506. Hansis, E., S.J. Davis, and J. Pongratz, 2015: Relevance of methodological choices for accounting of land use change carbon fluxes. Global Biogeochem. Cycles, 29, 1230–1246, doi:10.1002/2014GB004997.
507. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
508. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
509. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
510. Smith, N.G., and J.S. Dukes, 2013: Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2. Glob. Chang. Biol., 19, 45–63, doi:10.1111/j.1365-2486.2012.02797.x.
511. Houghton, R.A. et al., 2012: Carbon emissions from land use and land-cover change. Biogeosciences, 9, 5125–5142, doi:10.5194/bg-9-5125-2012.
512. Gasser, T., and P. Ciais, 2013: A theoretical framework for the net land-to-atmosphere CO2 flux and its implications in the definition of “emissions from land-use change.” Earth Syst. Dyn., 4, 171–186, doi:10.5194/esd-4-171-2013.
513. Pongratz, J., C.H. Reick, R.A. Houghton, and J.I. House, 2014: Terminology as a key uncertainty in net land use and land cover change carbon flux estimates. Earth Syst. Dyn., 5, 177–195, doi:10.5194/esd-5-177-2014.
514. Tubiello, F.N. et al., 2015: The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012. Glob. Chang. Biol., 21, 2655–2660, doi:10.1111/gcb.12865.
515. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
516. Hansis, E., S.J. Davis, and J. Pongratz, 2015: Relevance of methodological choices for accounting of land use change carbon fluxes. Global Biogeochem. Cycles, 29, 1230–1246, doi:10.1002/2014GB004997.
517. Hurtt, G.C. et al., 2011: Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change, 109, 117–161, doi:10.1007/s10584-011-0153-2.
518. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
519. FAO, 2015: FAOSTAT. Land use emissions database. http://faostat.fao.org/site/705/default.aspx .
520. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
521. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
522. Hansis, E., S.J. Davis, and J. Pongratz, 2015: Relevance of methodological choices for accounting of land use change carbon fluxes. Global Biogeochem. Cycles, 29, 1230–1246, doi:10.1002/2014GB004997.
523. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
524. Randerson, J., G.R. van der Werf, L. Giglio, G.J. Collagtz, and P.S. Kasibhatla, 2015: Global Fire Emissions Data Base, Version 4. Oak Ridge, Tennessee, USA. Retrieved from: http://www.globalfiredata.org/index.html .
525. Hooijer, A. et al., 2010: Current and future CO2 emissions from drained peatlands in Southeast Asia. Biogeosciences, 7, 1505–1514, doi:10.5194/bg-7-1505-2010.
526. Tyukavina, A. et al., 2015: Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environ. Res. Lett., 10, 74002–74002, doi:10.1088/1748-9326/10/7/074002.
527. Harris, Z.M., R. Spake, and G. Taylor, 2015: Land use change to bioenergy: A meta-analysis of soil carbon and GHG emissions. Biomass and Bioenergy, 82, 27–39, doi:10.1016/j.biombioe.2015.05.008.
528. Achard, F. et al., 2014: Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Chang. Biol., 20, 2540–2554, doi:10.1111/gcb.12605.
529. Baccini, A. et al., 2017: Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 358, 230–234, doi:10.1126/science.aam5962.
530. Tyukavina, A. et al., 2015: Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environ. Res. Lett., 10, 74002–74002, doi:10.1088/1748-9326/10/7/074002.
531. Baccini, A. et al., 2017: Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 358, 230–234, doi:10.1126/science.aam5962.
532. Houghton, R.A. et al., 2012: Carbon emissions from land use and land-cover change. Biogeosciences, 9, 5125–5142, doi:10.5194/bg-9-5125-2012.
533. Gasser, T., and P. Ciais, 2013: A theoretical framework for the net land-to-atmosphere CO2 flux and its implications in the definition of “emissions from land-use change.” Earth Syst. Dyn., 4, 171–186, doi:10.5194/esd-4-171-2013.
534. Pongratz, J., C.H. Reick, R.A. Houghton, and J.I. House, 2014: Terminology as a key uncertainty in net land use and land cover change carbon flux estimates. Earth Syst. Dyn., 5, 177–195, doi:10.5194/esd-5-177-2014.
535. Tubiello, F.N. et al., 2015: The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012. Glob. Chang. Biol., 21, 2655–2660, doi:10.1111/gcb.12865.
536. Tyukavina, A. et al., 2017: Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013. Sci. Adv., 3, e1601047, doi:10.1126/sciadv.1601047.
537. Arneth, A. et al., 2017: Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci., 10, 79–84. doi:10.1038/ngeo2882.
538. Erb, K.-H. et al., 2018: Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature, 553, 73–76, doi:10.1038/nature25138.
539. Sanderman, J., T. Hengl, and G.J. Fiske, 2017: Soil carbon debt of 12,000 years of human land use. Proc. Natl. Acad. Sci., 114, 9575–9580, doi:10.1073/pnas.1706103114.
540. Pugh, T.A.M. et al., 2015: Simulated carbon emissions from land-use change are substantially enhanced by accounting for agricultural management. Environ. Res. Lett., 10, 124008, doi:10.1088/1748-9326/10/12/124008.
541. Pongratz, J. et al., 2018: Models Meet Data: Challenges and Opportunities in Implementing Land Management in Earth System Models. 24, 1470–11487, doi: 10.1111/gcb.13988.
542. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
543. Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
544. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
545. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
546. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
547. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
548. IPCC, 2010a: Revisiting the Use of Managed Land as a Proxy for Estimating National Anthropogenic Emissions and Removals [Eggleston H.S., N. Srivastava N., K. Tanabe, and J. Baasansuren (eds.)]. Meeting Report, 5–7 May, 2009, INPE, São José dos Campos, Brazil. IGES, Japan 2010, 56 pp.
549. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
550. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
551. FAO-FRA, 2015: The Global Forest Resources Assessment 2015. Food and Agriculture Organization of the United Nations, Rome, Italy, 244 pp.
552. FAO-FRA, 2015: The Global Forest Resources Assessment 2015. Food and Agriculture Organization of the United Nations, Rome, Italy, 244 pp.
553. Kim, D., 2014: The effect of land-use change on the net exchange rates of greenhouse gases: A meta-analytical approach. doi:10.5194/bgd-11-1053-2014.
554. Hansen, M.C. et al., 2012: Carbon emissions from land use and land-cover change. Biogeosciences, 9, 5125–5142, doi:10.5194/bg-9-5125-2012.
555. FAO-FRA, 2015: The Global Forest Resources Assessment 2015. Food and Agriculture Organization of the United Nations, Rome, Italy, 244 pp.
556. Aragão, L.E.O.C. et al., 2018: 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun., 9, 536, doi:10.1038/s41467-017-02771-y.
557. Valade, A., C. Magand, S. Luyssaert, and V. Bellassen, 2017: Sustaining the sequestration efficiency of the European forest sector. For. Ecol. Manage., 405, 44–55, doi:10.1016/j.foreco.2017.09.009.
558. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
559. Zeng, Z. et al., 2017: Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nat. Clim. Chang., 7, 432–436, doi:10.1038/nclimate3299.
560. Ballantyne, A. et al., 2017: Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration. Nat. Clim. Chang., 7, 148–152, doi:10.1038/nclimate3204.
561. Piao, S. et al., 2018: Lower land-use emissions responsible for increased net land carbon sink during the slow warming period. Nat. Geosci., 11, 739–743, doi:10.1038/s41561-018-0204-7.
562. Sitch, S. et al., 2015: Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences, 12, 653–679, doi:10.5194/bg-12-653-2015.
563. Pan, Y. et al., 2011: A large and persistent carbon sink in the world’s forests. Science, 333, 988–993, doi:10.1126/science.1201609.
564. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
565. Andela, N. et al., 2017: A human-driven decline in global burned area. Science, 356, 1356–1362, doi:10.1126/science.aal4108.
566. Arora, V.K., and J.R. Melton, 2018: Reduction in global area burned and wildfire emissions since 1930s enhances carbon uptake by land. Nat. Commun., doi:10.1038/s41467-018-03838-0.
567. Kelly, R. et al., 2013: Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proc. Natl. Acad. Sci., 110, 13055–13060, doi:10.1073/pnas.1305069110.
568. Wang, Z. et al., 2017d: Human-induced erosion has offset one-third of carbon emissions from land cover change. Nat. Clim. Chang., 7, 345–349, doi:10.1038/nclimate3263.
569. Zhang, Y. et al., 2019: increased global land carbon sink due to aerosol-induced cooling. Global Biogeochem. Cycles, 33, 439–457, doi:10.1029/2018GB006051.
570. Humphrey, V. et al., 2018: Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature, 560, 628–631, doi:10.1038/s41586-018-0424-4. http://www.nature.com/articles/s41586-018-0424-4 .
571. Green, J.K. et al., 2019: Large influence of soil moisture on long-term terrestrial carbon uptake. Nature, 565, 476–479, doi:10.1038/s41586-018-0848-x.
572. Kolus, H.R. et al., 2019: Land carbon models underestimate the severity and duration of drought’s impact on plant productivity. Sci. Rep., 9, 2758, doi:10.1038/s41598-019-39373-1.
573. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
574. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
575. Hansis, E., S.J. Davis, and J. Pongratz, 2015: Relevance of methodological choices for accounting of land use change carbon fluxes. Global Biogeochem. Cycles, 29, 1230–1246, doi:10.1002/2014GB004997.
576. Tubiello, F.N. et al., 2013: The FAOSTAT database of greenhouse gas emissions from agriculture. Environ. Res. Lett., 8, 15009, doi:10.1088/1748-9326/8/1/015009.
577. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
578. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
579. IPCC, 2010a: Revisiting the Use of Managed Land as a Proxy for Estimating National Anthropogenic Emissions and Removals [Eggleston H.S., N. Srivastava N., K. Tanabe, and J. Baasansuren (eds.)]. Meeting Report, 5–7 May, 2009, INPE, São José dos Campos, Brazil. IGES, Japan 2010, 56 pp.
580. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
581. Hansis, E., S.J. Davis, and J. Pongratz, 2015: Relevance of methodological choices for accounting of land use change carbon fluxes. Global Biogeochem. Cycles, 29, 1230–1246, doi:10.1002/2014GB004997.
582. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
583. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
584. Tubiello, F.N. et al., 2013: The FAOSTAT database of greenhouse gas emissions from agriculture. Environ. Res. Lett., 8, 15009, doi:10.1088/1748-9326/8/1/015009.
585. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
586. Bond-Lamberty, B., V.L. Bailey, M. Chen, C.M. Gough, and R. Vargas, 2018: Globally rising soil heterotrophic respiration over recent decades. Nature, 560, 80–83, doi:10.1038/s41586-018-0358-x.
587. Brienen, R.J.W. et al., 2015: Long-term decline of the Amazon carbon sink. Nature, 519, 344–348, doi:10.1038/nature14283.
588. Berdanier, A.B. and J.S. Clark, 2016: Multiyear drought-induced morbidity preceding tree death in southeastern U.S. forests. Ecol. Appl., 26, 17–23, doi:10.1890/15-0274.
589. McDowell, N. et al., 2018: Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol., 219, 851–869, doi:10.1111/nph.15027.
590. Schuur, E.A.G. et al., 2015: Climate change and the permafrost carbon feedback. Nature, 520, 171–179, doi:10.1038/nature14338.
591. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
592. Anav, A. et al., 2013: Evaluating the land and ocean components of the Global Carbon Cycle in the CMIP5 Earth System Models. J. Clim., 26, 6801–6843, doi:10.1175/JCLI-D-12-00417.1.
593. Friedlingstein, P. et al., 2006: Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim., 19, 3337–3353, doi:10.1175/JCLI3800.1.
594. Friedlingstein, P. et al., 2014: Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim., doi:10.1175/JCLI-D-12-00579.1.
595. Cox, P.M. et al., 2013: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature, 494, 341–344, doi:10.1038/nature11882.
596. Prentice, I.C., Liang, X., Medlyn, B.E. and Wang, Y.P., 2015: Reliable, robust and realistic: The three R’s of next-generation land-surface modelling. Atmos. Chem. Phys., 15, 5987–6005, doi:10.5194/acp-15-5987-2015.
597. Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
598. Jones, C.D. et al., 2016: Simulating the Earth system response to negative emissions. Environ. Res. Lett., 11, 95012, doi:10.1088/1748-9326/11/9/095012.
599. Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
600. Jackson, R.B. et al., 2017: The ecology of soil carbon: Pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst., 48, 419–445, doi:10.1146/annurev-ecolsys-112414-054234.
601. Jones, C.D. et al., 2016: Simulating the Earth system response to negative emissions. Environ. Res. Lett., 11, 95012, doi:10.1088/1748-9326/11/9/095012.
602. Nisbet, E.G. et al., 2019: Very strong atmospheric methane growth in the four years 2014–2017: Implications for the Paris Agreement. Global Biogeochem. Cycles, 33, 318–342, doi:10.1029/2018GB006009.
603. Rice, A.L. et al., 2016: Atmospheric methane isotopic record favors fossil sources flat in 1980s and 1990s with recent increase. Proc. Natl. Acad. Sci., 113, 10791–10796, doi:10.1073/pnas.1522923113.
604. Bousquet, P. et al., 2006: Contribution of anthropogenic and natural sources to atmospheric methane variability. Nature, 443, 439–443, doi:10.1038/nature05132.
605. Bousquet, P. et al., 2011: Source attribution of the changes in atmospheric methane for 2006–2008. Atmos. Chem. Phys., 11, 3689–3700, doi:10.5194/acp-11-3689-2011.
606. Pison, I., B. Ringeval, P. Bousquet, C. Prigent, and F. Papa, 2013: Stable atmospheric methane in the 2000s: Key-role of emissions from natural wetlands. Atmos. Chem. Phys., 13, 11609–11623, doi:10.5194/acp-13-11609-2013.
607. Poulter, B. et al., 2017: Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics. Environ. Res. Lett., 12, doi:10.1088/1748-9326/aa8391.
608. Taraborrelli, D. et al., 2012: Hydroxyl radical buffered by isoprene oxidation over tropical forests. Nat. Geosci., 5, 190–193, doi:10.1038/ngeo1405.
609. Prather, M.J. et al., 2017: Global atmospheric chemistry – Which air matters. Atmos. Chem. Phys., 17, 9081–9102, doi:10.5194/acp-17-9081-2017.
610. Turner, A.J., C. Frankenberg, P.O. Wennberg, and D.J. Jacob, 2017: Ambiguity in the causes for decadal trends in atmospheric methane and hydroxyl. Proc. Natl. Acad. Sci., 114, 5367–5372, doi:10.1073/pnas.1616020114.
611. Rigby, M. et al., 2013: Re-evaluation of the lifetimes of the major CFCs and Biogeosciences CH3CCl3 using atmospheric trends. Atmos. Chem. Phys., 13, 2691–2702, doi:10.5194/acp-13-2691-2013.
612. McNorton, J. et al., 2016: Role of OH variability in the stalling of the global atmospheric CH4 growth rate from 1999 to 2006. Atmos. Chem. Phys., 16, 7943–7956, doi:10.5194/acp-16-7943-2016.
613. Nisbet, E.G. et al., 2019: Very strong atmospheric methane growth in the four years 2014–2017: Implications for the Paris Agreement. Global Biogeochem. Cycles, 33, 318–342, doi:10.1029/2018GB006009.
614. Nicely, J.M. et al., 2018: Changes in global tropospheric OH expected as a result of climate change over the last several decades. J. Geophys. Res. Atmos., 123, 10,774–10,795, doi:10.1029/2018JD028388.
615. Worden, J.R., et al., 2017: Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget. Nat. Commun., 8, 1–12, doi:10.1038/s41467-017-02246-0.
616. Schaefer, H. et al., 2016: A 21st-century shift from fossil-fuel to biogenic methane emissions indicated by 13CH4. Science, 352, 80–84, doi:10.1126/science.aad2705.
617. Bergamaschi, P. et al., 2013: Atmospheric CH4 in the first decade of the 21st century: Inverse modeling analysis using SCIAMACHY satellite retrievals and NOAA surface measurements. J. Geophys. Res. Atmos., 118, 7350–7369, doi:10.1002/jgrd.50480.
618. Melton, J.R. et al., 2013: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP). Biogeosciences, 10, 753–788, doi:10.5194/bg-10-753-2013.
619. Houweling, S. et al., 2014: A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements. Atmos. Chem. Phys., 14, 3991–4012, doi:10.5194/acp-14-3991-2014.
620. Pangala, S.R. et al., 2017: Large emissions from floodplain trees close the Amazon methane budget. Nature, 552, 230–234, doi:10.1038/nature24639.
621. Wolf, J., G.R. Asrar, and T.O. West, 2017: Revised methane emissions factors and spatially distributed annual carbon fluxes for global livestock. Carbon Balance Manag., 12, 16, doi:10.1186/s13021-017-0084-y.
622. Patra, P.K. et al., 2016: Regional methane emission estimation based on observed atmospheric concentrations (2002–2012). J. Meteorol. Soc. Japan. Ser. II, 94, 91–113, doi:10.2151/jmsj.2016-006.
623. Schaefer, H. et al., 2016: A 21st-century shift from fossil-fuel to biogenic methane emissions indicated by 13CH4. Science, 352, 80–84, doi:10.1126/science.aad2705.
624. Rice, A.L. et al., 2016: Atmospheric methane isotopic record favors fossil sources flat in 1980s and 1990s with recent increase. Proc. Natl. Acad. Sci., 113, 10791–10796, doi:10.1073/pnas.1522923113.
625. Hausmann, P., R. Sussmann, and D. Smale, 2015: Contribution of oil and natural gas production to renewed increase of atmospheric methane (2007–2014): Top-down estimate from ethane and methane column observations. Atmos. Chem. Phys. Discuss., 15, 35991–36028, doi:10.5194/acpd-15-35991-2015.
626. Dlugokencky, E.J., L.P. Steele, P.M. Lang, and K.A. Masarie, 1994: The growth rate and distribution of atmospheric methane. J. Geophys. Res., 99, 17021, doi:10.1029/94JD01245.
627. Schaefer, H. et al., 2016: A 21st-century shift from fossil-fuel to biogenic methane emissions indicated by 13CH4. Science, 352, 80–84, doi:10.1126/science.aad2705.
628. USEPA, 2012: Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990–2030. Office of Atmospheric Programs, Climate Change Division, U.S. Environmental Protection Agency, Washington, DC, USA, 176 pp.
629. Tubiello, F.N. et al., 2014: Agriculture, Forestry and Other Land Use Emissions by Sources and Removals by Sinks: 1990-2011 Analysis. FAO Statistics Division Working Paper Series, ESS/14-02, 4–89 pp.
630. Janssens-Maenhout, G. et al., 2017b: Fossil CO2 & GHG Emissions of All World Countries. JRC Science for Policy Report, EUR 28766 EN. Publications Office of the European Union, Luxembourg, doi:10.2760/709792, 239 pp.
631. USEPA, 2013: Global Mitigation of Non-CO2 Greenhouse Gases: 2010–2030. EPA-430-R-13-011, United States Environmental Protection Agency Office of Atmospheric Programs (6207J), Washington, DC, USA, 410 pp.
632. Tate, K.R., 2015: Soil methane oxidation and land-use change – From process to mitigation. Soil Biol. Biochem., 80, 260–272, doi:10.1016/j.soilbio.2014.10.010.
633. Yu, L., Y. Huang, W. Zhang, T. Li, and W. Sun, 2017: Methane uptake in global forest and grassland soils from 1981 to 2010. Sci. Total Environ., 607–608, 1163–1172, doi:10.1016/j.scitotenv.2017.07.082.
634. Dutaur, L. and L.V. Verchot, 2007: A global inventory of the soil CH4 sink. Global Biogeochem. Cycles, 21, 1–9, doi:10.1029/2006GB002734.
635. Edwards, M.E., L.B. Brubaker, A. V Lozhkin, and M. Patricia, 2005: Structurally novel biomes: A response to past warming in Beringia. Ecology, 86, 1696–1703, doi:10.1890/03-0787.
636. Zhuang, Q et al., 2013: Response of global soil consumption of atmospheric methane to changes in atmospheric climate and nitrogen deposition. Global Biogeochem. Cycles, 27, 650–663, doi:10.1002/gbc.20057.
637. Zhuang, Q et al., 2013: Response of global soil consumption of atmospheric methane to changes in atmospheric climate and nitrogen deposition. Global Biogeochem. Cycles, 27, 650–663, doi:10.1002/gbc.20057.
638. Tate, K.R., 2015: Soil methane oxidation and land-use change – From process to mitigation. Soil Biol. Biochem., 80, 260–272, doi:10.1016/j.soilbio.2014.10.010.
639. Yu, L., Y. Huang, W. Zhang, T. Li, and W. Sun, 2017: Methane uptake in global forest and grassland soils from 1981 to 2010. Sci. Total Environ., 607–608, 1163–1172, doi:10.1016/j.scitotenv.2017.07.082.
640. Xu, C., H. Liu, A.P. Williams, Y. Yin, and X. Wu, 2016a: Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob. Chang. Biol., 22, 2852–2860, doi:10.1111/gcb.13224.
641. Curry, C.L., 2009: The consumption of atmospheric methane by soil in a simulated future climate. Biogeosciences, 6, 2355–2367, doi:10.5194/bgd-6-6077-2009.
642. Zhuang, Q. et al., 2006: CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern high latitudes over the 21st century. Geophys. Res. Lett., 33, 2–6, doi:10.1029/2006GL026972.
643. Wuebbles, D. and K. Hayhoe, 2002: Atmospheric methane and global change. Earth-Science Rev., 57, 177–210, doi:10.1016/S0012-8252(01)00062-9.
644. Augustin, J., J. Couwenberg, and M. Minke, 2011: Peatlands and greenhouse gases (Sec. 3.1). In: Carbon Credits From Peatland Rewetting: Climate – Biodiversity – Land Use [Parish, T.F., A.Sirin, D.Charman, H. Joosten, Minayeva and L. Silvius, M. and Stringer, (eds.)]. Global Environment Centre and Wetlands International, Kuala Lumpur, Malaysia and Wageningen, Netherlands, pp. 13–19.
645. Strack, M., and J.M. Waddington, 2008: Spatiotemporal variability in peatland subsurface methane dynamics. J. Geophys. Res. Biogeosciences, 113, doi:10.1029/2007JG000472.
646. Abdalla, M. et al., 2016: Emissions of methane from northern peatlands: A review of management impacts and implications for future management options. Ecol. Evol., 6, 7080–7102, doi:10.1002/ece3.2469.
647. Osterloh, K., N. Tauchnitz, O. Spott, J. Hepp, S. Bernsdorf, and R. Meissner, 2018: Changes of methane and nitrous oxide emissions in a transition bog in central Germany (German National Park Harz Mountains) after rewetting. Wetl. Ecol. Manag., 26, 87–102, doi:10.1007/s11273-017-9555-x.
648. Christen, A. et al., 2016: Summertime greenhouse gas fluxes from an urban bog undergoing restoration through rewetting. Mires Peat, 17, 1–24, doi:10.19189/MaP.2015.OMB.207.
649. Koskinen, M., L. Maanavilja, M. Nieminen, K. Minkkinen, and E.-S. Tuittila, 2016: High methane emissions from restored Norway spruce swamps in southern Finland over one growing season. Mires Peat, 17, 1–13, doi:10.19189/MaP.2015.OMB.202.
650. Tuittila, E.-S. et al., 2000: Methane dynamics of a restored cut-away peatland. Glob. Chang. Biol., 6, 569–581, doi:10.1046/j.1365-2486.2000.00341.x.
651. Vanselow-Algan, M. et al., 2015: High methane emissions dominated annual greenhouse gas balances 30 years after bog rewetting. Biogeosciences, 12, 4361–4371, doi:10.5194/bg-12-4361-2015.
652. Abdalla, M. et al., 2016: Emissions of methane from northern peatlands: A review of management impacts and implications for future management options. Ecol. Evol., 6, 7080–7102, doi:10.1002/ece3.2469.
653. Drösler, M., L.V. Verchot, A. Freibauer, G. Pan, A. Freibauer, C.D. Evans, R.A. Bourboniere, J.P. Alm, S. Page, F. Agus, S. Sabiham, C. Wang, N. Srivastava, L. Borheau-Chavez, J. Couwenberg, K. Hergoualc’h, A. Hooijer, J. Jauhiainen, K. Minkkinen, N. French, T. Strand, A. Sirin, R. Mickler, K. Tansey, N. Larkin, 2014. Drained Inland Organic Soils, 2013. In: Task Force on National Greenhouse Gas Inventories of the IPCC, [Hiraishi T., T. Krug, K. Tanabe, N. Srivastava, B. Jamsranjav, M. Fukuda, and T. Troxler (eds.)]. Supplement to the 2006 IPCC Guidelines: Wetlands. Hayama, Japan: Institute for Global Environmental Strategies (IGES) on behalf of the Intergovernmental Panel on Climate Change (IPCC).
654. Sirin, A.A., G. Suvorov, M. Chistotin, and M. Glagolev, 2012: Values of methane emission from drainage channels. Doosigik, 2, 1–10.
655. Sirin, A.A., G. Suvorov, M. Chistotin, and M. Glagolev, 2012: Values of methane emission from drainage channels. Doosigik, 2, 1–10.
656. Wilson, D. et al., 2016: Multiyear greenhouse gas balances at a rewetted temperate peatland. Glob. Chang. Biol., 22, 4080–4095, doi:10.1111/gcb.13325.
657. Hergoualc’h, K.A., and L.V. Verchot, 2012: Changes in soil CH4 fluxes from the conversion of tropical peat swamp forests: a meta-analysis. J. Integr. Environ. Sci., 9, 93–101, doi:10.1080/1943815X.2012.679282.
658. Janssens-Maenhout, G. et al., 2017a: EDGAR v4.3.2 global atlas of the three major greenhouse gas emissions for the period 1970–2012. Earth Syst. Sci. Data Discuss., 2017, 1–55, doi:10.5194/essd-2017-79.
659. Tubiello, F.N. et al., 2013: The FAOSTAT database of greenhouse gas emissions from agriculture. Environ. Res. Lett., 8, 15009, doi:10.1088/1748-9326/8/1/015009.
660. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
661. Wells, K.C. et al., 2018: Top-down constraints on global N2O emissions at optimal resolution: Application of a new dimension reduction technique. Atmos. Chem. Phys., 18, 735–756, doi:10.5194/acp-18-735-2018.
662. Prokopiou, M. et al., 2018: Changes in the isotopic signature of atmospheric nitrous oxide and its global average source during the last three millennia. J. Geophys. Res. Atmos., 123, 10,757–10,773, doi:10.1029/2018JD029008.
663. Snider, D.M., J.J. Venkiteswaran, S.L. Schiff, and J. Spoelstra, 2015: From the ground up: Global nitrous oxide sources are constrained by stable isotope values. PLoS One, 10, e0118954, doi:10.1371/journal.pone.0118954.
664. Fowler, D. et al., 2015: Effects of global change during the 21st century on the nitrogen cycle. Atmos. Chem. Phys., 15, 13849–13893, doi:10.5194/acp-15-13849-2015.
665. Park, S. et al., 2012: Trends and seasonal cycles in the isotopic composition of nitrous oxide since 1940. Nat. Geosci., 5, 261–265, doi:10.1038/ngeo1421.
666. Toyoda, S. et al., 2013: Decadal time series of tropospheric abundance of N2O isotopomers and isotopologues in the Northern Hemisphere obtained by the long-term observation at Hateruma Island, Japan. J. Geophys. Res. Atmos., 118, 3369–3381, doi:10.1002/jgrd.50221.
667. Snider, D.M., J.J. Venkiteswaran, S.L. Schiff, and J. Spoelstra, 2015: From the ground up: Global nitrous oxide sources are constrained by stable isotope values. PLoS One, 10, e0118954, doi:10.1371/journal.pone.0118954.
668. Prokopiou, M. et al., 2018: Changes in the isotopic signature of atmospheric nitrous oxide and its global average source during the last three millennia. J. Geophys. Res. Atmos., 123, 10,757–10,773, doi:10.1029/2018JD029008.
669. USEPA, 2013: Global Mitigation of Non-CO2 Greenhouse Gases: 2010–2030. EPA-430-R-13-011, United States Environmental Protection Agency Office of Atmospheric Programs (6207J), Washington, DC, USA, 410 pp.
670. Tubiello, F.N. et al., 2015: The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012. Glob. Chang. Biol., 21, 2655–2660, doi:10.1111/gcb.12865.
671. Janssens-Maenhout, G. et al., 2017a: EDGAR v4.3.2 global atlas of the three major greenhouse gas emissions for the period 1970–2012. Earth Syst. Sci. Data Discuss., 2017, 1–55, doi:10.5194/essd-2017-79.
672. Thompson, R.L. et al., 2014: TransCom N2O model inter-comparison – Part 2: Atmospheric inversion estimates of N2O emissions. Atmos. Chem. Phys., 14, 6177–6194, doi:10.5194/acp-14-6177-2014.
673. Saikawa, E. et al., 2014: Global and regional emissions estimates for N2O. Atmos. Chem. Phys., 14, 4617–4641, doi:10.5194/acp-14-4617-2014.
674. Tian, H. et al., 2016: The terrestrial biosphere as a net source of greenhouse gases to the atmosphere. Nature, 531, 225–228, doi:10.1038/nature16946.
675. Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
676. Buitenhuis, E.T., P. Suntharalingam, and C. Le Quéré, 2018: Constraints on global oceanic emissions of N2O from observations and models. Biogeosciences, 15, 2161–2175, doi:10.5194/bg-15-2161-2018.
677. Saikawa, E. et al., 2014: Global and regional emissions estimates for N2O. Atmos. Chem. Phys., 14, 4617–4641, doi:10.5194/acp-14-4617-2014.
678. Janssens-Maenhout, G. et al., 2017b: Fossil CO2 & GHG Emissions of All World Countries. JRC Science for Policy Report, EUR 28766 EN. Publications Office of the European Union, Luxembourg, doi:10.2760/709792, 239 pp.
679. Zhang, G. et al., 2017: Extensive and drastically different alpine lake changes on Asia’s high plateaus during the past four decades. Geophys. Res. Lett., 44, 252–260, doi:10.1002/2016GL072033.
680. Janssens-Maenhout, G. et al., 2017b: Fossil CO2 & GHG Emissions of All World Countries. JRC Science for Policy Report, EUR 28766 EN. Publications Office of the European Union, Luxembourg, doi:10.2760/709792, 239 pp.
681. Saikawa, E. et al., 2014: Global and regional emissions estimates for N2O. Atmos. Chem. Phys., 14, 4617–4641, doi:10.5194/acp-14-4617-2014.
682. Saikawa, E. et al., 2014: Global and regional emissions estimates for N2O. Atmos. Chem. Phys., 14, 4617–4641, doi:10.5194/acp-14-4617-2014.
683. Wells, K.C. et al., 2018: Top-down constraints on global N2O emissions at optimal resolution: Application of a new dimension reduction technique. Atmos. Chem. Phys., 18, 735–756, doi:10.5194/acp-18-735-2018.
684. de Klein, C.A.M., M.A. Shepherd, and T.J. van der Weerden, 2014: Nitrous oxide emissions from grazed grasslands: Interactions between the N cycle and climate change – A New Zealand case study. Curr. Opin. Environ. Sustain., 9–10, 131–139, doi:10.1016/j.cosust.2014.09.016.
685. Tian, H. et al., 2018: The Global N2O Model Intercomparison Project. Bull. Am. Meteorol. Soc., 99, 1231–1251, doi:10.1175/BAMS-D-17-0212.1.
686. Chadwick, D.R. et al., 2018: The contribution of cattle urine and dung to nitrous oxide emissions: Quantification of country specific emission factors and implications for national inventories. Sci. Total Environ., 635, 607–617, doi:10.1016/j.scitotenv.2018.04.152.
687. Dangal, S.R.S. et al., 2019: Global nitrous oxide emissions from pasturelands and rangelands: Magnitude, spatiotemporal patterns, and attribution. Global Biogeochem. Cycles, 33, 200–222, doi:10.1029/2018GB006091.
688. Cardenas, L.M. et al., 2019: Nitrogen use efficiency and nitrous oxide emissions from five UK fertilised grasslands. Sci. Total Environ., 661, 696–710, doi:10.1016/j.scitotenv.2019.01.082.
689. Cardenas, L.M. et al., 2019: Nitrogen use efficiency and nitrous oxide emissions from five UK fertilised grasslands. Sci. Total Environ., 661, 696–710, doi:10.1016/j.scitotenv.2019.01.082.
690. Davidson, E.A., 2009: The contribution of manure and fertilizer nitrogen to atmospheric nitrous oxide since 1860. Nat. Geosci., 2, 659–662, doi:10.1038/ngeo608.
691. Oenema, O. et al., 2014: Reducing nitrous oxide emissions from the global food system. Curr. Opin. Environ. Sustain., 9–10, 55–64, doi:10.1016/j.cosust.2014.08.003.
692. Dangal, S.R.S. et al., 2019: Global nitrous oxide emissions from pasturelands and rangelands: Magnitude, spatiotemporal patterns, and attribution. Global Biogeochem. Cycles, 33, 200–222, doi:10.1029/2018GB006091.
693. IPCC, 2003: Good Practice Guidance for Land Use, Land-Use Change and Forestry. [Penman, J., M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K. Tanabe and F. Wagner (eds.)]. Institute for Global Environmental Strategies (IGES) for the IPCC, Hayama, Kanagawa, Japan, 590 pp.
694. Shcherbak, I., N. Millar, and G.P. Robertson, 2014: Global metaanalysis of the nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen. Proc. Natl. Acad. Sci., 111, 9199–9204, doi:10.1073/pnas.1322434111.
695. van Lent, J., K. Hergoualc’h, and L.V. Verchot, 2015: Reviews and syntheses: Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: A meta-analysis. Biogeosciences, 12, 7299–7313, doi:10.5194/bg-12-7299-2015.
696. Satria, 2017: Substantial N2O emissions from peat decomposition and N fertilization in an oil palm plantation exacerbated by hotspots. Environ. Res. Lett, 12, doi:10.1088/1748-9326/aa80f1.
697. McDaniel, M.D., D. Saha, M.G. Dumont, M. Hernández, and M.A. Adams, 2019: The effect of land-use change on soil CH4 and N2O Fluxes: A global meta-analysis. Ecosystems, doi:10.1007/s10021-019-00347-z.
698. van Lent, J., K. Hergoualc’h, and L.V. Verchot, 2015: Reviews and syntheses: Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: A meta-analysis. Biogeosciences, 12, 7299–7313, doi:10.5194/bg-12-7299-2015.
699. van Lent, J., K. Hergoualc’h, and L.V. Verchot, 2015: Reviews and syntheses: Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: A meta-analysis. Biogeosciences, 12, 7299–7313, doi:10.5194/bg-12-7299-2015.
700. van Lent, J., K. Hergoualc’h, and L.V. Verchot, 2015: Reviews and syntheses: Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: A meta-analysis. Biogeosciences, 12, 7299–7313, doi:10.5194/bg-12-7299-2015.
701. Meurer, K.H.E. et al., 2016: Direct nitrous oxide (N2O) fluxes from soils under different land use in Brazil—a critical review. Environ. Res. Lett., 11, 23001, doi:10.1088/1748-9326/11/2/023001.
702. McDaniel, M.D., D. Saha, M.G. Dumont, M. Hernández, and M.A. Adams, 2019: The effect of land-use change on soil CH4 and N2O Fluxes: A global meta-analysis. Ecosystems, doi:10.1007/s10021-019-00347-z.
703. UNEP, 2013: Drawing Down N2O to Protect Climate and the Ozone Layer: A UNEP Synthesis Report.. United Nations Environment Programme (UNEP), Nairobi, Kenya, 76 pp, http://www.unep.org/publications/ebooks/UNEPN2Oreport/ .
704. Williams, J. and P.J. Crutzen, 2010: Nitrous oxide from aquaculture. Nat. Geosci., 3, 143, doi:10.1038/ngeo804.
705. Bouwman, A.F. et al., 2013: Global trends and uncertainties in terrestrial denitrification and N2O emissions. Philos. Trans. R. Soc. B Biol. Sci., 368, 20130112–20130112, doi:10.1098/rstb.2013.0112.
706. Tian, H. et al., 2018: The Global N2O Model Intercomparison Project. Bull. Am. Meteorol. Soc., 99, 1231–1251, doi:10.1175/BAMS-D-17-0212.1.
707. Suntharalingam, P. et al., 2012: Quantifying the impact of anthropogenic nitrogen deposition on oceanic nitrous oxide. Geophys. Res. Lett., 39, 1–7, doi:10.1029/2011GL050778.
708. van Lent, J., K. Hergoualc’h, and L.V. Verchot, 2015: Reviews and syntheses: Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: A meta-analysis. Biogeosciences, 12, 7299–7313, doi:10.5194/bg-12-7299-2015.
709. Houghton, R.A. et al., 2012: Carbon emissions from land use and land-cover change. Biogeosciences, 9, 5125–5142, doi:10.5194/bg-9-5125-2012.
710. Hansis, E., S.J. Davis, and J. Pongratz, 2015: Relevance of methodological choices for accounting of land use change carbon fluxes. Global Biogeochem. Cycles, 29, 1230–1246, doi:10.1002/2014GB004997.
711. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
712. Randerson, J., G.R. van der Werf, L. Giglio, G.J. Collagtz, and P.S. Kasibhatla, 2015: Global Fire Emissions Data Base, Version 4. Oak Ridge, Tennessee, USA. Retrieved from: http://www.globalfiredata.org/index.html .
713. Sitch, S. et al. 2005: Impacts of future land cover changes on atmospheric CO2 and climate. Global Biogeochem. Cycles, 19, 1–15, doi:10.1029/2004GB002311.
714. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data, 10, 2141–2194, doi:10.5194/essd-10-2141-2018.
715. Pongratz, J., C.H. Reick, T. Raddatz, and M. Claussen, 2010: Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett., 37, 1–5, doi:10.1029/2010GL043010.
716. Friedlingstein, P. et al., 2014: Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim., doi:10.1175/JCLI-D-12-00579.1.
717. Lawrence, D.M. et al., 2016: The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: Rationale and experimental design. Geosci. Model Dev., 9, 2973–2998, doi:10.5194/gmd-9-2973-2016.
718. Zarin, D.J. et al., 2016: Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Chang. Biol., 22, 1336–1347, doi:10.1111/gcb.13153.
719. Baccini, A. et al., 2012: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Chang., 2, 182–185, doi:10.1038/nclimate1354.
720. Saatchi, S.S. et al., 2011: Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci., 108, 9899–9904, doi:10.1073/pnas.1019576108.
721. Tyukavina, A. et al., 2015: Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environ. Res. Lett., 10, 74002–74002, doi:10.1088/1748-9326/10/7/074002.
722. Harris, Z.M., R. Spake, and G. Taylor, 2015: Land use change to bioenergy: A meta-analysis of soil carbon and GHG emissions. Biomass and Bioenergy, 82, 27–39, doi:10.1016/j.biombioe.2015.05.008.
723. Baccini, A. et al., 2012: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Chang., 2, 182–185, doi:10.1038/nclimate1354.
724. Baccini, A. et al., 2017: Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 358, 230–234, doi:10.1126/science.aam5962.
725. Mitchard, E.T.A. et al., 2013: Uncertainty in the spatial distribution of tropical forest biomass: A comparison of pan-tropical maps. Carbon Balance Manag., 8, 10, doi:10.1186/1750-0680-8-10.
726. Saatchi, S. et al., 2015: Seeing the forest beyond the trees. Glob. Ecol. Biogeogr., 24, 606–610, doi:10.1111/geb.12256.
727. Avitabile, V., M. Herold, G.B.M. Heuvelink, A.G. P, de B. Jong, and V.G. Laurin, 2016: An integrated pan-tropical biomass map using multiple reference datasets. Glob. Chang. Biol., 22, 1406–1420.
728. Gatti, L.V. et al., 2014: Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature, 506, 76–80, doi:10.1038/nature12957.
729. Liu, J. et al., 2017a: Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science, 358, eaam5690, doi:10.1126/science.aam5690.
730. van der Laan-Luijkx, I.T. et al., 2017: The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: Implementation and global carbon balance 2001–2015. Geosci. Model Dev., 10, 2785–2800, doi:10.5194/gmd-10-2785-2017.
731. Tubiello, F.N. et al., 2013: The FAOSTAT database of greenhouse gas emissions from agriculture. Environ. Res. Lett., 8, 15009, doi:10.1088/1748-9326/8/1/015009.
732. FAO, 2015: FAOSTAT. Land use emissions database. http://faostat.fao.org/site/705/default.aspx .
733. Rossi, S., F.N. Tubiello, P. Prosperi, M. Salvatore, H. Jacobs, R. Biancalani, J.I. House, and L. Boschetti, 2016: FAOSTAT estimates of greenhouse gas emissions from biomass and peat fires. Clim. Change, 135, 699–711, doi:10.1007/s10584-015-1584-y.
734. Federici, S., F.N. Tubiello, M. Salvatore, H. Jacobs, and J. Schmidhuber, 2015: New estimates of CO2 forest emissions and removals: 1990–2015. For. Ecol. Manage., 352, 89–98, doi:10.1016/j.foreco.2015.04.022.
735. IPCC, 2003: Good Practice Guidance for Land Use, Land-Use Change and Forestry. [Penman, J., M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K. Tanabe and F. Wagner (eds.)]. Institute for Global Environmental Strategies (IGES) for the IPCC, Hayama, Kanagawa, Japan, 590 pp.
736. IPCC, 2006: 2006 IPCC Guidelines for National Greenhouse Gas Inventories – A Primer. [Eggleston H.S., K. Miwa, N. Srivastava, and K. Tanabe (eds.)]. Institute for Global Environmental Strategies (IGES) for the Intergovernmental Panel on Climate Change. IGES, Japan, 20 pp.
737. Cowie, A.L., M.U.F. Kirschbaum, and M. Ward, 2007: Options for including all lands in a future greenhouse gas accounting framework. Env. Sci Policy, 10, 306–321.
738. Lee, D. and Sanz, M.J., 2017: UNFCCC Accounting for Forests: What’s In and What’s Out of NDCs and REDD+. Climate and Land Use Alliance, 16 pp.
739. Canadell, J.G. et al., 2007: Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl. Acad. Sci., 104, 18866–18870, doi:10.1073/pnas.0702737104.
740. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
741. Le Quéré, C. et al., 2017: Global Carbon Budget 2017. Earth Syst. Sci. Data Discuss., 10, 1–79, doi:10.5194/essd-2017-123.
742. Hoffman, F.M. et al., 2014: Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models. J. Geophys. Res. Biogeosciences, 119, 141–162, doi:10.1002/2013JG002381.
743. Swann, A.L.S., F.M. Hoffman, C.D. Koven, and J.T. Randerson, 2016: Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl. Acad. Sci., 113, 10019–10024, doi:10.1073/pnas.1604581113.
744. Field, C.B., R.B. Jackson, and H.A. Mooney, 1995: Stomatal responses to increased CO2: implications from the plant to the global scale. Plant. Cell Environ., 18, 1214–1225, doi:10.1111/j.1365-3040.1995.tb00630.x.
745. Donohue, R.J., M.L. Roderick, T.R. McVicar, and G.D. Farquhar, 2013: Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett., 40, 3031–3035, doi:10.1002/grl.50563.
746. Körner, C., 2006: Plant CO2 responses: An issue of definition, time and resource supply. New Phytol., 172, 393–411, doi:10.1111/j.1469-8137.2006.01886.x.
747. Peñuelas, J. et al., 2017: Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol., 1, 1438–1445, doi:10.1038/s41559-017-0274-8.
748. Friend, A.D. et al., 2014a: Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl. Acad. Sci. U.S.A., 111, 3280–3285, doi:10.1073/pnas.1222477110.
749. Terrer, C. et al., 2017: Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition. New Phytol., 2, 507–522, doi:10.1111/nph.14872.
750. De Kauwe, M.G., T.F. Keenan, B.E. Medlyn, I.C. Prentice, and C. Terrer, 2016: Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat. Clim. Chang., 6, 892–893, doi:10.1038/nclimate3105.
751. Farrior, C.E., I. Rodriguez-Iturbe, R. Dybzinski, S.A. Levin, and S.W. Pacala, 2015: Decreased water limitation under elevated CO2 amplifies potential for forest carbon sinks. Proc. Natl. Acad. Sci., 112, 7213–7218, doi:10.1073/pnas.1506262112.
752. Keenan, T.F. et al., 2017: Corrigendum: Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun., 8, 16137, doi:10.1038/ncomms16137.
753. Sulman, B.N. et al., 2019: Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Global Biogeochem. Cycles, 33, 501–523, doi:10.1029/2018GB005973.
754. Norby, R.J., J.M. Warren, C.M. Iversen, B.E. Medlyn, and R.E. McMurtrie, 2010: CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl. Acad. Sci., 107, 19368–19373, doi:10.1073/pnas.1006463107.
755. Körner, C., 2015: Paradigm shift in plant growth control. Curr. Opin. Plant Biol., 25, 107–114, doi:10.1016/j.pbi.2015.05.003.
756. Feng, Z.Z. et al., 2015: Constraints to nitrogen acquisition of terrestrial plants under elevated CO2. Glob. Chang. Biol., 21, 3152–3168, doi:10.1111/gcb.12938.
757. Paschalis, A., G.G. Katul, S. Fatichi, S. Palmroth, and D. Way, 2017: On the variability of the ecosystem response to elevated atmospheric CO2 across spatial and temporal scales at the Duke Forest FACE experiment. Agric. For. Meteorol., 232, 367–383, doi:10.1016/j.agrformet.2016.09.003.
758. Terrer, C. et al., 2017: Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition. New Phytol., 2, 507–522, doi:10.1111/nph.14872.
759. Paschalis, A., G.G. Katul, S. Fatichi, S. Palmroth, and D. Way, 2017: On the variability of the ecosystem response to elevated atmospheric CO2 across spatial and temporal scales at the Duke Forest FACE experiment. Agric. For. Meteorol., 232, 367–383, doi:10.1016/j.agrformet.2016.09.003.
760. Berry, J.A., D.J. Beerling, and P.J. Franks, 2010: Stomata: Key players in the earth system, past and present. Curr. Opin. Plant Biol., 13, 233–240, doi:10.1016/j.pbi.2010.04.013.
761. Ainsworth, E.A., and A. Rogers, 2007: The response of photosynthesis and stomatal conductance to rising [CO2]: Mechanisms and environmental interactions. Plant, Cell Environ., 30, 258–270, doi:10.1111/j.1365-3040.2007.01641.x.
762. Keenan, T.F. et al., 2013: Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature, 499, 324–327, doi:10.1038/nature12291.
763. Laguë, M.M. and A.L.S. Swann, 2016: Progressive midlatitude afforestation: Impacts on clouds, global energy transport, and precipitation. J. Clim., 29, 5561–5573, doi:10.1175/jcli-d-15-0748.1.
764. Wolf, A., W.R.L. Anderegg, and S.W. Pacala, 2016a: Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proc. Natl. Acad. Sci., 113, E7222–E7230, doi:10.1073/pnas.1615144113.
765. Anderegg, W.R.L. et al., 2018a: Woody plants optimise stomatal behaviour relative to hydraulic risk. Ecol. Lett., 0, doi:10.1111/ele.12962.
766. Battipaglia, G. et al., 2013: Elevated CO2 increases tree-level intrinsic water use efficiency: Insights from carbon and oxygen isotope analyses in tree rings across three forest FACE sites. New Phytol., 197, 544–554, doi:10.1111/nph.12044.
767. Silva, L.C.R. and M. Anand, 2013: Probing for the influence of atmospheric CO2 and climate change on forest ecosystems across biomes. Glob. Ecol. Biogeogr., 22, 83–92, doi:10.1111/j.1466-8238.2012.00783.x.
768. van der Sleen, P. et al., 2014: No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased. Nat. Geosci., 8, 24–28, doi:10.1038/ngeo2313.
769. van der Sleen, P. et al., 2014: No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased. Nat. Geosci., 8, 24–28, doi:10.1038/ngeo2313.
770. Gedalof, Z. and A.A. Berg, 2010: Tree ring evidence for limited direct CO2 fertilization of forests over the 20th century. Global Biogeochem. Cycles, 24, n/a-n/a, doi:10.1029/2009GB003699.
771. Roy, J. et al., 2016: Elevated CO2 maintains grassland net carbon uptake under a future heat and drought extreme. Proc. Natl. Acad. Sci., 113, 6224–6229, doi:10.1073/pnas.1524527113.
772. Donohue, R.J., M.L. Roderick, T.R. McVicar, and G.D. Farquhar, 2013: Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett., 40, 3031–3035, doi:10.1002/grl.50563.
773. Morgan, J.A. et al., 2011: C4 grasses prosper as carbon dioxide eliminates desiccation in warmed semi-arid grassland. Nature, 476, 202–205, doi:10.1038/nature10274.
774. Derner, J.D. et al., 2003: Above- and below-ground responses of C3-C4species mixtures to elevated CO2 and soil water availability. Glob. Chang. Biol., 9, 452–460, doi:10.1046/j.1365-2486.2003.00579.x.
775. Reich, P.B., S.E. Hobbie, and T.D. Lee, 2014: Plant growth enhancement by elevated CO2 eliminated by joint water and nitrogen limitation. Nat. Geosci., 7, 920–924, doi:10.1038/ngeo2284.
776. Reich, P.B., S.E. Hobbie, T.D. Lee, and M.A. Pastore, 2018: Unexpected reversal of C3 versus C4 grass response to elevated CO2 during a 20-year field experiment. Science, 360, 317–320, doi:10.1126/science.aas9313.
777. Deryng, D. et al., 2016b: Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat. Clim. Chang., 6, 786–790, doi:10.1038/nclimate2995.
778. Nazemi, A. and H.S. Wheater, 2015: On inclusion of water resource management in Earth system models – Part 1: Problem definition and representation of water demand. Hydrol. Earth Syst. Sci., 19, 33–61, doi:10.5194/hess-19-33-2015.
779. Elliott, J. et al., 2014: Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl. Acad. Sci., 111, 3239–3244, doi:10.1073/pnas.1222474110.
780. Deryng, D. et al., 2016b: Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat. Clim. Chang., 6, 786–790, doi:10.1038/nclimate2995.
781. Los, S.O., 2013: Analysis of trends in fused AVHRR and MODIS NDVI data for 1982-2006: Indication for a CO2 fertilization effect in global vegetation. Global Biogeochem. Cycles, 27, 318–330, doi:10.1002/gbc.20027.
782. Sitch, S. et al., 2015: Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences, 12, 653–679, doi:10.5194/bg-12-653-2015.
783. Campbell, J.E. et al., 2017: Large historical growth in global terrestrial gross primary production. Nature, 544, 84–87, doi:10.1038/nature22030.
784. Keenan, T.F., and W.J. Riley, 2018: Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Chang., 8, 825–828, doi:10.1038/s41558-018-0258-y.
785. Schimel, D., B.B. Stephens, and J.B. Fisher, 2015: Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl. Acad. Sci., 112, 436–441, doi:10.1073/pnas.1407302112.
786. Los, S.O., 2013: Analysis of trends in fused AVHRR and MODIS NDVI data for 1982-2006: Indication for a CO2 fertilization effect in global vegetation. Global Biogeochem. Cycles, 27, 318–330, doi:10.1002/gbc.20027.
787. Shevliakova, E. et al. 2013: Historical warming reduced due to enhanced land carbon uptake. Proc. Natl. Acad. Sci., 110, 16730–16735, doi:10.1073/pnas.1314047110.
788. Chen, C. et al., 2019: China and India lead in greening of the world through land-use management. Nat. Sustain., 2, 122–129, doi:10.1038/s41893-019-0220-7.
789. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.
790. Friend, A.D. et al., 2014a: Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl. Acad. Sci. U.S.A., 111, 3280–3285, doi:10.1073/pnas.1222477110.
791. Körner, C., 2006: Plant CO2 responses: An issue of definition, time and resource supply. New Phytol., 172, 393–411, doi:10.1111/j.1469-8137.2006.01886.x.
792. Peñuelas, J. et al., 2017: Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol., 1, 1438–1445, doi:10.1038/s41559-017-0274-8.
793. Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–658.
794. Rogelj, J. et al., 2014: Disentangling the effects of CO2 and short-lived climate forcer mitigation. Proc. Natl. Acad. Sci., 111, 16325–16330, doi:10.1073/pnas.1415631111.
795. Kok, J.F., D.S. Ward, N.M. Mahowald, and A.T. Evan, 2018: Global and regional importance of the direct dust-climate feedback. Nat. Commun., 9, 241, doi:10.1038/s41467-017-02620-y.
796. Calvo, A.I. et al., 2013: Research on aerosol sources and chemical composition: Past, current and emerging issues. Atmos. Res., 120–121, 1–28, doi:10.1016/j.atmosres.2012.09.021.
797. Klimont, Z. et al., 2017: Global anthropogenic emissions of particulate matter including black carbon. Atmos. Chem. Phys., 17, 8681–8723, doi:10.5194/acp-17-8681-2017.
798. Fuzzi, S. et al., 2015a: Particulate matter, air quality and climate: Lessons learned and future needs. Atmos. Chem. Phys., 15, 8217–8299, doi:10.5194/acp-15-8217-2015.
799. Nousiainen, T., 2011: Optical modeling of mineral dust particles: A review. J. Quant. Spectrosc. Radiat. Transf., 110, 14–16, doi:10.1016/j.jqsrt.2009.02.002.
800. de Sá, S.S. et al., 2019: Contributions of biomass-burning, urban, and biogenic emissions to the concentrations and light-absorbing properties of particulate matter in central Amazonia during the dry season. Atmos. Chem. Phys. Discuss., 1–77, doi:10.5194/acp-2018-1309.
801. Suni, T. et al., 2015: The significance of land-atmosphere interactions in the Earth system – ILEAPS achievements and perspectives. Anthropocene, 12, 69–84, doi:10.1016/j.ancene.2015.12.001.
802. Andreae et al., 2002: Biogeochemical cycling of carbon, water, energy, trace gases, and aerosols in Amazonia: The LBA-EUSTACH experiments. J. Geophys. Res. D Atmos., 107, 1998–2000, doi:10.1029/2001JD000524.
803. Hodzic, A., P.S. Kasibhatla, D.S. Jo, C.D. Cappa, J.L. Jimenez, S. Madronich, and R.J. Park, 2016: Rethinking the global secondary organic aerosol (SOA) budget: Stronger production, faster removal, shorter lifetime. Atmos. Chem. Phys., 16, 7917–7941, doi:10.5194/acp-16-7917-2016.
804. Manish, S. et al., 2017: Recent advances in understanding secondary organic aerosol: Implications for global climate forcing. Rev. Geophys., 55, 509–559, doi:10.1002/2016RG000540.
805. Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–658.
806. IPCC, 2018: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, 1552 pp.
807. Prospero, J.M., P. Ginoux, O. Torres, S.E. Nicholson, and T.E. Gill, 2002: Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys., 40, 2–31, doi:10.1029/2000RG000095.
808. Ginoux, P., J.M. Prospero, T.E. Gill, N.C. Hsu, and M. Zhao, 2012: Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Rev. Geophys., 50, doi:10.1029/2012RG000388.
809. Kok, J.F., D.S. Ward, N.M. Mahowald, and A.T. Evan, 2018: Global and regional importance of the direct dust-climate feedback. Nat. Commun., 9, 241, doi:10.1038/s41467-017-02620-y.
810. Di Biagio, C. et al., 2017: Global scale variability of the mineral dust long-wave refractive index: A new dataset of in situ measurements for climate modeling and remote sensing. Atmos. Chem. Phys., 17, 1901–1929, doi:10.5194/acp-17-1901-2017.
811. Rocha-Lima, A. et al., 2018: A detailed characterization of the Saharan dust collected during the Fennec campaign in 2011: In situ ground-based and laboratory measurements. Atmos. Chem. Phys., 18, 1023–1043, doi:10.5194/acp-18-1023-2018.
812. Harrison, A.D. et al., 2016: Not all feldspars are equal: a survey of ice nucleating properties across the feldspar group of minerals. Atmos. Chem. Phys., 16, 10927–10940, doi:10.5194/acp-16-10927-2016.
813. Journet, E., Y. Balkanski, and S.P. Harrison, 2014: A new data set of soil mineralogy for dust-cycle modeling. Atmos. Chem. Phys., 14, 3801–3816, doi:10.5194/acp-14-3801-2014.
814. Perlwitz, J.P., C. Pérez García-Pando, and R.L. Miller, 2015: Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations. Atmos. Chem. Phys., 15, 11629–11652, doi:10.5194/acp-15-11629-2015.
815. Groß, S. et al., 2015: Optical properties of long-range transported Saharan dust over Barbados as measured by dual-wavelength depolarization Raman lidar measurements. Atmos. Chem. Phys., 15, 11067–11080, doi:10.5194/acp-15-11067-2015.
816. Price, H.C. et al., 2018: Atmospheric ice-nucleating particles in the dusty tropical Atlantic. J. Geophys. Res. Atmos., 123, 2175–2193, doi:10.1002/2017JD027560.
817. Kok, J.F., 2011: A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle. Proc. Natl. Acad. Sci., 108, 1016–1021, doi:10.1073/pnas.1014798108.
818. Kok, J.F., 2011: A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle. Proc. Natl. Acad. Sci., 108, 1016–1021, doi:10.1073/pnas.1014798108.
819. Wang, W., A.T. Evan, C. Flamant, and C. Lavaysse, 2015: On the decadal scale correlation between African dust and Sahel rainfall: The role of Saharan heat low-forced winds. Sci. Adv., 1, p.e1500646, doi:10.1126/sciadv.1500646.
820. Bullard, J.E. et al., 2016: High latitude dust in the Earth system. Rev. Geophys., 54, 447–485, doi:10.1002/2016RG000518.
821. Stanelle, T., I. Bey, T. Raddatz, C. Reick, and I. Tegen, 2014: Anthropogenically induced changes in twentieth century mineral dust burden and the associated impact on radiative forcing. J. Geophys. Res., 119, 13526–13546, doi:10.1002/2014JD022062.
822. Stanelle, T., I. Bey, T. Raddatz, C. Reick, and I. Tegen, 2014: Anthropogenically induced changes in twentieth century mineral dust burden and the associated impact on radiative forcing. J. Geophys. Res., 119, 13526–13546, doi:10.1002/2014JD022062.
823. Evan, A.T., C. Flamant, S. Fiedler, and O. Doherty, 2014: An analysis of aeolian dust in climate models. Geophys. Res. Lett., 41, 5996–6001, doi:10.1002/2014GL060545.
824. Evan, A.T., C. Flamant, M. Gaetani, and F. Guichard, 2016: The past, present and future of African dust. Nature, 531, 493–495, doi:10.1038/nature17149.
825. Evan, A.T., 2018: Surface winds and dust biases in climate models. Geophys. Res. Lett., 45, 1079–1085, doi:10.1002/2017GL076353.
826. Evan, A.T., C. Flamant, S. Fiedler, and O. Doherty, 2014: An analysis of aeolian dust in climate models. Geophys. Res. Lett., 41, 5996–6001, doi:10.1002/2014GL060545.
827. Stanelle, T., I. Bey, T. Raddatz, C. Reick, and I. Tegen, 2014: Anthropogenically induced changes in twentieth century mineral dust burden and the associated impact on radiative forcing. J. Geophys. Res., 119, 13526–13546, doi:10.1002/2014JD022062.
828. Kok, J.F., S. Albani, N.M. Mahowald, and D.S. Ward, 2014: An improved dust emission model – Part 2: Evaluation in the Community Earth System Model, with implications for the use of dust source functions. Atmos. Chem. Phys., 14, 13043–13061, doi:10.5194/acp-14-13043-2014.
829. Evans, S., P. Ginoux, S. Malyshev, and E. Shevliakova, 2016: Climate-vegetation interaction and amplification of Australian dust variability. Geophys. Res. Lett., 43, 11,823-11,830, doi:10.1002/2016GL071016.
830. Evans, S., P. Ginoux, S. Malyshev, and E. Shevliakova, 2016: Climate-vegetation interaction and amplification of Australian dust variability. Geophys. Res. Lett., 43, 11,823-11,830, doi:10.1002/2016GL071016.
831. Evans, S., P. Ginoux, S. Malyshev, and E. Shevliakova, 2016: Climate-vegetation interaction and amplification of Australian dust variability. Geophys. Res. Lett., 43, 11,823-11,830, doi:10.1002/2016GL071016.
832. Pu, B. and P. Ginoux, 2018: How reliable are CMIP5 models in simulating dust optical depth? Atmos. Chem. Phys., 18, 12491–12510, doi:10.5194/acp-18-12491-2018.
833. Evan, A.T., C. Flamant, M. Gaetani, and F. Guichard, 2016: The past, present and future of African dust. Nature, 531, 493–495, doi:10.1038/nature17149.
834. Pu, B. and P. Ginoux, 2018: How reliable are CMIP5 models in simulating dust optical depth? Atmos. Chem. Phys., 18, 12491–12510, doi:10.5194/acp-18-12491-2018.
835. Pu, B. and P. Ginoux, 2017: Projection of American dustiness in the late 21st century due to climate change. Sci. Rep., 7, 5553, doi:10.1038/s41598-017-05431-9.
836. Contini, D., R. Vecchi, and M. Viana, 2018: Carbonaceous aerosols in the atmosphere. Atmosphere, 9, 181, doi:10.3390/atmos9050181.
837. Tsigaridis, K. et al., 2014: The AeroCom evaluation and intercomparison of organic aerosol in global models. Atmos. Chem. Phys., 14, 10845–10895, doi:10.5194/acp-14-10845-2014.
838. Kulmala, M. et al., 2011: General overview: European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) – Integrating aerosol research from nano to global scales. Atmos. Chem. Phys., 11, 13061–130143, doi:10.5194/acp-11-13061-2011.
840. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
841. Robinson, A.L. et al., 2007: Rethinking organic aerosols: Semivolatile emissions and photochemical aging. Science, 315, 1259–1262, doi:10.1126/science.1133061.
842. Hodzic, A., P.S. Kasibhatla, D.S. Jo, C.D. Cappa, J.L. Jimenez, S. Madronich, and R.J. Park, 2016: Rethinking the global secondary organic aerosol (SOA) budget: Stronger production, faster removal, shorter lifetime. Atmos. Chem. Phys., 16, 7917–7941, doi:10.5194/acp-16-7917-2016.
843. Wang, J., B. Huang, D. Fu, P.M. Atkinson, and X. Zhang, 2016b: Response of urban heat island to future urban expansion over the Beijing–Tianjin–Hebei metropolitan area. Appl. Geogr., 70, 26–36, doi:10.1016/j.apgeog.2016.02.010.
844. Laskin, A., J. Laskin, and S.A. Nizkorodov, 2015: Chemistry of atmospheric brown carbon. Chem. Rev., 115, 4335–4382, doi:10.1021/cr5006167.
845. Liu, B. et al., 2016a: Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Chang., 6, 1130–1136, doi:10.1038/nclimate3115.
846. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
847. Saturno, J. et al., 2018: Black and brown carbon over central Amazonia: Long-term aerosol measurements at the ATTO site. Atmos. Chem. Phys., 18, 12817–12843, doi:10.5194/acp-18-12817-2018.
848. Rizzo, L.V. et al., 2013: Long term measurements of aerosol optical properties at a primary forest site in Amazonia. Atmos. Chem. Phys., 13, 2391–2413, doi:10.5194/acp-13-2391-2013.
849. Tsigaridis, K. et al., 2014: The AeroCom evaluation and intercomparison of organic aerosol in global models. Atmos. Chem. Phys., 14, 10845–10895, doi:10.5194/acp-14-10845-2014.
850. Fuzzi, S. et al., 2015a: Particulate matter, air quality and climate: Lessons learned and future needs. Atmos. Chem. Phys., 15, 8217–8299, doi:10.5194/acp-15-8217-2015.
851. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
852. Pöhlker, M.L. et al., 2016: Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction. Atmos. Chem. Phys., 16, 15709–15740, doi:10.5194/acp-16-15709-2016.
853. Thalman, R. et al., 2017: CCN activity and organic hygroscopicity of aerosols downwind of an urban region in central Amazonia: Seasonal and diel variations and impact of anthropogenic emissions. Atmos. Chem. Phys., 17, 11779–11801, doi:10.5194/acp-17-11779-2017.
854. Schmale, J. et al., 2018: Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories. Atmos. Chem. Phys., 185194, 2853–2881, doi:10.5194/acp-18-2853-2018.
855. Bowman, D.M.J.S. et al., 2011: The human dimension of fire regimes on Earth. J. Biogeogr., 38, 2223–2236, doi:10.1111/j.1365-2699.2011.02595.x.
856. Harrison, S.P., J.R. Marlon, and P.J. Bartlein, 2010: Fire in the Earth system. Chang. Clim. Earth Syst. Soc., 324, 21–48, doi:10.1007/978-90-481-8716-4_3.
857. Reddington, C.L. et al., 2016: Analysis of particulate emissions from tropical biomass burning using a global aerosol model and long-term surface observations. Atmos. Chem. Phys., 16, 11083–11106, doi:10.5194/acp-16-11083-2016.
858. Artaxo, P. et al., 2013: Atmospheric aerosols in Amazonia and land use change: From natural biogenic to biomass burning conditions. Faraday Discuss., 165, 203–235, doi:10.1039/c3fd00052d.
859. Marlon, J.R. et al., 2013: Global biomass burning: A synthesis and review of Holocene paleofire records and their controls. Quat. Sci. Rev., 65, 5–25, doi:10.1016/j.quascirev.2012.11.029.
860. van Marle, M.J.E. et al., 2017a: Fire and deforestation dynamics in Amazonia (1973–2014). Global Biogeochem. Cycles, 31, 24–38, doi:10.1002/2016GB005445.
861. Hantson, S. et al., 2016: The status and challenge of global fire modelling. Biogeosciences, 13, 3359–3375, doi:10.5194/bg-13-3359-2016.
862. van Marle, M.J.E. et al., 2017b: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015). Geosci. Model Dev., 10, 3329–3357, doi:10.5194/gmd-10-3329-2017.
863. van Marle, M.J.E. et al., 2017b: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015). Geosci. Model Dev., 10, 3329–3357, doi:10.5194/gmd-10-3329-2017.
864. Lamarque, J.-F. et al., 2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application. Atmos. Chem. Phys., 10, 7017–7039, doi:10.5194/acp-10-7017-2010.
865. Gidden, M.J. et al., 2018: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. Discuss., 1–42, doi:10.5194/gmd-2018-266.
866. Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev., 11, 369–408, doi:10.5194/gmd-11-369-2018.
867. Artaxo, P. et al., 2013: Atmospheric aerosols in Amazonia and land use change: From natural biogenic to biomass burning conditions. Faraday Discuss., 165, 203–235, doi:10.1039/c3fd00052d.
868. Fuzzi, S. et al., 2015a: Particulate matter, air quality and climate: Lessons learned and future needs. Atmos. Chem. Phys., 15, 8217–8299, doi:10.5194/acp-15-8217-2015.
869. Contini, D., R. Vecchi, and M. Viana, 2018: Carbonaceous aerosols in the atmosphere. Atmosphere, 9, 181, doi:10.3390/atmos9050181.
870. Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–658.
871. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
872. Fröhlich-Nowoisky, J. et al., 2016: Bioaerosols in the Earth system: Climate, health, and ecosystem interactions. Atmos. Res., 182, 346–376, doi:10.1016/j.atmosres.2016.07.018.
873. Pöschl, U., and M. Shiraiwa, 2015: Multiphase chemistry at the atmosphere–biosphere interface influencing climate and public health in the Anthropocene. Chem. Rev., 115, 4440–4475, doi:10.1021/cr500487s.
874. Huffman, J.A. et al., 2012: Size distributions and temporal variations of biological aerosol particles in the Amazon rainforest characterized by microscopy and real-time UV-APS fluorescence techniques during AMAZE-08. Atmos. Chem. Phys., 12, 11997–12019, doi:10.5194/acp-12-11997-2012.
875. Whitehead, J.D. et al., 2016: Biogenic cloud nuclei in the central Amazon during the transition from wet to dry season. Atmos. Chem. Phys., 16, 9727–9743, doi:10.5194/acp-16-9727-2016.
876. Scott, C.E. et al., 2015: Impact of gas-to-particle partitioning approaches on the simulated radiative effects of biogenic secondary organic aerosol. Atmos. Chem. Phys., 15, 12989–13001, doi:10.5194/acp-15-12989-2015.
877. Pöschl, U. et al., 2010: Rainforest aerosols as biogenic nuclei of clouds and precipitation in the Amazon. Science, 329, 1513–1516, doi:10.1126/science.1191056.
878. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
879. Klimont, Z. et al., 2017: Global anthropogenic emissions of particulate matter including black carbon. Atmos. Chem. Phys., 17, 8681–8723, doi:10.5194/acp-17-8681-2017.
880. Cohen, J.B. and C. Wang, 2014: Estimating global black carbon emissions using a top-down Kalman Filter approach. J. Geophys. Res. Atmos., 119, 307–323, doi:10.1002/2013JD019912.
881. Contini, D., R. Vecchi, and M. Viana, 2018: Carbonaceous aerosols in the atmosphere. Atmosphere, 9, 181, doi:10.3390/atmos9050181.
882. Klimont, Z. et al., 2017: Global anthropogenic emissions of particulate matter including black carbon. Atmos. Chem. Phys., 17, 8681–8723, doi:10.5194/acp-17-8681-2017.
883. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
884. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
885. Cohen, J.B. and C. Wang, 2014: Estimating global black carbon emissions using a top-down Kalman Filter approach. J. Geophys. Res. Atmos., 119, 307–323, doi:10.1002/2013JD019912.
886. Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev., 11, 369–408, doi:10.5194/gmd-11-369-2018.
887. Samset, B.H. et al., 2014: Modelled black carbon radiative forcing and atmospheric lifetime in AeroCom Phase II constrained by aircraft observations. Atmos. Chem. Phys., 14, 12465–12477, doi:10.5194/acp-14-12465-2014.
888. Boucher, O. et al., 2016: Jury is still out on the radiative forcing by black carbon. Proc. Natl. Acad. Sci., 113, E5092–E5093, doi:10.1073/pnas.1607005113.
889. Gustafsson, Ö., and V. Ramanathan, 2016: Convergence on climate warming by black carbon aerosols. Proc. Natl. Acad. Sci., 113, 4243–4245, doi:10.1073/pnas.1603570113.
890. Mahmood, R. et al., 2016: Seasonality of global and Arctic black carbon processes in the Arctic Monitoring and Assessment Programme models. J. Geophys. Res. Atmos., 121, 7100–7116, doi:10.1002/2016JD024849.
891. Contini, D., R. Vecchi, and M. Viana, 2018: Carbonaceous aerosols in the atmosphere. Atmosphere, 9, 181, doi:10.3390/atmos9050181.
892. Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–658.
893. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
894. de Coninck, H., A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, 2018, 2018: Strengthening and implementing the global response. In: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press.
895. Samset, B.H. et al., 2014: Modelled black carbon radiative forcing and atmospheric lifetime in AeroCom Phase II constrained by aircraft observations. Atmos. Chem. Phys., 14, 12465–12477, doi:10.5194/acp-14-12465-2014.
896. Boucher, O. et al., 2016: Jury is still out on the radiative forcing by black carbon. Proc. Natl. Acad. Sci., 113, E5092–E5093, doi:10.1073/pnas.1607005113.
897. Bond, T.C. et al., 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
898. Sand, M. et al., 2015: Response of Arctic temperature to changes in emissions of short-lived climate forcers. Nat. Clim. Chang., 6, 286. doi:10.1038/nclimate2880.
899. Ming, J., C. Xiao, Z. Du, and X. Yang, 2013: An overview of black carbon deposition in High Asia glaciers and its impacts on radiation balance. Adv. Water Resour., 55, 80–87, doi:10.1016/j.advwatres.2012.05.015.
900. Cholakian, A., A. Colette, G. Ciarelli, I. Coll, and M. Beekmann, 2018: Future climatic drivers and their effect on PM10 components in Europe and the Mediterranean Sea. Atmos. Chem. Phys. Discuss., 1–35, doi:10.5194/acp-2018-868.
901. Fiore, A.M. et al., 2012: Global air quality and climate. Chem. Soc. Rev., 41, 6663, doi:10.1039/c2cs35095e.
902. Lamarque, J.F. et al., 2013: The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Overview and description of models, simulations and climate diagnostics. Geosci. Model Dev., 6, 179–206, doi:10.5194/gmd-6-179-2013.
903. Tsigaridis, K. et al., 2014: The AeroCom evaluation and intercomparison of organic aerosol in global models. Atmos. Chem. Phys., 14, 10845–10895, doi:10.5194/acp-14-10845-2014.
904. van Marle, M.J.E. et al., 2017b: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015). Geosci. Model Dev., 10, 3329–3357, doi:10.5194/gmd-10-3329-2017.
905. Giglio, L., J.T. Randerson, and G.R. Van Der Werf, 2013: Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J. Geophys. Res. Biogeosciences, 118, 317–328, doi:10.1002/jgrg.20042.
906. Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–658.
907. Westervelt, D.M., L.W. Horowitz, V. Naik, J.C. Golaz, and D.L. Mauzerall, 2015: Radiative forcing and climate response to projected 21st century aerosol decreases. Atmos. Chem. Phys., 15, 12681–12703, doi:10.5194/acp-15-12681-2015.
908. Gonçalves, K. dos S. et al., 2018: Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region. Atmos. Environ., 184, 156–165, doi:10.1016/j.atmosenv.2018.03.057.
909. Shindell, D. et al., 2017: A climate policy pathway for near- and long-term benefits. Science, 356, 493–494, doi:10.1126/science.aak9521.
910. Guenther, A.B. et al., 2012: The model of emissions of gases and aerosols from nature version 2.1 (MEGAN2.1): An extended and updated framework for modeling biogenic emissions. Geosci. Model Dev., 5, 1471–1492, doi:10.5194/gmd-5-1471-2012.
911. Peñuelas, J. and M. Staudt, 2010: BVOCs and global change. Trends Plant Sci., 15, 133–144, doi:10.1016/j.tplants.2009.12.005.
912. Guenther, A. et al., 1995: A global model of natural volatile organic compound emissions. J. Geophys. Res., 100, 8873–8892, doi:10.1029/94JD02950.
913. Guenther, A.B. et al., 2012: The model of emissions of gases and aerosols from nature version 2.1 (MEGAN2.1): An extended and updated framework for modeling biogenic emissions. Geosci. Model Dev., 5, 1471–1492, doi:10.5194/gmd-5-1471-2012.
914. Bracho-Nunez, A., S. Welter, M. Staudt, and J. Kesselmeier, 2011: Plant-specific volatile organic compound emission rates from young and mature leaves of Mediterranean vegetation. J. Geophys. Res. Atmos., 116, 1–13, doi:10.1029/2010JD015521.
915. Peñuelas, J. and J. Llusià, 2003: BVOCs: Plant defense against climate warming? Trends Plant Sci., 8, 105–109, doi:10.1016/S1360-1385(03)00008-6.
916. Claeys, M. et al., 2004: Formation of secondary organic aerosols from isoprene and its gas-phase oxidation products through reaction with hydrogen peroxide. Atmos. Environ., 38, 4093–4098, doi:10.1016/j.atmosenv.2004.06.001.
917. Hu, W.W. et al., 2015: Characterization of a real-time tracer for isoprene epoxydiols-derived secondary organic aerosol (IEPOX-SOA) from aerosol mass spectrometer measurements. Atmos. Chem. Phys., 15, 11807–11833, doi:10.5194/acp-15-11807-2015.
918. de Sá, S.S. et al., 2018: Urban influence on the concentration and composition of submicron particulate matter in central Amazonia. Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2018-172.
919. de Sá, S.S. et al., 2019: Contributions of biomass-burning, urban, and biogenic emissions to the concentrations and light-absorbing properties of particulate matter in central Amazonia during the dry season. Atmos. Chem. Phys. Discuss., 1–77, doi:10.5194/acp-2018-1309.
920. Liu, C., C.E. Chung, F. Zhang, and Y. Yin, 2016b: The colors of biomass burning aerosols in the atmosphere. Sci. Rep., 6, doi:10.1038/srep28267.
921. Manish, S. et al., 2017: Recent advances in understanding secondary organic aerosol: Implications for global climate forcing. Rev. Geophys., 55, 509–559, doi:10.1002/2016RG000540.
922. Allan, J.D. et al., 2014: Airborne observations of IEPOX-derived isoprene SOA in the Amazon during SAMBBA. Atmos. Chem. Phys., 14, 11393–11407, doi:10.5194/acp-14-11393-2014.
923. Schulz, C. et al., 2018: Aircraft-based observations of isoprene epoxydiol-derived secondary organic aerosol (IEPOX-SOA) in the tropical upper troposphere over the Amazon region. Atmos. Chem. Phys. Discuss., 3, 1–32, doi:10.5194/acp-2018-232.
924. de Sá, S.S. et al., 2017: Influence of urban pollution on the production of organic particulate matter from isoprene epoxydiols in central Amazonia. Atmos. Chem. Phys., 17, 6611–6629, doi:10.5194/acp-17-6611-2017.
925. Schulz, C. et al., 2018: Aircraft-based observations of isoprene epoxydiol-derived secondary organic aerosol (IEPOX-SOA) in the tropical upper troposphere over the Amazon region. Atmos. Chem. Phys. Discuss., 3, 1–32, doi:10.5194/acp-2018-232.
926. Wang, J. et al., 2016a: Amazon boundary layer aerosol concentration sustained by vertical transport during rainfall. Nature, 539, 416–419, doi:10.1038/nature19819.
927. Andreae, M.O. et al., 2018: Aerosol characteristics and particle production in the upper troposphere over the Amazon Basin. Atmos. Chem. Phys., 18, 921–961, doi:10.5194/acp-18-921-2018.
928. Pöhlker, M.L. et al., 2018: Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and characteristics of biomass burning, long-range transport, and pristine rain forest aerosols. Atmos. Chem. Phys., 18, 10289–10331, doi:10.5194/acp-18-10289-2018.
929. Fuentes, J.D. et al., 2000: Biogenic hydrocarbons in the atmospheric boundary layer: A review. Bull. Am. Meteorol. Soc., 81, 1537–1575, doi:10.1175/1520-0477(2000)081<1537:BHITAB>2.3.CO;2.
930. Schmale, J. et al., 2018: Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories. Atmos. Chem. Phys., 185194, 2853–2881, doi:10.5194/acp-18-2853-2018.
931. Pöhlker, M.L. et al., 2018: Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and characteristics of biomass burning, long-range transport, and pristine rain forest aerosols. Atmos. Chem. Phys., 18, 10289–10331, doi:10.5194/acp-18-10289-2018.
932. Pöhlker, M.L. et al., 2016: Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction. Atmos. Chem. Phys., 16, 15709–15740, doi:10.5194/acp-16-15709-2016.
933. Unger, N., 2014a: On the role of plant volatiles in anthropogenic global climate change. Geophys. Res. Lett., 41, 8563–8569, doi:10.1002/2014GL061616.
934. Sporre, M.K., S.M. Blichner, I.H.H. Karset, R. Makkonen, and T.K. Berntsen, 2019: BVOC-aerosol-climate feedbacks investigated using NorESM. Atmos. Chem. Phys., 19, 4763–4782, doi:10.5194/acp-19-4763-2019.
935. Jardine, K. et al., 2011: Within-canopy sesquiterpene ozonolysis in Amazonia. J. Geophys. Res. Atmos., 116, 1–10, doi:10.1029/2011JD016243.
936. Jardine, K. et al., 2015: Green leaf volatile emissions during high temperature and drought stress in a Central Amazon rainforest. Plants, 4, 678–690, doi:10.3390/plants4030678.
937. Fuentes, J.D. et al., 2016: Linking meteorology, turbulence, and air chemistry in the amazon rain forest. Bull. Am. Meteorol. Soc., 97, 2329–2342, doi:10.1175/BAMS-D-15-00152.1.
938. Arneth, A. et al., 2010: Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci., 3, 525–532, doi:10.1038/ngeo905.
939. Kulmala, M., 2004: A new feedback mechanism linking forests, aerosols, and climate. Atmos. Chem. Phys., 4, 557–562, doi:10.5194/acp-4-557-2004.
940. Unger, N., X. Yue, and K.L. Harper, 2017: Aerosol climate change effects on land ecosystem services. Faraday Discuss., 200, 121–142, doi:10.1039/c7fd00033b.
941. Kulmala, M. et al., 2014: CO2-induced terrestrial climate feedback mechanism: From carbon sink to aerosol source and back. Boreal Environ. Res., 19, 122–131.Kundzewicz, Z.W. et al., 2014: Flood risk and climate change: Global and regional perspectives. Hydrol. Sci. J., 59, 1–28, doi:10.1080/02626667.2013.857411.
942. Cirino, G.G., R.A.F. Souza, D.K. Adams, and P. Artaxo, 2014: The effect of atmospheric aerosol particles and clouds on net ecosystem exchange in the Amazon. Atmos. Chem. Phys., 14, 6523–6543, doi:10.5194/acp-14-6523-2014.
943. Sena, E.T., A. McComiskey, and G. Feingold, 2016: A long-term study of aerosol-cloud interactions and their radiative effect at the Southern Great Plains using ground-based measurements. Atmos. Chem. Phys., 16, 11301–11318, doi:10.5194/acp-16-11301-2016.
944. Schafer, J.S. et al., 2002: Observed reductions of total solar irradiance by biomass-burning aerosols in the Brazilian Amazon and Zambian Savanna. Geophys. Res. Lett., 29, 4-1-4–4, doi:10.1029/2001GL014309.
945. Ometto, J.P.H.B., A.D. Nobre, H.R. Rocha, P. Artaxo, and L.A. Martinelli, 2005: Amazonia and the modern carbon cycle: Lessons learned. Oecologia, 143, 483–500, doi:10.1007/s00442-005-0034-3.
946. Oliveira, P.H.F. et al., 2007: The effects of biomass burning aerosols and clouds on the CO2 flux in Amazonia. Tellus, Ser. B Chem. Phys. Meteorol., 59, 338–349, doi:10.1111/j.1600-0889.2007.00270.x.
947. Sporre, M.K., S.M. Blichner, I.H.H. Karset, R. Makkonen, and T.K. Berntsen, 2019: BVOC-aerosol-climate feedbacks investigated using NorESM. Atmos. Chem. Phys., 19, 4763–4782, doi:10.5194/acp-19-4763-2019.
948. Kulmala, M., 2004: A new feedback mechanism linking forests, aerosols, and climate. Atmos. Chem. Phys., 4, 557–562, doi:10.5194/acp-4-557-2004.
949. Arneth, A. et al., 2017: Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci., 10, 79–84. doi:10.1038/ngeo2882.
950. Bourtsoukidis, E. et al., 2018: Strong sesquiterpene emissions from Amazonian soils. Nat. Commun., 9, doi:10.1038/s41467-018-04658-y.
951. Peñuelas, J., 2009: Phenology feedbacks on climate change. Science, 324, 887–888, doi:10.1126/science.1173004.
952. Sanderson, M.G., C.D. Jones, W.J. Collins, C.E. Johnson, and R.G. Derwent, 2003: Effect of climate change on isoprene emissions and surface ozone levels. Geophys. Res. Lett., 30, doi:10.1029/2003GL017642.
953. Pacifico, F., G.A. Folberth, C.D. Jones, S.P. Harrison, and W.J. Collins, 2012: Sensitivity of biogenic isoprene emissions to past, present, and future environmental conditions and implications for atmospheric chemistry. J. Geophys. Res. Atmos., 117, D22302, doi:10.1029/2012JD018276.
954. Unger, N., 2013: Isoprene emission variability through the twentieth century. J. Geophys. Res. Atmos., 118, 13, 606–613, doi:10.1002/2013JD020978.
955. Unger, N., 2014a: On the role of plant volatiles in anthropogenic global climate change. Geophys. Res. Lett., 41, 8563–8569, doi:10.1002/2014GL061616.
956. Fu, Y., and H. Liao, 2014: Impacts of land use and land cover changes on biogenic emissions of volatile organic compounds in China from the late 1980s to the mid-2000s: implications for tropospheric ozone and secondary organic aerosol. Tellus B Chem. Phys. Meteorol., 66, 24987, doi:10.3402/tellusb.v66.24987.
957. Scott, C.E. et al., 2017: Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions. Faraday Discuss., 200, 101–120, doi:10.1039/c7fd00028f.
958. Heald, C.L. and J.A. Geddes, 2016: The impact of historical land use change from 1850 to 2000 on secondary particulate matter and ozone. Atmos. Chem. Phys., 16, 14997–15010, doi:10.5194/acp-16-14997-2016.
959. Heald, C.L. and J.A. Geddes, 2016: The impact of historical land use change from 1850 to 2000 on secondary particulate matter and ozone. Atmos. Chem. Phys., 16, 14997–15010, doi:10.5194/acp-16-14997-2016.
960. Scott, C.E. et al., 2017: Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions. Faraday Discuss., 200, 101–120, doi:10.1039/c7fd00028f.
961. Unger, N., 2014b: Human land-use-driven reduction of forest volatiles cools global climate. Nat. Clim. Chang., 4, 907–910, doi:10.1038/nclimate2347.
962. Tsigaridis, K. et al., 2014: The AeroCom evaluation and intercomparison of organic aerosol in global models. Atmos. Chem. Phys., 14, 10845–10895, doi:10.5194/acp-14-10845-2014.
963. Scott, C.E. et al., 2017: Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions. Faraday Discuss., 200, 101–120, doi:10.1039/c7fd00028f.
964. Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev., 11, 369–408, doi:10.5194/gmd-11-369-2018.
965. Gidden, M.J. et al., 2018: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. Discuss., 1–42, doi:10.5194/gmd-2018-266.
966. Scott, C.E. et al., 2017: Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions. Faraday Discuss., 200, 101–120, doi:10.1039/c7fd00028f.
967. Scott, C.E. et al., 2017: Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions. Faraday Discuss., 200, 101–120, doi:10.1039/c7fd00028f.
968. Heald, C.L. and J.A. Geddes, 2016: The impact of historical land use change from 1850 to 2000 on secondary particulate matter and ozone. Atmos. Chem. Phys., 16, 14997–15010, doi:10.5194/acp-16-14997-2016.
969. Heald, C.L. and J.A. Geddes, 2016: The impact of historical land use change from 1850 to 2000 on secondary particulate matter and ozone. Atmos. Chem. Phys., 16, 14997–15010, doi:10.5194/acp-16-14997-2016.
970. Scott, C.E. et al., 2017: Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions. Faraday Discuss., 200, 101–120, doi:10.1039/c7fd00028f.
971. Unger, N., 2014b: Human land-use-driven reduction of forest volatiles cools global climate. Nat. Clim. Chang., 4, 907–910, doi:10.1038/nclimate2347.
972. Heald, C.L., and D.V. Spracklen, 2015: Land use change impacts on air quality and climate. Chem. Rev., 115, 4476–4496, doi:10.1021/cr500446g.
973. Scott, C.E. et al., 2017: Impact on short-lived climate forcers (SLCFs) from a realistic land-use change scenario via changes in biogenic emissions. Faraday Discuss., 200, 101–120, doi:10.1039/c7fd00028f.
974. Rosenkranz, M., T.A.M. Pugh, J.-P. Sghnitzler, and A. Arneth, 2015: Effect of land-use change and management on biogenic volatile organic compound emissions – selecting climate-smart cultivars. Plant. Cell Environ., 38, 1896–1912, doi:10.1111/pce.12453.
975. Peñuelas, J. and J. Llusià, 2003: BVOCs: Plant defense against climate warming? Trends Plant Sci., 8, 105–109, doi:10.1016/S1360-1385(03)00008-6.
976. Kulmala, M. et al., 2013: Climate feedbacks linking the increasing atmospheric CO2 concentration, BVOC emissions, aerosols and clouds in forest ecosystems. In: Biology, Controls and Models of Tree Volatile Organic Compound Emissions [Niinemets, Ü. and R. Monson (eds.)]. Springer, Dordrecht, Netherlands, pp. 489–508.
977. Sporre, M.K., S.M. Blichner, I.H.H. Karset, R. Makkonen, and T.K. Berntsen, 2019: BVOC-aerosol-climate feedbacks investigated using NorESM. Atmos. Chem. Phys., 19, 4763–4782, doi:10.5194/acp-19-4763-2019.
978. Scott, C.E. et al., 2018b: Substantial large-scale feedbacks between natural aerosols and climate. Nat. Geosci., 11, 44–48, doi:10.1038/s41561-017-0020-5.
979. Paasonen, P. et al., 2013: Warming-induced increase in aerosol number concentration likely to moderate climate change. Nat. Geosci., 6, 438–442, doi:10.1038/ngeo1800.
980. Rap, A. et al., 2018: Enhanced global primary production by biogenic aerosol via diffuse radiation fertilization. Nat. Geosci., 11, 640–644, doi:10.1038/s41561-018-0208-3.
981. Ashworth, K., G. Folberth, C.N. Hewitt, and O. Wild, 2012: Impacts of near-future cultivation of biofuel feedstocks on atmospheric composition and local air quality. Atmos. Chem. Phys., 12, 919–939, doi:10.5194/acp-12-919-2012.
982. Pacifico, F., G.A. Folberth, C.D. Jones, S.P. Harrison, and W.J. Collins, 2012: Sensitivity of biogenic isoprene emissions to past, present, and future environmental conditions and implications for atmospheric chemistry. J. Geophys. Res. Atmos., 117, D22302, doi:10.1029/2012JD018276.
983. Scott, C.E. et al., 2018a: Impact on short-lived climate forcers increases projected warming due to deforestation. Nat. Commun., 9, 157, doi:10.1038/s41467-017-02412-4.
984. Sporre, M.K., S.M. Blichner, I.H.H. Karset, R. Makkonen, and T.K. Berntsen, 2019: BVOC-aerosol-climate feedbacks investigated using NorESM. Atmos. Chem. Phys., 19, 4763–4782, doi:10.5194/acp-19-4763-2019.
985. Makkonen, R. et al., 2012: Air pollution control and decreasing new particle formation lead to strong climate warming. Atmos. Chem. Phys., 12, 1515–1524, doi:10.5194/acp-12-1515-2012.
986. Arneth, A. et al., 2010: Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci., 3, 525–532, doi:10.1038/ngeo905.
987. Hantson, S et al., 2017: Global isoprene and monoterpene emissions under changing climate, vegetation, CO2 and land use. Atmos. Environ., 155, 35–45, doi:10.1016/j.atmosenv.2017.02.010.
988. Brovkin, V. et al., 2004: Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years. Glob. Chang. Biol., 10, 1253–1266, doi:10.1111/j.1365-2486.2004.00812.x.
989. Simmons, C.T. and H.D. Matthews, 2016: Assessing the implications of human land-use change for the transient climate response to cumulative carbon emissions. Environ. Res. Lett., 11, doi:10.1088/1748-9326/11/3/035001.
990. Shevliakova, E. et al. 2013: Historical warming reduced due to enhanced land carbon uptake. Proc. Natl. Acad. Sci., 110, 16730–16735, doi:10.1073/pnas.1314047110.
991. Pongratz, J., C.H. Reick, T. Raddatz, and M. Claussen, 2010: Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett., 37, 1–5, doi:10.1029/2010GL043010.
992. Matthews, H.D.D., A.J.J. Weaver, K.J.J. Meissner, N.P.P. Gillett, and M. Eby, 2004: Natural and anthropogenic climate change: incorporating historical land cover change, vegetation dynamics and the global carbon cycle. Clim. Dyn., 22, 461–479, doi:10.1007/s00382-004-0392-2.
993. He, F. et al., 2014: Simulating global and local surface temperature changes due to Holocene anthropogenic land cover change. Geophys. Res. Lett., 41, 623–631, doi:10.1002/2013GL058085.
994. Zhang, Q., A.J. Pitman, Y.P. Wang, Y.J. Dai, and P.J. Lawrence, 2013a: The impact of nitrogen and phosphorous limitation on the estimated terrestrial carbon balance and warming of land use change over the last 156 yr. Earth Syst. Dyn., 4, 333–345, doi:10.5194/esd-4-333-2013.
995. Hua, W.J. and H.S. Chen, 2013: Recognition of climatic effects of land use/land cover change under global warming. Chinese Sci. Bull., doi:10.1007/s11434-013-5902-3.
996. Jones, A., W.D. Collins, and M.S. Torn, 2013b: On the additivity of radiative forcing between land use change and greenhouse gases. Geophys. Res. Lett., doi:10.1002/grl.50754.
997. Simmons, C.T. and H.D. Matthews, 2016: Assessing the implications of human land-use change for the transient climate response to cumulative carbon emissions. Environ. Res. Lett., 11, doi:10.1088/1748-9326/11/3/035001.
998. Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 659–740.
999. Claussen, M. et al., 2001: Biophysical versus biogeochemical feedbacks of large-scale land cover change. Geophys. Res. Lett., 28, 1011–1014, doi:10.1029/2000GL012471.
1000. Davin, E.L., and N. de Noblet-Ducoudre, 2010: Climatic impact of global-scale deforestation: Radiative versus nonradiative processes. J. Clim., 23, 97–112, doi:10.1175/2009JCLI3102.1.
1001. Boisier, J.P. et al., 2012: Attributing the impacts of land-cover changes in temperate regions on surface temperature and heat fluxes to specific causes: Results from the first LUCID set of simulations. J. Geophys. Res. Atmos., 117, 1–16, doi:10.1029/2011JD017106.
1002. Li, W. et al., 2017a: Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences, 14, 5053–5067, doi:10.5194/bg-14-5053-2017.
1003. Avitabile, V., M. Herold, G.B.M. Heuvelink, A.G. P, de B. Jong, and V.G. Laurin, 2016: An integrated pan-tropical biomass map using multiple reference datasets. Glob. Chang. Biol., 22, 1406–1420.
1004. Carvalhais, N. et al., 2014: Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature, 514, 213–217, doi:10.1038/nature13731.
1005. Le Quéré, C. et al., 2015: Global Carbon Budget 2015. Earth Syst. Sci. Data, 7, 349–396, doi:10.5194/essd-7-349-2015.
1006. Peng, S. et al., 2017: Sensitivity of land use change emission estimates to historical land use and land cover mapping. Global Biogeochem. Cycles, 31, 626–643, doi:10.1002/2015GB005360.
1007. Arneth, A. et al., 2017: Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci., 10, 79–84. doi:10.1038/ngeo2882.
1008. Pugh, T.A.M. et al., 2015: Simulated carbon emissions from land-use change are substantially enhanced by accounting for agricultural management. Environ. Res. Lett., 10, 124008, doi:10.1088/1748-9326/10/12/124008.
1009. Hansis, E., S.J. Davis, and J. Pongratz, 2015: Relevance of methodological choices for accounting of land use change carbon fluxes. Global Biogeochem. Cycles, 29, 1230–1246, doi:10.1002/2014GB004997.
1010. Pongratz, J., C.H. Reick, T. Raddatz, and M. Claussen, 2010: Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett., 37, 1–5, doi:10.1029/2010GL043010.
1011. Brovkin, V. et al., 2004: Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years. Glob. Chang. Biol., 10, 1253–1266, doi:10.1111/j.1365-2486.2004.00812.x.
1012. Matthews, H.D.D., A.J.J. Weaver, K.J.J. Meissner, N.P.P. Gillett, and M. Eby, 2004: Natural and anthropogenic climate change: incorporating historical land cover change, vegetation dynamics and the global carbon cycle. Clim. Dyn., 22, 461–479, doi:10.1007/s00382-004-0392-2.
1013. Simmons, C.T. and H.D. Matthews, 2016: Assessing the implications of human land-use change for the transient climate response to cumulative carbon emissions. Environ. Res. Lett., 11, doi:10.1088/1748-9326/11/3/035001.
1014. Ward, D.S., N.M. Mahowald, and S. Kloster, 2014: Potential climate forcing of land use and land cover change. Atmos. Chem. Phys., 14, 12701–12724, doi:10.5194/acp-14-12701-2014.
1015. Arneth, A. et al., 2017: Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci., 10, 79–84. doi:10.1038/ngeo2882.
1016. Cleveland, C. et al., 2015: Future productivity and carbon storage limited by terrestrial nutrient availability Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci., 8, 441, doi:10.1038/NGEO2413.
1017. Pongratz, J. et al., 2018: Models Meet Data: Challenges and Opportunities in Implementing Land Management in Earth System Models. 24, 1470–11487, doi: 10.1111/gcb.13988.
1018. Mahowald, N.M., D.S. Ward, S.C. Doney, P.G. Hess, and J.T. Randerson, 2017: Are the impacts of land use on warming underestimated in climate policy? Environ. Res. Lett., 12, 94016, doi:10.1088/1748-9326/aa836d.
1019. Ward, D.S., N.M. Mahowald, and S. Kloster, 2014: Potential climate forcing of land use and land cover change. Atmos. Chem. Phys., 14, 12701–12724, doi:10.5194/acp-14-12701-2014.
1020. Lloyd, A.H., T.S. Rupp, C.L. Fastie, and A.M. Starfield, 2003: Patterns and dynamics of treeline advance on the Seward Peninsula, Alaska. 108, D2, 8161, doi:10.1029/2001JD000852.
1021. Lucht, W. et al., 1995: Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296, 1687–1689, doi:10.1126/science.1071828.
1022. Forzieri, G., R. Alkama, D.G. Miralles, and A. Cescatti, 2017: Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science, 356, 1180–1184, doi:10.1126/science.aal1727.
1023. Qian, H., R. Joseph, and N. Zeng, 2010: Enhanced terrestrial carbon uptake in the northern high latitudes in the 21st century from the Coupled Carbon Cycle Climate Model Intercomparison Project model projections. Glob. Chang. Biol., 16, 641–656, doi:10.1111/j.1365-2486.2009.01989.x.
1024. Strengers, B.J. et al., 2010: Assessing 20th century climate-vegetation feedbacks of land-use change and natural vegetation dynamics in a fully coupled vegetation-climate model. Int. J. Climatol., 30, 2055–2065, doi:10.1002/joc.2132.
1025. De Noblet-Ducoudré, N. et al., 2012: Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: Results from the first set of LUCID experiments. J. Clim., 25, 3261–3281, doi:10.1175/JCLI-D-11-00338.1.
1026. Pongratz, J., C.H. Reick, T. Raddatz, and M. Claussen, 2010: Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett., 37, 1–5, doi:10.1029/2010GL043010.
1027. Pongratz, J., C.H. Reick, T. Raddatz, and M. Claussen, 2010: Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett., 37, 1–5, doi:10.1029/2010GL043010.
1028. Teuling, A.J. et al., 2010: Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci., 3, 722–727, doi:10.1038/ngeo950.
1029. Pitman, A., and N. de Noblet-Ducoudré, 2012: Human effects on climate through land-use-induced land-cover change. Futur. World’s Clim., 77–95, doi:10.1016/B978-0-12-386917-3.00004-X.
1030. Alkama, R., and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/science.aac8083.
1031. Quesada, B., A. Arneth, and N. de Noblet-Ducoudré, 2017a: Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate picture. J. Geophys. Res., 122, 5113–5131, doi:10.1002/2016JD025448.
1032. Pitman, A., and N. de Noblet-Ducoudré, 2012: Human effects on climate through land-use-induced land-cover change. Futur. World’s Clim., 77–95, doi:10.1016/B978-0-12-386917-3.00004-X.
1033. Avila, F.B., A.J. Pitman, M.G. Donat, L.V. Alexander, and G. Abramowitz, 2012: Climate model simulated changes in temperature extremes due to land cover change. J. Geophys. Res. Atmos., 117, 1–19, doi:10.1029/2011JD016382.
1034. Christidis, N., P.A. Stott, G.C. Hegerl, and R.A. Betts, 2013: The role of land use change in the recent warming of daily extreme temperatures. Geophys. Res. Lett., 40, 589–594, doi:10.1002/grl.50159.
1035. Lejeune, Q., E.L. Davin, L. Gudmundsson, and S.I. Seneviratne, 2018: Historical deforestation increased the risk of heat extremes in northern mid-latitudes 2. Nat. Clim. Chang., 8, 1–16, doi:10.1038/s41558-018-0131-z.
1036. Findell, K.L. et al., 2017: The impact of anthropogenic land use and land cover change on regional climate extremes. Nat. Commun., 8, 989, doi:10.1038/s41467-017-01038-w.
1037. Zaitchik, B.F., A.K. Macalady, L.R. Bonneau, and R.B. Smith, 2006: Europe’s 2003 heat wave: A satellite view of impacts and land–atmosphere feedbacks. Int. J. Climatol., 26, 743–769, doi:10.1002/joc.1280.
1038. Renaud, V., and M. Rebetez, 2008: Comparison between open site and below canopy climatic conditions in Switzerland during the exceptionally hot summer of 2003. Agric. For. Meteorol., 149, 873–880, doi:10.1016/j.agrformet.2008.11.006.
1039. Chen, L., P.A. Dirmeyer, Z. Guo, and N.M. Schultz, 2018: Pairing FLUXNET sites to validate model representations of land-use/land-cover change. Hydrol. Earth Syst. Sci., 22, 111–125, doi:10.5194/hess-22-111-2018.
1040. Duveiller, G., J. Hooker, and A. Cescatti, 2018: The mark of vegetation change on Earth’s surface energy balance. Nat. Commun., 9, 679, doi:10.1038/s41467-017-02810-8.
1041. Li, D. et al., 2015a: Contrasting responses of urban and rural surface energy budgets to heat waves explain synergies between urban heat islands and heat waves. Environ. Res. Lett., 10, 54009, doi:10.1088/1748-9326/10/5/054009.
1042. Lee, X. et al., 2011: Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384–387, doi:10.1038/nature10588.
1043. Renaud, V., and M. Rebetez, 2008: Comparison between open site and below canopy climatic conditions in Switzerland during the exceptionally hot summer of 2003. Agric. For. Meteorol., 149, 873–880, doi:10.1016/j.agrformet.2008.11.006.
1044. Zaitchik, B.F., A.K. Macalady, L.R. Bonneau, and R.B. Smith, 2006: Europe’s 2003 heat wave: A satellite view of impacts and land–atmosphere feedbacks. Int. J. Climatol., 26, 743–769, doi:10.1002/joc.1280.
1045. Renaud, V., J.L. Innes, M. Dobbertin, and M. Rebetez, 2011: Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007). Theor. Appl. Climatol., 105, 119–127, doi:10.1007/s00704-010-0361-0.
1046. Renaud, V., and M. Rebetez, 2008: Comparison between open site and below canopy climatic conditions in Switzerland during the exceptionally hot summer of 2003. Agric. For. Meteorol., 149, 873–880, doi:10.1016/j.agrformet.2008.11.006.
1047. De Noblet-Ducoudré, N. et al., 2012: Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: Results from the first set of LUCID experiments. J. Clim., 25, 3261–3281, doi:10.1175/JCLI-D-11-00338.1.
1048. Hurtt, G.C. et al., 2011: Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change, 109, 117–161, doi:10.1007/s10584-011-0153-2.
1049. Simmons, C.T. and H.D. Matthews, 2016: Assessing the implications of human land-use change for the transient climate response to cumulative carbon emissions. Environ. Res. Lett., 11, doi:10.1088/1748-9326/11/3/035001.
1050. Davies-Barnard, T., P.J. Valdes, J.S. Singarayer, F.M. Pacifico, and C.D. Jones, 2014: Full effects of land use change in the representative concentration pathways. Environ. Res. Lett., 9, doi:10.1088/1748-9326/9/11/114014.
1051. Boysen, L.R. et al., 2014: Global and regional effects of land-use change on climate in 21st century simulations with interactive carbon cycle. Earth Syst. Dyn. Discuss., 5, 443–472, doi:10.5194/esdd-5-443-2014.
1052. Pugh, T.A.M. et al., 2015: Simulated carbon emissions from land-use change are substantially enhanced by accounting for agricultural management. Environ. Res. Lett., 10, 124008, doi:10.1088/1748-9326/10/12/124008.
1053. Stocker, B.D., F. Feissli, K.M. Strassmann, R. Spahni, and F. Joos, 2014: Past and future carbon fluxes from land use change, shifting cultivation and wood harvest. Tellus, Ser. B Chem. Phys. Meteorol., 66, 23188, doi:10.3402/tellusb.v66.23188.
1054. Quesada, B., A. Arneth, and N. de Noblet-Ducoudré, 2017a: Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate picture. J. Geophys. Res., 122, 5113–5131, doi:10.1002/2016JD025448.
1055. Davies-Barnard, T., P.J. Valdes, J.S. Singarayer, A.J. Wiltshire, and C.D. Jones, 2015: Quantifying the relative importance of land cover change from climate and land use in the representative concentration pathways. Global Biogeochem. Cycles, 29, 842–853, doi:10.1002/2014GB004949.
1056. Boysen, L.R. et al., 2014: Global and regional effects of land-use change on climate in 21st century simulations with interactive carbon cycle. Earth Syst. Dyn. Discuss., 5, 443–472, doi:10.5194/esdd-5-443-2014.
1057. Davies-Barnard, T., P.J. Valdes, J.S. Singarayer, A.J. Wiltshire, and C.D. Jones, 2015: Quantifying the relative importance of land cover change from climate and land use in the representative concentration pathways. Global Biogeochem. Cycles, 29, 842–853, doi:10.1002/2014GB004949.
1058. Brovkin, V. et al., 2013: Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century. J. Clim., 26, 6859–6881, doi:10.1175/JCLI-D-12-00623.1.
1059. Sitch, S. et al. 2005: Impacts of future land cover changes on atmospheric CO2 and climate. Global Biogeochem. Cycles, 19, 1–15, doi:10.1029/2004GB002311.
1060. Davin, E.L., N. de Noblet-Ducoudré, and P. Friedlingstein, 2007: Impact of land cover change on surface climate: Relevance of the radiative forcing concept. Geophys. Res. Lett., 34, n/a-n/a, doi:10.1029/2007GL029678.
1061. Sitch, S. et al. 2005: Impacts of future land cover changes on atmospheric CO2 and climate. Global Biogeochem. Cycles, 19, 1–15, doi:10.1029/2004GB002311.
1062. Boysen, L.R. et al., 2014: Global and regional effects of land-use change on climate in 21st century simulations with interactive carbon cycle. Earth Syst. Dyn. Discuss., 5, 443–472, doi:10.5194/esdd-5-443-2014.
1063. Voldoire, A., 2006: Quantifying the impact of future land-use changes against increases in GHG concentrations. Geophys. Res. Lett., 33, 2–5, doi:10.1029/2005GL024354.
1064. Feddema, J.J. et al., 2005: The importance of land-cover change in simulating future climates. Science, 310, 1674–1678, doi:10.1126/science.1118160.
1065. Brovkin, V. et al., 2013: Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century. J. Clim., 26, 6859–6881, doi:10.1175/JCLI-D-12-00623.1.
1066. Sitch, S. et al. 2005: Impacts of future land cover changes on atmospheric CO2 and climate. Global Biogeochem. Cycles, 19, 1–15, doi:10.1029/2004GB002311.
1067. Feddema, J.J. et al., 2005: The importance of land-cover change in simulating future climates. Science, 310, 1674–1678, doi:10.1126/science.1118160.
1068. Quesada, B., A. Arneth, and N. de Noblet-Ducoudré, 2017a: Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate picture. J. Geophys. Res., 122, 5113–5131, doi:10.1002/2016JD025448.
1069. Quesada, B., N. Devaraju, N. de Noblet-Ducoudré, and A. Arneth, 2017b: Reduction of monsoon rainfall in response to past and future land use and land cover changes. Geophys. Res. Lett., 44, 1041–1050, doi:10.1002/2016GL070663.
1070. Wang, B., and Q. Ding, 2008: Global monsoon: Dominant mode of annual variation in the tropics. Dyn. Atmos. Ocean., 44, 165–183, doi:10.1016/j.dynatmoce.2007.05.002.
1071. Quesada, B., N. Devaraju, N. de Noblet-Ducoudré, and A. Arneth, 2017b: Reduction of monsoon rainfall in response to past and future land use and land cover changes. Geophys. Res. Lett., 44, 1041–1050, doi:10.1002/2016GL070663.
1072. Hirsch, A.L. et al., 2018: Biogeophysical impacts of land-use change on climate extremes in low-emission scenarios: Results from HAPPI-Land. Earth’s Futur., 6, 396–409, doi:10.1002/2017EF000744.
1073. Quesada, B., N. Devaraju, N. de Noblet-Ducoudré, and A. Arneth, 2017b: Reduction of monsoon rainfall in response to past and future land use and land cover changes. Geophys. Res. Lett., 44, 1041–1050, doi:10.1002/2016GL070663.
1074. Yim, S.Y., B. Wang, J. Liu, and Z. Wu, 2014: A comparison of regional monsoon variability using monsoon indices. Clim. Dyn., 43, 1423–1437, doi:10.1007/s00382-013-1956-9.
1075. Brovkin, V. et al., 2013: Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century. J. Clim., 26, 6859–6881, doi:10.1175/JCLI-D-12-00623.1.
1076. Pitman, A.J., 2003: The evolution of, and revolution in, land surface schemes designed for climate models. Int. J. Climatol., 23, 479–510, doi:10.1002/joc.893.
1077. Strengers, B.J. et al., 2010: Assessing 20th century climate-vegetation feedbacks of land-use change and natural vegetation dynamics in a fully coupled vegetation-climate model. Int. J. Climatol., 30, 2055–2065, doi:10.1002/joc.2132.
1078. Bonan, G.B., 2008: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320, 1444–1449, doi:10.1126/science.1155121.
1079. Ganopolski, A. et al., 2001: Climber-2: A climate system model of intermediate complexity. Part II: Model sensitivity. Clim. Dyn., 17, 735–751, doi:10.1007/s003820000144.
1080. Snyder, P.K., C. Delire, and J.A. Foley, 2004: Evaluating the influence of different vegetation biomes on the global climate. Clim. Dyn., 23, 279–302, doi:10.1007/s00382-004-0430-0.
1081. Devaraju, N., G. Bala, and A. Modak, 2015a: Effects of large-scale deforestation on precipitation in the monsoon regions: Remote versus local effects. Proc. Natl. Acad. Sci., 112, 3257–3262, doi:10.1073/pnas.1423439112.
1082. Longobardi, P., A. Montenegro, H. Beltrami, and M. Eby, 2016b: Deforestation induced climate change: Effects of spatial scale. PLoS One, 11, doi:10.1371/journal.pone.0153357.
1083. Perugini, L. et al., 2017: Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett., 12, 053002, doi:10.1088/1748-9326/aa6b3f.
1084. Devaraju, N., G. Bala, and R. Nemani, 2015b: Modelling the influence of land-use changes on biophysical and biochemical interactions at regional and global scales. Plant, Cell Environ., 38, 1931–1946, doi:10.1111/pce.12488.
1085. Snyder, P.K., 2010: The influence of tropical deforestation on the Northern Hemisphere climate by atmospheric teleconnections. Earth Interact., doi:10.1175/2010EI280.1.
1086. Longobardi, P., A. Montenegro, H. Beltrami, and M. Eby, 2016a: Deforestation induced climate change: Effects of spatial scale. PLoS One, doi:10.1371/journal.pone.0153357.
1087. Wang, Y., X. Yan, and Z. Wang, 2014b: The biogeophysical effects of extreme afforestation in modeling future climate. Theor. Appl. Climatol., 118, 511–521, doi:10.1007/s00704-013-1085-8.
1088. Bathiany, S., M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler, 2010a: Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model. Biogeosciences, 7, 1383–1399, doi:10.5194/bg-7-1383-2010.
1089. Lawrence, D. and K. Vandecar, 2015: Effects of tropical deforestation on climate and agriculture. Nat. Clim. Chang., 5, 27–36, doi:10.1038/nclimate2430.
1090. Lejeune, Q., E.L. Davin, B.P. Guillod, and S.I. Seneviratne, 2015: Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim. Dyn., 44, 2769–2786, doi:10.1007/s00382-014-2203-8.
1091. Perugini, L. et al., 2017: Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett., 12, 053002, doi:10.1088/1748-9326/aa6b3f.
1092. Perugini, L. et al., 2017: Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett., 12, 053002, doi:10.1088/1748-9326/aa6b3f.
1093. Kendra Gotangco Castillo, C., and K.R. Gurney, 2013: A sensitivity analysis of surface biophysical, carbon, and climate impacts of tropical deforestation rates in CCSM4-CNDV.J. Clim., 26, 805–821, doi:10.1175/JCLI-D-11-00382.1.
1094. Hagos, S. et al., 2014: Assessment of uncertainties in the response of the African monsoon precipitation to land use change simulated by a regional model. Clim. Dyn., 43, 2765–2775, doi:10.1007/s00382-014-2092-x.
1095. Boone, A.A. et al., 2016: The regional impact of Land-Use Land-cover Change (LULCC) over West Africa from an ensemble of global climate models under the auspices of the WAMME2 project. Clim. Dyn., 47, 3547–3573, doi:10.1007/s00382-016-3252-y.
1096. Xue, Y. et al., 2016: West African monsoon decadal variability and surface-related forcings: Second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II). Clim. Dyn., 47, 3517–3545, doi:10.1007/s00382-016-3224-2.
1097. Nogherotto, R., E. Coppola, F. Giorgi, and L. Mariotti, 2013: Impact of Congo Basin deforestation on the African monsoon. Atmos. Sci. Lett., 14, 45–51, doi:10.1002/asl2.416.
1098. Hartley, A.J., D.J. Parker, L. Garcia-Carreras, and S. Webster, 2016: Simulation of vegetation feedbacks on local and regional scale precipitation in West Africa. Agric. For. Meteorol., 222, 59–70, doi:10.1016/j.agrformet.2016.03.001.
1099. Klein, C. et al., 2017: Feedback of observed interannual vegetation change: A regional climate model analysis for the West African monsoon. Clim. Dyn., 48, 2837–2858, doi:10.1007/s00382-016-3237-x.
1100. Abiodun, B.J., Z.D. Adeyewa, P.G. Oguntunde, A.T. Salami, and V.O. Ajayi, 2012: Modeling the impacts of reforestation on future climate in West Africa. Theor. Appl. Climatol., 110, 77–96, doi:10.1007/s00704-012-0614-1.
1101. Butt, N., P.A. De Oliveira, and M.H. Costa, 2011: Evidence that deforestation affects the onset of the rainy season in Rondonia, Brazil. J. Geophys. Res. Atmos., 116, 2–9, doi:10.1029/2010JD015174.
1102. Wu, M. et al., 2017: Impacts of land use on climate and ecosystem productivity over the Amazon and the South American continent. Environ. Res. Lett., 12, 054016, doi:10.1088/1748-9326/aa6fd6.
1103. Spracklen, D.V., and L. Garcia-Carreras, 2015: The impact of Amazonian deforestation on Amazon Basin rainfall. Geophys. Res. Lett., 42, 9546–9552, doi:10.1002/2015GL066063.
1104. Ma, E., A. Liu, X. Li, F. Wu, and J. Zhan, 2013b: Impacts of vegetation change on the regional surface climate: A scenario-based analysis of afforestation in Jiangxi Province, China. Adv. Meteorol., 2013, doi:10.1155/2013/796163.
1105. Werth, D., 2005: The local and global effects of African deforestation. Geophys. Res. Lett., 32, 1–4, doi:10.1029/2005GL022969.
1106. Mabuchi, K., Y. Sato, and H. Kida, 2005: Climatic impact of vegetation change in the Asian tropical region. Part I: Case of the Northern Hemisphere summer. J. Clim., 18, 410–428, doi:10.1175/JCLI-3273.1.
1107. Tölle, M.H., S. Engler, and H.J. Panitz, 2017: Impact of abrupt land cover changes by tropical deforestation on Southeast Asian climate and agriculture. J. Clim., 30, 2587–2600, doi:10.1175/JCLI-D-16-0131.1.
1108. Wu, M. et al., 2017: Impacts of land use on climate and ecosystem productivity over the Amazon and the South American continent. Environ. Res. Lett., 12, 054016, doi:10.1088/1748-9326/aa6fd6.
1109. Prevedello, J.A., G.R. Winck, M.M. Weber, E. Nichols, and B. Sinervo, 2019: Impacts of forestation and deforestation on local temperature across the globe. PLoS One, 14, 1–18, doi:10.1371/journal.pone.0213368.
1110. Schultz, N.M., P.J. Lawrence, and X. Lee, 2017: Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. Biogeosciences, 122, 903–917, doi:10.1002/2016JG003653.
1111. Li, Y., M. Zhao, S. Motesharrei, Q. Mu, E. Kalnay, and S. Li, 2015b: Local cooling and warming effects of forests based on satellite observations. Nat. Commun., 6, 1–8, doi:10.1038/ncomms7603.
1112. Alkama, R., and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/science.aac8083.
1113. Abiodun, B.J., Z.D. Adeyewa, P.G. Oguntunde, A.T. Salami, and V.O. Ajayi, 2012: Modeling the impacts of reforestation on future climate in West Africa. Theor. Appl. Climatol., 110, 77–96, doi:10.1007/s00704-012-0614-1.
1114. Ma, D., M. Notaro, Z. Liu, G. Chen, and Y. Liu, 2013a: Simulated impacts of afforestation in East China monsoon region as modulated by ocean variability. Clim. Dyn., 41, 2439–2450, doi:10.1007/s00382-012-1592-9.
1115. Ornstein, L., I. Aleinov, and D. Rind, 2009: Irrigated afforestation of the Sahara and Australian Outback to end global warming. Clim. Change, 97, 409–437, doi:10.1007/s10584-009-9626-y.
1116. Kemena, T.P., K. Matthes, T. Martin, S. Wahl, and A. Oschlies, 2017: Atmospheric feedbacks in North Africa from an irrigated, afforested Sahara. Clim. Dyn., 0, 1–21, doi:10.1007/s00382-017-3890-8.
1117. Abiodun, B.J., Z.D. Adeyewa, P.G. Oguntunde, A.T. Salami, and V.O. Ajayi, 2012: Modeling the impacts of reforestation on future climate in West Africa. Theor. Appl. Climatol., 110, 77–96, doi:10.1007/s00704-012-0614-1.
1118. Phillips, T.J. et al., 2007: Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci., 104, 6550–6555, doi:10.1073/pnas.0608998104.
1119. Snyder, P.K., C. Delire, and J.A. Foley, 2004: Evaluating the influence of different vegetation biomes on the global climate. Clim. Dyn., 23, 279–302, doi:10.1007/s00382-004-0430-0.
1120. Longobardi, P., A. Montenegro, H. Beltrami, and M. Eby, 2016a: Deforestation induced climate change: Effects of spatial scale. PLoS One, doi:10.1371/journal.pone.0153357.
1121. Devaraju, N., G. Bala, and A. Modak, 2015a: Effects of large-scale deforestation on precipitation in the monsoon regions: Remote versus local effects. Proc. Natl. Acad. Sci., 112, 3257–3262, doi:10.1073/pnas.1423439112.
1122. Devaraju, N., N. de Noblet-Ducoudré, B. Quesada, and G. Bala, 2018: Quantifying the relative importance of direct and indirect biophysical effects of deforestation on surface temperature and teleconnections. J. Clim., 31, 3811–3829, doi:10.1175/JCLI-D-17-0563.1.
1123. Laguë, M.M. and A.L.S. Swann, 2016: Progressive midlatitude afforestation: Impacts on clouds, global energy transport, and precipitation. J. Clim., 29, 5561–5573, doi:10.1175/jcli-d-15-0748.1.
1124. Swann, A.L.S., I.Y. Fung, and J.C.H. Chiang, 2012: Mid-latitude afforestation shifts general circulation and tropical precipitation. Proc. Natl. Acad. Sci., 109, 712–716, doi:10.1073/pnas.1116706108.
1125. Gibbard, S., K. Caldeira, G. Bala, T.J. Phillips, and M. Wickett, 2005: Climate effects of global land cover change. Geophys. Res. Lett., 32, 1–4, doi:10.1029/2005GL024550.
1126. Wang, Y., X. Yan, and Z. Wang, 2014b: The biogeophysical effects of extreme afforestation in modeling future climate. Theor. Appl. Climatol., 118, 511–521, doi:10.1007/s00704-013-1085-8.
1127. Bright, R. et al., 2017: Local temperature response to land cover and management change driven by non-radiative processes. Nat. Clim. Chang., 7, 296–302, doi:10.1038/nclimate3250.
1128. Zhao, K. and R.B. Jackson, 2014: Biophysical forcings of land-use changes from potential forestry activities in North America. Ecol. Monogr., 84, 329–353, doi:10.1890/12-1705.1.
1129. Gálos, B., C. Mátyás, and D. Jacob, 2011: Regional characteristics of climate change altering effects of afforestation. Environ. Res. Lett., 6, 44010, doi:10.1088/1748-9326/6/4/044010.
1130. Gálos, B. et al., 2013: Case study for the assessment of the biogeophysical effects of a potential afforestation in Europe. Carbon Balance Manag., 8, 3, doi:10.1186/1750-0680-8-3.
1131. Wickham, J.D., T.G. Wade, and K.H. Riitters, 2013: Empirical analysis of the influence of forest extent on annual and seasonal surface temperatures for the continental United States. Glob. Ecol. Biogeogr., 22, 620–629, doi:10.1111/geb.12013.
1132. Ahlswede, B.J. and R.Q. Thomas, 2017: Community earth system model simulations reveal the relative importance of afforestation and forest management to surface temperature in eastern North America. Forests, 8, 1–10, doi:10.3390/f8120499.
1133. Anderson-Teixeira, K.J. et al., 2012: Climate-regulation services of natural and agricultural ecoregions of the Americas. Nat. Clim. Chang., 2, 177–181, doi:10.1038/nclimate1346.
1134. Anderson, R.G. et al., 2011: Biophysical considerations in forestry for climate protection. Front. Ecol. Environ., 9, 174–182, doi:10.1890/090179.
1135. Chen, G.S., M. Notaro, Z. Liu, and Y. Liu, 2012: Simulated local and remote biophysical effects of afforestation over the Southeast United States in boreal summer. J. Clim., 25, 4511–4522, doi:10.1175/JCLI-D-11-00317.1.
1136. Strandberg, G., and E. Kjellström, 2018: Climate impacts from afforestation and deforestation in Europe. Earth Interact., 23, 1–27, doi:10.1175/ei-d-17-0033.1.
1137. Davin, E.L., and N. de Noblet-Ducoudre, 2010: Climatic impact of global-scale deforestation: Radiative versus nonradiative processes. J. Clim., 23, 97–112, doi:10.1175/2009JCLI3102.1.
1138. Anav, A., P.M. Ruti, V. Artale, and R. Valentini, 2010: Modelling the effects of land-cover changes on surface climate in the Mediterranean region. Clim. Res., 41, 91–104, doi:10.3354/cr00841.
1139. Schultz, N.M., P.J. Lawrence, and X. Lee, 2017: Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. Biogeosciences, 122, 903–917, doi:10.1002/2016JG003653.
1140. Wickham, J., T.G. Wade, and K.H. Riitters, 2014: An isoline separating relatively warm from relatively cool wintertime forest surface temperatures for the southeastern United States. Glob. Planet. Change, 120, 46–53, doi:10.1016/j.gloplacha.2014.05.012.
1141. Juang, J.Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick, 2007: Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett., 34, 1–5, doi:10.1029/2007GL031296.
1142. Tang, B., X. Zhao, and W. Zhao, 2018: Local effects of forests on temperatures across Europe. Remote Sens., 10, 1–24, doi:10.3390/rs10040529.
1143. Peng, S.-S. et al., 2014: Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci., 111, 2915–2919, doi:10.1073/pnas.1315126111.
1144. Zhang, M. et al., 2014b: Response of surface air temperature to small-scale land clearing across latitudes. Environ. Res. Lett., 9, 034002, doi:10.1088/1748-9326/9/3/034002.
1145. Prevedello, J.A., G.R. Winck, M.M. Weber, E. Nichols, and B. Sinervo, 2019: Impacts of forestation and deforestation on local temperature across the globe. PLoS One, 14, 1–18, doi:10.1371/journal.pone.0213368.
1146. Li, Y., M. Zhao, S. Motesharrei, Q. Mu, E. Kalnay, and S. Li, 2015b: Local cooling and warming effects of forests based on satellite observations. Nat. Commun., 6, 1–8, doi:10.1038/ncomms7603.
1147. Alkama, R., and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/science.aac8083.
1148. Peng, S.-S. et al., 2014: Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci., 111, 2915–2919, doi:10.1073/pnas.1315126111.
1149. Juang, J.Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick, 2007: Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett., 34, 1–5, doi:10.1029/2007GL031296.
1150. Schultz, N.M., P.J. Lawrence, and X. Lee, 2017: Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. Biogeosciences, 122, 903–917, doi:10.1002/2016JG003653.
1151. Wickham, J., T.G. Wade, and K.H. Riitters, 2014: An isoline separating relatively warm from relatively cool wintertime forest surface temperatures for the southeastern United States. Glob. Planet. Change, 120, 46–53, doi:10.1016/j.gloplacha.2014.05.012.
1152. Juang, J.Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick, 2007: Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett., 34, 1–5, doi:10.1029/2007GL031296.
1153. Tang, B., X. Zhao, and W. Zhao, 2018: Local effects of forests on temperatures across Europe. Remote Sens., 10, 1–24, doi:10.3390/rs10040529.
1154. Prevedello, J.A., G.R. Winck, M.M. Weber, E. Nichols, and B. Sinervo, 2019: Impacts of forestation and deforestation on local temperature across the globe. PLoS One, 14, 1–18, doi:10.1371/journal.pone.0213368.
1155. Peng, S.-S. et al., 2014: Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci., 111, 2915–2919, doi:10.1073/pnas.1315126111.
1156. Zhang, M. et al., 2014b: Response of surface air temperature to small-scale land clearing across latitudes. Environ. Res. Lett., 9, 034002, doi:10.1088/1748-9326/9/3/034002.
1157. Li, D. et al., 2015a: Contrasting responses of urban and rural surface energy budgets to heat waves explain synergies between urban heat islands and heat waves. Environ. Res. Lett., 10, 54009, doi:10.1088/1748-9326/10/5/054009.
1158. Alkama, R., and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/science.aac8083.
1159. Lejeune, Q., E.L. Davin, L. Gudmundsson, and S.I. Seneviratne, 2018: Historical deforestation increased the risk of heat extremes in northern mid-latitudes 2. Nat. Clim. Chang., 8, 1–16, doi:10.1038/s41558-018-0131-z.
1160. Strandberg, G., and E. Kjellström, 2018: Climate impacts from afforestation and deforestation in Europe. Earth Interact., 23, 1–27, doi:10.1175/ei-d-17-0033.1.
1161. Gálos, B. et al., 2013: Case study for the assessment of the biogeophysical effects of a potential afforestation in Europe. Carbon Balance Manag., 8, 3, doi:10.1186/1750-0680-8-3.
1162. Sonntag, S., J. Pongratz, C.H. Reick, and H. Schmidt, 2016: Reforestation in a high-CO2 world—Higher mitigation potential than expected, lower adaptation potential than hoped for. Geophys. Res. Lett., 43, 6546–6553, doi:10.1002/2016GL068824.
1163. Sonntag, S. et al., 2018: Quantifying and comparing effects of climate engineering methods on the Earth system. Earth’s Futur., doi:10.1002/eft2.285.
1164. Dass, P., C. Müller, V. Brovkin, and W. Cramer, 2013: Can bioenergy cropping compensate high carbon emissions from large-scale deforestation of high latitudes? Earth Syst. Dyn., 4, 409–424, doi:10.5194/esd-4-409-2013.
1165. Longobardi, P., A. Montenegro, H. Beltrami, and M. Eby, 2016a: Deforestation induced climate change: Effects of spatial scale. PLoS One, doi:10.1371/journal.pone.0153357.
1166. Devaraju, N., G. Bala, and A. Modak, 2015a: Effects of large-scale deforestation on precipitation in the monsoon regions: Remote versus local effects. Proc. Natl. Acad. Sci., 112, 3257–3262, doi:10.1073/pnas.1423439112.
1167. Bathiany, S., M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler, 2010a: Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model. Biogeosciences, 7, 1383–1399, doi:10.5194/bg-7-1383-2010.
1168. Devaraju, N., N. de Noblet-Ducoudré, B. Quesada, and G. Bala, 2018: Quantifying the relative importance of direct and indirect biophysical effects of deforestation on surface temperature and teleconnections. J. Clim., 31, 3811–3829, doi:10.1175/JCLI-D-17-0563.1.
1169. Strandberg, G., and E. Kjellström, 2018: Climate impacts from afforestation and deforestation in Europe. Earth Interact., 23, 1–27, doi:10.1175/ei-d-17-0033.1.
1170. Gao, Y. et al., 2014: Biogeophysical impacts of peatland forestation on regional climate changes in Finland. Biogeosciences, 11, 7251–7267, doi:10.5194/bg-11-7251-2014.
1171. Schultz, N.M., P.J. Lawrence, and X. Lee, 2017: Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. Biogeosciences, 122, 903–917, doi:10.1002/2016JG003653.
1172. Arora, V.K. and A. Montenegro, 2011: Small temperature benefits provided by realistic afforestation efforts. Nat. Geosci., 4, 514–518, doi:10.1038/ngeo1182.
1173. Pitman, A.J. et al., 2011: Importance of background climate in determining impact of land-cover change on regional climate. Nat. Clim. Chang., 1, 472–475, doi:10.1038/nclimate1294.
1174. Montenegro, A. et al., 2009: The net carbon drawdown of small scale afforestation from satellite observations. Glob. Planet. Change, 69, 195–204, doi:10.1016/j.gloplacha.2009.08.005.
1175. Juang, J.Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick, 2007: Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett., 34, 1–5, doi:10.1029/2007GL031296.
1176. Wickham, J., T.G. Wade, and K.H. Riitters, 2014: An isoline separating relatively warm from relatively cool wintertime forest surface temperatures for the southeastern United States. Glob. Planet. Change, 120, 46–53, doi:10.1016/j.gloplacha.2014.05.012.
1177. Hagos, S. et al., 2014: Assessment of uncertainties in the response of the African monsoon precipitation to land use change simulated by a regional model. Clim. Dyn., 43, 2765–2775, doi:10.1007/s00382-014-2092-x.
1178. Voldoire, A., 2006: Quantifying the impact of future land-use changes against increases in GHG concentrations. Geophys. Res. Lett., 33, 2–5, doi:10.1029/2005GL024354.
1179. Feddema, J.J. et al., 2005: The importance of land-cover change in simulating future climates. Science, 310, 1674–1678, doi:10.1126/science.1118160.
1180. Strandberg, G., and E. Kjellström, 2018: Climate impacts from afforestation and deforestation in Europe. Earth Interact., 23, 1–27, doi:10.1175/ei-d-17-0033.1.
1181. Bright, R. et al., 2017: Local temperature response to land cover and management change driven by non-radiative processes. Nat. Clim. Chang., 7, 296–302, doi:10.1038/nclimate3250.
1182. O’Halloran, T.L. et al., 2012: Radiative forcing of natural forest disturbances. Glob. Chang. Biol., 18, 555–565, doi:10.1111/j.1365-2486.2011.02577.x.
1183. Erb, K.-H. et al., 2017: Land management: data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.
1184. Luyssaert, S. et al., 2018: Trade-offs in using European forests to meet climate objectives. Nature, 562, 259–262, doi:10.1038/s41586-018-0577-1.
1185. Jeong, J.H. et al., 2014a: Intensified Arctic warming under greenhouse warming by vegetation-atmosphere-sea ice interaction. Environ. Res. Lett., 9, doi:10.1088/1748-9326/9/9/094007.
1186. Bagley, J.E. et al., 2017: The influence of land cover on surface energy partitioning and evaporative fraction regimes in the U.S. Southern Great Plains. J. Geophys. Res. Atmos., 122, 5793–5807, doi:10.1002/2017JD026740.
1187. Erb, K.-H. et al., 2017: Land management: data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.
1188. Naudts, K. et al., 2016a: Mitigate climate warming. Science, 351, 597–601, doi:10.1126/science.aac9976.
1189. Luyssaert, S. et al., 2018: Trade-offs in using European forests to meet climate objectives. Nature, 562, 259–262, doi:10.1038/s41586-018-0577-1.
1190. Bonfils, C. and D. Lobell, 2007: Empirical evidence for a recent slowdown in irrigation-induced cooling. Proc. Natl. Acad. Sci., 104, 13582–13587, doi:10.1073/pnas.0700144104.
1191. Alter, R.E., E.S. Im, and E.A.B. Eltahir, 2015: Rainfall consistently enhanced around the Gezira Scheme in East Africa due to irrigation. Nat. Geosci., 8, 763–767, doi:10.1038/ngeo2514.
1192. Chen, X. and S.J. Jeong, 2018: Irrigation enhances local warming with greater nocturnal warming effects than daytime cooling effects. Environ. Res. Lett., 13, 24005, doi:10.1088/1748-9326/aa9dea.
1193. Christy, J.R., W.B. Norris, K. Redmond, and K.P. Gallo, 2006: Methodology and results of calculating central California surface temperature trends: Evidence of human-induced climate change? J. Clim., 19, 548–563, doi:10.1175/JCLI3627.1.
1194. Im, E.S. and E.A.B. Eltahir, 2014: Enhancement of rainfall and runoff upstream from irrigation location in a climate model of West Africa. Water Resour. Res., 50, 8651–8674, doi:10.1002/2014WR015592.
1195. Im, E.S, M.P. Marcella, and E.A.B. Eltahir, 2014: Impact of potential large-scale irrigation on the West African monsoon and its dependence on location of irrigated area. J. Clim., 27, 994–1009, doi:10.1175/JCLI-D-13-00290.1.
1196. Mueller, N.D., 2015: Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Chang., 6, 317–322, doi:10.1038/nclimate2825.
1197. Cook, B.I., T.R. Ault, and J.E. Smerdon, 2015: Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv., 1, e1400082–e1400082, doi:10.1126/sciadv.1400082.
1198. Han, S., and Z. Yang, 2013: Cooling effect of agricultural irrigation over Xinjiang, Northwest China from 1959 to 2006. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/2/024039.
1199. Huber, D., D. Mechem, and N. Brunsell, 2014: The effects of Great Plains irrigation on the surface energy balance, regional circulation, and precipitation. Climate, 2, 103–128, doi:10.3390/cli2020103.
1200. Alter, R.E., E.S. Im, and E.A.B. Eltahir, 2015: Rainfall consistently enhanced around the Gezira Scheme in East Africa due to irrigation. Nat. Geosci., 8, 763–767, doi:10.1038/ngeo2514.
1201. Im, E.S, M.P. Marcella, and E.A.B. Eltahir, 2014: Impact of potential large-scale irrigation on the West African monsoon and its dependence on location of irrigated area. J. Clim., 27, 994–1009, doi:10.1175/JCLI-D-13-00290.1.
1202. Han, S., and Z. Yang, 2013: Cooling effect of agricultural irrigation over Xinjiang, Northwest China from 1959 to 2006. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/2/024039.
1203. Chen, X. and S.J. Jeong, 2018: Irrigation enhances local warming with greater nocturnal warming effects than daytime cooling effects. Environ. Res. Lett., 13, 24005, doi:10.1088/1748-9326/aa9dea.
1204. Bonfils, C. and D. Lobell, 2007: Empirical evidence for a recent slowdown in irrigation-induced cooling. Proc. Natl. Acad. Sci., 104, 13582–13587, doi:10.1073/pnas.0700144104.
1205. Thiery, W. et al., 2017: Present-day irrigation mitigates heat extremes. J. Geophys. Res., 122, 1403–1422, doi:10.1002/2016JD025740.
1206. Mueller, N.D., 2015: Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Chang., 6, 317–322, doi:10.1038/nclimate2825.
1207. Chen, X. and S.J. Jeong, 2018: Irrigation enhances local warming with greater nocturnal warming effects than daytime cooling effects. Environ. Res. Lett., 13, 24005, doi:10.1088/1748-9326/aa9dea.
1208. Chen, X. and S.J. Jeong, 2018: Irrigation enhances local warming with greater nocturnal warming effects than daytime cooling effects. Environ. Res. Lett., 13, 24005, doi:10.1088/1748-9326/aa9dea.
1209. Christy, J.R., W.B. Norris, K. Redmond, and K.P. Gallo, 2006: Methodology and results of calculating central California surface temperature trends: Evidence of human-induced climate change? J. Clim., 19, 548–563, doi:10.1175/JCLI3627.1.
1210. Siebert, S., V. Henrich, K. Frenken, and J. Burke, 2013: Global map of irrigation areas Version 5. Food and Agriculture Organization of the United Nations, Rome, Italy. http://www.fao.org/nr/water/aquastat/irrigationmap/index10.stm .
1211. Chen, X. and S.J. Jeong, 2018: Irrigation enhances local warming with greater nocturnal warming effects than daytime cooling effects. Environ. Res. Lett., 13, 24005, doi:10.1088/1748-9326/aa9dea.
1212. Alter, R.E., E.S. Im, and E.A.B. Eltahir, 2015: Rainfall consistently enhanced around the Gezira Scheme in East Africa due to irrigation. Nat. Geosci., 8, 763–767, doi:10.1038/ngeo2514.
1213. Han, S., and Z. Yang, 2013: Cooling effect of agricultural irrigation over Xinjiang, Northwest China from 1959 to 2006. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/2/024039.
1214. Roy, S. et al., 2007: Impacts of the agricultural Green Revolution-induced land use changes on air temperatures in India. J. Geophys. Res. Atmos., 112, 1–13, doi:10.1029/2007JD008834.
1215. Shi, W., F. Tao, and J. Liu, 2013: Regional temperature change over the Huang-Huai-Hai Plain of China: The roles of irrigation versus urbanization. Int. J. Climatol., 34, 1181–1195, doi:10.1002/joc.3755.
1216. Bonfils, C. and D. Lobell, 2007: Empirical evidence for a recent slowdown in irrigation-induced cooling. Proc. Natl. Acad. Sci., 104, 13582–13587, doi:10.1073/pnas.0700144104.
1217. Lobell, D.B., C.J. Bonfils, L.M. Kueppers, and M.A. Snyder, 2008: Irrigation cooling effect on temperature and heat index extremes. Geophys. Res. Lett., 35, 1–5, doi:10.1029/2008GL034145.
1218. Lobell, D.B., and C. Bonfils, 2008: The effect of irrigation on regional temperatures: A spatial and temporal analysis of trends in California, 1934–2002. J. Clim., 21, 2063–2071, doi:10.1175/2007JCLI1755.1.
1219. Christy, J.R., W.B. Norris, K. Redmond, and K.P. Gallo, 2006: Methodology and results of calculating central California surface temperature trends: Evidence of human-induced climate change? J. Clim., 19, 548–563, doi:10.1175/JCLI3627.1.
1220. Mahmood, R. et al., 2006: Impacts of irrigation on 20th century temperature in the northern Great Plains. Glob. Planet. Change, 54, 1–18, doi:10.1016/j.gloplacha.2005.10.004.
1221. Mueller, N.D., 2015: Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Chang., 6, 317–322, doi:10.1038/nclimate2825.
1222. Alter, R.E., E.S. Im, and E.A.B. Eltahir, 2015: Rainfall consistently enhanced around the Gezira Scheme in East Africa due to irrigation. Nat. Geosci., 8, 763–767, doi:10.1038/ngeo2514.
1223. Im, E.S. and E.A.B. Eltahir, 2014: Enhancement of rainfall and runoff upstream from irrigation location in a climate model of West Africa. Water Resour. Res., 50, 8651–8674, doi:10.1002/2014WR015592.
1224. Im, E.S, M.P. Marcella, and E.A.B. Eltahir, 2014: Impact of potential large-scale irrigation on the West African monsoon and its dependence on location of irrigated area. J. Clim., 27, 994–1009, doi:10.1175/JCLI-D-13-00290.1.
1225. Niyogi, D., C. Kishtawal, S. Tripathi, and R.S. Govindaraju, 2010: Observational evidence that agricultural intensification and land use change may be reducing the Indian summer monsoon rainfall. Water Resour. Res., 46, 1–17, doi:10.1029/2008WR007082.
1226. Guimberteau, M., K. Laval, A. Perrier, and J. Polcher, 2012: Global effect of irrigation and its impact on the onset of the Indian summer monsoon. Clim. Dyn., 39, 1329–1348, doi:10.1007/s00382-011-1252-5.
1227. De Vrese, P., S. Hagemann, and M. Claussen, 2016: Asian irrigation, African rain: Remote impacts of irrigation. Geophys. Res. Lett., 43, 3737–3745, doi:10.1002/2016GL068146.
1228. Ridgwell, A., J.S. Singarayer, A.M. Hetherington, and P.J. Valdes, 2009: Tackling regional climate change by leaf albedo bio-geoengineering. Curr. Biol., 19, 146–150, doi:10.1016/j.cub.2008.12.025.
1229. Crook, J.A., L.S. Jackson, S.M. Osprey, and P.M. Forster, 2015: A comparison of temperature and precipitation responses to different earth radiation management geoengineering schemes. J. Geophys. Res., 120, 9352–9373, doi:10.1002/2015JD023269.
1230. Hirsch, A.L., M. Wilhelm, E.L. Davin, W. Thiery, and S.I. Seneviratne, 2017: Can climate-effective land management reduce regional warming? J. Geophys. Res., 122, 2269–2288, doi:10.1002/2016JD026125.
1231. Singarayer, J.S., A. Ridgwell, and P. Irvine, 2009: Assessing the benefits of crop albedo bio-geoengineering. Environ. Res. Lett., 4, doi:10.1088/1748-9326/4/4/045110.
1232. Singarayer, J.S. and T. Davies-Barnard, 2012: Regional climate change mitigation with crops: Context and assessment. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 370, 4301–4316, doi:10.1098/rsta.2012.0010.
1233. Lobell, D.B., G. Bala, and P.B. Duffy, 2006: Biogeophysical impacts of cropland management changes on climate. Geophys. Res. Lett., 33, 4–7, doi:10.1029/2005GL025492.
1234. Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci., 111, 9757–9761, doi:10.1073/pnas.1317323111.
1235. Carrer, D., G. Pique, M. Ferlicoq, X. Ceamanos, and E. Ceschia, 2018: What is the potential of cropland albedo management in the fight against global warming? A case study based on the use of cover crops. Environ. Res. Lett., 13, 44030, doi:10.1088/1748-9326/aab650.
1236. Kaye, J.P., and M. Quemada, 2017: Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev., 37, 4, doi:10.1007/s13593-016-0410-x.
1237. Campra, P., M. Garcia, Y. Canton, and A. Palacios-Orueta, 2008: Surface temperature cooling trends and negative radiative forcing due to land use change toward greenhouse farming in southeastern Spain. J. Geophys. Res. Atmos., 113, 1–10, doi:10.1029/2008JD009912.
1238. Seneviratne, S.I. et al., 2018: Land radiative management as contributor to regional-scale climate adaptation and mitigation. Nat. Geosci., 11, 88–96, doi:10.1038/s41561-017-0057-5.
1239. Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci., 111, 9757–9761, doi:10.1073/pnas.1317323111.
1240. Wilhelm, M., E.L. Davin, and S.I. Seneviratne, 2015: Climate engineering of vegetated land for hot extremes mitigation: An Earth system model sensitivity study. J. Geophy. Res. Atmos., 120, 2612–2623, doi:10.1002/2014JD022293.
1241. Kaye, J.P., and M. Quemada, 2017: Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev., 37, 4, doi:10.1007/s13593-016-0410-x.
1242. Lombardozzi, D.L. et al., 2018: Cover Crops May Cause Winter Warming in Snow-Covered Regions. Geophys. Res. Lett., 45, 9889–9897, doi:10.1029/2018GL079000.
1243. Ceschia, E. et al., 2017: Potentiel d’atténuation des changements climatiques par les couverts intermédiaires. Innov. Agron., 62, 43–58, doi:10.15454/1.517402718167511E12.
1244. Basche, A.D., F.E. Miguez, T.C. Kaspar, and M.J. Castellano, 2014: Do cover crops increase or decrease nitrous oxide emissions? A meta-analysis. J. Soil Water Conserv., 69, 471–482, doi:10.2489/jswc.69.6.471.
1245. Kaye, J.P., and M. Quemada, 2017: Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev., 37, 4, doi:10.1007/s13593-016-0410-x.
1246. Constantin, J. et al., 2010: Effects of catch crops, no till and reduced nitrogen fertilization on nitrogen leaching and balance in three long-term experiments. Agric. Ecosyst. Environ., 135, 268–278, doi:10.1016/j.agee.2009.10.005.
1247. Constantin, J. et al., 2011: Cumulative effects of catch crops on nitrogen uptake, leaching and net mineralization. Plant Soil, 341, 137–154, doi:10.1007/s11104-010-0630-9.
1248. Kaye, J.P., and M. Quemada, 2017: Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev., 37, 4, doi:10.1007/s13593-016-0410-x.
1249. Tribouillois, H., J. Constantin, and E. Justes, 2018: Cover crops mitigate direct greenhouse gases balance but reduce drainage under climate change scenarios in temperate climate with dry summers. Glob. Chang. Biol., 24, 2513–2529, doi:10.1111/gcb.14091.
1250. Seneviratne, S.I. et al., 2018: Land radiative management as contributor to regional-scale climate adaptation and mitigation. Nat. Geosci., 11, 88–96, doi:10.1038/s41561-017-0057-5.
1251. Garnaud, C. and L. Sushama, 2015: Biosphere-climate interactions in a changing climate over North America. J. Geophys. Res. Atmos., 120, 1091–1108, doi:10.1002/2014JD022055.
1252. Jeong, J.H. et al., 2014a: Intensified Arctic warming under greenhouse warming by vegetation-atmosphere-sea ice interaction. Environ. Res. Lett., 9, doi:10.1088/1748-9326/9/9/094007.
1253. O’ishi, R., and A. Abe-Ouchi, 2009: Influence of dynamic vegetation on climate change arising from increasing CO2. Clim. Dyn., 33, 645–663, doi:10.1007/s00382-009-0611-y.
1254. Port, U., V. Brovkin, and M. Claussen, 2012: The influence of vegetation dynamics on anthropogenic climate change. Earth Syst. Dyn., 3, 233–243, doi:10.5194/esd-3-233-2012.
1255. Strengers, B.J. et al., 2010: Assessing 20th century climate-vegetation feedbacks of land-use change and natural vegetation dynamics in a fully coupled vegetation-climate model. Int. J. Climatol., 30, 2055–2065, doi:10.1002/joc.2132.
1256. Port, U., V. Brovkin, and M. Claussen, 2012: The influence of vegetation dynamics on anthropogenic climate change. Earth Syst. Dyn., 3, 233–243, doi:10.5194/esd-3-233-2012.
1257. Wu, M. et al., 2016: Vegetation-climate feedbacks modulate rainfall patterns in Africa under future climate change. Earth Syst. Dyn., 7, 627–647, doi:10.5194/esd-7-627-2016.
1258. Yu, M., G. Wang, and H. Chen, 2016: Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets. J. Adv. Model. Earth Syst., 8, 370–386, doi:10.1002/2015MS000492.
1259. Port, U., V. Brovkin, and M. Claussen, 2012: The influence of vegetation dynamics on anthropogenic climate change. Earth Syst. Dyn., 3, 233–243, doi:10.5194/esd-3-233-2012.
1260. Yu, M., G. Wang, and H. Chen, 2016: Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets. J. Adv. Model. Earth Syst., 8, 370–386, doi:10.1002/2015MS000492.
1261. Port, U., V. Brovkin, and M. Claussen, 2012: The influence of vegetation dynamics on anthropogenic climate change. Earth Syst. Dyn., 3, 233–243, doi:10.5194/esd-3-233-2012.
1262. Wu, M. et al., 2016: Vegetation-climate feedbacks modulate rainfall patterns in Africa under future climate change. Earth Syst. Dyn., 7, 627–647, doi:10.5194/esd-7-627-2016.
1263. Wu, M. et al., 2016: Vegetation-climate feedbacks modulate rainfall patterns in Africa under future climate change. Earth Syst. Dyn., 7, 627–647, doi:10.5194/esd-7-627-2016.
1264. de Noblet-Ducoudré, N., M. Claussen, and C. Prentice, 2000: Mid-Holocene greening of the Sahara: First results of the GAIM 6000 year BP experiment with two asynchronously coupled atmosphere/biome models. Clim. Dyn., 16, 643–659, doi:10.1007/s003820000074.
1265. Rachmayani, R., M. Prange, and M. Schulz, 2015: North African vegetation-precipitation feedback in early and mid-Holocene climate simulations with CCSM3-DGVM. Clim. Past, 11, 175–185, doi:10.5194/cp-11-175-2015.
1266. Derksen, C., and R. Brown, 2012: Spring snow cover extent reductions in the 2008-2012 period exceeding climate model projections. Geophys. Res. Lett., 39, doi:10.1029/2012GL053387. doi:10.1029/2012GL053387.
1267. Brutel-Vuilmet, C., M. Ménégoz, and G. Krinner, 2013: An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models. Cryosphere, 7, 67–80, doi:10.5194/tc-7-67-2013.
1268. Brutel-Vuilmet, C., M. Ménégoz, and G. Krinner, 2013: An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models. Cryosphere, 7, 67–80, doi:10.5194/tc-7-67-2013.
1269. Perket, J., M.G. Flanner, and J.E. Kay, 2014: Diagnosing shortwave cryosphere radiative effect and its 21st century evolution in CESM.J. Geophys. Res., 119, 1356–1362, doi:10.1002/2013JD021139.
1270. Thackeray, C.W. and C.G. Fletcher, 2015: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions. Prog. Phys. Geogr., 40, 392–408, doi:10.1177/0309133315620999.
1271. Mudryk, L.R., P.J. Kushner, C. Derksen, and C. Thackeray, 2017: Snow cover response to temperature in observational and climate model ensembles. Geophys. Res. Lett., 44, 919–926, doi:10.1002/2016GL071789.
1272. Flanner, M.G., K.M. Shell, M. Barlage, D.K. Perovich, and M.A. Tschudi, 2011: Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nat. Geosci., 4, 151–155, doi:10.1038/ngeo1062.
1273. Singh, D., M.G. Flanner, and J. Perket, 2015: The global land shortwave cryosphere radiative effect during the MODIS era. Cryosphere, 9, 2057–2070, doi:10.5194/tc-9-2057-2015.
1274. Mudryk, L.R., P.J. Kushner, C. Derksen, and C. Thackeray, 2017: Snow cover response to temperature in observational and climate model ensembles. Geophys. Res. Lett., 44, 919–926, doi:10.1002/2016GL071789.
1275. Thackeray, C.W. and C.G. Fletcher, 2015: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions. Prog. Phys. Geogr., 40, 392–408, doi:10.1177/0309133315620999.
1276. Qu, X. and A. Hall, 2014: On the persistent spread in snow-albedo feedback. Clim. Dyn., 42, 69–81, doi:10.1007/s00382-013-1774-0.
1277. Flanner, M.G., K.M. Shell, M. Barlage, D.K. Perovich, and M.A. Tschudi, 2011: Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nat. Geosci., 4, 151–155, doi:10.1038/ngeo1062.
1278. Thackeray, C.W. and C.G. Fletcher, 2015: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions. Prog. Phys. Geogr., 40, 392–408, doi:10.1177/0309133315620999.
1279. Loranty, M.M., L.T. Berner, S.J. Goetz, Y. Jin, and J.T. Randerson, 2014: Vegetation controls on northern high latitude snow-albedo feedback: Observations and CMIP5 model simulations. Glob. Chang. Biol., 20, 594–606, doi:10.1111/gcb.12391.
1280. Wang, W. et al., 2016c: Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region. Cryosphere, 10, 1721–1737, doi:10.5194/tc-10-1721-2016.
1281. Thackeray, C.W. and C.G. Fletcher, 2015: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions. Prog. Phys. Geogr., 40, 392–408, doi:10.1177/0309133315620999.
1282. Schuur, E.A.G. et al., 2015: Climate change and the permafrost carbon feedback. Nature, 520, 171–179, doi:10.1038/nature14338.
1283. Hugelius, G. et al., 2014: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences, 11, 6573–6593, doi:10.5194/bg-11-6573-2014.
1284. Hugelius, G. et al., 2013: The northern circumpolar soil carbon database: Spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data, 5, 3–13, doi:10.5194/essd-5-3-2013.
1285. Olefeldt, D. et al., 2016: Circumpolar distribution and carbon storage of thermokarst landscapes. Nat. Commun., 7, 1–11, doi:10.1038/ncomms13043.
1286. McGuire, A.D. et al., 2018: Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl. Acad. Sci., 115, 201719903, doi:10.1073/pnas.1719903115.
1287. Schuur, E.A.G. et al., 2015: Climate change and the permafrost carbon feedback. Nature, 520, 171–179, doi:10.1038/nature14338.
1288. Koven, C.D. et al., 2015: A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 373, 20140423, doi:10.1098/rsta.2014.0423.
1289. McGuire, A.D. et al., 2018: Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl. Acad. Sci., 115, 201719903, doi:10.1073/pnas.1719903115.
1290. Mueller, B., and S.I. Seneviratne, 2012: Hot days induced by precipitation deficits at the global scale. Proc. Natl. Acad. Sci., 109, 12398–12403, doi:10.1073/pnas.1204330109.
1291. Quesada, B., R. Vautard, P. Yiou, M. Hirschi, and S.I. Seneviratne, 2012: Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat. Clim. Chang., 2, 736–741, doi:10.1038/nclimate1536.
1292. Miralles, D.G., P. Gentine, S.I. Seneviratne, and A.J. Teuling, 2018: Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. N.Y. Acad. Sci., 1436, 19–35, doi:10.1111/nyas.13912.
1293. Geirinhas, J.L., R.M. Trigo, R. Libonati, C.A.S. Coelho, and A.C. Palmeira, 2018: Climatic and synoptic characterization of heat waves in Brazil. Int. J. Climatol., 38, 1760–1776, doi:10.1002/joc.5294.
1294. Miralles, D.G., A.J. Teuling, C.C. Van Heerwaarden, and J.V.G. De Arellano, 2014: Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci., 7, 345–349, doi:10.1038/ngeo2141.
1295. Chiang, F., O. Mazdiyasni, and A. AghaKouchak, 2018: Amplified warming of droughts in southern United States in observations and model simulations. Sci. Adv., 4, eaat2380, doi:10.1126/sciadv.aat2380.
1296. Dong, J., and W.T. Crow, 2019: L-band remote-sensing increases sampled levels of global soil moisture-air temperature coupling strength. Remote Sens. Environ., 220, 51–58, doi:10.1016/j.rse.2018.10.024.
1297. Hirschi, M., B. Mueller, W. Dorigo, and S.I. Seneviratne, 2014: Using remotely sensed soil moisture for land-atmosphere coupling diagnostics: The role of surface vs. root-zone soil moisture variability. Remote Sens. Environ., 154, 246–252, doi:10.1016/j.rse.2014.08.030.
1298. Seneviratne, S.I. et al., 2013: Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophys. Res. Lett., 40, 5212–5217, doi:10.1002/grl.50956.
1299. Vogel, M.M. et al., 2017: Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks. Geophys. Res. Lett., 44, 1511–1519, doi:10.1002/2016GL071235.
1300. Donat, M.G., A.J. Pitman, and O. Angélil, 2018: Understanding and reducing future uncertainty in midlatitude daily heat extremes via land surface feedback constraints. Geophys. Res. Lett., 45, 10,627-10,636, doi:10.1029/2018GL079128.
1301. Miralles, D.G., P. Gentine, S.I. Seneviratne, and A.J. Teuling, 2018: Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. N.Y. Acad. Sci., 1436, 19–35, doi:10.1111/nyas.13912.
1302. Seneviratne, S.I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land-atmosphere coupling and climate change in Europe. Nature, 443, 205–209, doi:10.1038/nature05095.
1303. Fischer, E.M., J. Rajczak, and C. Schär, 2012: Changes in European summer temperature variability revisited. Geophys. Res. Lett., 39, doi:10.1029/2012GL052730.
1304. IPCC, 2013a: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–29.
1305. Sippel, S. et al., 2017: Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics. Earth Syst. Dyn., 8, 387–403, doi:10.5194/esd-8-387-2017.
1306. Ukkola, A.M., A.J. Pitman, M.G. Donat, M.G. De Kauwe, and O. Angélil, 2018: Evaluating the contribution of land-atmosphere coupling to heat extremes in CMIP5 Models. Geophys. Res. Lett., 45, 9003–9012, doi:10.1029/2018GL079102.
1307. Donat, M.G., A.J. Pitman, and O. Angélil, 2018: Understanding and reducing future uncertainty in midlatitude daily heat extremes via land surface feedback constraints. Geophys. Res. Lett., 45, 10,627-10,636, doi:10.1029/2018GL079128.
1308. Miralles, D.G., P. Gentine, S.I. Seneviratne, and A.J. Teuling, 2018: Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. N.Y. Acad. Sci., 1436, 19–35, doi:10.1111/nyas.13912.
1309. Hauser, M., R. Orth, and S.I. Seneviratne, 2017: Investigating soil moisture–climate interactions with prescribed soil moisture experiments: an assessment with the Community Earth System Model (version 1.2). Geosci. Model Dev., 10, 1665–1677, doi:10.5194/gmd-10-1665-2017.
1310. Berg, A. et al., 2016: Land-atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Chang., 6, 869–874, doi:10.1038/nclimate3029.
1311. Dirmeyer, P.A., Z. Wang, M.J. Mbuh, and H.E.Norton, 2014: Intensified land surface control on boundary layer growth in a changing climate. Geophys. Res. Lett., 45, 1290–1294, doi:10.1002/2013GL058826.Received.
1312. Guo, Z. et al., 2006: GLACE: The global land–atmosphere coupling experiment. Part II: Analysis. J. Hydrometeorol., 7, 611–625, doi:10.1175/JHM511.1.
1313. Tuttle, S. and G. Salvucci, 2016: Atmospheric science: Empirical evidence of contrasting soil moisture-precipitation feedbacks across the United States. Science, 352, 825–828, doi:10.1126/science.aaa7185.
1314. Yang, L., G. Sun, L. Zhi, and J. Zhao, 2018: Negative soil moisture-precipitation feedback in dry and wet regions. Sci. Rep., 8, 4026, doi:10.1038/s41598-018-22394-7.
1315. Froidevaux, P. et al., 2014: Influence of the background wind on the local soil moisture–precipitation feedback. J. Atmos. Sci., 71, 782–799, doi:10.1175/JAS-D-13-0180.1.
1316. Guillod, B.P., B. Orlowsky, D.G. Miralles, A.J. Teuling, and S.I. Seneviratne, 2015: Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nat. Commun., 6, 6443, doi:10.1038/ncomms7443.
1317. Ma, E., A. Liu, X. Li, F. Wu, and J. Zhan, 2013b: Impacts of vegetation change on the regional surface climate: A scenario-based analysis of afforestation in Jiangxi Province, China. Adv. Meteorol., 2013, doi:10.1155/2013/796163.
1318. McLeod, J., M. Shepherd, and C.E. Konrad, 2017: Spatio-temporal rainfall patterns around Atlanta, Georgia and possible relationships to urban land cover. Urban Clim., 21, 27–42, doi:10.1016/j.uclim.2017.03.004.
1319. Abiodun, B.J., Z.D. Adeyewa, P.G. Oguntunde, A.T. Salami, and V.O. Ajayi, 2012: Modeling the impacts of reforestation on future climate in West Africa. Theor. Appl. Climatol., 110, 77–96, doi:10.1007/s00704-012-0614-1.
1321. Ellison, D. et al., 2017: Trees, forests and water: Cool insights for a hot world. Glob. Environ. Chang., 43, 51–61, doi:10.1016/j.gloenvcha.2017.01.002.
1322. Layton, K. and D. Ellison, 2016: Induced precipitation recycling (IPR): A proposed concept for increasing precipitation through natural vegetation feedback mechanisms. Ecol. Eng., 91, 553–565, doi:10.1016/j.ecoleng.2016.02.031.
1323. Spracklen, D.V., S.R. Arnold, and C.M. Taylor, 2012: Observations of increased tropical rainfall preceded by air passage over forests. Nature, 489, 282–285, doi:10.1038/nature11390.
1324. Spracklen, D. V, J.C.A. Baker, L. Garcia-Carreras, and J.H. Marsham, 2018: The effects of tropical vegetation on rainfall. Annu. Rev. Environ. Resour., 43, 193–218, doi:10.1146/annurev-environ-102017-030136.
1325. Makarieva, A.M., V.G. Gorshkov, and B.L. Li, 2009: Precipitation on land versus distance from the ocean: Evidence for a forest pump of atmospheric moisture. Ecol. Complex., 6, 302–307, doi:10.1016/j.ecocom.2008.11.004.
1326. Spracklen, D. V, J.C.A. Baker, L. Garcia-Carreras, and J.H. Marsham, 2018: The effects of tropical vegetation on rainfall. Annu. Rev. Environ. Resour., 43, 193–218, doi:10.1146/annurev-environ-102017-030136.
1327. Zemp, D.C. et al., 2014: On the importance of cascading moisture recycling in South America. 13337–13359, doi:10.5194/acp-14-13337-2014.
1328. Staal, A. et al., 2018: Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Chang., 8, 539-543, doi:10.1038/s41558-018-0177-y.
1329. Spracklen, D.V., S.R. Arnold, and C.M. Taylor, 2012: Observations of increased tropical rainfall preceded by air passage over forests. Nature, 489, 282–285, doi:10.1038/nature11390.
1330. Ellison, D. et al., 2017: Trees, forests and water: Cool insights for a hot world. Glob. Environ. Chang., 43, 51–61, doi:10.1016/j.gloenvcha.2017.01.002.
1331. Layton, K. and D. Ellison, 2016: Induced precipitation recycling (IPR): A proposed concept for increasing precipitation through natural vegetation feedback mechanisms. Ecol. Eng., 91, 553–565, doi:10.1016/j.ecoleng.2016.02.031.
1332. Laguë, M.M. and A.L.S. Swann, 2016: Progressive midlatitude afforestation: Impacts on clouds, global energy transport, and precipitation. J. Clim., 29, 5561–5573, doi:10.1175/jcli-d-15-0748.1.
1333. Feddema, J.J. et al., 2005: The importance of land-cover change in simulating future climates. Science, 310, 1674–1678, doi:10.1126/science.1118160.
1335. Garcia, R.A., M. Cabeza, R. Altwegg, and M.B. Araújo, 2016: Do projections from bioclimatic envelope models and climate change metrics match? Glob. Ecol. Biogeogr., 25, 65–74, doi:10.1111/geb.12386.
1337. Devaraju, N., N. de Noblet-Ducoudré, B. Quesada, and G. Bala, 2018: Quantifying the relative importance of direct and indirect biophysical effects of deforestation on surface temperature and teleconnections. J. Clim., 31, 3811–3829, doi:10.1175/JCLI-D-17-0563.1.
1338. Quesada, B., A. Arneth, and N. de Noblet-Ducoudré, 2017a: Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate picture. J. Geophys. Res., 122, 5113–5131, doi:10.1002/2016JD025448.
1339. De Vrese, P., S. Hagemann, and M. Claussen, 2016: Asian irrigation, African rain: Remote impacts of irrigation. Geophys. Res. Lett., 43, 3737–3745, doi:10.1002/2016GL068146.
1340. Cowling, S.A., C.D. Jones, and P.M. Cox, 2009: Greening the terrestrial biosphere: Simulated feedbacks on atmospheric heat and energy circulation. Clim. Dyn., 32, 287–299, doi:10.1007/s00382-008-0481-8.
1341. Davin, E.L., and N. de Noblet-Ducoudre, 2010: Climatic impact of global-scale deforestation: Radiative versus nonradiative processes. J. Clim., 23, 97–112, doi:10.1175/2009JCLI3102.1.
1342. Wang, Y., X. Yan, and Z. Wang, 2014b: The biogeophysical effects of extreme afforestation in modeling future climate. Theor. Appl. Climatol., 118, 511–521, doi:10.1007/s00704-013-1085-8.
1344. Lorenz, R. et al., 2016: Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. J. Geophys. Res., 121, 607–623, doi:10.1002/2015JD024053.
1345. Layton, K. and D. Ellison, 2016: Induced precipitation recycling (IPR): A proposed concept for increasing precipitation through natural vegetation feedback mechanisms. Ecol. Eng., 91, 553–565, doi:10.1016/j.ecoleng.2016.02.031.
1346. Lawrence, D. and K. Vandecar, 2015: Effects of tropical deforestation on climate and agriculture. Nat. Clim. Chang., 5, 27–36, doi:10.1038/nclimate2430.
1347. Avissar, R. and D. Werth, 2005: Global hydroclimatological teleconnections resulting from tropical deforestation. J. Hydrometeorol., 6, 134–145, doi:10.1175/JHM406.1.
1348. Gedney, N. and P.J. Valdes, 2000: The effect of Amazonian deforestation on the northern hemisphere circulation and climate. Geophys. Res. Lett., 27, 3053, doi:10.1029/2000GL011794.
1349. Semazzi, F.H.M. and Y. Song, 2001: A GCM study of climate change induced by deforestation in Africa. Clim. Res., 17, 169–182, doi:10.3354/cr017169.
1350. Mabuchi, K., Y. Sato, and H. Kida, 2005: Climatic impact of vegetation change in the Asian tropical region. Part I: Case of the Northern Hemisphere summer. J. Clim., 18, 410–428, doi:10.1175/JCLI-3273.1.
1351. Werth, D., 2005: The local and global effects of African deforestation. Geophys. Res. Lett., 32, 1–4, doi:10.1029/2005GL022969.
1352. Esch, T. et al., 2017: Breaking new ground in mapping human settlements from space – The Global Urban Footprint. ISPRS J. Photogramm. Remote Sens., 134, 30–42, doi:10.1016/j.isprsjprs.2017.10.012.
1353. Zhou, Y. et al., 2015: A global map of urban extent from nightlights. Environ. Res. Lett., 10, 2000–2010, doi:10.1088/1748-9326/10/5/054011.
1354. United Nations, Department of Economic and Social Affairs, Population Division, 2017: World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working paper ESA/P/WP/248, 53 pp.
1355. Creutzig, F., G. Baiocchi, R. Bierkandt, P.-P. Pichler, and K.C. Seto, 2015b: Global typology of urban energy use and potentials for an urbanization mitigation wedge. Proc. Natl. Acad. Sci., 112, 6283–6288, doi:10.1073/pnas.1315545112.
1356. Seto K.C., S. Dhakal, A. Bigio, H. Blanco, G.C. Delgado, D. Dewar, L. Huang, A. Inaba, A. Kansal, S. Lwasa, J.E. McMahon, D.B. Müller, J. Murakami, H. Nagendra, and A. Ramaswami, 2014: Human Settlements, Infrastructure and Spatial Planning. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1000.
1357. Goldstein, B., M. Birkved, J. Fernández, and M. Hauschild, 2017: Surveying the environmental footprint of urban food consumption. J. Ind. Ecol., 21, 151–165, doi:10.1111/jiec.12384.
1358. Gudipudi, R. et al., 2019: The efficient, the intensive, and the productive: Insights from urban Kaya scaling. Appl. Energy, 236, 155–162, doi:10.1016/j.apenergy.2018.11.054.
1359. Santamouris, M., C. Cartalis, A. Synnefa, and D. Kolokotsa, 2015: On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings – A review. Energy Build., 98, 119–124, doi:10.1016/j.enbuild.2014.09.052.
1360. Wenz, L., A. Levermann and M. Auffhammer, 2017: North–south polarization of European electricity consumption under future warming. Proc. Natl. Acad. Sci., 114, E7910–E7918, doi:10.1073/pnas.1704339114.
1361. Du, X., and Z. Huang, 2017: Ecological and environmental effects of land use change in rapid urbanization: The case of Hangzhou, China. Ecol. Indic., 81, 243–251, doi:10.1016/j.ecolind.2017.05.040.
1362. Nizeyimana, E.L. et al., 2001: Assessing the impact of land conversion to urban use on soils with different productivity levels in the USA. Soil Sci. Soc. Am. J., 65, 391–402, doi:10.2136/sssaj2001.652391x.
1363. Pandey, B., Q. Zhang, and K.C. Seto, 2018: Time series analysis of satellite data to characterize multiple land use transitions: A case study of urban growth and agricultural land loss in India. J. Land Use Sci., 13, 221–237, doi:10.1080/1747423X.2018.1533042.
1364. Chen, J., 2007: Rapid urbanization in China: A real challenge to soil protection and food security. CATENA, 69, 1–15, doi: https://doi.org/10.1016/j.catena.2006.04.019 .
1365. United Nations, Department of Economic and Social Affairs, Population Division, 2017: World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working paper ESA/P/WP/248, 53 pp.
1366. Angel, S., J. Parent, D.L. Civco, A. Blei, and D. Potere, 2011: The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plann., 75, 53–107, doi.org/10.1016/j.progress.2011.04.001.
1367. Dahiya, B., 2012: Cities in Asia, 2012: Demographics, economics, poverty, environment and governance. Cities, 29, S44–S61, doi:10.1016/j.cities.2012.06.013. doi:10.1016/j.cities.2012.06.013.
1368. Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2014: Food self-sufficiency across scales: How local can we go? Environ. Sci. Technol., 48, 9463–9470, doi:10.1021/es5005939.
1369. Bren d’Amour, C. et al., 2016: Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci., 114, 8939–8944, doi:10.1073/pnas.1606036114.
1370. Adger, W.N. et al., 2015: Focus on environmental risks and migration: Causes and consequences. Environ. Res. Lett., 10, doi:10.1088/1748-9326/10/6/060201.
1371. Seto, K.C., M. Fragkias, B. Güneralp, and M.K. Reilly, 2011: A meta-analysis of global urban land expansion. PLoS One, 6, e23777, doi:10.1371/journal.pone.0023777.
1372. Geddes, A., W.N. Adger, N.W. Arnell, R. Black, and D.S.G. Thomas, 2012: Migration, environmental change, and the “challenges of governance.” Environ. Plan. C Gov. Policy, 30, 951–967, doi:10.1068/c3006ed.
1373. Barbero-Sierra, C., M.J. Marques, and M. Ruíz-Pérez, 2013: The case of urban sprawl in Spain as an active and irreversible driving force for desertification. J. Arid Environ., 90, 95–102, doi:10.1016/j.jaridenv.2012.10.014.
1374. Boettle, M., D. Rybski, and J.P. Kropp, 2016: Quantifying the effect of sea level rise and flood defence – A point process perspective on coastal flood damage. Nat. Hazards Earth Syst. Sci., 16, 559–576, doi:10.5194/nhess-16-559-2016.
1375. Revi, A., D.E. Satterthwaite, F. Aragón-Durand, J. Corfee-Morlot, R.B.R. Kiunsi, M. Pelling, D.C. Roberts, and W. Solecki, 2014: Urban Areas. Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 535–612.
1376. Adger, W.N. et al., 2015: Focus on environmental risks and migration: Causes and consequences. Environ. Res. Lett., 10, doi:10.1088/1748-9326/10/6/060201.
1377. Geddes, A., W.N. Adger, N.W. Arnell, R. Black, and D.S.G. Thomas, 2012: Migration, environmental change, and the “challenges of governance.” Environ. Plan. C Gov. Policy, 30, 951–967, doi:10.1068/c3006ed.
1378. Revi, A., D.E. Satterthwaite, F. Aragón-Durand, J. Corfee-Morlot, R.B.R. Kiunsi, M. Pelling, D.C. Roberts, and W. Solecki, 2014: Urban Areas. Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 535–612.
1379. Kuang, W. et al., 2017: An EcoCity model for regulating urban land cover structure and thermal environment: Taking Beijing as an example. Sci. China Earth Sci., 60, 1098–1109, doi:10.1007/s11430-016-9032-9.
1380. Wang, J., B. Huang, D. Fu, P.M. Atkinson, and X. Zhang, 2016b: Response of urban heat island to future urban expansion over the Beijing–Tianjin–Hebei metropolitan area. Appl. Geogr., 70, 26–36, doi:10.1016/j.apgeog.2016.02.010.
1381. Zhong, S. et al., 2017: Urbanization-induced urban heat island and aerosol effects on climate extremes in the Yangtze River Delta region of China. Atmos. Chem. Phys., 17, 5439–5457, doi:10.5194/acp-17-5439-2017.
1382. Torres-Valcárcel, Á.R., J. Harbor, A.L. Torres-Valcárcel, and C.J. González-Avilés, 2015: Historical differences in temperature between urban and non-urban areas in Puerto Rico. Int. J. Climatol., 35, 1648–1661, doi:10.1002/joc.4083.
1383. Li, H. et al., 2018a: A new method to quantify surface urban heat island intensity. Sci. Total Environ., 624, 262–272, doi:10.1016/j.scitotenv.2017.11.360.
1384. Doan, Q. Van, H. Kusaka, and Q.B. Ho, 2016: Impact of future urbanization on temperature and thermal comfort index in a developing tropical city: Ho Chi Minh City. Urban Clim., 17, 20–31, doi:10.1016/j.uclim.2016.04.003.
1385. Oke, T.R., G. Mills, A. Christen, and J.A. Voogt, 2017: Urban Climates. Cambridge University Press, Cambridge, 526 pp.
1386. Bader, D.A. et al., 2018: Urban climate science. In: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network [Rosenweigh, C. et al. (eds.)]. Urban Climate Change Research Network, Cambridge University Press, Cambridge, UK, pp. 27–60.
1387. Wienert, U., and W. Kuttler, 2005: The dependence of the urban heat island intensity on latitude – A statistical approach. Meteorol. Zeitschrift, 14, 677–686, doi:10.1127/0941-2948/2005/0069.
1388. Zhao, L., X. Lee, R.B. Smith, and K. Oleson, 2014: Strong contributions of local background climate to urban heat islands. Nature, 511, 216–219, doi:10.1038/nature13462.
1389. Ward, K., S. Lauf, B. Kleinschmit, and W. Endlicher, 2016: Heat waves and urban heat islands in Europe: A review of relevant drivers. Sci. Total Environ., 569–570, 527–539, doi:10.1016/j.scitotenv.2016.06.119.
1390. Argüeso, D., J.P. Evans, L. Fita, and K.J. Bormann, 2014: Temperature response to future urbanization and climate change. Clim. Dyn., 42, 2183–2199, doi:10.1007/s00382-013-1789-6.
1391. Alghamdi, A.S., and T.W. Moore, 2015: Detecting temporal changes in Riyadh’s urban heat island. Pap. Appl. Geogr., 1, 312–325, doi:10.1080/23754931.2015.1084525.
1392. Alizadeh-Choobari, O., P. Ghafarian, and P. Adibi, 2016: Inter-annual variations and trends of the urban warming in Tehran. Atmos. Res., 170, 176–185, doi:10.1016/j.atmosres.2015.12.001.
1393. Fujibe, F., 2009: Detection of urban warming in recent temperature trends in Japan. Int. J. Climatol., 29, 1811–1822, doi:10.1002/joc.1822.
1394. Hausfather, Z. et al., 2013: Quantifying the effect of urbanization on U.S. historical climatology network temperature records. J. Geophys. Res. Atmos., 118, 481–494, doi:10.1029/2012JD018509.
1395. Liao, W., D. Wang, X. Liu, G. Wang, and J. Zhang, 2017: Estimated influence of urbanization on surface warming in Eastern China using time-varying land use data. Int. J. Climatol., 37, 3197–3208, doi:10.1002/joc.4908.
1396. Sachindra, D.A., A.W.M.M. Ng, S. Muthukumaran, and B.J.C.C. Perera, 2016: Impact of climate change on urban heat island effect and extreme temperatures: A case-study. Q.J.R. Meteorol. Soc., 142, 172–186, doi:10.1002/qj.2642.
1397. Camilloni, I., and M. Barrucand, 2012: Temporal variability of the Buenos Aires, Argentina, urban heat island. Theor. Appl. Climatol., 107, 47–58, doi:10.1007/s00704-011-0459-z.
1398. Wang, J., Z. Yan, X.W. Quan, and J. Feng, 2017a: Urban warming in the 2013 summer heat wave in eastern China. Clim. Dyn., 48, 3015–3033, doi:10.1007/s00382-016-3248-7.
1399. Hamdi, R., 2010: Estimating urban heat island effects on the temperature series of Uccle (Brussels, Belgium) using remote sensing data and a land surface scheme. Remote Sens., 2, 2773–2784, doi:10.3390/rs2122773.
1400. Arsiso, B.K., G. Mengistu Tsidu, G.H. Stoffberg, and T. Tadesse, 2018: Influence of urbanization-driven land use/cover change on climate: The case of Addis Ababa, Ethiopia. Phys. Chem. Earth, doi:10.1016/j.pce.2018.02.009.
1401. Elagib, N.A., 2011: Evolution of urban heat island in Khartoum. Int. J. Climatol., 31, 1377–1388, doi:10.1002/joc.2159.
1402. Lokoshchenko, M.A., 2017: Urban heat island and urban dry island in Moscow and their centennial changes. J. Appl. Meteorol. Climatol., 56, 2729–2745, doi:10.1175/JAMC-D-16-0383.1.
1403. Robaa, S.M., 2013: Some aspects of the urban climates of Greater Cairo Region, Egypt. Int. J. Climatol., 33, 3206–3216, doi:10.1002/joc.3661.
1404. Li, D., and E. Bou-Zeid, 2013: Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts. J. Appl. Meteorol. Climatol., 52, 2051–2064, doi:10.1175/JAMC-D-13-02.1.
1405. Li, X., Y. Zhou, G.R. Asrar, M. Imhoff, and X. Li, 2017b: The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Sci. Total Environ., 605–606, 426–435, doi:10.1016/j.scitotenv.2017.06.229.
1406. Hamdi, R. et al., 2016: Evolution of urban heat wave intensity for the Brussels Capital Region in the ARPEGE-Climat A1B scenario. Urban Clim., 17, 176–195, doi:10.1016/j.uclim.2016.08.001.
1407. Founda, D., and M. Santamouris, 2017: Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an extremely hot summer (2012). Sci. Rep., 7, 1–11, doi:10.1038/s41598-017-11407-6.
1408. Wang, J., Z. Yan, X.W. Quan, and J. Feng, 2017a: Urban warming in the 2013 summer heat wave in eastern China. Clim. Dyn., 48, 3015–3033, doi:10.1007/s00382-016-3248-7.
1409. Watts, N., W.N. Adger, and P. Agnolucci, 2015: Health and climate change: Policy responses to protect public health. Environnement, Risques et Sante, 14, 466–468, doi:10.1016/S0140-6736(15)60854-6.
1410. Costa, H., G. Floater, H. Hooyberghs, S. Verbeke, K. De Ridder, 2016: Climate change , heat stress and labour productivity: A cost methodology for city economies. Working Paper No. 278, Centre for Climate Change Economics and Policy. and Working Paper No. 248, Grantham Research Institute on Climate Change and the Environment, London, UK, 15 pp.
1411. Kim, S. et al., 2014: Indirect land use change and biofuels: Mathematical analysis reveals a fundamental flaw in the regulatory approach. Biomass and Bioenergy, 71, 408–412, doi:10.1016/j.biombioe.2014.09.015.
1412. Sun, Y., X. Zhang, G. Ren, F.W. Zwiers, and T. Hu, 2016: Contribution of urbanization to warming in China. Nat. Clim. Chang., 6, 706–709, doi:10.1038/nclimate2956.
1413. Chen, B. et al., 2016a: Exploring the possible effect of anthropogenic heat release due to global energy consumption upon global climate: a climate model study. Int. J. Climatol., 36, 4790–4796, doi:10.1002/joc.4669.
1414. Founda, D., F. Pierros, M. Petrakis, and C. Zerefos, 2015: Interdecadal variations and trends of the Urban Heat Island in Athens (Greece) and its response to heat waves. Atmos. Res., 161–162, 1–13, doi:10.1016/j.atmosres.2015.03.016.
1415. Rafael, S. et al., 2017: Quantification and mapping of urban fluxes under climate change: Application of WRF-SUEWS model to Greater Porto area (Portugal). Environ. Res., 155, 321–334, doi:10.1016/j.envres.2017.02.033.
1416. Hinkel, K.M., and F.E. Nelson, 2007: Anthropogenic heat island at Barrow, Alaska, during winter: 2001–2005. J. Geophys. Res. Atmos., 112, 2001–2005, doi:10.1029/2006JD007837.
1417. Chrysanthou, A., G. Van Der Schrier, E.J.M. Van Den Besselaar, A.M.G. Klein Tank, and T. Brandsma, 2014: The effects of urbanization on the rise of the European temperature since 1960. Geophys. Res. Lett., 41, 7716–7722, doi:10.1002/2014GL061154.
1418. Dou, J., Y. Wang, R. Bornstein, and S. Miao, 2014: Observed spatial characteristics of Beijing urban climate impacts on summer thunderstorms. J. Appl. Meteorol. Climatol., 54, 94–105, doi:10.1175/JAMC-D-13-0355.1.
1419. Zhou, D. et al., 2016: Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China. Sci. Total Environ., 544, 617–626, doi:10.1016/j.scitotenv.2015.11.168.
1420. Zhou, B., D. Rybski, and J.P. Kropp, 2017: The role of city size and urban form in the surface urban heat island. Sci. Rep., 7, 4791, doi:10.1038/s41598-017-04242-2.
1421. Polydoros, A., T. Mavrakou, and C. Cartalis, 2018: Quantifying the trends in land surface temperature and surface urban heat island intensity in Mediterranean cities in view of smart urbanization. Urban Sci., 2, 16, doi:10.3390/urbansci2010016.
1422. Li, H. et al., 2018a: A new method to quantify surface urban heat island intensity. Sci. Total Environ., 624, 262–272, doi:10.1016/j.scitotenv.2017.11.360.
1423. Bader, D.A. et al., 2018: Urban climate science. In: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network [Rosenweigh, C. et al. (eds.)]. Urban Climate Change Research Network, Cambridge University Press, Cambridge, UK, pp. 27–60.
1424. Alizadeh-Choobari, O., P. Ghafarian, and P. Adibi, 2016: Inter-annual variations and trends of the urban warming in Tehran. Atmos. Res., 170, 176–185, doi:10.1016/j.atmosres.2015.12.001.
1425. Fujibe, F., 2009: Detection of urban warming in recent temperature trends in Japan. Int. J. Climatol., 29, 1811–1822, doi:10.1002/joc.1822.
1426. Lokoshchenko, M.A., 2017: Urban heat island and urban dry island in Moscow and their centennial changes. J. Appl. Meteorol. Climatol., 56, 2729–2745, doi:10.1175/JAMC-D-16-0383.1.
1427. Torres-Valcárcel, Á.R., J. Harbor, A.L. Torres-Valcárcel, and C.J. González-Avilés, 2015: Historical differences in temperature between urban and non-urban areas in Puerto Rico. Int. J. Climatol., 35, 1648–1661, doi:10.1002/joc.4083.
1428. Doan, Q. Van, H. Kusaka, and Q.B. Ho, 2016: Impact of future urbanization on temperature and thermal comfort index in a developing tropical city: Ho Chi Minh City. Urban Clim., 17, 20–31, doi:10.1016/j.uclim.2016.04.003.
1429. Elagib, N.A., 2011: Evolution of urban heat island in Khartoum. Int. J. Climatol., 31, 1377–1388, doi:10.1002/joc.2159.
1430. Liao, W., D. Wang, X. Liu, G. Wang, and J. Zhang, 2017: Estimated influence of urbanization on surface warming in Eastern China using time-varying land use data. Int. J. Climatol., 37, 3197–3208, doi:10.1002/joc.4908.
1431. Haberlie, A.M., W.S. Ashley, T.J. Pingel, 2014: The effect of urbanisation on the climatology of thunderstorm initiation. Q.J.R. Meteorol. Soc., 141, 663–675, doi:10.1002/qj.2499.
1432. McLeod, J., M. Shepherd, and C.E. Konrad, 2017: Spatio-temporal rainfall patterns around Atlanta, Georgia and possible relationships to urban land cover. Urban Clim., 21, 27–42, doi:10.1016/j.uclim.2017.03.004.
1433. Ganeshan, M. and R. Murtugudde, 2015: Nocturnal propagating thunderstorms may favor urban “hot-spots”: A model-based study over Minneapolis. Urban Clim., 14, 606–621, doi:10.1016/j.uclim.2015.10.005.
1434. Daniels, E.E., G. Lenderink, R.W.A. Hutjes, and A.A.M. Holtslag, 2016: Observed urban effects on precipitation along the Dutch west coast. Int. J. Climatol., 36, 2111–2119, doi:10.1002/joc.4458.
1435. Schlünzen, K.H., P. Hoffmann, G. Rosenhagen, and W. Riecke, 2010: Long-term changes and regional differences in temperature and precipitation in the metropolitan area of Hamburg. Int. J. Climatol., 30, 1121–1136, doi:10.1002/joc.1968.
1436. Liang, P. and Y. Ding, 2017: The long-term variation of extreme heavy precipitation and its link to urbanization effects in Shanghai during 1916–2014. Adv. Atmos. Sci., 34, 321–334, doi:10.1007/s00376-016-6120-0.
1437. Dou, J., Y. Wang, R. Bornstein, and S. Miao, 2014: Observed spatial characteristics of Beijing urban climate impacts on summer thunderstorms. J. Appl. Meteorol. Climatol., 54, 94–105, doi:10.1175/JAMC-D-13-0355.1.
1438. Lorenz, R. et al., 2016: Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. J. Geophys. Res., 121, 607–623, doi:10.1002/2015JD024053.
1439. Daniels, E.E., G. Lenderink, R.W.A. Hutjes, and A.A.M. Holtslag, 2016: Observed urban effects on precipitation along the Dutch west coast. Int. J. Climatol., 36, 2111–2119, doi:10.1002/joc.4458.
1440. Zhong, S. et al., 2017: Urbanization-induced urban heat island and aerosol effects on climate extremes in the Yangtze River Delta region of China. Atmos. Chem. Phys., 17, 5439–5457, doi:10.5194/acp-17-5439-2017.
1441. Zipper, S.C., J. Schatz, C.J. Kucharik, and S.P. Loheide, 2017: Urban heat island-induced increases in evapotranspirative demand. Geophys. Res. Lett., 44, 873–881, doi:10.1002/2016GL072190.
1442. Hamdi, R., P. Termonia, and P. Baguis, 2011: Effects of urbanization and climate change on surface runoff of the Brussels Capital Region: A case study using an urban soil-vegetation-atmosphere-transfer model. Int. J. Climatol., 31, 1959–1974, doi:10.1002/joc.2207.
1443. Pataki, D.E. et al., 2011: Socio-ecohydrology and the urban water challenge. Ecohydrology, 4, 341–347, doi:10.1002/eco.209.
1444. IPCC, 2013a: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–29.
1445. Shade, C. and P. Kremer, 2019: Predicting land use changes in philadelphia following green infrastructure policies. Land, 8, 28, doi:10.3390/land8020028.
1446. Wang, G.Q. et al., 2013: Simulating the impact of climate change on runoff in a typical river catchment of the Loess Plateau, China. J. Hydrometeorol., 14, 1553–1561, doi:10.1175/JHM-D-12-081.1.
1447. EPA, 2015: Stormwater Management in Response to Climate Change Impacts: Lessons from the Chesapeake Bay and Great Lakes Regions (Final Report). EPA/600/R-15/087F, 2015. U.S. Environmental Protection Agency, Washington, DC, USA, 80 pp.
1448. Xu, Q., R. Yang, Y.-X.X. Dong, Y.-X.X. Liu, and L.-R.R. Qiu, 2016b: The influence of rapid urbanization and land use changes on terrestrial carbon sources/sinks in Guangzhou, China. Ecol. Indic., 70, 304–316, doi:10.1016/j.ecolind.2016.05.052.
1449. Tao, Y. et al., 2015: Variation in ecosystem services across an urbanization gradient: A study of terrestrial carbon stocks from Changzhou, China. Ecol. Modell., 318, 210–216, doi:10.1016/j.ecolmodel.2015.04.027.
1450. Zhang, D. et al., 2015: Effects of forest type and urbanization on carbon storage of urban forests in Changchun, Northeast China. Chinese Geogr. Sci., 25, 147–158, doi:10.1007/s11769-015-0743-4.
1451. Bae, J., and Y. Ryu, 2015: Land use and land cover changes explain spatial and temporal variations of the soil organic carbon stocks in a constructed urban park. Landsc. Urban Plan., 136, 57–67, doi:10.1016/j.landurbplan.2014.11.015.
1452. Zhang, D. et al., 2015: Effects of forest type and urbanization on carbon storage of urban forests in Changchun, Northeast China. Chinese Geogr. Sci., 25, 147–158, doi:10.1007/s11769-015-0743-4.
1453. Sharma, S.B., S. Jain, P. Khirwadkar, and S. Kulkarni, 2013: The effects of air pollution on the environment and human health. Indian Journal of Research in Pharmacy and Biotechnoly, 1(3), 2320–3471.
1454. D’Amato, G., L. Cecchi, M. D’Amato, and G. Liccardi, 2010: Urban air pollution and climate change as environmental risk factors of respiratory allergy: An update. J. Investig. Allergol. Clin. Immunol., 20, 95–102.
1455. Harlan, S.L., and D.M. Ruddell, 2011: Climate change and health in cities: impacts of heat and air pollution and potential co-benefits from mitigation and adaptation. Curr. Opin. Environ. Sustain., 3, 126–134, doi:10.1016/j.cosust.2011.01.001.
1456. Shindell, D., G. Faluvegi, K. Seltzer, and C. Shindell, 2018: Quantified, localized health benefits of accelerated carbon dioxide emissions reductions. Nat. Clim. Chang., 8, 291–295, doi:10.1038/s41558-018-0108-y.
1457. de Coninck, H., A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, 2018, 2018: Strengthening and implementing the global response. In: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press.
1458. Davies, Z.G., J.L. Edmondson, A. Heinemeyer, J.R. Leake, and K.J. Gaston, 2011: Mapping an urban ecosystem service: Quantifying above-ground carbon storage at a city-wide scale. J. Appl. Ecol., 48, 1125–1134, doi:10.1111/j.1365-2664.2011.02021.x.
1459. Edmondson, J.L., Z.G. Davies, K.J. Gaston, and J.R. Leake, 2014: Urban cultivation in allotments maintains soil qualities adversely affected by conventional agriculture. J. Appl. Ecol., 51, 880–889, doi:10.1111/1365-2664.12254.
1460. Demuzere, M. et al., 2014: Mitigating and adapting to climate change: Multi-functional and multi-scale assessment of green urban infrastructure. J. Environ. Manage., 146, 107–115, doi:10.1016/j.jenvman.2014.07.025.
1461. Sussams, L.W., W.R. Sheate, and R.P. Eales, 2015: Green infrastructure as a climate change adaptation policy intervention: Muddying the waters or clearing a path to a more secure future? J. Environ. Manage., 147, 184–193, doi:10.1016/j.jenvman.2014.09.003.
1462. Elmqvist, T. et al., 2016: Ecosystem Services Provided by Urban Green Infrastructure, In Routledge Handbook of Ecosystem Services [Potschin, M., R. Haines-Young, R. Fish, and R.K. Turner (eds.)]. 1st ed. Routledge, Oxford, UK, 630 pp.
1463. Gill, S.E., J.F. Handley, A.R. Ennos, and S. Pauleit, 2007: Adapting cities for climate change: The role of the green infrastructure. Built Environ., 33, 115–133, doi:10.2148/benv.33.1.115.
1464. Revi, A., D.E. Satterthwaite, F. Aragón-Durand, J. Corfee-Morlot, R.B.R. Kiunsi, M. Pelling, D.C. Roberts, and W. Solecki, 2014: Urban Areas. Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 535–612.
1465. Cavan, G. et al., 2014: Urban morphological determinants of temperature regulating ecosystem services in two African cities. Ecol. Indic., 42, 43–57, doi:10.1016/j.ecolind.2014.01.025.
1466. Di Leo, N., F.J. Escobedo, and M. Dubbeling, 2016: The role of urban green infrastructure in mitigating land surface temperature in Bobo-Dioulasso, Burkina Faso. Environ. Dev. Sustain., 18, 373–392, doi:10.1007/s10668-015-9653-y.
1467. Di Leo, N., F.J. Escobedo, and M. Dubbeling, 2016: The role of urban green infrastructure in mitigating land surface temperature in Bobo-Dioulasso, Burkina Faso. Environ. Dev. Sustain., 18, 373–392, doi:10.1007/s10668-015-9653-y.
1468. Feyisa, G.L., K. Dons, and H. Meilby, 2014: Efficiency of parks in mitigating urban heat island effect: An example from Addis Ababa. Landsc. Urban Plan., 123, 87–95, doi:10.1016/j.landurbplan.2013.12.008.
1469. Zölch, T., J. Maderspacher, C. Wamsler, and S. Pauleit, 2016: Using green infrastructure for urban climate-proofing: An evaluation of heat mitigation measures at the micro-scale. Urban For. Urban Green., 20, 305–316, doi:10.1016/j.ufug.2016.09.011.
1470. Li, D. et al., 2019: Urban heat island: Aerodynamics or imperviousness? Sci. Adv., 5, doi:10.1126/sciadv.aau4299.
1471. Brown, S., and R.J. Nicholls, 2015: Subsidence and human influences in mega deltas: The case of the Ganges–Brahmaputra–Meghna. Sci. Total Environ., 527–528, 362–374, doi: https://doi.org/10.1016/j.scitotenv.2015.04.124 .
1472. Klemm, W., B.G. Heusinkveld, S. Lenzholzer, M.H. Jacobs, and B. Van Hove, 2015: Psychological and physical impact of urban green spaces on outdoor thermal comfort during summertime in The Netherlands. Build. Environ., 83, 120–128, doi:10.1016/j.buildenv.2014.05.013.
1473. Coma, J. et al., 2017: Vertical greenery systems for energy savings in buildings: A comparative study between green walls and green facades. Build. Environ., 111, 228–237, doi:10.1016/j.buildenv.2016.11.014.
1474. Zeleňáková, M., D.C. Diaconu, and K. Haarstad, 2017: Urban water retention measures. Procedia Eng., 190, 419–426, doi:10.1016/j.proeng.2017.05.358.
1475. Murata, T. and N. Kawai, 2018: Degradation of the urban ecosystem function due to soil sealing: Involvement in the heat island phenomenon and hydrologic cycle in the Tokyo Metropolitan Area. Soil Sci. Plant Nutr., 64, 145–155, doi:10.1080/00380768.2018.1439342.
1476. Scalenghe, R., and F.A. Marsan, 2009: The anthropogenic sealing of soils in urban areas. Landsc. Urban Plan., 90, 1–10, doi:10.1016/j.landurbplan.2008.10.011.
1477. Wilhelm, J.A. and R.G. Smith, 2018: Ecosystem services and land sparing potential of urban and peri-urban agriculture: A review. Renew. Agric. Food Syst., 33, 481–494, doi:10.1017/S1742170517000205.
1478. Gerster-Bentaya, M., 2013: Nutrition-sensitive urban agriculture. Food Secur., 5, 723–737, doi:10.1007/s12571-013-0295-3.
1479. Brodt, S., K.J. Kramer, A. Kendall, and G. Feenstra, 2013: Comparing environmental impacts of regional and national-scale food supply chains: A case study of processed tomatoes. Food Policy, 42, 106–114, doi:10.1016/j.foodpol.2013.07.004.
1480. Michalský, M., and P.S. Hooda, 2015: Greenhouse gas emissions of imported and locally produced fruit and vegetable commodities: A quantitative assessment. Environ. Sci. Policy, 48, 32–43, doi:10.1016/j.envsci.2014.12.018.
1481. Tobarra, M.A., L.A. López, M.A. Cadarso, N. Gómez, and I. Cazcarro, 2018: Is seasonal households’ consumption good for the nexus carbon/water footprint? The Spanish fruits and vegetables case. Environ. Sci. Technol., 52, 12066–12077, doi:10.1021/acs.est.8b00221.
1482. Goldstein, B., M. Birkved, J. Fernández, and M. Hauschild, 2017: Surveying the environmental footprint of urban food consumption. J. Ind. Ecol., 21, 151–165, doi:10.1111/jiec.12384.
1483. Moragues-Faus, A., and A. Marceau, 2018: Measuring progress in sustainable food cities: An indicators toolbox for action. Sustain., 11, 1–17, doi:10.3390/su11010045.
1484. Magarini, A. and A. Calori, 2015: Food and the Cities. Food Policies for Sustainable Cities. 1st ed. Edizioni Ambiente, Milán.
1485. Newman, L., C. Ling, and K. Peters, 2012: Between field and table: environmental impl ications of local food distribution. Int. J. Sustain. Soc., 5, 11, doi:10.1504/ijssoc.2013.050532.
1486. Hamilton, A.J. et al., 2014: Give peas a chance? Urban agriculture in developing countries. A review. Agron. Sustain. Dev., 34, 45–73, doi:10.1007/s13593-013-0155-8.
1487. Ercilla-Montserrat, M., P. Muñoz, J.I. Montero, X. Gabarrell, and J. Rieradevall, 2018: A study on air quality and heavy metals content of urban food produced in a Mediterranean city (Barcelona). J. Clean. Prod., 195, 385–395, doi: https://doi.org/10.1016/j.jclepro.2018.05.183 .
1488. IPCC, 2018: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, 1552 pp.
1489. Hawkes, C.V., B.G. Waring, J.D. Rocca, and S.N. Kivlin, 2017: Historical climate controls soil respiration responses to current soil moisture. Proc. Natl. Acad. Sci., 114, 6322–6327, doi:10.1073/pnas.1620811114.
1490. Smith, M.C., J.S. Singarayer, P.J. Valdes, J.O. Kaplan, and N.P. Branch, 2016a: The biogeophysical climatic impacts of anthropogenic land use change during the Holocene. Clim. Past, 12, 923–941, doi:10.5194/cp-12-923-2016.
1491. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1492. Minx, J.C. et al., 2018: Negative emissions – Part 1: Research landscape and synthesis. Environ. Res. Lett., doi:10.1088/1748-9326/aabf9b.
1493. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1494. Nemet, G.F. et al., 2018: Negative emissions – Part 3: Innovation and upscaling. Environ. Res. Lett., doi:10.1088/1748-9326/aabff4.
1495. Gren, I.-M., and A.Z. Aklilu, 2016: Policy design for forest carbon sequestration: A review of the literature. For. Policy Econ., 70, 128–136, doi:10.1016/j.forpol.2016.06.008.
1496. Smith, P. et al., 2008: Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. B Biol. Sci., 363, 789–813, doi:10.1098/rstb.2007.2184.
1498. Pradhan, P., D.E. Reusser, and J.P. Kropp, 2013: Embodied greenhouse gas emissions in diets. PLoS One, 8, e62228, doi:10.1371/journal.pone.0062228.
1499. Conant, R.T., C.E.P. Cerri, B.B. Osborne, and K. Paustian, 2017: Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl., doi:10.1002/eap.1473.
1500. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
1501. Smith, P. et al., 2008: Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. B Biol. Sci., 363, 789–813, doi:10.1098/rstb.2007.2184.
1503. Smith, P. et al., 2008: Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. B Biol. Sci., 363, 789–813, doi:10.1098/rstb.2007.2184.
1505. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
1506. FAO, 2015: FAOSTAT. Land use emissions database. http://faostat.fao.org/site/705/default.aspx .
1507. Hristov, A.N. et al., 2013: Mitigation of Greenhouse Gas Emissions in Livestock Production: A Review of Technical Options for Non-CO2 Emissions [Gerber, P.J., B. Henderson, and H.P.S. Makkar (eds.)]. FAO Animal Production and Health Paper No. 177. Food and Agriculture Organization of the United Nations, Rome, Italy, 226 pp.
1508. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1509. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
1510. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1511. Pradhan, P., D.E. Reusser, and J.P. Kropp, 2013: Embodied greenhouse gas emissions in diets. PLoS One, 8, e62228, doi:10.1371/journal.pone.0062228.
1512. Llonch, P., M.J. Haskell, R.J. Dewhurst, and S.P. Turner, 2017: Current available strategies to mitigate greenhouse gas emissions in livestock systems: An animal welfare perspective. Animal, doi:10.1017/S1751731116001440.
1513. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
1514. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1515. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1516. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1517. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1518. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1519. Beach, R.H. et al., 2015: Global mitigation potential and costs of reducing agricultural non-CO2 greenhouse gas emissions through 2030. J. Integr. Environ. Sci., 12, 87–105, doi:10.1080/1943815X.2015.1110183.
1520. Hussain, S. et al., 2015: Rice management interventions to mitigate greenhouse gas emissions: A review. Environ. Sci. Pollut. Res., 22, 3342–3360, doi:10.1007/s11356-014-3760-4.
1521. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1522. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1523. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1524. Hussain, S. et al., 2015: Rice management interventions to mitigate greenhouse gas emissions: A review. Environ. Sci. Pollut. Res., 22, 3342–3360, doi:10.1007/s11356-014-3760-4.
1525. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1526. Beach, R.H. et al., 2015: Global mitigation potential and costs of reducing agricultural non-CO2 greenhouse gas emissions through 2030. J. Integr. Environ. Sci., 12, 87–105, doi:10.1080/1943815X.2015.1110183.
1527. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1528. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1529. Zomer, R.J. et al., 2016: Global tree cover and biomass carbon on agricultural land: The contribution of agroforestry to global and national carbon budgets. Sci. Rep., 6, 29987. doi:10.1038/srep29987.
1530. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1531. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1532. Zomer, R.J. et al., 2016: Global tree cover and biomass carbon on agricultural land: The contribution of agroforestry to global and national carbon budgets. Sci. Rep., 6, 29987. doi:10.1038/srep29987.
1533. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1534. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1535. Busch, J., and J. Engelmann, 2017: Cost-effectiveness of reducing emissions from tropical deforestation, 2016–2050. Environ. Res. Lett., 13, 15001, doi:10.1088/1748-9326/aa907c.
1536. Baccini, A. et al., 2017: Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 358, 230–234, doi:10.1126/science.aam5962.
1537. Zarin, D.J. et al., 2016: Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Chang. Biol., 22, 1336–1347, doi:10.1111/gcb.13153.
1538. Federici, S., F.N. Tubiello, M. Salvatore, H. Jacobs, and J. Schmidhuber, 2015: New estimates of CO2 forest emissions and removals: 1990–2015. For. Ecol. Manage., 352, 89–98, doi:10.1016/j.foreco.2015.04.022.
1539. Carter, S., M. et al., 2015: Mitigation of agricultural emissions in the tropics: Comparing forest land-sparing options at the national level. Biogeosciences, 12, 4809–4825, doi:10.5194/bg-12-4809-2015.
1540. Houghton, R.A., B. Byers, and A.A. Nassikas, 2015: A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Chang., doi:10.1038/nclimate2869.
1541. Smith, P. et al., 2013a: How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Glob. Chang. Biol., 19, 2285–2302, doi:10.1111/gcb.12160.
1542. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
1543. Meli, P. et al., 2017: A global review of past land use, climate, and active vs. passive restoration effects on forest recovery. PLoS One, doi:10.1371/journal.pone.0171368.
1544. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1545. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1546. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1547. Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 85001, doi:10.1088/1748-9326/11/8/085001.
1548. Li, Y. et al., 2016: The role of spatial scale and background climate in the latitudinal temperature response to deforestation. Earth Syst. Dyn., 7, 167–181, doi:10.5194/esd-7-167-2016.
1549. Huang, J. et al., 2017: Dryland climate change: Recent progress and challenges. Rev. Geophys., 55, 719–778, doi:10.1002/2016RG000550.
1550. Sonntag, S., J. Pongratz, C.H. Reick, and H. Schmidt, 2016: Reforestation in a high-CO2 world—Higher mitigation potential than expected, lower adaptation potential than hoped for. Geophys. Res. Lett., 43, 6546–6553, doi:10.1002/2016GL068824.
1551. Lenton, T.M., 2014: The Global Potential for Carbon Dioxide Removal. Geoengineering of the Climate System [Harrison, R.M. and R.E. Hester (eds.)]. Royal Society of Chemistry, Cambridge, U.K., 52–79 pp. doi:10.1039/9781782621225-00052.
1552. McLaren, D., 2012: A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot., 90, 489–500, doi:10.1016/j.psep.2012.10.005.
1553. Lenton, T.M., 2010: The potential for land-based biological CO2 removal to lower future atmospheric CO2 concentration. Carbon Manag., doi:10.4155/cmt.10.12.
1554. Erb, K.-H. et al., 2018: Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature, 553, 73–76, doi:10.1038/nature25138.
1555. Dooley, K., and S. Kartha, 2018: Land-based negative emissions: Risks for climate mitigation and impacts on sustainable development. Int. Environ. Agreements Polit. Law Econ., doi:10.1007/s10784-017-9382-9.
1556. Yan, M., J. Liu, and Z. Wang, 2017: Global climate responses to land use and land cover changes over the past two millennia. Atmosphere, 8, 64, doi:10.3390/atmos8040064.
1557. Houghton, R.A., B. Byers, and A.A. Nassikas, 2015: A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Chang., doi:10.1038/nclimate2869.
1558. Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Glob. Chang. Biol., 24, 350–359, doi:10.1111/gcb.13876.
1559. Yan, M., J. Liu, and Z. Wang, 2017: Global climate responses to land use and land cover changes over the past two millennia. Atmosphere, 8, 64, doi:10.3390/atmos8040064.
1560. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1561. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1562. Yao, Y., S. Piao, and T. Wang, 2018: Future biomass carbon sequestration capacity of Chinese forests. Sci. Bull., 63, 1108–1117, doi:10.1016/j.scib.2018.07.015.
1563. Poorter, L. et al., 2016: Biomass resilience of neotropical secondary forests. Nature, 530, 211–214, doi:10.1038/nature16512.
1564. Tang, J., S. Luyssaert, A.D. Richardson, W. Kutsch, and I.A. Janssens, 2014: Steeper declines in forest photosynthesis than respiration explain age-driven decreases in forest growth. Proc. Natl. Acad. Sci., 111, 8856–8860, doi:10.1073/pnas.1320761111.
1565. Smith, M.C., J.S. Singarayer, P.J. Valdes, J.O. Kaplan, and N.P. Branch, 2016a: The biogeophysical climatic impacts of anthropogenic land use change during the Holocene. Clim. Past, 12, 923–941, doi:10.5194/cp-12-923-2016.
1566. Boysen, L.R., W. Lucht, and D. Gerten, 2017: Trade-offs for food production, nature conservation and climate limit the terrestrial carbon dioxide removal potential. Glob. Chang. Biol., 23, 4303–4317, doi:10.1111/gcb.13745.
1567. Sasaki, N. et al., 2016: Sustainable management of tropical forests can reduce carbon emissions and stabilize timber production. Front. Environ. Sci., 4, 50, doi:10.3389/fenvs.2016.00050.
1568. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1570. Erb, K.-H. et al., 2017: Land management: data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.
1571. Campioli, M. et al., 2015: Biomass production effciency controlled by management in temperate and boreal ecosystems.Nat. Geosci., 8, 843–846, doi: 10.1038/ngeo2553.
1572. Birdsey, R. and Y. Pan, 2015: Trends in management of the world’s forests and impacts on carbon stocks. For. Ecol. Manag., 355, 83–89, doi:10.1016/j.foreco.2015.04.031.
1573. Erb, K.-H. et al., 2016: Biomass turnover time in terrestrial ecosystems halved by land use.Nat. Geosci., 9, 674–678, doi:10.1038/NGE02782.
1574. Noormets, A. et al., 2015: Effects of forest management on productivity and carbon sequestration: A review and hypothesis. For. Ecol. Manage., 355, 124–140, doi:10.1016/j.foreco.2015.05.019.
1575. Wäldchen, J., E.-D. Schulze, I. Schöning, M. Schrumpf, and C. Sierra, 2013: The influence of changes in forest management over the past 200 years on present soil organic carbon stocks. For. Ecol. Manage., 289, 243–254, doi:10.1016/j.foreco.2012.10.014.
1576. Malhi, Y. et al., 2015: The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests. Glob. Chang. Biol., 21, 2283–2295, doi:10.1111/gcb.12859.
1577. Quesada, B., A. Arneth, E. Robertson, and N. de Noblet-Ducoudré, 2018: Potential strong contribution of future anthropogenic land-use and land-cover change to the terrestrial carbon cycle. Environ. Res. Lett., 13, 64023, doi:10.1088/1748-9326/aac4c3.
1578. Nabuurs, G.J., E.J.M.M. Arets, and M.J. Schelhaas, 2017: European forests show no carbon debt, only a long parity effect. For. Policy Econ., 75, 120–125, doi:10.1016/j.forpol.2016.10.009.
1579. Bosello, F., C. Carraro, and E. De Cian, 2009: An Analysis of Adaptation as a Response to Climate Change. Copenhagen Consensus Center, Frederiksberg, Denmark, 29 pp.
1580. Seidl, R. et al., 2017: Forest disturbances under climate change. Nat. Clim. Chang., 7, 395–402, doi:10.1038/nclimate3303.
1581. Laganière, J., D. Paré, E. Thiffault, and P.Y. Bernier, 2017: Range and uncertainties in estimating delays in greenhouse gas mitigation potential of forest bioenergy sourced from Canadian forests. GCB Bioenergy, 9, 358–369, doi:10.1111/gcbb.12327.
1582. Bernier, P. and D. Paré, 2013: Using ecosystem CO2 measurements to estimate the timing and magnitude of greenhouse gas mitigation potential of forest bioenergy. GCB Bioenergy, 5, 67–72, doi:10.1111/j.1757-1707.2012.01197.x.
1583. Mitchell, S.R., M.E. Harmon, and K.E.B. O’Connell, 2012: Carbon debt and carbon sequestration parity in forest bioenergy production. Glob. Chang. Biol. Bioenergy, 4, 818–827, doi:10.1111/j.1757-1707.2012.01173.x.
1584. Haberl, H. et al., 2012: Correcting a fundamental error in greenhouse gas accounting related to bioenergy. Energy Policy, 45, 18–23, doi:10.1016/j.enpol.2012.02.051.
1585. Haberl, H., 2013: Net land-atmosphere flows of biogenic carbon related to bioenergy: Towards an understanding of systemic feedbacks. GCB Bioenergy, 5, 351–357, doi:10.1111/gcbb.12071.
1586. Ter-Mikaelian, M.T. et al., 2015: Carbon debt repayment or carbon sequestration parity? Lessons from a forest bioenergy case study in Ontario, Canada. GCB Bioenergy, 7, 704–716, doi:10.1111/gcbb.12198.
1587. Macintosh, A., H. Keith, and D. Lindenmayer, 2015: Rethinking forest carbon assessments to account for policy institutions. Nat. Clim. Chang., 5, 946.
1588. Grassi, G., R. Pilli, J. House, S. Federici, and W.A. Kurz, 2018b: Science-based approach for credible accounting of mitigation in managed forests. Carbon Balance Manag., doi:10.1186/s13021-018-0096-2.
1589. Nabuurs, G.J., E.J.M.M. Arets, and M.J. Schelhaas, 2017: European forests show no carbon debt, only a long parity effect. For. Policy Econ., 75, 120–125, doi:10.1016/j.forpol.2016.10.009.
1590. Pingoud, K., T. Ekholm, R. Sievänen, S. Huuskonen, and J. Hynynen, 2018: Trade-offs between forest carbon stocks and harvests in a steady state – A multi-criteria analysis. J. Environ. Manage., doi:10.1016/j.jenvman.2017.12.076.
1591. Smyth, C., W.A. Kurz, G. Rampley, T.C. Lemprière, and O. Schwab, 2017a: Climate change mitigation potential of local use of harvest residues for bioenergy in Canada. GCB Bioenergy, 9, 817–832, doi:10.1111/gcbb.12387.
1592. Luyssaert, S. et al., 2018: Trade-offs in using European forests to meet climate objectives. Nature, 562, 259–262, doi:10.1038/s41586-018-0577-1.
1593. Valade, A., C. Magand, S. Luyssaert, and V. Bellassen, 2017: Sustaining the sequestration efficiency of the European forest sector. For. Ecol. Manage., 405, 44–55, doi:10.1016/j.foreco.2017.09.009.
1594. York, R., 2012: Do alternative energy sources displace fossil fuels? Nat. Clim. Chang., doi:10.1038/nclimate1451.
1595. Ter-Mikaelian, M.T., S.J. Colombo, and J. Chen, 2014: The burning question: Does forest bioenergy reduce carbon emissions? A review of common misconceptions about forest carbon accounting. J. For., 113, 57–68, doi:10.5849/jof.14-016.
1596. Naudts, K. et al., 2016b: Europe’s forest management did not mitigate climate warming. Science, 351, 597 LP-600, doi:10.1126/science.aad7270.
1597. Mitchell, S.R., M.E. Harmon, and K.E.B. O’Connell, 2012: Carbon debt and carbon sequestration parity in forest bioenergy production. Glob. Chang. Biol. Bioenergy, 4, 818–827, doi:10.1111/j.1757-1707.2012.01173.x.
1598. Haberl, H. et al., 2012: Correcting a fundamental error in greenhouse gas accounting related to bioenergy. Energy Policy, 45, 18–23, doi:10.1016/j.enpol.2012.02.051.
1599. Macintosh, A., H. Keith, and D. Lindenmayer, 2015: Rethinking forest carbon assessments to account for policy institutions. Nat. Clim. Chang., 5, 946.
1600. Laganière, J., D. Paré, E. Thiffault, and P.Y. Bernier, 2017: Range and uncertainties in estimating delays in greenhouse gas mitigation potential of forest bioenergy sourced from Canadian forests. GCB Bioenergy, 9, 358–369, doi:10.1111/gcbb.12327.
1601. Haberl, H., 2013: Net land-atmosphere flows of biogenic carbon related to bioenergy: Towards an understanding of systemic feedbacks. GCB Bioenergy, 5, 351–357, doi:10.1111/gcbb.12071.
1602. Lemprière, T.C. et al., 2013: Canadian boreal forests and climate change mitigation. Environ. Rev., 21, 293–321, doi:10.1139/er-2013-0039.
1603. Kurz, W.A., C. Smyth, and T. Lemprière, 2016: Climate change mitigation through forest sector activities: principles, potential and priorities. Unasylva, 67, 61–67.
1604. Keith, H. et al., 2014: Managing temperate forests for carbon storage: impacts of logging versus forest protection on carbon stocks. Ecosphere, 5, art75, doi:10.1890/ES14-00051.1.
1605. McLaren, D., 2012: A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot., 90, 489–500, doi:10.1016/j.psep.2012.10.005.
1606. Miner, R., 2010: Impact of the Global Forest Industry on Atmospheric Greenhouse Gases. Food and Agriculture Organization of the United Nations, Rome, Italy, 71 pp.
1607. Sathre, R. and J. O’Connor, 2010: Meta-analysis of greenhouse gas displacement factors of wood product substitution. Environ. Sci. Policy, 13, 104–114, doi:10.1016/j.envsci.2009.12.005.
1608. Nabuurs, G.-J., E.J.M.M. Arets, and M.-J. Schelhaas, 2018: Understanding the implications of the EU-LULUCF regulation for the wood supply from EU forests to the EU. Carbon Balance Manag., 13, 18, doi:10.1186/s13021-018-0107-3.
1609. Iordan, C.M., X. Hu, A. Arvesen, P. Kauppi, and F. Cherubini, 2018: Contribution of forest wood products to negative emissions: Historical comparative analysis from 1960 to 2015 in Norway, Sweden and Finland. Carbon Balance Manag., 13, 12, doi:10.1186/s13021-018-0101-9.
1610. Braun, M. et al., 2016: A holistic assessment of greenhouse gas dynamics from forests to the effects of wood products use in Austria. Carbon Manag., 7, 271–283, doi:10.1080/17583004.2016.1230990.
1611. Gustavsson, L. et al., 2017: Climate change effects of forestry and substitution of carbon-intensive materials and fossil fuels. Renew. Sustain. Energy Rev., doi:10.1016/j.rser.2016.09.056.
1612. Peñaloza, D., M. Erlandsson, J. Berlin, M. Wålinder, and A. Falk, 2018: Future scenarios for climate mitigation of new construction in Sweden: Effects of different technological pathways. J. Clean. Prod., 187, 1025–1035, doi:10.1016/j.jclepro.2018.03.285.
1613. Soimakallio, S., L. Saikku, L. Valsta, and K. Pingoud, 2016: Climate change mitigation challenge for wood utilization – The case of Finland. Environ. Sci. Technol., 50, 5127–5134, doi:10.1021/acs.est.6b00122.
1614. Grassi, G., R. Pilli, J. House, S. Federici, and W.A. Kurz, 2018b: Science-based approach for credible accounting of mitigation in managed forests. Carbon Balance Manag., doi:10.1186/s13021-018-0096-2.
1615. Sathre, R. and J. O’Connor, 2010: Meta-analysis of greenhouse gas displacement factors of wood product substitution. Environ. Sci. Policy, 13, 104–114, doi:10.1016/j.envsci.2009.12.005.
1616. Smyth, C., G. Rampley, T.C. Lemprière, O. Schwab, and W.A. Kurz, 2017b: Estimating product and energy substitution benefits in national-scale mitigation analyses for Canada. GCB Bioenergy, 9, 1071–1084, doi:10.1111/gcbb.12389.
1617. Nabuurs, G.J., O. Masera, K. Andrasko, P. Benitez-Ponce, R. Boer, M. Dutschke, E. Elsiddig, J. Ford-Robertson, P. Frumhoff, T. Karjalainen, O. Krankina, W.A. Kurz, M. Matsumoto, W. Oyhantcabal, N.H. Ravindranath, M.J. Sanz Sanchez, X. Zhang, 2007: In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Metz, B., O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 541–584.
1618. Lemprière, T.C. et al., 2013: Canadian boreal forests and climate change mitigation. Environ. Rev., 21, 293–321, doi:10.1139/er-2013-0039.
1619. Nabuurs, G.J., O. Masera, K. Andrasko, P. Benitez-Ponce, R. Boer, M. Dutschke, E. Elsiddig, J. Ford-Robertson, P. Frumhoff, T. Karjalainen, O. Krankina, W.A. Kurz, M. Matsumoto, W. Oyhantcabal, N.H. Ravindranath, M.J. Sanz Sanchez, X. Zhang, 2007: In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Metz, B., O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 541–584.
1620. Bathiany, S., M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler, 2010a: Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model. Biogeosciences, 7, 1383–1399, doi:10.5194/bg-7-1383-2010.
1621. Dass, P., C. Müller, V. Brovkin, and W. Cramer, 2013: Can bioenergy cropping compensate high carbon emissions from large-scale deforestation of high latitudes? Earth Syst. Dyn., 4, 409–424, doi:10.5194/esd-4-409-2013.
1622. Devaraju, N., N. de Noblet-Ducoudré, B. Quesada, and G. Bala, 2018: Quantifying the relative importance of direct and indirect biophysical effects of deforestation on surface temperature and teleconnections. J. Clim., 31, 3811–3829, doi:10.1175/JCLI-D-17-0563.1.
1623. Ganopolski, A. et al., 2001: Climber-2: A climate system model of intermediate complexity. Part II: Model sensitivity. Clim. Dyn., 17, 735–751, doi:10.1007/s003820000144.
1624. Snyder, P.K., C. Delire, and J.A. Foley, 2004: Evaluating the influence of different vegetation biomes on the global climate. Clim. Dyn., 23, 279–302, doi:10.1007/s00382-004-0430-0.
1625. West, P.C., G.T. Narisma, C.C. Barford, C.J. Kucharik, and J.A. Foley, 2011: An alternative approach for quantifying climate regulation by ecosystems. Front. Ecol. Environ., 9, 126–133, doi:10.1890/090015.
1626. Arora, V.K. and A. Montenegro, 2011: Small temperature benefits provided by realistic afforestation efforts. Nat. Geosci., 4, 514–518, doi:10.1038/ngeo1182.
1627. Erb, K.-H. et al., 2017: Land management: data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.
1628. Luyssaert, S. et al., 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Clim. Chang., 4, 389–393, doi:10.1038/nclimate2196.
1629. Luyssaert, S. et al., 2018: Trade-offs in using European forests to meet climate objectives. Nature, 562, 259–262, doi:10.1038/s41586-018-0577-1.
1630. Naudts, K. et al., 2016b: Europe’s forest management did not mitigate climate warming. Science, 351, 597 LP-600, doi:10.1126/science.aad7270.
1631. Anderson, R.G. et al., 2011: Biophysical considerations in forestry for climate protection. Front. Ecol. Environ., 9, 174–182, doi:10.1890/090179.
1632. Li, Y., M. Zhao, S. Motesharrei, Q. Mu, E. Kalnay, and S. Li, 2015b: Local cooling and warming effects of forests based on satellite observations. Nat. Commun., 6, 1–8, doi:10.1038/ncomms7603.
1633. Bright, R.M., K. Zhao, R.B. Jackson, and F. Cherubini, 2015: Quantifying surface albedo and other direct biogeophysical climate forcings of forestry activities. Glob. Chang. Biol., 21, 3246–3266, doi:10.1111/gcb.12951.
1634. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1635. Sommer, R., and D. Bossio, 2014: Dynamics and climate change mitigation potential of soil organic carbon sequestration. J. Environ. Manage., 144, 83–87, doi:10.1016/j.jenvman.2014.05.017.
1636. Lal, R., 2010: Managing soils and ecosystems for mitigating anthropogenic carbon emissions and advancing global food security. Bioscience, doi:10.1525/bio.2010.60.9.8.
1637. Lal, R., M. Griffin, J. Apt, L. Lave, and M.G. Morgan, 2004: Managing soil carbon. Science, 304, 393, doi: 10.1126/science.1093079.
1638. Conant, R.T., C.E.P. Cerri, B.B. Osborne, and K. Paustian, 2017: Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl., doi:10.1002/eap.1473.
1639. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1640. Frank, S. et al., 2017a: Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett., 12, 105004, doi:10.1088/1748-9326/aa8c83.
1641. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1642. Herrero, M. et al., 2015: Livestock and the environment: What have we learned in the past decade? Annu. Rev. Environ. Resour., 40, 177–202, doi:10.1146/annurev-environ-031113-093503.
1643. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
1644. McLaren, D., 2012: A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot., 90, 489–500, doi:10.1016/j.psep.2012.10.005.
1645. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1646. Poeplau, C. and A. Don, 2015: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agric. Ecosyst. Environ., 200, 33–41, doi:10.1016/j.agee.2014.10.024.
1647. Powlson, D.S. et al., 2014: Limited potential of no-till agriculture for climate change mitigation. Nat. Clim. Chang., 4, 678–683, doi:10.1038/nclimate2292.
1648. Smith, P. et al., 2016c: Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Chang., 6, 42–50, doi:10.1038/nclimate2870.
1649. Zomer, R.J., D.A. Bossio, R. Sommer, and L.V. Verchot, 2017: Global sequestration potential of increased organic carbon in cropland soils. Sci. Rep., doi:10.1038/s41598-017-15794-8.
1650. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1651. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1652. Frank, S. et al., 2017a: Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett., 12, 105004, doi:10.1088/1748-9326/aa8c83.
1653. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1654. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
1655. Henderson, B.B. et al., 2015: Greenhouse gas mitigation potential of the world’s grazing lands: Modeling soil carbon and nitrogen fluxes of mitigation practices. Agric. Ecosyst. Environ., 207, 91–100, doi:10.1016/j.agee.2015.03.029.
1656. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1657. Conant, R.T., C.E.P. Cerri, B.B. Osborne, and K. Paustian, 2017: Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl., doi:10.1002/eap.1473.
1658. Lal, R., 2010: Managing soils and ecosystems for mitigating anthropogenic carbon emissions and advancing global food security. Bioscience, doi:10.1525/bio.2010.60.9.8.
1659. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1660. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1661. Frank, S. et al., 2017a: Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett., 12, 105004, doi:10.1088/1748-9326/aa8c83.
1662. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1663. Powlson, D.S. et al., 2014: Limited potential of no-till agriculture for climate change mitigation. Nat. Clim. Chang., 4, 678–683, doi:10.1038/nclimate2292.
1664. McLaren, D., 2012: A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot., 90, 489–500, doi:10.1016/j.psep.2012.10.005.
1665. Zomer, R.J., D.A. Bossio, R. Sommer, and L.V. Verchot, 2017: Global sequestration potential of increased organic carbon in cropland soils. Sci. Rep., doi:10.1038/s41598-017-15794-8.
1666. Smith, P. et al., 2015: Global change pressures on soils from land use and management. Glob. Chang. Biol., 22, 1008–1028, doi:10.1111/gcb.13068.
1667. Sommer, R., and D. Bossio, 2014: Dynamics and climate change mitigation potential of soil organic carbon sequestration. J. Environ. Manage., 144, 83–87, doi:10.1016/j.jenvman.2014.05.017.
1668. Lal, R., 2010: Managing soils and ecosystems for mitigating anthropogenic carbon emissions and advancing global food security. Bioscience, doi:10.1525/bio.2010.60.9.8.
1669. Hassink, J. and A.P. Whitmore, 1997: A model of the physical protection of organic matter in soils. Soil Sci. Soc. Am. J., 61, 131, doi:10.2136/sssaj1997.03615995006100010020x.
1670. Smith, N.G., and J.S. Dukes, 2013: Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2. Glob. Chang. Biol., 19, 45–63, doi:10.1111/j.1365-2486.2012.02797.x.
1671. Palm, C., H. Blanco-Canqui, F. DeClerck, L. Gatere, and P. Grace, 2014: Conservation agriculture and ecosystem services: An overview. Agric. Ecosyst. Environ., 187, 87–105, doi:10.1016/j.agee.2013.10.010.
1672. Lal, R., 2013: Soil carbon management and climate change. Carbon Manag., 4, 439–462, doi:10.4155/cmt.13.31.
1673. Six, J., R.T. Conant, E.A. Paul, and K. Paustian, 2002: Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil, 241, 155–176, doi:10.1023/A:1016125726789.
1674. Feng, W., A.F. Plante, and J. Six, 2013: Improving estimates of maximal organic carbon stabilization by fine soil particles. Biogeochemistry, 112, 81–93, doi:10.1007/s10533-011-9679-7.
1675. West, T.O. et al., 2004: Carbon management response curves: Estimates of temporal soil carbon dynamics. Environ. Manage., 33, 507–518, doi:10.1007/s00267-003-9108-3.
1676. Smith, N.G., and J.S. Dukes, 2013: Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2. Glob. Chang. Biol., 19, 45–63, doi:10.1111/j.1365-2486.2012.02797.x.
1677. Lal, R., 2011: Sequestering carbon in soils of agro-ecosystems. Food Policy, 36, S33–S39, doi:10.1016/j.foodpol.2010.12.001.
1678. Smith, P. et al., 2008: Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. B Biol. Sci., 363, 789–813, doi:10.1098/rstb.2007.2184.
1679. Lorenz, R., and A.J. Pitman, 2014: Effect of land-atmosphere coupling strength on impacts from Amazonian deforestation. Geophys. Res. Lett., 41, 5987–5995, doi:10.1002/2014GL061017.
1680. Lal, R., 2013: Soil carbon management and climate change. Carbon Manag., 4, 439–462, doi:10.4155/cmt.13.31.
1681. Vermeulen, S.J., B.M. Campbell, and J.S.I. Ingram, 2012a: Climate change and food systems. Annu. Rev. Environ. Resour., 37, 195–222, doi:10.1146/annurev-environ-020411-130608.
1682. de Rouw, A. et al., 2010: Possibilities of carbon and nitrogen sequestration under conventional tillage and no-till cover crop farming (Mekong valley, Laos). Agric. Ecosyst. Environ., doi:10.1016/j.agee.2009.12.013.
1683. Poeplau, C. and A. Don, 2015: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agric. Ecosyst. Environ., 200, 33–41, doi:10.1016/j.agee.2014.10.024.
1684. Mazzoncini, M., T.B. Sapkota, P. Bàrberi, D. Antichi, and R. Risaliti, 2011: Long-term effect of tillage, nitrogen fertilization and cover crops on soil organic carbon and total nitrogen content. Soil Tillage Res., 114, 165–174, doi:10.1016/j.still.2011.05.001.
1685. Luo, Z., E. Wang, and O.J. Sun, 2010: Can no-tillage stimulate carbon sequestration in agricultural soils? A meta-analysis of paired experiments. Agric. Ecosyst. Environ., 139, 224–231, doi:10.1016/j.agee.2010.08.006.
1686. Powlson, D.S., C.M. Stirling, C. Thierfelder, R.P. White, and M.L. Jat, 2016: Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropical agro-ecosystems? Agric. Ecosyst. Environ., 220, 164–174, doi:10.1016/j.agee.2016.01.005.
1687. Zhang, H.L., R. Lal, X. Zhao, J.F. Xue, and F. Chen, 2014a: Opportunities and challenges of soil carbon sequestration by conservation agriculture in China. Advances in Agronomy, 124, 1–36, doi:10.1016/B978-0-12-800138-7.00001-2.
1688. Cheesman, S., C. Thierfelder, N.S. Eash, G.T. Kassie, and E. Frossard, 2016: Soil carbon stocks in conservation agriculture systems of Southern Africa. Soil Tillage Res., 156, 99–109, doi:10.1016/j.still.2015.09.018.
1689. Palm, C., H. Blanco-Canqui, F. DeClerck, L. Gatere, and P. Grace, 2014: Conservation agriculture and ecosystem services: An overview. Agric. Ecosyst. Environ., 187, 87–105, doi:10.1016/j.agee.2013.10.010.
1690. Govaerts, B. et al., 2009: Conservation agriculture and soil carbon sequestration: Between myth and farmer reality. CRC. Crit. Rev. Plant Sci., 28, 97–122, doi:10.1080/07352680902776358.
1691. Paustian, K., J. Six, E.T. Elliott, and H.W. Hunt, 2000: Management options for reducing CO2 emissions from agricultural soils. Biogeochemistry, 48, 147–163, doi:10.1023/A:1006271331703.
1692. Six, J. et al., 2004: The potential to mitigate global warming with no-tillage management is only realised when practised in the long term. Glob. Chang. Biol., 10, 155–160, doi:10.1111/j.1529-8817.2003.00730.x.
1693. van Kessel, C. et al., 2013: Climate, duration, and N placement determine N2O emissions in reduced tillage systems: A meta-analysis. Glob. Chang. Biol., 19, 33–44, doi:10.1111/j.1365-2486.2012.02779.x.
1694. Sisti, C.P.J. et al., 2004: Change in carbon and nitrogen stocks in soil under 13 years of conventional or zero tillage in southern Brazil. Soil Tillage Res., 76, 39–58, doi:10.1016/j.still.2003.08.007.
1695. Álvaro-Fuentes, J., M. V López Sánchez, C. Cantero-Martínez, and J.L. Arrúe Ugarte, 2008: Tillage effects on soil organic carbon fractions in Mediterranean dryland agroecosystems. Soil Sci. Soc. Am. J., 72, 541–547, doi:10.2136/sssaj2007.0164.
1696. Christopher, S.F., R. Lal, and U. Mishra, 2009: Regional study of no-till effects on carbon sequestration in the Midwestern United States. Soil Sci. Soc. Am. J., 73, 207–216, doi:10.2136/sssaj2007.0336.
1697. Alcántara, V., A. Don, R. Well, and R. Nieder, 2016: Deep ploughing increases agricultural soil organic matter stocks. Glob. Chang. Biol., doi:10.1111/gcb.13289.
1698. Haddaway, N.R. et al., 2017: How does tillage intensity affect soil organic carbon? A systematic review. Environ. Evid., 6, 30, doi:10.1186/s13750-017-0108-9.
1699. Luo, Z., E. Wang, and O.J. Sun, 2010: Can no-tillage stimulate carbon sequestration in agricultural soils? A meta-analysis of paired experiments. Agric. Ecosyst. Environ., 139, 224–231, doi:10.1016/j.agee.2010.08.006.
1700. Meurer, K.H.E., N.R. Haddaway, M.A. Bolinder, and T. Kätterer, 2018: Tillage intensity affects total SOC stocks in boreo-temperate regions only in the topsoil—A systematic review using an ESM approach. Earth-Science Rev., 177, 613–622, doi:10.1016/j.earscirev.2017.12.015.
1701. Wendt, J.W. and S. Hauser, 2013: An equivalent soil mass procedure for monitoring soil organic carbon in multiple soil layers. Eur. J. Soil Sci., 64, 58–65, doi:10.1111/ejss.12002.
1702. Powlson, D.S., A.P. Whitmore, and K.W.T. Goulding, 2011: Soil carbon sequestration to mitigate climate change: A critical re-examination to identify the true and the false. Eur. J. Soil Sci., 62, 42–55, doi:10.1111/j.1365-2389.2010.01342.x.
1703. Powlson, D.S. et al., 2014: Limited potential of no-till agriculture for climate change mitigation. Nat. Clim. Chang., 4, 678–683, doi:10.1038/nclimate2292.
1704. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1705. Li, C., S. Frolking, and K. Butterbach-Bahl, 2005: Carbon sequestration in arable soils is likely to increase nitrous oxide emissions, offsetting reductions in climate radiative forcing. Clim. Change, 72, 321–338, doi:10.1007/s10584-005-6791-5.
1706. Kaye, J.P., and M. Quemada, 2017: Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev., 37, 4, doi:10.1007/s13593-016-0410-x.
1707. Jacinthe, P.A. and R. Lal, 2001: A mass balance approach to assess carbon dioxide evolution during erosional events. L. Degrad. Dev., 12, 329–339, doi:10.1002/ldr.454.
1708. Lal, R., M. Griffin, J. Apt, L. Lave, and M.G. Morgan, 2004: Managing soil carbon. Science, 304, 393, doi: 10.1126/science.1093079.
1709. Stallard, R.F., 1998: Terrestrial sedimentation and the carbon cycle: Coupling weathering and erosion to carbon burial. Global Biogeochem. Cycles, 12, 231–257, doi:10.1029/98GB00741.
1710. Smith, S. V, W.H. Renwick, R.W. Buddemeier, and C.J. Crossland, 2001: Budgets of soil erosion and deposition for sediments and sedimentary organic carbon across the conterminous United States. Global Biogeochem. Cycles, 15, 697–707, doi:10.1029/2000GB001341.
1711. Van Oost, K. et al., 2007: The impact of agricultural soil erosion on the global carbon cycle. Science, 318, 626–629, doi:10.1126/science.1145724.
1712. Hoffman, F.M. et al., 2014: Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models. J. Geophys. Res. Biogeosciences, 119, 141–162, doi:10.1002/2013JG002381.
1713. Lehmann, J., D. Coumou, and K. Frieler, 2015: Increased record-breaking precipitation events under global warming. Clim. Change, 132, 501–515, doi:10.1007/s10584-015-1434-y.
1714. Weng, Z. et al., 2017: Biochar built soil carbon over a decade by stabilizing rhizodeposits. Nat. Clim. Chang., 7, 371–376, doi:10.1038/nclimate3276.
1715. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1716. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1717. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1718. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1719. Lenton, T.M., 2014: The Global Potential for Carbon Dioxide Removal. Geoengineering of the Climate System [Harrison, R.M. and R.E. Hester (eds.)]. Royal Society of Chemistry, Cambridge, U.K., 52–79 pp. doi:10.1039/9781782621225-00052.
1720. Lenton, T.M., 2010: The potential for land-based biological CO2 removal to lower future atmospheric CO2 concentration. Carbon Manag., doi:10.4155/cmt.10.12.
1721. Powell, T.W.R. and T.M. Lenton, 2012: Future carbon dioxide removal via biomass energy constrained by agricultural efficiency and dietary trends. Energy Environ. Sci., 5, 8116-8133, doi:10.1039/c2ee21592f.
1722. Woolf, D., J.E. Amonette, F.A. Street-Perrott, J. Lehmann, and S. Joseph, 2010: Sustainable biochar to mitigate global climate change. Nat. Commun., 1, doi:10.1038/ncomms1053.
1723. Pratt, K., and D. Moran, 2010: Evaluating the cost-effectiveness of global biochar mitigation potential. Biomass and Bioenergy, 34, 1149–1158, doi:10.1016/j.biombioe.2010.03.004.
1724. Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178.
1725. Roberts, K.G., B.A. Gloy, S. Joseph, N.R. Scott, and J. Lehmann, 2010: Life cycle assessment of biochar systems: Estimating the energetic, economic, and climate change potential. Environ. Sci. Technol., 44, 827–833, doi:10.1021/es902266r.
1726. Woolf, D., J.E. Amonette, F.A. Street-Perrott, J. Lehmann, and S. Joseph, 2010: Sustainable biochar to mitigate global climate change. Nat. Commun., 1, doi:10.1038/ncomms1053.
1727. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1728. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1729. Borchard, N. et al., 2019: Biochar, soil and land-use interactions that reduce nitrate leaching and N2O emissions: A meta-analysis. Sci. Total Environ., doi:10.1016/j.scitotenv.2018.10.060.
1730. Verheijen, F.G.A. et al., 2013: Reductions in soil surface albedo as a function of biochar application rate: Implications for global radiative forcing. Environ. Res. Lett., 8, 044008, doi:10.1088/1748-9326/8/4/044008.
1731. Bozzi, E., L. Genesio, P. Toscano, M. Pieri, and F. Miglietta, 2015: Mimicking biochar-albedo feedback in complex Mediterranean agricultural landscapes. Environ. Res. Lett., 10, doi:10.1088/1748-9326/10/8/084014.
1732. Hooijer, A. et al., 2010: Current and future CO2 emissions from drained peatlands in Southeast Asia. Biogeosciences, 7, 1505–1514, doi:10.5194/bg-7-1505-2010.
1733. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1734. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1735. Couwenberg, J., R. Dommain, and H. Joosten, 2010: Greenhouse gas fluxes from tropical peatlands in south-east Asia. Glob. Chang. Biol., 16, 1715–1732, doi:10.1111/j.1365-2486.2009.02016.x.
1736. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1737. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1738. Spencer, T. et al., 2016: Global coastal wetland change under sea-level rise and related stresses: The DIVA Wetland Change Model. Glob. Planet. Change, 139, 15–30, doi:10.1016/j.gloplacha.2015.12.018.
1739. Minayeva, T.Y. and A.A. Sirin, 2012: Peatland biodiversity and climate change. Biol. Bull. Rev., 2, 164–175, doi:10.1134/s207908641202003x.
1740. Sirin, A., T. Minayeva, A. Vozbrannaya, and S. Bartalev, 2011: How to avoid peat fires? Sci. Russ., N2, 13-21.
1741. Spahni, R., F. Joos, B.D. Stocker, M. Steinacher, and Z.C. Yu, 2013: Transient simulations of the carbon and nitrogen dynamics in northern peatlands: From the Last Glacial Maximum to the 21st century. Clim. Past, 9, 1287–1308, doi:10.5194/cp-9-1287-2013.
1742. Chaudhary, N., P.A. Miller, and B. Smith, 2017: Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model. Biogeosciences, doi:10.5194/bg-14-2571-2017.
1743. Ise, T., A.L. Dunn, S.C. Wofsy, and P.R. Moorcroft, 2008: High sensitivity of peat decomposition to climate change through water-table feedback. Nat. Geosci., 1, 763–766, doi:10.1038/ngeo331.
1744. Gallego-Sala, A. V et al., 2018: Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Chang., 8, 907–913, doi:10.1038/s41558-018-0271-1.
1745. Pendleton, L. et al., 2012: Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS One, 7, e43542, doi:10.1371/journal.pone.0043542.
1746. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1747. Howard, J. et al., 2017: Clarifying the role of coastal and marine systems in climate mitigation. Front. Ecol. Environ., 15, 42–50, doi:10.1002/fee.1451.
1748. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1749. Crooks, S., D. Herr, J. Tamelander, D. Laffoley, and J. Vandever, 2011: Mitigating Climate Change through Restoration and Management of Coastal Wetlands and Near-shore Marine Ecosystems: Challenges and Opportunities. Environment department papers, no.121. Marine Ecosystem Series. World Bank, Washington, DC, USA. 69 pp.
1750. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1751. McLeod, E. et al., 2011: A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ., 9, 552–560, doi:10.1890/110004.
1752. Duarte, C.M., I.J. Losada, I.E. Hendriks, I. Mazarrasa, and N. Marba, 2013: The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Chang., 3, 961–968, doi:10.1038/nclimate1970.
1753. Duarte, C.M., 2017: Reviews and syntheses: Hidden forests, the role of vegetated coastal habitats in the ocean carbon budget. Biogeosciences, 14, 301–310, doi:10.5194/bg-14-301-2017.
1754. Taillardat, P., D.A. Friess, and M. Lupascu, 2018: Mangrove blue carbon strategies for climate change mitigation are most effective at the national scale. Biol. Lett., 14, 20180251, doi:10.1098/rsbl.2018.0251.
1755. Jennerjahn, T.C. et al., 2017: Mangrove ecosystems under climate change. In: Mangrove Ecosystems: A Global Biogeographic Perspective [Rivera-Monroy, V.H., S.Y. Lee, E. Kristensen, and R.R. Twilley (eds.)]. Springer International Publishing, Cham, Switzerland, pp. 211–244.
1756. Kelleway, J.J. et al., 2017: Review of the ecosystem service implications of mangrove encroachment into salt marshes. Glob. Chang. Biol., 23, 3967–3983, doi:10.1111/gcb.13727.
1757. Dutta, M.K., T.S. Bianchi, and S.K. Mukhopadhyay, 2017: Mangrove Methane Biogeochemistry in the Indian Sundarbans: A Proposed Budget. Front. Mar. Sci., 4, 187, doi:10.3389/fmars.2017.00187.
1758. McLaren, D., 2012: A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot., 90, 489–500, doi:10.1016/j.psep.2012.10.005.
1759. Lenton, T.M., 2014: The Global Potential for Carbon Dioxide Removal. Geoengineering of the Climate System [Harrison, R.M. and R.E. Hester (eds.)]. Royal Society of Chemistry, Cambridge, U.K., 52–79 pp. doi:10.1039/9781782621225-00052.
1760. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1761. Turner, P.A., C.B. Field, D.B. Lobell, D.L. Sanchez, and K.J. Mach, 2018b: Unprecedented rates of land-use transformation in modelled climate change mitigation pathways. Nat. Sustain., 1, 240–245, doi:10.1038/s41893-018-0063-7.
1762. Lenton, T.M., 2010: The potential for land-based biological CO2 removal to lower future atmospheric CO2 concentration. Carbon Manag., doi:10.4155/cmt.10.12.
1763. Koornneef, J. et al., 2012: Global potential for biomass and carbon dioxide capture, transport and storage up to 2050. Int. J. Greenh. Gas Control, doi:10.1016/j.ijggc.2012.07.027.
1764. Powell, T.W.R. and T.M. Lenton, 2012: Future carbon dioxide removal via biomass energy constrained by agricultural efficiency and dietary trends. Energy Environ. Sci., 5, 8116-8133, doi:10.1039/c2ee21592f.
1765. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
1766. Chan, D. and Q. Wu, 2015: Significant anthropogenic-induced changes of climate classes since 1950. Sci. Rep., 5, 13487, doi:10.1038/srep13487.
1767. Schueler, V., S. Fuss, J.C. Steckel, U. Weddige, and T. Beringer, 2016: Productivity ranges of sustainable biomass potentials from non-agricultural land. Environ. Res. Lett., 11, 074026, doi:10.1088/1748-9326/11/7/074026.
1768. Wu, P., N. Christidis, and P. Stott, 2013: Anthropogenic impact on Earth’s hydrological cycle. Nat. Clim. Chang., 3, 807–810, doi:10.1038/nclimate1932.
1769. Searle, S. and C. Malins, 2015: A reassessment of global bioenergy potential in 2050. GCB Bioenergy, 7, 328–336, doi:10.1111/gcbb.12141.
1770. Wu, W. et al., 2019: Global advanced bioenergy potential under environmental protection policies and societal transformation measures. GCB Bioenergy, doi:10.1111/gcbb.12614.
1771. Heck, V., D. Gerten, W. Lucht, and A. Popp, 2018: Biomass-based negative emissions difficult to reconcile with planetary boundaries. Nat. Clim. Chang., doi:10.1038/s41558-017-0064-y.
1772. Fritz, S. et al., 2013: Downgrading recent estimates of land available for biofuel production. Environ. Sci. Technol., doi:10.1021/es303141h.
1773. Berndes, G., S. Ahlgren, P. Börjesson, and A.L. Cowie, 2013: Bioenergy and land use change—state of the art. Wiley Interdiscip. Rev. Energy Environ., 2, 282–303, doi:10.1002/wene.41.
1774. Creutzig, F. et al., 2015a: Bioenergy and climate change mitigation: An assessment. GCB Bioenergy, 7, 916–944, doi:10.1111/gcbb.12205.
1775. Woods, J., L. M, B. M, K.L. Kline, and A. Faaij, 2015: Land and bioenergy. In: Bioenergy & Sustainability: Bridging the Gaps, [Souza, G.M., R.L. Victoria, C.A. Joly, L.M. Verdade(eds.)], 258–295pp, São Paulo.
1776. Daioglou, V., J.C. Doelman, B. Wicke, A. Faaij, and D.P. van Vuuren, 2019: Integrated assessment of biomass supply and demand in climate change mitigation scenarios. Glob. Environ. Chang., doi:10.1016/j.gloenvcha.2018.11.012.
1777. Campbell, J.E., D.B. Lobell, R.C. Genova, and C.B. Field, 2008: The global potential of bioenergy on abandoned agriculture lands. Environ. Sci. Technol., 42, 5791–5794, doi:10.1021/es800052w.
1778. Campbell, J.E., D.B. Lobell, R.C. Genova, and C.B. Field, 2008: The global potential of bioenergy on abandoned agriculture lands. Environ. Sci. Technol., 42, 5791–5794, doi:10.1021/es800052w.
1779. Cai, X., X. Zhang, and D. Wang, 2011: Land availability for biofuel production. Environ. Sci. Technol., 45, 334–339, doi:10.1021/es103338e.
1780. Lewis, S.M., and M. Kelly, 2014: Mapping the potential for biofuel production on marginal lands: Differences in definitions, data and models across scales. Isprs Int. J. Geo-Information, 3, 430–459, doi:10.3390/ijgi3020430.
1781. Campbell, J.E., D.B. Lobell, R.C. Genova, and C.B. Field, 2008: The global potential of bioenergy on abandoned agriculture lands. Environ. Sci. Technol., 42, 5791–5794, doi:10.1021/es800052w.
1782. Cai, X., X. Zhang, and D. Wang, 2011: Land availability for biofuel production. Environ. Sci. Technol., 45, 334–339, doi:10.1021/es103338e.
1783. Lewis, S.M., and M. Kelly, 2014: Mapping the potential for biofuel production on marginal lands: Differences in definitions, data and models across scales. Isprs Int. J. Geo-Information, 3, 430–459, doi:10.3390/ijgi3020430.
1784. De Oliveira Bordonal, R. et al., 2015: Greenhouse gas balance from cultivation and direct land use change of recently established sugarcane (Saccharum officinarum) plantation in south-central Brazil. Renew. Sustain. Energy Rev., 52, 547–556, doi:10.1016/j.rser.2015.07.137.
1785. Elshout, P.M.F. et al., 2015: Greenhouse-gas payback times for crop-based biofuels. Nat. Clim. Chang., 5, 604–610, doi:10.1038/nclimate2642.
1786. Harper, A.B. et al., 2018: Land-use emissions play a critical role in land-based mitigation for Paris climate targets. Nature Communications doi:10.1038/s41467-018-05340-z.
1787. Daioglou, V. et al., 2017: Greenhouse gas emission curves for advanced biofuel supply chains. Nat. Clim. Chang., doi:10.1038/s41558-017-0006-8.
1788. Hudiburg, T.W., B.E. Law, C. Wirth, and S. Luyssaert, 2011: Regional carbon dioxide implications of forest bioenergy production. Nat. Clim. Chang., 1, 419–423, doi:10.1038/nclimate1264.
1789. Berndes, G., S. Ahlgren, P. Börjesson, and A.L. Cowie, 2013: Bioenergy and land use change—state of the art. Wiley Interdiscip. Rev. Energy Environ., 2, 282–303, doi:10.1002/wene.41.
1790. Guest, G., F. Cherubini, and A.H. Strømman, 2013: The role of forest residues in the accounting for the global warming potential of bioenergy. GCB Bioenergy, 5, 459–466, doi:10.1111/gcbb.12014.
1791. Lamers, P., and M. Junginger, 2013: The “debt” is in the detail: A synthesis of recent temporal forest carbon analyses on woody biomass for nergy. Biofuels, Bioprod. Biorefining, 7, 373–385, doi:10.1002/bbb.1407.
1792. Cherubini, F. et al., 2016: Global spatially explicit CO2 emission metrics for forest bioenergy. Sci. Rep., 6, 20186, doi:10.1038/srep20186 http://dx.doi.org/10.1038/srep20186 .
1793. Cintas, O. et al., 2017: Carbon balances of bioenergy systems using biomass from forests managed with long rotations: Bridging the gap between stand and landscape assessments. GCB Bioenergy, 9, 1238–1251, doi:10.1111/gcbb.12425.
1794. Laurance, S. et al., 2018: Compositional response of Amazon forests to climate change. Glob. Chang. Biol., 25, 39–56, doi:10.1111/gcb.14413.
1795. Valade, A. et al., 2018: Carbon costs and benefits of France’s biomass energy production targets. Carbon Balance Manag., 13, 26, doi:10.1186/s13021-018-0113-5.
1796. Baker, J.S., C.M. Wade, B.L. Sohngen, S. Ohrel, and A.A. Fawcett, 2019: Potential complementarity between forest carbon sequestration incentives and biomass energy expansion. Energy Policy, doi:10.1016/j.enpol.2018.10.009.
1797. Creutzig, F. et al., 2015a: Bioenergy and climate change mitigation: An assessment. GCB Bioenergy, 7, 916–944, doi:10.1111/gcbb.12205.
1798. Robertson, G.P. et al., 2017: Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes. Science, 356, eaal2324, doi:10.1126/science.aal2324.
1799. Vaughan, N.E. et al., 2018: Evaluating the use of biomass energy with carbon capture and storage in low emission scenarios. Environ. Res. Lett., 13, 44014, doi:10.1088/1748-9326/aaaa02.
1800. Daioglou, V. et al., 2017: Greenhouse gas emission curves for advanced biofuel supply chains. Nat. Clim. Chang., doi:10.1038/s41558-017-0006-8.
1801. Don, A. et al., 2012: Land-use change to bioenergy production in Europe: implications for the greenhouse gas balance and soil carbon. GCB Bioenergy, 4, 372–391, doi:10.1111/j.1757-1707.2011.01116.x.
1802. Robertson, G.P. et al., 2017: Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes. Science, 356, eaal2324, doi:10.1126/science.aal2324.
1803. Chum, H., A. Faaij, J. Moreira, G. Berndes, P. Dhamija, H. Dong, B. Gabrielle, A. Goss Eng, W. Lucht, M. Mapako, O. Masera Cerutti, T. McIntyre, T. Minowa, and K. Pingoud, 2011: Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation, (Edenhofer, O., R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, United Kingdom and New York, NY, USA, pp. 209–332.
1804. Liska, A.J. et al., 2014: Biofuels from crop residue can reduce soil carbon and increase CO2 emissions. Nat. Clim. Chang., 4, 398–401, doi:10.1038/nclimate2187.
1805. Monforti, F. et al., 2015: Optimal energy use of agricultural crop residues preserving soil organic carbon stocks in Europe. Renew. Sustain. Energy Rev., 44, 519–529, doi:10.1016/j.rser.2014.12.033.
1806. Zhao, G. et al., 2015: Sustainable limits to crop residue harvest for bioenergy: Maintaining soil carbon in Australia’s agricultural lands. GCB Bioenergy, 7, 479–487, doi:10.1111/gcbb.12145.
1807. Daioglou, V., E. Stehfest, B. Wicke, A. Faaij, and D.P. van Vuuren, 2016: Projections of the availability and cost of residues from agriculture and forestry. GCB Bioenergy, doi:10.1111/gcbb.12285.
1808. Chum, H., A. Faaij, J. Moreira, G. Berndes, P. Dhamija, H. Dong, B. Gabrielle, A. Goss Eng, W. Lucht, M. Mapako, O. Masera Cerutti, T. McIntyre, T. Minowa, and K. Pingoud, 2011: Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation, (Edenhofer, O., R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, United Kingdom and New York, NY, USA, pp. 209–332.
1809. Creutzig, F., G. Baiocchi, R. Bierkandt, P.-P. Pichler, and K.C. Seto, 2015b: Global typology of urban energy use and potentials for an urbanization mitigation wedge. Proc. Natl. Acad. Sci., 112, 6283–6288, doi:10.1073/pnas.1315545112.
1810. Staples, M.D., R. Malina, and S.R.H. Barrett, 2017: The limits of bioenergy for mitigating global life-cycle greenhouse gas emissions from fossil fuels. Nat. Energy, 2, 16202, doi:10.1038/nenergy.2016.202.
1811. Daioglou, V., J.C. Doelman, B. Wicke, A. Faaij, and D.P. van Vuuren, 2019: Integrated assessment of biomass supply and demand in climate change mitigation scenarios. Glob. Environ. Chang., doi:10.1016/j.gloenvcha.2018.11.012.
1812. Chum, H., A. Faaij, J. Moreira, G. Berndes, P. Dhamija, H. Dong, B. Gabrielle, A. Goss Eng, W. Lucht, M. Mapako, O. Masera Cerutti, T. McIntyre, T. Minowa, and K. Pingoud, 2011: Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation, (Edenhofer, O., R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, United Kingdom and New York, NY, USA, pp. 209–332.
1813. Creutzig, F., G. Baiocchi, R. Bierkandt, P.-P. Pichler, and K.C. Seto, 2015b: Global typology of urban energy use and potentials for an urbanization mitigation wedge. Proc. Natl. Acad. Sci., 112, 6283–6288, doi:10.1073/pnas.1315545112.
1814. Chum, H., A. Faaij, J. Moreira, G. Berndes, P. Dhamija, H. Dong, B. Gabrielle, A. Goss Eng, W. Lucht, M. Mapako, O. Masera Cerutti, T. McIntyre, T. Minowa, and K. Pingoud, 2011: Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation, (Edenhofer, O., R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, United Kingdom and New York, NY, USA, pp. 209–332.
1815. Gerbrandt, K. et al., 2016: Life cycle assessment of lignocellulosic ethanol: a review of key factors and methods affecting calculated GHG emissions and energy use. Curr. Opin. Biotechnol., 38, 63–70, doi:10.1016/j.copbio.2015.12.021.
1816. Oates, L.G. et al., 2016: Nitrous oxide emissions during establishment of eight alternative cellulosic bioenergy cropping systems in the North Central United States. GCB Bioenergy, 8, 539–549, doi:10.1111/gcbb.12268.
1817. Rowe, R.L. et al., 2016: Initial soil C and land-use history determine soil C sequestration under perennial bioenergy crops. GCB Bioenergy, 8, 1046–1060, doi:10.1111/gcbb.12311.
1818. Lai, L. et al., 2017: Soil nitrogen dynamics in switchgrass seeded to a marginal cropland in South Dakota. GCB Bioenergy, n/a-n/a, doi:10.1111/gcbb.12475.
1819. Robertson, G.P. et al., 2017: Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes. Science, 356, eaal2324, doi:10.1126/science.aal2324.
1820. Chum, H., A. Faaij, J. Moreira, G. Berndes, P. Dhamija, H. Dong, B. Gabrielle, A. Goss Eng, W. Lucht, M. Mapako, O. Masera Cerutti, T. McIntyre, T. Minowa, and K. Pingoud, 2011: Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation, (Edenhofer, O., R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, United Kingdom and New York, NY, USA, pp. 209–332.
1821. Wicke, B., P. Verweij, H. van Meijl, D.P. van Vuuren, and A.P.C. Faaij, 2012: Indirect land use change: Review of existing models and strategies for mitigation. Biofuels, 3, 87–100, doi:10.4155/bfs.11.154.
1822. Valin, H., et al., 2015: The Land Use Change Impact of Biofuels Consumed in the EU: Quantification of Area and Greenhouse Gas Impacts. ECOFYS Netherlands B.V., Utrecht, Netherlands, 261 pp.
1823. Ahlgren, S. and L. Di Lucia, 2014: Indirect land use changes of biofuel production – A review of modelling efforts and policy developments in the European Union. Biotechnol. Biofuels, doi:10.1186/1754-6834-7-35.
1824. Rajagopal, D. and R.J. Plevin, 2013: Implications of market-mediated emissions and uncertainty for biofuel policies. Energy Policy, 56, 75–82, doi:10.1016/j.enpol.2012.09.076.
1825. Finkbeiner, M., 2014: Indirect land use change – Help beyond the hype? Biomass and Bioenergy, 62, 218–221, doi:10.1016/j.biombioe.2014.01.024.
1826. Kim, S. et al., 2014: Indirect land use change and biofuels: Mathematical analysis reveals a fundamental flaw in the regulatory approach. Biomass and Bioenergy, 71, 408–412, doi:10.1016/j.biombioe.2014.09.015.
1827. Zilberman, D., 2017: Indirect land use change: much ado about (almost) nothing. GCB Bioenergy, 9, 485–488, doi:10.1111/gcbb.12368.
1828. Cintas, O. et al., 2017: Carbon balances of bioenergy systems using biomass from forests managed with long rotations: Bridging the gap between stand and landscape assessments. GCB Bioenergy, 9, 1238–1251, doi:10.1111/gcbb.12425.
1829. Duden, A.S. et al., 2017: Modeling the impacts of wood pellet demand on forest dynamics in southeastern United States. Biofuels, Bioprod. Biorefining, doi:10.1002/bbb.1803.
1830. Dale, V.H., E. Parish, K.L. Kline, and E. Tobin, 2017: How is wood-based pellet production affecting forest conditions in the southeastern United States? For. Ecol. Manage., doi:10.1016/j.foreco.2017.03.022.
1831. Baker, J.S., C.M. Wade, B.L. Sohngen, S. Ohrel, and A.A. Fawcett, 2019: Potential complementarity between forest carbon sequestration incentives and biomass energy expansion. Energy Policy, doi:10.1016/j.enpol.2018.10.009.
1832. Ahlgren, S. and L. Di Lucia, 2014: Indirect land use changes of biofuel production – A review of modelling efforts and policy developments in the European Union. Biotechnol. Biofuels, doi:10.1186/1754-6834-7-35.
1833. Valin, H., et al., 2015: The Land Use Change Impact of Biofuels Consumed in the EU: Quantification of Area and Greenhouse Gas Impacts. ECOFYS Netherlands B.V., Utrecht, Netherlands, 261 pp.
1834. Plevin, R.J., J. Beckman, A.A. Golub, J. Witcover, and M. O’Hare, 2015: Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change. Environ. Sci. Technol., doi:10.1021/es505481d.
1835. Taheripour, F., and W.E. Tyner, 2013: Induced land use emissions due to first and second generation biofuels and uncertainty in land use emission factors. Econ. Res. Int., 2013, 315787, doi:10.1155/2013/315787.
1836. Bento, A.M. and R. Klotz, 2014: Climate policy decisions require policy-based lifecycle analysis. Environ. Sci. Technol., 48, 5379–5387, doi:10.1021/es405164g.
1837. Kicklighter, D. et al., 2013: Climate impacts of a large-scale biofuels expansion. Geophys. Res. Lett., 40, 1624–1630, doi:10.1002/grl.50352.
1838. Georgescu, M., D.B. Lobell, and C.B. Field, 2011: Direct climate effects of perennial bioenergy crops in the United States. Proc. Natl. Acad. Sci., 108, 4307–4312, doi:10.1073/pnas.1008779108.
1839. Harding, K.J., T.E. Twine, A. VanLoocke, J.E. Bagley, and J. Hill, 2016: Impacts of second-generation biofuel feedstock production in the central U.S. on the hydrologic cycle and global warming mitigation potential. Geophys. Res. Lett., 43, 10,773–10,781, doi:10.1002/2016gl069981.
1840. Wang, M. et al., 2017b: On the long-term hydroclimatic sustainability of perennial bioenergy crop expansion over the United States. J. Clim., 30, 2535–2557, doi:10.1175/jcli-d-16-0610.1.
1841. Loarie, S.R., D.B. Lobell, G.P. Asner, Q. Mu, and C.B. Field, 2011: Direct impacts on local climate of sugar-cane expansion in Brazil. Nat. Clim. Chang., 1, 105–109, doi:10.1038/nclimate1067.
1842. Harding, K.J., T.E. Twine, A. VanLoocke, J.E. Bagley, and J. Hill, 2016: Impacts of second-generation biofuel feedstock production in the central U.S. on the hydrologic cycle and global warming mitigation potential. Geophys. Res. Lett., 43, 10,773–10,781, doi:10.1002/2016gl069981.
1843. Georgescu, M., D.B. Lobell, C.B. Field, and A. Mahalov, 2013: Simulated hydroclimatic impacts of projected Brazilian sugarcane expansion. Geophys. Res. Lett., 40, 972–977, doi:10.1002/grl.50206.
1844. Wang, M. et al., 2017b: On the long-term hydroclimatic sustainability of perennial bioenergy crop expansion over the United States. J. Clim., 30, 2535–2557, doi:10.1175/jcli-d-16-0610.1.
1845. Ashworth, K., G. Folberth, C.N. Hewitt, and O. Wild, 2012: Impacts of near-future cultivation of biofuel feedstocks on atmospheric composition and local air quality. Atmos. Chem. Phys., 12, 919–939, doi:10.5194/acp-12-919-2012.
1846. IPCC, 2005: Special Report on Carbon Dioxide Capture and Storage. Prepared by Working Group III of the Intergovernmental Panel on Climate Change. [Metz, B., O. Davidson, H.C. de Coninck, M. Loos, and L.A. Meyer, (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 443 pp.
1847. Manning, D.A.C., 2008: Biological enhancement of soil carbonate precipitation: Passive removal of atmospheric CO2, Mineral. Mag., 72, 639–649, doi:10.1180/minmag.2008.072.2.639.
1848. Beerling, D.J. et al., 2018: Farming with crops and rocks to address global climate, food and soil security. Nat. Plants, 4, 138–147, doi:10.1038/s41477-018-0108-y.
1849. Beerling, D.J. et al., 2018: Farming with crops and rocks to address global climate, food and soil security. Nat. Plants, 4, 138–147, doi:10.1038/s41477-018-0108-y.
1850. Lenton, T.M., 2010: The potential for land-based biological CO2 removal to lower future atmospheric CO2 concentration. Carbon Manag., doi:10.4155/cmt.10.12.
1851. Smith, M.C., J.S. Singarayer, P.J. Valdes, J.O. Kaplan, and N.P. Branch, 2016a: The biogeophysical climatic impacts of anthropogenic land use change during the Holocene. Clim. Past, 12, 923–941, doi:10.5194/cp-12-923-2016.
1852. Taylor, L.L. et al., 2016: Enhanced weathering strategies for stabilizing climate and averting ocean acidification. Nat. Clim. Chang., 6, 402–406, doi:10.1038/nclimate2882.
1853. Bajželj, B. and K. Richards, 2014: The positive feedback loop between the impacts of climate change and agricultural expansion and relocation. Land, 3, 898–916, doi:10.3390/land3030898. http://www.mdpi.com/2073-445X/3/3/898/ .
1854. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1855. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1856. Hiç, C., P. Pradhan, D. Rybski, and J.P. Kropp, 2016: Food surplus and its climate burdens. Environ. Sci. Technol., doi:10.1021/acs.est.5b05088.
1857. Bajželj, B. and K. Richards, 2014: The positive feedback loop between the impacts of climate change and agricultural expansion and relocation. Land, 3, 898–916, doi:10.3390/land3030898. http://www.mdpi.com/2073-445X/3/3/898/ .
1858. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
1859. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
1860. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
1861. Springmann, M., H.C.J. Godfray, M. Rayner, and P. Scarborough, 2016: Analysis and valuation of the health and climate change cobenefits of dietary change. Proc. Natl. Acad. Sci., 113, 4146–4151, doi:10.1073/pnas.1523119113.
1862. Tilman, D., and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13958.
1863. Hedenus, F., S. Wirsenius, and D.J.A. Johansson, 2014: The importance of reduced meat and dairy consumption for meeting stringent climate change targets. Clim. Change, 124, 79–91, doi:10.1007/s10584-014-1104-5.
1865. Tilman, D., and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13958.
1866. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
1867. Obersteiner, M. et al., 2016: Assessing the land resource–food price nexus of the Sustainable Development Goals. Sci. Adv., 2, e1501499–e1501499, doi:10.1126/sciadv.1501499.
1868. Humpenöder, F. et al., 2018: Large-scale bioenergy production: How to resolve sustainability trade-offs? Environ. Res. Lett., 13, 24011, doi:10.1088/1748-9326/aa9e3b.
1869. Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
1870. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
1871. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1872. Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 85001, doi:10.1088/1748-9326/11/8/085001.
1873. Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
1874. Tavoni, M. et al., 2015: Post-2020 climate agreements in the major economies assessed in the light of global models. Nat. Clim. Chang., 5, 119–126, doi:10.1038/nclimate2475.
1875. Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
1876. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
1877. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
1878. van Vuuren, D.P. et al., 2018: Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. Nat. Clim. Chang., 8, 1–7, doi:10.1038/s41558-018-0119-8.
1879. Frank, S. et al., 2017a: Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett., 12, 105004, doi:10.1088/1748-9326/aa8c83.
1880. Fujimori, S. et al., 2017: SSP3: AIM implementation of Shared Socioeconomic Pathways. Glob. Environ. Chang., 42, 268–283, doi:10.1016/J.GLOENVCHA.2016.06.009.
1881. Calvin, K. et al., 2017: The SSP4: A world of deepening inequality. Glob. Environ. Chang., 42, 284–296, doi:10.1016/J.GLOENVCHA.2016.06.010.
1882. Popp, A. et al., 2014: Land-use transition for bioenergy and climate stabilization: Model comparison of drivers, impacts and interactions with other land use based mitigation options. Clim. Change, 123, 495–509, doi:10.1007/s10584-013-0926-x.
1883. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
1884. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
1885. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
1886. Fujimori, S. et al., 2017: SSP3: AIM implementation of Shared Socioeconomic Pathways. Glob. Environ. Chang., 42, 268–283, doi:10.1016/J.GLOENVCHA.2016.06.009.
1887. Frank, S. et al., 2017b: Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett., 12, 105004, doi:10.1088/1748-9326/aa8c83.
1888. Humpenöder, F. et al., 2018: Large-scale bioenergy production: How to resolve sustainability trade-offs? Environ. Res. Lett., 13, 24011, doi:10.1088/1748-9326/aa9e3b.
1889. Obersteiner, M. et al., 2016: Assessing the land resource–food price nexus of the Sustainable Development Goals. Sci. Adv., 2, e1501499–e1501499, doi:10.1126/sciadv.1501499.
1890. Calvin, K. et al., 2014: Trade-offs of different land and bioenergy policies on the path to achieving climate targets. Clim. Change, 123, 691–704, doi:10.1007/s10584-013-0897-y.
1891. Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
1892. Fricko, O. et al., 2017: The marker quantification of the Shared Socioeconomic Pathway 2: A middle-of-the-road scenario for the 21st century. Glob. Environ. Chang., 42, 251–267, doi:10.1016/J.GLOENVCHA.2016.06.004.
1893. Stevanović, M. et al., 2017: Mitigation strategies for greenhouse gas emissions from agriculture and land-use change: Consequences for food prices. Environ. Sci. Technol., 51, 365–374, doi:10.1021/acs.est.6b04291.
1894. Frank, S. et al., 2017b: Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett., 12, 105004, doi:10.1088/1748-9326/aa8c83.
1895. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
1896. Kriegler, E. et al., 2017: Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century. Glob. Environ. Chang., 42, 297–315, doi:10.1016/J.GLOENVCHA.2016.05.015.
1897. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
1898. Calvin, K. et al., 2014: Trade-offs of different land and bioenergy policies on the path to achieving climate targets. Clim. Change, 123, 691–704, doi:10.1007/s10584-013-0897-y.
1899. Bertram, C. et al., 2018: Targeted policies can compensate most of the increased sustainability risks in 1.5°C mitigation scenarios. Environ. Res. Lett., 13, 64038, doi:10.1088/1748-9326/aac3ec.
1900. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
1901. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
1902. Obersteiner, M. et al., 2016: Assessing the land resource–food price nexus of the Sustainable Development Goals. Sci. Adv., 2, e1501499–e1501499, doi:10.1126/sciadv.1501499.
1903. van Vuuren, D.P. et al., 2018: Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. Nat. Clim. Chang., 8, 1–7, doi:10.1038/s41558-018-0119-8.
1904. Grubler, A. et al., 2018: A low energy demand scenario for meeting the 1.5 C target and sustainable development goals without negative emission technologies. Nat. Energy, 3, 515–527, doi:10.1038/s41560-018-0172-6.
1905. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
1906. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
1907. Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
1908. Huppmann, D., J. Rogelj, E. Kriegler, V. Krey, and K. Riahi, 2018: A new scenario resource for integrated 1.5°C research. Nat. Clim. Chang., 8, 1027–1030, doi:10.1038/s41558-018-0317-4.
1909. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
1910. Fuglestvedt, J. et al., 2018: Implications of possible interpretations of “greenhouse gas balance” in the Paris Agreement. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 376, 20160445, doi:10.1098/rsta.2016.0445.
1911. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
1912. Grassi, G. et al., 2017: The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang., 7, 220–226, doi:10.1038/nclimate3227.
1913. Forsell, N. et al., 2016: Assessing the INDCs’ land use, land use change, and forest emission projections. Carbon Balance Manag., 11, 26, doi:10.1186/s13021-016-0068-3.
1914. Grassi, G. et al., 2017: The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang., 7, 220–226, doi:10.1038/nclimate3227.
1915. Grassi, G. et al., 2017: The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang., 7, 220–226, doi:10.1038/nclimate3227.
1916. Forsell, N. et al., 2016: Assessing the INDCs’ land use, land use change, and forest emission projections. Carbon Balance Manag., 11, 26, doi:10.1186/s13021-016-0068-3.
1917. Grassi, G. et al., 2017: The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang., 7, 220–226, doi:10.1038/nclimate3227.
1918. Roberts, P.D., G.B. Stewart, and A.S. Pullin, 2006: Are review articles a reliable source of evidence to support conservation and environmental management? A comparison with medicine. Biol. Conserv., 132, 409–423, doi:10.1016/j.biocon.2006.04.034.
1919. Rogelj, J. et al., 2016: Paris Agreement climate proposals need a boost to keep warming well below 2°C. Nature, 534, 631. doi:10.1038/nature18307.
1920. Peters, G.P. and O. Geden, 2017: Catalysing a political shift from low to negative carbon. Nat. Clim. Chang., 7, 619–621, doi:10.1038/nclimate3369.
1921. Millar, R.J. et al., 2017: Emission budgets and pathways consistent with limiting warming to 1.5°c. Nat. Geosci., 10, 741–747, doi:10.1038/NGEO3031.
1922. Rogelj, J. et al., 2016: Paris Agreement climate proposals need a boost to keep warming well below 2°C. Nature, 534, 631. doi:10.1038/nature18307.
1923. Roberts, P.D., G.B. Stewart, and A.S. Pullin, 2006: Are review articles a reliable source of evidence to support conservation and environmental management? A comparison with medicine. Biol. Conserv., 132, 409–423, doi:10.1016/j.biocon.2006.04.034.
1924. UNEP, 2017: The Emissions Gap Report 2017: A UN Environment Synthesis Report. United Nations Environment Programme (UNEP), Nairobi, Kenya, 116 pp.
1925. Strefler, J. et al., 2018: Between Scylla and Charybdis: Delayed mitigation narrows the passage between large-scale CDR and high costs. Environ. Res. Lett., 13, 44015, doi:10.1088/1748-9326/aab2ba.
1926. Elmar, K. et al., 2018: Pathways limiting warming to 1.5°C: A tale of turning around in no time? Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 376, 20160457, doi:10.1098/rsta.2016.0457.
1927. Busch, F.A., and R.F. Sage, 2017: The sensitivity of photosynthesis to O2 and CO2 concentration identifies strong Rubisco control above the thermal optimum. New Phytol., 213, 1036–1051, doi:10.1111/nph.14258.
1928. Rogers, A. et al., 2017: A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol., 213, 22–42, doi:10.1111/nph.14283.
1929. Anderegg, W.R.L. et al., 2016: Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. Proc. Natl. Acad. Sci., 113, 5024–5029, doi:10.1073/pnas.1525678113.
1930. Tjoelker, M.G., 2018: The role of thermal acclimation of plant respiration under climate warming: Putting the brakes on a runaway train? Plant Cell Environ., 41, 501–503, doi:10.1111/pce.13126.
1931. Sulman, B.N., R.P. Phillips, A.C. Oishi, E. Shevliakova, and S.W. Pacala, 2014: Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Chang., 4, 1099–1102, doi:10.1038/nclimate2436.
1932. Wieder, W.R. et al., 2018: Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models. Glob. Chang. Biol., 24, 1563–1579, doi:10.1111/gcb.13979.
1933. Davidson, E.A., I.A. Janssens, E.A. Davidson, and I.A. Janssens, 2006a: Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165–173, doi:10.1038/nature04514.
1934. Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
1935. Busch, F.A., and R.F. Sage, 2017: The sensitivity of photosynthesis to O2 and CO2 concentration identifies strong Rubisco control above the thermal optimum. New Phytol., 213, 1036–1051, doi:10.1111/nph.14258.
1936. Rogers, A. et al., 2017: A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol., 213, 22–42, doi:10.1111/nph.14283.
1937. Malhi, Y., C. Doughty, and D. Galbraith, 2011: The allocation of ecosystem net primary productivity in tropical forests. Philos. Trans. R. Soc. B Biol. Sci., 366, 3225–3245, doi:10.1098/rstb.2011.0062.
1938. Rogers, A. et al., 2017: A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol., 213, 22–42, doi:10.1111/nph.14283.
1939. Tjoelker, M.G., 2018: The role of thermal acclimation of plant respiration under climate warming: Putting the brakes on a runaway train? Plant Cell Environ., 41, 501–503, doi:10.1111/pce.13126.
1940. Vanderwel, M.C. et al., 2015: Global convergence in leaf respiration from estimates of thermal acclimation across time and space. New Phytol., 207, 1026–1037, doi:10.1111/nph.13417.
1941. Tjoelker, M.G., 2018: The role of thermal acclimation of plant respiration under climate warming: Putting the brakes on a runaway train? Plant Cell Environ., 41, 501–503, doi:10.1111/pce.13126.
1942. Todd-Brown, K.E.O. et al., 2013: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences, 10, 1717–1736, doi:10.5194/bg-10-1717-2013.
1943. Hawkes, C.V., B.G. Waring, J.D. Rocca, and S.N. Kivlin, 2017: Historical climate controls soil respiration responses to current soil moisture. Proc. Natl. Acad. Sci., 114, 6322–6327, doi:10.1073/pnas.1620811114.
1944. Todd-Brown, K.E.O. et al., 2013: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences, 10, 1717–1736, doi:10.5194/bg-10-1717-2013.
1945. Sulman, B.N., R.P. Phillips, A.C. Oishi, E. Shevliakova, and S.W. Pacala, 2014: Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Chang., 4, 1099–1102, doi:10.1038/nclimate2436.
1946. Singh, B.K., R.D. Bardgett, P. Smith, and D.S. Reay, 2010: Microorganisms and climate change: Terrestrial feedbacks and mitigation options. Nat. Rev. Microbiol., 8, 779–790, doi:10.1038/nrmicro2439.
1947. Wieder, W.R. et al., 2018: Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models. Glob. Chang. Biol., 24, 1563–1579, doi:10.1111/gcb.13979.
1948. Slot, M. et al, 2014: Thermal acclimation of leaf respiration of tropical trees and lianas: Response to experimental canopy warming, and consequences for tropical forest carbon balance. Glob. Chang. Biol., 20, 2915–2926, doi:10.1111/gcb.12563.
1949. Rogers, A. et al., 2017: A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol., 213, 22–42, doi:10.1111/nph.14283.
1950. Tan, Z.H. et al., 2017: Optimum air temperature for tropical forest photosynthesis: Mechanisms involved and implications for climate warming. Environ. Res. Lett., 12, doi:10.1088/1748-9326/aa6f97.
1951. Tjoelker, M.G., 2018: The role of thermal acclimation of plant respiration under climate warming: Putting the brakes on a runaway train? Plant Cell Environ., 41, 501–503, doi:10.1111/pce.13126.
1952. Slot, M. et al, 2014: Thermal acclimation of leaf respiration of tropical trees and lianas: Response to experimental canopy warming, and consequences for tropical forest carbon balance. Glob. Chang. Biol., 20, 2915–2926, doi:10.1111/gcb.12563.
1953. Li, W. et al., 2018b: Recent changes in global photosynthesis and terrestrial ecosystem respiration constrained from multiple observations. Geophys. Res. Lett., 45, 1058–1068, doi:10.1002/2017GL076622.
1954. Davidson, E.A., I.A. Janssens, E.A. Davidson, and I.A. Janssens, 2006a: Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165–173, doi:10.1038/nature04514.
1955. Tjoelker, M.G., 2018: The role of thermal acclimation of plant respiration under climate warming: Putting the brakes on a runaway train? Plant Cell Environ., 41, 501–503, doi:10.1111/pce.13126.
1956. Smith, N.G., and J.S. Dukes, 2013: Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2. Glob. Chang. Biol., 19, 45–63, doi:10.1111/j.1365-2486.2012.02797.x.
1957. Smith, N.G., S.L. Malyshev, E. Shevliakova, J. Kattge, and J.S. Dukes, 2016b: Foliar temperature acclimation reduces simulated carbon sensitivity to climate. Nat. Clim. Chang., 6, 407–411, doi:10.1038/nclimate2878.
1958. Atkin, O.K. et al., 2015: Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. New Phytol., 206, 614–636, doi:10.1111/nph.13253.
1959. Heskel, M.A. et al., 2016: Convergence in the temperature response of leaf respiration across biomes and plant functional types. Proc. Natl. Acad. Sci., 113, 3832–3837, doi:10.1073/pnas.1520282113.
1960. Huntingford, C. et al., 2017: Implications of improved representations of plant respiration in a changing climate. Nat. Commun., 8, 1602, doi:10.1038/s41467-017-01774-z.
1961. Heskel, M.A. et al., 2016: Convergence in the temperature response of leaf respiration across biomes and plant functional types. Proc. Natl. Acad. Sci., 113, 3832–3837, doi:10.1073/pnas.1520282113.
1962. Atkin, O.K. et al., 2015: Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. New Phytol., 206, 614–636, doi:10.1111/nph.13253.
1963. Slot, M., and K. Kitajima, 2015: General patterns of acclimation of leaf respiration to elevated temperatures across biomes and plant types. Oecologia, 177, 885–900, doi:10.1007/s00442-014-3159-4.
1964. Tjoelker, M.G., 2018: The role of thermal acclimation of plant respiration under climate warming: Putting the brakes on a runaway train? Plant Cell Environ., 41, 501–503, doi:10.1111/pce.13126.
1965. Slot, M. et al, 2014: Thermal acclimation of leaf respiration of tropical trees and lianas: Response to experimental canopy warming, and consequences for tropical forest carbon balance. Glob. Chang. Biol., 20, 2915–2926, doi:10.1111/gcb.12563.
1966. Tan, Z.H. et al., 2017: Optimum air temperature for tropical forest photosynthesis: Mechanisms involved and implications for climate warming. Environ. Res. Lett., 12, doi:10.1088/1748-9326/aa6f97.
1967. Vanderwel, M.C. et al., 2015: Global convergence in leaf respiration from estimates of thermal acclimation across time and space. New Phytol., 207, 1026–1037, doi:10.1111/nph.13417.
1968. Slot, M., and K. Winter, 2017: Photosynthetic acclimation to warming in tropical forest tree Seedlings. J. Exp. Bot., 68, 2275–2284, doi:10.1093/jxb/erx071.
1969. Yamori, W., K. Hikosaka, and D.A. Way, 2014: Temperature response of photosynthesis in C3, C4, and CAM plants: Temperature acclimation and temperature adaptation. Photosynth. Res., 119, 101–117, doi:10.1007/s11120-013-9874-6.
1970. Rogers, A. et al., 2017: A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol., 213, 22–42, doi:10.1111/nph.14283.
1971. Lloyd, J., and G.D. Farquhar, 2008: Effects of rising temperatures and [CO2] on the physiology of tropical forest trees. Philos. Trans. R. Soc. B Biol. Sci., 363, 1811–1817, doi:10.1098/rstb.2007.0032.
1972. Kattge, J. et al., 2007: Temperature acclimation in a biochemical model of photosynthesis: A reanalysis of data from 36 species. Plant, Cell Environ., 30, 1176–1190, doi:10.1111/j.1365-3040.2007.01690.x.
1973. Mercado, L.M. et al., 2018: Large sensitivity in land carbon storage due to geographical and temporal variation in the thermal response of photosynthetic capacity. New Phytol., 218, 1462–1477, doi:10.1111/nph.15100.
1974. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.
1975. Rogers, A. et al., 2017: A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol., 213, 22–42, doi:10.1111/nph.14283.
1976. Sellers, P.J. et al., 1996: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation. J. Clim., 9, 676–705, doi:10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2.
1977. Bonan, G.B., 2008: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320, 1444–1449, doi:10.1126/science.1155121.
1978. Brodribb, T.J., 2009: Xylem hydraulic physiology: The functional backbone of terrestrial plant productivity. Plant Sci., 177, 245–251, doi:10.1016/j.plantsci.2009.06.001.
1979. Choat, B. et al., 2012: Global convergence in the vulnerability of forests to drought. Nature, 491, 752–755, doi:10.1038/nature11688.
1980. Sperry, J.S., and D.M. Love, 2015: What plant hydraulics can tell us about responses to climate-change droughts. New Phytol., 207, 14–27, doi:10.1111/nph.13354.
1981. Novick, K.A. et al., 2016: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Chang., 6, 1023–1027, doi:10.1038/nclimate3114.
1982. Sulman, B.N., D.T. Roman, K. Yi, L.X. Wang, R.P. Phillips, and K.A. Novick, 2016: High atmospheric demand for water can limit forest carbon uptake and transpiration as severely as dry soil. Geophys. Res. Lett., 43, 9686–9695, doi:10.1002/2016gl069416.
1983. Seneviratne, S.I. et al., 2018: Land radiative management as contributor to regional-scale climate adaptation and mitigation. Nat. Geosci., 11, 88–96, doi:10.1038/s41561-017-0057-5.
1984. Sperry, J.S., and D.M. Love, 2015: What plant hydraulics can tell us about responses to climate-change droughts. New Phytol., 207, 14–27, doi:10.1111/nph.13354.
1985. Anderegg, W.R.L. et al., 2018a: Woody plants optimise stomatal behaviour relative to hydraulic risk. Ecol. Lett., 0, doi:10.1111/ele.12962.
1986. Buotte, P.C. et al., 2019: Near-future forest vulnerability to drought and fire varies across the western United States. Glob. Chang. Biol., 25, 290–303, doi:10.1111/gcb.14490.
1987. Breshears, D.D. et al., 2005: Regional vegetation die-off in response to global-change-type drought. Proc. Natl. Acad. Sci., 102, 15144–15148, doi:10.1073/pnas.0505734102.
1988. Kurz, W.A. et al., 2008: Mountain pine beetle and forest carbon feedback to climate change. Nature, 452, 987–990, doi:10.1038/nature06777.
1989. Allen, C.D. et al., 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage., 259, 660–684, doi:10.1016/j.foreco.2009.09.001.
1990. Anderegg, W.R.L. et al., 2018b: Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature, 561, 538–541, doi:10.1038/s41586-018-0539-7.
1991. Bohrer, G. et al., 2005: Finite element tree crown hydrodynamics model (FETCH) using porous media flow within branching elements: A new representation of tree hydrodynamics. Water Resour. Res., 41, doi:10.1029/2005wr004181.
1992. Anderegg, W.R.L. et al., 2016: Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. Proc. Natl. Acad. Sci., 113, 5024–5029, doi:10.1073/pnas.1525678113.
1993. Sperry, J.S., and D.M. Love, 2015: What plant hydraulics can tell us about responses to climate-change droughts. New Phytol., 207, 14–27, doi:10.1111/nph.13354.
1994. Wolf, A., W.R.L. Anderegg, and S.W. Pacala, 2016a: Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proc. Natl. Acad. Sci., 113, E7222–E7230, doi:10.1073/pnas.1615144113.
1995. Rogers, A. et al., 2017: A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol., 213, 22–42, doi:10.1111/nph.14283.
1996. Hartmann, H. et al., 2018: Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol., 218, 15–28, doi:10.1111/nph.15048.
1997. McDowell, N.G. et al., 2011: The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol., 26, 523–532, doi:10.1016/j.tree.2011.06.003.
1998. Powell, T.L. et al., 2013: Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought. New Phytol., 200, 350–365, doi:10.1111/nph.12390.
1999. Sperry, J.S., and D.M. Love, 2015: What plant hydraulics can tell us about responses to climate-change droughts. New Phytol., 207, 14–27, doi:10.1111/nph.13354.
2000. Rowland, L. et al., 2015: Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature, 528, 119.doi:10.1038/nature15539.
2001. Hartmann, H. et al., 2018: Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol., 218, 15–28, doi:10.1111/nph.15048.
2002. Anderegg, W.R.L. et al., 2016: Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. Proc. Natl. Acad. Sci., 113, 5024–5029, doi:10.1073/pnas.1525678113.
2003. Xu, C., H. Liu, A.P. Williams, Y. Yin, and X. Wu, 2016a: Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob. Chang. Biol., 22, 2852–2860, doi:10.1111/gcb.13224.
2004. Chitra-Tarak, R. et al., 2017: The roots of the drought: Hydrology and water uptake strategies mediate forest-wide demographic response to precipitation. J. Ecol., 106, 1495–1507, doi:10.1111/1365-2745.12925.
2005. Zhang, W.-f. et al., 2013c: New technologies reduce greenhouse gas emissions from nitrogenous fertilizer in China. Proc. Natl. Acad. Sci., 110, 8375–8380, doi:10.1073/pnas.1210447110.
2006. Norby, R.J., J.M. Warren, C.M. Iversen, B.E. Medlyn, and R.E. McMurtrie, 2010: CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl. Acad. Sci., 107, 19368–19373, doi:10.1073/pnas.1006463107.
2007. Sardans, J., A. Rivas-Ubach, and J. Peñuelas, 2012: The C:N:P stoichiometry of organisms and ecosystems in a changing world: A review and perspectives. Perspect. Plant Ecol. Evol. Syst., 14, 33–47, doi:10.1016/j.ppees.2011.08.002.
2008. Reich, P.B. and S.E. Hobbie, 2013: Decade-long soil nitrogen constraint on the CO2 fertilization of plant biomass. Nat. Clim. Chang., 3, 278–282, doi:10.1038/nclimate1694.
2009. Feng, Z.Z. et al., 2015: Constraints to nitrogen acquisition of terrestrial plants under elevated CO2. Glob. Chang. Biol., 21, 3152–3168, doi:10.1111/gcb.12938.
2010. Terrer, C. et al., 2017: Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition. New Phytol., 2, 507–522, doi:10.1111/nph.14872.
2011. Du, C.J., X.D. Wang, M.Y. Zhang, J. Jing, and Y.H. Gao, 2019: Effects of elevated CO2 on plant C-N-P stoichiometry in terrestrial ecosystems: A meta-analysis. Sci. Total Environ., 650, 697–708, doi:10.1016/j.scitotenv.2018.09.051.
2012. Craine, J.M. et al., 2018: Isotopic evidence for oligotrophication of terrestrial ecosystems. Nat. Ecol. Evol., 2, 1735–1744, doi:10.1038/s41559-018-0694-0.
2013. Jonard, M. et al., 2015: Tree mineral nutrition is deteriorating in Europe. Glob. Chang. Biol., 21, 418–430, doi:10.1111/gcb.12657.
2014. Liang, J., X. Qi, L. Souza, and Y. Luo, 2016: Processes regulating progressive nitrogen limitation under elevated carbon dioxide: A meta-analysis. Biogeosciences, 13, 2689–2699, doi:10.5194/bg-13-2689-2016.
2015. Rutting, T., 2017: Nitrogen mineralization, not N-2 fixation, alleviates progressive nitrogen limitation – Comment on “Processes regulating progressive nitrogen limitation under elevated carbon dioxide: a meta-analysis” by Liang et al. (2016). Biogeosciences, 14, 751–754, doi:10.5194/bg-14-751-2017.
2016. Du, C.J., X.D. Wang, M.Y. Zhang, J. Jing, and Y.H. Gao, 2019: Effects of elevated CO2 on plant C-N-P stoichiometry in terrestrial ecosystems: A meta-analysis. Sci. Total Environ., 650, 697–708, doi:10.1016/j.scitotenv.2018.09.051.
2017. Drake, J.E. et al., 2011: Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long-term enhancement of forest productivity under elevated CO2. Ecol. Lett., 14, 349–357, doi:10.1111/j.1461-0248.2011.01593.x.
2018. Zak, D.R., K.S. Pregitzer, M.E. Kubiske, and A.J. Burton, 2011: Forest productivity under elevated CO2 and O3: Positive feedbacks to soil N cycling sustain decade-long net primary productivity enhancement by CO2. Ecol. Lett., 14, 1220–1226, doi:10.1111/j.1461-0248.2011.01692.x.
2019. Hungate, B.A. et al., 2013: Cumulative response of ecosystem carbon and nitrogen stocks to chronic CO2 exposure in a subtropical oak woodland. New Phytol., 200, 753–766, doi:10.1111/nph.12333.
2020. Talhelm, A.F. et al., 2014: Elevated carbon dioxide and ozone alter productivity and ecosystem carbon content in northern temperate forests. Glob. Chang. Biol., 20, 2492–2504, doi:10.1111/gcb.12564.
2021. Du, C.J., X.D. Wang, M.Y. Zhang, J. Jing, and Y.H. Gao, 2019: Effects of elevated CO2 on plant C-N-P stoichiometry in terrestrial ecosystems: A meta-analysis. Sci. Total Environ., 650, 697–708, doi:10.1016/j.scitotenv.2018.09.051.
2022. Phillips, R.P. et al., 2011: Enhanced root exudation induces microbial feedbacks to N cycling in a pine forest under long-term CO2 fumigation. Ecol. Lett., 14, 187–194, doi:10.1111/j.1461-0248.2010.01570.x.
2023. Terrer, C. et al., 2017: Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition. New Phytol., 2, 507–522, doi:10.1111/nph.14872.
2024. Elbert, W. et al., 2012: Contribution of cryptogamic covers to the global cycles of carbon and nitrogen. Nat. Geosci., 5, 459–462, doi:10.1038/ngeo1486.
2025. Sulman, B.N. et al., 2017: Feedbacks between plant N demand and rhizosphere priming depend on type of mycorrhizal association. Ecol. Lett., 20, 1043–1053, doi:10.1111/ele.12802.
2026. Orwin, K.H., M.U.F. Kirschbaum, M.G. St John, and I.A. Dickie, 2011: Organic nutrient uptake by mycorrhizal fungi enhances ecosystem carbon storage: A model-based assessment. Ecol. Lett., 14, 493–502, doi:10.1111/j.1461-0248.2011.01611.x.
2027. Baskaran, P., R. et al., 2017: Modelling the influence of ectomycorrhizal decomposition on plant nutrition and soil carbon sequestration in boreal forest ecosystems. New Phytol., 213, 1452–1465, doi:10.1111/nph.14213.
2028. Pellitier, P.T. and D.R. Zak, 2018: Ectomycorrhizal fungi and the enzymatic liberation of nitrogen from soil organic matter: Why evolutionary history matters. New Phytol., 217, 68–73, doi:10.1111/nph.14598.
2029. Terrer, C. et al., 2017: Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition. New Phytol., 2, 507–522, doi:10.1111/nph.14872.
2030. Houlton, B.Z., S.L. Morford, and R.A. Dahlgren, 2018: Convergent evidence for widespread rock nitrogen sources in Earth’s surface environment. Science, 360, 58–62, doi:10.1126/science.aan4399.
2031. Reed, S.C., X. Yang, and P.E. Thornton, 2015: Incorporating phosphorus cycling into global modeling efforts: A worthwhile, tractable endeavor. New Phytol., 208, 324–329, doi:10.1111/nph.13521.
2032. Schulte-Uebbing, L. and W. de Vries, 2018: Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: A meta-analysis. Glob. Chang. Biol., 24, e416–e431, doi:10.1111/gcb.13862.
2033. Ellsworth, D.S. et al., 2017: Elevated CO2 does not increase eucalypt forest productivity on a low-phosphorus soil. Nat. Clim. Chang., 7, 279–282, doi:10.1038/nclimate3235.
2034. Turner, B.L., T. Brenes-Arguedas, and R. Condit, 2018a: Pervasive phosphorus limitation of tree species but not communities in tropical forests. Nature, 555, 367–370, doi:10.1038/nature25789.
2035. Sardans, J. and J. Peñuelas, 2015: Potassium: A neglected nutrient in global change. Glob. Ecol. Biogeogr., 24, 261–275, doi:10.1111/geb.12259.
2036. Zhao, C. et al., 2017: Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci., 114(35), 9326–9331, doi:10.1073/pnas.1701762114.
2037. Wright, S.J. et al., 2018: Plant responses to fertilization experiments in lowland, species-rich, tropical forests. Ecology, 99, 1129–1138, doi:10.1002/ecy.2193.
2038. Peñuelas, J. et al., 2013: Human-induced nitrogen-phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun., 4, doi:10.1038/ncomms3934.
2039. Peñuelas, J. et al., 2017: Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol., 1, 1438–1445, doi:10.1038/s41559-017-0274-8.
2040. Wang, R. et al., 2017c: Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100. Glob. Chang. Biol., 23, 4854–4872, doi:10.1111/gcb.13766.
2041. Schulte-Uebbing, L. and W. de Vries, 2018: Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: A meta-analysis. Glob. Chang. Biol., 24, e416–e431, doi:10.1111/gcb.13862.
2042. Yuan, X., L. Wang, and E. Wood, 2018: Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bull. Am. Meteorol. Soc., 99, S86–S90, doi:10.1175/BAMS-D-17-0077.1.
2043. Wang, R. et al., 2017c: Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100. Glob. Chang. Biol., 23, 4854–4872, doi:10.1111/gcb.13766.
2044. Schulte-Uebbing, L. and W. de Vries, 2018: Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: A meta-analysis. Glob. Chang. Biol., 24, e416–e431, doi:10.1111/gcb.13862.
2045. Liu, L. and T.L. Greaver, 2009: A review of nitrogen enrichment effects on three biogenic GHGs: The CO2 sink may be largely offset by stimulated N2O and CH4 emission. Ecol. Lett., 12, 1103–1117, doi:10.1111/j.1461-0248.2009.01351.x.
2046. Peñuelas, J. et al., 2013: Human-induced nitrogen-phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun., 4, doi:10.1038/ncomms3934.
2047. Janssens, I.A. et al., 2010: Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci., 3, 315–322, doi:10.1038/ngeo844.
2048. Chen, W., Y. Zhang, X. Mai, and Y. Shen, 2016b: Multiple mechanisms contributed to the reduced stability of Inner Mongolia grassland ecosystem following nitrogen enrichment. Plant Soil, 409, 283–296, doi:10.1007/s11104-016-2967-1.
2049. Joosten, H., 2015: Current soil carbon loss and land degradation globally-where are the hotspots and why there? In: Soil Carbon: Science, Management and Policy for Multiple Benefits [Banwart, S.A., E. Noellemeyer, and E. Milne (eds.)]. Wallingford, UK, pp. 224–234.
2050. Köchy, M., R. Hiederer, and A. Freibauer, 2015: Global distribution of soil organic carbon – Part 1: Masses and frequency distributions of SOC stocks for the tropics, permafrost regions, wetlands, and the world. SOIL, 1, 351–365, doi:10.5194/soil-1-351-2015.
2051. Tifafi, M., B. Guenet, and C. Hatté, 2018: Large differences in global and regional total soil carbon stock estimates based on SoilGrids, HWSD, and NCSCD: Intercomparison and evaluation based on field data from USA, England, Wales, and France. Global Biogeochem. Cycles, 32, 42–56, doi:10.1002/2017GB005678.
2052. Bond-Lamberty, B., V.L. Bailey, M. Chen, C.M. Gough, and R. Vargas, 2018: Globally rising soil heterotrophic respiration over recent decades. Nature, 560, 80–83, doi:10.1038/s41586-018-0358-x.
2053. Gumbricht, T. et al., 2017: An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Glob. Chang. Biol., 23, 3581–3599, doi:10.1111/gcb.13689.
2054. Dargie, G.C. et al., 2019: Congo Basin peatlands: Threats and conservation priorities. Mitig. Adapt. Strateg. Glob. Chang., 24, 669–686, doi:10.1007/s11027-017-9774-8.
2055. González-Jaramillo, V. et al., 2016: Assessment of deforestation during the last decades in Ecuador using NOAA-AVHRR satellite data. Erdkunde, 70, 217–235, doi:10.3112/erdkunde.2016.03.02.
2056. Rumpel, C., and I. Kögel-Knabner, 2011: Deep soil organic matter – A key but poorly understood component of terrestrial C cycle. Plant Soil, 338, 143–158, doi:10.1007/s11104-010-0391-5.
2057. Okuno, M., and T. Nakamura, 2003: Radiocarbon dating of tephra layers: recent progress in Japan. Quat. Int., 105, 49–56, doi:10.1016/S1040-6182(02)00150-7.
2058. Hicks Pries, C.E., C. Castanha, R.C. Porras, and M.S. Torn, 2017: The whole-soil carbon flux in response to warming. Science, 355, 1420 LP-1423.
2059. Fontaine, S., P. Barre, N. Bdioui, B. Mary, and C. Rumpel, 2007: Stability of organic carbon in deep soil layers. Nature, 450, 277–281. doi:10.1038/nature06275.
2060. Berhe, A.A., J. Harte, J.W. Harden, and M.S. Torn, 2007: The significance of the erosion-induced terrestrial carbon sink. Bioscience, 57, 337–346, doi:10.1641/B570408.
2061. Asefaw, B.A., J.W. Harden, M.S. Torn, and J. Harte, 2008: Linking soil organic matter dynamics and erosion-induced terrestrial carbon sequestration at different landform positions. J. Geophys. Res. Biogeosciences, 113, 1–12, doi:10.1029/2008JG000751. https://doi.org/10.1029/2008JG000751 .
2062. Wang, Z. et al., 2017e: Does drought in China show a significant decreasing trend from 1961 to 2009? Sci. Total Environ., 579, 314–324, doi:10.1016/j.scitotenv.2016.11.098.
2063. Auffret, M.D. et al., 2016: The role of microbial community composition in controlling soil respiration responses to temperature. PLoS One, 11, e0165448, doi:10.1371/journal.pone.0165448.
2064. Shao, P. et al., 2013: Soil microbial respiration from observations and Earth System Models. Environ. Res. Lett., 8, 034034, doi:10.1088/1748-9326/8/3/034034.
2065. Davidson, E.A., I.A. Janssens, E.A. Davidson, and I.A. Janssens, 2006a: Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165–173, doi:10.1038/nature04514.
2066. Dungait, J.A.J., D.W. Hopkins, A.S. Gregory, and A.P. Whitmore, 2012: Soil organic matter turnover is governed by accessibility not recalcitrance. Glob. Chang. Biol., 18, 1781–1796, doi:10.1111/j.1365-2486.2012.02665.x.
2067. Crowther, T.W. et al., 2016: Quantifying global soil carbon losses in response to warming. Nature, 540, 104–108, doi:10.1038/nature20150.
2068. Gestel, N. Van et al., 2018: Brief communications arising predicting soil carbon loss with warming. Nat. Publ. Gr., 554, E4–E5, doi:10.1038/nature25745.
2069. Melillo, J.M. et al., 2017: Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science, 358, 101–105, doi:10.1126/science.aan2874.
2070. Conant, R.T. et al., 2011: Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward. Glob. Chang. Biol., 17, 3392–3404, doi:10.1111/j.1365-2486.2011.02496.x.
2071. Knorr, W., I.C. Prentice, J.I. House, and E.A. Holland, 2005: Long-term sensitivity of soil carbon turnover to warming. Nature, 433, 298–301, doi:10.1038/nature03226.
2072. Davidson, E.A., I.A. Janssens, E.A. Davidson, and I.A. Janssens, 2006a: Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165–173, doi:10.1038/nature04514.
2073. Dungait, J.A.J., D.W. Hopkins, A.S. Gregory, and A.P. Whitmore, 2012: Soil organic matter turnover is governed by accessibility not recalcitrance. Glob. Chang. Biol., 18, 1781–1796, doi:10.1111/j.1365-2486.2012.02665.x.
2074. Nishina, K. et al., 2014: Quantifying uncertainties in soil carbon responses to changes in global mean temperature and precipitation. Earth Syst. Dyn., 5, 197–209, doi:10.5194/esd-5-197-2014.
2075. Tian, H. et al., 2015: Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions. Global Biogeochem. Cycles, 29, 775–792, doi:10.1002/2014GB005021.
2076. Monard, C., C. Mchergui, N. Nunan, F. Martin-Laurent, and L. Vieublé-Gonod, 2012: Impact of soil matric potential on the fine-scale spatial distribution and activity of specific microbial degrader communities. FEMS Microbiol. Ecol., 81, 673–683, doi:10.1111/j.1574-6941.2012.01398.x.
2077. Moyano, F.E., S. Manzoni, and C. Chenu, 2013: Responses of soil heterotrophic respiration to moisture availability: An exploration of processes and models. Soil Biol. Biochem., 59, 72–85, doi:10.1016/j.soilbio.2013.01.002.
2078. Yan, Z. et al., 2018: A moisture function of soil heterotrophic respiration that incorporates microscale processes. Nat. Commun., 9, 2562, doi:10.1038/s41467-018-04971-6.
2079. Hawkes, C.V., B.G. Waring, J.D. Rocca, and S.N. Kivlin, 2017: Historical climate controls soil respiration responses to current soil moisture. Proc. Natl. Acad. Sci., 114, 6322–6327, doi:10.1073/pnas.1620811114.
2080. Huang, W., and S.J. Hall, 2017: Elevated moisture stimulates carbon loss from mineral soils by releasing protected organic matter. Nat. Commun., 8, 1774, doi:10.1038/s41467-017-01998-z.
2081. Luo, Y., S. Wan, D. Hui, and L.L. Wallace, 2001: Acclimatization of soil respiration to warming in a tall grass prairie. Nature, 413, 622–625, doi:10.1038/35098065.
2082. Bradford, M.A. et al., 2008: Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett., 11, 1316–1327, doi:10.1111/j.1461-0248.2008.01251.x.
2083. Zhou, J. et al., 2011: Microbial mediation of carbon-cycling feedbacks to climate warming. Nat. clinate Chang., 2, 106–110. doi:10.1038/nclimate1331.
2084. Carey, J.C. et al., 2016: Temperature response of soil respiration largely unaltered with experimental warming. Proc. Natl. Acad. Sci., 113, 13797–13802, doi:10.1073/pnas.1605365113.
2085. Teramoto, M., N. Liang, M. Takagi, J. Zeng, and J. Grace, 2016: Sustained acceleration of soil carbon decomposition observed in a 6-year warming experiment in a warm-temperate forest in southern Japan. Sci. Rep., 6, 35563. doi:10.1038/srep35563.
2086. Schindlbacher, A., J. Schnecker, M. Takriti, W. Borken, and W. Wanek, 2015: Microbial physiology and soil CO2 efflux after 9 years of soil warming in a temperate forest – no indications for thermal adaptations. Glob. Chang. Biol., 21, 4265–4277, doi:10.1111/gcb.12996.
2087. Hartley, I.P., D.W. Hopkins, M.H. Garnett, M. Sommerkorn, and P.A. Wookey, 2008: Soil microbial respiration in arctic soil does not acclimate to temperature. Ecol. Lett., 11, 1092–1100, doi:10.1111/j.1461-0248.2008.01223.x.
2088. Karhu, K. et al., 2014: Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature, 513, 81–84, doi:10.1038/nature13604.
2089. Camenzind, T., S. Hättenschwiler, K.K. Treseder, A. Lehmann, and M.C. Rillig, 2018: Nutrient limitation of soil microbial processes in tropical forests. Ecol. Monogr., 88, 4–21, doi:10.1002/ecm.1279.
2090. Fahey, C., K. Winter, M. Slot, and K. Kitajima, 2016: Influence of arbuscular mycorrhizal colonization on whole-plant respiration and thermal acclimation of tropical tree seedlings. Ecol. Evol., 6, 859–870, doi:10.1002/ece3.1952.
2091. Allison, S.D., M.D. Wallenstein, and M.A. Bradford, 2010: Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci., 3, 336–340, doi:10.1038/ngeo846.
2092. Frey, S.D., J. Lee, J.M. Melillo, and J. Six, 2013: The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Chang., 3, 395, doi:10.1038/nclimate1796.
2093. Wieder, W.R., G.B. Bonan, and S.D. Allison, 2013: Global Soil Carbon Projections are Improved by Modeling Microbial Processes. Nat. Clim. Chang., 3, 909. doi:/10.1038/nclimate1951.
2094. García-Palacios, P. et al., 2015: Are there links between responses of soil microbes and ecosystem functioning to elevated CO2, N deposition and warming? A global perspective. Glob. Chang. Biol., 21, 1590–1600, doi:10.1111/gcb.12788.
2095. Crowther, T.W. et al., 2015: Biotic interactions mediate soil microbial feedbacks to climate change. Proc. Natl. Acad. Sci., 112, 7033–7038, doi:10.1073/pnas.1502956112.
2096. Buchkowski, R.W., M.A. Bradford, A.S. Grandy, O.J. Schmitz, and W.R. Wieder, 2017: Applying population and community ecology theory to advance understanding of belowground biogeochemistry. Ecol. Lett., 20, 231–245, doi:10.1111/ele.12712.
2097. Averill, C., B.L. Turner, and A.C. Finzi, 2014: Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature, 505, 543–545, doi:10.1038/nature12901.
2098. Lajtha, K. et al., 2014b: Changes to particulate versus mineral-associated soil carbon after 50 years of litter manipulation in forest and prairie experimental ecosystems. Biogeochemistry, 119, 341–360, doi:10.1007/s10533-014-9970-5.
2099. Liu, L. et al.., 2009: Enhanced litter input rather than changes in litter chemistry drive soil carbon and nitrogen cycles under elevated CO2: A microcosm study. Glob. Chang. Biol., 15, 441–453, doi:10.1111/j.1365-2486.2008.01747.x. https://doi.org/10.1111/j.1365-2486.2008.01747.x .
2100. Lajtha, K., R.D. Bowden, and K. Nadelhoffer, 2014a: Litter and root manipulations provide insights into soil organic matter dynamics and stability. Soil Sci. Soc. Am. J., 78, S261, doi:10.2136/sssaj2013.08.0370nafsc.
2101. van Groenigen, K.J., X. Qi, C.W. Osenberg, Y. Luo, and B.A. Hungate, 2014: Faster decomposition under increased atmospheric CO2 limits soil carbon storage. Science, 344, 508–509, doi:10.1126/science.1249534.
2102. Kuzyakov, Y., I. Bogomolova, and B. Glaser, 2014: Biochar stability in soil: Decomposition during eight years and transformation as assessed by compound-specific14C analysis. Soil Biol. Biochem., 70, 229–236, doi:10.1016/j.soilbio.2013.12.021.
2103. Cheng, L. et al., 2017: Warming enhances old organic carbon decomposition through altering functional microbial communities. Isme J., 11, 1825. https://doi.org/10.1038/ismej.2017.48 .
2104. Finzi, A.C. et al., 2015: Rhizosphere processes are quantitatively important components of terrestrial carbon and nutrient cycles. Glob. Chang. Biol., 21, 2082–2094, doi:10.1111/gcb.12816.
2105. Kuzyakov, Y., and E. Blagodatskaya, 2015: Microbial hotspots and hot moments in soil: Concept & review. Soil Biol. Biochem., 83, 184–199, doi:10.1016/j.soilbio.2015.01.025.
2106. Hopkins, F.M. et al., 2014: Increased belowground carbon inputs and warming promote loss ofsoil organic carbon through complementary microbial responses. Soil Biol. Biochem., 76, 57–69, doi:10.1016/j.soilbio.2014.04.028.
2107. Guenet, B., et al., 2018: Impact of priming on global soil carbon stocks. Glob. Chang. Biol., 24, 1873–1883, doi:10.1111/gcb.14069.
2108. Sulman, B.N., R.P. Phillips, A.C. Oishi, E. Shevliakova, and S.W. Pacala, 2014: Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Chang., 4, 1099–1102, doi:10.1038/nclimate2436.
2109. van Groenigen, K.J., X. Qi, C.W. Osenberg, Y. Luo, and B.A. Hungate, 2014: Faster decomposition under increased atmospheric CO2 limits soil carbon storage. Science, 344, 508–509, doi:10.1126/science.1249534.
2110. Hartley, I.P. et al., 2012: A potential loss of carbon associated with greater plant growth in the European Arctic. Nat. Clim. Chang., 2, 875–879, doi:10.1038/nclimate1575.
2111. Sayer, E.J., M.S. Heard, H.K. Grant, T.R. Marthews, and E.V.J. Tanner, 2011: Soil carbon release enhanced by increased tropical forest litterfall. Nat. Clim. Chang., 1, 304–307, doi:10.1038/nclimate1190.
2112. Kögel-Knabner, I. et al., 2008: Organo-mineral associations in temperate soils: Integrating biology, mineralogy, and organic matter chemistry. J. Plant Nutr. Soil Sci., 171, 61–82, doi:10.1002/jpln.200700048.
2113. Kleber, M. et al., 2011: Old and stable soil organic matter is not necessarily chemically recalcitrant: Implications for modeling concepts and temperature sensitivity. Glob. Chang. Biol., 17, 1097–1107, doi:10.1111/j.1365-2486.2010.02278.x.
2114. Marschner, B. et al., 2008: How relevant is recalcitrance for the stabilization of organic matter in soils? J. Plant Nutr. Soil Sci., 171, 91–110, doi:10.1002/jpln.200700049. doi:10.1002/jpln.200700049.
2115. Schmidt, M. et al., 2011: Persistance of soil organic matter as an ecosystem property. Nature, 478, 49–56, doi:10.1038/nature10386.
2116. Lützow, M.V. et al., 2006: Stabilization of organic matter in temperate soils: Mechanisms and their relevance under different soil conditions – A review. Eur. J. Soil Sci., 57, 426–445, doi:10.1111/j.1365-2389.2006.00809.x.
2117. Keiluweit, M. et al., 2015: Mineral protection of soil carbon counteracted by root exudates. Nat. Clim. Chang., 5, 588–595, doi:10.1038/nclimate2580.
2118. Kallenbach, C.M., S.D. Frey, and A.S. Grandy, 2016: Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun., 7, 13630, doi:10.1038/ncomms13630.
2119. Kleber, M. et al., 2011: Old and stable soil organic matter is not necessarily chemically recalcitrant: Implications for modeling concepts and temperature sensitivity. Glob. Chang. Biol., 17, 1097–1107, doi:10.1111/j.1365-2486.2010.02278.x.
2120. Hopkins, F.M. et al., 2014: Increased belowground carbon inputs and warming promote loss ofsoil organic carbon through complementary microbial responses. Soil Biol. Biochem., 76, 57–69, doi:10.1016/j.soilbio.2014.04.028.
2121. Blanco-Canqui, H. and R. Lal, 2004: Mechanisms of carbon sequestration in soil aggregates. Crit. Rev. Plant Sci., 23, 481–504, doi:10.1080/07352680490886842.
2122. Six, J. et al., 2004: The potential to mitigate global warming with no-tillage management is only realised when practised in the long term. Glob. Chang. Biol., 10, 155–160, doi:10.1111/j.1529-8817.2003.00730.x.
2123. Keiluweit, M., P.S. Nico, M. Kleber, and S. Fendorf, 2016: Are oxygen limitations under recognized regulators of organic carbon turnover in upland soils? Biogeochemistry, 127, 157–171, doi:10.1007/s10533-015-0180-6.
2124. Wang, J., Z. Yan, X.W. Quan, and J. Feng, 2017a: Urban warming in the 2013 summer heat wave in eastern China. Clim. Dyn., 48, 3015–3033, doi:10.1007/s00382-016-3248-7.
2125. Sulman, B.N., R.P. Phillips, A.C. Oishi, E. Shevliakova, and S.W. Pacala, 2014: Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Chang., 4, 1099–1102, doi:10.1038/nclimate2436.
2126. Riley, W., J. Tang, and W.J. Riley, 2014: Weaker soil carbon – Climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Chang., 5, 56, doi:10.1038/nclimate2438.
2127. Wieder, W.R., C.C. Cleveland, W.K. Smith, and K. Todd-Brown, 2015: Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci., 8, 441–444, doi:10.1038/NGEO2413.
2128. EDGAR v4.3.2 global atlas of the three major greenhouse gas emissions for the period 1970–2012. Earth Syst. Sci. Data Discuss., 2017, 1–55, doi:10.5194/essd-2017-79
2129. Janssens-Maenhout, G. et al., 2017b: Fossil CO2 & GHG Emissions of All World Countries. JRC Science for Policy Report, EUR 28766 EN. Publications Office of the European Union, Luxembourg, doi:10.2760/709792, 239 pp.
2130. Tubiello, F.N. et al., 2013: The FAOSTAT database of greenhouse gas emissions from agriculture. Environ. Res. Lett., 8, 15009, doi:10.1088/1748-9326/8/1/015009.
2131. Grassi, G. et al., 2018a: Reconciling global model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Chang., 1–35.
2132. Meurer, K.H.E. et al., 2016: Direct nitrous oxide (N2O) fluxes from soils under different land use in Brazil—a critical review. Environ. Res. Lett., 11, 23001, doi:10.1088/1748-9326/11/2/023001.
2133. Tian, H. et al., 2016: The terrestrial biosphere as a net source of greenhouse gases to the atmosphere. Nature, 531, 225–228, doi:10.1038/nature16946.
2134. Sporre, M.K., S.M. Blichner, I.H.H. Karset, R. Makkonen, and T.K. Berntsen, 2019: BVOC-aerosol-climate feedbacks investigated using NorESM. Atmos. Chem. Phys., 19, 4763–4782, doi:10.5194/acp-19-4763-2019.
2135. Arneth, A. et al., 2010: Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci., 3, 525–532, doi:10.1038/ngeo905.
2136. Griscom, B.W. et al., 2017a: Natural climate solutions. Proc. Natl. Acad. Sci., 114, 11645–11650, doi:10.1073/pnas.1710465114.
2137. Hawken, P. (ed.), 2017: Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming. Penguin Books, 240 pp.
2138. Paustian, K. et al., 2016: Climate-smart soils. Nature, 532, 49–57, doi:10.1038/nature17174.
2139. Beach, R.H. et al., 2015: Global mitigation potential and costs of reducing agricultural non-CO2 greenhouse gas emissions through 2030. J. Integr. Environ. Sci., 12, 87–105, doi:10.1080/1943815X.2015.1110183.
2140. Dickie, A., C. Streck, S. Roe, M. Zurek, F. Haupt, and A. Dolginow, 2014: Strategies for Mitigating Climate Change in Agriculture:Recommentations for Philanthropy-Executive Summary. Climate Focus and California Environmental Associates, and the Climate and Land Use Alliance. 87 pp.
2141. Herrero, M. et al., 2015: Livestock and the environment: What have we learned in the past decade? Annu. Rev. Environ. Resour., 40, 177–202, doi:10.1146/annurev-environ-031113-093503.
2142. Herrero, M. et al., 2016: Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang., 6, 452–461, doi:10.1038/nclimate2925.
2143. Hussain, S. et al., 2015: Rice management interventions to mitigate greenhouse gas emissions: A review. Environ. Sci. Pollut. Res., 22, 3342–3360, doi:10.1007/s11356-014-3760-4.
2144. Hristov, A.N. et al., 2013: Mitigation of Greenhouse Gas Emissions in Livestock Production: A Review of Technical Options for Non-CO2 Emissions [Gerber, P.J., B. Henderson, and H.P.S. Makkar (eds.)]. FAO Animal Production and Health Paper No. 177. Food and Agriculture Organization of the United Nations, Rome, Italy, 226 pp.
2145. Zhang, W.-f. et al., 2013c: New technologies reduce greenhouse gas emissions from nitrogenous fertilizer in China. Proc. Natl. Acad. Sci., 110, 8375–8380, doi:10.1073/pnas.1210447110.
2146. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
2147. Busch, J., and J. Engelmann, 2017: Cost-effectiveness of reducing emissions from tropical deforestation, 2016–2050. Environ. Res. Lett., 13, 15001, doi:10.1088/1748-9326/aa907c.
2148. Baccini, A. et al., 2017: Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 358, 230–234, doi:10.1126/science.aam5962.
2149. Zarin, D.J. et al., 2016: Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Chang. Biol., 22, 1336–1347, doi:10.1111/gcb.13153.
2150. Houghton, R.A., B. Byers, and A.A. Nassikas, 2015: A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Chang., doi:10.1038/nclimate2869.
2151. Federici, S., F.N. Tubiello, M. Salvatore, H. Jacobs, and J. Schmidhuber, 2015: New estimates of CO2 forest emissions and removals: 1990–2015. For. Ecol. Manage., 352, 89–98, doi:10.1016/j.foreco.2015.04.022.
2152. Carter, S., M. et al., 2015: Mitigation of agricultural emissions in the tropics: Comparing forest land-sparing options at the national level. Biogeosciences, 12, 4809–4825, doi:10.5194/bg-12-4809-2015.
2153. Smith, N.G., and J.S. Dukes, 2013: Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2. Glob. Chang. Biol., 19, 45–63, doi:10.1111/j.1365-2486.2012.02797.x.
2154. Pearson et al. (2017)
2155. Hooijer, A. et al., 2010: Current and future CO2 emissions from drained peatlands in Southeast Asia. Biogeosciences, 7, 1505–1514, doi:10.5194/bg-7-1505-2010.
2156. Howard, J. et al., 2017: Clarifying the role of coastal and marine systems in climate mitigation. Front. Ecol. Environ., 15, 42–50, doi:10.1002/fee.1451.
2157. Pendleton, L. et al., 2012: Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS One, 7, e43542, doi:10.1371/journal.pone.0043542.
2158. Fuss, S. et al., 2018: Negative emissions – Part 2: Costs, potentials and side effects. Environ. Res. Lett., submitted, 2–4, doi:10.1088/1748-9326/aabf9f.
2159. Dooley, K., and S. Kartha, 2018: Land-based negative emissions: Risks for climate mitigation and impacts on sustainable development. Int. Environ. Agreements Polit. Law Econ., doi:10.1007/s10784-017-9382-9.
2160. Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 85001, doi:10.1088/1748-9326/11/8/085001.
2161. Yan, M., J. Liu, and Z. Wang, 2017: Global climate responses to land use and land cover changes over the past two millennia. Atmosphere, 8, 64, doi:10.3390/atmos8040064.
2162. Sonntag, S., J. Pongratz, C.H. Reick, and H. Schmidt, 2016: Reforestation in a high-CO2 world—Higher mitigation potential than expected, lower adaptation potential than hoped for. Geophys. Res. Lett., 43, 6546–6553, doi:10.1002/2016GL068824.
2163. Lenton, T.M., 2014: The Global Potential for Carbon Dioxide Removal. Geoengineering of the Climate System [Harrison, R.M. and R.E. Hester (eds.)]. Royal Society of Chemistry, Cambridge, U.K., 52–79 pp. doi:10.1039/9781782621225-00052.
2164. McLaren, D., 2012: A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot., 90, 489–500, doi:10.1016/j.psep.2012.10.005.
2165. Lenton, T.M., 2010: The potential for land-based biological CO2 removal to lower future atmospheric CO2 concentration. Carbon Manag., doi:10.4155/cmt.10.12.
2166. Sasaki, N. et al., 2016: Sustainable management of tropical forests can reduce carbon emissions and stabilize timber production. Front. Environ. Sci., 4, 50, doi:10.3389/fenvs.2016.00050.
2167. Sasaki, N. et al., 2016: Sustainable management of tropical forests can reduce carbon emissions and stabilize timber production. Front. Environ. Sci., 4, 50, doi:10.3389/fenvs.2016.00050.
2168. Zomer, R.J., D.A. Bossio, R. Sommer, and L.V. Verchot, 2017: Global sequestration potential of increased organic carbon in cropland soils. Sci. Rep., doi:10.1038/s41598-017-15794-8.
2169. Couwenberg, J., R. Dommain, and H. Joosten, 2010: Greenhouse gas fluxes from tropical peatlands in south-east Asia. Glob. Chang. Biol., 16, 1715–1732, doi:10.1111/j.1365-2486.2009.02016.x.
2170. Conant, R.T., C.E.P. Cerri, B.B. Osborne, and K. Paustian, 2017: Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl., doi:10.1002/eap.1473.
2171. Sanderman, J., T. Hengl, and G.J. Fiske, 2017: Soil carbon debt of 12,000 years of human land use. Proc. Natl. Acad. Sci., 114, 9575–9580, doi:10.1073/pnas.1706103114.
2172. Frank, S. et al., 2017a: Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett., 12, 105004, doi:10.1088/1748-9326/aa8c83.
2173. Henderson, B.B. et al., 2015: Greenhouse gas mitigation potential of the world’s grazing lands: Modeling soil carbon and nitrogen fluxes of mitigation practices. Agric. Ecosyst. Environ., 207, 91–100, doi:10.1016/j.agee.2015.03.029.
2174. Sommer, R., and D. Bossio, 2014: Dynamics and climate change mitigation potential of soil organic carbon sequestration. J. Environ. Manage., 144, 83–87, doi:10.1016/j.jenvman.2014.05.017.
2175. Lal, R., 2010: Managing soils and ecosystems for mitigating anthropogenic carbon emissions and advancing global food security. Bioscience, doi:10.1525/bio.2010.60.9.8.
2176. Zomer, R.J., D.A. Bossio, R. Sommer, and L.V. Verchot, 2017: Global sequestration potential of increased organic carbon in cropland soils. Sci. Rep., doi:10.1038/s41598-017-15794-8.
2177. Smith, M.C., J.S. Singarayer, P.J. Valdes, J.O. Kaplan, and N.P. Branch, 2016a: The biogeophysical climatic impacts of anthropogenic land use change during the Holocene. Clim. Past, 12, 923–941, doi:10.5194/cp-12-923-2016.
2178. Poeplau, C. and A. Don, 2015: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agric. Ecosyst. Environ., 200, 33–41, doi:10.1016/j.agee.2014.10.024.
2179. Powlson, D.S. et al., 2014: Limited potential of no-till agriculture for climate change mitigation. Nat. Clim. Chang., 4, 678–683, doi:10.1038/nclimate2292.
2180. Powell, T.W.R. and T.M. Lenton, 2012: Future carbon dioxide removal via biomass energy constrained by agricultural efficiency and dietary trends. Energy Environ. Sci., 5, 8116-8133, doi:10.1039/c2ee21592f.
2181. Woolf, D., J.E. Amonette, F.A. Street-Perrott, J. Lehmann, and S. Joseph, 2010: Sustainable biochar to mitigate global climate change. Nat. Commun., 1, doi:10.1038/ncomms1053.
2182. Roberts, K.G., B.A. Gloy, S. Joseph, N.R. Scott, and J. Lehmann, 2010: Life cycle assessment of biochar systems: Estimating the energetic, economic, and climate change potential. Environ. Sci. Technol., 44, 827–833, doi:10.1021/es902266r.
2183. Pratt, K., and D. Moran, 2010: Evaluating the cost-effectiveness of global biochar mitigation potential. Biomass and Bioenergy, 34, 1149–1158, doi:10.1016/j.biombioe.2010.03.004.
2184. Turner, B.L., T. Brenes-Arguedas, and R. Condit, 2018a: Pervasive phosphorus limitation of tree species but not communities in tropical forests. Nature, 555, 367–370, doi:10.1038/nature25789.
2185. Koornneef, J. et al., 2012: Global potential for biomass and carbon dioxide capture, transport and storage up to 2050. Int. J. Greenh. Gas Control, doi:10.1016/j.ijggc.2012.07.027.
2186. Bajželj, B. and K. Richards, 2014: The positive feedback loop between the impacts of climate change and agricultural expansion and relocation. Land, 3, 898–916, doi:10.3390/land3030898. http://www.mdpi.com/2073-445X/3/3/898/ .
2187. Springmann, M., H.C.J. Godfray, M. Rayner, and P. Scarborough, 2016: Analysis and valuation of the health and climate change cobenefits of dietary change. Proc. Natl. Acad. Sci., 113, 4146–4151, doi:10.1073/pnas.1523119113.
2188. Tilman, D., and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13958.
2189. Hedenus, F., S. Wirsenius, and D.J.A. Johansson, 2014: The importance of reduced meat and dairy consumption for meeting stringent climate change targets. Clim. Change, 124, 79–91, doi:10.1007/s10584-014-1104-5.
2190. Miner, R., 2010: Impact of the Global Forest Industry on Atmospheric Greenhouse Gases. Food and Agriculture Organization of the United Nations, Rome, Italy, 71 pp.
2191. Bailis et al. (2015)
2192. Bajželj, B. and K. Richards, 2014: The positive feedback loop between the impacts of climate change and agricultural expansion and relocation. Land, 3, 898–916, doi:10.3390/land3030898. http://www.mdpi.com/2073-445X/3/3/898/ .
2193. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
2194. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
2195. Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
2196. Huppmann, D., J. Rogelj, E. Kriegler, V. Krey, and K. Riahi, 2018: A new scenario resource for integrated 1.5°C research. Nat. Clim. Chang., 8, 1027–1030, doi:10.1038/s41558-018-0317-4.
2197. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
2198. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
2199. Fricko, O. et al., 2017: The marker quantification of the Shared Socioeconomic Pathway 2: A middle-of-the-road scenario for the 21st century. Glob. Environ. Chang., 42, 251–267, doi:10.1016/J.GLOENVCHA.2016.06.004.
2200. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
2201. Kriegler, E. et al., 2017: Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century. Glob. Environ. Chang., 42, 297–315, doi:10.1016/J.GLOENVCHA.2016.05.015.
2202. Bertram, C. et al., 2018: Targeted policies can compensate most of the increased sustainability risks in 1.5°C mitigation scenarios. Environ. Res. Lett., 13, 64038, doi:10.1088/1748-9326/aac3ec.
2203. van Vuuren, D.P. et al., 2018: Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. Nat. Clim. Chang., 8, 1–7, doi:10.1038/s41558-018-0119-8.
2204. Grubler, A. et al., 2018: A low energy demand scenario for meeting the 1.5 C target and sustainable development goals without negative emission technologies. Nat. Energy, 3, 515–527, doi:10.1038/s41560-018-0172-6.
2205. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
2206. Tan, Z.H. et al., 2017: Optimum air temperature for tropical forest photosynthesis: Mechanisms involved and implications for climate warming. Environ. Res. Lett., 12, doi:10.1088/1748-9326/aa6f97.
2207. Turner, P.A., C.B. Field, D.B. Lobell, D.L. Sanchez, and K.J. Mach, 2018b: Unprecedented rates of land-use transformation in modelled climate change mitigation pathways. Nat. Sustain., 1, 240–245, doi:10.1038/s41893-018-0063-7.
2208. Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, 331–345, doi:10.1016/J.GLOENVCHA.2016.10.002.
2209. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.
2210. Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
2211. Huppmann, D., J. Rogelj, E. Kriegler, V. Krey, and K. Riahi, 2018: A new scenario resource for integrated 1.5°C research. Nat. Clim. Chang., 8, 1027–1030, doi:10.1038/s41558-018-0317-4.
2212. Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°c. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.