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

=== 2.3.3 Biologically Important Physical Changes in Freshwater Systems ===

<div id="h2-6-siblings" class="h2-siblings"></div>

Physical changes are fundamental drivers of change at all levels of biological organisation, from individual species, to communities, whole ecosystems. The climate hazards specific to freshwater systems not documented elsewhere in AR6 are summarised here.

<div id="2.3.3.1" class="h3-container"></div>

<span id="observed-change-in-thermal-habitat-and-oxygen-availability"></span>
==== 2.3.3.1 Observed Change in Thermal Habitat and Oxygen Availability ====

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

Since AR5, evidence of changes in the temperature of lakes and rivers has continued to increase. Global warming rates for lake surface waters were estimated as 0.21°C–0.45°C per decade between 1970 and 2010, exceeding sea-surface temperature (SST) trends of 0.09°C per decade between 1980 and 2017 ( ''robust evidence'' , ''high agreement'' ) (Figure 2.2; ( [[#Schneider--2010|Schneider and Hook, 2010]] ; [[#Kraemer--2015|Kraemer et al., 2015]] ; [[#O’Reilly--2015|O’Reilly et al., 2015]] ; [[#Woolway--2020b|Woolway et al., 2020b]] ). Warming of lake surface water temperatures was variable within regions ( [[#O’Reilly--2015|O’Reilly et al., 2015]] ) but more homogeneous than deep-water temperature changes ( [[#Pilla--2020|Pilla et al., 2020]] ). Because temperature trends in lakes can vary vertically, horizontally and seasonally, complex changes have occurred in the amount of habitat available to aquatic organisms at particular depths and temperatures ( [[#Kraemer--2021|Kraemer et al., 2021]] ).

<div id="_idContainer007" class="Figure"></div>

[[File:cca4001e49eec573dada90dcc9bfbf0a IPCC_AR6_WGII_Figure_2_002.png]]

'''Figure 2.2 | Observed global trends in lake and river surface water temperature.'''

'''(a)''' Left panel: map of temperatures of lakes (1970–2010).

'''(b)''' Left panel: map of temperatures of rivers (1901–2010). Note that the trends of river water temperatures are not directly comparable within rivers or to lakes, since time periods are not consistent across river studies. Right panels (a) and (b) depict water temperature trends along a latitudinal gradient highlighting the above average warming rates in northern Polar Regions (polar amplification). Data sources for lakes: ( [[#O’Reilly--2015|O’Reilly et al., 2015]] ; [[#Carrea--2019|Carrea and Merchant, 2019]] ; [[#Woolway--2020a|Woolway et al., 2020a]] ; [[#Woolway--2020b|Woolway et al., 2020b]] ). Data sources for rivers: ( [[#Webb--1992|Webb and Walling, 1992]] ; [[#Langan--2001|Langan et al., 2001]] ; [[#Daufresne--2004|Daufresne et al., 2004]] ; [[#Moatar--2006|Moatar and Gailhard, 2006]] ; [[#Lammers--2007|Lammers et al., 2007]] ; [[#Patterson--2007|Patterson et al., 2007]] ; [[#Webb--2007|Webb and Nobilis, 2007]] ; [[#Durance--2009|Durance and Ormerod, 2009]] ; [[#Kaushal--2010|Kaushal et al., 2010]] ; [[#Pekárová--2011|Pekárová et al., 2011]] ; [[#Jurgelėnaitė--2012|Jurgelėnaitė et al., 2012]] ; [[#Markovic--2013|Markovic et al., 2013]] ; [[#Arora--2016|Arora et al., 2016]] ; [[#Latkovska--2016|Latkovska and Apsīte, 2016]] ; [[#Marszelewski--2016|Marszelewski and Pius, 2016]] ; [[#Jurgelėnaitė--2017|Jurgelėnaitė et al., 2017]] ).

Changes in river water temperatures ranged from −1.21°C to +1.076°C per decade between 1901 and 2010 ( ''medium evidence, medium agreement'' ) ( [[#Hari--2006|Hari et al., 2006]] ; [[#Kaushal--2010|Kaushal et al., 2010]] ; [[#Jurgelėnaitė--2012|Jurgelėnaitė et al., 2012]] ; [[#Li--2012|Li et al., 2012]] ; [[#Latkovska--2016|Latkovska and Apsīte, 2016]] ; [[#Marszelewski--2016|Marszelewski and Pius, 2016]] ). The more rapid increase in surface water temperature in lakes and rivers in regions with cold winters ( [[#O’Reilly--2015|O’Reilly et al., 2015]] ) can, in part, be attributed to the amplified warming in polar and high-latitude regions ( ''robust evidence'' , ''high agreement'' ) ( [[#Screen--2010|Screen and Simmonds, 2010]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ).

Shifts in thermal regime: Since AR5, the trend that lake waters mix less frequently continues ( [[#Butcher--2015|Butcher et al., 2015]] ; [[#Adrian--2016|Adrian et al., 2016]] ; [[#Richardson--2017|Richardson et al., 2017]] ; [[#Woolway--2017|Woolway et al., 2017]] ). This results from greater warming of surface temperatures relative to deep-water temperatures, and the loss of ice during winter which prevents inverse thermal stratification in north temperate lakes ( ''robust evidence, high agreement'' ) ( [[#Adrian--2009|Adrian et al., 2009]] ; [[#Winslow--2015|Winslow et al., 2015]] ; [[#Adrian--2016|Adrian et al., 2016]] ; [[#Schwefel--2016|Schwefel et al., 2016]] ; [[#Richardson--2017|Richardson et al., 2017]] ) ''.''

Oxygen availability: increased water temperature and reduced mixing cause a decrease in dissolved oxygen. In 400 lakes, dissolved oxygen in surface and deep waters declined by 4.1 and 16.8%, respectively, between 1980 and 2017 ( [[#Jane--2021|Jane et al., 2021]] ). The deepest water layers are expected to experience an increase in hypoxic conditions by &gt;25% due to fewer complete mixing events, with strong repercussions for nutrient dynamics and the loss of thermal habitat ( ''robust evidence, high agreement'' ) ( [[#Straile--2010|Straile et al., 2010]] ; [[#Zhang--2015|Zhang et al., 2015]] ; [[#Schwefel--2016|Schwefel et al., 2016]] ).

<div id="2.3.3.2 " class="h3-container"></div>

<span id="observed-changes-in-water-level"></span>
==== 2.3.3.2 Observed Changes in Water Level ====

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

Depending on how the intensification of the global water cycle affects individual lake water budgets, the amount of water stored in specific lakes may increase, decrease or have no substantial cumulative effect ( [[#Notaro--2015|Notaro et al., 2015]] ; [[#Pekel--2016|Pekel et al., 2016]] ; [[#Rodell--2018|Rodell et al., 2018]] ; [[#Busker--2019|Busker et al., 2019]] ; [[#Woolway--2020b|Woolway et al., 2020b]] ). The magnitude of hydrological changes that can be assuredly attributed to climate change remains uncertain ( [[#Hegerl--2015|Hegerl et al., 2015]] ; [[#Gronewold--2019|Gronewold and Rood, 2019]] ; [[#Kraemer--2020|Kraemer et al., 2020]] ). Attribution of water storage variation in lakes due to climate change is facilitated when such variations occur coherently across broad geographic regions and long time scales, preferably absent of other anthropogenic hydrological influences ( [[#Watras--2014|Watras et al., 2014]] ; [[#Kraemer--2020|Kraemer et al., 2020]] ). There is increasing awareness that climate change contributes to the loss of small temporary ponds which cover a greater global area than lakes ( [[#Bagella--2016|Bagella et al., 2016]] ).

Lakes fed by glacial melt water are growing in response to climate change and glacier retreat ( ''robust evidence'' , ''high agreement'' ) ( [[#Shugar--2020|Shugar et al., 2020]] ). Water storage increases on the Tibetan Plateau (Figure 2.3a) have been attributed to changes in glacier melt, permafrost thaw, precipitation and runoff, in part as a result of climate change ( [[#Huang--2011|Huang et al., 2011]] ; [[#Meng--2019|Meng et al., 2019]] ; [[#Wang--2020a|Wang et al., 2020a]] ). ''High confidence'' in attribution of these trends to climate change is supported by long-term ground survey data and observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission ( [[#Ma--2010|Ma et al., 2010]] ; [[#Rodell--2018|Rodell et al., 2018]] ; [[#Kraemer--2020|Kraemer et al., 2020]] ).

<div id="_idContainer009" class="Figure"></div>

[[File:409f4936dac4e9a7def021157381fcfa IPCC_AR6_WGII_Figure_2_003.png]]

'''Figure 2.3 | Change in water extent in the Tibetan Plateau and annual mean global river flow.'''

'''(a)''' Changes in water storage on the Tibetan Plateau. Map of the Qinghai–Tibetan Plateau, Asia, showing the percent change in surface water extent from 1984 to 2019 based on LANDSAT imagery. Increases in surface water extent in this region are mainly caused by climate change-mediated increases in precipitation and glacial melt (Source: EC JRC/Google; ( [[#Pekel--2016|Pekel et al., 2016]] ).

'''(b)''' Global map of the median trend in annual mean river flow derived from 7250 observatories around the world (in 1971–2010). Some regions are drying (northeast Brazil, southern Australia and the Mediterranean) and others are wetting (northern Europe), mainly caused by large-scale shifts in precipitation, changes in factors that influence evapotranspiration and alterations of the timing of snow accumulation and melt driven by rising temperatures (Source: ( [[#Gudmundsson--2021|Gudmundsson et al., 2021]] ).

In the Arctic, lake area has increased in regions with continuous permafrost, and decreased in regions where permafrost is thinner and discontinuous ( ''robust evidence'' , ''high agreement'' ) (See Chapter 4) ( [[#Smith--2005|Smith et al., 2005]] ; [[#Andresen--2015|Andresen and Lougheed, 2015]] ; [[#Nitze--2018|Nitze et al., 2018]] ; [[#Mekonnen--2021|Mekonnen et al., 2021]] ).

<div id="2.3.3.3" class="h3-container"></div>

<span id="observed-changes-in-discharge"></span>
==== 2.3.3.3 Observed Changes in Discharge ====

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

Analysis of river flows from 7250 observatories around the world covering the years 1971–2010 and identified spatially complex patterns, with reductions in northeastern Brazil, southern Australia and the Mediterranean, and increases in northern Europe ( ''medium evidence'' , ''medium agreement'' ) ( [[#Gudmundsson--2021|Gudmundsson et al., 2021]] ). More than half of global rivers undergo periodic drying that reduces river connectivity ( ''medium evidence'' , ''medium agreement'' ). Increased frequency and intensity of droughts may cause perennial rivers to become intermittent and intermittent rivers to disappear ( ''medium evidence'' , ''medium agreement'' ), threatening freshwater fish in habitats already characterised by heat and droughts ( [[#Datry--2016|Datry et al., 2016]] ; [[#Schneider--2017|Schneider et al., 2017]] ; [[#Jaric--2019|Jaric et al., 2019]] ). In high-altitude/latitude streams, reduced glacier and snowpack extent, earlier snowmelt and altered precipitation patterns, attributed to climate change, have increased flow intermittency ( [[#Siebers--2019|Siebers et al., 2019]] ; [[#Gudmundsson--2021|Gudmundsson et al., 2021]] ). Patterns in flow regimes can be directly linked to a variety of processes shaping freshwater biodiversity, so any climate change-induced changes in flow regimes and river connectivity are expected to alter species composition as well as having societal impacts (See [[IPCC:Wg2:Chapter:Chapter-3|Chapter 3]] in ( [[#IPCC--2018b|IPCC, 2018b]] )) ( [[#Bunn--2002|Bunn and Arthington, 2002]] ; [[#Thomson--2012|Thomson et al., 2012]] ; [[#Chessman--2015|Chessman, 2015]] ; [[#Kakouei--2018|Kakouei et al., 2018]] ).

<div id="2.3.3.4" class="h3-container"></div>

<span id="observed-loss-of-ice"></span>
==== 2.3.3.4 Observed Loss of Ice ====

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

Studies since AR5 have confirmed ongoing and accelerating loss of lake and river ice in the Northern Hemisphere ( ''robust evidence'' , ''high agreement'' ) (Figure 2.4). In recent decades, systems have been freezing later in winter and thawing earlier in spring, reducing ice duration by &gt;2 weeks per year and leading to an increasing numbers of years with a loss of perennial ice cover, intermittent ice cover or even an absence of ice ( [[#Adrian--2009|Adrian et al., 2009]] ; [[#Kirillin--2012|Kirillin et al., 2012]] ; [[#Paquette--2015|Paquette et al., 2015]] ; [[#Adrian--2016|Adrian et al., 2016]] ; [[#Park--2016|Park et al., 2016]] ; [[#Roberts--2017|Roberts et al., 2017]] ; [[#Sharma--2019|Sharma et al., 2019]] ). The global extent of river ice declined by 25% between 1984 and 2018 ( [[#Yang--2020|Yang et al., 2020]] ). This trend has been more pronounced at higher latitudes, consistent with enhanced polar warming (large geographic coverage) ( [[#Du--2017|Du et al., 2017]] ). Empirical long-term and remote-sensing data gathered in an increasingly large number of freshwater systems supports ''very high confidence'' in attributing these trends to climate change. For the decline of glaciers, snow and permafrost, see [[IPCC:Wg2:Chapter:Chapter-4|Chapter 4]] (this report) and the Special Report on the Ocean and Cryosphere in a Changing Climate ( [[#IPCC--2019b|IPCC, 2019b]] ).

<div id="_idContainer011" class="Figure"></div>

[[File:9cd76d4f821922ce20c8a4a40ef3ed8e IPCC_AR6_WGII_Figure_2_004.png]]

'''Figure 2.4 | Global ice cover trends of lakes and rivers.'''

'''(a)''' Spatial distribution of current (light grey areas) and future (coloured areas) Northern Hemisphere lakes that may experience intermittent winter ice cover with climate warming. Projections were based on current conditions (1970–2010) and four established air temperature projections (Data source: ( [[#Sharma--2019|Sharma et al., 2019]] ).

'''(b)''' Spatial distribution of projected change in Northern Hemisphere river ice duration under the RCP4.5 emission scenario by 2080–2100 relative to the period 2009–2029. White areas refer to rivers without ice cover in the period 2009–2029 (zero days). Reference period isolines indicate river ice duration in the period 2009–2029. Coloured areas depict loss of ice duration in days. Blue areas depict a projected increase in river ice duration. Grey land areas indicate a lack of Landsat-observable rivers (Data source: ( [[#Yang--2020|Yang et al., 2020]] ).

<div id="2.3.3.5 " class="h3-container"></div>

<span id="extreme-weather-events-and-freshwater-systems"></span>
==== 2.3.3.5 Extreme Weather Events and Freshwater Systems ====

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

Since AR5, numerous drastic short-term responses have been observed in lakes and rivers, to both expected seasonal extreme events and unexpected supra-seasonal extremes extending over multiple seasons. Consequences for ecosystem functioning are not well understood ( [[#Bogan--2015|Bogan et al., 2015]] ; [[#Death--2015|Death et al., 2015]] ; [[#Stockwell--2020|Stockwell et al., 2020]] ) ''.'' Increasing frequencies of severe floods and droughts attributed to climate change are major threats for river ecosystems ( [[#Peters--2016|Peters et al., 2016]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ). While extreme floods cause massive physical disturbance, moderate floods can have positive effects, providing woody debris that contributes to habitat complexity and diversity, flushing fine sediments, dissolving organic carbon and providing important food sources from terrestrial origins ( [[#Peters--2016|Peters et al., 2016]] ; [[#Talbot--2018|Talbot et al., 2018]] ). Droughts reduce river habitat diversity and connectivity, threatening aquatic species, especially in deserts and arid regions ( [[#Bogan--2015|Bogan et al., 2015]] ; [[#Death--2015|Death et al., 2015]] ; [[#Ledger--2015|Ledger and Milner, 2015]] ; [[#Jaric--2019|Jaric et al., 2019]] ).

Rivers already under stress from human activities such as urban development and farming on floodplains are prone to reduced resilience to future extreme events ( ''medium confidence'' ) ( [[#Woodward--2016|Woodward et al., 2016]] ; [[#Talbot--2018|Talbot et al., 2018]] ). Thus, the potential for floods to become catastrophic for ecosystem services is exacerbated by LULCC ( [[#Peters--2016|Peters et al., 2016]] ; [[#Talbot--2018|Talbot et al., 2018]] ). However, biota can recover rapidly from extreme flood events if river geomorphology is not greatly altered. If instream habitat is strongly affected, recovery, if it occurs, takes much longer, resulting in a decline of biodiversity ( ''medium confidence'' ) ( [[#Thorp--2010|Thorp et al., 2010]] ; [[#Death--2015|Death et al., 2015]] ; [[#Poff--2018|Poff et al., 2018]] ).

However, not all extreme events will have a biological impact, depending, in particular, on the timing, magnitude and frequency of events and the antecedent conditions ( [[#Bailey--2016|Bailey and van de Pol, 2016]] ; [[#Stockwell--2020|Stockwell et al., 2020]] ; [[#Jennings--2021|Jennings et al., 2021]] ; [[#Thayne--2021|Thayne et al., 2021]] ). For instance, an extreme wind event may have little impact on phytoplankton in a lake that was fully mixed prior to the event. Conversely, the effects of a storm on phytoplankton communities may compound when lakes have not yet recovered from a previous storm or if periods of drought alternate with periods of intense precipitation ( ''limited evidence'' ) ( [[#Leonard--2014|Leonard et al., 2014]] ; [[#Stockwell--2020|Stockwell et al., 2020]] ).

In summary, extreme events (heat waves, storms and loss of ice) affect lakes in terms of water temperature, water level, light, oxygen concentrations and nutrient dynamics, which, in turn, affect primary production, fish communities and GHG emissions ( ''high confidence'' ). These impacts are modified by levels of solar radiation, wind speed and precipitation ( [[#Woolway--2020a|Woolway et al., 2020a]] ). Droughts have a negative impact on water quality in streams and lakes by increasing water temperature, salinity, the frequency of algal blooms and contaminant concentrations, and reducing concentrations of nutrients and dissolved oxygen ( ''medium confidence'' ) ( [[#Peters--2016|Peters et al., 2016]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ; [[#Woolway--2020a|Woolway et al., 2020a]] ). Understanding how these pressures subsequently cascade through freshwater ecosystems will be essential for future projections of their resistance and resilience towards extreme events ( [[#Leonard--2014|Leonard et al., 2014]] ; [[#Stockwell--2020|Stockwell et al., 2020]] ). See Table SM2.1 for specific examples of observed changes.

<div id="2.3.3.6 " class="h3-container"></div>

<span id="projected-changes-in-physical-characteristics-of-lakes-and-rivers"></span>
==== 2.3.3.6 Projected Changes in Physical Characteristics of Lakes and Rivers ====

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

Given the strength of relationship between past GSAT and warming trends at lake surfaces (Figure 2.2; [[#2.3.3.1|Section 2.3.3.1]] ) and projected increases in heat waves, surface water temperatures are projected to continue to increase ( [[#Woolway--2021|Woolway et al., 2021]] ). Mean May to October lake surface temperatures in 46,557 European lakes were projected to be 2.9°C, 4.5°C and 6.5°C warmer by 2081–2099 compared to the historic period (1981–1999) under RCP2.0, RCP6.0 and RCP8.5, respectively ( [[#Woolway--2020a|Woolway et al., 2020a]] ). Under RCP2.6, the average intensity of lake heat waves increases from 3.7°C to 4.0°C and the average duration from 7.7 to 27.0 days, relative to the historic period (1970–1999). For RCP8.5, warming increases to 5.4°C and duration increases dramatically to 95.5 days ( ''medium confidence'' ) ( [[#Woolway--2021|Woolway et al., 2021]] ).

Worldwide alterations in lake mixing regimes in response to climate change are projected ( [[#Kirillin--2010|Kirillin, 2010]] ). Most prominently, monomictic lakes—undergoing one mixing event in most years—will become permanently stratified, while lakes that are currently dimictic—mixing twice per year—will become monomictic by 2080–2100 ( ''medium confidence'' ) ( [[#Woolway--2019|Woolway and Merchant, 2019]] ). Nevertheless, predicting mixing behaviour remains an important challenge and attribution to climate change remains difficult ( [[#Schwefel--2016|Schwefel et al., 2016]] ; [[#Bruce--2018|Bruce et al., 2018]] ).

Under climate projections of 3.2°C warming, 4.6% of the ice-covered lakes in the Northern Hemisphere could switch to intermittent winter ice cover (Figure 2.4a; ( [[#Sharma--2019|Sharma et al., 2019]] ). Unfrozen and warmer lakes lose more water to evaporation ( [[#Wang--2018b|Wang et al., 2018b]] ). By 2100, global annual lake evaporation will increase by 16%, relative to 2006–2015, under RCP8.5 ( [[#Woolway--2020b|Woolway et al., 2020b]] ). Moreover, melting of ice decreases the ratio of sensible to latent heat flux, thus channelling more energy into evaporation ( ''medium confidence'' ) ( [[#Wang--2018b|Wang et al., 2018b]] ). In the periods 2009–2029 and 2080–2100, average duration of river ice is projected to decline by 7.3 and 16.7 days under RCP4.5 and RCP8.5, respectively (Figure 2.4b; [[#Yang--2020|Yang et al., 2020]] ).

Projections of lake water storage are limited by the absence of reliable, long-term, homogenous and spatially resolved hydrologic observations ( [[#Hegerl--2015|Hegerl et al., 2015]] ). This uncertainty is reflected in the widely divergent projections in response to future climate changes in individual lakes ( [[#Angel--2010|Angel and Kunkel, 2010]] ; [[#MacKay--2012|MacKay and Seglenieks, 2012]] ; [[#Malsy--2012|Malsy et al., 2012]] ; [[#Notaro--2015|Notaro et al., 2015]] ) . Selecting models that perform well when comparing hindcasted to observed past water storage variation often does little to reduce water storage projection uncertainty ( [[#Angel--2010|Angel and Kunkel, 2010]] ). This wide range of potential changes complicates lake management. For information on observed and projected changes in the global water cycle and hydrological regimes for streams, lakes, wetland, groundwater and their implications on water quality and societies, see Chapter 4, this report, and ( [[#Douville--2021|Douville et al., 2021]] ). For the role of weather and climate extremes on the global water cycle, see ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ).

In summary, with ongoing climate warming and an increase in the frequency and intensity of extreme events, observed increases in water temperature, losses of ice and shifts in thermal regime are projected to continue ( ''high confidence'' ).

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

'''Cross-Chapter Box EXTREMES | Ramifications of Climatic Extremes for Marine, Terrestrial, Freshwater and Polar Natural Systems'''
<div id="h2-30-siblings" class="h2-siblings"></div>

Authors: Rebecca Harris (Australia, Chapter 2, CCP3), Philip Boyd (Australia, Chapter 3), Rita Adrian (Germany, Chapter 2), Jörn Birkmann (Germany, Chapter 8), Sarah Cooley (USA, Chapter 3), Simon Donner (Canada, Chapter 3), Mette Mauritzen (Norway, Chapter 3), Guy Midgley (South Africa, Chapter 16); Camille Parmesan (France/USA/UK, Chapter 2), Dieter Piepenburg (Germany, Chapter 13, CCP6), Marie-Fanny Racault (UK/France, Chapter 3), Björn Rost (Germany, Chapter 3, CCP6), David Schoeman (Australia, Chapter 3), Stavana E. Strutz (USA/Chapter 2), Maarten van Aalst (the Netherlands, Chapter 16).

'''Introduction'''
Increases in the frequency and magnitudes of extreme events, attributed to anthropogenic climate change by WGI ( [[#IPCC--2021a|IPCC, 2021a]] ), are now causing profound negative effects across all realms of the world (marine, terrestrial, freshwater and polar) ( ''medium confidence'' ) ( [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ) (Sections 2.3.1, 2.3.2, 2.3.3.5, 2.4.2.2, Chapter 3, Chapters 9–12, this report). Changes to population abundance, species distributions, local extirpations, and global extinctions are leading to long-term, potentially irreversible shifts in the composition, structure and function of natural systems ( ''medium confidence'' ) ( [[#Frolicher--2018|Frolicher and Laufkotter, 2018]] ; [[#Harris--2018a|Harris et al., 2018a]] ; [[#Maxwell--2019|Maxwell et al., 2019]] ; [[#Smale--2019|Smale et al., 2019]] ). These effects have widespread ramifications for ecosystems and the services they provide—physical habitat, erosion control, carbon storage, nutrient cycling and water quality—with knock-on effects for tourism, fisheries, forestry and other natural resources (2.4.3, 2.4.4, 2.5.1, 2.5.2, 2.5.3, 2.5.4) ( [[#Kaushal--2018|Kaushal et al., 2018]] ; [[#Heinze--2021|Heinze et al., 2021]] ; [[#Pörtner--2021|Pörtner et al., 2021]] ).

Increasingly, the magnitude of extreme events is exceeding the values projected for mean conditions for 2100, regardless of emissions scenario (Figure Cross-Chapter Box EXTREMES.1). This has collapsed the timeline that organisms and natural communities have to acclimate or adapt to climate change ( ''medium confidence)'' . Consequently, rather than having decades to identify, develop and adopt solutions, actions to build resilience and assist recovery following extreme events are required quickly if they are to be effective.

[[File:b2e556fbf88237045cc93d611988a4b9 IPCC_AR6_WGII_Figure_2_Cross-Chapter-Box-EXTREMES_1.png]]

'''Figure Cross-Chapter Box EXTREMES.1 |''' '''A conceptual illustration of how extinction risk is affected by changes in the frequency, duration and magnitude of extreme weather or climate even''' '''ts''' '''(e.''' '''g,. drought, fire, flood and heat waves).''' Many organisms have adapted to cope with long- and short-term climate variability, but as the magnitude and frequency of extreme events increases, superimposed on the long-term climate trend, the threshold between survivable extreme weather events (yellow) and extremes that carry a high risk of causing population or species extinctions (red) is crossed more frequently. This can lead to local extinction events with insufficient time between to enable recovery, resulting in long-term, irreversible changes to the composition, structure and function of natural systems. When the extreme event occurs over a large area relative to the distribution of a species (e.g., a hurricane impacting an island which is the only place a given species occurs), a single extreme event can drive the global extinction of a species.

Recent extremes highlight the characteristics that enable natural systems to resist or recover from events, helping natural resource managers to develop solutions to improve the resilience of natural communities and identify the limits to adaptation ( [[#Bergstrom--2021|Bergstrom et al., 2021]] ).

'''Marine Heat Waves'''
Consensus is emerging that anthropogenic climate change has significantly increased the likelihood of recent marine heat waves (MHWs) ( ''medium confidence'' ) ( [[#Oliver--2018|Oliver et al., 2018]] ; [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). A widespread MHW occurred in the northeast Pacific in 2013–2015, with upper ocean temperature anomalies of up to 6.2°C relative to 2002–2012 ( [[#Gentemann--2017|Gentemann et al., 2017]] ). This event, termed the ‘Blob’, enhanced surface water stratification, decreasing nutrient supply, primary and community production and leading to widespread changes to open ocean and coastal ecosystems, with geographical shifts of key species across trophic levels, mass strandings of marine mammals, seabird mortalities and the closure of commercially important fisheries ( [[#Cavole--2016|Cavole et al., 2016]] ; [[#Piatt--2020|Piatt et al., 2020]] ). The MHW reappeared in 2019 (‘Blob 2.0’) ( [[#Amaya--2020|Amaya et al., 2020]] ), with similarly high temperature anomalies extending from Alaska to California, but the ecological effects of this event are expected to differ because the Blob originated in winter, and Blob2.0 intensified in summer ( [[#Amaya--2020|Amaya et al., 2020]] ). Modelling suggests rapid shifts in the geographic distributions of important fish species in response to MHWs ( [[#Cheung--2020|Cheung and Frolicher, 2020]] ), with projected decreased biomass and distributional shifts of fish at least four times faster and larger than the effects of decadal-scale mean changes throughout the 21st century under RCP8.5 ( ''high confidence'' ) ( [[#Cheung--2020|Cheung and Frolicher, 2020]] ). MHWs can also dramatically increase CH 4 emissions from oceans, a significant positive feedback to global warming (see also Chapter 3, this report) ( [[#Borges--2019|Borges et al., 2019]] ).

The Arctic region is warming more than twice as fast as the global mean, and polar organisms and ecosystems are likely to be particularly vulnerable to heat waves due to their specific thermal niches and physiological thresholds and also the lack of poleward ‘refugia’ ( ''high confidence'' ). The consequences of MHWs are exacerbated by concomitant sea ice melting and the freshening of surface waters, leading to secondary effects due to osmotic stress and failing pH homeostasis. Since sea ice-associated organisms are often critical components of polar food chains, cascading effects up to the top predators are expected. In 2015–2016, a MHW occurred in the Gulf of Alaska/Bering Sea ( [[#Walsh--2018|Walsh et al., 2018]] ) which was unprecedented in terms of surface temperatures and ocean heat content, geographical extent, depth range and persistence, impacting the entire marine food web. Persistent warming favoured some phytoplankton species and triggered one of the largest algal blooms recorded in this region, with concomitant oyster farm closures due to uncommon paralytic shellfish-poisoning events ( [[#Walsh--2018|Walsh et al., 2018]] ). There were also massive die-offs of common guillemots ( ''Uria aalge'' ) and puffins ( ''Fratercula cirrhata)'' , attributed to starvation resulting from warming-induced effects on food supply ( [[#Jones--2019|Jones et al., 2019]] ). A 2017 survey found a 71% decline in the abundance of Pacific cod ( ''Gadus macrocephalus'' ) since 2015, likely due to an increase in metabolic demand and reduced prey supply during the MHWs ( [[#Barbeaux--2020|Barbeaux et al., 2020]] ).

'''Terrestrial Heat Waves'''
Heat waves are now regularly occurring that exceed the physiological thresholds of some species, including birds and other small endotherms such as flying foxes ( ''high confidence)'' (Sections 2.4.2.2, 2.4.2.6) ''.'' Heat waves in Australia, North America and southern Africa have caused mass mortality events due to lethal hyperthermia and dehydration ( [[#Saunders--2011|Saunders et al., 2011]] ; [[#Conradie--2020|Conradie et al., 2020]] ; [[#McKechnie--2021|McKechnie et al., 2021]] ), reducing fitness ( [[#du%20Plessis--2012|du Plessis et al., 2012]] ; [[#Andrew--2017|Andrew et al., 2017]] ; [[#Sharpe--2019|Sharpe et al., 2019]] ; [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-2/van-de-Ven--2019 van de Ven et al., 2019] ; [[#van%20de%20Ven--2020|van de Ven et al., 2020]] ), breeding success, and recruitment ( [[#Kennedy--2013|Kennedy et al., 2013]] ; [[#Wiley--2016|Wiley and Ridley, 2016]] ; [[#Ratnayake--2019|Ratnayake et al., 2019]] ) and affecting daily activity and geographic distributions ( [[#Albright--2017|Albright et al., 2017]] ). They also place enormous demands on wildlife management agencies and pose risks to human health ( [[#Welbergen--2008|Welbergen et al., 2008]] ).

<div id="_idContainer013" class="Box_Header-continued"></div>

Cross-Chapter Box EXTREMES

Recent mortality events affected 14 species of bird and fruit bats ( ''Epomophorus wahlbergi'' ) in South Africa when maximum air temperatures exceeded 43–45°C in 2020 ( [[#McKechnie--2021|McKechnie et al., 2021]] ). Passerine birds seem more vulnerable to lethal hyperthermia, due to the relative inefficiency of panting to lose heat ( [[#McKechnie--2021|McKechnie et al., 2021]] ) and also their small size, as heat tolerance generally increases with body mass ( [[#McKechnie--2021|McKechnie et al., 2021]] ). Several mass mortality events of flying foxes ( ''Pteropus poliocephalus, P. alecto'' ) have occurred in eastern Australia when maximum air temperatures exceeded 42° ( [[#Welbergen--2008|Welbergen et al., 2008]] ). Nineteen such events occurred between 1994 and 2008, compared to three events prior to 1994. In January 2002, maximum temperatures exceeded the 30-year average mean daily maximum by up to 16.5° and killed &gt;3500 individuals ( [[#Welbergen--2008|Welbergen et al., 2008]] ). In 2014, an estimated 45,500 flying foxes died in a single day, when average maximum temperatures were ≥8°C above average ( [[#Bureau%20of%20Meteorology--2014|Bureau of Meteorology, 2014]] ). Drought compounds the impacts, as mortality increases when water availability is low ( [[#Welbergen--2008|Welbergen et al., 2008]] ; [[#Mo--2020|Mo and Roache, 2020]] ; [[#McKechnie--2021|McKechnie et al., 2021]] ).

Antarctica encountered its first recorded heat wave in 2020. Record high temperatures occurred in East Antarctica ( [[#Robinson--2020|Robinson et al., 2020]] ), with a maximum (9.2°) temperature ~7° above the mean maximum, and minimum temperatures &gt; 0°. Record high temperatures (18.3°) were also recorded in West Antarctica ( [[#Robinson--2020|Robinson et al., 2020]] ). It is too soon to know the impact on polar life, but such abrupt heating is expected to have wide-ranging effects on biota, from flash-flooding and dislodgement of plants, to excess meltwater supplying moisture to arid polar ecosystems ( [https://www.ipcc.ch/chapter/cross-chapter-paper-6 Cross-Chapter Paper 6] Polar). Heat waves in Siberia in 2016, 2018 and 2020, with air temperature anomalies &gt;6°, were associated with extensive wildfires, pest infestations and melting permafrost ( [[#Overland--2021|Overland and Wang, 2021]] ).

'''Freshwater Extremes'''
Heat waves, storms and floods affect the thermal regime and biogeochemical functioning of lakes and rivers ( [[#Woolway--2017|Woolway and Merchant, 2017]] ; [[#Vicente-Serrano--2020|Vicente-Serrano et al., 2020]] ). Extreme heat waves lead to abnormally high water temperatures ( [[#Till--2019|Till et al., 2019]] ) and reduce the mixing of lakes ( [[#Woolway--2021|Woolway et al., 2021]] ), causing a decrease in oxygen and deep-water oxygen renewal ( [[#Zhang--2015|Zhang et al., 2015]] ). Ectotherms such as fish and invertebrates are particularly susceptible to such temperature and oxygen stress ( [[#Stoks--2014|Stoks et al., 2014]] ). Their metabolic demands increase with rising temperature and a suitable habitat is eroded due to both high temperatures and lower oxygen concentrations in lakes and rivers. [[#Till--2019|Till et al. (2019)]] attributed 502 fish kill events in the Wisconsin lakes (USA) to warmer summers in lakes that experienced abnormally high water temperatures. Such events are predicted to double by 2041–2059 and increase four-fold by 2081–2099 compared to historical levels ( [[#Till--2019|Till et al., 2019]] ). This anticipated increase in die-offs may facilitate warm-water fish species displacing cool-water species ( [[#Hansen--2017|Hansen et al., 2017]] ; [[#Jennings--2021|Jennings et al., 2021]] ). Floods mobilise nutrients and sediment, and aid dispersal of invasive species in rivers ( [[#Death--2015|Death et al., 2015]] ), while drought extremes reduce river connectivity, threatening biodiversity in rivers (section 2.3.3.5) ( [[#Tickner--2020|Tickner et al., 2020]] ).

<div id="_idContainer014" class="Box_Header-continued"></div>

Cross-Chapter Box EXTREMES

'''Learnings from Recent Extremes'''
These examples show that the impact of an extreme event is a function of its characteristics and those of the exposed ecosystem. The timing, frequency, absolute magnitude and geographic extent of the extreme event, relative to antecedent conditions and the life cycle, resistance and resilience of the natural community, all determine the biological response (Figure Cross-Chapter Box Extremes.1) ( [[#Hillebrand--2018|Hillebrand et al., 2018]] ; [[#Gruber--2021|Gruber et al., 2021]] ). The impact appears to be greater when extreme events occur more frequently, particularly when the interval between events is insufficient to allow recovery to previous population sizes (e.g., frequent fires and coral bleaching) or coincides with vulnerable life-cycle stages, even when populations are adapted to cope with such disturbances. Events occurring over large spatial areas reduce the potential for recolonisation from nearby populations (e.g., regional droughts causing widespread declines). Often the magnitude of extreme events exceeds historical levels, so organisms are less likely to be adapted to them, particularly when several extremes coincide (e.g., high water temperature and drought) ( [[#Duke--2017|Duke et al., 2017]] ). When hazards occur simultaneously (compound events), the impacts of extremes can be substantially aggravated, triggering a cascade of effects in ecosystems ( [[#Gruber--2021|Gruber et al., 2021]] ).

Several characteristics of natural systems are associated with greater vulnerability to extreme events (Figure Cross-Chapter Box EXTREMES.2), knowledge of which can inform solutions to build resilience and aid recovery ( [[#Robinson--2020|Robinson et al., 2020]] ). Resilience can be built prior to an event by minimising additional disturbances, such as water extraction from river systems, pollution of aquatic systems, fragmentation of land and LULCCs. Managing landscapes to reduce fragmentation and increase habitat extent, connectivity and heterogeneity, by increasing the number and extent of reserves, may provide local refugia from extreme events and enhance post-event recolonisation, but may be less effective for marine systems ( [[IPCC:Wg2:Chapter:Chapter-3#3.6|Section 3.6]] ). Maintaining taxonomic, phylogenetic and functional diversity is important, as more diverse systems may be more stable in the face of disturbances ( [[#Pimm--1984|Pimm, 1984]] ; [[#García-Palacios--2018|García-Palacios et al., 2018]] ).

[[File:4b6c427145d927083e4221f1db0f1bdb IPCC_AR6_WGII_Figure_2_Cross-Chapter-Box-Extremes-2.png]] Figure Cross-Chapter Box EXTREMES.2 | Characteristics of natural systems that affect vulnerability and help identify solutions—both prior to and after extreme events—to build resistance, resilience and recovery

Several characteristics increase vulnerability: low or narrow thermal tolerance, high habitat specificity, low dispersal ability, long generation times, low competitive ability and life-cycle constraints that limit recovery or recolonisation. Populations living close to one or more limiting factors near range edges are also vulnerable ( [[#Arafeh-Dalmau--2019|Arafeh-Dalmau et al., 2019]] ). Understanding these characteristics can inform management intervention to aid recovery following an extreme event. For instance, knowledge of the flying fox’s physiological temperature threshold led to successful interventions, including misting populations to reduce mortality ( [[#Mo--2020|Mo and Roache, 2020]] ), and the development of a ‘heat stress forecaster’, an online tool which uses weather forecasts to identify roosts at risk of extreme heat events ( [[#Ratnayake--2019|Ratnayake et al., 2019]] ). This early warning system increases the preparedness of wildlife management and conservation agencies, enabling efficient allocation of management resources towards the locations that are likely to be the most affected.

Monitoring following extreme events can help identify immediate impacts and the potential for cascading interactions, such as changes to competitive interactions following range shifts, impacts on freshwater ecosystems following wildfires and the spread of invasive species. Ongoing monitoring of recovery and effectiveness of management intervention is important, focussing on habitat-forming species (e.g., kelp, corals and dominant tree species) and keystone species (e.g., filter-feeders, macrophytes and top predators), as the loss of these species can lead to ecosystem tipping points, beyond which the system may not recover ( [[#Collins--2019|Collins et al., 2019]] ) (Sections 2.5.3; 3.4.4.1; 3.4.4.1.4; chapters 9–15, this report).

The acute impacts of extreme events, in addition to the chronic stress of changing mean conditions, are accelerating and amplifying the biological effects of climate change. This amplification is being observed globally and in all realms where life exists. Extreme events are compressing the timeline available for natural systems to adapt, and impeding our ability to identify, develop and adopt solutions. Recent events highlight the urgent need to mitigate global GHG emissions and identify solutions to halt accelerating impacts on natural systems ( [[#Díaz--2020|Díaz et al., 2020]] ).

<div id="_idContainer017" class="Box_Header-continued"></div>

Cross-Chapter Box EXTREMES

<div id="2.4" class="h1-container"></div>

<span id="observed-impacts-of-climate-change-on-species-communities-biomes-key-ecosystems-and-their-services"></span>