Editing IPCC:AR6/WGI/Chapter-5 (section)

== 5.4 Biogeochemical Feedbacks on Climate Change ==

<div id="h1-5-siblings" class="h1-siblings"></div>

This section covers biogeochemical feedbacks on climate change, which represent one of the largest sources of uncertainty in climate change projections. The relevant processes are discussed (Sections 5.4.1 to 5.4.4), prior to discussing the simulation and projection of the carbon cycle in Earth system models ([[#5.4.5|Section 5.4.5]]), emergent constraints on future projections ([[#5.4.6|Section 5.4.6]]), non-CO <sub>2</sub> feedbacks ([[#5.4.7|Section 5.4.7]]), combined feedback assessment ([[#5.4.8|Section 5.4.8]]), possible biogeochemical abrupt changes ([[#5.4.9|Section 5.4.9]]), long-term carbon cycle projections ([[#5.4.10|Section 5.4.10]]), and near-term prediction of ocean and land carbon sinks (5.4.11).

<div id="5.4.1" class="h2-container"></div>

<span id="direct-co-2-effect-on-land-carbon-uptake"></span>
=== 5.4.1 Direct CO <sub>2</sub> Effect on Land Carbon Uptake ===

<div id="h2-20-siblings" class="h2-siblings"></div>

The AR5 (WGI, Box 6.3) and SRCCL ([[#IPCC--2019a|IPCC, 2019a]]) concluded with ''high confidence'' that rising atmospheric CO <sub>2</sub> increases leaf-level photosynthesis. This effect is represented in all ESMs. New studies since AR5 add evidence that the leaf-level CO <sub>2</sub> fertilization is modulated by acclimation of photosynthesis to long-term CO <sub>2</sub> exposure, growth temperature, seasonal drought, and nutrient availability, but these effects are not yet routinely represented in ESMs ([[#Smith--2013|Smith and Dukes, 2013]] ; [[#Baig--2015|Baig et al., 2015]] ; [[#Kelly--2016|Kelly et al., 2016]] ; [[#Drake--2017|Drake et al., 2017]] ; [[#Jiang--2020a|Jiang et al., 2020a]]). Cross-Chapter Box 5.1 assesses multiple lines of evidence, which suggest that the ratio of plant CO <sub>2</sub> uptake to water loss – plant water-use efficiency (WUE) – increases in near proportionality to atmospheric CO <sub>2</sub> . Despite advances in the regional coverage of field experiments, observations of the consequences of CO <sub>2</sub> fertilization at ecosystem level are still scarce, in particular from outside the temperate zone ([[#Song--2019|Song et al., 2019]]). New syntheses since AR5 corroborate that the effect of elevated CO <sub>2</sub> on plant growth and ecosystem carbon storage is generally positive (''high confidence''), but is modulated by temperature, water and nutrient availability ([[#Reich--2014|Reich et al., 2014]] ; [[#Obermeier--2017|Obermeier et al., 2017]] ; [[#Peñuelas--2017|Peñuelas et al., 2017]] ; [[#Hovenden--2019|Hovenden et al., 2019]] ; [[#Song--2019|Song et al., 2019]]). Plant carbon allocation, changes in plant community composition, disturbance, and natural plant mortality are important processes affecting the magnitude of the response, but are currently poorly represented in models (De Kauwe et al., 2014; [[#Friend--2014|Friend et al., 2014]] ; [[#Reich--2018|Reich et al., 2018]] ; A.P. [[#Walker--2019|]] [[#Walker--2019|Walker et al., 2019]] ; K. [[#Yu--2019|]] [[#Yu--2019|Yu et al., 2019]]), and thus contribute strongly to uncertainty in ESM projections ([[#Arora--2020|Arora et al., 2020]]).

Field studies with elevated CO <sub>2</sub> have demonstrated that the initial stimulation of above-ground growth may decline if insufficient nutrients such as nitrogen or phosphorus are available ([[#Finzi--2007|Finzi et al., 2007]] ; [[#Norby--2010|Norby et al., 2010]] ; [[#Hungate--2013|Hungate et al., 2013]] ; [[#Reich--2013|Reich and Hobbie, 2013]] ; [[#Talhelm--2014|Talhelm et al., 2014]] ; [[#Terrer--2018|Terrer et al., 2018]]). Model-data syntheses have demonstrated that capturing the observed long-term effect of elevated CO <sub>2</sub> depends on the ability of models to predict the effect of vegetation on soil biogeochemistry ([[#Zaehle--2014|Zaehle et al., 2014]] ; [[#Koven--2015b|Koven et al., 2015b]] ; [[#Medlyn--2015|Medlyn et al., 2015]] ; [[#Walker--2015|Walker et al., 2015]]). Meta-analyses of CO <sub>2</sub> manipulation experiments point to increased soil microbial activity and accelerated turnover of soil organic matter ([[#van%20Groenigen--2017|van Groenigen et al., 2017]]) as a result of increased below-ground carbon allocation by plants ([[#Song--2019|Song et al., 2019]]), and increased root exudation or mycorrhizal activity due to enhanced plant nutrient requirements under elevated CO <sub>2</sub> ([[#Drake--2011|Drake et al., 2011]] ; [[#Terrer--2016|Terrer et al., 2016]] ; [[#Meier--2017|Meier et al., 2017]]). These effects are not considered in most ESMs. One global model that attempts to represent these processes suggests that elevated CO <sub>2</sub> -related carbon accumulation is reduced in soils but increased in vegetation relative to more conventional models ([[#Sulman--2019|Sulman et al., 2019]]).

Our understanding of the effects of phosphorus limitation is less developed than for nitrogen, but a growing body of literature suggests that it is just as important, particularly in regions with highly weathered soils ([[#Wang--2018|Wang et al., 2018]] ; [[#Terrer--2019|Terrer et al., 2019]] ; [[#Du--2020|Du et al., 2020]]). CO <sub>2</sub> experiments collectively show that soil phosphorus is an important constraint on the CO <sub>2</sub> fertilization effect on plant biomass ([[#Terrer--2019|Terrer et al., 2019]] ; [[#Jiang--2020a|Jiang et al., 2020a]]). For example, despite increases in photosynthesis after four years of CO <sub>2</sub> exposure, a free-air CO <sub>2</sub> enrichment experiment in a phosphorus-limited mature forest ecosystem did not find an increase in biomass production ([[#Jiang--2020b|Jiang et al., 2020b]]). The lack of free-air CO <sub>2</sub> enrichment experiments in phosphorus-limited tropical forests limits our understanding of the role of phosphorus availability in constraining the CO <sub>2</sub> fertilization effect globally ([[#Norby--2016|Norby et al., 2016]] ; [[#Fleischer--2019|Fleischer et al., 2019]]). Models accounting for the effects of phosphorus availability, in addition to nitrogen, generally show an even stronger reduction of the response of ecosystem carbon storage to elevated CO <sub>2</sub> ([[#Goll--2012|Goll et al., 2012]] ; [[#Zhang--2014|Zhang et al., 2014]] ; X. [[#Yang--2019|Yang et al., 2019]]). Insufficient data and uncertainties in the process formulation cause large uncertainty in the magnitude of this effect ([[#Medlyn--2016|Medlyn et al., 2016]] ; [[#Fleischer--2019|Fleischer et al., 2019]]).

Consistent with AR5 (WGI, Section 6.4.2), the CO <sub>2</sub> fertilization effect <sub></sub> is the dominant cause for the projected increase in land carbon uptake between 1860 and 2100 in ESMs (Figures 5.26 and 5.27, and Table 5.5; [[#Arora--2020|Arora et al., 2020]]). In the CMIP6 ensemble, the increase of land carbon storage due to CO <sub>2</sub> fertilization is a global phenomenon but is strongest in the tropics (Figure 5.26). The resulting increase of productivity is a key driver of increases in vegetation and soil carbon storage. However, consistent with earlier findings ([[#Todd-Brown--2013|Todd-Brown et al., 2013]] ; [[#Friend--2014|Friend et al., 2014]] ; [[#Hajima--2014|Hajima et al., 2014]]), processes affecting vegetation carbon-use efficiency and turnover, such as allocation changes, mortality, and vegetation structural changes, as well as the pre-industrial soil carbon turnover time, also play an important role ([[#Arora--2020|Arora et al., 2020]]).

As a major advance since AR5 (WGI, Section 6.4.2), six out of 11 models in the C4MIP-CMIP6 ensemble account for nitrogen cycle dynamics over land (Table 5.4). On average, these models exhibit a 25–30% lower CO <sub>2</sub> fertilization effect on land carbon storage, compared to models that do not account for nitrogen cycle dynamics (Figure 5.29 and Table 5.5). The only model in the C <sup>4</sup> MIP-CMIP6 ensemble that explicitly represents the effect of P availability on plant growth suggests the lowest carbon storage response to increasing CO <sub>2</sub> ([[#Arora--2020|Arora et al., 2020]]). The lower CO <sub>2</sub> effect due to decreased nutrient availability is generally consistent with analyses of the implicit nutrient limitation in CMIP5 simulations ([[#Wieder--2015|Wieder et al., 2015]] ; [[#Zaehle--2015|Zaehle et al., 2015]]) and independent assessments by stand-alone land models ([[#Zaehle--2010|Zaehle et al., 2010]] ; [[#Wårlind--2014|Wårlind et al., 2014]] ; [[#Zhang--2014|Zhang et al., 2014]] ; [[#Goll--2017|Goll et al., 2017]] ; [[#Meyerholt--2020|Meyerholt et al., 2020]]). The simulated effects are generally consistent with expectations based on independent observations ([[#Walker--2021|Walker et al., 2021]]). However, the magnitude of nutrient feedbacks in these models is poorly constrained by observations, owing to the limited geographic distribution of available observations and the uncertain scaling of results obtained from manipulation experiments to transient system dynamics ([[#Song--2019|Song et al., 2019]] ; [[#Wieder--2019|Wieder et al., 2019]] ; [[#Meyerholt--2020|Meyerholt et al., 2020]]).

Our understanding of the various biological processes that affect the strength of the CO <sub>2</sub> fertilization effect on photosynthesis and its impact on carbon storage in vegetation and soils, (in particular regarding the limitations imposed by nitrogen and phosphorus availability), has developed since AR5 (WGI, Box 6.2). Based on consistent behaviour across all CMIP6 ESMs, there is ''high confidence'' that CO <sub>2</sub> fertilization of photosynthesis acts as an important negative feedback on anthropogenic climate change, by reducing the rate at which CO <sub>2</sub> accumulates in the atmosphere. Since AR5 (WGI, Box 6.2), an increasing number of CMIP6 ESMs account for nutrient cycles. The consistent results found in their model projections suggests with ''high confidence'' that limited nutrient availability will limit the CO <sub>2</sub> fertilization effect ([[#Arora--2020|Arora et al., 2020]]). The magnitude of the direct CO <sub>2</sub> effect on land carbon uptake, and its limitation by nutrients, remains uncertain.

<div id="5.4.2" class="h2-container"></div>

<span id="direct-co-2-effects-on-projected-ocean-carbon-uptake"></span>
=== 5.4.2 Direct CO <sub>2</sub> Effects on Projected Ocean Carbon Uptake ===

<div id="h2-21-siblings" class="h2-siblings"></div>

In AR5 (WGI, Section 6.4.2) there was ''high agreement'' that CMIP5 ESMs project continued ocean CO <sub>2</sub> uptake through to 2100, with higher uptake corresponding to higher concentration or emissions pathways. There has been no significant change in the magnitude of the sensitivity of ocean carbon uptake to increasing atmospheric CO <sub>2</sub> , or in the inter-model spread, between the CMIP5 and CMIP6 era ([[#Arora--2020|Arora et al., 2020]]). The analysis from emissions and concentration-driven CMIP5 model projections show that the ocean sink stops growing beyond 2050 across all emissions scenarios ([[#5.4.5.3|Section 5.4.5.3]]). CMIP6 models also show a similar time evolution of global ocean CO <sub>2</sub> uptake to CMIP5 models over the 21st century (Figure 5.25) with decreasing net ocean CO <sub>2</sub> uptake ratio to anthropogenic CO <sub>2</sub> emissions under SSP5-8.5.

The projected weakening of ocean carbon uptake is driven by a combination of decreasing carbonate buffering capacity and warming, which are positive feedbacks under weak to no mitigation scenarios (SSP4 and 5). In high mitigation scenarios (SSP1-2.6), weakening ocean carbon uptake is driven by decreasing emissions (Cross-Chapter Box 5.3). The detailed understanding of carbonate chemistry in seawater that has accumulated over more than half a century (e.g., [[#Revelle--1957|Revelle and Suess, 1957]] ; [[#Egleston--2010|Egleston et al., 2010]]), provides ''high confidence'' that the excess CO <sub>2</sub> dissolved in seawater leads to a non-linear reduction of the CO <sub>2</sub> buffering capacity, that is smaller dissolved inorganic carbon (DIC) increase with respect to ''p'' CO <sub>2</sub> increase along with the increase in cumulative ocean CO <sub>2</sub> uptake. Recent studies ([[#Katavouta--2018|Katavouta et al., 2018]] ; [[#Jiang--2019|Jiang et al., 2019]] ; [[#Arora--2020|Arora et al., 2020]] ; [[#Rodgers--2020|Rodgers et al., 2020]]) suggest with ''medium confidence'' that the decrease in the ocean CO <sub>2</sub> uptake ratio to anthropogenic CO <sub>2</sub> emissions, under low to no mitigation scenarios over the 21st century, is predominantly attributable to the ocean carbon-concentration feedback through the reduction of the seawater CO <sub>2</sub> buffering capacity, but with contributions from physical drivers such as warming and wind stress (''medium confidence'') and biological drivers (''low confidence'') (Sections 5.2.1.3.3 and 5.4.4).

Projected increases in ocean DIC due to anthropogenic CO <sub>2</sub> uptake amplify the sensitivity of carbonate system variables to perturbations of DIC in the surface ocean, for example via the amplitude of the seasonal cycle of ''p'' CO <sub>2</sub> , which impacts the mean annual air–sea fluxes ([[#Hauck--2015|Hauck et al., 2015]] ; [[#Fassbender--2018|Fassbender et al., 2018]] ; [[#Landschützer--2018|Landschützer et al., 2018]] ; SROCC, [[#5.2.2.3|Section 5.2.2.3]]). A larger amplification of the surface ocean ''p'' CO <sub>2</sub> seasonality occurs in the subtropics where ''p'' CO <sub>2</sub> seasonality is dominated by temperature seasonality, with the summer increase in the difference in ''p'' CO <sub>2</sub> <sup></sup> between surface water and the overlying atmosphere reaching 3μatm per decade between 1990 and 2030 under RCP8.5 ([[#Schlunegger--2019|Schlunegger et al., 2019]] ; [[#Rodgers--2020|Rodgers et al., 2020]]). In contrast, the impact of biological production on the seasonal cycle of ''p'' CO <sub>2</sub> in summer in the Southern Ocean strengthens the drawdown of CO <sub>2</sub> ([[#Hauck--2015|Hauck et al., 2015]]).

Overall, there is ''medium confidence'' on three outcomes in the ocean from projected CO <sub>2</sub> uptake under medium to high CO <sub>2</sub> concentration scenarios: (i) a weakening of the buffering capacity, which impacts the airborne fraction via the reduction of the ocean CO <sub>2</sub> buffering capacity due to cumulative ocean CO <sub>2</sub> uptake, which reduces the net ocean CO <sub>2</sub> uptake ratio to anthropogenic CO <sub>2</sub> emissions ([[#Katavouta--2018|Katavouta et al., 2018]] ; [[#Arora--2020|Arora et al., 2020]] ; [[#Rodgers--2020|Rodgers et al., 2020]]); (ii) an amplification of the seasonal cycle of CO <sub>2</sub> variables, which impacts both the ocean sink and ocean acidification ([[#Hauck--2015|Hauck et al., 2015]]); (iii) a decrease in the aragonite and calcite saturation levels in the ocean, which negatively impacts the calcification rates of marine organisms (''high confidence'') and forms a negative feedback on the uptake of CO <sub>2</sub> ([[#McNeil--2016|McNeil and Sasse, 2016]]) (Cross-Chapter Box 5.3).

<div id="5.4.3" class="h2-container"></div>

<span id="climate-effect-on-land-carbon-uptake"></span>
=== 5.4.3 Climate Effect on Land Carbon Uptake ===

<div id="h2-22-siblings" class="h2-siblings"></div>

The AR5 assessed with ''medium confidence'' that future climate change will decrease land carbon uptake relative to the case with constant climate, but with a poorly constrained magnitude (AR5 WGI, Chapter 6, Executive Summary). Ongoing uncertainty in the magnitude and geographic pattern of the feedbacks ([[#5.4.5|Section 5.4.5]]), continues to support a ''medium confidence'' assessment that future climate change will decrease land carbon uptake relative to the case with constant climate.

<div id="5.4.3.1" class="h3-container"></div>

<span id="plant-physiology"></span>
==== 5.4.3.1 Plant Physiology ====

<div id="h3-29-siblings" class="h3-siblings"></div>

Plant productivity is highly dependent on local climate. In cold environments, warming has generally led to an earlier onset of the growing season, and with it an increase in early season vegetation productivity (e.g., [[#Forkel--2016|Forkel et al., 2016]]). However, this trend is affected by the adverse effects of climate variability, and other emerging limitations on vegetation production by water, energy and nutrients, which may gradually reduce the effects of warming ([[#Piao--2017|Piao et al., 2017]] ; [[#Buermann--2018|Buermann et al., 2018]] ; [[#Liu--2019|Liu et al., 2019]]). At centennial time scales, boreal forest expansion may act as a climate-driven carbon sink ([[#Pugh--2018|Pugh et al., 2018]]).

In tropical and temperate environments, temperature simultaneously affects the metabolic rates of photosynthetic processes within leaf tissues, as well as the vapour pressure deficit that drives transpiration, its control by leaf stomata, and the resulting soil and plant tissue water content. Thus the direct effect of warming on photosynthesis can be positive, negative, or invariant depending on the environmental context ([[#Lin--2012|Lin et al., 2012]] ; [[#Yamori--2014|Yamori et al., 2014]] ; [[#Smith--2017|Smith and Dukes, 2017]] ; [[#Grossiord--2020|Grossiord et al., 2020]]). Observations and models suggest that the vapour pressure deficit effects are stronger than direct temperature effects on enzyme activities ([[#Smith--2020|Smith et al., 2020]]), and that acclimation of photosynthetic optimal temperature may mitigate productivity losses of tropical forests under climate change ([[#Kattge--2007|Kattge and Knorr, 2007]] ; [[#Tan--2017|Tan et al., 2017]] ; [[#Kumarathunge--2019|Kumarathunge et al., 2019]]). Some models have begun to include these acclimation responses in photosynthesis and autotrophic respiration ([[#Lombardozzi--2015|Lombardozzi et al., 2015]] ; [[#Smith--2015|Smith et al., 2015]] ; [[#Huntingford--2017|Huntingford et al., 2017]] ; [[#Mercado--2018|Mercado et al., 2018]]).

<div id="5.4.3.2" class="h3-container"></div>

<span id="fire-and-other-disturbances"></span>
==== 5.4.3.2 Fire and Other Disturbances ====

<div id="h3-30-siblings" class="h3-siblings"></div>

The SRCCL assessed that climate change is playing an increasing role in determining wildfire regimes alongside human activity (''medium confidence''), with future climate variability expected to enhance the recurrence and severity of wildfires in many biomes, such as tropical rainforests (''high confidence''). Projections of increased fire weather in a warmer climate are widespread ([[IPCC:Wg1:Chapter:Chapter-12#12.3.2.8|Section 12.3.2.8]]) and may drive increased fire frequency and severity in several regions, including Arctic and boreal ecosystems ([[#Gauthier--2015|Gauthier et al., 2015]] ; X.J. [[#Walker--2019|]] [[#Walker--2019|Walker et al., 2019]]), Mediterranean-type ecosystems ([[#Turco--2014|Turco et al., 2014]] ; [[#Jin--2015|Jin et al., 2015]]), degraded tropical forests ([[#Aragão--2018|Aragão et al., 2018]]), and tropical forest-savanna transition zones ([[#Lehmann--2014|Lehmann et al., 2014]]).

Wildfire is included in some CMIP6 ESMs (Table 5.4) and is thus only partially represented in estimates of carbon–climate feedbacks from these models. The CMIP5 ESMs that include fire project an 8–58% increase of fire carbon emissions under future scenarios, with higher emissions under higher warming scenarios; the ensemble spread is driven by differing factors such as population density, fire management, and other land-use processes ([[#Kloster--2017|Kloster and Lasslop, 2017]]). Fire dynamics in CMIP6 models, as evaluated in land-only configurations of CMIP6-generation land surface models, also show large variations but better agreement with observations ([[#Teckentrup--2019|Teckentrup et al., 2019]] ; [[#Hantson--2020|Hantson et al., 2020]] ; [[#Lasslop--2020|Lasslop et al., 2020]]).

Climate change also drives changes to vegetation composition and ecosystem carbon storage through other disturbances such as forest dieback that lead to biome shifts in tropical forests ([[#Cox--2004|Cox et al., 2004]] ; [[#Jones--2009|Jones et al., 2009]] ; [[#Brando--2014|Brando et al., 2014]] ; [[#Le%20Page--2017|Le Page et al., 2017]] ; [[#Zemp--2017|Zemp et al., 2017]]), and temperate and boreal regions ([[#Joos--2001|Joos et al., 2001]] ; [[#Lucht--2006|Lucht et al., 2006]] ; [[#Scheffer--2012|Scheffer et al., 2012]] ; [[#Lasslop--2016|Lasslop et al., 2016]]). The AR5 assessed that large-scale loss of tropical forests due to climate change is ''unlikely'' (WGI, Section 6.4.9). Newer ecosystem modelling approaches that include a greater degree of ecosystem heterogeneity and diversity show a reduced sensitivity of such forest dieback-type changes ([[#Levine--2016|Levine et al., 2016]] ; [[#Sakschewski--2016|Sakschewski et al., 2016]]), supporting the AR5 assessment ([[#5.4.9|Section 5.4.9]]). Beyond such biome shifts, observations of tropical forests also show that increasing tree mortality rates within tropical forests may reduce carbon turnover times and storage ([[#Brienen--2015|Brienen et al., 2015]]), that increased tree mortality rates in tropical forests and elsewhere are expected with increased temperatures and vapour pressure deficit (Cross-Chapter Box 5.1; [[#Allen--2015|Allen et al., 2015]] ; [[#McDowell--2018|McDowell et al., 2018]] ; [[#Grossiord--2020|Grossiord et al., 2020]]), and that these processes are not well represented in ESMs ([[#Powell--2013|Powell et al., 2013]] ; [[#Fisher--2018|Fisher et al., 2018]]). An ensemble of land models that includes ecological processes such as forest demography shows that changes to mortality may be a more important driver of carbon dynamics than changes to productivity ([[#Friend--2014|Friend et al., 2014]]).

Overall, climate change will force widespread increases in fire weather throughout the world ([[IPCC:Wg1:Chapter:Chapter-12#12.3.2.8|Section 12.3.2.8]]). Because of incomplete inclusion of fire in ESMs, a separate compilation of fire-driven carbon–climate feedback estimates is shown in Figure 5.29, based on results from [[#Eliseev--2014a|Eliseev et al. (2014a)]] and [[#Harrison--2018|Harrison et al. (2018)]] . There is ''low agreement'' in magnitude and ''medium agreement'' in sign which leads to an assessment of ''medium confidence'' that fire represents a positive carbon–climate feedback, but ''very low confidence'' in the magnitude of that feedback. Other disturbances such as tree mortality will increase across several ecosystems (''medium agreement'') with decreased vegetation carbon (''medium confidence''). However, the lack of model agreement and key process representation in ESMs leads to a ''low confidence'' assessment in the projected magnitude of this feedback.

<div id="5.4.3.3" class="h3-container"></div>

<span id="soil-carbon"></span>
==== 5.4.3.3 Soil Carbon ====

<div id="h3-31-siblings" class="h3-siblings"></div>

Changes to soil carbon stocks in response to climate change are a potentially strong positive feedback ([[#Cox--2000|Cox et al., 2000]]). Since AR5 (WGI, Section 6.4.2), progress has been made in understanding soil carbon dynamics, and associated feedbacks. Advances include: (i) an increased understanding of and ability to quantify high-latitude soil carbon feedbacks (Box 5.1); (ii) increased understanding of the causes responsible for soil carbon persistence on long time scales, particularly the interactions between decomposers and soil organic matter and mineral assemblages ([[#Kleber--2007|Kleber et al., 2007]] ; [[#Schmidt--2011|Schmidt et al., 2011]] ; [[#Luo--2016|Luo et al., 2016]]); and (iii) increased understanding of soil carbon dynamics in subsurface layers ([[#Hicks%20Pries--2017|Hicks Pries et al., 2017]] ; [[#Balesdent--2018|Balesdent et al., 2018]]).

CMIP6 ESMs predict losses of soil carbon with warming, which are larger than climate-driven vegetation carbon losses ([[#Arora--2020|Arora et al., 2020]]). As in CMIP5 ([[#Todd-Brown--2013|Todd-Brown et al., 2013]]), there is also a large CMIP6 ensemble spread in climate-driven soil carbon changes, partially driven by a large spread in the current soil carbon stocks predicted by the models. In CMIP5 ESMs, much of the soil carbon losses with warming can be traced to decreased carbon inputs, with a weaker contribution from changing soil carbon lifetimes due to faster decomposition rates ([[#Koven--2015b|Koven et al., 2015b]]), which may be an artefact of the lack of permafrost carbon (Box 5.1). Isotopic constraints suggest that CMIP5 ESMs systematically overestimated the transient sensitivity of soil <sup>14</sup> C responses to atmospheric <sup>14</sup> C changes, implying that the models respond too quickly to changes in either inputs or turnover times, and that therefore the soil contribution to all feedbacks may be weaker than currently projected ([[#He--2016|He et al., 2016]]). Using natural gradients of soil carbon turnover as a constraint on long-term responses to warming suggests that both CMIP5 and CMIP6 ESMs may systematically underestimate the temperature sensitivity at high latitudes, and may overestimate the temperature sensitivity in the tropics ([[#Koven--2017|Koven et al., 2017]] ; [[#Wieder--2018|Wieder et al., 2018]] ; [[#Varney--2020|Varney et al., 2020]]), although experimental soil warming in tropical forests suggest high sensitivity of decomposition to warming in those regions as well ([[#Nottingham--2020|Nottingham et al., 2020]]).

Peat soils, where thick organic layers build up due to saturated and anoxic conditions, represent another possible source of carbon to the atmosphere. Peats could dry, and decompose or burn as a result of climate change in both high ([[#Chaudhary--2020|Chaudhary et al., 2020]]) and tropical ([[#Cobb--2017|Cobb et al., 2017]]) latitudes, and in combination with anthropogenic drainage of peatlands ([[#Warren--2017|Warren et al., 2017]]). Peat carbon dynamics are not included in the majority of CMIP6 ESMs. Soil microbial dynamics shift in response to temperature, giving rise to complex longer-term trophic effects that are more complex than the short-term sensitivity of decomposition to temperature. Such responses are observed in response to long-term warming experiments ([[#Melillo--2017|Melillo et al., 2017]]). While most CMIP6 ESMs do not include microbial dynamics, simplified global soil models that do include such dynamics show greater uncertainty in projections of soil carbon changes, despite agreeing more closely with current observations, than the linear models used in most ESMs ([[#Wieder--2013|Wieder et al., 2013]] ; [[#Guenet--2018|Guenet et al., 2018]]).

In nutrient-limited ecosystems, prolonged soil warming can induce a fertilization effect through increased decomposition, which increases nutrient availability and thereby vegetation productivity ([[#Melillo--2011|Melillo et al., 2011]]). Models that include this process tend to show a weaker carbon–climate feedback than those that do not ([[#Thornton--2009|Thornton et al., 2009]] ; [[#Zaehle--2010|Zaehle et al., 2010]] ; [[#Wårlind--2014|Wårlind et al., 2014]] ; [[#Meyerholt--2020|Meyerholt et al., 2020]]). In CMIP6, six out of 11 ESMs include a representation of the nitrogen cycle, and the mean of those models predicts a weaker carbon–climate feedback than the overall ensemble mean ([[#Arora--2020|Arora et al., 2020]] ; [[#5.4.8|Section 5.4.8]]). These models only partly account for the interactions of nutrient effects with other processes, such as shifts of vegetation zones under climate changes ([[#Sakaguchi--2016|Sakaguchi et al., 2016]]) leading to either changes in species composition or changes in plant tissue nutrient to carbon ratios ([[#Thomas--2015|Thomas et al., 2015]] ; [[#Achat--2016|Achat et al., 2016]] ; [[#Du--2019|Du et al., 2019]]).

The ''high agreement'' and multiple lines of evidence that warming increases decomposition rates lead to ''high confidence'' that warming will, overall, result in carbon losses relative to a constant climate and contribute to the positive carbon–climate feedback ([[#5.4.8|Section 5.4.8]]). However, the wide spread in ESM projections and the lack of model representation of key processes that may amplify or mitigate soil carbon losses on longer time scales (including microbial dynamics, permafrost, peatlands, and nutrients) lead to ''low confidence'' in the magnitude of global soil carbon losses with warming.

<div id="box-5.1" class="h2-container box-container"></div>
<div class="container-box col-regular">