Editing IPCC:AR6/SRCCL/Chapter-2 (section)

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

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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 <sub>2</sub> 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.

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<span id="figure-2.13"></span>
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'''Figure 2.13'''

<span id="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"></span>
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'''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 […]'''
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[[File:672f6436c7a4bb3e207c23b651582b0f Figure-2.13-1024x461.jpg]]

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.

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=== 2.5.1 Impacts of historical and future anthropogenic land cover changes ===

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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.

<div id="section-2-5-1-1-impacts-of-global-historical-land-cover-changes-on-climate"></div>

<span id="impacts-of-global-historical-land-cover-changes-on-climate"></span>
==== 2.5.1.1 Impacts of global historical land cover changes on climate ====

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'''''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) <sup>[[#fn:r988|988]]</sup> and Simmons and Matthews (2016) <sup>[[#fn:r989|989]]</sup> , to larger warming simulated by three models (&gt;+0.14°C; Shevliakova et al. 2013 <sup>[[#fn:r990|990]]</sup> ; Pongratz et al. 2010 <sup>[[#fn:r991|991]]</sup> ; Matthews et al. 2004 <sup>[[#fn:r992|992]]</sup> ). When starting from the Holocene period, He et al. (2014) <sup>[[#fn:r993|993]]</sup> 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 <sup>[[#fn:1|1]]</sup> ). 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 <sup>[[#fn:r994|994]]</sup> ; Hua and Chen 2013 <sup>[[#fn:r995|995]]</sup> ; Jones et al. 2013b <sup>[[#fn:r996|996]]</sup> ; Simmons and Matthews 2016 <sup>[[#fn:r997|997]]</sup> ) (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 <sup>[[#fn:r998|998]]</sup> ). 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 <sup>[[#fn:r999|999]]</sup> ). 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) <sup>[[#fn:r1000|1000]]</sup> , Boisier et al. (2012) <sup>[[#fn:r1001|1001]]</sup> ).

Historical land cover changes have contributed to the increase in atmospheric CO <sub>2</sub> 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) <sup>[[#fn:r1002|1002]]</sup> , Avitabile et al. (2016) <sup>[[#fn:r1003|1003]]</sup> , Carvalhais et al. (2014) <sup>[[#fn:r1004|1004]]</sup> , Le Quéré et al. (2015) <sup>[[#fn:r1005|1005]]</sup> ), or estimated from DGVMs (+0.24 ± 0.12°C) (Peng et al. 2017 <sup>[[#fn:r1006|1006]]</sup> ; Arneth et al. 2017 <sup>[[#fn:r1007|1007]]</sup> ; Pugh et al. 2015 <sup>[[#fn:r1008|1008]]</sup> ; Hansis et al. 2015 <sup>[[#fn:r1009|1009]]</sup> ) and global climate models (+0.20 ± 0.05°C) (Pongratz et al. 2010 <sup>[[#fn:r1010|1010]]</sup> ; Brovkin et al. 2004 <sup>[[#fn:r1011|1011]]</sup> ; Matthews et al. 2004 <sup>[[#fn:r1012|1012]]</sup> ; Simmons and Matthews 2016 <sup>[[#fn:r1013|1013]]</sup> ).

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 <sub>4</sub> , N <sub>2</sub> O and non-GHG emissions from land (Ward et al. 2014 <sup>[[#fn:r1014|1014]]</sup> ; Arneth et al. 2017 <sup>[[#fn:r1015|1015]]</sup> ; Cleveland et al. 2015 <sup>[[#fn:r1016|1016]]</sup> ; Pongratz et al. 2018 <sup>[[#fn:r1017|1017]]</sup> ). 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 <sup>[[#fn:r1018|1018]]</sup> ; Ward et al. 2014 <sup>[[#fn:r1019|1019]]</sup> ). 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 <sub>2</sub> 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 <sup>[[#fn:r1020|1020]]</sup> ; Lucht et al. 1995 <sup>[[#fn:r1021|1021]]</sup> ; 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 <sup>[[#fn:r1022|1022]]</sup> ). At the same time, cooling occurs due to increased evapotranspiration during the growing season, along with enhanced photosynthesis, in essence, increased CO <sub>2</sub> sink (Qian et al. 2010 <sup>[[#fn:r1023|1023]]</sup> ). 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 <sup>[[#fn:r1024|1024]]</sup> ). 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 <sub>2</sub> content since pre- industrial times (De Noblet-Ducoudré et al. 2012 <sup>[[#fn:r1025|1025]]</sup> ; 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 <sup>[[#fn:r1026|1026]]</sup> ) reported a 20th century biophysical cooling of –0.10°C, and Strengers et al. (2010) <sup>[[#fn:r1027|1027]]</sup> 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 <sup>[[#fn:r1028|1028]]</sup> ; Pitman and de Noblet-Ducoudré 2012 <sup>[[#fn:r1029|1029]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1030|1030]]</sup> ). Quesada et al. (2017a) <sup>[[#fn:r1031|1031]]</sup> 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 <sub>2</sub> and sea-surface temperatures, but with a lack of model agreement on the sign of changes (Pitman et al. 2012 <sup>[[#fn:r1032|1032]]</sup> ). In some regions, the impact of land cover change masks or amplifies the effect of increased CO <sub>2</sub> on extremes (Avila et al. 2012 <sup>[[#fn:r1033|1033]]</sup> ; Christidis et al. 2013 <sup>[[#fn:r1034|1034]]</sup> ). 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) <sup>[[#fn:r1035|1035]]</sup> 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) <sup>[[#fn:r1036|1036]]</sup> 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 <sup>[[#fn:r1037|1037]]</sup> ; Renaud and Rebetez 2008 <sup>[[#fn:r1038|1038]]</sup> ), 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 <sup>[[#fn:r1039|1039]]</sup> ; Duveiller et al. 2018 <sup>[[#fn:r1040|1040]]</sup> ; Li et al. 2015a <sup>[[#fn:r1041|1041]]</sup> ; Lee et al. 2011 <sup>[[#fn:r1042|1042]]</sup> ). Furthermore, trees also locally dampen the amplitude of heat extremes (Renaud and Rebetez 2008 <sup>[[#fn:r1043|1043]]</sup> ; Zaitchik et al. 2006 <sup>[[#fn:r1044|1044]]</sup> ) although this result depends on the forest type, coniferous trees providing less cooling effect than broadleaf trees (Renaud et al. 2011 <sup>[[#fn:r1045|1045]]</sup> ; Renaud and Rebetez 2008 <sup>[[#fn:r1046|1046]]</sup> ).

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<span id="table-2.4"></span>
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'''Table 2.4'''

<span id="change-in-mean-global-annual-surface-air-temperature-resulting-from-anthropogenic-land-cover-change-over-the-historical-period."></span>
'''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).

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{| class="wikitable"
|-

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

|}
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'''Figure 2.14'''

<span id="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"></span>
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'''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) […]'''
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[[File:08e4c0dfc6985de239e57e8cfecc576b Figure-2.14-1024x724.jpg]]

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 <sub>2</sub> 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

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'''Figure 2.15'''

<span id="simulated-changes-in-mean-surface-air-temperature-ºc-between-the-pre-industrial-period-18701900-and-the-present-day-19722002-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"></span>
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'''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 […]'''
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[[File:f4afc1705c5c633841db1ca2f70ea59f Figure-2.15-1024x481.jpg]]

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) <sup>[[#fn:r1047|1047]]</sup> . 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 <sub>2</sub> ). The CO <sub>2</sub> 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).

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<div id="section-2-5-1-2-impacts-of-future-global-land-cover-changes-on-climate"></div>

<span id="impacts-of-future-global-land-cover-changes-on-climate"></span>
==== 2.5.1.2 Impacts of future global land cover changes on climate ====

<div id="section-2-5-1-2-impacts-of-future-global-land-cover-changes-on-climate-block-1"></div>

'''''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 <sup>[[#fn:r1048|1048]]</sup> ) 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 <sup>[[#fn:r1049|1049]]</sup> ; Davies-Barnard et al. 2014 <sup>[[#fn:r1050|1050]]</sup> ; Boysen et al. 2014 <sup>[[#fn:r1051|1051]]</sup> ) (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 <sup>[[#fn:r1052|1052]]</sup> ; Stocker et al. 2014 <sup>[[#fn:r1053|1053]]</sup> ). This biogeochemical warming is compensated for by a biophysical cooling ( ''medium confidence'' ) of –0.10 ± 0.14°C (Quesada et al. 2017a <sup>[[#fn:r1054|1054]]</sup> ; Davies-Barnard et al. 2015 <sup>[[#fn:r1055|1055]]</sup> ; Boysen et al. 2014 <sup>[[#fn:r1056|1056]]</sup> ). 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 <sup>[[#fn:r1057|1057]]</sup> ). 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 <sup>[[#fn:r1058|1058]]</sup> ), 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 <sub>2</sub> in the atmosphere from land conversions (Sitch et al. 2005 <sup>[[#fn:r1059|1059]]</sup> ). 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 <sup>[[#fn:r1060|1060]]</sup> ). The authors derived a biophysical climatic sensitivity to land use change of about –0.3°C W.m <sup>–2</sup> for their model, whereas a warming of about 1°C W.m <sup>–2</sup> is obtained in response to changes in atmospheric CO <sub>2</sub> 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 <sup>[[#fn:r1061|1061]]</sup> ). 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 <sup>–1</sup> respectively in the Amazon and Central Africa). They also report opposite changes – that is, increased rainfall of about 0.25 mm day <sup>–1</sup> 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 <sup>[[#fn:r1062|1062]]</sup> ). Voldoire (2006) <sup>[[#fn:r1063|1063]]</sup> 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) <sup>[[#fn:r1064|1064]]</sup> 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 <sup>[[#fn:r1065|1065]]</sup> ; Sitch et al. 2005 <sup>[[#fn:r1066|1066]]</sup> ; Feddema et al. 2005 <sup>[[#fn:r1067|1067]]</sup> ). Quesada et al. (2017a <sup>[[#fn:r1068|1068]]</sup> , b <sup>[[#fn:r1069|1069]]</sup> ) 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 <sup>–1</sup> 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 <sup>[[#fn:r1070|1070]]</sup> )). 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 <sup>[[#fn:r1071|1071]]</sup> ).

'''''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 <sup>[[#fn:r1072|1072]]</sup> ). 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.

<div id="section-2-5-1-2-impacts-of-future-global-land-cover-changes-on-climate-block-2"></div>

<span id="table-2.5"></span>
<!-- START IMG -->
<!-- TABLE IMG -->
<!-- IMG TITLE -->
'''Table 2.5'''

<span id="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."></span>
<!-- IMG CAPTION -->
 '''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.
<!-- IMG FILE -->
[[File:06f33ed15db05e99f31259a8e97d2ea1 table-2.5.png]]

<!-- END IMG -->
<div id="section-2-5-1-2-impacts-of-future-global-land-cover-changes-on-climate-block-3"></div>

<span id="figure-2.16"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.16'''

<span id="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-20712100-and-the-end-of-the-20th-century-19762005-and-the-percent-change-is-calculated-with-reference-to-19762005.-grey-bars-refer-to"></span>
<!-- IMG CAPTION -->
'''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 […]'''
<!-- IMG FILE -->
[[File:b7f35600beedd6a93242d3be3b32c1ff Figure-2.16-1024x659.jpg]]

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 <sup>[[#fn:r1073|1073]]</sup> ). 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) <sup>[[#fn:r1074|1074]]</sup> . The changes have been simulated by five climate models (Brovkin et al. 2013) <sup>[[#fn:r1075|1075]]</sup> . 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.

<!-- END IMG -->
<span id="impacts-of-specific-land-use-changes"></span>
=== 2.5.2 Impacts of specific land use changes ===

<div id="section-2-5-2-1-impacts-of-deforestation-and-forestation"></div>

<span id="impacts-of-deforestation-and-forestation"></span>
==== 2.5.2.1 Impacts of deforestation and forestation ====

<div id="section-2-5-2-1-impacts-of-deforestation-and-forestation-block-1"></div>

Deforestation or forestation <sup>[[#fn:2|2]]</sup> , wherever it occurs, triggers simultaneously warming and cooling of the surface and of the atmosphere via changes in its various characteristics (Pitman 2003 <sup>[[#fn:r1076|1076]]</sup> ; Strengers et al. 2010 <sup>[[#fn:r1077|1077]]</sup> ; Bonan 2008 <sup>[[#fn:r1078|1078]]</sup> ). Following deforestation, warming results from (i) the release of  CO <sub>2</sub> 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 <sub>2</sub> 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 <sub>2</sub> 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 <sup>[[#fn:r1079|1079]]</sup> ; Snyder et al. 2004 <sup>[[#fn:r1080|1080]]</sup> ; Devaraju et al. 2015a <sup>[[#fn:r1081|1081]]</sup> ; Longobardi et al. 2016 <sup>[[#fn:r1082|1082]]</sup> ; Perugini et al. 2017 <sup>[[#fn:r1083|1083]]</sup> ). 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) <sup>[[#fn:r1084|1084]]</sup> , Snyder (2010) <sup>[[#fn:r1085|1085]]</sup> , Longobardi et al. (2016a) <sup>[[#fn:r1086|1086]]</sup> ) (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 <sup>[[#fn:r1087|1087]]</sup> ; Bathiany et al. 2010 <sup>[[#fn:r1088|1088]]</sup> ).

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 <sup>[[#fn:r1089|1089]]</sup> ; Lejeune et al. 2015 <sup>[[#fn:r1090|1090]]</sup> ; Perugini et al. 2017 <sup>[[#fn:r1091|1091]]</sup> ). In their review, Perugini et al. (2017) <sup>[[#fn:r1092|1092]]</sup> reported an average simulated decrease of –288 ± 75 mm yr <sup>–1</sup> (95% confidence interval). Inversely large-scale forestation increases tropical rainfall by 41 ± 21 mm yr <sup>–1</sup> . 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 <sup>–1</sup> and –220 ± 100 mm yr <sup>–1</sup> ). Biogeochemical effects in response to pan-tropical deforestation, particularly CO <sub>2</sub> 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 <sup>[[#fn:r1093|1093]]</sup> ).

Specific model-based deforestation studies have been carried out for Africa (Hagos et al. 2014 <sup>[[#fn:r1094|1094]]</sup> ; Boone et al. 2016 <sup>[[#fn:r1095|1095]]</sup> ; Xue et al. 2016 <sup>[[#fn:r1096|1096]]</sup> ; Nogherotto et al. 2013 <sup>[[#fn:r1097|1097]]</sup> ; Hartley et al. 2016 <sup>[[#fn:r1098|1098]]</sup> ; Klein et al. 2017 <sup>[[#fn:r1099|1099]]</sup> ; Abiodun et al. 2012 <sup>[[#fn:r1100|1100]]</sup> ), southern America (Butt et al. 2011 <sup>[[#fn:r1101|1101]]</sup> ; Wu et al. 2017 <sup>[[#fn:r1102|1102]]</sup> ; Spracklen and Garcia-Carreras 2015 <sup>[[#fn:r1103|1103]]</sup> ; Lejeune et al. 2015) and Southeast Asia (Ma et al. 2013b <sup>[[#fn:r1104|1104]]</sup> ; Werth and Avissar 2005 <sup>[[#fn:r1105|1105]]</sup> ; Mabuchi et al. 2005 <sup>[[#fn:r1106|1106]]</sup> ; Tölle et al. 2017 <sup>[[#fn:r1107|1107]]</sup> ). 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 <sup>[[#fn:r1108|1108]]</sup> ).

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 <sup>[[#fn:r1109|1109]]</sup> ; Schultz et al. 2017 <sup>[[#fn:r1110|1110]]</sup> ; Li et al. 2015b <sup>[[#fn:r1111|1111]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1112|1112]]</sup> ). 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 <sup>[[#fn:r1113|1113]]</sup> ), eastern China (Ma et al. 2013a <sup>[[#fn:r1114|1114]]</sup> ) or the Saharan and Australian deserts (Ornstein et al. 2009 <sup>[[#fn:r1115|1115]]</sup> ; Kemena et al. 2017 <sup>[[#fn:r1116|1116]]</sup> ) 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 <sup>[[#fn:r1117|1117]]</sup> ). 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 <sub>2</sub> 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 <sup>[[#fn:r1118|1118]]</sup> ; Snyder et al. 2004 <sup>[[#fn:r1119|1119]]</sup> ; Longobardi et al. 2016a <sup>[[#fn:r1120|1120]]</sup> ; Devaraju et al. 2015a <sup>[[#fn:r1121|1121]]</sup> , 2018 <sup>[[#fn:r1122|1122]]</sup> ). There is ''medium agreement'' on a global and latitudinal biophysical warming in response to forestation (Laguë and Swann 2016 <sup>[[#fn:r1123|1123]]</sup> ; Swann et al. 2012 <sup>[[#fn:r1124|1124]]</sup> ; Gibbard et al. 2005 <sup>[[#fn:r1125|1125]]</sup> ; Wang et al. 2014b <sup>[[#fn:r1126|1126]]</sup> ) (Figure 2.17), but this is based on a smaller number of studies.

<div id="section-2-5-2-1-impacts-of-deforestation-and-forestation-block-2"></div>

<span id="figure-2.17"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.17'''

<span id="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"></span>
<!-- IMG CAPTION -->
'''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 […]'''
<!-- IMG FILE -->
[[File:1124de0ee2732fc48f4e1c95cb2e0d83 Figure-2.17-1024x680.jpg]]

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 <sub>2</sub> 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.

<!-- END IMG -->
<div id="section-2-5-2-1-impacts-of-deforestation-and-forestation-block-3"></div>

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 <sup>[[#fn:r1127|1127]]</sup> ; Zhao and Jackson 2014 <sup>[[#fn:r1128|1128]]</sup> ; Gálos et al. 2011 <sup>[[#fn:r1129|1129]]</sup> , 2013 <sup>[[#fn:r1130|1130]]</sup> ; Wickham etal.2013 <sup>[[#fn:r1131|1131]]</sup> ;Ahlswede and Thomas 2017 <sup>[[#fn:r1132|1132]]</sup> ; Anderson-Teixeira et al. 2012 <sup>[[#fn:r1133|1133]]</sup> ; Anderson et al. 2011 <sup>[[#fn:r1134|1134]]</sup> ; Chen et al. 2012 <sup>[[#fn:r1135|1135]]</sup> ; Strandberg and Kjellström 2018 <sup>[[#fn:r1136|1136]]</sup> ). 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 <sup>[[#fn:r1137|1137]]</sup> ; Anav et al. 2010 <sup>[[#fn:r1138|1138]]</sup> ). 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 <sup>[[#fn:r1139|1139]]</sup> ; Wickham et al. 2014 <sup>[[#fn:r1140|1140]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1141|1141]]</sup> ; Tang et al. 2018 <sup>[[#fn:r1142|1142]]</sup> ; Peng et al. 2014 <sup>[[#fn:r1143|1143]]</sup> ; Zhang et al. 2014b <sup>[[#fn:r1144|1144]]</sup> ; Prevedello et al. 2019 <sup>[[#fn:r1145|1145]]</sup> ; Li et al. 2015b <sup>[[#fn:r1146|1146]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1147|1147]]</sup> ). 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 <sup>[[#fn:r1148|1148]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1149|1149]]</sup> ).

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 <sup>[[#fn:r1150|1150]]</sup> ; Wickham et al. 2014 <sup>[[#fn:r1151|1151]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1152|1152]]</sup> ; Tang et al. 2018 <sup>[[#fn:r1153|1153]]</sup> ; Prevedello et al. 2019 <sup>[[#fn:r1154|1154]]</sup> ; Peng et al. 2014 <sup>[[#fn:r1155|1155]]</sup> ; Zhang et al. 2014b <sup>[[#fn:r1156|1156]]</sup> ; Li et al. 2015b <sup>[[#fn:r1157|1157]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1158|1158]]</sup> ). 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) <sup>[[#fn:r1159|1159]]</sup> found systematic warming of the hottest summer days following historical deforestation in the northern mid-latitudes, and this echoes Strandberg and Kjellström (2018) <sup>[[#fn:r1160|1160]]</sup> 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) <sup>[[#fn:r1161|1161]]</sup> 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 <sup>[[#fn:r1162|1162]]</sup> , 2018 <sup>[[#fn:r1163|1163]]</sup> ). 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) <sup>[[#fn:r1164|1164]]</sup> , Longobardi et al. (2016a) <sup>[[#fn:r1165|1165]]</sup> , Devaraju et al. (2015a) <sup>[[#fn:r1166|1166]]</sup> , Bathiany et al. (2010) <sup>[[#fn:r1167|1167]]</sup> , Devaraju et al. (2018) <sup>[[#fn:r1168|1168]]</sup> ) 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) <sup>[[#fn:r1169|1169]]</sup> and Gao et al. (2014) <sup>[[#fn:r1170|1170]]</sup> : 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) <sup>[[#fn:r1171|1171]]</sup> , 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) <sup>[[#fn:r1172|1172]]</sup> 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 <sub>2</sub> , 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 <sup>[[#fn:r1173|1173]]</sup> ; Montenegro et al. 2009 <sup>[[#fn:r1174|1174]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1175|1175]]</sup> ; Wickham et al. 2014 <sup>[[#fn:r1176|1176]]</sup> ; Hagos et al. 2014 <sup>[[#fn:r1177|1177]]</sup> ; Voldoire 2006 <sup>[[#fn:r1178|1178]]</sup> ; Feddema et al. 2005 <sup>[[#fn:r1179|1179]]</sup> ; Strandberg and Kjellström 2018 <sup>[[#fn:r1180|1180]]</sup> ). 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 <sub>2</sub> 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 <sup>[[#fn:r1181|1181]]</sup> ).

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 <sub>2</sub> .

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 <sub>2</sub> (O’Halloran et al. 2012 <sup>[[#fn:r1182|1182]]</sup> ), 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 <sub>2</sub> by land. The resulting climate effects depend on the duration of the perturbation and of the following recovery of vegetation.

<div id="section-2-5-2-2-impacts-of-changes-in-land-management"></div>

<span id="impacts-of-changes-in-land-management"></span>
==== 2.5.2.2 Impacts of changes in land management ====

<div id="section-2-5-2-2-impacts-of-changes-in-land-management-block-1"></div>

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 <sup>[[#fn:r1183|1183]]</sup> ). 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) <sup>[[#fn:r1184|1184]]</sup> ).

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) <sup>[[#fn:r1185|1185]]</sup> for double cropping, Bagley et al. (2017) <sup>[[#fn:r1186|1186]]</sup> for the timing of the growing season and Erb et al. (2017) <sup>[[#fn:r1187|1187]]</sup> 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 <sub>2</sub> 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 <sup>[[#fn:r1188|1188]]</sup> ; Luyssaert et al. 2018 <sup>[[#fn:r1189|1189]]</sup> ).

''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 <sup>[[#fn:r1190|1190]]</sup> ; Alter et al. 2015 <sup>[[#fn:r1191|1191]]</sup> ; Chen and Jeong 2018 <sup>[[#fn:r1192|1192]]</sup> ; Christy et al. 2006 <sup>[[#fn:r1193|1193]]</sup> ; Im and Eltahir 2014 <sup>[[#fn:r1194|1194]]</sup> ; Im et al. 2014 <sup>[[#fn:r1195|1195]]</sup> ; Mueller et al. 2015 <sup>[[#fn:r1196|1196]]</sup> ). Decreases in maximum daytime temperature can locally be as large as –3°C to –8°C (Cook et al. 2015 <sup>[[#fn:r1197|1197]]</sup> ; Han and Yang 2013 <sup>[[#fn:r1198|1198]]</sup> ; Huber et al. 2014 <sup>[[#fn:r1199|1199]]</sup> ; Alter et al. 2015 <sup>[[#fn:r1200|1200]]</sup> ; Im et al. 2014 <sup>[[#fn:r1201|1201]]</sup> ). 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 <sup>[[#fn:r1202|1202]]</sup> ; Chen and Jeong 2018 <sup>[[#fn:r1203|1203]]</sup> ) while being quite larger in California, USA (–0.14°C to –0.25°C/decade) (Bonfils and Lobell 2007 <sup>[[#fn:r1204|1204]]</sup> ). 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 <sup>[[#fn:r1205|1205]]</sup> ; Mueller et al. 2015 <sup>[[#fn:r1206|1206]]</sup> ).

There is ''no agreement'' on changes in night-time temperatures, as discussed in Chen and Jeong (2018) <sup>[[#fn:r1207|1207]]</sup> 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 <sup>[[#fn:r1208|1208]]</sup> ; Christy et al. 2006 <sup>[[#fn:r1209|1209]]</sup> ), 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.

<div id="section-2-5-2-2-impacts-of-changes-in-land-management-block-2"></div>

<span id="figure-2.18"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.18'''

<span id="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"></span>
<!-- IMG CAPTION -->
'''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 […]'''
<!-- IMG FILE -->
[[File:8a2d4154a588ac50461beb56b94e25c9 Figure-2.18-724x1024.jpg]]

Global map of areas equipped for irrigation (colours), expressed as a percentage of total area, or irrigation fraction. Source: Siebert et al. (2013) <sup>[[#fn:r1210|1210]]</sup> . 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) <sup>[[#fn:r1211|1211]]</sup> . Tmax refers to the warmest daily temperature while Tmin to the coldest one, which generally occurs at night (Alter et al. 2015 <sup>[[#fn:r1212|1212]]</sup> ; Han and Yang 2013 <sup>[[#fn:r1213|1213]]</sup> ; Roy et al. 2007 <sup>[[#fn:r1214|1214]]</sup> ; Shi et al. 2013 <sup>[[#fn:r1215|1215]]</sup> ; Bonfils and Lobell 2007 <sup>[[#fn:r1216|1216]]</sup> ; Lobell et al. 2008 <sup>[[#fn:r1217|1217]]</sup> ; Lobell and Bonfils 2008 <sup>[[#fn:r1218|1218]]</sup> ; Christy et al. 2006 <sup>[[#fn:r1219|1219]]</sup> ; Mahmood et al. 2006 <sup>[[#fn:r1220|1220]]</sup> ; Mueller et al. 2015 <sup>[[#fn:r1221|1221]]</sup> ).

<!-- END IMG -->
<div id="section-2-5-2-2-impacts-of-changes-in-land-management-block-3"></div>

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 <sup>[[#fn:r1222|1222]]</sup> ) and increased in the east/northeast when crops are located further east in Sudan (Im and Eltahir 2014 <sup>[[#fn:r1223|1223]]</sup> ; Im et al. 2014 <sup>[[#fn:r1224|1224]]</sup> ) 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 <sup>[[#fn:r1225|1225]]</sup> ; Guimberteau et al. 2012 <sup>[[#fn:r1226|1226]]</sup> ). Results from a modelling study by De Vrese et al. (2016) <sup>[[#fn:r1227|1227]]</sup> 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 <sup>[[#fn:r1228|1228]]</sup> ; Crook et al. 2015 <sup>[[#fn:r1229|1229]]</sup> ; Hirsch et al. 2017 <sup>[[#fn:r1230|1230]]</sup> ; Singarayer et al. 2009 <sup>[[#fn:r1231|1231]]</sup> ; Singarayer and Davies-Barnard 2012 <sup>[[#fn:r1232|1232]]</sup> ), abandon tillage (Lobell et al. 2006 <sup>[[#fn:r1233|1233]]</sup> ; Davin et al. 2014 <sup>[[#fn:r1234|1234]]</sup> ), include cover crops into rotation in areas where soils are darker than vegetation (Carrer et al. 2018 <sup>[[#fn:r1235|1235]]</sup> ; Kaye and Quemada 2017 <sup>[[#fn:r1236|1236]]</sup> ) or use greenhouses (as in Campra et al. (2008) <sup>[[#fn:r1237|1237]]</sup> ). See Seneviratne et al. (2018) <sup>[[#fn:r1238|1238]]</sup> 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 <sup>[[#fn:r1239|1239]]</sup> ; Wilhelm et al. 2015 <sup>[[#fn:r1240|1240]]</sup> ; 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.

<div id="section-2-5-2-2-impacts-of-changes-in-land-management-block-4"></div>

<span id="figure-2.19"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.19'''

<span id="change-in-summer-julyaugust-daily-maximum-temperature-ºc-resulting-from-increased-surface-albedo-in-unploughed-versus-ploughed-land-in-a-southern-and-b-northern-europe-during-the-period-19862009.-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."></span>
<!-- IMG CAPTION -->
'''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. […]'''
<!-- IMG FILE -->
[[File:08d354f66f961d81b064ddf459156770 Figure-2.19-1024x398.jpg]]

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)

<!-- END IMG -->
<div id="section-2-5-2-2-impacts-of-changes-in-land-management-block-5"></div>

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 <sup>[[#fn:r1241|1241]]</sup> ; Lombardozzi et al. 2018 <sup>[[#fn:r1242|1242]]</sup> ). In addition, evapotranspiration greater than that of bare soil during this transitional period reduces soil temperature (Ceschia et al. 2017 <sup>[[#fn:r1243|1243]]</sup> ). 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 <sub>2</sub> O emissions (Basche et al. 2014 <sup>[[#fn:r1244|1244]]</sup> ; Kaye and Quemada 2017 <sup>[[#fn:r1245|1245]]</sup> ), in particular if fertilisation of the subsequent crop is reduced (Constantin et al. 2010 <sup>[[#fn:r1246|1246]]</sup> , 2011 <sup>[[#fn:r1247|1247]]</sup> ). The use of cover crops thus substantially improves the GHG budget of croplands (Kaye and Quemada 2017 <sup>[[#fn:r1248|1248]]</sup> ; Tribouillois et al. 2018 <sup>[[#fn:r1249|1249]]</sup> ). 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) <sup>[[#fn:r1250|1250]]</sup> 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.

<span id="amplifyingdampening-climate-changes-via-land-responses"></span>
=== 2.5.3 Amplifying/dampening climate changes via land responses ===

<div id="section-2-5-3-amplifying-dampening-climate-changes-via-land-responses-block-1"></div>

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 <sub>2</sub> , 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 <sub>2</sub> (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.

<div id="section-2-5-3-amplifying-dampening-climate-changes-via-land-responses-block-2"></div>

<span id="figure-2.20"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.20'''

<span id="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-regionallocal-level.-grey-arrows-and-boxes-refer-to-what-we-consider-herein-as-imposed-changes-that-is"></span>
<!-- IMG CAPTION -->
'''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, […]'''
<!-- IMG FILE -->
[[File:2ef84d76969a00271ecd285a3ded3639 Figure-2.20-1024x507.jpg]]

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 <sub>2</sub> fluxes). References supporting this figure can be found in each of those sections.

<!-- END IMG -->
<div id="section-2-5-3-1-effects-of-changes-in-land-cover-and-productivity-resulting-from-global-warming"></div>

<span id="effects-of-changes-in-land-cover-and-productivity-resulting-from-global-warming"></span>
==== 2.5.3.1 Effects of changes in land cover and productivity resulting from global warming ====

<div id="section-2-5-3-1-effects-of-changes-in-land-cover-and-productivity-resulting-from-global-warming-block-1"></div>

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 <sup>[[#fn:r1251|1251]]</sup> ; Jeong et al. 2014a <sup>[[#fn:r1252|1252]]</sup> ; O’ishi and Abe-Ouchi 2009 <sup>[[#fn:r1253|1253]]</sup> ; Port et al. 2012 <sup>[[#fn:r1254|1254]]</sup> ; Strengers et al. 2010 <sup>[[#fn:r1255|1255]]</sup> ). 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 <sup>[[#fn:r1256|1256]]</sup> ; Wu et al. 2016 <sup>[[#fn:r1257|1257]]</sup> ; Yu et al. 2016 <sup>[[#fn:r1258|1258]]</sup> ). The reverse is true where warming generates increases in rainfall and thus greening. As an example, Port e tal. (2012) <sup>[[#fn:r1259|1259]]</sup> simulated decreases in tree cover and shortened growing season in the Amazon, despite the CO <sub>2</sub> 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 <sup>[[#fn:r1260|1260]]</sup> ; Port et al. 2012 <sup>[[#fn:r1261|1261]]</sup> ; Wu et al. 2016 <sup>[[#fn:r1263|1263]]</sup> ). 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 <sup>[[#fn:r1264|1264]]</sup> ; Rachmayani et al. 2015 <sup>[[#fn:r1265|1265]]</sup> ).

<div id="section-2-5-3-1-effects-of-changes-in-land-cover-and-productivity-resulting-from-global-warming-block-2"></div>

<span id="figure-2.21"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.21'''

<span id="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"></span>
<!-- IMG CAPTION -->
'''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 […]'''
<!-- IMG FILE -->
[[File:d22ae8a9968926035f16a50fcbc1f7d9 Figure-2.21-1024x691.jpg]]

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.

<!-- END IMG -->
<div id="section-2-5-3-2-feedbacks-to-climate-from-high-latitude-land-surface-changes"></div>

<span id="feedbacks-to-climate-from-high-latitude-land-surface-changes"></span>
==== 2.5.3.2 Feedbacks to climate from high-latitude land-surface changes ====

<div id="section-2-5-3-2-feedbacks-to-climate-from-high-latitude-land-surface-changes-block-1"></div>

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 <sup>[[#fn:r1266|1266]]</sup> ; Brutel-Vuilmet et al. 2013 <sup>[[#fn:r1267|1267]]</sup> ) warming is, and will continue to be, enhanced in boreal regions ( ''high confidence'' ) (Brutel-Vuilmet et al. 2013 <sup>[[#fn:r1268|1268]]</sup> ; Perket et al. 2014 <sup>[[#fn:r1269|1269]]</sup> ; Thackeray and Fletcher 2015 <sup>[[#fn:r1270|1270]]</sup> ; Mudryk et al. 2017 <sup>[[#fn:r1271|1271]]</sup> ). 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 <sup>–2</sup> (Flanner et al. 2011 <sup>[[#fn:r1272|1272]]</sup> ; Singh et al. 2015 <sup>[[#fn:r1273|1273]]</sup> ). 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) <sup>[[#fn:r1274|1274]]</sup> 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 <sup>–2</sup> K <sup>–1</sup> , which amounts to about 7% of the strength of the globally dominant water vapour feedback (e.g., Thackeray and Fletcher (2015) <sup>[[#fn:r1275|1275]]</sup> . While climate models do represent this feedback, a persistent spread in the modelled feedback strength has been noticed (Qu and Hall 2014 <sup>[[#fn:r1276|1276]]</sup> ) and, on average, the simulated snow albedo feedback strength tends to be somewhat weaker than in reality ( ''medium confidence'' ) (Flanner et al. 2011 <sup>[[#fn:r1277|1277]]</sup> ; Thackeray and Fletcher 2015 <sup>[[#fn:r1278|1278]]</sup> ). 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 <sup>[[#fn:r1279|1279]]</sup> ; Wang et al. 2016c <sup>[[#fn:r1280|1280]]</sup> ; Thackeray and Fletcher 2015 <sup>[[#fn:r1281|1281]]</sup> ).

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 <sub>2</sub> and/or CH <sub>4</sub> (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 <sup>[[#fn:r1282|1282]]</sup> ). The most recent comprehensive estimates establish a total soil organic carbon storage in permafrost of about 1500 ± 200 PgC (Hugelius et al. 2014 <sup>[[#fn:r1283|1283]]</sup> , 2013 <sup>[[#fn:r1284|1284]]</sup> ; Olefeldt et al. 2016 <sup>[[#fn:r1285|1285]]</sup> ), 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) <sup>[[#fn:r1286|1286]]</sup> ), but representations of some critical processes such as thermokarst formation are still in their infancy (Schuur et al. 2015) <sup>[[#fn:r1287|1287]]</sup> . Recent model-based estimates of future permafrost carbon release (Koven et al. 2015 <sup>[[#fn:r1288|1288]]</sup> ; McGuire et al. 2018 <sup>[[#fn:r1289|1289]]</sup> ) 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'' ).

<div id="section-2-5-3-3-feedbacks-related-to-changes-in-soil-moisture-resulting-from-global-warming"></div>

<span id="feedbacks-related-to-changes-in-soil-moisture-resulting-from-global-warming"></span>
==== 2.5.3.3 Feedbacks related to changes in soil moisture resulting from global warming ====

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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 <sup>[[#fn:r1290|1290]]</sup> ; Quesada et al. 2012 <sup>[[#fn:r1291|1291]]</sup> ; Miralles et al. 2018 <sup>[[#fn:r1292|1292]]</sup> ; Geirinhas et al. 2018 <sup>[[#fn:r1293|1293]]</sup> ; Miralles et al. 2014 <sup>[[#fn:r1294|1294]]</sup> ; Chiang et al. 2018 <sup>[[#fn:r1295|1295]]</sup> ; Dong and Crow 2019 <sup>[[#fn:r1296|1296]]</sup> ; Hirschi et al. 2014 <sup>[[#fn:r1297|1297]]</sup> ).

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 <sup>[[#fn:r1298|1298]]</sup> ; Vogel et al. 2017 <sup>[[#fn:r1299|1299]]</sup> ; Donat et al. 2018 <sup>[[#fn:r1300|1300]]</sup> ; Miralles et al. 2018 <sup>[[#fn:r1301|1301]]</sup> ). 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 <sup>[[#fn:r1302|1302]]</sup> ; Fischer et al. 2012 <sup>[[#fn:r1303|1303]]</sup> ). 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 <sup>[[#fn:r1304|1304]]</sup> ), to weaknesses in the representation of soil moisture–atmosphere interactions in climate models (Sippel et al. 2017 <sup>[[#fn:r1305|1305]]</sup> ; Ukkola et al. 2018 <sup>[[#fn:r1306|1306]]</sup> ; Donat et al. 2018 <sup>[[#fn:r1307|1307]]</sup> ; Miralles et al. 2018 <sup>[[#fn:r1308|1308]]</sup> ) 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 <sup>[[#fn:r1309|1309]]</sup> ).

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 <sup>[[#fn:r1310|1310]]</sup> ). 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 <sup>[[#fn:r1311|1311]]</sup> ; Guo et al. 2006 <sup>[[#fn:r1312|1312]]</sup> ). 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 <sup>[[#fn:r1313|1313]]</sup> ; Yang et al. 2018 <sup>[[#fn:r1314|1314]]</sup> ; Froidevaux et al. 2014 <sup>[[#fn:r1315|1315]]</sup> ; Guillod et al. 2015 <sup>[[#fn:r1316|1316]]</sup> ).

<span id="non-local-and-downwind-effects-resulting-from-changes-in-land-cover"></span>
=== 2.5.4 Non-local and downwind effects resulting from changes in land cover ===

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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.

# Any action on land that affects photosynthesis and respiration has an impact on the atmospheric CO <sub>2</sub> 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 <sub>2</sub> .
# 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) <sup>[[#fn:r1317|1317]]</sup> , McLeod et al. (2017) <sup>[[#fn:r1318|1318]]</sup> , Abiodun et al. (2012) <sup>[[#fn:r1319|1319]]</sup> , Keys (2012) <sup>[[#fn:r1320|1320]]</sup> ). Forests, for example, provide water vapour to the atmosphere which supports terrestrial precipitation downwind (Ellison et al. 2017 <sup>[[#fn:r1321|1321]]</sup> ; Layton and Ellison 2016 <sup>[[#fn:r1322|1322]]</sup> ; Spracklen et al. 2012 <sup>[[#fn:r1323|1323]]</sup> , 2018 <sup>[[#fn:r1324|1324]]</sup> ). 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 <sup>[[#fn:r1325|1325]]</sup> ). This cascading moisture recycling (succession of evapotranspiration, water vapour transport and condensation-rainfall) has been observed in South America (Spracklen et al. 2018 <sup>[[#fn:r1326|1326]]</sup> ; Zemp et al. 2014 <sup>[[#fn:r1327|1327]]</sup> ; Staal et al. 2018 <sup>[[#fn:r1328|1328]]</sup> ; Spracklen et al. 2012 <sup>[[#fn:r1329|1329]]</sup> ). 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 <sup>[[#fn:r1330|1330]]</sup> ; Layton and Ellison, 2016 <sup>[[#fn:r1331|1331]]</sup> ) (Figure 2.22).
# 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 <sup>[[#fn:r1332|1332]]</sup> ; Feddema et al. 2005 <sup>[[#fn:r1333|1333]]</sup> , Badger and Dirmeyer 2016 <sup>[[#fn:r1334|1334]]</sup> ; Garcia 2016 <sup>[[#fn:r1335|1335]]</sup> ; Stark 2015 <sup>[[#fn:r1336|1336]]</sup> ; Devaraju 2018 <sup>[[#fn:r1337|1337]]</sup> ; Quesada et al. 2017a <sup>[[#fn:r1338|1338]]</sup> ) (Figure 2.23).

De Vrese et al. (2016) <sup>[[#fn:r1339|1339]]</sup> 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 <sup>[[#fn:r1340|1340]]</sup> ; Davin and de Noblet-Ducoudre 2010 <sup>[[#fn:r1341|1341]]</sup> ; Wang et al. 2014b <sup>[[#fn:r1342|1342]]</sup> , Notaro Liu 2007 <sup>[[#fn:r1343|1343]]</sup> ). 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) <sup>[[#fn:r1344|1344]]</sup> .

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.

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<span id="figure-2.22"></span>
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'''Figure 2.22'''

<span id="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"></span>
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'''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 […]'''
<!-- IMG FILE -->
[[File:ad7e328fa9f1907a1389979ce24bba6e Figure-2.22-1024x724.jpg]]

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 <sup>[[#fn:r1345|1345]]</sup> ). 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.

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<span id="figure-2.23"></span>
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'''Figure 2.23'''

<span id="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"></span>
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'''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 […]'''
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[[File:96cdb17f744ee3684efe61e96eecfb37 Figure-2.23-1024x482.png]]

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) <sup>[[#fn:r1346|1346]]</sup> . 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 <sup>[[#fn:r1347|1347]]</sup> ; Gedney and Valdes 2000 <sup>[[#fn:r1348|1348]]</sup> ; Semazzi and Song 2001 <sup>[[#fn:r1349|1349]]</sup> ; Werth 2002; Mabuchi et al. 2005 <sup>[[#fn:r1350|1350]]</sup> ; Werth 2005 <sup>[[#fn:r1351|1351]]</sup> )

<!-- END IMG -->
<div id="section-2-5-4-non-local-and-downwind-effects-resulting-from-changes-in-land-cover-block-4" class="box"></div>

<span id="ccb4-climate-change-and-urbanisation"></span>