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

==== 2.5.3.5 Feedbacks between Ecosystems and Climate ====

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The possibility of feedbacks and interactions between climate drivers and biological systems or ecological processes was identified as a significant emerging issue in AR5, and has since also been highlighted in the SRCCL and the SR1.5. It is virtually certain that land cover changes affect regional and global climate through changes to albedo, evapotranspiration and roughness ( ''very high confidence'' ) ( [[#Perugini--2017|Perugini et al., 2017]] ). There is growing evidence that biosphere-related climate processes are being affected by climate change in combination with disturbance and LULCC ( ''high confidence'' ) ( [[#Jia--2019|Jia et al., 2019]] ). It is virtually certain that land surface change caused by disturbances such as forest fires, hurricanes, phenological changes, insect outbreaks and deforestation affect carbon, water and energy exchanges, thereby influencing weather and climate ( ''very high confidence'' ) (Table 2.4; Figure 2.10) ( [[#Bright--2013|Bright et al., 2013]] ; [[#Brovkin--2013|Brovkin et al., 2013]] ; [[#Naudts--2016|Naudts et al., 2016]] ; [[#Prăvălie--2018|Prăvălie, 2018]] ).

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'''Figure 2.10 | Terrestrial ecosystem feedbacks, which affect the Earth’s climate system dynamics.''' Perturbations and implications for climate system dynamics (warming/cooling) are shown for the three global forest biomes (adapted from Figure 5 in ( [[#Prăvălie--2018|Prăvălie, 2018]] ). The strength of the mechanism is estimated in general terms, based on the magnitude of carbon storage and evaporative cooling processes that characterise each forest biome ( [[#Bonan--2008|Bonan, 2008]] ). Carbon storage includes forest biomass, without accounting for carbon dynamics in soil, peat and underlying permafrost deposits. Implications of bio-geochemical shifts were only estimated in relation to the intensification of the carbon cycle and increase in biomass at high latitudes, assuming nitrogen availability for the stoichiometric demands of forest vegetation.

Feedbacks can be positive or negative (i.e., amplify or dampen the original forcing), vary spatially and seasonally, and act over large geographic areas and long time periods (more than decades), making them difficult to observe and quantify directly ( [[#Schimel--2015|Schimel et al., 2015]] ; [[#Canadell--2021|Canadell et al., 2021]] ). Due to the positive impacts of CO 2 on vegetation growth and ecosystem carbon storage ( ''high confidenc'' e) (Sections 2.4.4.4, 2.5.5.4) ( [[#Canadell--2021|Canadell et al., 2021]] ), the associated climate feedback is negative (i.e., increased removal of atmospheric CO 2 and dampened warming, compared to an absence of the feedback). By contrast, projected global losses of carbon in warmer climates ( [[#Canadell--2021|Canadell et al., 2021]] ) imply a positive climate feedback. WGI ( [[#Canadell--2021|Canadell et al., 2021]] ) assesses an overall increase in land carbon uptake through the 21st century. However, the overall strength of the carbon cycle–climate feedback remains very uncertain. One of the underlying reasons may be complex interactions with ecosystem water balance and nitrogen and phosphorous availability, which are poorly constrained by observational evidence and incompletely captured in ESMs ( [[#2.5.2.10|Section 2.5.2.10]] ) ( [[#Huntzinger--2017|Huntzinger et al., 2017]] ; [[#Canadell--2021|Canadell et al., 2021]] ).

Land ecosystems contribute substantially to global emissions of nitrous oxide and methane . As with CO 2 , these emissions respond both directly and indirectly to atmospheric CO 2 concentration and climate change, and this gives rise to potential additional bio-geochemical feedbacks in the climate system. A large part of these emissions stem from land and water management, such as fertilizer application, rice production, aquaculture or animal husbandry ( [[#Jia--2019|Jia et al., 2019]] ). However, nearly 60% of total nitrous oxide emissions (in 2007–2016) has been estimated to stem from natural ecosystems, especially in the Tropics ( [[#Tian--2019|Tian et al., 2019]] ; [[#Canadell--2021|Canadell et al., 2021]] ), while freshwater wetlands and peatlands are estimated to contribute between 83% (top-down estimates) and 40% (bottom-up estimates) of total natural CH 4 (and 31 and 20% of total methane emissions, respectively) for the period 2008–2017 ( [[#Canadell--2021|Canadell et al., 2021]] ). Median CH 4 emissions from northern-latitude wetlands in 2100 were estimated to be 12.1 and 13.5 PgC in emission scenarios leading to 1.5°C and 2°C warming, respectively ( [[#Comyn-Platt--2018|Comyn-Platt et al., 2018]] ). Likewise, global warming has been attributed to soil N 2 O emission increases since the pre-industrial period of 0.8 (0.3–1.3) TgN yr -1 ( [[#Tian--2020|Tian et al., 2020]] ). Overall, climate feedbacks from future altered land ecosystem emissions of CH 4 or N 2 O are uncertain, but are expected to be small ( [[#Canadell--2021|Canadell et al., 2021]] ).

Changes in regional biodiversity are integral parts of ecosystem–climate feedback loops, including and beyond carbon cycle processes (Figure 2.10; Table 2.4). For instance, the impacts of climate-induced altered animal composition and trophic cascades on ecosystem carbon turnover (see Sections 2.4.4.4, 2.5.3.4) could be a substantive contribution to carbon–climate feedbacks ( ''low confidence'' ). Additional surface–atmosphere feedbacks that arise from changes in vegetation cover and subsequently altered albedo, evapotranspiration or roughness (often summarised as biophysical feedbacks) can be regionally relevant and could amplify or dampen vegetation cover changes ( [[#Jia--2019|Jia et al., 2019]] ).

Climate-induced shifts towards forests in what is currently tundra would be expected to reduce regional albedo especially in spring, but also during parts of winter when trees are snow-free (whereas tundra vegetation would be covered in snow), which amplifies warming regionally ( ''high confidence'' ) ( [[#Perugini--2017|Perugini et al., 2017]] ; [[#Jia--2019|Jia et al., 2019]] ). Trees would also enhance momentum absorption compared to low tundra vegetation, thus impacting surface–atmosphere mixing of latent and sensible heat fluxes ( [[#Jia--2019|Jia et al., 2019]] ). Boreal forests insulate and stabilize permafrost and reduce fluctuations of ground temperature: the amplitude of variation of ground surface temperatures was 28°C at a forested site, compared to 60°C in nearby grassland ( [[#2.5.2.7|Section 2.5.2.7]] ) ( [[#Bonan--1989|Bonan, 1989]] ; [[#Stuenzi--2021a|Stuenzi et al., 2021a]] ; [[#Stuenzi--2021b|Stuenzi et al., 2021b]] ). Likewise, a shift in moist tropical forests towards vegetation with drought-tolerant traits could possibly reduce evapotranspiration, increase albedo, alter heat transfer at the surface and lead to a negative feedback to precipitation ( [[#2.5.2.6|Section 2.5.2.6]] ) ( [[#Jia--2019|Jia et al., 2019]] ). In savannas, restoration of woody vegetation has been shown to enhance cloud formation and precipitation in response to enhanced transpiration and turbulent mixing, leading to a positive feedback on woody cover ( [[#Syktus--2016|Syktus and McAlpine, 2016]] ). While this has not yet been systematically explored, similar feedbacks might also emerge from a CO 2 -induced woody cover increase in savannas ( ''low confidence'' ) ( [[#2.5.2.5|Section 2.5.2.5]] ).

Since biophysical feedbacks can contribute to both surface temperature warming or cooling, analyses so far suggest that, on a global scale, the net impact on climate change is small ( [[#Perugini--2017|Perugini et al., 2017]] ; [[#Jia--2019|Jia et al., 2019]] ), unless these feedbacks also accelerate vegetation mortality and lead to substantive carbon losses ( [[#Zemp--2017a|Zemp et al., 2017a]] ; [[#Lemordant--2019|Lemordant and Gentine, 2019]] ). More than one-third of the Earth’s land surface has at least 50% of its evapotranspiration regulated by vegetation, and in some regions between 40 and &gt;80% of the land’s evaporated water is returned to land as precipitation. Locally, both directly human-mediated and climate change-mediated changes in vegetation cover can therefore notably affect annual average freshwater availability to human societies, especially if negative feedbacks amplify the reduction of vegetation cover, evapotranspiration and precipitation ( ''medium confidence'' ) ( [[#Keys--2016|Keys et al., 2016]] ; [[#Keys--2018|Keys and Wang-Erlandsson, 2018]] ).

Since AR5, freshwater ecosystems (lakes, reservoirs, rivers and ponds) have been increasingly recognised as important sources of GHG emissions (CO 2 , CH 4 and N 2 O) into the atmosphere. Key mechanisms which contribute to rising GHG emissions from freshwater ecosystems are the temperature imbalance between photosynthesis and respiration (respiration increases more than photosynthesis with rising temperature), CO 2 and CH 4 emissions from exposed sediments during droughts, increased transport of matter from land to water, changes in water retention time in rivers and lakes and the effects of temperature on lake stratification and anoxia that favour CH 4 emissions.

DelSontro et al. (2018) assembled the largest global data set to date on emission rates from lakes of CO 2 , CH 4 and N 2 O and found that they co-vary with lake size and trophic state. They estimated that moderate global increases in eutrophication of lakes could translate to 5–40% increases in the GHG effect in the atmosphere. Moreover, they estimated that GHG emissions from lakes and impoundments in past decades accounted for 1.25–2.30 PgCO 2 yr -1 ( [[#DelSontro--2018|DelSontro et al., 2018]] ), thus around 20% of global CO 2 emissions from the burning of fossil fuels (9.4 PgCO 2 yr -1 ) ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ).

Global warming will strongly enhance freshwater CH 4 emissions through a disproportionate increase in ebullition (gas flux) by 6–20% per 1°C increase in water temperature ( [[#Aben--2017|Aben et al., 2017]] ). It can be expected that ongoing eutrophication enhanced by climate change-related increases in the release of sediment nutrients and the loading of organic carbon and nutrients from catchments will enhance CH 4 ebullition on a global scale ( [[#Aben--2017|Aben et al., 2017]] ; [[#DelSontro--2018|DelSontro et al., 2018]] ; [[#Bartosiewicz--2019|Bartosiewicz et al., 2019]] ; [[#Beaulieu--2019|Beaulieu et al., 2019]] ; [[#Sanches--2019|Sanches et al., 2019]] ). The strongest increase in ebullition is expected in shallow waters where sediment temperatures are strongly related to atmospheric temperature ( [[#Aben--2017|Aben et al., 2017]] ). Given that small ponds and shallow lakes are the most abundant freshwater ecosystems globally, these may become hot spots of CH 4 ebullition in the future ( [[#Aben--2017|Aben et al., 2017]] ). On average, CH 4 , CO 2 and N 2 O account for 75, 23 and 2% of the total CO 2 -equivalent emissions, respectively, in lakes ( [[#DelSontro--2018|DelSontro et al., 2018]] ).

Furthermore, the exposure of lake and river sediments during droughts activates the decomposition of buried organic carbon. In dry river beds, mineralisation of buried organic matter is likely to increase with climate change as anoxic sediments are oxygenated downwards during drying, along with pulses of microbial activity following re-wetting of desiccated sediment. Conservative estimates indicate that adding emissions from exposed sediments of dry inland waters across diverse ecosystem types and climate zones to current global estimates of CO 2 emissions could result in a 6% (~0.12 PgC yr −1 ) increase of total inland water CO 2 emission rates covering streams and rivers (334 mmol m -2 day -1 ), lakes and reservoirs (320 mmol m -2 day -1 ) and small ponds (148 mmol m -2 day -1 ) ( [[#Marcé--2019|Marcé et al., 2019]] ; [[#Keller--2020|Keller et al., 2020]] ).

Overall, uncertainty as to the quantity of carbon fluxes within freshwater ecosystems and between terrestrial and freshwater systems, and subsequent emissions to the atmosphere remains very ''high'' ( [[#Raymond--2013|Raymond et al., 2013]] ; [[#Catalán--2016|Catalán et al., 2016]] ; [[#Stanley--2016|Stanley et al., 2016]] ; [[#Evans--2017|Evans et al., 2017]] ; [[#Drake--2018|Drake et al., 2018]] ; [[#Seekell--2018|Seekell et al., 2018]] ; [[#Sanches--2019|Sanches et al., 2019]] ; [[#Bodmer--2020|Bodmer et al., 2020]] ; [[#Keller--2020|Keller et al., 2020]] ; [[#Canadell--2021|Canadell et al., 2021]] ) (see Table SM2.1.). Projections of carbon fluxes are, for example, challenged by the complex interaction between rising water temperatures, loss of ice, changes in hydrology, ecosystem productivity, increased extreme events and variation in terrestrial-matter transport. While we are still short of empirical data, particularly in the Tropics ( [[#DelSontro--2018|DelSontro et al., 2018]] ), improvements in sensor technology ( [[#Eugster--2011|Eugster et al., 2011]] ; [[#Gonzalez-Valencia--2014|Gonzalez-Valencia et al., 2014]] ; [[#Maeck--2014|Maeck et al., 2014]] ; [[#Delwiche--2015|Delwiche et al., 2015]] ) and the use of statistically robust survey designs ( [[#Beaulieu--2016|Beaulieu et al., 2016]] ; [[#Wik--2016|Wik et al., 2016]] ) have improved the accuracy of measurements of GHG emissions in freshwater ecosystems. Global networks such as the Global Lakes Ecological Observatory Network (GLEON) increasingly allow a global view of carbon fluxes, thereby improving estimates of the contribution of freshwater ecosystems to global GHG emissions to the atmosphere.

In summary for freshwater systems, Drake et al. (2018) aggregated contemporary estimates of CO 2 and CH 4 emissions from freshwater ecosystems with global estimates made by [[#Raymond--2013|Raymond et al. (2013)]] , and arrived at an estimate of 3.9 PgC yr -1 . Rivers and streams accounted for 85% and lakes and reservoirs for 15% of the emissions ( [[#Raymond--2013|Raymond et al., 2013]] ). This trend will continue under scenarios of nutrient loading to inland waters over the next century where increased CH 4 emission of inland water has an atmospheric impact of 1.7–2.6 PgC/CO 2 -eq y −1 , which is equivalent to 18–33% of annual CO 2 emissions from burning fossil fuels ( ''medium evidence'' , ''medium agreement'' ) ( [[#Beaulieu--2019|Beaulieu et al., 2019]] ). For comparison, annual uptake of CO 2 in land ecosystems is estimated as 3.4 (± 0.9) PgC yr −1 ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). The freshwater numbers combine CO 2 and CH 4 and are thus not directly comparable. However, they are indicative of the importance of better accounting for freshwater systems in global carbon budgets.

'''Table 2.4 |''' Terrestrial and freshwater ecosystem feedbacks which affect the Earth’s climate system dynamics, according to ( [[#Prăvălie--2018|Prăvălie, 2018]] ).

{| class="wikitable"
|-
! '''Perturbation'''

! '''Implications for warming/feedback mechanism'''

'''The Earth’s climate system dynamics'''

|-
| ''Phenological changes''

(sections 2.4.2.4, 2.4.2.5)

| Increased primary productivity and plant growth with CO 2 fertilisation ( [[#Mao--2016|Mao et al., 2016]] ; [[#Wang--2018a|Wang et al., 2018a]] ); increasing growing season length ( [[#Peñuelas--2009|Peñuelas et al., 2009]] ; [[#Barichivich--2013|Barichivich et al., 2013]] ); reduced diurnal temperature range through evapotranspiration (mid latitudes) and albedo (high latitudes) caused by vegetation greening ( [[#Jeong--2011|Jeong et al., 2011]] ); increased CO 2 storage in biomass (cooling) ( [[#Keenan--2014|Keenan et al., 2014]] ); reduced albedo in snow-covered regions as canopies become taller and darker (warming); increased evapotranspiration, a key component of the global water cycle and energy balance which influences global rainfall, temperature and atmospheric motion ( [[#Zeng--2017|Zeng et al., 2017]] )

|-
| ''Insect outbreaks''

(sections 2.4.4.2

| Reduced carbon uptake and storage (warming); increased surface albedo (cooling) ( [[#Landry--2016|Landry et al., 2016]] ); increased CO 2 emissions (warming); decreased LAI and gross primary productivity ( [[#Ghimire--2015|Ghimire et al., 2015]] ), leading to reduced evapotranspiration and increased land surface temperature ( [[#Bright--2013|Bright et al., 2013]] )

|-
| ''Range shifts''

(sections 2.4.2.1, 2.4.2.2, 2.4.2.3, 2.4.2.5, 2.4.3)

| Reduced albedo in snow-covered regions as trees expand polewards (warming) ( [[#Chae--2015|Chae et al., 2015]] ); enhanced permafrost thawing; expansion of insect outbreak range, increasing forest impact ( [[#Pureswaran--2018|Pureswaran et al., 2018]] ); biome-dependent changes in albedo and evapotranspiration regimes ( [[#Naudts--2016|Naudts et al., 2016]] ); reduction in snow and ice albedo in freshwater due to loss of ice (warming) ( [[#Lang--2018|Lang et al., 2018]] )

|-
| ''Die-off and large-scale mortality events''

(sections 2.4.2.2, 2.4.4.3)

| Decreased GPP; decline in carbon storage (warming); increased CO 2 emissions; increased solar radiation, reduced soil moisture and higher surface runoff; albedo effects ( [[#Lewis--2011|Lewis et al., 2011]] ; [[#Prăvălie--2018|Prăvălie, 2018]] )

|-
| ''Deforestation''

(sections 2.4.3.6, 2.4.3.7)

| Reduced carbon storage (warming) ( [[#Pugh--2019a|Pugh et al., 2019a]] ); increase in (regional) surface air temperature due to reduced evaporation (less cooling); increased albedo in high-latitude systems (regional radiative cooling) ( [[#Loranty--2014|Loranty et al., 2014]] ); increased air temperature and diurnal temperature variation ( [[#Alkama--2016|Alkama and Cescatti, 2016]] ), locally and globally ( [[#Winckler--2019|Winckler et al., 2019]] ); reduced precipitation ( [[#Perugini--2017|Perugini et al., 2017]] ); decreased biogenic volatile organic compounds (BVOC) and aerosol emissions (warming through direct and indirect aerosol effects; cooling associated with reduction in atmospheric methane ( [[#Jia--2019|Jia et al., 2019]] )

|-
| ''Forest degradation''

(sections 2.4.3.6, 2.4.3.7)

| Reduced carbon storage (warming) ( [[#de%20Paula--2015|de Paula et al., 2015]] ; [[#Bustamante--2016|Bustamante et al., 2016]] ; [[#de%20Andrade--2017|de Andrade et al., 2017]] ; [[#Mitchard--2018|Mitchard, 2018]] )

|-
| ''Fragmentation''

| Carbon losses because biomass is less developed at forest edges ( [[#Pütz--2014|Pütz et al., 2014]] ; [[#Chaplin-Kramer--2015|Chaplin-Kramer et al., 2015]] ; [[#Haddad--2015|Haddad et al., 2015]] )

|-
| ''Air pollution''

| Decreased plant productivity, transpiration and carbon sequestration in forests with lower biomass due to ozone toxicity ( [[#Sitch--2007|Sitch et al., 2007]] ; [[#Ainsworth--2012|Ainsworth et al., 2012]] ); increased (regional) productivity due to increase in diffuse solar radiation caused by terrestrial aerosols ( [[#Xie--2021|Xie et al., 2021]] )

|-
| ''Declining populations of megafauna''

| Changes to physical and chemical properties of organic matter, soils and sediments influence carbon uptake and storage ( [[#Schmitz--2018|Schmitz et al., 2018]] ); increased or decreased carbon storage biomass and carbon storage, with differences across biomes determined by floristic structure and animal size ( [[#Bello--2015|Bello et al., 2015]] ; [[#Osuri--2016|Osuri et al., 2016]] ; [[#Peres--2016|Peres et al., 2016]] ; [[#He--2017|He et al., 2017]] ; [[#Berzaghi--2018|Berzaghi et al., 2018]] ; [[#Schmitz--2018|Schmitz et al., 2018]] ; [[#He--2019|He et al., 2019]] )

|-
| ''Fire''

(sections 2.4.4.2, 2.5.3.2)

| Increased carbon and aerosol emissions ( [[#van%20der%20Werf--2017|van der Werf et al., 2017]] ); surface warming ( [[#Liu--2019b|Liu et al., 2019b]] ); albedo effect dependent on ecosystem and species-level traits ( [[#Rogers--2015|Rogers et al., 2015]] ; [[#Chen--2018a|Chen et al., 2018a]] ) (initial albedo decreases post-fire; increased albedo where snow exposure is increased by canopy removal and species composition changes during recovery); black carbon deposition on snow and sea ice (short-term) ( [[#Randerson--2006|Randerson et al., 2006]] ); indirect increases in carbon emissions due to soil erosion ( [[#Caon--2014|Caon et al., 2014]] )

|-
| ''Changes in forest composition'' (sections 2.4.3.6, 2.4.3.7, 2.5.2.6, 2.5.2.7)

| Reduced carbon storage due to the decline of biomass (warming) ( [[#McIntyre--2015|McIntyre et al., 2015]] )

|-
| ''Woody encroachment in non-forested ecosystems''

(sections 2.4.3.3, 2.4.3.4, 2.4.3.5, 2.5.2.3, 2.5.2.4, 2.5.2.5, Box 2.1)

| Reduced production, increased water use, reduced albedo and altered land–atmosphere feedbacks; increased carbon storage in woody savannas ( [[#Zhou--2017|Zhou et al., 2017]] ; [[#Mureva--2018|Mureva et al., 2018]] ); uncertain feedbacks to the carbon cycle (some suggest an increase, others a decrease)

|-
| ''NPP shifts''

(section 2.4.4.5)

| Reduced albedo following high-latitude expansion of trees caused by photosynthetic enhancement of growth (cooling); increased photosynthesis and net ecosystem production (NEP) ( [[#Fernandez-Martinez--2019|Fernandez-Martinez et al., 2019]] ); increased NPP in nutrient-limited ecosystems due to increased nitrogen deposition from agriculture and combustion ( [[#Du--2018|Du and de Vries, 2018]] ; [[#Schulte-Uebbing--2018|Schulte-Uebbing and de Vries, 2018]] ); nutrient-limited lakes are likely to become less productive, while nutrient-rich lakes are likely to become more productive due to warming-induced prolongation of stable stratification ( [[#Adrian--2016|Adrian et al., 2016]] ; [[#Kraemer--2017|Kraemer et al., 2017]] )

|-
| ''Bio-geochemical shifts''

| Decline in carbon storage due to nitrogen limitation in nutrient-limited systems (warming) ( [[#Reich--2014|Reich et al., 2014]] ; [[#Wieder--2015|Wieder et al., 2015]] ); increased carbon storage on land ( [[#Peñuelas--2013|Peñuelas et al., 2013]] ) and in lakes ( [[#Heathcote--2015|Heathcote et al., 2015]] ; [[#Mendonça--2017|Mendonça et al., 2017]] ); increase in CO 2 and CH 4 emissions from freshwater ecosystems due to increased eutrophication ( [[#DelSontro--2018|DelSontro et al., 2018]] ), the imbalance between losses and gains of CO 2 by photosynthesis and respiration (the metabolic theory of ecology), enhanced emissions from exposed river and lake sediments during droughts and re-wetting ( [[#Marcé--2019|Marcé et al., 2019]] ; [[#Keller--2020|Keller et al., 2020]] ), enhanced CH 4 ebullition of seasonally hypoxic lakes ( [[#Aben--2017|Aben et al., 2017]] ; [[#DelSontro--2018|DelSontro et al., 2018]] ; [[#Bartosiewicz--2019|Bartosiewicz et al., 2019]] ; [[#Beaulieu--2019|Beaulieu et al., 2019]] ; [[#Sanches--2019|Sanches et al., 2019]] ) and increased transfer of organic carbon from land to water (particularly in permafrost areas) ( [[#Wauthy--2018|Wauthy et al., 2018]] )

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