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

=== 10.6.4 Mediterranean Summer Warming ===

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==== 10.6.4.1 Motivation and Regional Context ====

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The Mediterranean region is loosely denoted as the region that surrounds the Mediterranean Sea, and it is characterized by complex orography and strong land–sea contrasts. The region contains a dense and growing human population, with large regional differences: whereas the population of the European Mediterranean countries has been relatively stable or even declining during the past decades, the population of countries in Mediterranean areas of the Middle East and North Africa has quadrupled between 1960 and 2015, and the degree of urbanization has risen from 35 to 64% during the same period ( [[#Cramer--2018|Cramer et al., 2018]] ) and during the more recent period 2000–2020 the urban expansion rate has exceeded 5% ( [[#Kuang--2021|Kuang et al., 2021]] ).

The Mediterranean region has experienced significant climate variability over recent decades and has been affected in particular by severe heatwaves and droughts (Sections 8.3, 11.3, 11.6 and 12.4; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Increasing summer temperatures will enhance the frequency and intensity of such extreme events and will cause additional environmental and socio-economic pressure on the region.

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==== 10.6.4.2 The Region’s Climate ====

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The Mediterranean has a heterogeneous climate that is partly semi-arid, especially along the southern coast of the Mediterranean Sea ( [[#Lionello--2012|Lionello et al., 2012]] ). It is characterized by mild humid winters and dry warm or hot summers, which are associated with large scale subsidence that is partly related to the downward branch of the Hadley circulation. Other factors affecting the Mediterranean circulation include the monsoon heating over Asia ( [[#Rodwell--1996|Rodwell and Hoskins, 1996]] ; [[#Cherchi--2014|Cherchi et al., 2014]] ; [[#Ossó--2019|Ossó et al., 2019]] ) and circulation anomalies induced by topography ( [[#Simpson--2015|Simpson et al., 2015]] ). Seasonal and interannual variability is strongly linked to natural modes of variability ( [[#10.6.4.4|Section 10.6.4.4]] ). The Mediterranean Sea acts as an evaporation source that dominates the regional hydrological cycle, which is characterized by local cyclogenesis and a separate branch of the mid-latitude storm track ( [[#Lionello--2016|Lionello et al., 2016]] ). It also affects remote locations such as the Sahel ( [[#Park--2016|Park et al., 2016]] ; [[#10.4.2.1|Section 10.4.2.1]] ). Strong storms can develop over the Mediterranean. Among these, Medicanes are particularly destructive and exhibit several similarities with tropical cyclones ( [[#Cavicchia--2014|Cavicchia et al., 2014]] ; [[#Kouroutzoglou--2015|Kouroutzoglou et al., 2015]] ; [[#Gaertner--2018|Gaertner et al., 2018]] ). The Mediterranean region is also characterized by strong land-atmosphere coupling and feedbacks ( [[#Seneviratne--2006|Seneviratne et al., 2006]] ) generating prolonged droughts and intense heatwaves, which can also affect continental Europe ( [[#Zampieri--2009|Zampieri et al., 2009]] ). Other aspects of Mediterranean climate include regional winds, which can be very strong due to the channelling effect ( [[#Obermann--2018|Obermann et al., 2018]] ) and extreme rainfall during autumn ( [[#Ducrocq--2014|Ducrocq et al., 2014]] ; [[#Ribes--2019|Ribes et al., 2019]] ).

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==== 10.6.4.3 Observational Issues ====

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The Mediterranean region spans a wide variety of countries and economies. This has led to large differences in the existence and availability of observational records, with the southern part of the area being sparsely covered by meteorological stations (Figure 10.20b). Consequently, basin-wide, homogeneous, quality controlled observational datasets are lacking, especially before the advent of substantial satellite observations in the 1970s. Observational uncertainties exist also for those regions that are covered by high quality networks such as European Climate Assessment &amp; Dataset (ECA&amp;D; [[#Flaounas--2012|Flaounas et al., 2012]] ).

Large differences of up to 7°C between the CRU and UDEL datasets have been found especially over mountainous areas, such as the [[IPCC:Wg1:Chapter:Atlas|Atlas]] in Morocco ( [[#Zittis--2017|Zittis and Hadjinicolaou, 2017]] ; [[#Strobach--2019|Strobach and Bel, 2019]] ). Bucchignani et al. (2016a, b) compared three different datasets (CRU, UDEL, and MERRA) with the available ground observations and found that although the geographical distribution of the bias is qualitatively similar for the three datasets, differences exist, with the absolute bias being generally lower in Modern-Era Retrospective Analysis for Research and Applications (MERRA) especially over North Africa during the summer and winter season. There is ''high confidence'' that the sparse monitoring network in parts of the Mediterranean region strongly increases the uncertainty across different gridded datasets ( [[#10.2.2.3|Section 10.2.2.3]] , Figure 10.20b,c).

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==== 10.6.4.4 Relevant Anthropogenic and Natural Drivers ====

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The Mediterranean summer climate is affected by large-scale modes of natural variability, the most dominant being the NAO (Annex IV) in winter and the summer NAO in summer ( [[#Folland--2009|Folland et al., 2009]] ; [[#Bladé--2012|Bladé et al., 2012]] ), although regional differences exist. The influence of those modes of variability over the eastern Mediterranean is recognized by some studies ( [[#Chronis--2011|Chronis et al., 2011]] ; [[#Kahya--2011|Kahya, 2011]] ; [[#Black--2012|Black, 2012]] ; [[#Bladé--2012|Bladé et al., 2012]] ), but disputed by others ( [[#Ben-Gai--2001|Ben-Gai et al., 2001]] ; [[#Ziv--2006|Ziv et al., 2006]] ; [[#Donat--2014|Donat et al., 2014]] ; [[#Turki--2016|Turki et al., 2016]] ; [[#Zamrane--2016|Zamrane et al., 2016]] ; [[#Han--2019|Han et al., 2019]] ). During positive summer NAO phase, associated with an upper-level trough over the Balkans, the Mediterranean is anomalously wet ( [[#Bladé--2012|Bladé et al., 2012]] ). Drivers of Mediterranean climate variability include modes of variability such as the AMV ( [[#Sutton--2012|Sutton and Dong, 2012]] ) and the Asian monsoon ( [[#Rodwell--1996|Rodwell and Hoskins, 1996]] ; [[#Logothetis--2020|Logothetis et al., 2020]] ). In addition, the increase of GHGs (e.g., [[#Zittis--2019|Zittis et al., 2019]] ), the decrease of anthropogenic aerosols over Europe and the Mediterranean since the 1980s resulting from air pollution policies ( [[#Turnock--2016|Turnock et al., 2016]] ), and anthropogenic land-use change ( [[#Millán--2014|Millán, 2014]] ; MedECC 2020) have been shown to be linked to the regional warming. The role of the zonal averaged circulation as a driver for the Mediterranean climate has been stressed by ( [[#Garfinkel--2020|Garfinkel et al., 2020]] ).

The attribution of observed Mediterranean summer warming to above drivers and implications for future projections will be discussed in Sections 10.6.4.5 and 10.6.4.6.

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==== 10.6.4.5 Model Simulation and Attribution Over the Historical Period ====

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Observational datasets show large agreement on the historical (1960–2014) temperature evolution at basin-wide scale (Figure 10.20e), with an enhanced warming since the 1990s, and the early decades of the 21st century being on average approximately more than 1°C warmer than late 19th century levels ( [[#van%20der%20Schrier--2013|van der Schrier et al., 2013]] ; [[#Cramer--2018|Cramer et al., 2018]] ; [[#Lionello--2018|Lionello and Scarascia, 2018]] ; Figure 10.20e). Over recent decades, the surface air temperature of the Mediterranean including the Mediterranean Sea has warmed by around 0.4°C per decade ( [[#Macias--2013|Macias et al., 2013]] ). Observed trends over land show large geographical heterogeneity (Figure 10.20d) and notable differences exist amongst different datasets at grid point scale (Figure 10.20c; [[#Qasmi--2021|Qasmi et al., 2021]] ).

Several mechanisms have been proposed for the enhanced Mediterranean warming, although their relative importance and the possible interplay between them are not fully understood. Circulation changes might have contributed to this enhanced warming (Figure 10.20a). [[#Sutton--2012|Sutton and Dong (2012)]] argued that the AMV induced a shift around the 1990s towards warmer southern European summers. This mechanism is associated with a linear baroclinic atmospheric response to the AMV-related surface heat flux. Also [[#O’Reilly--2017|O’Reilly et al. (2017)]] related warm summer decades to the AMV, but the connection was shown to be mainly thermodynamic. [[#Qasmi--2021|Qasmi et al. (2021)]] estimate an increase in Mediterranean summer temperature of 0.2°C–0.8°C during a positive AMV.

Increased warming over land compared to the sea is expected due to the lapse-rate changes associated with tropospheric moisture contrasts ( [[#Kröner--2017|Kröner et al., 2017]] ; [[#Byrne--2018|Byrne and O’Gorman, 2018]] ; [[#Brogli--2019b|Brogli et al., 2019b]] ; Figure 10.20a). Enhanced land–sea temperature contrast leads to relative humidity and soil moisture feedbacks ( [[#Rowell--2006|Rowell and Jones, 2006]] ), the latter also depending on weather regimes ( [[#Quesada--2012|Quesada et al., 2012]] ). The globally enhanced land–sea contrast in near surface temperature is also a robust result in CMIP5 and CMIP6 models ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.1|Section 4.5.1.1]] ).

Due to its semi-arid climate, strong atmosphere–land coupling has contributed to the larger increase of mean summer temperature compared to the increase of annual mean temperature ( [[#Seneviratne--2006|Seneviratne et al., 2006]] ). In particular, during drought spells, limits to evaporation due to low soil moisture provide a positive feedback and enhances the intensity of heatwaves ( [[#Lorenz--2016|Lorenz et al., 2016]] ; Box 11.1). By comparing reanalysis-driven RCM simulations with observations, [[#Knist--2017|Knist et al. (2017)]] found that RCMs are able to reproduce soil moisture interannual variability, spatial patterns, and annual cycles of surface fluxes over the period 1990–2008, revealing a strong land–atmosphere coupling especially in southern Europe in summer. In addition cloud feedbacks can modulate the Mediterranean summer temperature ( [[#Mariotti--2012|Mariotti and Dell’Aquila, 2012]] ).

The observed trends over 1901–2010 are outside the range of internal variability shown in CMIP5 pre-industrial control experiments and consistent with, or greater than those simulated by experiments including both anthropogenic and natural forcings ( [[#Knutson--2013|Knutson et al., 2013]] ) and therefore partly attributable to anthropogenic forcing. The decrease of anthropogenic aerosols over Europe including the Mediterranean resulting from European de-industrialisation and air pollution policies ( [[#Turnock--2016|Turnock et al., 2016]] ) has been highlighted as an important contributor to the observed warming ( [[#Ruckstuhl--2008|Ruckstuhl et al., 2008]] ; [[#Philipona--2009|Philipona et al., 2009]] ; [[#de%20Laat--2013|de Laat and Crok, 2013]] ; [[#Nabat--2014|Nabat et al., 2014]] ; [[#Besselaar--2015|Besselaar et al., 2015]] ; [[#Dong--2017|Dong et al., 2017]] ; [[#Boé--2020a|Boé et al., 2020a]] ). [[#Pfeifroth--2018|Pfeifroth et al. (2018)]] argue that this brightening is mainly due to cloud changes caused by the indirect aerosol effect with a minor role for the direct aerosol effect, in contrast to [[#Nabat--2014|Nabat et al. (2014)]] and [[#Boers--2017|Boers et al. (2017)]] who attribute it to the direct aerosol effect. Using model sensitivity experiments, [[#Nabat--2014|Nabat et al. (2014)]] also associated the increase in Mediterranean SST since 1980–2012 with the decrease in aerosol concentrations (Atlas.8.2, Atlas.8.3 and Atlas.8.5).

Over the period 1960–2014, observed trends over land are consistent with those of most of the multi-model or SMILEs ensembles (Figure 10.20f), although large differences exist for individual models and ensemble members. The modelled ensemble-mean trends show large geographical variations. Generally, both global and regional models often underestimate the observed trend especially over parts of North Africa, Italy, the Balkans and Turkey. The cold bias in global models is related to simulated SLP trends that are anti-correlated to the observed trend, which is probably due to systematic model errors ( [[#Boé--2020b|Boé et al., 2020b]] ). Biases in the simulation of soil-moisture and cloud-cover might also have contributed to the underestimation of the warming trend in GCMs ( [[#van%20Oldenborgh--2009|van Oldenborgh et al., 2009]] ). The CORDEX results (at both 0.44° and 0.11° resolution) show consistently smaller values than those in global models and the available datasets (Figure 10.20g; [[#Vautard--2021|Vautard et al., 2021]] ). This is partly due to the overestimation in the temperature evolution before 1990 (Figure 10.20e), possibly because of differences in the aerosol forcing ( [[#Boé--2020a|Boé et al., 2020a]] ), although the driving global models also have a cold bias ( [[#Vautard--2021|Vautard et al., 2021]] ). Cold biases for recent decades are also found in Med-CORDEX simulations ( [[#Dell’Aquila--2018|Dell’Aquila et al., 2018]] ) and by RCM simulations over the southern part of the Mediterranean, Middle East and North Africa region ( [[#Almazroui--2016|Almazroui, 2016]] ; [[#Almazroui--2016a|Almazroui et al., 2016a]] , b; [[#Zittis--2017|Zittis and Hadjinicolaou, 2017]] ; [[#Ozturk--2018|Ozturk et al., 2018]] ), although higher resolution, new bare soil albedo and modified aerosol parametrization significantly improve the results ( [[#Bucchignani--2016a|Bucchignani et al., 2016a]] , b, 2018). Despite large differences in the multi-model mean trend (Figure 10.20g), in most of the land points the observed trend lies within the model range in all ensembles. For the SST bias exhibited by coupled RCMs the choice of driving global model has the largest impact ( [[#Darmaraki--2019|Darmaraki et al., 2019]] ; [[#Soto-Navarro--2020|Soto-Navarro et al., 2020]] ).

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==== 10.6.4.6 Future Climate Information From Global Simulations ====

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The Mediterranean is expected to be one of the most prominent and vulnerable climate change hotspots ( [[#Diffenbaugh--2012|Diffenbaugh and Giorgi, 2012]] ). CMIP5, CMIP6, HighResMIP and CORDEX ( [[#10.6.4.7|Section 10.6.4.7]] ) simulations all project a future warming for the 21st century that ranges between 3.5°C and 8.75°C for RCP8.5 at the end of this century for those ending at 2100 (Figure 10.21a, b). CMIP6 results project more pronounced warming than CMIP5 for a given emissions scenario and time period (Figure 10.21c; [[#Coppola--2020|Coppola et al., 2020]] ). However, when analysing the Mediterranean warming in terms of mean global warming levels, the two ensembles largely agree, showing that summer warming is projected to reach values up to 40–50% larger than the global annual warming, largely independent of models and emissions scenarios (Figure 10.21d). Large regional differences exist, with enhanced warming projected over Turkey, the Balkans, the Iberian Peninsula and North African regions (Figures 10.14a, 10.21c; [[#Almazroui--2020a|Almazroui et al., 2020a]] ) and reaching, locally, values of up to double the global mean ( [[#Lionello--2018|Lionello and Scarascia, 2018]] ). The enhanced summer warming also increases the amplitude of the seasonal cycle ( [[#Yettella--2018|Yettella and England, 2018]] ).

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[[File:007e4c2219b09c7224cd75140ad29075 IPCC_AR6_WGI_Figure_10_20.png]]

'''Figure''' '''10.20 |''' '''Aspects of Mediterranean summer warming.''' '''(a)''' Mechanisms and feedbacks involved in enhanced Mediterranean summer warming. '''(b)''' Locations of observing stations in E-OBS and [[#Donat--2014|Donat et al. (2014)]] . '''(c)''' Differences in temperature observational datasets (NOAA Global Temp, Berkeley Earth, CRUTEM4 and GISTEMP) with respect to E-OBS for the land points between the Mediterranean Sea and 46°N and west of 30°E. '''(d)''' Observed summer (June to August) surface air temperature linear trends (°C decade <sup>–1</sup> ) over the 1960–2014 period from Berkeley Earth. '''(e)''' Time series of area averaged Mediterranean (25°N–50°N, 10°W–40°E) land point summer temperature anomalies (°C, baseline 1995–2014). Dark blue, brown and turquoise lines show low-pass filtered temperature of Berkeley Earth, CRU TS and HadCRUT5, respectively. Orange, light blue and green lines show low-pass filtered ensemble means of HighResMIP (4 members), CORDEX EUR-44 (20 members) and CORDEX EUR-11 (37 members). Blue and red lines and shadings show low-pass filtered ensemble means and standard deviations of CMIP5 (41 members) and CMIP6 (36 members). The filter is the same as the one used in Figure 10.10. '''(f)''' Distribution of 1960–2014 Mediterranean summer temperature linear trends (°C decade <sup>–1</sup> ) for observations (black crosses), CORDEX EUR-11 (green circles), CORDEX EUR-44 (light blue circles), HighResMIP (orange circles), CMIP6 (red circles), CMIP5 (blue circles) and selected SMILEs (grey box-and-whisker plots, MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF). Ensemble means are also shown. CMIP6 models showing a very high ECS (Box. 4.1) have been marked with a black cross. All trends are estimated using ordinary least-squares and box-and-whisker plots follow the methodology used in Figure 10.6. '''(g)''' Ensemble mean differences with respect to the Berkeley Earth linear trend for 1960–2014 (°C decade <sup>–1</sup> ) of CMIP5, CMIP6, HighResMIP, CORDEX EUR-44 and CORDEX EUR-11. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

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[[File:d024a23587bfc4b1a98f1def61fbd518 IPCC_AR6_WGI_Figure_10_21.png]]

'''Figure 10.''' '''21 |''' '''Projected Mediterranean summer warming. (a)''' Time series of area averaged Mediterranean (25°N–50°N, 10°W–40°E) land point summer surface air temperature anomalies (°C, baseline period is 1995–2014). Orange, light blue and green lines show low-pass filtered ensemble means of HighResMIP (highres-future, four members), CORDEX EUR-44 (RCP8.5, 20 members) and CORDEX EUR-11 (RCP8.5, 37 members). Blue and dark red lines and shadings show low-pass filtered ensemble means and standard deviations of CMIP5 (RCP8.5, 41 members) and CMIP6 (SSP5-8.5, 36 members). The filter is the same as the one used in Figure 10.10. The box-and-whisker plots show long-term (until 2081–2100) temperature changes of different CMIP6 scenarios with respect to the baseline period (SSP1-2.6 in dark blue, SSP2-4.5 in yellow, SSP3-7.0 in red, SSP5-8.5 in dark red). '''(b)''' Distribution of 2015–2050 Mediterranean summer temperature linear trends (°C per decade) for CORDEX EUR-11 (RCP8.5, green circles), CORDEX EUR-44 (RCP8.5, light blue circles), HighResMIP (highres-future, orange circles), CMIP6 (SSP5-8.5, dark red circles), CMIP5 (RCP8.5, blue circles) and selected SMILEs (grey box-and-whisker plots, MIROC6, CSIRO-Mk3-6-0 and MPI-ESM). Ensemble means are also shown. CMIP6 models showing a very high ECS (Box 4.1) have been marked with a black cross. All trends are estimated using ordinary least-squares and box-and-whisker plots follow the methodology used in Figure 10.6. '''(c)''' Projections of ensemble mean 2015–2050 linear trends (°C per decade) of CMIP5 (RCP8.5), CORDEX EUR-44 (RCP8.5), CORDEX EUR-11 (RCP8.5), CMIP6 (SSP5-8.5) and HighResMIP (highres-future). All trends are estimated using ordinary least-squares. '''(d)''' Projected Mediterranean summer warming in comparison to global annual mean warming of CMIP5 (dashed lines, RCP2.6 in dark blue, RCP4.5 in light blue, RCP6.0 in orange and RCP8.5 in red) and CMIP6 (solid lines, SSP1-2.6 in dark blue, SSP2-4.5 in yellow, SSP3-7.0 in red and SSP5-8.5 in dark red) ensemble means. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

As noted in [[#10.6.4.4|Section 10.6.4.4]] , the Mediterranean summer climate is affected by large-scale circulation patterns, of which the summer NAO is the most important ( [[#Folland--2009|Folland et al., 2009]] ; [[#Bladé--2012|Bladé et al., 2012]] ). [[#Barcikowska--2020|Barcikowska et al. (2020)]] highlight the importance of correctly simulating the summer NAO impact on the Mediterranean climate, as it partly offsets the anthropogenic warming signal in the western and central Mediterranean.

Climate models project a reduction in precipitation in all seasons, and a northward and eastward expansion of the Mediterranean climate, with the affected areas becoming more arid with an increased summer drying (Atlas.8.5; [[#Alessandri--2015|Alessandri et al., 2015]] ; [[#Mariotti--2015|Mariotti et al., 2015]] ; [[#Rajczak--2017|Rajczak and Schär, 2017]] ; [[#Waha--2017|Waha et al., 2017]] ; [[#Barredo--2018|Barredo et al., 2018]] ; [[#Lionello--2018|Lionello and Scarascia, 2018]] ; [[#Spinoni--2018|Spinoni et al., 2018]] , 2020). The drying can contribute to the enhanced warming by land surface feedbacks ( [[#Whan--2015|Whan et al., 2015]] ; [[#Lorenz--2016|Lorenz et al., 2016]] ; [[#Russo--2019|Russo et al., 2019]] ). A negative feedback to this dryness-induced warming might be provided by an enhanced moisture transport into the dry area associated with the dynamical response of the atmosphere ( [[#Zhou--2021|]] [[#Zhou--2021|Zhou et al., 2021]] ). Due to the arid climate, no positive soil moisture-temperature feedback is found over the North African regions of the Mediterranean, where the surface energy budget is mostly governed by radiative cooling ( [[#Lelieveld--2016|Lelieveld et al., 2016]] ), implying that soil moisture feedbacks are not contributing to enhanced warming over those regions.

Over the Mediterranean region, daily maximum temperature is projected to increase more than the daily minimum. Consequently, the difference between daytime maxima and nighttime minima is expected to increase, particularly in summer ( [[#Lionello--2018|Lionello and Scarascia, 2018]] ). Temperature extremes will be affected as well, with a dramatic increase in the number of warm days and reduction of cold nights ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ; [[#Lionello--2020|Lionello and Scarascia, 2020]] ). The Mediterranean summer warming will also increase the frequency and intensity of heatwaves ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

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==== 10.6.4.7 Future Climate Information From Regional Downscaling ====

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To unravel the complex interactions and feedbacks over the region on a range of spatial and temporal scales, regional downscaling projects are being developed to provide more comprehensive and detailed information on the future of the Mediterranean. The importance of regional downscaling for investigating the subregional details caused by the complex morphology of the Mediterranean region is a well-known issue in the literature ( [[#Planton--2012|Planton et al., 2012]] ), which has been addressed in many studies since AR5. Recent examples of dynamical downscaling are EURO-CORDEX ( [[#Jacob--2014|Jacob et al., 2014]] ) and Med-CORDEX ( [[#Ruti--2016|Ruti et al., 2016]] ; [[#Somot--2018|Somot et al., 2018]] ), but earlier activities have included ENSEMBLES ( [[#Déqué--2012|Déqué et al., 2012]] ; [[#Fernández--2019|Fernández et al., 2019]] ), PRUDENCE ( [[#Christensen--2002|Christensen et al., 2002]] ), CIRCE ( [[#Gualdi--2013|Gualdi et al., 2013]] ) and ESCENA ( [[#Jiménez-Guerrero--2013|Jiménez-Guerrero et al., 2013]] ).

From an analysis of CORDEX results, studies showed that southern Europe is projected to face a robust non-linear increase in temperature larger than the global mean ( [[#Zittis--2019|Zittis et al., 2019]] ), EURO-CORDEX projections, that are driven by CMIP5 global models, project a less pronounced warming than that of CMIP6 ( [[#Coppola--2021|Coppola et al., 2021]] ; see Figure 10.21c). The non-linear increase is especially evident for both hot and cold extremes ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ; [[#Maule--2017|Maule et al., 2017]] ; [[#Jacob--2018|Jacob et al., 2018]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ). In particular, [[#Dosio--2018|Dosio and Fischer (2018)]] showed that in many places in southern Europe and the Mediterranean, the increase in the number of nights with temperature above 20°C is more than 60% larger under 2°C warming compared to 1.5°C. Over the region, the projected temperature increase, including a higher probability of severe heatwaves ( [[#Russo--2015|Russo et al., 2015]] ), is accompanied by a reduction in precipitation ( [[#Jacob--2014|Jacob et al., 2014]] ; [[#Dosio--2016|Dosio, 2016]] ; [[#Rajczak--2017|Rajczak and Schär, 2017]] ), resulting in projected increases of drought frequency and severity ( [[#Spinoni--2018|Spinoni et al., 2018]] , 2020; [[#Raymond--2019|Raymond et al., 2019]] ). Also, the frequency and severity of marine heatwaves of the Mediterranean Sea are projected to increase ( [[#Darmaraki--2019|Darmaraki et al., 2019]] ; see [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] and Atlas.8.4).

Only a limited number of RCM simulations for the MENA domain are currently available. For the southern and eastern Mediterranean, they project a mean warming ranging from 3°C for RCP4.5 to 9°C for RCP8.5 at the end of this century compared to its beginning ( [[#Bucchignani--2018|Bucchignani et al., 2018]] ; [[#Ozturk--2018|Ozturk et al., 2018]] ). The frequency and duration of heatwaves and annual number of extremely hot days (i.e., those with maximum temperature &gt;50°C) in the southern Mediterranean will increase substantially. For 2070–2099 with respect to 1971–2000 the latter might even reach 70 days for RCP8.5 ( [[#Lelieveld--2016|Lelieveld et al., 2016]] ; [[#Almazroui--2019|Almazroui, 2019]] ; [[#Driouech--2020|Driouech et al., 2020]] ; [[#Varela--2020|Varela et al., 2020]] ).

Despite the large efforts of these regional downscaling projects, the global model–RCM matrix is still sparse and lacking a systematic design to explore the uncertainty sources (e.g., global model, RCM, scenario, resolution) ( [[#10.3|Section 10.3]] ). Focusing on the Iberian peninsula, [[#Fernández--2019|Fernández et al. (2019)]] argued that the driving global model is the main contributor to uncertainty in the ensemble. Physically consistent but implausible temperature changes in RCMs can occur. An example is a strong temperature increase over the Pyrenees due to excessive snow cover in the present climate ( [[#Fernández--2019|Fernández et al., 2019]] ). Based on an older set of RCM simulations (ENSEMBLES), [[#Déqué--2012|Déqué et al. (2012)]] also argued that the largest source of uncertainty in the temperature response over southern Europe is the choice of the driving global model (whereas for summer precipitation the choice of the RCM dominates the uncertainty). Finally, [[#Boé--2020a|Boé et al. (2020a)]] found that over a large area of Europe, including parts of the Mediterranean, RCMs project a summer warming 1.5°C–2°C colder than in their driving global models for the end of the 21st century. This is caused by differences in solar radiation related to the absence of time-varying anthropogenic aerosols in RCMs ( [[#Boé--2020a|Boé et al., 2020a]] ; [[#Gutiérrez--2020|Gutiérrez et al., 2020]] ), which also affects the noted differences in cloud cover between global models and RCMs ( [[#Bartók--2017|Bartók et al., 2017]] ).

Statistical downscaling studies for the Mediterranean confirm the results from global model and RCM studies, with large agreement among future projections showing lower rates of warming in winter and spring, and, in most cases, higher ones in summer and autumn ( [[#Jacobeit--2014|Jacobeit et al., 2014]] ).

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==== 10.6.4.8 Storyline Approaches ====

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The atmospheric circulation is influenced by large-scale, often slowly varying components of the climate system, such as ocean, sea ice and soil moisture. Historical and future changes of the atmospheric circulation depend, among other factors, on how these drivers have changed and will change. [[#Zappa--2017|Zappa and Shepherd (2017)]] have analysed this for the Mediterranean region and developed a set of storylines based on different plausible evolutions of those drivers and their impact on the Mediterranean winter climate. Important identified drivers during winter are tropical and polar amplification of global warming and the polar stratospheric vortex ( [[#Manzini--2014|Manzini et al., 2014]] ; [[#Simpson--2018|Simpson et al., 2018]] ), with implications for precipitation. [[#Zappa--2019|Zappa (2019)]] discusses the relative amplitude of tropical and Arctic warming, response of the AMOC, patterns of Pacific SST change, and changes in stratospheric vortex strength as possible drivers of the Mediterranean summer climate and stresses that given the present state of knowledge, alternative storylines based on these drivers should be considered as equally plausible future manifestations of regional climate change. Brogli et al. (2019a, b) and [[#Kröner--2017|Kröner et al. (2017)]] have revealed thermodynamic processes, lapse rate, and circulation as important drivers for Mediterranean summer climate.

Low-likelihood high-impact events might affect future Mediterranean climate. An example of such an event is the collapse of the AMOC ( [[#Weijer--2019|Weijer et al., 2019]] ), that would bring widespread cooling over the Northern Hemisphere. For the Mediterranean this is estimated to be a few degrees Celsius during summer in the case of a total collapse ( [[#Jackson--2015|Jackson et al., 2015]] ).

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==== 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.

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

'''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]] ).

<div id="cross-chapter-box-10.4" class="h2-container box-container"></div>

'''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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