Editing IPCC:AR6/WGII/Chapter-10 (section)

=== 10.4.2 Terrestrial and Freshwater Ecosystems ===

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

Sub-regional diversity of ecosystems is high in Asia (Section 10.2.2). Climate-impact drivers of Asian terrestrial ecosystems (ATS) change are global warming, precipitation and Asian monsoon alteration, permafrost thawing and extreme events like dust storms. Observed and projected changes in ATS are affected by several interacting factors. Non-climatic human-related drivers are change of land use, change of human use of natural resources, including species and ecosystems overexploitation as well as other non-sustainable use, socioeconomic changes and direct impacts of rising greenhouse gases (GHGs). Ecosystem vulnerability has resulted from complex interactions of CIDs and non-climate drivers. Species interaction and natural variability of organisms, species and ecosystems is currently poorly understood, and much more work still needs to be done to unravel these multiple stressors (i.e., [[#Berner--2013|Berner et al., 2013]] ; [[#Brazhnik--2015|Brazhnik and Shugart, 2015]] ).

<div id="10.4.2.1" class="h3-container"></div>

<span id="observed-impacts"></span>
==== 10.4.2.1 Observed Impacts ====

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

<div id="10.4.2.1.1" class="h4-container"></div>

<span id="biomes-and-mountain-treeline"></span>
===== 10.4.2.1.1 Biomes and mountain treeline =====

<div id="h4-1-siblings" class="h4-siblings"></div>

Changes in biomes in Asia are compatible with a response to regional surface air temperature increase ( [[#Arias--2021|Arias et al., 2021]] ) ( ''medium agreement, medium evidence'' ). Expansion of the boreal forest and reduction of the tundra area is observed for about 60% of latitudinal and altitudinal sites in Siberia ( [[#Rees--2020|Rees et al., 2020]] ). In Central Siberia, the changes in climate and disturbance regimes are shifting the southern taiga ecotone northward ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). In Taimyr, no significant changes in the forest boundary have been observed during the past three decades ( [[#Pospelova--2017|Pospelova et al., 2017]] ). For the Japanese archipelago, it is suggested that the change in tree community composition along the temperature gradient is a response to past and/or current climate changes ( [[#Suzuki--2015|Suzuki et al., 2015]] ).

Alpine treeline position in Asian mountains in recent decades either moves upwards in North Asia or demonstrates multi-directional shifts in Himalaya ( ''high confidence'' ). Since AR5, in North Asia new evidence has appeared of tree expansion into mountain tundra and steppe, of intensive reproduction and increase in tree stands productivity in the past 30–100 years at the upper treeline in the Ural Mountains ( [[#Shiyatov--2015|Shiyatov and Mazepa, 2015]] ; [[#Zolotareva--2017|Zolotareva and Zolotarev, 2017]] ; [[#Moiseev--2018|Moiseev et al., 2018]] ; [[#Sannikov--2018|Sannikov et al., 2018]] ; [[#Fomin--2020|Fomin et al., 2020]] ; [[#Gaisin--2020|Gaisin et al., 2020]] ), in the Russian Altai Mountains ( [[#Kharuk--2017a|Kharuk et al., 2017a]] ; [[#Cazzolla%20Gatti--2019|Cazzolla Gatti et al., 2019]] ) and in the Putorana Mountains ( [[#Kirdyanov--2012|Kirdyanov et al., 2012]] ; [[#Pospelova--2017|Pospelova et al., 2017]] ; [[#Grigor’ev--2019|Grigor’ev et al., 2019]] ). Lower treelines in the southernmost ''Larix sibirica'' forests in the Saur Mountains, eastern Kazakhstan, have suffered from increased drought stress in recent decades causing forest regeneration and tree growth decrease, and tree mortality increase ( [[#Dulamsuren--2013|Dulamsuren et al., 2013]] ). In Jeju Island, Republic of Korea, recent warming has enhanced ''Quercus mongolica'' growth at its higher distribution and has led to ''Abies koreana'' (ABKO) growth reduction at all elevations, except the highest locality. Thus, the combination of warming, increasing competition and frequent tropical cyclone disturbances could lead to population decline or even extinction of ABKO at Jeju Island ( [[#Altman--2020|Altman et al., 2020]] ). In the Himalaya, the treeline over recent decades either moves upwards ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Suwal--2016|Suwal et al., 2016]] ; [[#Sigdel--2018|Sigdel et al., 2018]] ; [[#Tiwari--2018|Tiwari and Jha, 2018]] ) or does not show upslope advance ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Gaire--2017|Gaire et al., 2017]] ; [[#Singh--2018c|Singh et al., 2018c]] ), or moves downwards ( [[#Bhatta--2018|Bhatta et al., 2018]] ). In the Tibetan Plateau, the treeline either shifted upwards or showed no significant upwards shift ( [[#Wang--2019c|Wang et al., 2019c]] ). This can be explained by site-specific complex interaction of positive effect of warming on tree growth, and negative effects of drought stress, change in snow precipitation, inter- and intraspecific interactions of trees and shrubs, land-use change (especially grazing) and other factors ( [[#Liang--2014|Liang et al., 2014]] ; [[#Lenoir--2015|Lenoir and Svenning, 2015]] ; [[#Tiwari--2017|Tiwari et al., 2017]] ; [[#Sigdel--2018|Sigdel et al., 2018]] ; [[#Tiwari--2018|Tiwari and Jha, 2018]] ; [[#Sigdel--2020|Sigdel et al., 2020]] ). It is largely unknown how broader-scale climate inputs, such as pre-monsoon droughts, interact with local-scale factors to govern treeline response patterns ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Müller--2016|Müller et al., 2016]] ; [[#Bhatta--2018|Bhatta et al., 2018]] ; [[#Singh--2019b|Singh et al., 2019b]] ).

<div id="10.4.2.1.2" class="h4-container"></div>

<span id="species-ranges-and-biodiversity"></span>
===== 10.4.2.1.2 Species ranges and biodiversity =====

<div id="h4-2-siblings" class="h4-siblings"></div>

Since AR5, new evidence has appeared of alterations in terrestrial and freshwater species, populations and communities in line with climate change across Asia ( ''medium to high confidence'' ) ( [[#Arias--2021|Arias et al., 2021]] ). In North Asia, temperature increase and droughts have promoted spread northward of the current silk moth outbreak (has affected nearly 2.5 × 10 6 ha) in Central Siberia dark taiga since 2014 ( [[#Kharuk--2017b|Kharuk et al., 2017b]] ; [[#Kharuk--2020|Kharuk et al., 2020]] ). The climatic range of the Colorado potato beetle ( ''Leptinotarsa decemlineata'' ) in 1991–2010 expanded east- and northward in Siberia and the Russian Far East compared with the 1951–1970 range ( [[#Popova--2014|Popova, 2014]] ). The climatic range of ''Ixodes ricinus'' , a vector of dangerous human diseases, expanded into Central Asia and south of the Russian Far East ( [[#Semenov--2020|Semenov et al., 2020]] ). A butterfly ( ''Melanargia russiae'' ) in the Middle Urals moved northward ( [[#Zakharova--2017|Zakharova et al., 2017]] ). Thrush birds in West Siberia penetrated northward up to the limits of the sparse woodlands ( [[#Ryzhanovskiy--2019a|Ryzhanovskiy, 2019a]] ). The increase in the length of frost-free period observed in the Ilmen Nature Reserve, Middle Urals, during recent decades is supposed to be interlinked with changes in the amplitude and frequency of population waves of bank vole ( [[#Kiseleva--2020|Kiseleva, 2020]] ). In Katunskiy Biosphere Reserve, Russian Altai, in the period 2005–2015, alpine plant species have shifted towards higher altitudes by 5.3 m on average ( [[#Artemov--2018|Artemov, 2018]] ). Wild reindeer herds in Taimyr, north of Central Siberia, migrated northward to the Arctic Sea coast in hot summers between 1999–2003 and 2009–2016 because of an earlier massive emergence of bloodsucking insects ( [[#Pospelova--2017|Pospelova et al., 2017]] ). In Yakutia, the ranges of red deer, elk and the northern pika are expanding, and the winter survival of the mouse-like rodents has increased ( [[#Safronov--2016|Safronov, 2016]] ). In the Chukchi Sea, in recent decades the average duration polar bears spent onshore increased by 30 d ( [[#Rode--2015b|Rode et al., 2015b]] ) in line with global warming and the rapid decline of their sea ice habitat ( [[#Derocher--2013|Derocher et al., 2013]] ; [[#Jenssen--2015|Jenssen et al., 2015]] ; [[#Rode--2015a|Rode et al., 2015a]] ).

In Central Kazakh Steppe, in line with warming, in 2018 there were more ‘southern’ sub-arid species in the communities and fewer relatively ‘northern’ boreal and polyzonal species of ground beetles (Carabidae) and black beetles (Tenebrionidae) than in 1976–1978 ( [[#Mordkovich--2020|Mordkovich et al., 2020]] ). The present distribution of Asian black birch ( ''Betula davurica'' Pall.) in East and North Asia was formed as a result of northward expansion during post-Last Glacial Maximum global warming ( [[#Shitara--2018|Shitara et al., 2018]] ). Both upper and lower limits of avifauna of two New Guinean mountains, Mt. Karimui and Karkar Island, have been shifting upslope since 1965 ( [[#Freeman--2014|Freeman and Freeman, 2014]] ). In Republic of Korea, for the past 60 years, the northern boundary line of 63 southern butterfly species has moved further north ( [[#Bae--2020|Bae et al., 2020]] ). The change in the butterflies’ occurrence in this period has been influenced mostly by large-scale reforestation, not by climate change ( [[#Kwon--2021|Kwon et al., 2021]] ). Warming-driven geographic range shift was recorded in 87% of 124 endemic plant species studied in the Sikkim Himalaya in the periods 1849–1850 and 2007–2010 ( [[#Telwala--2013|Telwala et al., 2013]] ). In Darjeeling, India, significant change in lichen community structure was shown in response to climate change and anthropogenic pollution ( [[#Bajpai--2016|Bajpai et al., 2016]] ).

The observed loss of biodiversity and habitat of animals and plants has been linked to climate change in some parts of Asia ( ''high confidence'' ). Climate change, together with human disturbances, have caused local extinction of some large and medium-sized mammals during the past three centuries in China ( [[#Wan--2019|Wan et al., 2019]] ). Climate change has shown significant impacts on subalpine plant species at low altitudes and latitudes in Republic of Korea and may impose a big threat to these plant species ( [[#Adhikari--2018|Adhikari et al., 2018]] ; [[#Kim--2019c|Kim et al., 2019c]] ). Climate change has caused habitat loss of amphibians ( [[#Surasinghe--2011|Surasinghe, 2011]] ) and extinction of some endemic species in Sri Lanka ( [[#Kottawa-Arachchi--2017|Kottawa-Arachchi and Wijeratne, 2017]] ).

There is evidence that climate change can alter species interaction or spatial distribution of invasive species in Asia ( ''high confidence'' ). Climate warming has enhanced the competitive ability of the native species ( ''Sparganium angustifolium'' ) against the invasive species ( ''Egeria densa'' ) in China under a mesocosm experiment in a greenhouse ( [[#Yu--2018e|Yu et al., 2018e]] ). It has also increased the non-target effect on a native plant ( ''Alternanthera sessilis'' ) by a biological control beetle ( ''Agasicles hygrophila'' ) in China due to range expansion of the beetle and change of phenology of the plant ( [[#Lu--2015|Lu et al., 2015]] ). Climate warming has expanded the distribution of invasive bamboos ( ''Phyllostachys edulis'' and ''P. bambusoides'' ) northward and upslope in Japan ( [[#Takano--2017|Takano et al., 2017]] ), while soil dry-down rates have been a key driver of invasion of dwarf bamboo ( ''Sasa kurilensis'' ) in central Hokkaido above and below the treeline ( [[#Winkler--2016|Winkler et al., 2016]] ).

Climate change along with land-use and land-cover change influences soil organic carbon content, microbial biomass C, microbial respiration and the soil carbon cycle in the Hyrcanian forests of Iran ( [[#Soleimani--2019|Soleimani et al., 2019]] ; [[#Francaviglia--2020|Francaviglia et al., 2020]] ). In the fir forest ecosystems of the Tibetan Plateau, winter warming affects the ammonia-oxidising bacteria and archaea, thus altering the nitrogen cycle ( [[#Huang--2016|Huang et al., 2016]] ). Ecosystem carbon pool in the spruce forests of the northeast Tibetan Plateau was reduced by about 25% by deforestation due to recent decades of climate warming as well as wood pasture and logging ( [[#Wagner--2015|Wagner et al., 2015]] ). In Mongolia’s forest steppe, recent decades of drought- and land-use-induced deforestation has reduced the ecosystem carbon stock density by about 40% ( [[#Dulamsuren--2016|Dulamsuren et al., 2016]] ). In Inner Mongolia, the predicted decreases in precipitation and warming for most of the temperate grassland region could lead to a pH change, which would contribute to a soil C-N-P decoupling that could reduce plant growth and production in arid ecosystems ( [[#Jiao--2016|Jiao et al., 2016]] ).

In Central Asia, in the Vakhsh, Kafirnigan and Kyzylsu river basins, Tajikistan, it has been shown that temperature stimulates algal species diversity, while precipitation and altitude suppress it ( [[#Barinova--2015|Barinova et al., 2015]] ). In line with the warming of Lake Baikal, Russia, since the 1990s in the lake’s south basin, there have been shifts in diatom community composition towards higher abundances of the cosmopolitan ''Synedra acus'' and a decline in endemic species, mainly ''Cyclotella minuta'' and ''Stephanodiscus meyerii'' , and to a lesser extent ''Aulacoseira baicalensis'' and ''A. skvortzowii'' ( [[#Roberts--2018|Roberts et al., 2018]] ). In Gonghai Lake, North China, diatom biodiversity has increased remarkably from 1966, but began to decline after 1990 presumably in response to rapid climate warming ( [[#Yan--2018|Yan et al., 2018]] ).

<div id="10.4.2.1.3" class="h4-container"></div>

<span id="wildfires"></span>
===== 10.4.2.1.3 Wildfires =====

<div id="h4-3-siblings" class="h4-siblings"></div>

Climate change, human activity and lightning determine increases in wildfire severity and area burned in North Asia (high detection with medium-to-low attribution to climate change). In North Asia, the extent of fire-affected areas in boreal forest can be millions of hectares in a single extreme fire year ( [[#Duane--2021|Duane et al., 2021]] ) and nearly doubled between 1970 and 1990 ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). During recent decades, the number, area and frequency of forest fires increased in Putorana Plateau (north of Central Siberia), in larch-dominated forests of Central Siberia and in Siberian forests as a whole. This increase is in line with an increase in the average annual air temperature, air temperature anomalies, droughts and the length of fire season ( [[#Ponomarev--2016|Ponomarev et al., 2016]] ; [[#Kharuk--2017|Kharuk and Ponomarev, 2017]] ; [[#Pospelova--2017|Pospelova et al., 2017]] ). The number of forest fires and damaged areas in Gangwon Province and the Yeongdong area in the 2000s increased by factors of 1.7 and 5.6, respectively, compared with the 1990s ( [[#Bae--2020|Bae et al., 2020]] ). Climate change is not the sole cause of the increase in forest fire severity ( [[#Wu--2014|Wu et al., 2014]] ; [[#Wu--2018d|Wu et al., 2018d]] ). Ignition is often facilitated by lightning ( [[#Canadell--2021|Canadell et al., 2021]] ), and over 80% of fires in Siberia are ''likely'' anthropogenic in origin (e.g., ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). Gas field development and Indigenous tundra burning practices that may get out of control contribute to fire frequency in the forest–tundra of West Siberia ( [[#Adaev--2018|Adaev, 2018]] ; [[#Moskovchenko--2020|Moskovchenko et al., 2020]] ). Climate change in combination with socioeconomic changes has resulted in an increase in fire severity and area burned in South Siberia, and illegal logging increases fire danger in forest–steppe Scots pine stands ( [[#Ivanova--2010|Ivanova et al., 2010]] ; [[#Schaphoff--2016|Schaphoff et al., 2016]] ).

<div id="10.4.2.1.4" class="h4-container"></div>

<span id="phenology-growth-rate-and-productivity"></span>
===== 10.4.2.1.4 Phenology, growth rate and productivity =====

<div id="h4-4-siblings" class="h4-siblings"></div>

In East and North Asia, satellite measurements and ground-based observations in recent decades demonstrate either an increase in the length of plant growth season over sub-regions or in some territories in line with climate warming, or do not show any significant trend in other territories ( ''high confidence'' ). In recent decades in China, there has been an increasing trend in annual mean grassland net primary production (NPP), average leaf area index and lengthening of the local growing season ( [[#Piao--2015|Piao et al., 2015]] ; [[#Zhang--2017b|Zhang et al., 2017b]] ; [[#Xia--2019|Xia et al., 2019]] ). Nevertheless, phenology patterns vary across different studies, species and parts of China. In most regions of Northeast China, start date and length of land surface phenology from 2000 to 2015 had advanced by approximately 1 d yr −1 , except in the needle-leaf and cropland areas ( [[#Zhang--2017d|Zhang et al., 2017d]] ). For Inner Mongolia, it has been shown that neither the start of growing season (SOS) nor the end of growing season (EOS) presented detectable progressive patterns at the regional level in 1998–2012, except for the steppe–desert (6% of the total area) ( [[#Sha--2016|Sha et al., 2016]] ). In the Tianshan Mountains in China, the NPP of only 2 out of 12 types of vegetation increased in spring, and the NPP of only one type increased in autumn from 2000–2003 to 2012–2016 ( [[#Hao--2019|Hao et al., 2019]] ). In Republic of Korea, from 1970 to 2013, the SOS has advanced by 2.7 d per decade, and the EOS has been delayed by 1.4 d per decade ( [[#Jung--2015|Jung et al., 2015]] ). During the past decade, leaf unfolding has accelerated at a rate of 1.37 d yr −1 , and the timing of leaf fall has been delayed at a rate of 0.34 d yr −1 ( [[#Kim--2019d|Kim et al., 2019d]] ). Cherry blossoms are predicted to flower 6.3 and 11.2 d earlier after 2090 according to scenarios RCP4.5 and RCP8.5, respectively ( [[#Bae--2020|Bae et al., 2020]] ). On the Tibetan Plateau, it was found that the SOS has advanced and the EOS has been delayed over the past 30–40 years ( [[#Yang--2017|Yang et al., 2017]] ). Using normalised difference vegetation index (NDVI) datasets and ground-based Budburst data ( [[#Wang--2017c|Wang et al., 2017c]] ) found no consistent evidence that the SOS has been advancing or delaying over the Tibetan Plateau during the past two to three decades. The discrepancies among different studies in the trends of spring phenology over the Tibetan Plateau could be largely attributed to the use of different phenology retrieval methods. An uncertainty exists with the relationship between land-surface phenology and climate change estimated by satellite-derived NDVI because these indices are usually composite products of a number of days (e.g., 16 d) that could fail to capture more details. Besides, due to lack of ''in situ'' observations, the SOS and EOS at large areas cannot be easy defined ( [[#Zhang--2017d|Zhang et al., 2017d]] ).

In North Asia, in Central Siberia and south of West Siberia, the growth index of Siberian larch based on tree-ring width increased with the onset of warming and changed in antiphase with aridity in the 1980s ( [[#Kharuk--2018|Kharuk et al., 2018]] ). In Mongolia and Kazakhstan, the temperature increase over the previous decade promoted radial stem increment of the Siberian larch. However, the simultaneous influence of increased temperature, decreased precipitation and increased anthropogenic pressure resulted in widespread declines in forest productivity and reduced forest regeneration, and increased tree mortality ( [[#Dulamsuren--2013|Dulamsuren et al., 2013]] ; [[#Lkhagvadorj--2013a|Lkhagvadorj et al., 2013a]] ; [[#Lkhagvadorj--2013b|Lkhagvadorj et al., 2013b]] ; [[#Dulamsuren--2014|Dulamsuren et al., 2014]] ; [[#Khansaritoreh--2017|Khansaritoreh et al., 2017]] ). In Eastern Taimyr, growing season, the number of flowering shoots, annual increment, success of seed ripening and vegetation biomass have increased considerably in recent decades ( [[#Pospelova--2017|Pospelova et al., 2017]] ). In Vishera Nature Reserve, northern Ural Mountains, annual temperature has increased in recent decades in parallel with a summer temperature drop and an increase in summer frost numbers. As a result, trends in vegetation change are mostly unreliable ( [[#Prokosheva--2017|Prokosheva, 2017]] ).

In Asia, the date of arrival of migrant birds to nesting areas and the date of departure from winter areas are changing consistently with climate change ( ''medium confidence'' ). Time of arrival of the grey crow to the Lower Ob river region, northwest Siberia, shifted to earlier dates in the period 1970–2017, which is consistent with an increase in the daily average temperatures on the day of arrival ( [[#Ryzhanovskiy--2019b|Ryzhanovskiy, 2019b]] ). In Ilmen Nature Reserve, Urals, an earlier arrival of the majority of nesting bird species has not been observed in recent decades. This is explained by the fact that other factors, such as the weather of each spring month of particular years, population density in the previous nesting period, the seed yield of the main feeding plants and migration of wintering species from adjacent areas, determinate the long-term dynamics of bird arrival ( [[#Zakharov--2016|Zakharov, 2016]] ; [[#Zakharov--2018|Zakharov, 2018]] ). In Yokohama, Japan, observations since 1986 have revealed that the arrival of six winter bird species came later and the departure earlier than in the past, due to warmer temperatures ( [[#Kobori--2012|Kobori et al., 2012]] ; [[#Cohen--2018|Cohen et al., 2018]] ). Some papers corroborate that earlier start and later end of phenological events in Asia are associated with global warming; however, other papers do not confirm such a connection. Comparison and synthesis of results is impeded by usage of different metrics, measurement methods and models (e.g., [[#Hao--2019|Hao et al., 2019]] ). Relative contribution of climatic stress and other factors to phenology and plant growth trends are poorly understood (e.g., [[#Andreeva--2019|Andreeva et al., 2019]] ).

<div id="10.4.2.2" class="h3-container"></div>

<span id="projected-impacts"></span>
==== 10.4.2.2 Projected Impacts ====

<div id="h3-7-siblings" class="h3-siblings"></div>

<div id="10.4.2.2.1" class="h4-container"></div>

<span id="biomes-and-mountain-treeline-1"></span>
===== 10.4.2.2.1 Biomes and mountain treeline =====

<div id="h4-5-siblings" class="h4-siblings"></div>

Across Asia, under a range of representative concentration pathways (RCPs) and other scenarios, rising temperatures are expected to contribute to a northward shift of biome boundaries and an upwards shift of mountain treeline ( ''medium confidence'' ). Northward shift and area change of bioclimatic zones in Siberia ( [[#Anisimov--2017|Anisimov et al., 2017]] ; [[#Torzhkov--2019|Torzhkov et al., 2019]] ) and northeast Asia ( [[#Choi--2019|Choi et al., 2019]] ) are projected. Projected changes in vegetation in China at the end of the 21st century reveal that the area covered by cold–dry potential vegetation decreases as the area covered by warm–humid potential vegetation increases ( [[#Zhao--2017a|Zhao et al., 2017a]] ). Forest expansion into mountain tundra of the northern Urals is expected ( [[#Sannikov--2018|Sannikov et al., 2018]] ). In Republic of Korea, projected under RCP4.5 and RCP8.5 in the 2070s, suitable area loss of six subalpine tree species, namely, Korean fir, Khingan fir, Sargent juniper, Yeddo spruce, Korean yew and Korean arborvitae, range from 17.7 ± 20.1% to 65.2 ± 34.7%, respectively ( [[#Lee--2021b|Lee et al., 2021b]] ). Korean fir forests would be replaced by temperate forests at lower elevations, while they would continuously persist at the highest elevations on Mt. Halla, Jeju Island and Republic of Korea ( [[#Lim--2018|Lim et al., 2018]] ). Himalayan birch at its upper distribution boundary either is projected to move upwards ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Bobrowski--2018|Bobrowski et al., 2018]] ) or considered to downslope as a response to global-change-type droughts ( [[#Liang--2014|Liang et al., 2014]] ). Upwards shift in elevation of bioclimatic zones, decreases in area of the highest elevation zones and large expansion of the lower tropical and sub-tropical zones can be expected by the year 2050 throughout the transboundary Kailash Sacred Landscape of China, India and Nepal, and ''likely'' within the Himalayan region more generally ( [[#Zomer--2014|Zomer et al., 2014]] ).

In North Asia, a shift is projected in the dominant biomes from conifers to deciduous species across Russia after 20 years of altered climate conditions ( [[#Shuman--2015|Shuman et al., 2015]] ). In South Siberia, [[#Brazhnik--2015|Brazhnik and Shugart (2015)]] projected a shift from the boreal forest to the steppe biome. [[#Rumiantsev--2013|Rumiantsev et al. (2013)]] also project a positive northward shift of vegetation boundaries for the greater part of West Siberia in line with warming; however, no shift for the north of West Siberia and negative shift for the southern Urals and northwest Kazakhstan are projected for 2046–2065. The replacement of forest–steppe with steppe at the lower treeline in South Siberia is projected ( [[#Brazhnik--2015|Brazhnik and Shugart, 2015]] ), and retreat of larch forests from the southernmost strongholds of boreal forest in eastern Kazakhstan is expected as part of a global process of forest dieback in semiarid regions ( [[#Dulamsuren--2013|Dulamsuren et al., 2013]] ). In North Asia, tree growth is intertwined with permafrost, snowpack, insect outbreaks, wildfires, seed dispersal and climate (e.g., [[#Klinge--2018|Klinge et al., 2018]] ). It is challenging to isolate the affects of individual factors, particularly since they can interact on one another in unanticipated ways because the underlying mechanisms are not well understood ( [[#Berner--2013|Berner et al., 2013]] ; [[#Brazhnik--2015|Brazhnik and Shugart, 2015]] ). The accuracy of treeline-shift projections is limited because projections are based on vegetation models which do not consider all the factors ( [[#Tishkov--2020|Tishkov et al., 2020]] ). The regional vegetation model structure and parameterisation can affect model performance, and the corresponding projections can differ significantly ( [[#Shuman--2015|Shuman et al., 2015]] ).

<div id="10.4.2.2.2" class="h4-container"></div>

<span id="species-ranges-and-biodiversity-1"></span>
===== 10.4.2.2.2 Species ranges and biodiversity =====

<div id="h4-6-siblings" class="h4-siblings"></div>

Considerable changes in plant and animal species distribution under warming stress are expected in Asia until 2100 ( ''high confidence'' ). In East Asia, ''Cunninghamia lanceolate'' , a fast-growing and widely distributed coniferous timber species in China, is projected to increase distribution, to decrease the establishment probability and to reduce total NPP by the 2050s ( [[#Liu--2014c|Liu et al., 2014c]] ). In the monsoon regions of Asia, by the end of the 21st century, NPP is projected to increase by 9–45% ( [[#Ito--2016|Ito et al., 2016]] ). Under climate change on the Korean Peninsula (KP), the potential habitat for ''Abies nephrolepis'' is the northern part of KP, and ''A. koreana'' will disappear from Jeju Island and shrink significantly in the KP ( [[#Yun--2018|Yun et al., 2018]] ), while evergreen forests will expand to the northern part of KP ( [[#Koo--2018|Koo et al., 2018]] ; [[#Lim--2018|Lim et al., 2018]] ). It is expected that under projected warming, fig species in China will expand to higher latitudes and altitudes ( [[#Chen--2018c|Chen et al., 2018c]] ). In Japan, under the A1B scenario, 89% of the area currently covered by the ''Fagus crenata'' -dominant forest type will be replaced by ''Quercus'' spp.-dominant forest types ( [[#Matsui--2018|Matsui et al., 2018]] ). Current trends of climate change will reduce distribution of tall (2–2.5 m high) herb communities in Japan, and will increase suitably for them in the Russian Far East ( [[#Korznikov--2019|Korznikov et al., 2019]] ). A range expansion of ''Lobaria pindarensis'' , an endemic epiphytic lichen in the HKH region, is projected to move to the northeast and to higher altitudes in response to climate change, although the species’ low dispersal abilities and the local availability of trees as a substratum will considerably limit latitudinal and altitudinal shifts ( [[#Devkota--2019|Devkota et al., 2019]] ).

The climatic range of Italian locust ( ''Calliptamus italicus'' L.) under RCP4.5 will expand north- and east-ward to Siberia, the Russian Far East and Central Asia ( [[#Popova--2016|Popova et al., 2016]] ). In Krasnoyarsk Krai, Siberia, it is projected that the needle cast disease caused by fungi from the genus ''Lophodermium'' Chevall. in the Scots pine nurseries would shift northward up to 2080 under A2 and B1 scenarios ( [[#Tchebakova--2016|Tchebakova et al., 2016]] ). All four RCP scenarios showed north-ward expansion of vulnerable regions to pine wilt disease in China, Republic of Korea, the Russian Far East and Japan under climate conditions in 2070 ( [[#Hirata--2017|Hirata et al., 2017]] ), and during 2026–2050 in Japan ( [[#Matsuhashi--2020|Matsuhashi et al., 2020]] ). It is noteworthy that disease expansion depends not only on climatic factors but also on the dispersal capacity of insect vectors, the transportation of infected logs to non-infected regions and the susceptibility of host trees (e.g., [[#Gruffudd--2016|Gruffudd et al., 2016]] ). The suitable habitat area of the snow leopard ''Panthera uncia'' is projected to increase by 20% under the IPCC Scenario A1B by 2080: for the seven northernmost snow leopard range states (Afghanistan, Tajikistan, Uzbekistan, Kyrgyzstan, Kazakhstan, Russia and Mongolia) the suitable habitat area will increase, while habitat loss is expected on the southern slope of the Himalaya and the southeast Tibetan Plateau ( [[#Farrington--2016|Farrington and Li, 2016]] ). Climate change projected under four RCP scenarios will not affect the distribution patterns of Turkestan Rock Agama ''Paralaudakia lehmanni'' (Nikolsky 1896; [[#Sancholi--2018|Sancholi, 2018]] ). In Iran, among 37 studied species of plants and animals, the ranges of 30 species are expected to shrink and ranges of 7 species are expected to increase between 2030 and 2099 under climate-change stress ( [[#Yousefi--2019|Yousefi et al., 2019]] ).

Future climate change would cause biodiversity and habitat loss in many parts of Asia using modelling approaches ( ''high confidence'' ). [[#Warren--2018|Warren et al. (2018)]] projected that extirpation risks to terrestrial taxa (plants, amphibians, reptiles, birds and mammals) from 2°C to 4.5°C global warming in 12 ‘priority places’ in Asia, under the assumption of no adaptation (i.e., dispersal) by the 2080s, is from 12.2–26.4% to 29–56% (Table 10.1; Figure 10.4). Under different scenarios, future climate change could reduce the extent of a suitable habitat for giant pandas ( [[#Fan--2014|Fan et al., 2014]] ), moose ( ''Alces alces'' ) ( [[#Huang--2016|Huang et al., 2016]] ), black muntjac ( ''Muntiacus crinifrons'' ) ( [[#Lei--2016|Lei et al., 2016]] ) and the Sichuan snub-nosed monkey ( ''Rhinopithecus roxellana'' ) ( [[#Zhang--2019d|Zhang et al., 2019d]] ) in China; the Persian leopard ( ''Panthera pardus saxicolor'' ) in Iran ( [[#Ashrafzadeh--2019a|Ashrafzadeh et al., 2019a]] ); the Bengal tiger ( [[#Mukul--2019|Mukul et al., 2019]] ) in India; and four tree-snail species ( ''Amphidromus'' ) in Thailand ( [[#Klorvuttimontara--2017|Klorvuttimontara et al., 2017]] ). However, climate change would have little impact on the habitats of the Asian elephant, but would cause extinction of the Hoolock gibbon in Bangladesh by 2070 ( [[#Alamgir--2015|Alamgir et al., 2015]] ). Climate change would increase the distribution of the Mesopotamian spiny-tailed lizard ( ''Saara loricate'' ) in Iran ( [[#Kafash--2016|Kafash et al., 2016]] ). Future climate change would reduce the suitable habitat of certain protected plants ( [[#Zhang--2014|Zhang et al., 2014]] ) including ''Polygala tenuifolia'' Wild ( [[#Lei--2016|Lei et al., 2016]] ); relict species in East Asia ( [[#Tang--2018|Tang et al., 2018]] ); tree ''Abies'' ( [[#Ran--2018|Ran et al., 2018]] ) in China; two threatened medicinal plants ( ''Fritillaria cirrhosa'' and ''Lilium nepalense'' ) in Nepal ( [[#Rana--2017|Rana et al., 2017]] ); a medicinal and vulnerable plant species ''Daphne mucronata'' ( [[#Abolmaali--2018|Abolmaali et al., 2018]] ) and ''Bromus tomentellus'' in Iran ( [[#Sangoony--2016|Sangoony et al., 2016]] ); a valuable threatened tree species, ''Dysoxylum binectariferum'' , in Bangladesh ( [[#Sohel--2016|Sohel et al., 2016]] ); and plant diversity in Republic of Korea ( [[#Lim--2018|Lim et al., 2018]] ).

'''Table 10.1 |''' Projected extirpation risks: percentage of taxa (plants, amphibians, reptiles, birds and mammals) for 2°C and 4.5°C global warming in ‘priority places’ in Asia, without adaptation by the 2080s. (From [[#Warren--2018|Warren et al., 2018]] ).

{| class="wikitable"
|-
! Priority places

! At 2°C (%)

! At 4.5°C (%)

|-
| Mekong

| 26.4

| 55.2

|-
| Baikal

| 22.8

| 49.5

|-
| Yangtze

| 20

| 42.6

|-
| Coral Triangle

| 19.2

| 41.8

|-
| Western Ghats

| 18.8

| 41.67

|-
| New Guinea

| 19.8

| 41.2

|-
| Atlai-Syan

| 18.6

| 37

|-
| Sumatra

| 16.8

| 37

|-
| Borneo

| 17.6

| 36.8

|-
| Amur

| 14.2

| 35.6

|-
| Eastern Himalayas

| 12.2

| 29

|-
| Black sea

| 26.2

| 56

|}

<div id="_idContainer012" class="Figure"></div>

[[File:a091579abc41f0455382430f112766ce IPCC_AR6_WGII_Figure_10_004.png]]

'''Figure 10.4 |''' '''Location of ‘priority places’ in Asia.''' (Modified from [[#Warren--2018|Warren et al., 2018]] ).

The impact of future climate change on invasive species may be species- or region specific ( ''medium confidence'' ). Climate change would promote invasion of a highly invasive aquatic plant ''Eichhornia crassipes'' ( [[#You--2014|You et al., 2014]] ), ''Ambrosia artemisiifolia'' ( [[#Qin--2014|Qin et al., 2014]] ), alligator weed ( ''Alternanthera philoxeroides'' ) ( [[#Wu--2016|Wu et al., 2016]] ), invasive alien plant ''Solidago canadensis'' ( [[#Xu--2014|Xu et al., 2014]] ), three invasive woody oil-plant species ( ''Jatropha curcas, Ricinus communis'' and ''Aleurites moluccana'' ) ( [[#Dai--2018|Dai et al., 2018]] ), and 90 of ~150 poisonous plant species ( [[#Zhang--2017a|Zhang et al., 2017a]] ) in China; six mostly highly invasive species ( ''Ageratum houstonianum'' Mill., ''Chromolaena odorata'' (L.) R.M. King &amp; H. Rob., ''Hyptis suaveolens'' (L.) Poit., ''Lantana camara'' L ''.'' , ''Mikania micrantha'' Kunth and ''Parthenium hysterophorus'' L.) in Nepal (Shrestha et al. 2018); 11 invasive plant species in the western Himalaya ( [[#Thapa--2018|Thapa et al., 2018]] ); alien plants in Georgia ( [[#Slodowicz--2018|Slodowicz et al., 2018]] ); the invasive green anole ( ''Anolis carolinensis'' ) in Japan ( [[#Suzuki-Ohno--2017|Suzuki-Ohno et al., 2017]] ); the Giant African Snail in India ( [[#Sarma--2015|Sarma et al., 2015]] ); and a major insect vector ( ''Monochamus alternatus'' ) of the pine wilt disease ( [[#Kim--2016b|Kim et al., 2016b]] ) and melon thrips ( ''Thrips palmi'' Karny) ( [[#Park--2014|Park et al., 2014]] ) in Republic of Korea. In contrast, a few studies have projected that climate change would inhibit the invasion of one exotic species ( ''Spartina alterniflora'' ) ( [[#Ge--2015|Ge et al., 2015]] ), alien invasive weeds ( [[#Wan--2017|Wan et al., 2017]] ), an invasive plant ( ''Galinsoga parviflora'' ) ( [[#Bi--2019|Bi et al., 2019]] ) and an invasive species ( ''Galinsoga quadriradiata'' ) ( [[#Yang--2018b|Yang et al., 2018b]] ) in China; and two invasive plants ( ''Chromolaena odorata'' and ''Tridax procumben'' s) in India ( [[#Panda--2019|Panda and Behera, 2019]] ).

Five of 15 endemic freshwater fish species in Iran will lose some parts of their current suitable range under climate change by 2070 ( [[#Yousefi--2020|Yousefi et al., 2020]] ). In line with projected large increases in mean water temperature, the strongest increase is projected in exceeded frequency and magnitude of maximum temperature tolerance values for freshwater minnow ( ''Zacco platypus'' ) in East Asia for 2031–2100 ( [[#Van%20Vliet--2013|Van Vliet et al., 2013]] ). Climate change under the A1B scenario is projected to decrease diversity (–0.1%) along with increased local richness (+15%) and range size (+19%) of stream macroinvertebrates in the Changjiang River catchment, southeast China, for the period 2021–2050, while land-use change is predicted to have the strongest negative impact ( [[#Kuemmerlen--2015|Kuemmerlen et al., 2015]] ). The Asian clam ''Corbicula fluminea'' Müller, an invasive species native to southeast China, the Republic of Korea and southeast Russia, is projected to invade Southeast Asia under all four RCP scenarios for the 2041–2060 and 2061–2080 periods ( [[#Gama--2017|Gama et al., 2017]] ). Projected SLR, related aquatic salinisation and alteration in fish species composition may have a negative impact on poor households in southwest coastal Bangladesh ( [[#Dasgupta--2017a|Dasgupta et al., 2017a]] ).

<div id="10.4.2.2.3" class="h4-container"></div>

<span id="wildfires-1"></span>
===== 10.4.2.2.3 Wildfires =====

<div id="h4-7-siblings" class="h4-siblings"></div>

Under regional projections for North Asia, warmer climate will increase forest fire severity by the late 21st century ( ''medium confidence'' ). For the southern taiga in Tuva Republic, Central Siberia, in a warmer climate, both the annual area burned and fire intensity will increase by 2100. For the central taiga in the Irkutsk region, the annual area burned as well as crown fire-to-ground fire ratiowill increase by the late 21st century compared with the historical (1960–1990) estimate. This moves forest composition towards greater contribution of hardwoods (e.g., ''Betula'' spp., ''Populus'' spp.) ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). This shifting was also proved by observations in northern Mongolia, where boreal forest fires ''likely'' promote the relative dominance of ''B. platyphylla'' and threaten the existence of the evergreen conifers, ''Picea obovata'' and ''Pinus sibirica'' ( [[#Otoda--2013|Otoda et al., 2013]] ). For Tuva Republic, warming ambient temperatures increase the potential evapotranspiration demands on vegetation, but if no concurrent increase in precipitation occurs, vegetation becomes stressed and either dies from temperature-based drought stress or more easily succumbs to insects, fire, pathogens or wind throw ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). Although [[#Torzhkov--2019|Torzhkov et al. (2019)]] also projected fire risk (FR) increase in Tuva Republic, they expect FR decrease in the Irkutsk region and Yakutia under RCP8.5, and FR decrease in major parts of Central and East Siberia under RCP4.5 for 2090–2099. This discrepancy is due to differences in models, climate projections, fire severity metrics and other assumptions. According to global projections, FR will increase in Central Asia, Russia, China and India under a range of scenarios ( [[#Sun--2019|Sun et al., 2019]] ).

<div id="10.4.2.3" class="h3-container"></div>

<span id="vulnerabilities-to-key-drivers"></span>
==== 10.4.2.3 Vulnerabilities to Key Drivers ====

<div id="h3-8-siblings" class="h3-siblings"></div>

Both natural and managed ecosystems, ecosystem services and livelihoods in Asia will potentially be substantially impacted by changing climate ( [[#Wu--2018d|Wu et al., 2018d]] ). There will be increased risk for biodiversity, particularly many endemic and threatened species of fauna and flora already under environmental pressure from land-use change and other regional and global processes ( [[#Zomer--2014|Zomer et al., 2014]] ; [[#Rashid--2015|Rashid et al., 2015]] ; [[#Choi--2019|Choi et al., 2019]] ). Biomes shift not only serves as a signal of climate change but also provides important information for resources management and ecotone ecosystem conservation. A widespread upwards encroachment of subalpine forests would displace regionally unique alpine tundra habitats and possibly cause the loss of alpine species ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ). In North Asia, emissions from fires reduce forests’ ability to regulate climate. A warmer and longer growing season will increase vulnerability to fires, although fires can be attributed both to climate warming and to other human and natural influences. Recent field-based observations revealed that the forests in South Siberia are losing their ability to regenerate after fire and other landscape disturbances under a warming climate ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). Data support the hypothesis of a climate-driven increase in fire frequency in boreal forests with the possible turning of boreal forests from a carbon sink to a carbon source ( [[#Ponomarev--2016|Ponomarev et al., 2016]] ; [[#Schaphoff--2016|Schaphoff et al., 2016]] ; [[#Brazhnik--2017|Brazhnik et al., 2017]] ; [[#Ponomarev--2018|Ponomarev et al., 2018]] ); however, warming resulting from forest fire is partly offset by cooling in response to increased surface albedo of burned areas in a snow-on period ( [[#Chen--2018|Chen and Loboda, 2018]] ; [[#Chen--2018a|Chen et al., 2018a]] ; [[#Jia--2019|Jia et al., 2019]] ; [[#Lasslop--2019|Lasslop et al., 2019]] ).

<div id="10.4.2.4" class="h3-container"></div>

<span id="adaptation-options-1"></span>
==== 10.4.2.4 Adaptation Options ====

<div id="h3-9-siblings" class="h3-siblings"></div>

Modelling of the interactions between climate-induced vegetation shifts, wildfire and human activities can provide keys to how people in Asia may be able to adapt to climate change ( [[#Kicklighter--2014|Kicklighter et al., 2014]] ; [[#Tian--2020|Tian et al., 2020]] ). Conservation and sustainable development would benefit from being tailored and modified considering the changing climatic conditions and shifting biomes, mountain belts and species ranges ( [[#Pörtner--2021|Pörtner et al., 2021]] ). Expanding the nature reserves would help species conservation; to facilitate species movements across climatic gradients, an increase in landscape connectivity can be elaborated by setting up habitat corridors between nature reserves and along elevational and other climatic gradients ( [[#Brito-Morales--2018|Brito-Morales et al., 2018]] ; [[#D’Aloia--2019|D’Aloia et al., 2019]] ; [[#United%20Nations%20Climate%20Change%20Secretariat--2019|United Nations Climate Change Secretariat, 2019]] ). Assisted migration of species should be considered for isolated habitats as mountain summits or where movements are constrained by poor dispersal ability. Introducing seeds of the species to new regions will help to protect them from the extinction risk caused by climate change ( [[#Mazangi--2016|Mazangi et al., 2016]] ). In Asian boreal forests, a strategy and integrated programmes should be developed for adaptation of the forests to global climate change, including sustainable forest management, firefighting infrastructure and forest fuel management, afforestation, as well as institutional, social and other measures in line with Sustainable Development Goal (SDG) 15 ‘Life on Land’ ( [[#Isaev--2013|Isaev and Korovin, 2013]] ; [[#Kattsov--2014|Kattsov and Semenov, 2014]] ; [[#Bae--2020|Bae et al., 2020]] ). Improvements in forest habitat quality can reduce the negative impacts of climate change on biodiversity and ecosystem services ( [[#Choi--2021|Choi et al., 2021]] ). Adaptation options for freshwater ecosystems in Asia include increasing connectivity in river networks, expanding protected areas, restoring hydrological processes of wetlands and rivers, creating shade to lower temperatures for vulnerable species, assisted translocation and migration of species ( [[#Hassan--2020|Hassan et al., 2020]] ; Chapter 2). Reduction of non-climate anthropogenic impacts can enhance the adaptive capacity of ecosystems ( [[#Tchebakova--2016|Tchebakova et al., 2016]] ).

<div id="box-10.3" class="h2-container box-container"></div>

'''Box 10.3 | Case Study on Sand and Dust Storm, Climate Change in West Asia’s Iranian Region'''
<div id="h2-23-siblings" class="h2-siblings"></div>

The West Asia region, especially the Tigris–Euphrates alluvial plain, has been recognised as one of the most important dust-source areas in the world ( [[#Cao--2015|Cao et al., 2015]] ). The inhabitants of each of these settlements have experienced a decline in dust storms in recent decades, since the late 1980s at Nouakchott, since 2004 at Zabol and since the late 1970s at Minqin. Iran is mostly arid or semiarid, with deserts making up at least 25 million hectares of the country (NASA, 2018). Iran is experiencing unprecedented climate-related problems such as drying of lakes and rivers, dust storms, record-breaking temperatures, droughts and floods ( [[#Vaghefi--2019|Vaghefi et al., 2019]] ). There are three key factors responsible for the generation of sand and dust storms: strong wind, lack of vegetation and absence of rainfall (EcoMENA, 2020). It seems that all of this is closely related to the heating surface and the occurrence of local dry instabilities ( [[#Ghasem--2012|Ghasem et al., 2012]] ). According to EcoMENA (2020), sand and dust storms cause significant negative impacts on society, the economy and the environment at the local, regional and global levels. The seasonality of the numbers of dusty days (NDD) in Iran shows the highest frequency in summer followed by spring and autumn ( [[#Modarres--2018|Modarres and Sadeghi, 2018]] ). In the past decade, West Asia has witnessed more frequent and intensified dust storms affecting Iran and other Persian Gulf countries ( [[#Nabavi--2016|Nabavi et al., 2016]] ). In terms of long-term frequency of dust events, observational analyses show an overall rising trend of the frequency of Iran’s dust events in recent years ( [[#Alizadeh-Choobari--2016|Alizadeh-Choobari et al., 2016]] ). Results show that there has been a direct relationship between dust event, drought and years of intensive drought ( [[#Dastorani--2019|Dastorani and Jafari, 2019]] ). Compared with the period 1980–2004, in the period 2025–2049, Iran is ''likely'' to experience more extended periods of extreme maximum temperatures in the southern part of the country, more extended periods of dry (for ≥120 d: precipitation &lt;2 mm, T max ≥30°C) as well as wet (for ≤3 d: total precipitation ≥110 mm) conditions and a higher frequency of floods ( [[#Vaghefi--2019|Vaghefi et al., 2019]] ). The slope of precipitation in West Asia shows that during the period 2016–2045 in January, February, July and August, precipitation would increase and decrease in other months of the year ( [[#Ahmadi--2018|Ahmadi et al., 2018]] ). Temperatures in Central Asia have risen significantly within recent decades, whereas mean precipitation remains almost unchanged ( [[#Haag--2019|Haag et al., 2019]] ); however, climatic trends can vary greatly between different sub-regions, across altitudinal levels and within seasons ( [[#Haag--2019|Haag et al., 2019]] ).

<div id="10.4.3" class="h2-container"></div>

<span id="ocean-and-coastal-ecosystems"></span>