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

=== 2.5.2 Projected Changes at Level of Biomes and Whole Ecosystems ===

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==== 2.5.2.1 Global Overview, Assessment of Ecosystem-Level Models and Sources of Uncertainties ====

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Shifts in terrestrial biome and changes in ecosystem processes in response to climate change are most frequently projected with dynamic global vegetation models (DGVMs) or land-surface models that form part of ESMs, which use gridded climate variables, atmospheric CO 2 concentration and information on soil properties as input variables. Since AR5, most DGVMs have been upgraded to capture carbon–nitrogen cycle interactions (e.g., ( [[#Le%20Quéré--2018|Le Quéré et al., 2018]] ), many also include a representation of wildfire and fire–vegetation interactions ( [[#Rabin--2017|Rabin et al., 2017]] ) and a small number now also account for land management (e.g., wood removal from forests and crop fertilisation harvest of irrigation ( [[#Arneth--2017|Arneth et al., 2017]] ). Other forms of disturbance, such as tree mortality, in response to, for example, episodic weather extremes or insect pest outbreaks, are relatively poorly represented or not at all, although they demonstrably impact calculated carbon cycling ( [[#Pugh--2019a|Pugh et al., 2019a]] ). Simulated biome shifts are generally in agreement in projecting broad patterns on a global scale but vary greatly regarding the simulated trends in historical and future carbon uptake or losses, both regionally and globally ( [[#Chang--2017|Chang et al., 2017]] ; [[#Canadell--2021|Canadell et al., 2021]] ).

Similar to other models, models to project large-scale changes in vegetation and ecosystem processes have to deal with structural uncertainty (associated with the choice and the representation of processes in models), input-data uncertainty (associated with variability in initial conditions and parameter values) and error propagation (associated with coupling models) ( [[#Rounsevell--2019|Rounsevell et al., 2019]] ). The IPBES methodological assessment report on scenarios and models of biodiversity and ecosystem services provides a comprehensive overview over the relevant issues ( [[#Ferrier--2016|Ferrier et al., 2016]] ).

In order to assess performance, most models have been individually evaluated against a range of observations. Moreover, in the annual updates of the global carbon budgets, a model has to meet a small set of basic criteria to have its output included ( [[#Le%20Quéré--2018|Le Quéré et al., 2018]] ). More systematic benchmarking approaches have also been proposed that utilise a range of different datasets ( [[#Kelley--2013|Kelley et al., 2013]] ; [[#Chang--2017|Chang et al., 2017]] ) to assess multiple simulated processes. These methods, in principle, facilitate assigning quality scores to models based on their overall performance ( [[#Kelley--2013|Kelley et al., 2013]] ). So far, this scoring does not yet allow a clear quality ranking of models, since individual DGVMs tend to score well for some variables and badly for others. A recent comparison of global fire–vegetation model outputs was also able to clearly identify outliers when using a formalised benchmarking and scoring approach ( [[#Hantson--2020|Hantson et al., 2020]] ). However, benchmarking does not address sources of uncertainty and it would be advisable to perform ‘perturbed-physics’ experiments, in which multiple model parameters are varied in parallel more frequently as a means to test parameter-value uncertainty ( [[#Wramneby--2008|Wramneby et al., 2008]] ; [[#Booth--2012|Booth et al., 2012]] ; [[#Lienert--2018|Lienert and Joos, 2018]] ).

Species diversity impacts ecosystem functioning and hence ecosystem services ( [[#Hooper--2012|Hooper et al., 2012]] ; [[#Mokany--2016|Mokany et al., 2016]] ). So far, however, integrated modelling of ecosystem processes and biodiversity across multiple trophic levels and food webs is in its infancy ( [[#Harfoot--2014|Harfoot et al., 2014]] ). Whether or not the enhanced integration of state, function and functional diversity across multiple trophic levels in models will markedly alter projections of how ecosystems respond to climate change thus remains an open research question.

Beyond dynamic simulation of biome shifts and carbon cycling, which are important aspects of climate regulation, DGVMs can also provide information on a number of variables closely linked to other ecosystem services such as water availability, air quality or food provisioning ( [[#Krause--2017|Krause et al., 2017]] ; [[#Rabin--2020|Rabin et al., 2020]] ). However, they are not intended to provide a comprehensive assessment of ecosystem services. For these, other approaches applied but, to date, these are mostly applied on regional scales and are only weakly dynamic ( [[#Ferrier--2016|Ferrier et al., 2016]] ).

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==== 2.5.2.2 Projected Changes Globally at the Biome Level ====

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Climate change and the associated change in atmospheric CO 2 levels already exacerbate other human-caused impacts on the structure and composition of land and freshwater ecosystems, such as LULCC, nitrogen deposition and pollution. The relative importance of these drivers for ecosystems over the coming decades will likely differ between biomes, but climate change and atmospheric CO 2 will be pervasive unless there is a rapid lowering of fossil-fuel emissions and warming ( ''high confidence'' ) ( [[#Pereira--2010|Pereira et al., 2010]] ; [[#Warren--2011|Warren, 2011]] ; [[#Ostberg--2013|Ostberg et al., 2013]] ; [[#Davies-Barnard--2015|Davies-Barnard et al., 2015]] ; [[#Pecl--2017|Pecl et al., 2017]] ; [[#Ostberg--2018|Ostberg et al., 2018]] ). Global vegetation and ESMs agree on climate change-driven shifts of biome boundaries of potentially hundreds of kilometres over this century, combined with several substantial alterations that take place within biomes (e.g., changes in phenology, canopy structure and functional diversity, etc.). Large discrepancies exist between models and between scenarios regarding the region and the speed of change ( [[#Gonzalez--2010|Gonzalez et al., 2010]] ; [[#Pereira--2010|Pereira et al., 2010]] ; [[#Pecl--2017|Pecl et al., 2017]] ), but robust understanding is emerging in that the degree of impact increases in high-emission and high-warming scenarios ( ''high confidence'' ) (Figure 2.9).

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[[File:f307bbaf4831631b9ffa621bd753e09d IPCC_AR6_WGII_Figure_2_009.png]]

'''Figure 2.9 | Projected fraction of global terrestrial area that could experience a biome shift by 2100.''' Shifts due to climate change (filled symbols) or a combination of climate change and LUC (outline symbols), from publications in Supplementary Table SM2 '''.''' 3 (projected vulnerabilities and risks of ecosystems to biome shifts). Filled circles ( [[#Bergengren--2011|Bergengren et al., 2011]] ), filled squares ( [[#Alo--2008|Alo and Wang, 2008]] ), filled diamonds ( [[#Gonzalez--2010|Gonzalez et al., 2010]] ), filled triangle pointing up ( [[#Scholze--2006|Scholze et al., 2006]] ), filled triangle pointing down ( [[#Sitch--2008|Sitch et al., 2008]] ), filled triangle on its side ( [[#Li--2018|Li et al., 2018]] ), filled cross ( [[#Warszawski--2013|Warszawski et al., 2013]] ), outlined circle ( [[#Ostberg--2018|Ostberg et al., 2018]] )and outlined diamond ( [[#Eigenbrod--2015|Eigenbrod et al., 2015]] ).

Substantial changes in vegetation structure and ecosystem processes are already happening (see [[#2.4|Section 2.4]] ). Many of these observations have already been projected to take place as early as at least IPCC AR3 ( [[#Rosenzweig--2007|Rosenzweig et al., 2007]] ), and can they now be increasingly tested for their robustness with observational evidence. These multiple changes in response to warming (and changes in precipitation and increasing atmospheric CO 2 levels that go hand-in-hand with warming) are further expected for already relatively small additional temperature increases. In particular, in cold (boreal and tundra) regions, as well as in dry regions ( ''high confidence'' ), alterations of 2–47% of the areal extent of terrestrial ecosystems in scenarios of &lt;2°C warming above pre-industrial levels have been projected, increasing drastically with higher-warming scenarios ( [[#Warren--2011|Warren, 2011]] ; [[#Wårlind--2014|Wårlind et al., 2014]] ). More recent work, applying also probabilistic methods, confirm the risk of drastic changes in vegetation cover (e.g., forest to non-forest or vice versa) at the end of the 21st century even for approximately 2°C warming scenarios, especially in tundra, and also in tropical forest and savannah regions, with more subtle changes (within a given biome type) likely to occur in all regions ( [[#Ostberg--2013|Ostberg et al., 2013]] ; [[#Ostberg--2018|Ostberg et al., 2018]] ). Model studies have found 5–20% of terrestrial ecosystems affected by warming of around 2°C–3°C, increasing to above one-third at a warming of 4°C–5°C ( [[#Ostberg--2013|Ostberg et al., 2013]] ; [[#Warszawski--2013|Warszawski et al., 2013]] ).

In general, vegetation types are projected to be moving into their ‘neighbouring’ climates, depending on whether temperature or precipitation is expected to be the predominant factor and how vegetation interacts with the increasing CO 2 levels in the atmosphere ( [[#Wårlind--2014|Wårlind et al., 2014]] ; [[#Scheiter--2015|Scheiter et al., 2015]] ; [[#Schimel--2015|Schimel et al., 2015]] ; [[#Huntzinger--2017|Huntzinger et al., 2017]] ). For instance, boreal or temperate forest vegetation is simulated to migrate polewards, closed tropical (moist) forest is expected to transition towards dry tropical forest types, while climate-driven degradation might expand arid vegetation cover (Sections 2.5.2.2–2.5.2.9). However, ‘novel ecosystems’, that is, communities with no current or historical equivalent because of the novel combinations of abiotic conditions under climate change, are expected to be increasingly common in the future ( ''medium confidence'' ), although the regions where these novel ecosystems might emerge are still disputed ( [[#Reu--2014|Reu et al., 2014]] ; [[#Radeloff--2015|Radeloff et al., 2015]] ; [[#Ordonez--2016|Ordonez et al., 2016]] ). The possibility of these novel ecosystems and the communities that live within them are a challenge for current modelling of ecosystem shifts, and new approaches to conservation will be required that are designed to adapt to rapid changes in species composition and the ensuing challenges.

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==== 2.5.2.3 Risk to Arid Regions ====

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Shifts in arid system structure and functioning that have been observed to date ( [[#2.4.3.3|Section 2.4.3.3]] ) are projected to continue ''(medium confidence)'' . These include widespread woody plant encroachment, notably in savanna systems in Africa, Australia and South America, and are attributed to interactions of LULCC, climate change and CO 2 fertilisation effects ( [[#Fensholt--2012|Fensholt et al., 2012]] ; [[#Fang--2017|Fang et al., 2017]] ; [[#Stevens--2017|Stevens et al., 2017]] ). Arid Mongolian steppe grassland did not respond to experimentally elevated CO 2 ( [[#Song--2019|Song et al., 2019]] ). Woody encroachment is projected to continue or not reverse in North American drylands ( [[#Caracciolo--2016|Caracciolo et al., 2016]] ) and southern African arid ecosystems ( [[#Moncrieff--2014b|Moncrieff et al., 2014b]] ). Dryland woody encroachment may increase carbon stocks, depending on emissions scenario ( [[#Martens--2021|Martens et al., 2021]] ), but reduce soil water and biodiversity of grassland-dependent species diversity ( [[#Archer--2017|Archer et al., 2017]] ). Warm season (C4) grass expansion into arid shrublands risks sudden ecosystem transformation due to introduced wildfire ( [[#Bradley--2016|Bradley et al., 2016]] ), a risk anticipated for grass-invaded desert ecosystems of Australia and the southwestern USA (Horn and St. Clair, 2017). Novel fire regimes in grassy shrublands have enhanced grass cover locally in the southern African Nama-Karoo ( [[#du%20Toit--2015|du Toit et al., 2015]] ).

Range retractions are projected for endemic plants in southern Africa ( [[#Young--2016|Young et al., 2016]] ) and dry woodlands in Morocco ( [[#Alba-Sánchez--2015|Alba-Sánchez et al., 2015]] ). Increasing thermal stress is projected to increase woody plant mortality in the Sonoran Desert ecosystems ( [[#Munson--2016|Munson et al., 2016]] ) and facilitate perennial grass replacement by xeric shrubs in the southwestern USA ( [[#Bestelmeyer--2018|Bestelmeyer et al., 2018]] ). Ecological effects may occur rapidly when extreme events compound long-term trends ( [[#Hoover--2015|Hoover et al., 2015]] ), but evolve more slowly as opportunity costs accumulate due to warming (Cross-Chapter Paper 3) ( [[#Cunningham--2021|Cunningham et al., 2021]] ).

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==== 2.5.2.4 Risk to Mediterranean-Type Ecosystems (MTEs) ====

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The regions containing MTEs all show ''high confidence'' in projected increases in the intensity and frequency of hot extremes and decreases in the intensity and frequency of cold extremes, and ''medium confidence'' in increasing ecological drought due to increased evapotranspiration (in all regions) and reduced rainfall (excluding California, USA, where model agreement is low) (see WGI Chapter 11). Projections also show a ''robust'' increase in the intensity and frequency of heavy precipitation in the event of ≥2°C warming for MTEs in South Africa, the Mediterranean Basin and California, USA, but are less clear for Australia and Chile ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ).

MTEs are characterised by the distinctive seasonal timing of precipitation and temperature, and the disruption of this regime is likely to be critical for their maintenance. Unfortunately, projections of changes in rainfall seasonality have received less attention and are far more uncertain than many other aspects of climate change ( [[#Pascale--2016|Pascale et al., 2016]] ; [[#Breinl--2020|Breinl et al., 2020]] ), thus limiting our ability to predict the ecological consequences of climate change in MTEs. Responses to experimental manipulation of rainfall seasonality show the potential for shifts in plant functional composition and diversity loss, but results vary with soil type ( [[#van%20Blerk--2021|van Blerk et al., 2021]] ).

Unfortunately, global- and regional-scale dynamic vegetation models show a poor performance for large areas of MTEs, because they do not characterise shrub and Crassulacean acid metabolism (CAM)-photosynthetic plant functional types well ( [[#Moncrieff--2015|Moncrieff et al., 2015]] ). Furthermore, the grain of these models is too coarse for quantifying impacts to many vegetation formations which are patchy or of limited extent (e.g., small stands of trees). There is ''high confidence'' that observations of high mortality in trees and other growth forms, reduced reproductive and recruitment success, range shifts, community shifts towards more thermophilic species and type conversions are set to continue, due to either direct climate impacts through drought and other extreme weather events or to their interaction with factors like fire and pathogens (Sections 2.4.3.5, 2.4.3.6; 2.4.3.7; 2.4.4.2; 2.4.4.3; 2.5.2.5, 2.5.2.6, 2.5.2.7, 2.5.4).

Fire is a key driver across most MTEs due to summer-dry conditions. Climate projections for the MTEs translate into high confidence that periods of low fuel moisture will become more severe and prolonged, and that episodes of extreme fire weather will become more frequent and severe (see ( [[#Douville--2021|Douville et al., 2021]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] )). This will lead to the birth of novel fire regimes in MTEs, characterised by an increase in the probability of greater burned area and extreme wildfire events (e.g., megafires), with associated loss of human life and property, long-term impacts on ecosystems and acceleration of the possible loss of resilience and capacity to recover ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ; [[#González--2018|González et al., 2018]] ; [[#Boer--2020|Boer et al., 2020]] ; [[#Moreira--2020|Moreira et al., 2020]] ; [[#Nolan--2020|Nolan et al., 2020]] ; [[#Duane--2021|Duane et al., 2021]] ; [[#Gallagher--2021|Gallagher et al., 2021]] ).

Fire is virtually certain to have additional impacts through compound events (see Section 11.8 in ( [[#Seneviratne--2021|Seneviratne et al., 2021]] )). Extreme post-fire weather is extremely likely to continue to impact diversity ( [[#Slingsby--2017|Slingsby et al., 2017]] ), retard vegetation regrowth ( [[#Slingsby--2020a|Slingsby et al., 2020a]] ) and accelerate vegetation shifts ( [[#Batllori--2019|Batllori et al., 2019]] ). Any increases in the intensity and frequency of heavy precipitation are highly likely to compromise soil stability in recently burnt areas ( [[#Morán-Ordóñez--2020|Morán-Ordóñez et al., 2020]] ). The impacts of fire often depend on interactions with non-climatic factors such as habitat fragmentation ( [[#Slingsby--2020b|Slingsby et al., 2020b]] ) and management ( [[#Steel--2015|Steel et al., 2015]] ) or the spread of flammable exotic plantation forestry and invasive species ( [[#Kraaij--2018|Kraaij et al., 2018]] ; [[#McWethy--2018|McWethy et al., 2018]] ). Managing these factors provides opportunities for adaptation and mitigation ( [[#Moreira--2020|Moreira et al., 2020]] ). (See sections 2.4.4.2 and 2.5.3.2).

Human adaptation and mitigation responses to climate change may create additional threats to MTEs. MTEs have dry summers by definition, posing a challenge for the year-round supply of water to growing human populations and agriculture. With recent major droughts in all MTEs ( [[#2.4.3.6|Section 2.4.3.6]] ), there is increasing reliance on groundwater for the bulk of the water supply ( [[#Kaiser--2018|Kaiser and Macleod, 2018]] ). The majority of groundwater systems have exceeded or are rapidly approaching their environmental flow limits ( [[#de%20Graaf--2019|de Graaf et al., 2019]] ), threatening human populations and ecosystems that depend on these systems for their persistence through unfavourable climatic conditions ( [[#McLaughlin--2017|McLaughlin et al., 2017]] ). Similarly, much of the MTEs are open shrublands and grasslands and proposed extensive tree-planting to sequester atmospheric CO 2 could result in a loss of biodiversity and threaten water security ( [[#Doblas-Miranda--2017|Doblas-Miranda et al., 2017]] ; [[#Bond--2019|Bond et al., 2019]] ).

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==== 2.5.2.5 Risk to Grasslands and Savannas ====

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Worldwide, woody cover is increasing in savannas ( [[#Buitenwerf--2012|Buitenwerf et al., 2012]] ; [[#Donohue--2013|Donohue et al., 2013]] ; [[#Stevens--2017|Stevens et al., 2017]] ), as a result of interactions of elevated CO 2 and altered fire and herbivory impacts, some of which stems from LULCC ''(high confidence)'' (see [[#2.4.3.5|Section 2.4.3.5]] ; Cross-Chapter Paper 3.2) ( [[#Venter--2018|Venter et al., 2018]] ; [[#Wu--2021|Wu et al., 2021]] ). In some regions, altered climate may also contribute (Cross-Chapter Paper 3.2). Elevated CO 2 benefits plants with C3 photosynthesis (often woody plants), more than C4 species ( [[#Moncrieff--2014a|Moncrieff et al., 2014a]] ; [[#Scheiter--2015|Scheiter et al., 2015]] ; [[#Knorr--2016a|Knorr et al., 2016a]] ). Increases in woody vegetation in grassy ecosystems could provide some carbon increase ( ''medium confidence'' ) ( [[#Zhou--2017|Zhou et al., 2017]] ; [[#Mureva--2018|Mureva et al., 2018]] ), but is expected to decrease biodiversity ( [[#Smit--2015|Smit and Prins, 2015]] ; [[#Abreu--2017|Abreu et al., 2017]] ; [[#Andersen--2019|Andersen and Steidl, 2019]] ) and water availability ( [[#Honda--2016|Honda and Durigan, 2016]] ; [[#Stafford--2017|Stafford et al., 2017]] ) and alter ecosystem services like grazing and wood provision ( ''high confidence'' ) ( [[#Anadón--2014b|Anadón et al., 2014b]] ).

The relative importance of climate, disturbance (e.g., fire/herbivory) and plant feedbacks in shaping present and future savanna distribution varies between continents ( [[#Lehmann--2014|Lehmann et al., 2014]] ), which makes projections of changing the biome extent challenging ( [[#Moncrieff--2016|Moncrieff et al., 2016]] ). It has been shown that simulation studies that do not account for CO 2 interactions but only consider climate change impacts do not realistically capture the future distribution of savannas ( ''high confidence'' ) ( [[#Higgins--2012|Higgins and Scheiter, 2012]] ; [[#Moncrieff--2016|Moncrieff et al., 2016]] ; [[#Scheiter--2020|Scheiter et al., 2020]] ). Due to the continued strong effect of CO 2 on tree and shrub-to-grass ratios in future, models suggest a loss of savanna extent and conversion into closed canopy forest/thicket and an expansion of savanna-type vegetation into arid grasslands ( [[#Wårlind--2014|Wårlind et al., 2014]] ; [[#Moncrieff--2016|Moncrieff et al., 2016]] ). In arid savannas and their interface to grasslands, survival of woody vegetation (which may be stimulated to grow by increasing CO 2 ) will depend on their capacity to survive potentially more severe and frequent droughts ( [[#Sankaran--2019|Sankaran and Staver, 2019]] ). Across a range of models, for RCP4.5 future climate change and CO 2 concentrations, savanna expanse declines by around 50% (converting to closed canopy systems) by 2070 in Africa and South America, 25% in Asia and with small changes in Australia ( [[#Moncrieff--2016|Moncrieff et al., 2016]] ; [[#Kumar--2021|Kumar et al., 2021]] ). Future fire-spread is expected to be reduced with increased woody dominance ( [[#Scheiter--2015|Scheiter et al., 2015]] ; [[#Knorr--2016b|Knorr et al., 2016b]] ; [[#Scheiter--2020|Scheiter et al., 2020]] ), feeding back to further increase tree-to-grass ratios ( ''high confidence'' ).

Like the tropical forest biome, savannas are at a high risk, given the projected climate changes in combination with LULCC (see Cross-Chapter Paper 3). About 50% of the Brazilian Cerrado has been converted to agricultural land and pastures ( [[#Lehman--2016|Lehman and Parr, 2016]] ), and African savannas have been proposed to follow a similar tropical agricultural revolution pathway to enhance agronomic prosperity ( [[#Ryan--2016|Ryan et al., 2016]] ). In fact, indirect climate change impacts arising from mitigation efforts may be particularly perilous to savannas; extensive tree-planting to restore ecosystems and remove CO 2 from the atmosphere, as pledged, for example, under the African Forest Restoration Initiative, could lead to carbon losses and the loss of biodiversity as well as damage the water balance if trees are planted on what was naturally grassland or savanna (Box 2.2; FAQ 2.6) ( [[#Bond--2019|Bond et al., 2019]] ).

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==== 2.5.2.6 Risk to Tropical Forests ====

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Key factors affecting the future distribution of tropical humid and dry forests are amounts and seasonalities of precipitation, increased temperatures, prolonged droughts and droughted-moderated fires ( ''robust evidence'' , ''high agreement'' ) ( [[#Bonai--2016|Bonai et al., 2016]] ; [[#Corlett--2016|Corlett, 2016]] ; [[#Lyra--2017|Lyra et al., 2017]] ; [[#Anderson--2018|Anderson et al., 2018]] ; [[#da%20Silva--2018|da]] [[#Silva--2018|Silva et al., 2018]] ; [[#Fontes--2018|Fontes et al., 2018]] ; [[#O’Connell--2018|O’Connell et al., 2018]] ; [[#Aguirre-Gutiérrez--2019|Aguirre-Gutiérrez et al., 2019]] ; [[#Bartlett--2019|Bartlett et al., 2019]] ; [[#Brando--2019|Brando et al., 2019]] ; [[#Stan--2019|Stan and Sanchez-Azofeifa, 2019]] ). The probability of severe drought is projected to quadruple in natural areas in Brazil with &gt;2°C warming ( [[#Barbosa--2016|Barbosa and Lakshmi Kumar, 2016]] ; [[#Marengo--2020|Marengo et al., 2020]] ). Most multi-model studies assuming rapid economic growth/business-as-usual scenarios (A2, A1B and RCP8.5) show an increase in future woody biomass and areas of woody cover towards the end of the 21st century in temperate regions ( [[#Boit--2016|Boit et al., 2016]] ; [[#Nabuurs--2017|Nabuurs et al., 2017]] ) and tropical forests in East Africa ( [[#Ross--2021|Ross et al., 2021]] ) but a decrease in the remaining tropical regions ( [[#Anadón--2014a|Anadón et al., 2014a]] ; [[#Boit--2016|Boit et al., 2016]] ; [[#Lyra--2017|Lyra et al., 2017]] ; [[#Nabuurs--2017|Nabuurs et al., 2017]] ; [[#Maia--2020|Maia et al., 2020]] ). Terrestrial species are predicted to shift to cooler temperatures and higher elevations ( [[#Pecl--2017|Pecl et al., 2017]] ). Tropical species are more susceptible to climate warming than temperate species ( [[#Rehm--2016|Rehm and Feeley, 2016]] ; [[#Sentinella--2020|Sentinella et al., 2020]] ). This susceptibility will be exacerbated by road-building increasing the ease of access into forests ( [[#Brinck--2017|Brinck et al., 2017]] ; [[#Taubert--2018|Taubert et al., 2018]] ; [[#Bovendorp--2019|Bovendorp et al., 2019]] ; [[#Senior--2019|Senior et al., 2019]] ). Furthermore, most tropical cloud forest species are unable to invade grasslands and this will increase the risk of extinctions in tropical cloud forests ( [[#Rehm--2015|Rehm and Feeley, 2015]] ).

SLR as the result of climate change is likely to influence mangroves in all regions, with greater impact on North and Central America, Asia, Australia and East Africa than on West Africa and South America ( ''robust evidence'' , ''high agreement'' ) ( [[#Alongi--2015|Alongi, 2015]] ; [[#Ward--2016|Ward et al., 2016]] ). On a small scale, mangroves are potentially moving landward ( [[#Di%20Nitto--2014|Di Nitto et al., 2014]] ), while on a large scale they will continue to expand poleward ( [[#Alongi--2015|Alongi, 2015]] ).

Most simulations predict a significant geographical shift of transition areas between tropical forests and savanna in the tropical and subtropical Americas and Himalayas ( [[#Anadón--2014a|Anadón et al., 2014a]] ; [[#Rashid--2015|Rashid et al., 2015]] ). Forest dieback, as postulated for the Amazon region, does not occur in the majority of simulations ( [[#Malhi--2009|Malhi et al., 2009]] ; [[#Poulter--2010|Poulter et al., 2010]] ; [[#Rammig--2010|Rammig et al., 2010]] ; [[#Higgins--2012|Higgins and Scheiter, 2012]] ; [[#Huntingford--2013|Huntingford et al., 2013]] ; [[#Davies-Barnard--2015|Davies-Barnard et al., 2015]] ; [[#Sakschewski--2016|Sakschewski et al., 2016]] ; [[#Wu--2016a|Wu et al., 2016a]] ). Model projections of future biodiversity in tropical forests are rare. Arguably, species are most vulnerable to climate change effects at higher altitudes or at the dry end of tropical forest occurrence ( ''medium evidence'' , ''medium agreement'' ) ( [[#Krupnick--2013|Krupnick, 2013]] ; [[#Nobre--2016|Nobre et al., 2016]] ; [[#Trisurat--2018|Trisurat, 2018]] ). Tropical lowlands are expected to lose plant species as temperatures rise above species’ heat tolerance, but could also generate novel communities of heat-tolerant species ( ''robust evidence'' , ''high agreement'' ) ( [[#Colwell--2008|Colwell et al., 2008]] ; [[#Trisurat--2009|Trisurat et al., 2009]] ; [[#Trisurat--2011|Trisurat et al., 2011]] ; [[#Krupnick--2013|Krupnick, 2013]] ; [[#Zomer--2014a|Zomer et al., 2014a]] ; [[#Zomer--2014b|Zomer et al., 2014b]] ; [[#Sullivan--2020|Sullivan et al., 2020]] ; [[#Pomoim--2021|Pomoim et al., 2021]] ).

Statistical models that correlate data on species abundance with information on human pressures, such as LUCs ( [[#Srichaichana--2019|Srichaichana et al., 2019]] ), population density ( [[#Leclère--2020|Leclère et al., 2020]] ) and hunting ( [[#Mockrin--2011|Mockrin et al., 2011]] ), found in tropical and subtropical forests that birds, invertebrates, mammals and reptiles show a decline in their probability of presence with declining forest cover, particularly pronounced in forest specialists or species with narrow ranges ( [[#Newbold--2014|Newbold et al., 2014]] ). Different soil fauna groups showed different responses in abundance and diversity to climate change conditions ( [[#Coyle--2017|Coyle et al., 2017]] ; [[#Facey--2017|Facey et al., 2017]] ) but these responses can impact decomposition rates and biogeochemical cycles ( ''medium evidence'' , ''low agreement'' ).

Invasive plant species are predicted to expand upward by 500–1,500 m in the western Himalayas ( [[#Thapa--2018|Thapa et al., 2018]] ), and by 6–35% yr -1 from the current extent in South America ( ''robust evidence'' , ''high agreement'' ) ( [[#Bhattarai--2014|Bhattarai and Cronin, 2014]] ). Global assessment ( [[#Wang--2017|Wang et al., 2017]] ) also revealed that ecoregions of high-elevation tropical forests and subtropical coniferous forests have a high risk of invasive plant expansion in low-CO 2- emission scenarios, with negative impacts on ecosystem functioning and local livelihoods ( [[#Shrestha--2019|Shrestha et al., 2019]] ).

The impact of unsustainable land use on tropical forests continues in all regions (see Cross-Chapter Paper 7). Projected climate changes will not only impact biodiversity but also the livelihoods of affected people ( ''robust evidence'' , ''high agreement'' ). Increased drought drives crop failures that cause local communities to expand their agricultural area by further clearing native forests ( [[#Desbureaux--2018|Desbureaux and Damania, 2018]] ). Climate change is projected to enlarge the area of suitability for booming tree crops such as oil palm, acacia, Eucalyptus and rubber ( [[#Koninck--2011|Koninck et al., 2011]] ; [[#Cramb--2015|Cramb et al., 2015]] ; [[#Nath--2016|Nath, 2016]] ; [[#Hurni--2017|Hurni et al., 2017]] ; [[#Li--2017|Li et al., 2017]] ; [[#Varkkey--2018|Varkkey et al., 2018]] ). An increase of 8% in the area of rubber plantations in Yunnan Province, China, between 2002–2010 to higher altitudes due to decreased environmental limits, has potentially increased pressure on the remaining biodiversity both within and outside of protected areas ( [[#Zomer--2014a|Zomer et al., 2014a]] ). As a consequence, the suitable area for mammals is projected to be reduced by 47.7% (RCP2.6) and 67.7% (RCP8.5) by 2070, with large variability depending on the different species (Cross-Chapter Paper 7) ( [[#Brodie--2016|Brodie, 2016]] ). To minimize these potential threats, the Yunnan provincial government has identified suitable areas for the establishment of national parks, including the Asian Elephant National Park since 2006. And the government of China developed a national park system in 2013 across the country.

<div id="2.5.2.7" class="h3-container"></div>

<span id="risks-to-boreal-and-temperate-forests"></span>
==== 2.5.2.7 Risks to Boreal and Temperate Forests ====

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As in the Arctic, warming substantially exceeding the global average has already been observed in the northern parts of the temperate and boreal forest zone ( [[#Gauthier--2015|Gauthier et al., 2015]] ), and is projected to continue (see [https://www.ipcc.ch/chapter/cross-chapter-paper-6 Cross-Chapter Paper 6] and ( [[#Lee--2021|Lee et al., 2021]] )). As a consequence, boreal tree species are expected to move northwards (or in mountainous regions, upwards) into regions dominated by tundra, unless constrained by edaphic features, and temperate species are projected to grow in regions currently occupied by southern boreal forest ( ''high confidence'' ). In both biomes, deciduous trees are simulated to grow increasingly in regions currently dominated by conifers ( [[#Wårlind--2014|Wårlind et al., 2014]] ; [[#Boulanger--2017|Boulanger et al., 2017]] ). These simulation results have been supported by observational examples. In eastern Siberia, fire disturbance of larch-dominated forest was followed by recovery to birch-dominated forest ( [[#Stuenzi--2020|Stuenzi and Schaepman-Strub, 2020]] ). In Alberta, lodgepole pine ( ''Pinus contorta'' ) lost its dominant status after attacks by mountain pine beetles ( ''Dendroctonus ponderosae'' ) caused the canopy to switch to non-pine conifers and broadleaf trees ( [[#Axelson--2018|Axelson et al., 2018]] ). In contrast to the examples above, some boreal forests have proven resilient to disturbances including recent unprecedented insect outbreaks ( [[#Campbell--2019a|Campbell et al., 2019a]] ; [[#Prendin--2020|Prendin et al., 2020]] ).

Reforestation, either natural or anthropogenic, leads to summer cooling and winter warming of the ground, while forest thinning or removal by fire has reverse effects, deepening the upper layer that is free of permafrost ( [[#Stuenzi--2021a|Stuenzi et al., 2021a]] ). Interactions between permafrost and vegetation are important. For example, trees in the east Siberian taiga obtained water mostly from rain in wet summers and permafrost melt water in dry summers ( [[#Sugimoto--2002|Sugimoto et al., 2002]] ), suggesting that these forests will be particularly vulnerable to the combination of drought with the retraction of permafrost further underground due to climate warming.

<div id="2.5.2.8 " class="h3-container"></div>

<span id="risk-to-peatland-systems"></span>
==== 2.5.2.8 Risk to Peatland Systems ====

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The overall effect of climate change on the extent of northern peatlands is still debated ( ''limited evidence'' , ''low agreement'' ). It is expected that climate change will drive the expansion of high-latitude peatlands poleward of their present distribution due to warming, permafrost degradation and glacier retreat, which could provide new land and conditions favourable for peat development ( ''limited evidence'' , ''medium agreement'' ) ( [[#Zhang--2017b|Zhang et al., 2017b]] ), as seen during the last de-glacial warming ( ''robust evidence'' , ''high agreement'' ) ( [[#MacDonald--2006|MacDonald et al., 2006]] ; [[#Jones--2010|Jones and Yu, 2010]] ; [[#Ratcliffe--2018|Ratcliffe et al., 2018]] ). Peatland area loss (shrinking) near the southern limit of their current distribution or in areas where the climate becomes unsuitable is also expected ( ''medium evidence'' , ''medium agreement'' ) ( [[#2.3|Section 2.3.4.3.2]] ) ( [[#Finkelstein--2011|Finkelstein and Cowling, 2011]] Gallego-Sala and Prentice, 2013; [[#Schneider--2016|Schneider et al., 2016]] ; [[#Müller--2020|Müller and Joos, 2020]] ) ( [[#Müller--2021|Müller and Joos, 2021]] ), but they could persist if moisture is maintained via their capacity to self-regulate. In western Canada, a study suggests that peatlands may persist until 2100, even though the climate will be less suitable ( [[#Schneider--2016|Schneider et al., 2016]] ). Simulations suggest that climate change-driven increases in temperature and atmospheric CO 2 could drive reductions in the northern peatland area up to 18% (SSP1–2.6), 41% (SSP2–4.5) and 61% (SSP5–8.5) by 2300 ( [[#Müller--2020|Müller and Joos, 2020]] ). This is in contrast with the findings of northern peatland persistence and expansion under RCP2.6 and RCP6.0 scenarios in 1861–2099 by another modelling study ( [[#Qiu--2020|Qiu et al., 2020]] ). In the Tropics, the only available study suggests peatland area will increase until 2300, mainly due to increases in precipitation and the CO 2 fertilisation effect ( [[#Müller--2020|Müller and Joos, 2020]] ; [[#Müller--2021|Müller and Joos, 2021]] ).

The combination of changes in climate and land use represents a substantial risk to peatland carbon stocks, but full assessment is impeded because peatlands are yet to be included in ESMs ( ''limited evidence'' , ''high agreement'' ) ( [[#Loisel--2021|Loisel et al., 2021]] ). It is expected that the carbon balance of peatlands globally will switch from sink to source in the near future (2020–2100), mainly because tropical peatland emissions, together with those from climate change-driven permafrost thaw, will likely surpass the carbon gain expected from climate change-driven enhanced plant productivity in northern high latitudes ( [[#Gallego-Sala--2018|Gallego-Sala et al., 2018]] ; [[#Chaudhary--2020|Chaudhary et al., 2020]] ; [[#Turetsky--2020|Turetsky et al., 2020]] ; [[#Loisel--2021|Loisel et al., 2021]] ) which are mainly caused by groundwater drawdown ( ''robust evidence'' , ''medium agreement'' ) ( [[#Hirano--2014|Hirano et al., 2014]] ; [[#Brouns--2015|Brouns et al., 2015]] ; [[#Cobb--2017|Cobb et al., 2017]] ; [[#Itoh--2017|Itoh et al., 2017]] ; [[#Evans--2021|Evans et al., 2021]] ) ''.'' The overall northern peatland carbon sink has been simulated to persist for at least 300 years under RCP2.6, but not under RCP8.5 ( [[#Qiu--2020|Qiu et al., 2020]] ).

Increases in the extent, severity and duration of fires are expected in all peatland regions in the future due to temperature increases ( [[IPCC:Wg2:Chapter:Chapter-4#4.3.1|Section 4.3.1.1]] ), changes in precipitation patterns ( [[IPCC:Wg2:Chapter:Chapter-4#4.3.1|Section 4.3.1.2]] ) and increases in ignition sources (e.g., lightning) ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.3.2|Section 5.4.3.2]] ), with associated rapid carbon losses to the atmosphere ( ''medium evidence'' , ''high agreement'' ) ( [[#Dadap--2019|Dadap et al., 2019]] ; [[#Chen--2021a|Chen et al., 2021a]] ; [[#Nelson--2021|Nelson et al., 2021]] ). For example, drought has been linked to fires in Southeast Asian peatlands ( [[#Field--2009|Field et al., 2009]] ) and there are predicted decreases in mean summer precipitation (10–30%) for high and low RCPs, particularly over the Indonesian region, by the mid and late 21st century ( [[IPCC:Wg2:Chapter:Chapter-12#12.4|Section 12.4.2.2]] ) ( [[#Tangang--2020|Tangang et al., 2020]] ; [[#Taufik--2020|Taufik et al., 2020]] ). During wet years, the fire probability in Indonesian peatlands also significantly increases (by 15–40%) when temperatures in July to October surpass 0.5°C anomalies compared to the 1995–2015 baseline ( [[#Fernandes--2017|Fernandes et al., 2017]] ). Overall, current evidence suggests that peat carbon losses via fire have the potential to be equal to, or greater than, losses due to human peatland drainage and disturbance ( ''limited evidence'' , ''high agreement'' ) ( [[#Turetsky--2015|Turetsky et al., 2015]] ).

Regarding permafrost peatlands, studies differ, with some projecting a net loss and others a net gain of carbon ( ''medium evidence'' , ''low agreement'' ) ( [[#Estop-Aragonés--2018|Estop-Aragonés et al., 2018]] ; [[#Hugelius--2020|Hugelius et al., 2020]] ; [[#Loisel--2021|Loisel et al., 2021]] ; [[#Väliranta--2021|Väliranta et al., 2021]] ). In some permafrost peatlands, prolonged and warmer growing seasons due to climate change ( [[#2.3|Section 2.3.4.3.1]] ), along with increases in nitrogen deposition since 1850 ( [[#Lamarque--2013|Lamarque et al., 2013]] ), are promoting plant primary productivity. Other studies indicate that increased nitrogen-mediated sequestration could be exceeded by increased decomposition due to climate change-driven warming and fire ( ''medium evidence'' , ''low agreement'' ) ( [[#Natali--2012|Natali et al., 2012]] ; [[#Vonk--2015|Vonk et al., 2015]] ; [[#Keuper--2017|Keuper et al., 2017]] ; [[#Burd--2018|Burd et al., 2018]] ; [[#Estop-Aragonés--2018|Estop-Aragonés et al., 2018]] ; [[#Gallego-Sala--2018|Gallego-Sala et al., 2018]] ; [[#Serikova--2018|Serikova et al., 2018]] ; [[#Wild--2019|Wild et al., 2019]] ; [[#Chaudhary--2020|Chaudhary et al., 2020]] ; [[#Hugelius--2020|Hugelius et al., 2020]] ).

Any climate change- or human-driven degradation of peatlands will also entail losses in water storage ( ''limited evidence'' , ''high agreement'' ) ( [[#Wooster--2012|Wooster et al., 2012]] ; [[#Hirano--2015|Hirano et al., 2015]] ; [[#Cole--2019|Cole et al., 2019]] ; [[#Taufik--2019|Taufik et al., 2019]] ) and biodiversity ( [[#Harrison--2013|Harrison, 2013]] ; [[#Lampela--2017|Lampela et al., 2017]] ; [[#Renou-Wilson--2019|Renou-Wilson et al., 2019]] ). The environmental archive contained in peat that preserves records of vegetation, hydrology, climate change, pollution and/or human disturbances is also being lost as peatlands degrade ( [[#Kasischke--2006|Kasischke and Turetsky, 2006]] ; [[#MacDonald--2006|MacDonald et al., 2006]] ; [[#Turunen--2008|Turunen, 2008]] ; [[#Field--2009|Field et al., 2009]] ; [[#Flannigan--2009|Flannigan et al., 2009]] ; [[#Jones--2010|Jones and Yu, 2010]] ; [[#Kasischke--2010|Kasischke et al., 2010]] ; [[#Peterson--2010|Peterson et al., 2010]] ; [[#Finkelstein--2011|Finkelstein and Cowling, 2011]] ; [[#Rooney--2012|Rooney et al., 2012]] ; [[#Gallego-Sala--2013|Gallego-Sala and Colin Prentice, 2013]] ; [[#Lamarque--2013|Lamarque et al., 2013]] ; [[#Hirano--2014|Hirano et al., 2014]] ; [[#Brouns--2015|Brouns et al., 2015]] ; [[#Turetsky--2015|Turetsky et al., 2015]] ; [[#Miettinen--2016|Miettinen et al., 2016]] ; [[#Schneider--2016|Schneider et al., 2016]] ; [[#Cobb--2017|Cobb et al., 2017]] ; [[#Fernandes--2017|Fernandes et al., 2017]] ; [[#Itoh--2017|Itoh et al., 2017]] ; [[#Gallego-Sala--2018|Gallego-Sala et al., 2018]] ; [[#Greiser--2018|Greiser and Joosten, 2018]] ; [[#Ratcliffe--2018|Ratcliffe et al., 2018]] ; [[#Dadap--2019|Dadap et al., 2019]] ; [[#Leifeld--2019|Leifeld et al., 2019]] ; [[#Chaudhary--2020|Chaudhary et al., 2020]] ; [[#Hoyt--2020|Hoyt et al., 2020]] ; [[#Müller--2020|Müller and Joos, 2020]] ; [[#Qiu--2020|Qiu et al., 2020]] ; [[#Tangang--2020|Tangang et al., 2020]] ; [[#Taufik--2020|Taufik et al., 2020]] ; [[#Turetsky--2020|Turetsky et al., 2020]] ; [[#Chen--2021a|Chen et al., 2021a]] ; [[#Evans--2021|Evans et al., 2021]] ; [[#Loisel--2021|Loisel et al., 2021]] ; [[#Nelson--2021|Nelson et al., 2021]] ; [[#Qiu--2021|Qiu et al., 2021]] ).

<div id="2.5.2.9" class="h3-container"></div>

<span id="risks-to-polar-tundra-ecosystems"></span>
==== 2.5.2.9 Risks to Polar Tundra Ecosystems ====

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For boreal–tundra systems, AR5 projected the transformation of species composition, land cover and permafrost extent, decreasing albedo and increasing GHG emissions ( ''medium confidence'' ). SR1.5 classified tundra and boreal forests as particularly vulnerable to degradation and encroachment by woody shrubs ( ''high confidence'' ). The SROCC projected climate-related changes to arctic hydrology, wildfires and abrupt thaw ( ''high confidence'' ) and the broad disappearance of arctic near-surface permafrost this century, with important consequences for global climate ( ''very high confidence'' ) ''.'' [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-2 Chapter 2] of AR6 has focused on new key findings about observed and projected changes in tundra vegetation and related hydrology, with implications for feedbacks to the climate system.

Due to the rapid warming at high northern latitudes, the Arctic tundra is one of the terrestrial biomes where climate change impacts are already clearly visible ( [[#Settele--2014|Settele et al., 2014]] ; [[#Uboni--2016|Uboni et al., 2016]] ). Climate models project that warming of the Arctic is likely to continue at more than double the global rate. Compared to the period 1995–2014, mean annual surface air temperatures in the Arctic tundra are projected to increase by 7.9°C–10°C by the end of the century in scenarios of high GHG emissions (RCP7.0 and RCP8.5). In scenarios of low GHG emissions (RCP1.9 and RCP2.6), the projected increase is 2.6°C–3.2°C ( [[#Lee--2021|Lee et al., 2021]] ). The Arctic is also projected to have amongst the largest increases in precipitation globally, but with ''high'' uncertainty. In contrast to climate change, LUC is projected to be very low in Arctic tundra systems ( [[#van%20Asselen--2013|van Asselen and Verburg, 2013]] ).

Models of vegetation response to climate project acceleration in the coming decades of observed increases in shrub dominance and boreal forest encroachment that have been driven by recent warming ( [[#Settele--2014|Settele et al., 2014]] ), leading to a shrinking of the area of tundra globally ( ''medium confidence'' ) ( [[#Mod--2016|Mod and Luoto, 2016]] ; [[#Gang--2017|Gang et al., 2017]] ). Simulating changes in tundra vegetation is complicated by permafrost dynamics (e.g., the formation of thaw ponds and draining of existing ponds), changes in precipitation and low nutrient availability (which may promote the abundance of graminoids) ( [[#van%20der%20Kolk--2016|van der Kolk et al., 2016]] ). Changes in vegetation, when combined with warming and increased precipitation effects on soil thawing and carbon cycling, are projected to modify GHG emissions and have biophysical feedbacks to regional and global climate. High uncertainty in modelled carbon cycle changes arises from differences between the vegetation models ( [[#Nishina--2015|Nishina et al., 2015]] ; [[#Ito--2016|Ito et al., 2016]] ). In addition, climate change is expected to strongly interact with other factors, such as fire, to further increase uncertainty in projections of tundra ecosystem function ( [[#Jiang--2017|Jiang et al., 2017]] ).

<div id="2.5.2.10" class="h3-container"></div>

<span id="committed-impacts-of-climate-change-on-terrestrial-ecosystems-and-implications-of-overshoot"></span>
==== 2.5.2.10 Committed Impacts of Climate Change on Terrestrial Ecosystems and Implications of Overshoot ====

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Projections point to potentially large changes of canopy structure and composition within and across the terrestrial biomes in response to climate change and changes in atmospheric CO 2 . These changes will contribute to altered ecosystem carbon uptake and losses, biophysical climate feedbacks (Sections 2.3.2; 2.4.4; 2.5.3.2; 2.5.3.3. 2.5.3.4, 2.5.3.5, Figure 2.10, Table 2.4) and multiple other ecosystem services (Sections 2.5.3, 2.5.4) as well impacts on biodiversity (Sections 2.4.2, 2.4.3, 2.4.4, 2.4.5, 2.5.1.3, 2.5.1.4, 2.5.2, Figure Box 2.1.1, Table Box 2.1.1, Table SM2.4). Until now, most studies project changes over next decades until the end of this century.

However, there is an increasing body of literature that has found continued, longer-term responses of ecosystems to climate change, so-called ‘committed changes’, that arise from lags that exist in many systems. Many processes in ecosystems take more than a few decades to quasi-equilibrate to environmental changes. Therefore, the trends of changing vegetation cover identified in simulations of transient warming continue to show up in simulations that hold climate change at low levels of warming ( ''medium confidence'' ) ( [[#Boulton--2017|Boulton et al., 2017]] ; [[#Pugh--2018|Pugh et al., 2018]] ; [[#Scheiter--2020|Scheiter et al., 2020]] ). Such changes, which could tip ecosystems into an alternative state, could also be triggered by a ‘warming overshoot’ if global warming were to exceed a certain threshold, even if mean temperatures afterwards decline again ( [[#Albrich--2020a|Albrich et al., 2020a]] ).

For instance, even if warming achieved by 2100 remained constant after 2100, such committed responses continue to occur. These include: (1) continued Amazon forest loss ( [[#Boulton--2017|Boulton et al., 2017]] ), consistent with results in [[#Pugh--2018|Pugh et al. (2018)]] that found continued tropical forest cover loss across a range of models and simulation setups, and (2) across Africa, an increased shift towards woody C3 vegetation was found in equilibrium state, the overall response depending on the atmospheric CO 2 concentration ( [[#Scheiter--2020|Scheiter et al., 2020]] ). In [[#Pugh--2018|Pugh et al. (2018)]] , the opposite was found for boreal forest cover, which showed a strong committed increase. The committed changes in vegetation composition correspond to large committed changes in terrestrial carbon uptake and losses ( [[#Boulton--2017|Boulton et al., 2017]] ; [[#Pugh--2018|Pugh et al., 2018]] ; [[#Scheiter--2020|Scheiter et al., 2020]] ), and would plausibly also appear in other ecosystem functioning and services. These studies point to the importance of having not only a multi-decadal but also a multi-century perspective when exploring the impacts of political decisions on climate change mitigation taken now. Even if climate-warming targets are met, published evidence so far suggests that fundamental changes in some ecosystems are ''likely'' as these correspond to well-understood ecosystem physiological responses that trigger long-term changes in composition.

<div id="box-2.1" class="h2-container box-container"></div>

'''Box 2.1 | Assessing Past Projections of Ecosystem Change against Observations'''
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To assess future climate change impacts on ecosystems, we use models to project their future distribution. Comparing the trends in the observed changes against the projections can help assess the strength of the model projections. In this box, we compare observed trends of changes in ecosystem structure to projections highlighted in previous IPCC reports, specifically AR3 ( [[#IPCC--2001|IPCC, 2001]] ), AR4 ( [[#Fischlin--2007|Fischlin et al., 2007]] ) and AR5 ( [[#Settele--2014|Settele et al., 2014]] ). We use this to assess how well the projections are matching up with observed changes. The map represents studies documenting observed changes in common plant functional groups (e.g., trees, grasses and shrubs). Studies documenting changes in plant functional groups were collated from published papers in natural and semi-natural areas. Studies were included if climate change or interactions between climate change and land use showed a causal link to the observed change (Table SM2.4). Studies were excluded if the changes were only from landscape/land use transformation (e.g., deforestation). In each paper, we recorded the geographical location and type of functional change, and noted the causes. Observed changes are plotted onto a biome map derived from the WWF ecoregions database ( [[#Olson--2001|Olson et al., 2001]] ). Trends in changing plant functional types are good indicators of potential biome shifts and are used to assess how observations match up with projections.

[[File:b08a5740a487993ed517554892a93800 IPCC_AR6_WGII_Figure_2_Box_2_1_1.png]]

'''Figure Box 2.1.1 |''' '''Observed changes in the distribution of plant functional types that are caused by climate change or a combination of land use and climate change.''' Shifts in plant functional types are indicative of shift in biome function and structure. Based upon studies listed in Table SM2.4 and section 2.4.

'''Table Box 2.1.1 |''' Comparison of projections on biome change from AR3, AR4 and AR5 ( [[#IPCC--2001|IPCC, 2001]] ; [[#Fischlin--2007|Fischlin et al., 2007]] ; [[#Settele--2014|Settele et al., 2014]] ), with observed changes in ecosystems as assessed in this report (see [[#2.4|Section 2.4]] , Figure Box 2.1.1, Table SM2.4). Observed changes marked in bold show good agreement with past projections; those in red show mismatch with observations and projections.

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

! '''AR3'''

! '''AR4'''

! '''AR5'''

! '''Observed trends 1990–2021'''

|-
| ''MTEs''

| Increased disturbance by fire and warming will cause a loss of unique habitats

| Loss of 65% of area due to warming. Increased fire frequencies will favour resprouting plants. An increase in grass dominance. Forest expansion within MTEs due to elevated CO 2 .

| Range contractions of all species

| Increase in water deficit '''and fire activity''' (Sections 2.4.3.6, 2.4.4.2) '''causing a decline in diversity;''' tree mortality (Fig. Box 2.1.1) with resprouting trees worst affected.

'''Increasing dominance of grasses (often alien).''' Increasing dominance of deciduous over evergreen species (Fig. Box 2.1.1).

|-
| ''Tundra''

| Tree and shrub encroachment into tundra

| Increased woody plant growth due to longer and warmer growing seasons and shrub tundra replacing dwarf tundra

Poleward expansion of tundra into polar desert and encroachment of coniferous trees into tundra

| Continued woody expansion in tundra regions with reduced surface albedo due to less snow and more woody cover

| '''Increase in woody shrub cover in tundra and expansion of boreal forest into tundra''' (Fig. Box 2.1.1, 2.4.3.4).

|-
| ''Boreal forest''

| Reduced productivity due to weather-related disturbances (e.g., increased fire risk).

Deciduous broadleaf tree encroachment into boreal forest.

| Extensive boreal tree spread into tundra.

Boreal forest dieback within boreal zone and contraction of boreal forest at southern ecotone with continental grasslands

|
| '''Expansion into tundra and upslope treeline advance''' ( [[#2.4.3.8|Section 2.4.3.8]] and Fig Box 2.1.1).

'''Increased mortality due to drought, fire, beetle infestations''' (Sections 2.4.3.8, 2.4.42.1, 2.4.4.3.1).

|-
| ''Tropical forest''

| Increasing CO 2 concentration would increase NPP

| Increases in forest productivity and biomass through increased CO 2 with localised decreases in the Amazon. Shift in forest species composition.

Expansion of forest area into mesic savanna.

| Shift in the climate envelope of moist tropical forests but forests are less likely to undergo major retractions or expansions than suggested in AR4

| '''Expansion of tropical forest into savannas in Africa, Asia, South America''' ( [[#2.4.3.7|Section 2.4.3.7]] , Fig. Box 2.1.1).

'''Forest biomass increases (though slowing)''' ( [[#2.4.4.4|Section 2.4.4.4]] ).

Forest degradation from drought, warming, fire and shorter residence time of trees ( [[#2.4.3.7|Section 2.4.3.7]] )

'''Shift in species composition''' towards species with more aridity-adapted traits ( [[#2.4.3.7|Section 2.4.3.7]] ).

|-
| ''Temperate forest''

| Forest decline and increased mortality

| Increase in tree mortality from drought-related declines.

A general increase of deciduous vegetation at the expense of evergreen vegetation is predicted at all latitudes.

|
| Map indicates a shift towards deciduous species in western North America (Fig. Box 2.1.1).

'''Tree death due to interactions of drought, pest outbreaks and fire''' (2.4.3.8, 2.4.4.2.1., 2.4.4.3.1)

|-
| ''Grasslands and savannas''

| Increasing CO 2 concentration will increase NPP

| Increased tree dominance in savannas and grasslands (from elevated CO 2 ), with C3 plants benefitting more than C4 plants

| Rising CO 2 will increase the likelihood of woodier states (but the transition will vary in different environments)

| '''Greening and encroachment across tropical and temperate savannas in Africa, Asia, Australia and America''' ( [[#2.4.3.5|Section 2.4.3.5]] ).

'''Expansion of trees into grasslands and advancement of tree lines.'''

Signs of increased C4 grass productivity in drought conditions. Increased C3 grass productivity ( [[#2.4.3.5|Section 2.4.3.5]] ).

|-
| ''Desert/arid shrublands''

|
| An increase in desert vegetation productivity was projected in southern Africa, the Sahel, central Australia, the Arabian Peninsula and parts of central Asia due to a positive impact of rising atmospheric CO 2

|
| '''Greening''' (increased leaf area index [LAI] and woody cover) and increased herbaceous production are occurring at desert–grassland interfaces (Cross-Chapter Paper 3)

|}

Assessment: There is high agreement between observations and projections of tree death in temperate and boreal forests, with current projections (AR6) indicating this trend will continue (Sections 2.4.4.3, 2.5.3.3, 2.5.4). Forest death is most widely recorded in central Europe and western North America (Fig. Box 2.1.1). There is also very high agreement between observations and projections of woody encroachment in savannas, grasslands and tundra, with projections also indicating that this trend is likely to continue (Sections 2.4.3.5, 2.4.3.9, 2.5.2.5, 2.5.2.9, 2.5.4). Observations of desert-greening show good agreement with earlier projections. Patterns of desertification are also occurring, although the geographical match between projections and observations shows moderate agreement, likely due to the strong role of land use in this process. Projections of tropical forest expansion into mesic savannas and boreal forest expansion into tundra also show agreement with the observations.

Projections of the future of Mediterranean shrublands, deserts, xeric shrublands and temperate grassy systems are limited, making assessment of this relationship less clear. It is also unclear, due to limited observations, how widespread a shift there is from deciduous forest species to evergreen forest species. Some observations suggest this is occurring, but it is not clear how widespread this change is and if the geographical pattern is as projected.

<div id="2.5.3" class="h2-container"></div>

<span id="risk-assessment-of-ecosystems-and-related-services"></span>