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

== 10.6 Comprehensive Examples of Steps Toward Constructing Regional Climate Information ==

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=== 10.6.1 Introduction ===

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This section presents three comprehensive examples of steps for distilling regional climate information from the multiple sources of regional climate information presented in this chapter. These examples build on the general framework presented in [[#10.5|Section 10.5]] , examining in particular the strengths and challenges in linking the different sources, while also exposing the assumptions behind and consequences of decisions made in the process. The examples are framed taking into account societal perspectives that provide context for their regional climate statements. Although the nature of an IPCC Working Group I assessment precludes engaging with users of climate information ( [[#10.5|Section 10.5]] ), we do cite relevant national and regional reports that give user perspectives to set a foundation from which one could distil climate information for users. We have chosen the recent Cape Town drought, Indian summer-monsoon trends and the Mediterranean summer warming because they provide a geographically diverse set of locations and relevant processes and because most of the components for constructing regional climate information outlined in Chapter 10 are directly relevant to each case.

The three comprehensive examples follow a similar structure:

# Motivation and regional context.
# The region’s climate.
# Observational issues.
# Relevant anthropogenic and natural drivers.
# Model simulation and attribution over the historical period.
# Future climate information from global simulations.
# Future climate information from regional downscaling.
# Storylines.
# Climate information distilled from multiple lines of evidence.

Following this structure, construction of the regional climate information presented in these examples depends on an assessment of observational uncertainty relative to the magnitude of a climate change signal ( [[#10.2|Section 10.2]] ), the evaluations of model performance to judge the fitness-for-purpose of a given model ( [[#10.3|Section 10.3]] ), and expert judgement. These factors contribute to attribution of historical climate change signals ( [[#10.4|Section 10.4]] ), recognizing that attribution must account for the interplay between externally forced signals and unforced internal variability. This interplay is explored using multiple model ensembles, including, when appropriate and feasible, single-model initial-condition large ensembles (SMILEs). The multiple lines of evidence for the climate information may conflict, thus requiring distillation of the evidence ( [[#10.5|Section 10.5]] ) to arrive at climate-change statements. When moving from global climate information to climate information at the regional scale, following the structure above provides a basis for arriving at relevant and credible climate information. The comprehensive examples of distilling climate information thus show the value of working with multiple lines of evidence to develop robust climate change information for a region.

In addition to the three comprehensive examples, this section contains two additional examples analysing multiple sources of regional climate information. Box 10.3 on urban climate assesses information that provides a foundation for understanding climatic behaviour in urban areas and its projected change. Cross-Chapter Box 10.4 on climate change over the Hindu Kush Himalaya assembles information rooted in several chapters and previous assessment reports to assess understanding of several climate elements (temperature, precipitation, snow and glaciers, and extreme events) for the region and their projected changes.

As these examples will show, the distillation process of regional climate information from multiple lines of evidence can vary substantially from one case to another. Confidence in the distilled regional climate information is enhanced when there is agreement across multiple lines of evidence, but the outcome of distilling regional climate information can be limited by inconsistent or contradictory sources.

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=== 10.6.2 Cape Town Drought ===

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==== 10.6.2.1 Motivation and Regional Context ====

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Cape Town’s ‘Day Zero’ water crisis in 2018 threatened a shut-down of water supply to 3.4 million inhabitants of the city and resulted in domestic water use restriction of 50 litres per person per day lasting for nine months (pre-drought unconstrained water use was about 170 litres per person per day, [[#DWA--2013|DWA, 2013]] ), punitive water tariffs, and temporary closure of irrigation systems. Problems with water supply in many large cities in developing countries are endemic and rarely reported internationally. The water crisis in Cape Town attracted considerable international attention to a city with functional government structures, well-developed services (compared to other urban centres in Africa), a centre of international tourism, and an economic hub with GDP of 22 billion USD (about 7,500 USD per capita, [[#Gallie--2018|Gallie et al., 2018]] ). Economic and social impacts of the crisis were significant. Loss of revenue for companies of all sizes resulted not only from the scaling down of water-dependent activities, but also from the need to invest in water-efficient technologies and processes. Tourism was affected through reduced arrivals and bookings, although only temporarily ( [[#CTT--2018|CTT, 2018]] ). In the agricultural sector, 30,000 people were laid-off and production dropped by 20% ( [[#Piennaar--2018|Piennaar and Boonzaaier, 2018]] ). The crisis initially polarized society, with conflict emerging between various water users and erosion of trust in the government, but eventually social cohesion and an acute awareness of limited water resources emerged ( [[#Robins--2019|Robins, 2019]] ).

Cape Town’s crisis resulted from a combination of a strong, rare multi-year meteorological drought (Figure 10.18), estimated at 1 in 300 years ( [[#Wolski--2018|Wolski, 2018]] ), and factors related to the nature of the water supply system, operational water management and water resource policies. Cape Town was very successful in implementing water-saving actions after the previous drought of 2000–2003, reducing water losses from over 22% to 15% ( [[#Frame--2007|Frame and Killick, 2007]] ; [[#DWA--2013|DWA, 2013]] ), breaking the previous coupling of growth in water demand with growth in population. As a consequence, Cape Town won a Water Smart City award from the C40 Cities program only three years prior to the crisis. However, the water-saving actions, together with changing priorities in water resource provision from infrastructure-oriented towards resource and demand management, may well have led to delays in implementation of the expansion of water supply infrastructure ( [[#Muller--2018|Muller, 2018]] ). The expansion plan, formulated a decade prior to the crisis, included an expectation of long-term climate-change drying in the region ( [[#DWAF--2007|DWAF, 2007]] ). The crisis also exposed structural deficiencies of water management and inadequacy of a policy process in which decisions about local water resources are taken at a national level, particularly in a situation of political tension ( [[#Visser--2018|Visser, 2018]] ). The crisis was widely seen as a harbinger of future problems to be faced by the city, and a highlight of vulnerability of many cities in the world resulting from the interplay of three factors: (i) the fast urban-population growth, (ii) the economic, policy, infrastructural and water resource paradigms and constraints, and (iii) anthropogenic climate change.

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[[File:f0461f69d2ef1358dc143bf1faf712d8 IPCC_AR6_WGI_Figure_10_18.png]]

'''Figure 10.1''' '''8 |''' '''Historical and projected rainfall and Southern Annular Mode (SAM) over the Cape Town region. (a)''' Yearly accumulation of rainfall (in mm) obtained by summing monthly totals between January and December, with the drought years 2015 (orange), 2016 (red), and 2017 (purple) highlighted in colour. '''(b)''' Monthly rainfall for the drought years (in colour) compared with the 1981–2014 climatology (grey line). Rainfall in (a) and (b) is the average of 20 quality controlled and gap-filled series from stations within the Cape Town region (31°S–35°S, 18°W–20.5°W). '''(c)''' Time series of the SAM index and of historical and projected rainfall anomalies (%, baseline 1980–2010) over the Cape Town region. Observed data presented as 30-year running means of relative total annual rainfall over the Cape Town region for station-based data (black line, average of 20 stations as in (a) and (b), and gridded data (average of all gridcells falling within 31°S–35°S, 18°W–20.5°W), GPCC (green line) and CRU TS (olive line). Model ensemble results presented as the 90th-percentile range of relative 30-year running means of rainfall and the SAM index from 35 CMIP5 (blue shading) and 35 CMIP6 (red shading) simulations, 6 CORDEX simulations driven by 1 to 10 GCMs (cyan shading), 6 CCAM (purple shading) simulations from individual ensemble members, and 50 members from the MIROC6 SMILE simulations (orange shading). The light blue, dark red and yellow lines correspond to NCEP/NCAR, ERA20C and 20CR, respectively. The SAM index is calculated from sea level pressure reanalysis and GCM data as per [[#Gong--1999|Gong and Wang (1999)]] and averaged over the aforementioned bounding box. CMIP5, CORDEX and CCAM projections use RCP8.5, and CMIP6 and MIROC6 SMILE projections use SSP5-8.5. '''(d)''' Historical and projected trends in rainfall over the Cape Town region and in the SAM index. Observations and gridded data processed as in (c). Trends calculated as Theil-Sen trend with block-bootstrap confidence interval estimate. Markers show median trend, bars 95% confidence interval. Global models in each CMIP group were ordered according to the magnitude of trend in rainfall, and the same order is maintained in panels showing trends in the SAM. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

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<span id="the-regions-climate"></span>
==== 10.6.2.2 The Region’s Climate ====

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An evaluation of the relative role of rainfall and temperature signal in the 2015–2017 hydrological drought gives a strong indication that lack of rainfall was the primary driver ( [[#Otto--2018|Otto et al., 2018]] ) leading to the 2018 water crisis. Thus, the remainder of this section focuses on rainfall. [[IPCC:Wg1:Chapter:Chapter-11#11.6|Section 11.6]] offers a discussion of African drought over broader areas, including mechanisms relevant to them.

Cape Town is located at the south-western tip of Africa, within an approximately 100 km × 300 km region that receives 80% of its rainfall during the austral winter (March to October), with the largest portion in June to August. In the vicinity of Cape Town, rainfall is strongly heterogeneous, ranging from about 300 mm/year on coastal plains to &gt;2,000 mm/year in mountain ranges. The Cape Town water supply relies on surface water reservoirs located in a few small mountain catchments (about 800 km <sup>2</sup> in total). The Cape Town region receives 85% of its rainfall from a series of cold fronts forming within mid-latitude cyclones. The remainder is brought in by infrequent cut-off lows that occur throughout the year ( [[#Favre--2013|Favre et al., 2013]] ). This creates a very strong water resource dependency on a single rainfall delivery mechanism that may be strongly affected by anthropogenic climate change ( [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] and [[#10.6.2.6|Section 10.6.2.6]] ).

The 2015–2017 drought had strong low-rainfall anomalies in shoulder seasons (March to May and September to November, though weaker in the latter), and average rainfall in June and July ( [[#Sousa--2018a|Sousa et al., 2018a]] ; [[#Mahlalela--2019|Mahlalela et al., 2019]] ). The anomaly resulted from fewer rainfall events and lower average intensity of events. The anomaly was strongest in the mountainous region where the water supply system’s catchments are located ( [[#Wolski--2021|Wolski et al., 2021]] ).

Although the 2015–2017 drought was unprecedented in the historical record, the Cape Town region has experienced other droughts of substantial magnitude, notably in the 1930s, 1970s and more recently in 2000–2003. Long-term (&gt;90 years) rainfall trends are mixed in sign, location-dependent, and weak ( [[#Kruger--2017|Kruger and Nxumalo, 2017]] ; [[#Wolski--2021|Wolski et al., 2021]] ); mid-term (about 50 years) trends are similarly mixed in sign ( [[#MacKellar--2014|MacKellar et al., 2014]] ). In the south-western part of the region, rainfall is mostly decreasing in the post 1981 period, particularly in December–January–February and March–April–May, although there is no trend or a weak wetting in June–July–August ( [[#Sousa--2018a|Sousa et al., 2018a]] ; [[#Wolski--2021|Wolski et al., 2021]] ). Rainfall trends of similar magnitude and duration to the post-1981 trend accompanied previous strong droughts in the region ( [[#Wolski--2021|Wolski et al., 2021]] ).

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<span id="observational-issues"></span>
==== 10.6.2.3 Observational Issues ====

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South Africa and the Cape Town region have good instrumental weather data. Records start in the late 1800s, with in excess of 10 gauges reporting since the 1920s, expanding to about 80 gauges in the 1980s, but the number of stations has declined since. The mountains have only a few stations, which receive more than 1000 mm per year. In view of the strong heterogeneity of rainfall, changes in the number of stations contributing to datasets such as Climatic Research Unit (CRU) and Global Precipitation Climatology Project results in a lack of consistency between them, which limits their reliability in the region ( [[#10.2|Section 10.2]] ; [[#Wolski--2021|Wolski et al., 2021]] ).

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<span id="relevant-anthropogenic-and-natural-drivers"></span>
==== 10.6.2.4 Relevant Anthropogenic and Natural Drivers ====

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Because the primary rainfall mechanism is frontal rain, the most relevant large-scale drivers are those that affect cyclogenesis, frontogenesis and the mid-latitude westerlies’ latitudinal position and moisture supply. These drivers and, thus, the region’s rainfall are linked to the Antarctic Oscillation (AAO; [[#Reason--2005|Reason and Rouault, 2005]] ) or Southern Annular Mode (SAM), the dominant monthly and interannual mode of Southern Hemisphere atmospheric variability, and a measure of the pressure gradient between mid- and high latitudes. (See Sections 3.3, 3.7, 4.3 and Annex IV.2.2 for more general discussion of the SAM.) While in the post-1930 period, the SAM displays a long-term positive trend, the Cape Town region’s rainfall does not, and only the post-1979 trends of rainfall and SAM are conceptually consistent. For example, a positive trend in the SAM is associated with a negative trend in rainfall ( [[#10.6.2.5|Section 10.6.2.5]] and Figure 10.18). There is also good agreement between the seasonality of the SAM and rainfall trends in the post-1979 period: a drying trend appears strongly in December to February and March to May, but not in June to August and September to November ( [[#Wolski--2021|Wolski et al., 2021]] ), and trends in the SAM have similar seasonal dependence (E.-P. [[#Lim--2016|]] [[#Lim--2016|Lim et al., 2016]] ; [[IPCC:Wg1:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ). Additionally, there is a similar seasonal pattern in the post-1979 trends in indices capturing the southern edge of the Hadley circulation ( [[#Grise--2018|Grise et al., 2018]] ).

In the longer-term, Cape Town regional rainfall is characterized by a multi-decadal scale quasi-periodicity (Figure 10.18; [[#Dieppois--2019|Dieppois et al., 2019]] ; [[#Wolski--2021|Wolski et al., 2021]] ), with the 2015–2017 drought and previous strong droughts (1930s and 1970s) occurring during the rainfall’s periodic low phases. However, the studies linking the Cape Town 2015–2017 drought to the hemispheric processes expressed by the SAM ( [[#Sousa--2018a|Sousa et al., 2018a]] ; [[#Burls--2019|Burls et al., 2019]] ; [[#Mahlalela--2019|Mahlalela et al., 2019]] ) focused almost exclusively on the post-1979 period, when global reanalyses are available. Detailed understanding of the drivers of previous (1930s and 1970s) Cape Town region droughts and the role of hemispheric processes expressed by the SAM in the pre-1979 period is missing.

The Cape Town regional rainfall is also potentially linked to other hemispheric phenomena, such as the expansion of the tropics and, specifically, the South Atlantic high-pressure system and the position of the subtropical jet, which share some variability with the SAM. The relationships between these phenomena and Cape Town rainfall have not been thoroughly investigated outside of the context of the 2015–2017 drought, but the drought itself was associated with poleward expansion of the subtropical anticyclones in the South Atlantic and South Indian oceans and (a resulting) poleward displacement of the moisture corridor across the South Atlantic ( [[#Sousa--2018a|Sousa et al., 2018a]] ), as well as a weaker subtropical jet ( [[#Mahlalela--2019|Mahlalela et al., 2019]] ). [[#Burls--2019|Burls et al. (2019)]] also link the decline in the number of rainy days to the increase in sea level pressure along the poleward flank of the South Atlantic high-pressure system and the intensity of the post-frontal ridging high. Additionally, there is a possible linkage between Cape Town rainfall and near-shore cold sea surface temperature (SST) anomalies arising from Ekman upwelling due to reduced westerly and increased south-easterly winds. These might lead to suppression of convection and reduction of rainfall over land ( [[#Rouault--2010|Rouault et al., 2010]] ). All these phenomena are conceptually consistent with the poleward migration of the westerlies and expansion of the tropics.

Rainfall in the Cape Town region also responds to SST anomalies in the south-east Atlantic, including the Agulhas Current retroflection region, which may drive intensification of low-pressure systems, leading to the trailing front strengthening as it makes landfall over the Cape Town region ( [[#Reason--2005|Reason and Jagadheesha, 2005]] ). There are also linkages at the seasonal time scale between the Cape Town regional rainfall and Antarctic sea ice ( [[#Blamey--2007|Blamey and Reason, 2007]] ).

In addition to mid-latitude controls, subtropical processes also play a role in the Cape Town region’s rainfall variability. The 10°S–30°S region of the subtropical Atlantic, parts of the South American continent and even parts of the African continent north of Cape Town are sources of moisture for atmospheric river events contributing to frontal rainfall ( [[#Blamey--2018|Blamey et al., 2018]] ; [[#Ramos--2019|Ramos et al., 2019]] ), with implications for the 2015–2017 drought ( [[#Sousa--2018a|Sousa et al., 2018a]] ). Also, the second major rainfall contributing system, cut-off-lows, is conditional on moisture supply from the subtropics ( [[#Abba%20Omar--2020|Abba Omar and Abiodun, 2020]] ).

Although El Niño–Southern Oscillation (ENSO) influences climate in southern Africa, any relationship between ENSO and Cape Town’s rainfall is weak and inconsistent, showing the strongest impact in May to June ( [[#Philippon--2012|Philippon et al., 2012]] ). ENSO, however, does influence large-scale processes and phenomena relevant to the drought, though the relationship between ENSO and the SAM is complex, with each ENSO event influencing the SAM differently in different seasons ( [[#Ding--2012|Ding et al., 2012]] ). Similarly, ENSO affects meridional circulation and thus the subtropical anticyclone as well as the polar and subtropical jets ( [[#Seager--2019|Seager et al., 2019]] ), but only modifying, not controlling, their role in Cape Town’s rainfall.

Paleoclimate studies reveal that long-term variability in the winter rainfall region of South Africa (including Cape Town) is consistent with a general framework of warming/cooling-induced latitudinal migration of the westerlies and transformation of the subtropical high-pressure belt and associated hemispherical processes (see section 10.2.3.2 for assessment of paleoclimate analysis). The synchronicity of winter rainfall with Antarctic ice-core-derived polar temperature anomalies is consistently revealed in studies using different paleoclimate proxies and time scales of 1400 years ( [[#Stager--2012|Stager et al., 2012]] ), about 3000 years ( [[#Hahn--2016|Hahn et al., 2016]] ) and 12,000 years ( [[#Weldeab--2013|Weldeab et al., 2013]] ). Changes in rainfall regimes at shorter (decadal) time scales appear to reflect influence of local processes such as the Agulhas current’s interaction with the Atlantic, resulting in changes in SST and coastal upwelling, as well as modification of the wind tracks by topography ( [[#Stager--2012|Stager et al., 2012]] ).

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==== 10.6.2.5 Model Simulation and Attribution Over the Historical Period ====

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Due to the small scale of the Cape Town region, robust comparison of CMIP simulations to observations is difficult. However, in general, CMIP5 models capture the seasonality well, such as the dominance of austral winter rains, although they overestimate the peak and underestimate the shoulder season rainfall ( [[#Mahlalela--2019|Mahlalela et al., 2019]] ). Trends in rainfall are particularly difficult to assess as they are generally weak and depend strongly on the time period and dataset adopted for the analyses ( [[#10.6.2.3|Section 10.6.2.3]] ). A multi-method attribution study ( [[#Otto--2018|Otto et al., 2018]] ) estimates the probability of the 2015–2017 drought to have increased by a factor of three since pre-industrial times (with a wide 95% confidence interval of 1.5 to 6). However, throughout the 20th century, a substantial portion of the global models (about 36% of CMIP5 and 44% of CMIP6 models, as well as many of the MIROC SMILE members) simulate a statistically significant (95% level) decline in total annual rainfall, while there is no robust long-term trend in observations (Figure 10.18). [[#10.4|Section 10.4]] offers a more detailed assessment of attribution challenges.

Global models capture the overall behaviour of the observed main hemispherical processes, such as the expansion of the tropics, a positive trend in SAM and the poleward shift of the westerly jet. However, they fail to capture details of their observed climatology and variability ( [[#Simpson--2016|Simpson and Polvani, 2016]] ), and the magnitudes of simulated trends vary, though the models typically underestimate observed trends in these processes ( [[#Purich--2013|Purich et al., 2013]] ; [[#Staten--2018|Staten et al., 2018]] ). In general, CMIP5 models do capture the SAM-regional rainfall association, although not consistently across all seasons ( [[#Purich--2013|Purich et al., 2013]] ; E.-P. [[#Lim--2016|]] [[#Lim--2016|Lim et al., 2016]] ).

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<span id="future-climate-information-from-global-simulations"></span>
==== 10.6.2.6 Future Climate Information from Global Simulations ====

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Global models show strong consistency in a drying signal for the Cape Town region, with the reduction in total annual rainfall of up to 20% by the end of the 21st century in CMIP5 RCP8.5 and CMIP6 SSP5-8.5 simulations (Figure 10.18; [[#Almazroui--2020c|Almazroui et al., 2020c]] ). The consistency across the models is a robust signal compared to the rest of southern Africa, where the climate change signal varies spatially: stronger drying in the west and moderate drying or weak wetting in the east ( [[#DEA--2013|DEA, 2013]] , 2018; see Atlas.4.4 for further discussion of southern Africa precipitation projections). Rainfall changes projected for the Cape Town region are consistent with projected changes in hemispheric-scale processes and regional-scale dynamics that point toward reduced frequency of frontal systems affecting that region. These changes include robust signals in CMIP5 models for the Southern Hemisphere for a poleward expansion of the tropics ( [[#Hu--2013b|Hu et al., 2013b]] ), poleward displacement of mid-latitude storm tracks ( [[#Chang--2012|Chang et al., 2012]] ), increased strength and poleward shift of the westerly winds ( [[#Bracegirdle--2018|Bracegirdle et al., 2018]] ) and subtropical jet-streams ( [[#Chenoli--2017|Chenoli et al., 2017]] ), and a shift toward a more positive phase of the SAM (E.-P. [[#Lim--2016|]] [[#Lim--2016|Lim et al., 2016]] ). However, despite the consistency in circulation changes, the emergence of anthropogenic rainfall change above unforced variability in West Southern Africa remains uncertain for annual rainfall throughout most of the 21st century, even under SSP5-8.5 (Figure 10.15).

There is also a substantial increase in the frequency of conditions supporting atmospheric rivers and water vapour transport towards the south-west coast of southern Africa in the projected climate ( [[#Espinoza--2018|Espinoza et al., 2018]] ). This behaviour has strong implications for the region, as most topographically high locations receive rainfall from persistent atmospheric rivers ( [[#Blamey--2018|Blamey et al., 2018]] ). A thorough understanding of the role of atmospheric rivers in the Cape Town region under a changing climate is missing.

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<span id="future-climate-information-from-regional-downscaling"></span>
==== 10.6.2.7 Future Climate Information from Regional Downscaling ====

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Dynamical downscaling studies implemented with a stretched-grid model ( [[#Engelbrecht--2009|Engelbrecht et al., 2009]] ) revealed a signal compatible with the driving CMIP5 ensemble, that is, consistent drying throughout the region, amplifying in time, irrespective of the considered emissions scenario and the generation of global models ( [[#DEA--2013|DEA, 2013]] , 2018). A multi-model CORDEX ensemble indicates a robust signal of reduction of total annual rainfall in the future, although there is less agreement on how changes in rainfall occurrence may evolve in the region, such as through fewer consecutive rain days or longer dry spells ( [[#Abiodun--2017|Abiodun et al., 2017]] ; [[#Maúre--2018|Maúre et al., 2018]] ). For the end of the century under RCP8.5, [[#Dosio--2019|Dosio et al. (2019)]] also found drying. Moreover, in their analysis, the drying is associated with an increase in the number of consecutive dry days and a reduction in number of rainy days. Their results are consistent with the driving global models for all the precipitation indices, and they are robust independent of the choice of the regional climate model (RCM) or global model. However, collectively, these analyses indicate that uncertainty remains in the characteristics of the precipitation decrease.

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<span id="storyline-approaches"></span>
==== 10.6.2.8 Storyline Approaches ====

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There is a consistency in rainfall projections with the projections of rainfall drivers and with the general understanding of the influence of global warming on the circulation dynamics and rainfall patterns in the region. Thus, the expansion of the South Atlantic high-pressure system, related to widespread warming of the tropics and poleward shift of the subsiding limb of the Hadley cell, is associated with the southward displacement of the subtropical jet, and southward migration of mid-latitude westerlies and storm tracks, in addition to changes in the SAM ( [[#10.6.2.4|Section 10.6.2.4]] ). These effects are also relatively consistent with recent (post-1980s) declines in rainfall in the Cape Town region. The storyline of an extended drought is thus a set of events that can yield reduced rainfall in the Cape Town region: a poleward shift of the downward branch of the Hadley cell that produces a sustained southward shift in mid-latitude westerlies and storm tracks. The behaviour is potentially reinforced by changes in the SAM.

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<span id="climate-information-distilled-from-multiple-lines-of-evidence"></span>
==== 10.6.2.9 Climate Information Distilled From Multiple Lines of Evidence ====

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There is ''high agreement'' among observational data and reanalyses that the recent (post-1979) downward trend in the Cape Town region’s rainfall leading to the 2015–2017 drought is related to the hemispheric processes of poleward shift in the westerlies and expansion of the Hadley circulation. However, there is less support for the precipitation–circulation relationship in historical CMIP5 and CMIP6 simulations. As a consequence, there is only ''medium confidence'' that these process changes produced the 2015–2017 drought leading to the 2018 water crisis.

For the water-resource planner who has to deal with potential drought like the 2015–2017 event, several lines of evidence indicate future drying: the projected precipitation by global models and RCMs of different spatial resolutions, and the observed and projected changes of circulation patterns consistent with drier conditions, the paleoclimatic evidence confirming a millennial-scale circulation–rainfall link. However, the distillation is limited by a lack of information about whether or not a relationship between Cape Town precipitation and large-scale circulation processes adequately explains droughts in the twentieth century prior to 1979.

Thus, although a clear association appears in observations from 1979 onward between increasing GHG concentrations, drying in the Cape Town region and behaviour of a key circulation process, the SAM, further analysis suggests caution. Not all global models show the historical post-1979 association among these factors, and when the observational record is extended back further to times when the anthropogenic greenhouse forcing was weaker, there is no strong association between the SAM and Cape Town drought. Thus, there is only ''medium confidence'' in the expectation of a future drier climate for Cape Town.

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<span id="indian-summer-monsoon"></span>
=== 10.6.3 Indian Summer Monsoon ===

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==== 10.6.3.1 Motivation and Regional Context ====

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The Indian summer monsoon provides 80% of the country’s annual rainfall from June to September, supplying the majority of water for agriculture, industry, drinking and sanitation to over a billion people. Any variations in the monsoon on time scales from days to decades can have large impacts ( [[#Challinor--2006|Challinor et al., 2006]] ; [[#Gadgil--2006|Gadgil and Gadgil, 2006]] ). Evidence from paleoclimate records (Sections 8.3.2.4.1) shows ''high confidence'' in a weakened Indian monsoon during cold epochs of the past such as the Younger Dryas (12,800–11,600 years ago) as measured by speleothem oxygen isotopes ( [[#Kathayat--2016|Kathayat et al., 2016]] ). There is a pressing need to understand if the monsoon will change in the future under anthropogenic forcing and to quantify such changes. Multiple datasets have shown robust negative trends since the 1950s until the turn of the century ( [[#Bollasina--2011|Bollasina et al., 2011]] ) followed by a recovery ( [[#Jin--2017|Jin and Wang, 2017]] ), yet repeated assessments project the monsoon to increase in strength under enhanced GHG forcing ( [[#Christensen--2007|Christensen et al., 2007]] , 2013; Sections 8.3.2.4.1 and 8.4.2.4.1). The apparent contradiction between future projections and observed historical trends makes the region an ideal choice for an in-depth assessment. The reader is also referred to the South Asia (SAS) regional assessment of precipitation extremes ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ), which is not discussed here for brevity.

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<span id="the-regional-climate-of-india"></span>
==== 10.6.3.2 The Regional Climate of India ====

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Local geography gives rise to distinct differences in societal experience of the summer monsoon. The south-westerly monsoon winds are incident upon the Western Ghats mountains on the west coast, leading to orographic enhancement and heavy rains ( [[#Shige--2017|Shige et al., 2017]] ), which supply rivers with water for much of the southern peninsula, often the subject of inter-regional water disputes. The northern plains contain the Ganges river and also India’s most intensive agriculture, both rainfed and irrigated. Synoptic systems known as monsoon depressions cross the northern east coast, supplying much of the rain in central India ( [[#Hunt--2019|Hunt and Fletcher, 2019]] ). Further north, the eastern Himalayas are dominated by the summer monsoon, while the western Himalayas receive most rainfall from western disturbances during winter ( [[#Palazzi--2013|Palazzi et al., 2013]] ). Meanwhile, south-eastern India sits under a rain shadow (the only region to receive more rainfall during the winter monsoon).

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==== 10.6.3.3 Observational Issues for India ====

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India has one of the oldest rain-gauge networks in the world, leading to the production of numerous observational products (reviewed in [[#Khouider--2020|Khouider et al., 2020]] ). Gridded gauge-based products dating back to the 19th century reveal pronounced decadal variability ( [[#Sontakke--2008|Sontakke et al., 2008]] ). Trends for India over the whole 20th century are inconclusive ( [[#Knutson--2018|Knutson and Zeng, 2018]] ), although declining over central and northern areas ( [[#Roxy--2015|Roxy et al., 2015]] ). Assessment of multiple observational datasets covering the Indian summer monsoon reveals significant declining rainfall over the second half of the 20th century ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.1|Section 8.3.2.4.1]] and Figure 10.19c,d). A subsequent recovery has been noted since the early 2000s ( [[#Jin--2017|Jin and Wang, 2017]] ).

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[[File:6a7505023aeaa709cd7d8681f24af85e IPCC_AR6_WGI_Figure_10_19.png]]

'''Figure 10.''' '''19 |''' '''Changes in the Indian summer monsoon in the historical and future periods.''' Observational uncertainty demonstrated by a snapshot of rain-gauge density (% of 0.05° subgrid boxes containing at least one gauge) in the APHRO-MA 0.5° daily precipitation dataset for June to September 1956. '''(b)''' Multi-model ensemble (MME) mean bias of 34 CMIP6 models for June to September precipitation (mm day <sup>–1</sup> ) compared to CRU TS observations for the 1985–2010 period. '''(c)''' Maps of rainfall trends (mm day <sup>–1</sup> per decade) in CRU TS observations (1950–2000), the CMIP6 MME-mean of SSP5-8.5 future projections for 2015–2100 (34 models), the CMIP6 hist-GHG and hist-aer runs, both measured over 1950 to 2000. '''(d)''' Low-pass filtered time series of June to September precipitation anomalies (%, relative to 1995–2014 baseline) averaged over the central India box shown in panel (b). The averaging region (20°N–28°N, 76°E–87°E) follows other works ( [[#Bollasina--2011|Bollasina et al., 2011]] ; [[#Jin--2017|Jin and Wang, 2017]] ; [[#Huang--2020b|Huang et al., 2020b]] ). Time series are shown for CRU TS (brown), GPCC (dark blue), REGEN (green), APHRO-MA (light brown) observational estimates and the IITM all-India rainfall product (light blue) in comparison with the CMIP6 mean of 13 models for the all-forcings historical (pink) the aerosol-only (hist-aer, grey) and greenhouse gas-only (hist-GHG, blue). Dark red and blue lines show low-pass filtered MME-mean change in the CMIP6 historical/SSP5-8.5 (34 models) and CMIP5 historical/RCP8.5 (41 models) experiments for future projections to 2100. The filter is the same as that used in Figure 10.11 (d). To the right, box-and-whisker plots show the 2081–2100 change averaged over the CMIP5 (blue) and CMIP6 (dark red) ensembles. Note that some models exceed the plotting range (CMIP5: GISS-E2-R-CC, GISS-E2-R, IPSL-CM5B-LRl and CMIP6: CanESM5-CanOE, CanESM5 and GISS-E2-1-G). '''(e)''' Precipitation linear trend (% per decade) over Central India for historical 1950–2000 (left) and future 2015–2100 (right) periods in Indian Monsoon rainfall in observed estimates (black crosses), the CMIP5 historical-RCP8.5 simulations (blue), the CMIP6 ensemble (dark red) for historical all-forcings experiment and SSP5-8.5 future projection, the CMIP6 hist-GHG (light blue triangles), hist-aer (grey triangles) and historical all-forcings (same sample as for hist-aer and hist-GHG, pink circles). Ensemble means are also shown. Box-and-whisker plots show the trend distribution of the three coupled and the d4PDF atmosphere-only (for past period only) SMILEs used throughout (Chapter 10 and follow the methodology used in Figure 10.6. '''(f)''' Example spread of trends (mm day <sup>–1</sup> per decade) for the period 2016–2045 in RCP8.5 SMILE experiments of the MPI-ESM model, showing the difference between the three driest and three wettest trends among ensemble members over central India. All trends are estimated using ordinary least-squares regression. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

Observational products containing critical inhomogeneities in gauge distribution and reporting over time are acknowledged as suitable for mesoscale analysis ( [[#Rajeevan--2009|Rajeevan and Bhate, 2009]] ), while use for climate trends requires consistent reporting over time from quality-controlled gauges (e.g., about 2000 gauges since the 1950s in [[#Rajeevan--2006|Rajeevan et al., 2006]] ). A newer 0.25°-gridded product covering 1901 onwards ( [[#Pai--2014|Pai et al., 2014]] , 2015), based on Shepard’s interpolation method for irregularly-spaced stations ( [[#Shepard--1968|Shepard, 1968]] ), shows increased intensity of daily rainfall and extremes over some regions, especially in the late-20th century. However, changes to the inputted gauges may have introduced an artificial jump in extreme rainfall since 1975 over central India ( [[#10.2.2.3|Section 10.2.2.3]] ; [[#Lin--2019|Lin and Huybers, 2019]] ). They suggest that this method may have masked declines in mean rainfall and highlight the need for availability of raw gauge data to allow transparent assessments. [[#Khouider--2020|Khouider et al. (2020)]] have successfully tested a probabilistic interpolation method for India to overcome problems inherent in algorithms based on inverse-distance weighting when applied to data-sparse regions. An example snapshot of the uneven distribution of rain gauges in a common observational product is shown in Figure 10.19a.

The uncertainty among local and international observational products for India can pose challenges when evaluating climate models (as in [[#10.2.2.6|Section 10.2.2.6]] ; [[#Prakash--2015|Prakash et al., 2015]] ). For the seasonal mean summer monsoon rainfall, [[#Collins--2013a|Collins et al. (2013a)]] found large biases separating many CMIP5 models from the available observational products. However, for seasonal mean variability, the spread across observational products was larger than across the CMIP5 ensemble.

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==== 10.6.3.4 Relevant Anthropogenic and Natural Drivers for Long-term Change ====

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The relevant drivers for long-term change in the mean Indian summer monsoon are summarized briefly:

* Increased greenhouse gas (GHG) concentrations (chiefly CO <sub>2</sub> ) are a strong contributor to changes in the monsoon, with repercussions for the meridional temperature contrast driving the monsoon circulation ( [[#Ueda--2006|Ueda et al., 2006]] ; [[#Roxy--2015|Roxy et al., 2015]] ), for the monsoon winds in the lower troposphere ( [[#Cherchi--2011|Cherchi et al., 2011]] ; [[#Krishnan--2013|Krishnan et al., 2013]] ), or for the availability of moisture from the Indian Ocean ( [[#May--2011|May, 2011]] ).
* Industrial emissions of sulphate aerosol predominantly in the Northern Hemisphere could change inter-hemispheric energy transports and weaken the monsoon ( [[#Polson--2014|Polson et al., 2014]] ; [[#Undorf--2018|Undorf et al., 2018]] ). The effect of local anthropogenic emissions of black carbon (chiefly from cooking fires) is uncertain ( [[#Lau--2006|Lau and Kim, 2006]] ; [[#Nigam--2010|Nigam and Bollasina, 2010]] ).
* India’s green revolution over the late-20th century led to considerable land-use change, with massive expansion of agriculture at the expense of forest and shrublands. As a result, India’s northern plains feature widespread irrigation, suggested to be a cause of drying ( [[#Mathur--2020|Mathur and AchutaRao, 2020]] ).
* Decadal modes of variability such as the Pacific Decadal Variability (PDV, Annex IV) and Atlantic Multi-decadal Variability (AMV, Annex IV), which may be partly forced ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ), are known to cause decadal modulation of the monsoon ( [[#Krishnamurthy--2014|Krishnamurthy and Krishnamurthy, 2014]] ; [[#Naidu--2020|Naidu et al., 2020]] ).

The interplay of these external and internal drivers is key to understanding past and future monsoon change.

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==== 10.6.3.5 Model Simulation and Attribution of Drying Over the Historical Period ====

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The robust decline of Indian summer monsoon rainfall averaged over India in the second half of the 20th century ( [[#10.6.3.3|Section 10.6.3.3]] ) is not in line with expectations arising from thermodynamic constraints on the water cycle in a warming world ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.2|Section 8.2.2]] ) and has been regarded as a puzzle ( [[#Goswami--2006|Goswami et al., 2006]] ). Assessing the attribution of 20th-century changes to Indian rainfall is the subject of coordinated modelling under the Global Monsoon MIP (GMMIP; [[#Zhou--2016|Zhou et al., 2016]] ), but is complicated by long-standing dry biases in coupled CMIP3, CMIP5 ( [[#Sperber--2013|Sperber et al., 2013]] ) and CMIP6 (Figure 10.19b) global models. These dry biases are connected to a lower tropospheric circulation that is too weak ( [[#Sperber--2013|Sperber et al., 2013]] ) and wet biases in the equatorial Indian Ocean ( [[#Bollasina--2013|Bollasina and Ming, 2013]] ). [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.1|Section 8.3.2.4.1]] finds ''high confidence'' that anthropogenic aerosol emissions have dominated the observed declining trends of countrywide Indian summer monsoon rainfall, consistent with findings at the global-monsoon scale ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.2|Section 3.3.3.2]] ).

Stronger Northern Hemisphere aerosol emissions cool it relative to the Southern Hemisphere, increasing northward energy transport at the expense of moisture transport towards India ( [[#Bollasina--2011|Bollasina et al., 2011]] ). The attribution to anthropogenic aerosols is supported in CMIP5 single-forcing experiments, including some testing the sensitivity to local and remote emissions ( [[#Guo--2015|Guo et al., 2015]] , 2016; [[#Shawki--2018|Shawki et al., 2018]] ), comparing CMIP5 GCMs forced by both aerosol and GHG to GHG only ( [[#Salzmann--2014|Salzmann et al., 2014]] ) and reducing emissions to pre-industrial levels ( [[#Takahashi--2018|Takahashi et al., 2018]] ). The large spread between individual model realisations of comparable magnitude to the aerosol-induced signal suggested to [[#Salzmann--2014|Salzmann et al. (2014)]] that internal variability may also play a role over regions such as northern-central India. Further uncertainty surrounds the level of radiative forcing. [[#Dittus--2020|Dittus et al. (2020)]] forced a GCM with historical aerosol emissions scaled between 0.2 and 1.5 times their observed values, representing the spread in CMIP5 effective radiative forcing. The strongest forcing led to around 0.5 mm day <sup>–1</sup> less late-20th century Indian monsoon rainfall than the weakest ( [[#Shonk--2020|Shonk et al., 2020]] ). Meanwhile, the uncertainty surrounding aerosol–cloud interactions could change the sign of long-term precipitation trends ( [[#Takahashi--2018|Takahashi et al., 2018]] ).

There is some evidence that declining Indian monsoon rainfall is due to regional SST warming patterns, themselves arising due to radiative forcing from GHG (e.g., in the Indian Ocean, [[#Guemas--2013|Guemas et al., 2013]] ). [[#Roxy--2015|Roxy et al. (2015)]] artificially raised SST in a GCM in the equatorial Indian Ocean (the region of strongest observed SST warming), leading to a weakened monsoon. [[#Annamalai--2013|Annamalai et al. (2013)]] used a GCM to suggest instead that preferential warming of the western North Pacific may force a Rossby-wave response to its west that weakens the monsoon through dry advection and subsidence. These hypotheses are not borne out in GHG-forced future projections ( [[#10.6.3.6|Section 10.6.3.6]] ).

A small anthropogenic contribution may be expected from local land-use/land-cover changes and land management. India is the world’s most irrigated region with around 0.5 mm/day in places, although peaking higher in summer ( [[#Cook--2015b|Cook et al., 2015b]] ; [[#McDermid--2017|McDermid et al., 2017]] ). Including irrigation in GCMs and RCMs slows the monsoon circulation and diminishes rainfall ( [[#Lucas-Picher--2011|Lucas-Picher et al., 2011]] ; [[#Guimberteau--2012|Guimberteau et al., 2012]] ; [[#Shukla--2014|Shukla et al., 2014]] ; [[#Tuinenburg--2014|Tuinenburg et al., 2014]] ; [[#Cook--2015b|Cook et al., 2015b]] ) due to reduced surface temperature ( [[#Thiery--2017|Thiery et al., 2017]] ), reducing the monsoon wind and moisture fluxes towards India ( [[#Mathur--2020|Mathur and AchutaRao, 2020]] ). However, implementation methodologies for irrigation in climate models are simplified and often do not account for spatial heterogeneity while overestimating demand and supply ( [[#10.3.3.6|Section 10.3.3.6]] ; [[#Nazemi--2015|Nazemi and Wheater, 2015]] ; [[#Pokhrel--2016|Pokhrel et al., 2016]] ). Changing forest cover to agricultural land in an RCM ( [[#Paul--2016|Paul et al., 2016]] ) finds weakened summer monsoon rainfall especially in central and eastern India, due to decreased local evapotranspiration. Decreased evapotranspiration from a warmer surface since the 1950s in the CMIP5 ensemble may also feedback on the supply of moisture ( [[#Ramarao--2015|Ramarao et al., 2015]] ). Based on an AGCM study and literature review, [[#Krishnan--2016|Krishnan et al. (2016)]] support the role of land-use/land-cover change in adding to the effects of aerosol in weakening the monsoon, in addition to dynamic effects on the circulation caused by rapid warming of the Indian Ocean.

In addition to anthropogenic forcing, there is evidence that internal variability in the Pacific is a significant driver. [[#Huang--2020b|Huang et al. (2020b)]] compared a large perturbed-physics ensemble (HadCM3C) with a SMILE for the historical period. Ensemble members replicating the negative Indian rainfall trend were accompanied by a strong phase change in the PDV from negative to positive, consistent with SST observations. [[#Jin--2017|Jin and Wang (2017)]] have demonstrated increasing Indian monsoon rainfall since 2002 in a variety of observed datasets, suggesting the increase is due either to a change in dominance of a particular forcing (for example from aerosol to GHG) or to a change in phase of internal variability such as the PDV. [[#Huang--2020b|Huang et al. (2020b)]] partially attribute the rainfall recovery to a phase change in the PDV, supported by a SMILE study combined with reanalyses ( [[#Ha--2020|Ha et al., 2020]] ).

The drying trend of Indian summer monsoon rainfall since the mid-20th century can be attributed with ''high confidence'' to aerosol as the dominant anthropogenic forcing with a further contribution from internal variability, supported by the review of [[#Wang--2021|]] [[#Wang--2021|B. Wang et al. (2021)]] including CMIP6 results. Understanding the interplay between anthropogenic and internal drivers will be important for understanding future change.

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<span id="future-climate-projections-from-global-simulations"></span>
==== 10.6.3.6 Future Climate Projections from Global Simulations ====

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The AR5 ( [[#Christensen--2013|Christensen et al., 2013]] ) concluded that Indian summer monsoon rainfall will strengthen under all RCP future climate scenarios, while the circulation will weaken ( ''medium confidence'' ). SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) found only ''low confidence'' in projections of monsoon change at 1.5°C and 2°C, or any difference between them. The AR6 assessment of ( [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.4.1|Section 8.4.2.4.1]] ) finds more precipitation in future projections (also depicted in Figure 10.19c,d,e), supported by reviews of CMIP3, CMIP5 and CMIP6 models ( [[#Turner--2012|Turner and Annamalai, 2012]] ; [[#Kitoh--2017|Kitoh, 2017]] ; Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ; [[#Wang--2021|]] [[#Wang--2021|B. Wang et al., 2021]] ).

Given the assessment for a future wetter monsoon dominated by GHG emissions and attribution of the late-20th century decline to aerosol (Sections 8.3.2.4.1 and 10.6.3.5), the change between dominant forcings will lead, at some point, to a positive trend. For example, RCP4.5 experiments in an AGCM forced by coupled model-derived future SSTs showed continuation of 20th-century drying, before a rainfall recovery ( [[#Krishnan--2016|Krishnan et al., 2016]] ). By holding aerosol emissions at 2005 levels, lower monsoon rainfall is found throughout the 21st century than in a standard RCP8.5 scenario ( [[#Zhao--2019|Zhao et al., 2019]] ), suggesting that the timing of the recovery will be partially controlled by the rate at which aerosol emissions decline. The spread in spatial distribution of aerosol emissions in SSPs may also play a role in near-term projections ( [[#Samset--2019|Samset et al., 2019]] ). Under divergent air-quality policies, SSP3 features a dipole of declining sulphate emissions for China but increases over India, leading to suppression of GHG-related precipitation increases there ( [[#Wilcox--2020|Wilcox et al., 2020]] ). For the near-term future around the mid-21st century, the interplay between internal variability and external forcing must be considered ( [[#Singh--2019|Singh and AchutaRao, 2019]] ). [[#Huang--2020a|Huang et al. (2020a)]] used two SMILEs to show that internal variability related to PDV could potentially overcome the GHG-forced upward trend in Indian monsoon rainfall, consistent with assessments of the global monsoon for the near term ( [[IPCC:Wg1:Chapter:Chapter-4#4.4.1.4|Section 4.4.1.4]] ). Emergence of the anthropogenic signal for South Asian precipitation is shown from the 2050s onwards in CMIP6 (Figure 10.15b).

In long-term projections, robust signals consist of a weakened upper-tropospheric meridional temperature gradient, either due to upper-level heating over the tropical Pacific ( [[#Sooraj--2015|Sooraj et al., 2015]] ) or Indian oceans ( [[#Sabeerali--2018|Sabeerali and Ajayamohan, 2018]] ) in CMIP5, and increased seasonal mean rainfall, including in CMIP6 ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ; [[#Wang--2021|]] [[#Wang--2021|B. Wang et al., 2021]] ). The weakened temperature gradient combines with increased atmospheric stability to weaken the monsoon overturning circulation, with some findings showing northward movement of the lower-tropospheric monsoon winds in response to a stronger land–sea temperature contrast in CMIP3 and CMIP5 ( [[#Sandeep--2015|Sandeep and Ajayamohan, 2015]] ; [[#Endo--2018|Endo et al., 2018]] ). The northward shift was also found in the genesis of synoptic systems (monsoon depressions) in a single high-resolution AGCM forced by an ensemble of SSTs derived from four GCMs under the RCP8.5 scenario ( [[#Sandeep--2018|Sandeep et al., 2018]] ).

Projections can also be expressed in terms of global-mean warming levels (GWLs) rather than time horizons (Cross-Chapter Box 11.1). Advancing on SR1.5, amplification of mean and extreme monsoon rainfall at 2.0°C compared to 1.5°C has been found both by an AGCM forced by future SST patterns ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ) and by using time slices in CMIP5 GCMs ( [[#Yaduvanshi--2019|Yaduvanshi et al., 2019]] ; J. [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ). These findings are consistent with the general scaling of Indian monsoon precipitation per degree of warming in CMIP5 ( [[#Zhang--2019|Zhang et al., 2019]] ) and CMIP6 ( [[#Wang--2021|]] [[#Wang--2021|B. Wang et al., 2021]] ). Increasing GWLs also lead to emergence of the anthropogenic signal over larger proportions of the South Asian region (Figure 10.15a).

Decomposition of the increased rainfall signal showed that while the dynamic component led to a drying tendency, this was overcome by the thermodynamic contribution ( [[#Sooraj--2015|Sooraj et al., 2015]] ; Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ). Alternative decomposition experiments using AGCMs and their coupled counterparts found increases in the lower-tropospheric temperature gradient and monsoon rainfall to be dominated by the fast radiative response to GHG increase rather than SST changes ( [[#Li--2017|Li and Ting, 2017]] ; [[#Endo--2018|Endo et al., 2018]] ). The response to SST forcing featured a large model spread, particularly arising from the dynamic component ( [[#Li--2017|Li and Ting, 2017]] ). [[#Chen--2015|Chen and Zhou (2015)]] found that the Indo-Pacific SST warming pattern dominated the uncertainty in Indian monsoon rainfall change. Finally, in assessing the relative impact of CO <sub>2</sub> radiative forcing and plant physiological changes in quadrupled CO <sub>2</sub> experiments in four Earth system models, [[#Cui--2020|Cui et al. (2020)]] showed little impact of plant physiology on annual rainfall for the Indian region.

While several of the above studies selected model subsets to constrain future projections based on standard performance metrics of the historical period, such as pattern correlation and root-mean-square error, [[#Latif--2018|Latif et al. (2018)]] included a performance measure based on agreement with historical rainfall trends. This is an unproven constraint for regional projections ( [[#10.3.3.9|Section 10.3.3.9]] ), since the 20th-century rainfall trend over India is assessed to have been driven chiefly by aerosol and other factors such as PDV (Sections 8.3.2.4.1 and 10.6.3.5), while the dominant late-21st century forcing is GHG emissions. Modern emergent-constraint techniques ( [[#10.3.4.2|Section 10.3.4.2]] ) are being applied to the Indian monsoon such as G. [[#Li--2017|]] [[#Li--2017|Li et al. (2017)]] , who found that models with excessive tropical western Pacific rainfall tend to project a greater Indian monsoon rainfall change in future, due to an exaggerated cloud-radiation feedback. Correcting for this bias reduces the future change.

In summary, long-term future scenarios dominated by GHG increases (such as the RCPs) suggest increases in Indian summer monsoon rainfall ( ''high confidence'' ), dominated by thermodynamic mechanisms leading to increases in the available moisture. In the near-term, there is ''high confidence'' ( ''medium agreement'' , ''robust evidence'' ) that increased rainfall trends due to GHGs could be overcome by aerosol forcing or internal variability.

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<span id="future-climate-projections-from-regional-downscaling"></span>
==== 10.6.3.7 Future Climate Projections from Regional Downscaling ====

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Coordinated monsoon-relevant dynamical downscaling efforts such as CORDEX South Asia ( [[#Gutowski%20Jr.--2016|Gutowski Jr. et al., 2016]] ; [[#Choudhary--2018|Choudhary et al., 2018]] ) are relevant to the Indian summer monsoon, first with assessment of their added value ( [[#10.3.3.2|Section 10.3.3.2]] and Atlas.5.3.3). [[#Singh--2017|Singh et al. (2017)]] compared nine CORDEX-South Asia RCMs against their driving CMIP5 GCMs, for present-day rainfall patterns and processes related to intra-seasonal variability. They found no consistent improvement other than for spatial patterns (e.g., rainfall close to better-resolved orography); some characteristics were made worse. Both the rainfall pattern and its bias were worsened in CORDEX compared to CMIP5 in [[#Mishra--2018|Mishra et al. (2018)]] . In contrast, [[#Varikoden--2018|Varikoden et al. (2018)]] found improved representation of historical rainfall patterns, such as over the Western Ghats mountains (consistent with [[#Singh--2017|Singh et al., 2017]] ), reducing the dry bias; improvements were not found over the northern plains, which are dominated by synoptic variability known as monsoon depressions. Similarly, [[#Sabin--2013|Sabin et al. (2013)]] compared a uniform 1° resolution model ensemble with another zoomed to about 35 km over South Asia. Local zooming improved simulation of orographic precipitation and the monsoon trough. For the future, a surrogate approach (like pseudo-global warming, see [[#10.3.2.2|Section 10.3.2.2]] ) was used in an RCM to test the impacts of warming or moistening on monsoon depressions ( [[#Sørland--2016|Sørland and Sorteberg, 2016]] ; [[#Sørland--2016|Sørland et al., 2016]] ). The depressions are found to give more rainfall in future, dominated by strengthened synoptic circulation from the warming perturbation. By forcing an RCM with a perturbed parameter ensemble of a GCM, [[#Bal--2016|Bal et al. (2016)]] made projections under SRES A1B for the 2020s, 2050s and 2080s. They noted increases in rainfall of 15–24% for India. Finally, evidence from a single CORDEX South Asia RCM showed a mixed signal for changes in peak season rainfall under RCP2.6 and RCP8.5 ( [[#Ashfaq--2021|Ashfaq et al., 2021]] ).

Statistical downscaling and other post-processing require calibration in historical conditions (e.g., [[#Akhter--2019|Akhter et al., 2019]] ) and assessment of fitness-for-purpose ( [[#10.3.3.9|Section 10.3.3.9]] ) before use for future projections. Given the noted biases in GCM monsoon simulation ( [[#10.6.3.5|Section 10.6.3.5]] ), [[#Vigaud--2013|Vigaud et al. (2013)]] used a variant of quantile mapping to bias adjust ( [[#10.3.1.3.2|Section 10.3.1.3.2]] and Cross-Chapter Box 10.2) GCM outputs. For the historical period, the pattern, mean and seasonal cycle of rainfall versus the input GCMs were improved. Increased future monsoon rain, albeit in older SRES A2 projections, was found for southern India. [[#Salvi--2013|Salvi et al. (2013)]] used regression-based perfect prognosis ( [[#10.3.1.3.1|Section 10.3.1.3.1]] ) for the whole country at 0.5° resolution based on five ensemble members of a GCM in SRES scenarios. They noted increases over rainy regions of west coast and north-east India, but decreases in the north, west and south-east. [[#Madhusoodhanan--2018|Madhusoodhanan et al. (2018)]] statistically downscaled 20 CMIP5 models to 0.05° resolution. While the global models projected increased rainfall, the downscaled ensemble depicted both increasing and decreasing trends, indicating uncertainty. However, key physical processes operating at below-GCM scale cannot be resolved nor calibrated for, such as aspects of the flow around topography. This is notably an issue given the resolution disparity between the driving global models and output, and the regional challenges in observational data used for calibration ( [[#10.6.3.3|Section 10.6.3.3]] ).

There are mixed messages as to whether downscaling adds value to climate projections of the Indian summer monsoon; however, there is ''high confidence'' in projections of precipitation changes in orographic regions given the consistent improved representation in these regions among several dynamical downscaling studies.

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<span id="storyline-approaches-for-india"></span>
==== 10.6.3.8 Storyline Approaches for India ====

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Formal storyline approaches (see Box 10.2) have been used infrequently for the Indian summer monsoon, representing a knowledge gap. In an expert-elicitation approach ( [[#Dessai--2018|Dessai et al., 2018]] ), physically plausible futures substantiated by climate processes were constructed, focusing on a river basin in southern India. Possible outcomes were framed based on changes in two drivers: availability of moisture from the Arabian Sea and strength of the low-level flow. The narratives identified were able to explain 70% of the variance in monsoon rainfall over 1979–2013, the implication being that climate uncertainties could be easily communicated to stakeholders in the context of present-day variability.

The storylines terminology could be used to loosely describe the interplay between internal variability and forced change (see [[#10.6.3.6|Section 10.6.3.6]] ), such as considering the difference between groups of wettest and driest ensemble members of a SMILE for the near-term future in Figure 10.19f. However, given the interest in low-likelihood high-impact scenarios ( [[#Sutton--2018|Sutton, 2018]] ), we can also consider possible storylines for the Indian monsoon constructed from evidence in paleoclimate records and modelling. For example, a future AMOC collapse could cause reduced monsoon rainfall ( [[IPCC:Wg1:Chapter:Chapter-8#8.6.1|Section 8.6.1]] ; [[#Liu--2017|Liu et al., 2017]] ), offsetting increases expected due to GHG. Large tropical volcanic eruptions are also known to weaken the Asian summer monsoon, in observations and model simulations over the last millennium ( [[IPCC:Wg1:Chapter:Chapter-8#8.5.2.3|Section 8.5.2.3]] ; [[#Zambri--2017|Zambri et al., 2017]] ), although a hemispheric dependence is found, with Southern Hemisphere eruptions even strengthening the monsoon around India ( [[#Zuo--2019|Zuo et al., 2019]] ). Typically, future climate projections do not consider plausible eruption scenarios and their mitigating effects on greenhouse warming (see also Cross-Chapter Box 4.1). A single-model ensemble ( [[#Bethke--2017|Bethke et al., 2017]] ) demonstrates a future drier Indian monsoon relative to conditions in which volcanic eruptions are not considered, although the effects of GHG warming dominate beyond the mid-term.

The few studies on low-likelihood high-impact scenarios, often in single models, together with findings in SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), noting the small radiative forcing in 1.5°C or 2°C scenarios, or the absence of large aerosol emissions at the end of the 21st century in RCPs, give us ''low confidence'' in abrupt changes to the monsoon on this time scale.

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<span id="regional-climate-information-distilled-from-multiple-lines-of-evidence"></span>
==== 10.6.3.9 Regional Climate Information Distilled from Multiple Lines of Evidence ====

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Above, we presented assessments from observational and model attribution studies of the historical period, followed by future climate projections in global and regional models, and storylines approaches including low-likelihood high impact events. Miscellaneous lines of evidence are considered here.

Our assessment could also be informed by attempting to constrain future projections of the Indian summer monsoon using paleoclimate evidence. In modelling work of the mid-Holocene ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ), the increased obliquity (axial tilt) and altered orbital precession lead to an enhanced monsoon with a stronger dynamic component (strengthening the mean monsoon overturning) controlling the increase in monsoon rainfall. In future climates however, the dynamic contribution decreases ( [[#10.6.3.6|Section 10.6.3.6]] ), yet the increased thermodynamic component (greater moisture availability) overcomes this to cause a wetter monsoon. Monsoon changes under different epochs may not be governed by the same mechanisms ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ; [[#Hill--2019|Hill, 2019]] ), making the mid-Holocene, in particular, unsuitable as a period to compare with.

Finally, the recent national climate-change assessment for India ( [[#Krishnan--2020|Krishnan et al., 2020]] ) has distilled multiple lines of evidence to show declining summer monsoon rainfall over the second half of the 20th century, attributable to emissions of anthropogenic aerosols, while future projections informed by CMIP5 modelling and dominated by GHG forcing show increased mean rainfall by the end of the 21st century.

There is ''very high confidence'' ( ''robust evidence'' , ''high agreement'' ) of a negative trend of summer monsoon rainfall over the second half of the 20th century averaged over all of India. There is ''medium agreement'' over trends at the regional level owing to uncertainty among observational products, which hinders model evaluation, downscaling and assessment of changes to extremes. There is ''high confidence'' ( ''robust evidence'' , ''medium agreement'' ) that anthropogenic aerosol emissions over the Northern Hemisphere and internal variability have contributed to the negative trend, while there is ''high confidence'' ( ''robust evidence'' , ''medium agreement'' ) that Indian summer monsoon rainfall will increase at the end of the 21st century in response to increased GHG forcing, due to the dominance of thermodynamic mechanisms. No contradictory evidence is found from downscaling methods. The contrast between declining rainfall in the observational record and long-term future increases can be explained using multiple lines of evidence. They are not contradictory since they are attributable to different mechanisms (primarily aerosols and greenhouse gases, respectively). The long-term future changes are generally consistent across global (including at high resolution) and regional climate models, and supported by theoretical arguments. Furthermore, while there are subtle differences found in past periods with a climate similar to the future climate (the mid-Holocene), different physical mechanisms at play suggest that paleoclimate evidence does not reduce confidence in the future projections. In the near term, there is ''high confidence'' that internal variability will dominate.

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<span id="mediterranean-summer-warming"></span>
=== 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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<span id="the-regions-climate-1"></span>
==== 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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<span id="observational-issues-1"></span>
==== 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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<span id="relevant-anthropogenic-and-natural-drivers-1"></span>
==== 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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<span id="model-simulation-and-attribution-over-the-historical-period-1"></span>
==== 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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<span id="future-climate-information-from-global-simulations-1"></span>
==== 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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<span id="future-climate-information-from-regional-downscaling-1"></span>
==== 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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<span id="storyline-approaches-1"></span>
==== 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]] ).

<div id="10.6.4.9" class="h3-container"></div>

<span id="climate-information-distilled-from-multiple-lines-of-evidence-1"></span>
==== 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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