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

==== 10.6.4.9 Climate Information Distilled From Multiple Lines of Evidence ====

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There is ''very high confidence'' ( ''high agreement'' , ''robust evidence'' ) that the Mediterranean region has experienced a summer temperature increase in recent decades that is faster than the increase for the Northern Hemisphere summer mean. There is also ''very high confidence'' ( ''high agreement'' , ''robust evidence'' ) that the projected Mediterranean summer temperature increase will be larger than the global warming level, with an increase in the frequency and intensity of heatwaves.

Traditionally, the distillation process to produce contextualized, policy relevant information has taken place at regional or national level. For example, the potential effects of climate change on public health are discussed in several national climate change and adaptation reports ( [[#Bruci--2016|Bruci et al., 2016]] ; [[#MoARE--2016|MoARE, 2016]] ; [[#MoE--2016|MoE, 2016]] ; [[#MoEP--2018|MoEP, 2018]] ; [[#MoEU--2018|MoEU, 2018]] ). Although these reports are extremely helpful and widely used for the development of national adaptation policies, they are often based on non-comprehensive and heterogeneous sources of climate information (e.g., [[#MEEN--2018|MEEN, 2018]] ; [[#MoE/UNDP/GEF--2019|MoE/UNDP/GEF, 2019]] ). For instance, future climate change projections are based on a limitednumber of socio-economic scenarios and climate model simulations, which are also often not evaluated comprehensively (e.g., [[#Bruci--2016|Bruci et al., 2016]] ; [[#MoARE--2016|MoARE, 2016]] ; [[#MoEU--2018|MoEU, 2018]] ). In addition, these reports are often not peer-reviewed, not availablein English, and mainly limited to the country level, thus making it difficult to compare the details of the climate information across them.

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'''Box 10.3 | Urban Climate: Processes and Trends'''

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Urban areas have special interactions with the climate system that produce heat islands. This box presents information about these processes, how they are parametrized in climate modules, and on the role of urban monitoring networks. A discussion on the observed climate trends and climate change projections for urban areas follows.

'''Urban heat island'''

During nighttime, urban centres are often several degrees warmer than the surrounding rural area, a phenomenon known as the nighttime canopy urban heat island effect ( [[#Bader--2018|Bader et al., 2018]] ; [[#Kuang--2019|Kuang, 2019]] ; [[#Li--2019|Li et al., 2019]] ; Y. [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] a). While green and blue infrastructures can mitigate the urban heat island effect, three main factors contribute to its development ( [[#Hamdi--2020|Hamdi et al., 2020]] ; [[#Masson--2020|Masson et al., 2020]] ): (i) three-dimensional urban geometry including building density and plan area, street aspect ratio and building height; (ii) thermal characteristics of impervious surfaces; and (iii) anthropogenic heat release, either from building energy consumption, especially waste heat from air conditioning systems, or as direct emissions from industry, traffic, or human metabolism ( [[#Ichinose--1999|Ichinose et al., 1999]] ; [[#Sailor--2011|Sailor, 2011]] ; [[#de%20Munck--2013|de Munck et al., 2013]] ; [[#Bohnenstengel--2014|Bohnenstengel et al., 2014]] ; [[#Chow--2014|Chow et al., 2014]] ; [[#Salamanca--2014|Salamanca et al., 2014]] ; [[#Dou--2017|Dou and Miao, 2017]] ; [[#Ma--2017a|Ma et al., 2017a]] ; [[#Chrysoulakis--2018|Chrysoulakis et al., 2018]] ; [[#Takane--2019|Takane et al., 2019]] ). Urban heat island magnitude is also affected by aerosols due to air pollution in urban areas ( [[#Cheng--2020|Cheng et al., 2020]] ; [[#Han--2020|Han et al., 2020]] ) and by local background climate ( [[#Zhao--2014|Zhao et al., 2014]] ; [[#Ward--2016|Ward et al., 2016]] ).

'''Monitoring network'''

Long-term climate datasets (a year or more) at the small spatial scales required to resolve processes of interest for cities (&lt;1 km) are scarce ( [[#Bader--2018|Bader et al., 2018]] ; [[#Caluwaerts--2020|Caluwaerts et al., 2020]] ). Moreover, urban observation sites often represent only parts of the urban environment and are suboptimal for detecting urban effects (e.g., sites in city parks). Recently, city-scale climate monitoring networks as well as satellite and ground-based remote sensing are being used (though still missing in Global South cities; Technical Annex I), enhancing our understanding of the urban microclimate and its interaction with climate change, and providing key information for users (F. [[#Chen--2012|]] [[#Chen--2012|Chen et al., 2012]] ; [[#Barlow--2017|Barlow et al., 2017]] ; [[#Bader--2018|Bader et al., 2018]] ). It has been found that harmonization of collection practices, instrumentation, station locations, and quality control methodologies across urban environments needs improvement to facilitate collaborative research ( [[#Muller--2013|Muller et al., 2013]] ; [[#Barlow--2017|Barlow et al., 2017]] ). Real time crowdsourcing data is becoming available ( [[#10.2.4|Section 10.2.4]] ). The urban climate community is making efforts to understand how these methods can complement traditional datasets ( [[#Meier--2017|Meier et al., 2017]] ; [[#Zheng--2018|Zheng et al., 2018]] ; [[#Langendijk--2019b|Langendijk et al., 2019b]] ; [[#Venter--2020|Venter et al., 2020]] ).

'''Urban modules in climate models'''

Exchanges of heat, water and momentum between the urban surface and its overlying atmosphere are calculated using specific surface-atmosphere exchange schemes. Three different schemes, here in order of increasing complexity, can be distinguished ( [[#Masson--2006|Masson, 2006]] ; [[#Grimmond--2010|Grimmond et al., 2010]] , 2011; [[#Chen--2011|Chen et al., 2011]] ; [[#Best--2015|Best and Grimmond, 2015]] ): (i) in the slab or bulk approach, the three-dimensional city structure is not resolved but cities are represented by modifying soil and vegetation parameters within land surface models, increasing roughness length and displacement height (e.g., [[#Seaman--1989|Seaman et al., 1989]] ; [[#Dandou--2005|Dandou et al., 2005]] ; [[#Best--2006|Best et al., 2006]] ; [[#Liu--2006|Liu et al., 2006]] ). The energy balance is often modifiedto account for the radiation trapped by the urban canopy, heat storage, evaporation and anthropogenic heat fluxes. (ii) Single-layer urban canopy modules use a simplified geometry (urban canyon, with three surface types: roof, road and wall) that approximately capture the three-dimensional dynamical and thermal physical processes influencing radiative and energy fluxes ( [[#Masson--2000|Masson, 2000]] ; [[#Kusaka--2001|Kusaka et al., 2001]] ). (iii) Multi-layer urban canopy modules compute urban effects vertically, allowing a direct interaction with the planetary boundary layer ( [[#Brown--2000|Brown, 2000]] ; [[#Martilli--2002|Martilli et al., 2002]] ; [[#Hagishima--2005|Hagishima et al., 2005]] ; [[#Dupont--2006|Dupont and Mestayer, 2006]] ; [[#Hamdi--2008|Hamdi and Masson, 2008]] ; [[#Schubert--2012|Schubert et al., 2012]] ). Building-energy models that estimate anthropogenic heat from a building for given atmospheric conditions can be incorporated. Recent model development has focused on improving the representation of urban vegetation ( [[#Lee--2016|Lee et al., 2016]] ; [[#Redon--2017|Redon et al., 2017]] ; [[#Mussetti--2020|Mussetti et al., 2020]] ).

Global ( [[#McCarthy--2010|McCarthy et al., 2010]] ; [[#Oleson--2011|Oleson et al., 2011]] ; [[#Zhang--2013|Zhang et al., 2013]] ; H. [[#Chen--2016|Chen et al., 2016]] ; [[#Katzfey--2020|Katzfey et al., 2020]] ; [[#Sharma--2020|Sharma et al., 2020]] ; [[#Hertwig--2021|Hertwig et al., 2021]] ) and regional modelling groups ( [[#Oleson--2011|Oleson et al., 2011]] ; [[#Kusaka--2012a|Kusaka et al., 2012a]] ; [[#McCarthy--2012|McCarthy et al., 2012]] ; [[#Hamdi--2014|Hamdi et al., 2014]] ; Trusilova et al., 2016; Daniel et al., 2019; [[#Halenka--2019|Halenka et al., 2019]] ; [[#Langendijk--2019a|Langendijk et al., 2019a]] ) are beginning to implement these urban parametrizations within the land surface component of their models. There is ''very high confidence'' ( ''robust evidence'' and ''high agreement'' ) that while all types of urban parametrizations generally simulate radiation exchanges in a realistic way, they have strong biases when simulating latent heat fluxes, though recent research incorporating in-canyon vegetation processes improved their performance. There is ''medium confidence'' ( ''medium evidence'' , ''high agreement'' ) ( [[#Kusaka--2012b|Kusaka et al., 2012b]] ; [[#McCarthy--2012|McCarthy et al., 2012]] ; [[#Hamdi--2014|Hamdi et al., 2014]] ; [[#Trusilova--2016|Trusilova et al., 2016]] ; [[#Jänicke--2017|Jänicke et al., 2017]] ; [[#Daniel--2019|Daniel et al., 2019]] ) that a simple single-layer parametrization, is sufficient for the correct simulation of the urban heat island magnitude and its interplay with regional climate change.

Box 10.3

'''Observed trends'''

There is ''medium evidence'' but ''high agreement'' ( [[#Parker--2010|Parker, 2010]] ; [[#Zhang--2013|Zhang et al., 2013]] ; H. [[#Chen--2016|Chen et al., 2016]] ) that the global annual mean surface air temperature response to urbanization is negligible. There is very high confidence that the different observed warming trend in cities as compared to their surroundings can partly be attributed to urbanization (Box 10.3, Figure 1; [[#Park--2017|Park et al., 2017]] ).

[[File:49ddec5a5add910d395337af9250d05d IPCC_AR6_WGI_Box_10_3_Figure_1.png]]

'''Box 10.3, Figure 1 |'''

'''Urban warming compared to global GHG-induced warming. (a)''' Change in the annual mean surface air temperature over the period 1950–2018 based on the local linear trend retrieved from CRU TS (°C per 68 years). This background warming is compared to the local warming that has been reported during 1950–2018 in the literature from historical urbanization. The relative share of the total warming as percentage between the urban warming and the surrounding warming is plotted in a circle for each city. This map has been compiled from a review study ( [[#Hamdi--2020|Hamdi et al., 2020]] ). '''(b)''' Low-pass filtered time series of the annual mean temperature (°C) observed in the urban station of Tokyo (red line) and the rural reference station in Choshi (blue line) in Japan. The filter is the same as the one used in Figure 10.10. '''(c)''' Uncertainties in the relative share of urban warming with respect to the total warming (%) related to the use of different global observational datasets: CRU TS (brown circles), Berkeley Earth (dark blue downward triangle), HadCRUT5 (cyan upward triangle), Cowtan Way (orange plus) and GISTEMP (purple squares). Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).
There is ''very high confidence'' ( ''robust evidence'' and ''high agreement'' ) that the annual mean minimum temperature is more affected by urbanization than the maximum temperature ( [[#Ezber--2007|Ezber et al., 2007]] ; [[#Fujibe--2009|Fujibe, 2009]] ; [[#Hamdi--2010|Hamdi, 2010]] ; [[#Elagib--2011|Elagib, 2011]] ; [[#Camilloni--2012|Camilloni and Barrucand, 2012]] ; [[#Hausfather--2013|Hausfather et al., 2013]] ; [[#Robaa--2013|Robaa, 2013]] ; [[#Argüeso--2014|Argüeso et al., 2014]] ; [[#Alghamdi--2015|Alghamdi and Moore, 2015]] ; [[#Alizadeh-Choobari--2016|Alizadeh-Choobari et al., 2016]] ; [[#Sachindra--2016|Sachindra et al., 2016]] ; [[#Liao--2017|Liao et al., 2017]] ; [[#Lokoshchenko--2017|Lokoshchenko, 2017]] ; J. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Arsiso--2018|Arsiso et al., 2018]] ). Beside temperature, urbanization can induce an urban dryness island, which refers to lower relative humidity in cities than in nearby rural locations ( [[#Lokoshchenko--2017|Lokoshchenko, 2017]] ; [[#Bian--2020|Bian et al., 2020]] ) and the urban wind island, where slower wind speeds are observed in cities ( [[#Wu--2017|Wu et al., 2017]] ; [[#Bader--2018|Bader et al., 2018]] ; [[#Peng--2018|Peng et al., 2018]] ). There is ''medium confidence'' ( ''medium evidence'' and ''medium agreement'' ) ( [[#Schlünzen--2010|Schlünzen et al., 2010]] ; [[#Ganeshan--2013|Ganeshan et al., 2013]] ;

[[#Ganeshan--2015|Ganeshan and Murtugudde, 2015]] ; [[#Haberlie--2015|Haberlie et al., 2015]] ; [[#Daniels--2016|Daniels et al., 2016]] ; [[#Liang--2017|Liang and Ding, 2017]] ; [[#McLeod--2017|McLeod et al., 2017]] ; [[#Li--2020|]] [[#Li--2020b|Li et al., 2020b]] ) that cities induce increases in mean and extreme precipitation over and downwind of the city especially in the afternoon and early evening.

'''Climate projections'''

Estimates of the urban heat island under further climate change are ''very uncertain'' because studies using different methods report contrasting results. However, there is ''very high confidence'' ( ''robust evidence'' and ''high agreement'' ) that the projected change of the urban heat island under climate change conditions is one order of magnitude less than the projected warming in both urban and rural areas under simulation constraints of no urban growth ( [[#McCarthy--2010|McCarthy et al., 2010]] , [[#McCarthy--2012|2012]] ; [[#Oleson--2011|Oleson et al., 2011]] ; [[#Früh--2011|Früh et al., 2011]] ; [[#Adachi--2012|Adachi et al., 2012]] ; [[#Kusaka--2012a|Kusaka et al., 2012a]] ; [[#Oleson--2012|Oleson, 2012]] ; [[#Hamdi--2014|Hamdi et al., 2014]] ; [[#Sachindra--2016|Sachindra et al., 2016]] ; [[#Hatchett--2016|Hatchett et al., 2016]] ; [[#Arsiso--2018|Arsiso et al., 2018]] ; [[#Hoffmann--2018|Hoffmann et al., 2018]] ).

Combining climate change conditions together with urban growth scenarios, there is ''very high confidence'' ( ''robust evidence'' and ''high agreement'' ) that future urbanization will amplify the projected air temperature warming irrespective of the background climate ( [[#Georgescu--2013|Georgescu et al., 2013]] ; [[#Argüeso--2014|Argüeso et al., 2014]] ; [[#Mahmood--2014|Mahmood et al., 2014]] ; [[#Doan--2016|Doan et al., 2016]] ; [[#Kim--2016|Kim et al., 2016]] ; [[#Kusaka--2016|Kusaka et al., 2016]] ; [[#Grossman-Clarke--2017|Grossman-Clarke et al., 2017]] ; [[#Kaplan--2017|Kaplan et al., 2017]] ; [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). Urbanization will have a strong influence on minimum temperatures that could be locally comparable in magnitude to the global GHG-induced warming ( [[#Berckmans--2019|Berckmans et al., 2019]] ) ''.'' There is ''very high confidence'' ( ''robust evidence'' and ''high agreement'' ) for the combination of future urban development and more frequent occurrence of extreme climatic events, such as heatwaves ( [[#Hamdi--2016|Hamdi et al., 2016]] ; [[#Bader--2018|Bader et al., 2018]] ; [[#He--2021|He et al., 2021]] ).

The choice of urban planning scenarios and RCM projections shows a large sensitivity during nighttime, up to 0.6°C ( [[#Kusaka--2016|Kusaka et al., 2016]] ). The sensitivity is significantly less than the uncertainties arising from global emissions scenarios or global model projections. However, there is a large difference between RCM simulations with and without urban land use, indicating that this impact is comparable to the uncertainties related to the use of different global model projections ( [[#Hamdi--2014|Hamdi et al., 2014]] ; [[#Kusaka--2016|Kusaka et al., 2016]] ; [[#Daniel--2019|Daniel et al., 2019]] ). Therefore, impact assessments and adaptation plans for urban areas require high spatial resolution climate projections along with models that represent urban processes, ensemble dynamical and statistical downscaling, and local-impact models ( [[#Masson--2014|Masson et al., 2014]] ; [[#Baklanov--2018|Baklanov et al., 2018]] , [[#Baklanov--2020|2020]] ; Duchêne et al., 2020; [[#Schoetter--2020|Schoetter et al., 2020]] ; [[#Le%20Roy--2021|Le Roy et al., 2021]] ; [[#Zhao--2021|Zhao et al., 2021]] ).

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'''Cross-Chapter Box 10.4 | Climate Change over the Hindu Kush Himalaya'''

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'''Coordinators:''' Izuru Takayabu (Japan), Andrew Turner (United Kingdom), Zhiyan Zuo (China)

'''Contributors:''' Bhupesh Adhikary (Nepal), Muhammad Adnan (Pakistan), Muhammad Amjad (Pakistan), Subimal Ghosh (India), Rafiq Hamdi (Belgium),Akm Saiful Islam (Bangladesh), Richard G. Jones (United Kingdom), Martin Jury (Austria), Asif Khan (Pakistan), Akio Kitoh (Japan), Krishnan Raghavan (India), Lucas Ruiz (Argentina), Laurent Terray (France)

The Hindu Kush Himalaya (HKH) constitutes the largest glacierized region outside the poles and provides the headwaters for several major rivers ( [[#Sharma--2019|Sharma et al., 2019]] ). Since the 1960s, the HKH has experienced significant trends in the mean and extremes of temperature and precipitation, accompanied by glacier mass loss and retreat, snowmelt and permafrost degradation ( [[#Yao--2012a|Yao et al., 2012a]] , b; [[#Azam--2018|Azam et al., 2018]] ; [[#Bolch--2019|Bolch et al., 2019]] ; [[#Krishnan--2019a|Krishnan et al., 2019a]] , b; [[#Chug--2020|Chug et al., 2020]] ; [[#Sabin--2020|Sabin et al., 2020]] ). Observational uncertainty and lack of consistent, high-quality datasets hamper reliable assessments of climate change and model evaluation over several mountain areas, including the HKH ( [[#10.2.2|Section 10.2.2]] ). This box assesses observed and projected climate change in the extended HKH (outline in Cross-Chapter Box 10.4, Figure 1a), in which we include the Tibetan Plateau (TP) and Pamir mountains.

'''Temperature trends'''

Little evidence was presented in the AR5 ( [[#IPCC--2014|IPCC, 2014]] ) other than increased minimum and maximum temperature trends in the western Himalaya ( [[#Hartmann--2013|Hartmann et al., 2013]] ). The SROCC assessed that HKH (named High Mountain Asia) surface-air temperature has warmed more rapidly than the global mean over recent decades ( ''high confidence'' ). Annual mean HKH surface air temperature increased significantly (about 0.1°C per decade) over 1901–2014 ( [[#Ren--2017|Ren et al., 2017]] ), although Cross-Chapter Box 10.4, Figure 1d shows an observational range of 0.20°C–0.25°C per decade over 1961–2014. There is a rising trend of extreme warm events and fewer extreme cold events over 1961–2015 ( [[#Krishnan--2019b|Krishnan et al., 2019b]] ; [[#Wester--2019|Wester et al., 2019]] ). However, summer cooling over the Karakoram (western HKH) was reported for 1960–2010 ( [[#Forsythe--2017|Forsythe et al., 2017]] ). A key relevant process is elevation-dependent warming (EDW; reviewed in [[#Pepin--2015|Pepin et al., 2015]] ), leading to warming of 2°C–2.5°C at 5000 m over 1961–2006, but only 0.5°C at sea level ( [[#Xu--2016|Xu et al., 2016]] ). However, EDW behaviour appears to depend on region, time period and elevation (D. [[#Guo--2019|]] [[#Guo--2019|Guo et al., 2019]] ; b. [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] ) and understanding is limited by the sparse observational network ( [[#You--2020|You et al., 2020]] ). Observational and model analyses have attributed EDW to GHG and black carbon emissions, accelerating warming by snow-albedo feedback ( [[#Ming--2012|Ming et al., 2012]] ; [[#Gautam--2013|Gautam et al., 2013]] ; [[#Xu--2016|Xu et al., 2016]] ; [[#Yan--2016|Yan et al., 2016]] ; [[#Lau--2018|Lau and Kim, 2018]] ; Y. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ), or the more pronounced cooling effect of scattering aerosols at low elevations and stratospheric ozone depletion ( [[#Guo--2012|Guo and Wang, 2012]] ; [[#Zeng--2015|Zeng et al., 2015]] ). There is ''high confidence'' that the eastern and central HKH has exhibited rising temperatures (Cross-Chapter Box 10.4, Figure 1), with warming dependent on season and elevation. There is ''high confidence'' that much of the warming can be attributed to GHGs, but the effect of albedo has only ''medium confidence'' . There is ''high confidence'' in more frequent extreme warm events and fewer extreme cold events over the eastern Himalayas in the last five decades.

[[File:78b209f88dc471916b40e6cca062fd17 IPCC_AR6_WGI_CCBox_10_4_Figure_1.png]]

'''Cross-chapter Box 10.4, Figure'''

'''1 |'''

'''Historical annual-mean surface air temperature linear trend (°C per decade) and its attribution over the Hindu Kush Himalaya (HKH) region. (a)''' Observed trends from Berkeley Earth (also showing the HKH outline), CRU TS (also showing the AR6 Tibetan Plateau (TIB) outline, for ease of comparison to the Interactive Atlas), APHRO-MA and JRA-55 datasets over 1961–2014. '''(b)''' Models showing the coldest, median and warmest HKH temperature linear trends among the CMIP6 historical ensemble over 1961–2014. '''(c)''' Low-pass-filtered time series of annual-mean surface air temperature anomalies (°C, baseline 1961–1980) over the HKH region as outlined in panel (a), showing means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue). Observed datasets are Berkeley Earth (dark blue), CRU (brown), APHRO-MA (light green) and JRA-55 (dark green). The filter is the same as that used in Figure 10.10. '''(d)''' Distribution of annual mean surface air temperature trends (°C per decade) over the HKH region from 1961 to 2014 for ensemble means, the aforementioned observed and reanalysis data (black crosses), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs used throughout (Chapter 10 (grey shading). Ensemble means are also shown. All trends are estimated using ordinary least-squares regression and box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).
Cross-Chapter Box 10.4

'''Precipitation trends'''

Annual and summer precipitation over the central-eastern HKH show decreasing trends over 1979–2010 in multiple observed datasets, attributable to a weakening South Asian monsoon ( [[#Yao--2012a|Yao et al., 2012a]] ; [[#Palazzi--2013|Palazzi et al., 2013]] ; [[#Roxy--2015|Roxy et al., 2015]] ). There are contradictory trends in the western HKH ( [[#Azmat--2017|Azmat et al., 2017]] ; [[#Yadav--2017|Yadav et al., 2017]] ; H. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Meher--2018|Meher et al., 2018]] ), where most precipitation is associated with western disturbances on the subtropical westerly jet, but trends in western disturbance activity are unclear ( [[#Kumar--2015|Kumar et al., 2015]] ; [[#Hunt--2019|Hunt et al., 2019]] ; [[#Krishnan--2019a|Krishnan et al., 2019a]] ). There has been an increased frequency and intensity of extreme precipitation over the central-western HKH but contrasting evidence in the east ( [[#Sheikh--2015|Sheikh et al., 2015]] ; [[#Talchabhadel--2018|Talchabhadel et al., 2018]] ). The number of consecutive wet days has increased over 1961–2012, but with no uniform trend in consecutive dry days ( [[#Zhan--2017|Zhan et al., 2017]] ). There is ''medium confidence'' that the eastern-central HKH has experienced decreased summer precipitation ( [[#10.6.3|Section 10.6.3]] ). There is ''medium confidence'' in the increase of summer extreme precipitation over the western HKH.

'''Glacier trends'''

The SROCC assessed that snow cover has declined in duration, depth and accumulated mass at lower elevations in mountain regions, including the HKH ( ''high confidence'' ). Glaciers are losing mass ( ''very high confidence'' ) and permafrost is warming ( ''high confidence'' ) over high mountains in recent decades, and it is ''very likely'' that atmospheric warming is the main driver. A significant reduction in HKH glacier area has been observed since the 1970s, with smaller glaciers generally shrinking faster (e.g., [[#Bolch--2019|Bolch et al., 2019]] ). HKH glacier mass loss took place at the lowest rate among high mountain areas in the last 20 years, although with one of the largest total losses ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.1|Section 9.5.1.1]] and Figure 9.20; [[#Shean--2020|Shean et al., 2020]] ). The highest mass-loss rates occurred in the eastern and northern HKH, while gains occurred in the west (e.g., [[#Shean--2020|Shean et al., 2020]] ). Glacier mass gain has been coined as the ‘Karakoram anomaly’ (Sections 8.3.1.7.1 and 9.5.1), explained by a combination of low temperature sensitivity of debris-covered glaciers, a decrease in summer air temperatures, and increased snowfall possibly linked to evapotranspiration from irrigated agriculture ( [[#You--2017|You et al., 2017]] ; [[#Bolch--2019|Bolch et al., 2019]] ; [[#de%20Kok--2020a|de Kok et al., 2020a]] ; [[#Farinotti--2020|Farinotti et al., 2020]] ). Meanwhile, increased air temperature and decreased snowfall explain the glacier mass decrease elsewhere ( [[#Bonekamp--2019|Bonekamp et al., 2019]] ; [[#de%20Kok--2020b|de Kok et al., 2020b]] ; [[#Farinotti--2020|Farinotti et al., 2020]] ; [[#Shean--2020|Shean et al., 2020]] ). There is ''high confidence'' that glaciers in most HKH regions have thinned, retreated and lost mass since the 1970s.

'''Projections'''

In AR5, the HKH was projected to continue warming over the 21st century, faster than the ''likely'' ranges for the global mean and South Asia. New CMIP5 results show temperature increases across mountainous HKH by about 1°C–2°C (in some places in summer 4°C–5°C) during 2021–2050 compared to 1961–1990 ( [[#Shrestha--2015|Shrestha et al., 2015]] ). Projected warming differs by up to 1°C between east and west, with higher values in winter ( [[#Sanjay--2017|Sanjay et al., 2017]] ; see Interactive Atlas). Statistically significant mean warming (0.30°C–0.90°C per decade until the end of the 21st century) across all RCPs has been projected by CORDEX South Asia ( [[#Dimri--2018|Dimri et al., 2018]] ). CMIP6 models report that north-western South Asia, including the western Himalayas, is projected to experience temperature increases exceeding 6°C by the end of the 21st century under SSP5-8.5 relative to 1995–2014 ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ). Results from CMIP5, CMIP6 and CORDEX ensembles for different warming levels are shown in the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] and summarized in Figure Atlas.20. The HKH will ''likely'' continue warming in the coming decades.

The SR1.5 (IPCC, 2018b) stated that heavy precipitation risk in high-elevation regions is projected to be higher at 2°C compared to 1.5°C of global warming ( ''medium confidence'' ). CMIP5 models project increased annual or summer monsoon precipitation over the HKH in the 21st century ( [[#Palazzi--2015|Palazzi et al., 2015]] ; [[#Kitoh--2016|Kitoh and Arakawa, 2016]] ), intensifying by about 22% in the hilly south-eastern Himalaya and TP for the long term in RCP8.5, but with no trends in the western HKH ( [[#Rajbhandari--2015|Rajbhandari et al., 2015]] ; [[#Krishnan--2019a|Krishnan et al., 2019a]] ). CMIP6 projects an increase of winter precipitation over the western Himalayas, with a corresponding decrease in the east ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ). HKH projections are subject to large uncertainties in CMIP5 and CORDEX ( [[#Hasson--2013|Hasson et al., 2013]] , 2017; [[#Mishra--2015|Mishra, 2015]] ; [[#Sanjay--2017|Sanjay et al., 2017]] ). CORDEX, in particular, has inherent limitations at reproducing the characteristics of summer monsoon rainfall variability ( [[#Singh--2017|Singh et al., 2017]] ). There is ''medium confidence'' that HKH precipitation will increase in the coming decades.

The SROCC assessed that glaciers will lose substantial mass ( ''high confidence'' ) and permafrost will undergo increasing thaw and degradation ( ''very high confidence'' ) over high mountain regions (including the HKH), with stronger changes for higher emissions scenarios. Regional differences in warming and precipitation projections and glacier properties cause considerable differences in glacier response within High Mountain Asia ( [[#Kraaijenbrink--2017|Kraaijenbrink et al., 2017]] ). Glacier mass loss will accelerate through the 21st century, increasing with RCP after 2030 ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] ; [[#Marzeion--2014|Marzeion et al., 2014]] ). Loss of between 40 ± 25% to 69 ± 21 % of 2015 glacier volume is expected by 2100 in RCP 2.6 and RCP 8.5, respectively ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] and Figure 9.21). Glacier mass loss is expected due to decreased snowfall, increased snowline elevations and longer melt seasons. However, due to projection uncertainties, simplicity of the models, and limited observations, there is ''medium confidence'' in the magnitude and timing of glacier mass changes ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] ). Glacier mass in HKH will decline through the 21st century ( ''high confidence'' ), more so under high-emissions scenarios.

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