Editing IPCC:AR6/WGI/Chapter-6 (section)

=== 6.2.2 Emissions by Natural Systems ===

<div id="h2-11-siblings" class="h2-siblings"></div>

This section assesses our current understanding of SLCF emissions by natural systems. Many naturally occurring emission processes in the Earth system have been perturbed by the growing influence of human activities either directly (e.g., deforestation, agriculture) or via human-induced atmospheric CO <sub>2</sub> increase and climate change, and therefore cannot be considered as purely natural emissions. The temporal evolution and spatial distribution of natural SLCF emissions are highly variable and their estimates rely on models with rather uncertain parametrizations for production mechanisms. For some SLCFs, the natural processes by which emissions occur are also not well understood. In the following sections, we assess the level of confidence in present-day SLCF emissions by natural systems, in their perturbation since the pre-industrial period and their sensitivity to future changes. When available, the assessment also includes estimates from the CMIP6 model ensemble. Note that volcanic SO <sub>2</sub> emissions are discussed in [[IPCC:Wg1:Chapter:Chapter-2#2.2.2|Section 2.2.2]] and natural sources of methane and N <sub>2</sub> O are assessed in Sections 5.2.2 and 5.2.3.

<div id="6.2.2.1" class="h3-container"></div>

<span id="lightning-no-x"></span>
==== 6.2.2.1 Lightning NO <sub>x</sub> ====

<div id="h3-1-siblings" class="h3-siblings"></div>

Lightning contributes approximately 10% of the total NO <sub>x</sub> emissions ( [[#Murray--2016|Murray, 2016]] ). Since lightning NO <sub>x</sub> (LNO <sub>x</sub> ) is predominantly released in the upper troposphere, it has a disproportionately large impact on ozone and OH, and on the lifetime of methanecompared with surface NO <sub>x</sub> emissions. Whereas the global spatial and temporal distribution of lightning flashes can be characterized thanks to satellite-borne and ground sensors ( [[#Virts--2013|Virts et al., 2013]] ; [[#Cecil--2014|Cecil et al., 2014]] ), constraining the amount of NO <sub>x</sub> produced per flash (Miyazaki et al. , 2014; Medici et al. , 2017; Nault et al. , 2017; Marais et al. , 2018; D.J. Allen et al. , 2019; Bucsela et al. , 2019) and its vertical allocation ( [[#Koshak--2014|Koshak et al., 2014]] ; [[#Medici--2017|Medici et al., 2017]] ) has been more elusive.

Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) models in CMIP5 used a range of LNO <sub>x4</sub> between 1.2–9.7 TgN y <sup>−1</sup> (Lamarque et al., 2013b). In CMIP6, the corresponding LNO <sub>x</sub> range is between 3.2–7.6 TgN y <sup>−1</sup> ( [[#Griffiths--2021|Griffiths et al., 2021]] ). All CMIP6 models (as well as most models included in CMIP5, [[#Young--2013|Young et al., 2013]] ) apply a parametrization that relates cloud-top height to lightning intensity ( [[#Price--1992|Price and Rind, 1992]] ), projecting an increase in LNO <sub>x</sub> in a warmer world in the range of 0.27–0.61 TgN yr <sup>−1</sup> °C <sup>–1</sup> ( [[#Thornhill--2021a|Thornhill et al., 2021a]] ). However, models using parametrizations based on convection ( [[#Grewe--2001|Grewe et al., 2001]] ), updraft mass flux ( [[#Allen--2002|Allen and Pickering, 2002]] ) or ice flux ( [[#Finney--2016a|Finney et al., 2016a]] ) show either much less sensitivity or a negative response ( [[#Finney--2016b|Finney et al., 2016b]] , 2018; [[#Clark--2017|Clark et al., 2017]] ).

In summary, the total present-day global lightning NO <sub>x</sub> emissions are still estimated to be within a factor of two. There is ''high confidence'' that LNO <sub>x</sub> are perturbed by climate change; however, there is ''low confidence'' in the sign of the change due to fundamental uncertainties in parametrizations. <sub></sub>

<div id="6.2.2.2" class="h3-container"></div>

<span id="no-x-emissions-from-soils"></span>
==== 6.2.2.2 NO <sub>x</sub> emissions from Soils ====

<div id="h3-2-siblings" class="h3-siblings"></div>

Soil NO <sub>x</sub> (SNO <sub>x</sub> ) emissions occur in connection with complex biogenic/microbial nitrification and denitrification processes ( [[#Ciais--2013|Ciais et al., 2013]] ), which in turn are sensitive – in a non-linear manner – to temperature, precipitation, soil moisture, carbon and nutrient content, and the biome itself (e.g., [[#Hudman--2012|Hudman et al., 2012]] ). Global SNO <sub>x</sub> estimates, based on observationally constrained chemistry-transport model and vegetation model studies, show a broad range between 4.7–16.8 TgN yr <sup>–1</sup> ( [[#Young--2018|Young et al., 2018]] ). This estimate is generally larger than the current source strength used in CMIP6 simulations, which is prescribed using an early empirical estimate, typically scaled to about 5 TgN yr <sup>–1</sup> ( [[#Yienger--1995|Yienger and Levy, 1995]] ).

By the end of the 21st century, the overall nitrogen fixation in non-agricultural ecosystems could be 40% larger than in 2000, due to increased enzyme activity with growing temperatures, but the emission rates of NO (and N <sub>2</sub> O) could be dominated by changes in precipitation patterns and evapotranspiration fluxes ( [[#Fowler--2015|Fowler et al., 2015]] ). Current Earth system models (ESMs) incorporate biophysical and biogeochemical processes only to a limited extent ( [[#Jia--2019|Jia et al., 2019]] ), precluding adequate climate sensitivity studies for SNO <sub>x</sub> . Hence, while the current strength source of soil NO <sub>x</sub> has been better constrained over the last decade, adequate representations of SNO <sub>x</sub> and how it escapes from the canopy, which could provide quantitative estimates of climate-driven changes in SNO <sub>x</sub> , are still missing in ESMs.

<div id="6.2.2.3" class="h3-container"></div>

<span id="vegetation-emissions-of-organic-compounds"></span>
==== 6.2.2.3 Vegetation Emissions of Organic Compounds ====

<div id="h3-3-siblings" class="h3-siblings"></div>

A wide range of BVOCs are emitted from vegetation with the dominant compounds being isoprene and monoterpenes but also including sesquiterpenes, alkenes, alcohols, aldehydes and ketones. The photooxidation of BVOC emissions plays a fundamental role in atmospheric composition by controlling the regional and global budgets of ozone and organic aerosols, and impacting the lifetime of methane and other reactive components ( [[#Arneth--2010b|Arneth et al., 2010b]] ; [[#Heald--2015|Heald and Spracklen, 2015]] ). Substantial uncertainty exists across different modelling frameworks for estimates of global total BVOC emissions and individual compound emissions ( [[#Messina--2016|Messina et al., 2016]] ). Global isoprene emissions estimates differ by a factor of two from 300–600 TgC yr <sup>–1</sup> and global monoterpene emissions estimates by a factor of five from 30–150 TgC yr <sup>–1</sup> ( [[#Messina--2016|Messina et al., 2016]] ). A main driver of the uncertainty ranges is the choice of basal emissions rates assigned to different plant functional types in the model; however, the smaller uncertainty range for isoprene than for monoterpenes is not fully understood ( [[#Arneth--2008|Arneth et al., 2008]] ). The evaluation of global BVOC emissions is challenging because of poor measurement data coverage in many regions and the lack of year-round measurements ( [[#Unger--2013|Unger et al., 2013]] ). Several observational approaches have been developed in the past few years to improve understanding of BVOC emissions, including indirect methods such as the measurement of the OH loss rate in forested environments ( [[#Yang--2016|Yang et al., 2016]] ) and application of the variability in satellite formaldehyde concentrations ( [[#Palmer--2006|Palmer et al., 2006]] ; [[#Barkley--2013|Barkley et al., 2013]] ; [[#Stavrakou--2014|Stavrakou et al., 2014]] ). Direct space-borne isoprene retrievals using infrared radiance (IR) measurements have very recently become available ( [[#Fu--2019|Fu et al., 2019]] ; [[#Wells--2020|Wells et al., 2020]] ). Collectively these approaches have identified weaknesses in the ability of the parametrizations in global models to reproduce BVOC emissions hotspots ( [[#Wells--2020|Wells et al., 2020]] ). However, none of the current observational approaches have yet been able to reduce the uncertainty ranges in global emissions estimates.

At the plant level, BVOC emissions rates and composition depend strongly on plant species with plants tending to emit either isoprene or monoterpenes but not both. Photosynthetic activity is a main driver of isoprene and monoterpene production. Therefore, radiation and temperature, along with leaf-water status, phenological state and atmospheric CO <sub>2</sub> mixing ratio, affect emissions directly (on the leaf scale) and indirectly (via plant productivity; Guenther et al. , 2012; Loreto et al. , 2014; Niinemets et al. , 2014) . CO <sub>2</sub> directly influences the isoprene-synthesis process, with inhibition under increasing atmospheric CO <sub>2</sub> ( [[#Rosenstiel--2003|Rosenstiel et al., 2003]] ; [[#Possell--2005|Possell et al., 2005]] ; [[#Wilkinson--2009|Wilkinson et al., 2009]] ). Direct CO <sub>2</sub> inhibition has been observed for some monoterpene compounds ( [[#Loreto--2001|Loreto et al., 2001]] ; [[#Llorens--2009|Llorens et al., 2009]] ). Severe/long-term water stress may reduce emissions whilst mild/short-term water stress may temporarily amplify or maintain BVOC emissions to protect plants against ongoing stress ( [[#Peñuelas--2010|Peñuelas and Staudt, 2010]] ; [[#Potosnak--2014|Potosnak et al., 2014]] ; [[#Genard-Zielinski--2018|Genard-Zielinski et al., 2018]] ). Furthermore, observations in the Amazon indicate that the chemical composition of monoterpene emissions could also change under elevated temperature conditions ( [[#Jardine--2016|Jardine et al., 2016]] ). In addition, all these processes are investigated over short time scales but the long-term response of BVOC emissions depends on how the vegetation itself responds to the altered climate state (including temperature and water stress).

Global BVOC emissions are highly sensitive to environmental changes including changes in climate, atmospheric CO <sub>2</sub> <sub>,</sub> and vegetation composition and cover changes in natural and managed lands. Recent global modelling studies agree that global isoprene emissions have declined since the pre-industrial period, driven predominantly by anthropogenic land-use and land-cover change (LULCC) with results converging on a 10–25% loss of isoprene emissions between 1850 and the present day ( [[#Lathière--2010|Lathière et al., 2010]] ; [[#Unger--2013|Unger, 2013]] , 2014; [[#Acosta%20Navarro--2014|Acosta Navarro et al., 2014]] ; [[#Heald--2016|Heald and Geddes, 2016]] ; [[#Hantson--2017|Hantson et al., 2017]] ; [[#Hollaway--2017|Hollaway et al., 2017]] ; [[#Scott--2017|Scott et al., 2017]] ). The historical evolution of monoterpene and sesquiterpene emissions is less well studied and there is no robust consensus on even the sign of the change ( [[#Acosta%20Navarro--2014|Acosta Navarro et al., 2014]] ; [[#Hantson--2017|Hantson et al., 2017]] ). Future global isoprene and monoterpene emissions depend strongly on the climate and land-use scenarios considered ( [[#Hantson--2017|Hantson et al., 2017]] ; [[#Szogs--2017|Szogs et al., 2017]] ). BVOC emissions will be sensitive to future land-based climate change mitigation strategies including afforestation and bioenergy, with impacts of bioenergy depending on the choice of crops ( [[#Szogs--2017|Szogs et al., 2017]] ).

Most CMIP6 models use overly simplistic parametrizations and project an increase in global BVOC emissions in response to warming temperatures ( [[#Turnock--2020|Turnock et al., 2020]] ). This good agreement actually reflects the lack of diversity in BVOC-emissions parametrizations in global models that do not fully account for the complex processes influencing emissions that are discussed above.

Overall, we assess that historical global isoprene emissions declined between the pre-industrial period and the present day by 10–25% ( ''low confidence'' ) but historical changes in global monoterpenes and sesquiterpenes are too uncertain to provide an assessment. Future changes in BVOCs depend strongly on the evolution of climate and land use and are strongly sensitive to land-based climate change mitigation strategies. However, the net response of BVOC emissions is uncertain due to the complexity of processes that are hard to constrain observationally and are considered with various degrees of details in models.

<div id="6.2.2.4" class="h3-container"></div>

<span id="land-emissions-of-dust-particles"></span>
==== 6.2.2.4 Land Emissions of Dust Particles ====

<div id="h3-4-siblings" class="h3-siblings"></div>

The emission of dust particles into the atmosphere results from a natural process, namely saltation bombardment of the soil by large wind-blown particles, such as sand grains, and from disintegration of saltating particle clusters ( [[#Kok--2012|Kok et al., 2012]] ). The occurrence and intensity of dust emissions are controlled by soil properties, vegetation and near-surface wind, making dust emissions sensitive to climate change and LULCC ( [[#Jia--2019|Jia et al., 2019]] ). In addition, dust can be directly emitted through human activities, such as agriculture, off-road vehicles, building construction and mining, and indirectly emitted through hydrological changes due to human actions such as water diversion for irrigation (e.g., [[#Ginoux--2012|Ginoux et al., 2012]] ). Estimates of the anthropogenic fraction of global dust vary from less than 10% to over 60% suggesting that the human contribution to the global dust budget is quite uncertain ( [[#Ginoux--2012|Ginoux et al., 2012]] ; [[#Stanelle--2014|Stanelle et al., 2014]] ; [[#Xi--2016|Xi and Sokolik, 2016]] ). Reconstruction of global dust (deposition) from paleo records indicate factor of two to four changes between the different climate regimes in the glacial and interglacial periods ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.6|Section 2.2.6]] ).

An extremely limited number of studies have explored the evolution of global dust sources since pre-industrial times ( [[#Mahowald--2010|Mahowald et al., 2010]] ; [[#Stanelle--2014|Stanelle et al., 2014]] ). A modelling study estimated a 25% increase in global dust emissions between the late 19th century and the present, due to agricultural land expansion and climate change ( [[#Stanelle--2014|Stanelle et al., 2014]] ). CMIP5 models were unable to capture the observed variability of annual and longer time scales in North African dust emissions ( [[#Evan--2014|Evan et al., 2014]] ), however, more recent ESMs with process-based dust emissions schemes that account for changes in vegetation and climate in a more consistent manner, better match the observations ( [[#Kok--2014|Kok et al., 2014]] ; [[#Evans--2016|Evans et al., 2016]] ). Feedbacks between the global dust cycle and the climate system (Section 6.4.5) could account for a substantial fraction of the total aerosol feedbacks in the climate system with an order of magnitude enhancement on a regional scale ( [[#Kok--2018|Kok et al., 2018]] ). In summary, there is ''high confidence'' that atmospheric dust source and loading are sensitive to changes in climate and land use, however, there is ''low confidence'' in quantitative estimates of dust emission response to climate change.

<div id="6.2.2.5" class="h3-container"></div>

<span id="oceanic-emissions-of-marine-aerosols-and-precursors"></span>
==== 6.2.2.5 Oceanic Emissions of Marine Aerosols and Precursors ====

<div id="h3-5-siblings" class="h3-siblings"></div>

Oceans are a significant source of marine aerosols that influence climate directly by scattering and absorbing solar radiation or indirectly through the formation of cloud condensation nuclei (CCN) and ice nucleating particles (INPs). Marine aerosols consist of primary sea-spray particles and secondary aerosols produced by the oxidation of emitted precursors, such as dimethylsulphide (DMS) and numerous other BVOCs. Sea-spray particles, composed of sea salt and primary organic aerosols (POA), are produced by wind-induced wave breaking as well as the direct mechanical disruption of waves. The understanding of sea-spray emissions has increased substantially over the last five years, however, the knowledge of formation pathways and factors influencing their emissions continue to have large uncertainties ( [[#Forestieri--2018|Forestieri et al., 2018]] ; [[#Saliba--2019|Saliba et al., 2019]] ). The emission rate of sea-spray particles is predominantly controlled by wind speed. Since AR5, the influence of other factors, including sea surface temperature, wave history and salinity is increasingly evident (Callaghan et al. , 2014; Grythe et al. , 2014; Ovadnevaite et al. , 2014; Salter et al. , 2014; Barthel et al. , 2019) . Marine POA, often the dominant submicron component of sea spray, are emitted as a result of oceanic biological activity, however the biological processes by which these particles are produced remain poorly characterized contributing to large uncertainties in global marine POA emissions estimates (Tsigaridis et al. , 2014; Cravigan et al. , 2020; Hodzic et al. , 2020) '''.''' Furthermore, the particle size and chemical composition of sea-spray particles, and how these evolve in response to changing climate factors and dynamic oceanic biology, continue to have large uncertainties.

DMS, the largest natural source of sulphur in the atmosphere, is produced by marine phytoplankton and is transferred from ocean water to the atmosphere due to wind-induced mixing of surface water. DMS oxidizes to produce sulphate aerosols and contributes to the formation of CCN. Since AR5, the range in global DMS flux estimates reduced from 10–40 TgS yr <sup>–1</sup> to 9–34 TgS yr <sup>–1</sup> with a ''very likely'' range of 18–24 TgS yr <sup>–1</sup> based on sea-surface measurements and satellite observations ( [[#Lana--2011|Lana et al., 2011]] ). DMS production, and consequently emissions, have been shown to respond to multiple stressors, including climate warming, eutrophication, and ocean acidification. However, large uncertainties in process-based understanding of the mechanisms controlling DMS emissions, from physiological to ecological, limit our knowledge of past variations and our capacity to project future changes.

Overall, there is ''low confidence'' in the magnitude and changes in marine aerosol emissions in response to shifts in climate and marine ecosystem processes.

<div id="6.2.2.6" class="h3-container"></div>

<span id="open-biomass-burning-emissions"></span>
==== 6.2.2.6 Open Biomass Burning Emissions ====

<div id="h3-6-siblings" class="h3-siblings"></div>

Emissions from open biomass burning (including forest, grassland, peat fires and agricultural waste burning) represent about 30%, 10%, 15% and 40% of present-day global emissions of CO, NO <sub>x</sub> , BC and OC, respectively ( [[#van%20Marle--2017|van Marle et al., 2017]] ; [[#Hoesly--2018|Hoesly et al., 2018]] ). Wildfires also play an important role in several atmospheric chemistry–climate feedback mechanisms ( [[#Bowman--2009|Bowman et al., 2009]] ; [[#Fiore--2012|Fiore et al., 2012]] ) and fire events occurring near populated areas induce severe air pollution episodes ( [[#Marlier--2020|Marlier et al., 2020]] ; [[#Rooney--2020|Rooney et al., 2020]] ; [[#Yu--2020|Yu et al., 2020]] ).

For the last two decades, model-based emissions estimates are constrained by remote-sensing capacity to detect active fires and area burned. In AR5, biomass burning emissions were derived from a satellite product ( [[#Lamarque--2010|Lamarque et al., 2010]] ). Since then, improvements in detection of small fires has enhanced the agreement with higher-resolution and ground-based data on burned area in several regions ( [[#Randerson--2012|Randerson et al., 2012]] ; [[#Mangeon--2015|Mangeon et al., 2015]] ), especially for areas subjected to agricultural waste burning ( [[#Chuvieco--2016|Chuvieco et al., 2016]] , 2019). The updated emissions factors and the contribution of forest versus savanna fires lead to significantly higher global emissions of NO <sub>x</sub> and lower emissions of OC and CO in CMIP6, compared with CMIP5. A recent compilation and assessment of emissions factors ( [[#Andreae--2019|Andreae, 2019]] ) indicates that the emissions factors from [[#Akagi--2011|Akagi et al. (2011)]] , primarily used to produce the CMIP6 datasets, differ by ±50% for CO, OC, BC and NO <sub>x</sub> , depending on the biome, and would imply, for example, up to 10–30% higher OC and BC emissions from tropical forest fires.

The historical (pre-satellite era) dataset for CMIP6 considers advances in knowledge of past fire dynamics (new fire proxy datasets, such as charcoal in sediments and levoglucosan in ice cores) and visibility records from weather stations ( [[#Marlon--2016|Marlon et al., 2016]] ; [[#van%20Marle--2017|van Marle et al., 2017]] ). At a global level, CMIP5 and CMIP6 emissions trends are similar, however, there are substantial differences at the regional level, especially for the USA, South America (south of Amazonia) and Southern Hemisphere Africa ( [[#van%20Marle--2017|van Marle et al., 2017]] ).

Globally, the CMIP5 estimates ( [[#Lamarque--2010|Lamarque et al., 2010]] ), indicated a gradual decline of open biomass burning emissions from 1920 to about 1950 and then steady, and stronger than CMIP6, increase towards 2000. In contrast, CMIP6 biomass burning emissions ( [[#van%20Marle--2017|van Marle et al., 2017]] ) increase only slightly over 1750–2015 – they peak during the 1990s after which they decrease gradually, which is consistent with the assessment of fire trends in Chapter 5. Therefore, the CMIP6 evolution has a smaller difference between pre-industrial and present-day emissions than CMIP5, resulting in a lower radiative forcing of biomass burning SLCFs, possibly leading to a lower effect on climate ( [[#van%20Marle--2017|van Marle et al., 2017]] ).

Climate warming, especially through change in temperature and precipitation, will generally increase the risk of fire ( [[#Jia--2019|Jia et al., 2019]] , see also Chapter 12) and can also affect the fire injection and plume height ( [[#Veira--2016|Veira et al., 2016]] ), but occurrence of fires and their emissions in the future strongly depends on anthropogenic factors, such as population density, land use and fire management ( [[#Veira--2016|Veira et al., 2016]] ). Consequently, future emissions vary widely with increases and decreases amongst the SSP scenarios due to different land-use change scenarios.

In summary, there has been an improvement in the knowledge of biomass burning emissions by reducing key uncertainties highlighted in AR5. However, systematic assessment of remaining uncertainties is limited, with a lower limit of uncertainties due to emissions factors of 30%, and larger uncertainties due to burning-activity estimates, especially at regional level. Overall, a ''medium confidence'' in current global biomass burning SLCF emissions and their evolution over the satellite era is assessed. There is ''low'' to ''medium confidence'' in SLCF emissions from biomass burning from the pre-industrial period to the 1980s, which rely on the incorporation of several proxy data, with limited spatial representativeness. Nevertheless, uncertainties in the absolute value of pre-industrial emissions remain high, limiting confidence in radiative forcing estimates.

<div id="6.3" class="h1-container"></div>

<span id="evolution-of-atmospheric-slcf-abundances"></span>