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

== Atlas.8 Europe ==

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The assessment in this section focuses on changes in average temperature and precipitation (rainfall and snow), including the most recent years of observations, updates to observed datasets, the consideration of recent studies using CMIP5 and those using CMIP6 and CORDEX simulations. Assessment of changes in extremes is in [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] (Tables 11.16–11.18) and climatic impact-drivers in [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] (Table 12.7).

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=== Atlas.8.1 Key Features of the Regional Climate and Findings From Previous IPCC Assessments ===

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==== Atlas.8.1.1 Key Features of the Regional Climate ====

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Westerly winds and the accompanying Atlantic storm track with cyclones and anticyclones travelling from the Atlantic towards inland Europe are the main climatic features that characterize daily to interannual variability in the European region. The Siberian High in winter determines cold weather in Eastern Europe and can affect other regions with cold outbreaks. Intra-seasonal and interannual variations are driven by modes of climate variability such as the North Atlantic Oscillation (NAO; Table Atlas.1 and Annex IV.2). Global warming can lead to systematic changes in regional climate variability via thermodynamic responses such as altered lapse rates ( [[#Kröner--2017|Kröner et al., 2017]] ; [[#Brogli--2019|Brogli et al., 2019]] ) and land-atmosphere feedbacks ( [[#Zampieri--2011|Zampieri and Lionello, 2011]] ; [[#Boé--2014|Boé and Terray, 2014]] ). Regional feedbacks involving the land-sea contrast, sea surface, land surface, clouds, aerosols, radiation and other processes modulate the regional response to enhanced warming.

Four climatic regions are defined for Europe (Figure Atlas.24). The Mediterranean region (MED) in the south is characterized by mild winters and hot and dry summers (Mediterranean climate; [[IPCC:Wg1:Chapter:Chapter-10#10.6.4.2|Section 10.6.4.2]] ). It covers both Europe and Africa, and MED assessments in this section generally imply the entire MED domain unless stated otherwise. The Western and Central Europe region (WCE) has distinct summer and winter seasons with increasing continentality of climate eastwards. The Northern Europe region (NEU), close to the Atlantic Ocean, is characterized by high humidity and relatively mild winters, and strong exposure to the Atlantic storm track. Eastern Europe (EEU) covers the western part of Russia and neighbouring territories and has continental characteristics. Many regional datasets and model projections assessed here do not sufficiently cover the EEU region.

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==== Atlas.8.1.2 Findings From Previous IPCC Assessments ====

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The AR5 WGII ( [[#Kovats--2014|Kovats et al., 2014]] ) reports with ''high confidence'' that observed climate trends show regionally varying changes in temperature and rainfall in Europe. The average temperature in Europe has continued to increase, with seasonally different rates of warming being greatest in high latitudes in Northern Europe. Annual precipitation has increased in Northern Europe and decreased in parts of Southern Europe. The SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) reports with ''high confidence'' that a reduction in snow cover at low elevation and glacier extent is observed in recent decades, with consequent changes in annual and seasonal runoff patterns. According to the SRCCL report ( [[#IPCC--2019b|IPCC, 2019b]] ) there is ''high agreement'' that observed vegetation greening and forestation in the last 30 years cools summer surface temperature and warms winter temperature due to decreased snow cover and increased snow shading in forested areas. It is ''very likely'' that aerosol column amounts have declined over Europe since the mid-1980s.

The AR5 ( [[#Collins--2013|Collins et al., 2013]] ) reports that the ability of models to simulate the climate in Europe has improved in many important aspects. Particularly relevant for this region are increased model resolution and a better representation of the land surface processes in many of the models that participated in CMIP5. The spread in climate model projections is still substantial, partly due to pronounced internal variability in this region (particularly NAO and AMO). In the winter half year, NEU and WCE are ''likely'' to have increased mean precipitation associated with increased atmospheric moisture and moisture convergence, and intensification in extratropical cyclone activity. No change or a moderate reduction is projected for MED. In the summer half year, it is ''likely'' that NEU and WCE mean precipitation will have only small changes with a notable reduction in MED. According to SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), these precipitation changes are more pronounced at 2°C than at 1.5°C of global warming. For a 2°C global warming level, an increase in runoff is projected for north-eastern Europe while decreases are projected in the Mediterranean region, where runoff differences between 1.5°C and 2°C global warming will be most prominent ( ''medium confidence'' ). According to SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) the RCP8.5 projections lead to a loss of more than 80% of the ice mass from small glaciers by the end of century in Central Europe ( ''high confidence'' ). Snow cover and glaciers are projected to decrease throughout the 21st century.

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=== Atlas.8.2 Assessment and Synthesis ofObservations, Trends and Attribution ===

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To support climatological analyses and model evaluation, national meteorological and hydrological services are increasingly making available high spatial and temporal resolution gridded and in situ homogenized and quality-checked datasets ( [[#Déqué--2008|Déqué and Somot, 2008]] ; [[#Vidal--2010|Vidal et al., 2010]] ; [[#Rauthe--2013|Rauthe et al., 2013]] ; [[#Noël--2015|Noël et al., 2015]] ; [[#Spinoni--2015b|Spinoni et al., 2015b]] ; [[#Ruti--2016|Ruti et al., 2016]] ; [[#Fantini--2018|Fantini et al., 2018]] ; [[#Lussana--2018|Lussana et al., 2018]] ; [[#Herrera--2019|Herrera et al., 2019]] ; [[#Skrynyk--2020|Skrynyk et al., 2020]] ). The inclusion of additional station data and data rescue activities lead to a better representation of extreme precipitation statistics than the global- or continental-scale datasets ( [[#Atlas.1.4.1|Atlas.1.4.1]] ). Recent gridded products merging radar and station data allow higher spatial and temporal resolutions to be reached ( [[#Haiden--2011|Haiden et al., 2011]] ; [[#Tabary--2012|Tabary et al., 2012]] ; [[#Berg--2016|Berg et al., 2016]] ; [[#Fumière--2020|Fumière et al., 2020]] ). A number of regional reanalysis products has become available for the European region ( [[#Bollmeyer--2015|Bollmeyer et al., 2015]] ; [[#Bach--2016|Bach et al., 2016]] ; [[#Dahlgren--2016|Dahlgren et al., 2016]] ; [[#Landelius--2016|Landelius et al., 2016]] ). A European ensemble of regional reanalyses from 1961 to 2019 is shown to add accuracy and reliability in comparison to global reanalysis products, but also introduces additional uncertainties, especially for threshold-based climate indices ( [[#Kaiser-Weiss--2019|Kaiser-Weiss et al., 2019]] ). However, gridded European datasets are unreliable over data-sparse regions. Also, many datasets employ different approaches to interpolation and gridding, which adds to their uncertainty and complicates comparative evaluations ( [[#Fantini--2018|Fantini et al., 2018]] ; [[#Kotlarski--2019|Kotlarski et al., 2019]] ; [[#Berthou--2020|Berthou et al., 2020]] ). For some sub-regions and performance metrics, differences between datasets have been shown to be of the same magnitude as errors in regional climate models ( [[#Prein--2016|Prein et al., 2016]] ; [[#Prein--2017|Prein and Gobiet, 2017]] ; [[#Fantini--2018|Fantini et al., 2018]] ), but observational uncertainty is substantially reduced when datasets of similar nature and representativeness are used ( [[#Kotlarski--2019|Kotlarski et al., 2019]] ).

In addition to the global display of observed temperature and precipitation trends in Figure Atlas.11, annual mean temperature and precipitation trends between 1980 and 2015 calculated from the gridded ensemble E-OBS dataset ( [[#Cornes--2018|Cornes et al., 2018]] ) are shown in Figure Atlas.23, together with time series of temperature and precipitation anomalies relative to the 1980–2015 mean value from E-OBS, CRU, EWEMBI and Berkeley for temperature, and E-OBS, CRU, GPCC and GPCP for precipitation (see also Figure 2.11 for global mean values, and [[#Atlas.1.4.1|Atlas.1.4.1]] for description of global datasets).

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[[File:d16110155935d9d2ed66213584ddae69 IPCC_AR6_WGI_Atlas_Figure_23.png]]

'''Figure Atlas.23''' '''|''' '''(a) Mean 1980–2015 trend ofannual mean surface air temperature (°C per decade) from E-OBS''' ( [[#Cornes--2018|Cornes et al., 2018]] ). Data for non-European countries in the MED area are masked out. '''(b)''' Time series of mean annual temperature anomaly relative to the 1980–2015 period (shown with grey shading) aggregated for the land area in each of the four European sub-regions, from E-OBS, CRU, Berkeley and ERA5 (see [[#Atlas.1.4.1|Atlas.1.4.1]] for description of global datasets). Mean trends for 1901–2015, 1961–2015 and 1980–2015 are shown for each dataset in corresponding colours in the same units as panel (a) (see legend in upper panel). '''(c)''' As panel (a) for annual mean precipitation (mm day <sup>–1</sup> per decade). '''(d)''' As panel (b) for annual mean precipitation, from datasets E-OBS, CRU, GPCC and GPCP. Note that E-OBS data are not shown in panels (b) and (d) for the region EEU. For the MED region data are aggregated over the European countries alone. Trends have been calculated using ordinary least squares regression and the crosses indicate non-significant trend values (at the 0.1 level) following the method of [[#Santer--2008|Santer et al. (2008)]] to account for serial correlation. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

In NEU continued warming has been observed, particularly during spring. An annual mean temperature increase of 0.4°C per decade was reported between 1970 and 2008 ( [[#Rutgersson--2015|Rutgersson et al., 2015]] ). In WCE temperature increases since the mid-20th century have been documented for Poland ( [[#Degirmendžić--2004|Degirmendžić et al., 2004]] ) and Ukraine ( [[#Boychenko--2016|Boychenko et al., 2016]] ; [[#Balabukh--2017|Balabukh and Malitskaya, 2017]] ). Land-only observations indicate a rapid increase in summer (JJA) mean surface air temperature since the mid-1990s ( [[#Dong--2017|Dong et al., 2017]] ). In Eastern Europe no significant trend in winter mean air temperatures was found between 1881 and 2016 in Belarus ( [[#Loginov--2018|Loginov et al., 2018]] ). In parts of the European area of the MED, spring and summer temperatures are reported to increase faster than in the other seasons (see the Mediterranean case study in [[IPCC:Wg1:Chapter:Chapter-10#10.6.4|Section 10.6.4]] and Figure 10.18; [[#Brunetti--2006|Brunetti et al., 2006]] ; [[#Homar--2009|Homar et al., 2009]] ; [[#Lionello--2012|Lionello et al., 2012]] ; [[#Philandras--2015|Philandras et al., 2015]] ; [[#Gonzalez-Hidalgo--2016|Gonzalez-Hidalgo et al., 2016]] ; [[#Vicente-Serrano--2017|Vicente-Serrano et al., 2017]] ). Figure Atlas.23 shows that since 1980 in each European region all datasets show a consistent warming of annual mean temperature of 0.04°C yr <sup>–1</sup> to 0.05°C yr <sup>–1</sup> . Trends in European land temperature cannot be explained without accounting for anthropogenic warming offset by anthropogenic aerosol emissions ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.1|Section 3.3.1.1]] and Figure 3.9). It is ''virtually certain'' that annual mean temperature continues to increase in each European subdomain.

Multi-decadal trends in mean precipitation are generally small and non-significant. Apart from difficulties related to observational coverage ( [[#Prein--2017|Prein and Gobiet, 2017]] ), gauge undercatch (e.g., [[#Murphy--2020|Murphy et al., 2020]] ) and data inhomogeneity (e.g., [[#Camuffo--2013|Camuffo et al., 2013]] ), strong interannual and multi-decadal variability is dominant over at least the last two centuries. However, significant precipitation trends have been recorded for recent periods, for example in south-western Europe between 1960 and 2000 ( [[#Peña-Angulo--2020|Peña-Angulo et al., 2020]] ), and between 1961 and 2015 in NEU (Interactive Atlas). Also, some studies suggest that in the MED precipitation has declined and more frequent and severe meteorological droughts have occurred between 1960 and 2000 ( [[#Spinoni--2015a|Spinoni et al., 2015a]] ; [[#Gudmundsson--2016|Gudmundsson and Seneviratne, 2016]] ), and in some regions cannot be explained without anthropogenic forcing ( [[IPCC:Wg1:Chapter:Chapter-10#10.4.1.2|Section 10.4.1.2]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ). Other studies suggest that this trend can be seen as an expression of multi-decadal internal variability driven mainly by the North Atlantic Oscillation (Table Atlas.1; [[#Kelley--2012|Kelley et al., 2012]] ; [[#Zittis--2018|Zittis, 2018]] ). Global dimming and brightening also are reported to affect precipitation trends in the Mediterranean region ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.6|Section 8.3.1.6]] and Figure 8.7).

The large-scale spatial patterns of the E-OBS annual mean precipitation trend between 1980 and 2015 shown in Figure Atlas.23 is broadly consistent with trends derived from CRU, GPCP and GPCC (Figure Atlas.11) but with more explicit spatial detail. Trends calculated for regional averages are sensitive to the selection of the time window: for 1980–2015 annual mean precipitation averaged over the regions shows a positive trend (not significant at p = 0.05), while for CRU and GPCC the trend calculated over 1901–2015 is positive for NEU, EEU and WCE, and non-significant for MED. Precipitation trends in the MED are significant only in selected areas ( [[#Lionello--2012|Lionello et al., 2012]] ; [[#MedECC--2020|MedECC, 2020]] ). Also the NEU trends show large spatial variability and are subject to decadal variability related to NAO ( [[#Heikkilä--2012|Heikkilä and Sorteberg, 2012]] ), but are generally positive over the 20th century (Figure Atlas.23). There is ''medium confidence'' that annual mean precipitation in NEU, WCE and EEU has increased since the early 20th century. In the European Mediterranean, observed land precipitation trends show pronounced variability within the region, with magnitude and sign of trend in the past century depending on time period and exact study region ( ''medium confidence'' ).

Trends in snowfall and snowmelt are related to seasonal changes in both temperature and precipitation. In EEU, melt onset dates have advanced by one to two weeks in the 1979–2012 period ( [[#Mioduszewski--2015|Mioduszewski et al., 2015]] ). Over Eurasia, trends in spring and early summer snow cover extent increased over the 1971–2014 period ( [[#Hernández-Henríquez--2015|Hernández-Henríquez et al., 2015]] ). Between 1966 and 2012, averaged over entire Eurasia, monthly mean snow depth decreased in autumn and increased in winter and spring ( [[#Zhong--2018|Zhong et al., 2018]] ), while the snow cover extent was reported to have decreased during the past 40 years ( [[#Bulygina--2011|Bulygina et al., 2011]] ). In NEU late winter and early spring snow depth and snow cover decreases since the early 1960s are reported over Finland ( [[#Luomaranta--2019|Luomaranta et al., 2019]] ) and Norway ( [[#Rizzi--2018|Rizzi et al., 2018]] ) with a dependence on altitude ( [[#Skaugen--2012|Skaugen et al., 2012]] ), while winter snow depth increased in northern Sweden ( [[#Kohler--2006|Kohler et al., 2006]] ). It is ''very likely'' that since the early 1980s in snow-dominant areas in NEU and EEU the length of the snowfall season is reduced with regional warming, and the melt onset dates have advanced.

The increasing trend in surface shortwave radiation, documented in AR5 ( [[#Hartmann--2013|Hartmann et al., 2013]] ) to have occurred since the 1980s and referred to as a brightening effect, is substantiated over Europe and the Mediterranean region ( [[#Nabat--2014|Nabat et al., 2014]] ; [[#Sanchez-Lorenzo--2015|Sanchez-Lorenzo et al., 2015]] ; [[#Cherif--2020|Cherif et al., 2020]] ). This increasing trend has been attributed to the decrease in anthropogenic sulphate aerosols over the 1980–2012 period ( [[#Nabat--2014|Nabat et al., 2014]] ). In model sensitivity experiments, the aerosol trend has been quantified to explain 81 ± 16% of the European surface shortwave trend and 23 ± 5% of the European surface temperature warming. It is ''likely'' that trends in anthropogenic aerosols in Europe have generated positive trends in shortwave radiation and surface temperature since the 1980s (Sections 6.3.3.1, 8.3.1.6 and 10.6.4).

Assessments of observed European trends in meteorological extremes and CIDs are reported elsewhere in this report. [[IPCC:Wg1:Chapter:Chapter-11#11.3.5|Section 11.3.5]] documents and attributes an increase in the frequency and extent of heatwaves and daily maximum temperatures, and [[IPCC:Wg1:Chapter:Chapter-11#11.6.2|Section 11.6.2]] discusses the uncertainty concerning the detection of trends in meteorological droughts, and the role of increasing atmospheric evaporative demand on hydrological and ecological/agricultural droughts. [[IPCC:Wg1:Chapter:Chapter-8#8.3.1|Section 8.3.1.8]] reports on increasing aridity trends in the Mediterranean related to soil moisture declines and increases in atmospheric water vapor demand. [[IPCC:Wg1:Chapter:Chapter-11#11.4.2|Section 11.4.2]] reports on the increased likelihood and intensity of daily precipitation extremes, while Sections 11.5.2 and 12.4.5.2 discuss implications for peak streamflow. [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.5|Section 12.4.5.5]] discusses the increased likelihood of wildfires, while [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.3|Section 12.4.5.3]] discusses the substantial decadal variability in mean wind speed and the trends in wind storms and gusts. The acceleration of sea level rise in the Atlantic and European seas has been discussed in [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.5|Section 12.4.5.5]] .

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=== Atlas.8.3 Assessment of Model Performance ===

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A globalevaluation of annual mean temperature and precipitation from the CMIP6 ensemble is presented in Sections 3.3.1 and 3.3.2 respectively. In general, annual mean temperature is slightly underestimated at high latitudes and overestimated in the MED area. Temporal evolution of decadal temperature oscillations in Europe simulated by the CMIP6 historical simulations is well reproduced ( [[#Fan--2020|Fan et al., 2020]] ). [[#Fernandez-Granja--2021|Fernandez-Granja et al. (2021)]] report an overall improvement of CMIP6 compared to CMIP5 to reproduce atmospheric weather patterns over Europe.

Regional climate models (RCMs; [[IPCC:Wg1:Chapter:Chapter-10#10.3.1.2|Section 10.3.1.2]] ) have been extensively evaluated for a range of climate features over Europe ( [[#Casanueva--2016|Casanueva et al., 2016]] ; [[#Vaittinada%20Ayar--2016|Vaittinada Ayar et al., 2016]] ; [[#Krakovska--2017|Krakovska et al., 2017]] ; [[#Terzago--2017|Terzago et al., 2017]] ; [[#Cavicchia--2018|Cavicchia et al., 2018]] ; [[#Drobinski--2018|Drobinski et al., 2018]] ; [[#Fantini--2018|Fantini et al., 2018]] ; [[#Harzallah--2018|Harzallah et al., 2018]] ; [[#Ivanov--2018|Ivanov et al., 2018]] ; [[#Panthou--2018a|Panthou et al., 2018a]] ). Standard assessments of RCMs driven by reanalyses, typically run at 12–25 km spatial resolution, confirm that the Euro-CORDEX and Med-CORDEX ensembles are capable of reproducing the salient features of European climate ( [[#Kotlarski--2014|Kotlarski et al., 2014]] ; [[#Krakovska--2018|Krakovska, 2018]] ) and represent European circulation features realistically ( [[#Cardoso--2016|Cardoso et al., 2016]] ; [[#Drobinski--2018|Drobinski et al., 2018]] ; [[#Flaounas--2018|Flaounas et al., 2018]] ; [[#Sanchez-Gomez--2018|Sanchez-Gomez and Somot, 2018]] ). Seasonal and regionally averaged temperature biases generally do not exceed 1.5°C, while precipitation biases can be up to ±40% ( [[#Kotlarski--2014|Kotlarski et al., 2014]] ). Extensive evaluation of a large collection of RCM–GCM combinations show a general wet, cold and windy bias compared to observations and reanalyses, but none of the models is systematically performing best or worst ( [[#Vautard--2021|Vautard et al., 2021]] ). Higher-resolution simulations do show improved performance in reproducing the spatial patterns and seasonal cycle of not only extreme precipitation but also mean precipitation over all European regions (see Sections 10.3.3.4 and 10.3.3.5 for an extensive evaluation of the added value of increased simulation resolution; [[#Mayer--2015|Mayer et al., 2015]] ; [[#Fantini--2018|Fantini et al., 2018]] ; [[#Soares--2018|Soares and Cardoso, 2018]] ; [[#Ciarlo%60--2021|Ciarlo` et al., 2021]] ).

In line with findings reported in [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.8|Section 10.3.3.8]] , several studies argue that both GCMs and RCMs underestimate the observed trend in European summer temperature ( [[#Dosio--2016|Dosio, 2016]] ; [[#Boé--2020b|Boé et al., 2020b]] ), indicating that essential processes are missing or that the natural variability is not correctly sampled ( [[#Dell’Aquila--2018|Dell’Aquila et al., 2018]] ). [[#Nabat--2014|Nabat et al. (2014)]] argued that including realistic aerosol variations enables climate models to correctly reproduce the summer warming trend (as is required for attributing continental annual temperature trends, [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.1|Section 3.3.1.1]] ). However, other studies showed models to be sensitive also to local effects, such as land surface processes, convection, microphysics and snow albedo ( [[#Vautard--2013|Vautard et al., 2013]] ; [[#Davin--2016|Davin et al., 2016]] ). In Euro-CORDEX the warm and dry summer bias over southern and south-eastern Europe is reduced compared to the previous ENSEMBLES simulations ( [[#Katragkou--2015|Katragkou et al., 2015]] ; [[#Giot--2016|Giot et al., 2016]] ; [[#Prein--2017|Prein and Gobiet, 2017]] ; [[#Dell’Aquila--2018|Dell’Aquila et al., 2018]] ). Natural variability has strongly affected the historical warming and large ensembles are necessary for a correct estimation of the forced signal versus natural variability ( [[#Aalbers--2018|Aalbers et al., 2018]] ; [[#Lehner--2020|Lehner et al., 2020]] ).

Specific assessments of convection-permitting RCMs (CPRCMs, running at a resolution of typically 1 to 3 km and designed for extreme precipitation characteristics) is undertaken in [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.4.1|Section 10.3.3.4.1]] . A unique CPRCM ensemble has been applied over the great Alpine domain and improves representation of mean and extreme precipitation compared to coarser resolution models ( [[#Ban--2021|Ban et al., 2021]] ; [[#Pichelli--2021|Pichelli et al., 2021]] ). The role of aerosol forcing is increasingly analysed as new and more realistic aerosol datasets become available ( [[#Nabat--2013|Nabat et al., 2013]] ; [[#Pavlidis--2020|Pavlidis et al., 2020]] ), and as RCMs begin to include interactive aerosols ( [[#Nabat--2012|Nabat et al., 2012]] , 2015, 2020; [[#Drugé--2019|Drugé et al., 2019]] ). Explicitly accounting for aerosol effects in RCMs leads to improved representation of the surface shortwave radiation at various scales: long-term means ( [[#Gutiérrez--2018|Gutiérrez et al., 2018]] ), day-to-day variability ( [[#Nabat--2015|Nabat et al., 2015]] ), and long-term trends ( [[#Nabat--2014|Nabat et al., 2014]] ).

New, or updated, higher-resolution, coupled atmosphere-ocean-ice model systems have been found to improve simulations of observed climate features over the Baltic area compared to atmosphere-only model versions, including correlation between precipitation and SST, between surface heat-flux components and SST, and weather events like convective snow bands over the Baltic Sea (e.g., [[#Tian--2013|Tian et al., 2013]] ; [[#Van%20Pham--2014|Van Pham et al., 2014]] ; [[#Gröger--2015|Gröger et al., 2015]] ; S. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ; [[#Pham--2017|Pham et al., 2017]] ). Coupled atmosphere–land–river–ocean regional climate system models (RCSMs) from Med-CORDEX have similar skill as the ENSEMBLES and the Euro-CORDEX ensembles to represent decadal variability of Mediterranean climate and its extremes ( [[#Cavicchia--2018|Cavicchia et al., 2018]] ; [[#Dell’Aquila--2018|Dell’Aquila et al., 2018]] ; [[#Gaertner--2018|Gaertner et al., 2018]] ). [[#Panthou--2018a|Panthou et al. (2018a)]] showed that, over land, differences between atmosphere-only and coupled RCMs are confined to coastal areas that are directly influenced by SST anomalies. In contrast, [[#Van%20Pham--2014|Van Pham et al. (2014)]] showed significant differences in seasonal mean temperature across a widespread continental domain.

Statistical downscaling methods are assessed in [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.7|Section 10.3.3.7]] , including the intercomparison and evaluation activities performed in the framework of VALUE and Euro-CORDEX over Europe.

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=== Atlas.8.4 Assessment and Synthesis of Projections ===

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Simulations from CMIP5 and CMIP6 indicate pronounced geographical patterns and scenario dependence of the projections of mean temperature and precipitation. Global warming projected under SSP5-8.5 emissions in CMIP6 exceeds the warming projected by RCP8.5 emissions in CMIP5 ( [[IPCC:Wg1:Chapter:Chapter-4#4.3|Section 4.3]] ; [[#Forster--2020|Forster et al., 2020]] ). In selected regions in Europe CMIP6 also projects a systematically higher mean temperature than CMIP5 ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). The annual mean projections from CMIP5, CMIP6 and 0.11° resolution EURO-CORDEX contained in the Interactive Atlas are shown for the four European regions in Figure Atlas.24. For each region and season a warming offset between the pre-industrial (1850–1900) and the recent past (1995–2014) baselines is also shown. The results confirm higher CMIP6 long-term annual mean warming rates for WCE, EEU and MED and a larger inter-model spread for each region. For given GWLs, regional annual mean temperature change in CMIP5 and CMIP6 are largely consistent and higher than the global average, most prominently in EEU. For high warming levels the CMIP5 subset of eight GCMs used to drive the EURO-CORDEX simulations show a lower annual mean temperature change than the full CMIP5 ensemble in each of the European sub-regions. This illustrates the large inter-model spread and implications for subsampling a relatively small subset from the full ensemble. Regional warming is strongest in continental EEU away from the Atlantic and in MED during summer ( [[#Lionello--2018|Lionello and Scarascia, 2018]] ). The assessment of EURO-CORDEX projections for levels of global warming of 1.5°C and 2.0°C indicate enhanced local warming even at relatively low global warming levels, particularly towards the north in winter ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ; [[#Teichmann--2018|Teichmann et al., 2018]] ).

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[[File:2d52a26cd191d9b715797c12594aba3b IPCC_AR6_WGI_Atlas_Figure_24.png]]

'''Figure Atlas.24''' '''|''' '''Regional changes over land in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Europe (warming since the 1850–1900 pre-industrial baseline is also provided as an offset).''' Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See [[#Atlas.1.3|Atlas.1.3]] for more details on reference regions ( [[#Iturbide--2020|Iturbide et al., 2020]] ) and [[#Atlas.1.4|Atlas.1.4]] for details on model data selection and processing. The script used to generate this figure is available online ( [[#Iturbide--2021|Iturbide et al., 2021]] ) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

Some signatures of climate change projected by GCMs are modified by RCMs and CPRCMs. Projections of temperature, precipitation and wind in RCMs may deviate from GCM signals dependent on the dominant atmospheric circulation ( [[#Kjellström--2018|Kjellström et al., 2018]] ). In many areas RCMs produce lower warming rates and higher precipitation (less drying) in summer ( [[#Fernández--2019|Fernández et al., 2019]] ; [[#Boé--2020a|Boé et al., 2020a]] ). Also, for mean surface shortwave radiation, systematic differences between GCM and RCM outputs are found ( [[#Bartók--2017|Bartók et al., 2017]] ; [[#Gutiérrez--2020|Gutiérrez et al., 2020]] ). Although RCMs generally have a smaller bias for the present climate ( [[#Sørland--2018|Sørland et al., 2018]] ) and better cloud representation ( [[#Bartók--2017|Bartók et al., 2017]] ), the representation of aerosol forcing ( [[#Boé--2020a|Boé et al., 2020a]] ; [[#Gutiérrez--2020|Gutiérrez et al., 2020]] ), air-sea coupling ( [[#Boé--2020a|Boé et al., 2020a]] ) or vegetation response to elevated atmospheric CO <sub>2</sub> ( [[#Schwingshackl--2019|Schwingshackl et al., 2019]] ) give rise to systematic biases in RCM projections. The comparison between EURO-CORDEX and the CMIP5 subset shown in Figure Atlas.24 illustrates that the RCMs primarily modify the climate change warming signal from the driving GCMs for MED and WCE in summer ( [[#Boé--2020a|Boé et al., 2020a]] ).

Changes in precipitation clearly show a seasonal signature and a meridional gradient over Europe. Mean precipitation increases by 4–5% per °C of global warming in NEU, EEU and WCE in DJF, and decreases in summer in WCE and MED (Figure Atlas.24; [[#Jacob--2018|Jacob et al., 2018]] ). CMIP5 projections of precipitation change in MED are strongest in DJF in the south, while changes in JJA are dominant in the northern (European) part of MED ( [[#Lionello--2018|Lionello and Scarascia, 2018]] ). The European north–south gradient in precipitation response is confirmed by the EURO-CORDEX experiment ( [[#Coppola--2021a|Coppola et al., 2021a]] ), but Figure Atlas.24 shows that the JJA precipitation reduction in WCE projected by CMIP5 and CMIP6 at higher warming levels has ''low confidence'' in the CORDEX simulations. Precipitation in JJA in EEU is reduced in CMIP6, while little change is shown in CMIP5. Quantitative estimations of climate change features from regional climate projections in Eastern Europe ( [[#Partasenok--2015|Partasenok et al., 2015]] ; [[#Kattsov--2017|Kattsov et al., 2017]] ) have ''low confidence'' due to the use of relatively small ensembles of GCMs and/or RCMs, and limited evaluation of model performance in the region.

Over specific geographic features such as high mountains, RCMs further modify the climate change signal of precipitation simulated by the low-resolution GCMs ( [[#Giorgi--2016|Giorgi et al., 2016]] ; [[#Torma--2020|Torma and Giorgi, 2020]] ). This is especially true for summer precipitation over the Alps where opposite signs of changes in mean and extreme precipitation are generated by the CMIP5 GCM ensemble and the 12-km Med-CORDEX and EURO-CORDEX RCM ensembles ( [[IPCC:Wg1:Chapter:Chapter-10#10.6.4.7|Section 10.6.4.7]] ; [[#Giorgi--2016|Giorgi et al., 2016]] ).

Regional warming is ''virtually certain'' to extend the observed downward trends in snow accumulation, snow water equivalent and length of the snow cover season in NEU and at low altitudes in mountainous areas in the Alps and Pyrenees ( ''very high confidence'' ). This is supported by regional and global multi-model and/or single-model ensemble projections including CMIP5, PRUDENCE, ENSEMBLES and EURO-CORDEX ( [[#Jylhä--2008|Jylhä et al., 2008]] ; [[#Steger--2013|Steger et al., 2013]] ; [[#Mankin--2015|Mankin and Diffenbaugh, 2015]] ; [[#Schmucki--2015|Schmucki et al., 2015]] ; [[#Marty--2017|Marty et al., 2017]] ; [[#Frei--2018|Frei et al., 2018]] ), and attributed to changes in the snowfall fraction of precipitation and to increased snowmelt. In mountain areas a strong dependence of projected snow trends on altitude is shown, with most pronounced effects below 1500 m ( [[#López-Moreno--2009|López-Moreno et al., 2009]] ). [[#Terzago--2017|Terzago et al. (2017)]] showed a large positive bias in the amplitude of the annual snow cycle of EURO-CORDEX 0.11° simulations driven by GCM projections, while reanalysis-driven RCMs showed good agreement with in situ observations.

Regional ocean warming in projections with RCSMs for the Baltic and North seas ( [[#Gröger--2015|Gröger et al., 2015]] ) and for the Mediterranean ( [[#Darmaraki--2019|Darmaraki et al., 2019]] ) is associated with increased intensity and frequency of marine heatwaves in the Mediterranean ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.5|Section 12.4.5.5]] ), strong freshening in the Baltic, and, for some simulations, changes in the circulation in response to non-uniform changes in air-sea interaction ( [[#Dieterich--2019|Dieterich et al., 2019]] ). Med-CORDEX RCSM and CMIP5 GCM results agree well on the Mediterranean SST warming rate ( [[#Mariotti--2015|Mariotti et al., 2015]] ; [[#Darmaraki--2019|Darmaraki et al., 2019]] ); see also the Interactive Atlas.

Assessments of projected changes in meteorological extremes and CIDs are reported elsewhere in this report. Extreme precipitation and temperature often exhibit a different response to global warming than mean values. Increased intensity and frequency of extreme temperatures and heatwaves is assessed in Sections 11.3.5 and 12.4.5.1. Changes in the hydrological cycle include enhanced soil moisture decline in southern Europe, drying in summer and autumn in Central Europe, and spring drought due to early snowmelt in Northern Europe (Sections 8.4.1, 11.6.5 and 12.4.5.2). Changes in mean and extreme wind are very uncertain ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.3|Section 12.4.5.3]] ), while sea level rise will increase the frequency of occurrence of extreme sea level at most European coasts ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.5|Section 12.4.5.5]] ).

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=== Atlas.8.5 Summary ===

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An assessment of recent literature largely confirms the findings of previous IPCC reports but with additional detail and (in some cases) higher confidence due to improvements in observations, reanalyses and methods. Observational datasets with global coverage are complemented by the E-OBS gridded ensemble temperature and precipitation dataset, a range of regional observational analyses, and regional reanalysis products. New RCM experiments, including CPRCMs and regional coupled climate system models, mostly coordinated under the umbrella of CORDEX, have generated many new projections and process studies.

The representation of mean European climate features by GCMs and RCMs is improved compared to previous IPCC assessments ( ''medium confidence'' ), in spite of persisting biases in annual mean and seasonal temperature and precipitation characteristics. The added value of regional downscaling of GCMs by RCM projections for summer mean temperature, precipitation and shortwave radiation is constrained by the representation of processes that lead to a systematic difference between RCM and driving GCM, such as aerosol forcing ( ''medium confidence'' ).

It is ''virtually certain'' that annual mean temperature continues to increase in each European region. There is ''medium confidence'' that annual mean precipitation in NEU, WCE and EEU has increased since the early 20th century. In the European Mediterranean trends in annual mean precipitation contain substantial spatial and temporal variability ( ''medium confidence'' ). It is ''very likely'' that since the early 1980s in snow-dominated areas in NEU and EEU the length of the snowfall season is reduced with regional warming, and the melt onset dates have advanced. It is ''likely'' that decreasing trends in anthropogenic aerosols in Europe have generated positive trends in shortwave radiation and surface temperature since the 1980s.

At increasing levels of global warming, there is ''very'' ''high confidence'' that temperature will increase in all European areas at a rate exceeding global mean temperature increases, while increased mean precipitation amounts at high latitudes in DJF and reduced JJA precipitation in southern Europe will occur with ''medium confidence'' for global warming levels below 2°C, and with ''high confidence'' for higher warming levels. At high latitudes and low-altitude mountain areas in Europe strong declines in snow accumulation are ''virtually certain'' to occur with further increasing regional temperatures ( ''very high confidence'' ).

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