Anthropogenic influence on excess warming in Europe during recent decades

Anthropogenic influence on excess warming in Europe during recent decades | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Anthropogenic influence on excess warming in Europe during recent decades Buwen Dong, Rowan Sutton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4523385/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 11 You are reading this latest preprint version Abstract Over the period 1979–2022, European surface air temperatures warmed around three times faster than global mean temperatures in both winter and summer. Here we define “excess” European warming as the difference between the rate of European regional warming and the rate of global warming and investigate the causes. We estimate that about 40% (in winter) and 29% (in summer) of excess European warming is “dynamical” - attributable to changes in atmospheric circulation. We show that the rate of European warming simulated in CMIP6 models compares well with the observations, but only because these models warm too fast in the global mean; excess European warming is underestimated, particularly in winter. The CMIP6 models simulate well the magnitude of the thermodynamic component of excess European warming since 1979 in both winter and summer, but do not simulate the dynamical contribution. The models suggest greenhouse gas induced warming made the largest contribution to excess thermodynamic warming in winter, whereas changes in anthropogenic aerosols made the largest contribution in summer. They also imply a substantially reduced future rate of excess European warming in summer. However, the failure of current models to simulate observed circulation trends also implies large uncertainty in future rates of European warming. Earth and environmental sciences/Climate sciences/Climate change/Attribution Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Trends in surface air temperature (SAT) during recent decades exhibit a non-uniform pattern with amplified warming over Europe in both winter and summer (Fig. 1 a, b). Indeed, Europe has been the fastest warming continent on the Earth, warming more than twice as fast as the global mean over recent decades (Fig. 1 e, f), consistent with previous studies 1–3 . The rapid warming in summer is associated with increases in intense and longer-lasting extreme heatwaves, sometimes coupled with droughts, and has led to Europe being identified as a heatwave hotspot 4–11 . Meanwhile, the warming in winter is associated extreme rainfall events, increased runoff, and risk of flooding, related to changes in atmospheric circulation and precipitation characteristics 7, 10, 11–14 . In view of these changes in climate extremes and their impacts, further understanding European excess warming, including whether it may continue in the future, is an important challenge. For the purposes of this study, we define excess European warming as the difference between the rate of European regional warming and the rate of global warming. Regional warming on decadal-multidecadal scales is influenced by global and regional drivers, such as radiative forcings from anthropogenic greenhouse gases (GHGs) 1, 15–16 , anthropogenic aerosol emissions (AER) 16, 17–19 , changes in solar activity and large-scale volcanic eruptions 20 , and by modes of internal climate variability, such as the North Atlantic Oscillation (NAO) 21–25 , and Atlantic Multidecadal Variability (AMV) 26–28 . Previous studies have demonstrated the important influence of atmospheric circulation on warming over Europe in both winter and summer 1, 21, 23–25, 29 and for understanding trends in hot extremes in Europe 8, 11, 30–31 . However, it remains unclear whether the relevant changes in atmospheric circulation are the result of natural climate variability or are responses to natural or anthropogenic external forcings 11, 32 . Furthermore, past studies concluded that previous generations of CMIP3 and CMIP5 models 33 tended to underestimate the observed warming trend in summer over Western Europe, linked in part to a failure to simulate observed atmospheric circulation changes over the Atlantic sector 1, 34–35 . Given the outstanding knowledge gaps, there is a clear need to investigate further, with a new generation of climate models, the drivers and underlying mechanisms that may be responsible for the observed excess warming in Europe, and the extent to which these mechanisms may be accurately simulated in that latest models. In this study, we first analyze observational and reanalysis data sets to quantify regional warming trends over Europe in boreal winter and summer - including the excess regional warming relative to the global mean - focusing on the period 1979–2022. Next, we investigate the contribution of atmospheric circulation to excess European warming using a simple regression-based method. Lastly, we use multimodel simulations from the coupled model intercomparison project phase 6 (CMIP6) 36 and detection and attribution model intercomparison project (DAMIP) single forcing experiments 37 to investigate the drivers and physical processes that contribute to excess European warming (see Methods). Results Excess warming over Europe in observations and reanalyses The spatial patterns of surface air temperature (SAT) trends based on ERA5 reanalysis during last four decades show relatively large warming trends over Europe in both winter and summer (Fig. 1 a-b) with excess local warming relative to global means (Fig. 1 c-d). The linear trends of SAT over Europe are around three times higher than the trends in global mean SAT (Fig. 1 e-f). Four observational datasets and the ERA5 reanalysis show very consistent interannual variability of area averaged SAT over Europe and the globe, and also show robust linear trends (Fig. 1 g-h). The multi-dataset mean trends of area averaged SAT over Europe during 1979–2022 are 0.52 \(\pm\) 0.015 and 0.48 \(\pm\) 0.014 o C decade −1 (Table 1 ) corresponding to total changes of 2.29±0.065 and 2.10±0.063 o C) in winter and summer respectively, and these trends are 3.1 and 2.8 times higher than the corresponding global mean trends (0.17 \(\pm\) 0.008 and 0.17 \(\pm\) 0.006 o C decade −1 , corresponding to total changes of 0.76 \(\pm\) 0.018 and 0.75 \(\pm\) 0.013 o C). Estimating the contribution of atmospheric circulation to excess European warming Associated with the excess European warming in winter, atmospheric circulation trends based on ERA5 show a spatial pattern over the North Atlantic sector which projects onto a positive NAO pattern in SLP, accompanied by a strengthening and eastward extension of the North Atlantic jet (Fig. 2 a-c). The circulation trends based on the JRA55 reanalysis show very similar features (Supplementary Fig. S1 ). To quantify the contribution of atmospheric circulation trends to regional warming, we employ a simple regression-based method (see Methods). Specifically, we use the relationship between atmospheric circulation and SAT on interannual timescales to estimate the contribution from atmospheric circulation to the multidecadal warming trend. This approach is appropriate because the dominant patterns of atmospheric circulation are very similar on the different timescales. However, as it considers only one pattern of circulation, it may be considered to provide a lower bound on the circulation-related warming trend. Figure 2 d-f show regression patterns for detrended winter SAT, 500hPa zonal wind and SLP onto a detrended index of European SAT, thus highlighting interannual relationships. The patterns indicate that interannual warming over Europe is associated with a positive NAO phase and enhanced westerlies over the North Atlantic. As the circulation patterns are similar to those associated with the multi-decadal trend (Fig. 2 b-c), we use a NAO index to quantify the contribution of atmospheric circulation change to excess warming over Europe (see Methods). This estimated contribution is shown in Fig. 2 g, with the residual warming shown in Fig. 2 h. For the European area average, the results suggest that 40% of the excess warming (26% of the total warming) in winter is explained by atmospheric circulation (Fig. 2 i, Table 2 ). To test robustness of this result to an alternative choice for the circulation index, we repeated the analysis using an index of zonal wind at 500 hPa, averaged over the North Atlantic jet exit region (45 o N-60 o N, 50 o W-20 o E). The results show that 37% of the excess warming (24% of the total warming) is explained by atmospheric circulation (Supplementary Fig. S2). These numbers are less than those obtained using the NAO index, which suggests that the NAO index is a better choice for a single index (Fig. 2 g, h, i, Supplementary Fig. S2g, h, i). Associated with excess warming trends over Europe in summer (Fig. 3 a), atmospheric circulation trends based on ERA5 reanalysis show an anomalous low over the North Atlantic, accompanied by a southward displacement of the North Atlantic jet, and an anomalous high over Europe, accompanied by a dipole structure of zonal wind trends over the European sector with a weakening of Mediterranean jet and increased westerlies to the north (Fig. 3 b-c). Regression patterns of detrended circulation interannual variability onto a detrended SAT index over Europe show that warm summers are associated with an anomalous high over Europe, associated with a dipole pattern in zonal winds (Fig. 3 d-f). This pattern of circulation anomalies over Europe is similar to that associated with the multi-decadal warming trend, although the pattern of anomalies over the North Atlantic is quite different. Focussing on Europe, we define a summer circulation index as the area averaged geopotential height over Europe (The trend in zonally averaged geopotential height over the same latitude band is first removed - see Methods). Following the same procedure as for winter, we use this index to quantify the contribution of atmospheric circulation change to excess summer warming over Europe. This estimated contribution is shown in Fig. 3 g, with the residual warming shown in Fig. 3 h. For the European area average, the results suggest that 29% of the excess warming (19% of the total warming) in summer is explained by atmospheric circulation (Fig. 3 i, Table 2 ). To test the robustness of this result to an alternative choice for the circulation index, we repeated the analysis using an index of zonal wind at 500 hPa, averaged over the Mediterranean (35 o N-50 o N, 0 o -50 o E) in summer. The results suggest that that 23% of the excess warming (15% of the total warming) is explained by atmospheric circulation (Supplementary Fig. S4). As was the case for winter, these numbers are again less than those obtained using the initial index, which suggests that geopotential height index is a better choice for summer. Attribution of the observed warming trends To investigate drivers of the observed excess warming over Europe we analyse multi-model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) 36 , including both historical all forcing simulations (ALL) and single forcing simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) 37 for the period 1979–2020 (see Methods, Supplementary Table 1). Similar to features seen in observations (Fig. 1 a-b), Multi-model mean (MMM) responses to ALL forcing changes show large warming trends over the continents in the northern hemisphere in both winter and summer (Fig. 4 a, b, Supplementary Fig. S5a, b). However, the model simulated warming over the continents is more uniform than is seen in observations. As a result, the ratio of European warming to global mean warming in the ALL simulations gives values of 2-2.5 ( Fig. 4 i, j ), which are 20–30% lower than the observed values . To identify the contribution of greenhouse gas (GHG), anthropogenic aerosols (AER) and natural (NAT) forcings to the excess warming over Europe, we use DAMIP single forcing experiments 37 . MMM results indicate a widespread more or less uniform warming of SAT over Eurasian continent in response to GHG forcing (Fig. 4 b, f, Supplementary Fig. S5b, f). By contrast AER simulations show spatially inhomogeneous trends of SAT in summer, characterized by enhanced warming over mid-high latitude Eurasia and reduced warming over tropical Africa, and South and East Asia (Fig. 4 g, Supplementary Fig. S5g). These results strongly suggest that AER forcing changes have been an important driver of the observed excess warming over Europe in summer over recent decades, consistent with other recent studies 16–19 . AER induced warming trends in winter are weak over the Eurasian continent (Fig. 4 c, Supplementary Fig. S5c). NAT forcing induced SAT trends are generally weak and not consistent among different models in both winter and summer (Fig. 4 d, h, Supplementary Fig. S5d, h). Quantitative comparisons between the SAT trends over Europe in reanalyses and model responses to different forcings are shown in Fig. 5 and in Table 1 . The MMM surface warming in winter over Europe in response to ALL forcings during 1979–2020 exhibits a trend of 0.4 \(5\pm 0.101\) o C decade −1 (see Methods), which is slightly weaker (by about 13%) than the mean value based on the four observations and ERA5 reanalysis (0.52 \(\pm 0.015\) o C decade − 1 ) during 1979–2022. However, the MMM global warming is about 47% stronger than that based on observations (0.25 vs 0.17 o C decade −1 ). This is consistent with higher climate sensitivity in CMIP6 models by comparison with CMIP5 models 38–40 . As a result, the excess warming relative to global mean observed on Europe is severely underestimated (by about 43%) in the MMM . Responses to different forcings show that the absolute and excess winter warming over Europe in the models is predominantly due to GHG forcings, with AER and NAT forcings making weak contributions (Fig. 5 , Table 1 ). In summer, the MMM simulated warming trend of 0.51 ± 0.068 o C decade − 1 in response to ALL forcing changes is slightly stronger (by about 6%) than the mean value based on observations (0.48 ± 0.014 o C decade − 1 ). However, similar to winter, the MMM global warming in summer is about 47% stronger than was observed (Fig. 5 , Table 1 ). As a result, the excess European warming relative to the global mean is underestimated by 16% in CMIP6 MMM simulations. The largest contribution to excess European warming in the model simulations is from AER forcing, with an additional smaller contribution from GHG forcing. The responses to NAT forcing are generally weak and make a negligible contribution to the excess European warming. An additional feature to note in the model simulations is that the sum of MMM European warming trends in response to GHG, AER, and NAT is about 10% weaker in summer and winter than the trends in response to ALL forcings, while the sum of global mean trends is very close to those in response to ALL forcing in both seasons (Fig. 5 , Table 1 ). Three factors may be important for explaining this discrepancy. First, land use and land cover change (included in ALL) might have played a role in local warming over Europe 41–42 . Secondly, there could be nonlinear interactions between the responses to different forcings 43–44 . A third possibility is that the discrepancy may be attributable to internal variability. Understanding these contributions is an important area for future research. The contribution of atmospheric circulation to excess European warming in CMIP6 models We now investigate the contribution of atmospheric circulation to excess European warming in the CMIP6 models. First, we evaluate model simulated interannual variability of SAT and atmospheric circulation over Europe in CMIP6 historical simulations for the period 1979–2020. The standard deviations of circulation indices and European SAT indices show some spread among models and ensemble members but the observed values lie within model range in both seasons (Supplementary Fig. S6. In winter, the MMM SAT interannual variability over Europe is very close to that observed while the MMM NAO interannual variability is slightly lower that the observed value. In summer, interannual variability of European SAT in most models and ensemble members has a similar value to that observed, while interannual variability of the geopotential height index is higher than is observed in some models and ensemble members. The MMM regression patterns of circulation to European SAT in model historical simulations in both seasons show very similar characteristics (Supplementary Fig. S7) in comparison with those based on ERA5 reanalysis (Fig. 2 d, e, f, Fig. 3 d, e, f). These similar circulation patterns associated with European SAT variability in model simulations and reanalyses indicate a feasibility to investigate the role of atmospheric circulation trends in European warming trends in the CMIP6 model historical simulations. Comparison between the observed trends in circulation indices and the trend distributions derived from the CMIP6 ALL simulations show observed trends in both summer and winter are outside the ranges of those in model simulations (Fig. 6 a, b, see Methods). For winter, this result is consistent with the findings of ref 32. For summer, the result is in line with the findings of ref 11, who focussed however on heat extremes rather than mean warming. The MMM circulation trends in winter in ALL simulations project onto the positive NAO phase with increased westerlies downstream of the North Atlantic jet, predominantly due to the response to GHG, but with very weak magnitude (Supplementary Fig. S8a, b) in comparison with the trends seen in reanalyses (Fig. 2 b, c, Supplementary Fig. S1 b, c). Circulation responses to individual forcings are also very weak (Supplementary Fig. S8). The MMM summer geopotential height trends in ALL do not show the observed increase in height over Europe, but responses to AER forcing show a zonal dipole pattern which has some similarities to the observed trend, albeit with a magnitude about 10% of that based on reanalyses (Supplementary Fig. S9, Fig. 3 c, Supplementary Fig. S3c). An important consequence of the very weak forced circulation trends seen in the CMIP6 simulations is that the estimated atmospheric circulation contribution to excess European warming is also very small (Fig. 6 c, d, Table 2 ). In winter, atmospheric circulation changes explain 5% of the excess European warming (2% of the total warming) in CMIP6 simulations, which is far less than 40% of the excess warming (26% of the total warming) estimated from the ERA5 reanalysis. In summer, atmospheric circulation changes in CMIP6 MMM simulations play a weak damping role for European warming (-3.8% for excess European warming and − 2% for the total European warming) since the MMM gives a negative geopotential height anomaly over Europe (Fig. 6 b, Supplementary Fig. S9a), which is in contrast to the positive contribution to excess European warming (by 29%) and total European warming (by 19%) estimated from the ERA5 reanalysis. Therefore, the excess European warming seen in the CMIP6 MMM simulations is almost entirely “thermodynamic” rather than “dynamic”, in both winter and summer . This observation invites us to look again at Fig. 5 . Alongside the observed trends, this figure also shows an estimate of the thermodynamic component of observed warming (computed from the residual difference between the actual observed warming and the estimated circulation-related contribution). As the CMIP6 MMM simulations fail to capture the circulation-related contribution, it is arguably more appropriate to compare these model results with the observed estimate for the thermodynamic contribution to warming. If we focus on excess European warming (Fig. 5 b, d) we see that the trends found in the ALL simulations compare in magnitude extremely well with the estimated thermodynamic contribution in both winter and summer. This suggests that the models may be simulating well this thermodynamic contribution to excess European warming, whilst failing to simulate the dynamic contribution. Discussion In this study, we have investigated the causes of “excess” European warming in recent decades, this being a measure of the extent to which Europe has warmed more rapidly than the global mean in both winter and summer. Expressed as a ratio, European temperatures warmed around three times faster, in both seasons, than global mean temperatures. We have shown that this excess warming has both dynamic and thermodynamic contributions. The dynamic contributions are related to trends in atmospheric circulation and, by using a simple regression-based method, we have estimated that these trends can account for about 40% (26%) and 29% (19%) of the excess (total) European warming in winter and summer respectively. These numbers are likely to be a lower bound, as they are based on the contribution of a single dominant pattern of atmospheric variability. Refs 11 and 30 reported estimates of the dynamic contribution to trends in summer heat extremes over Western Europe using different approaches. Their estimates of 24% for the trend over 1950–2022 (Ref 11) and 33% for the trend over 1979–2021 (Ref 30) are similar to our value of 26% for the mean summer warming. Analyses of CMIP6 MMM simulations show that models reproduce the magnitude of observed European warming quite well. These results suggest that CMIP6 models, unlike previous generations of CMIP3 and CMIP5 models 1, 35 do not underestimate European warming in winter and summer; however, they get the right answer for the wrong reasons. As a result, the ratio of European warming to global mean warming in the CMIP6 MMM simulations gives values of 2-2.5, which are 20–30% lower than the observed values. The CMIP6 MMM simulations underestimate the magnitude of excess European warming in both winter (by about 43%) and summer (by about 16%), but reproduce the magnitude of thermodynamic excess warming very well in both seasons. However, CMIP6 models do not capture the magnitude of the dynamic warming as a forced response in either season, and the observed circulation trends lie outside the ranges of those in CMIP6 ALL forcing simulations. The weakness compared to observations of the regional circulation trends simulated in CMIP6 models has been highlighted in a number of recent studies 1, 11, 30, 32 . For example, Ref 32 showed that the observed wintertime North Atlantic jet strengthening during 1951–2020 is greater than in any of the 303 simulations from 44 CMIP6 climate models. Ref 30 showed that observed heat extreme trends in summer over Western Europe during 1979–2021 stand out above the 95th percentile of trend distribution simulated by CMIP6 multimodel ensembles. And Ref 11 showed that none of 170 CMIP6 ensemble simulations exhibited a trend in a circulation–induced heat extremes over Western Europe as large as was observed during 1950–2022. These findings are also in line with previous studies which focused on related aspects of European (or wider) climate change 11, 16, 24–25, 30, 32, 44–45 and suggest – as also highlighted in these previous studies – either that the circulation response to external forcings is underestimated in the CMIP6 MMM or that low frequency internal variability is underestimated, or both. In either case, these findings imply substantial uncertainty concerning the future rate of European warming, and particularly the dynamical component. However, the situation regarding the thermodynamic contribution to excess European warming looks better. For this component, the CMIP6 MMM agrees well with the observational estimate for both winter and summer. Furthermore, the CMIP6 DAMIP simulations provide quantitative estimates of the contributions from different forcings, identifying GHG forcing as the dominant contribution to the excess warming in winter, but AER forcing as the dominant contribution to the excess warming in summer. Our findings are consistent with previous studies 16, 19 that have highlighted the importance of AER forcing for understanding summer warming in Europe, but also extend these results by quantifying the contributions of different forcings using DAMIP results and by considering excess warming in both winter and summer. These findings have implications for projections. In particular, the large AER contribution to the excess warming in summer is unlikely to be continued in future as it arises from declines in aerosol precursor emissions since the 1970s, which are not expected to continue in future. Thus – if other influences are equal – we should expect excess warming in summer to be lower (perhaps by as much as 60% of the thermodynamic contribution) in future decades than in the recent past. A reduction of the AER contribution would also reduce excess warming in winter, but by a much smaller percentage (up to 20% of the thermodynamic contribution). Methods Reanalysis and observational data sets. The reanalysis datasets used in this study are monthly mean zonal winds on pressure levels and surface air temperature (SAT) from the new state-of-the-art climate reanalysis of the European Centre for Medium Range Weather Forecast (ERA5) 46 , and the Japanese 55-year Reanalysis Project (JRA55) reanalysis 47 during 1979–2022. The observed monthly mean SAT data sets used are the HadCRUT5 dataset 48 , the NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp) 49 , the GISS Surface Temperature Analysis version 4 (GISTEMP v4) 50 , and Berkeley Earth Surface Temperatures (BEST) 51 . These reanalysis and observational data sets were interpolated to a common grid with a horizontal resolution of 1.875° longitude by 1.25° latitude. We used monthly mean data to construct boreal winter (December, January, February, DJF) and summer (June, July, August, JJA) means and investigated the trends of surface warming over Europe averaged over the region (45°N-65°N, 10°W-60°E) in winter and over the region (35 o N-65 o N, 10 o W-60 o E) in summer. DJF in a year is the mean of December in the previous year, January and February in the current year. We compared these regional warming trends with trends of global means to quantify excess warming over Europe in two seasons. The significance of warming trends at each grid point and the area averaged temperature was tested by the Mann-Kendall nonparametric method. The multi-data mean warming trend was the arithmetic mean of trends based on four observation data sets and ERA5 reanalysis. Estimating the contribution of atmospheric circulation to excess European warming. To quantify the influence of atmospheric circulation change on regional warming trends in observations/reanalyses, we first regressed the detrended atmospheric circulation variables in winter and summer seasons to the detrended surface air temperature index averaged over the region (45°N-65°N, 10°W-60°E) for winter and over the region (35°N-65°N, 10°W-60°E) for summer, where the trends show excess warming compared to the corresponding trends of global means. As discussed in the main text, since the dominant patterns of circulation associated with interannual variability are similar to those associated with the multi-decadal (1979–2022) trends in both seasons (Figs. 2 and 3 ), we next defined a circulation index to quantify the variability in the relevant patterns. For winter, we used an NAO index defined as the difference of area averaged SLP over a southern box (36°N-38°N, 28°W-22°W) around Azores and a northern box (64°N-66°N, 26°W-21°W) around Iceland. For summer, we used an index of area averaged geopotential height over the region (40°N-65°N, 5°-50°E); in this case we subtracted the trend in the zonally averaged value over the same latitude band to remove the global warming influence on geopotential height. In a third step, we regressed detrended SAT interannual variations during 1979–2022 onto the detrended circulation indices in winter and summer to obtain spatial patterns of SAT associated with circulation index variability. We then estimated the circulation-related SAT trend by scaling these interannual patterns in proportion to the magnitude of the trend in the appropriate circulation index (Figs. 2 g and 3 g). The difference between the raw SAT trends and the circulation-related part provides a residual warming pattern (Figs. 2 h and 3 h), which we interpret over Europe as being primarily related to thermodynamic processes. In mathematical terms, the regression of detrended SAT onto the detrended circulation index may be expressed as: SAT detrended = a + b x CI detrended ( 1 ) where SAT detrended and CI detrended are detrended surface air temperature and circulation indices respectively, b is the estimated slope and a is the estimated intercept. Then, we rescaled SAT variability using the raw circulation index (CI raw ): SAT CI = a + b x CI raw ( 2 ) where SAT CI is total SAT variation (including trend) resulting from circulation variability. The trend in SAT CI is the estimated dynamical contribution to warming at each grid point (Figs. 2 g and 3 g), and area averaged trends of SAT CI provide estimates of the regional warming trend attributable to changes in circulation. We repeated our calculations using the ERA5 reanalysis and JRA55 reanalysis and obtained very similar results. We also explored the sensitivity to alternative choices for the circulation indices, namely a zonal wind circulation index at 500 hPa averaged over the North Atlantic jet exit region (45 o N-60 o N, 50 o W-20 o E) in winter, and averaged over the Mediterranean (35 o N-50 o N, 0 o -50 o E) in summer and results. As described in the main text, the results were similar to those obtained with our original indices (Supplementary Fig. S2 and S4). We note that our method is significantly simpler to some alternative methods 11, 30 that have been used in the literature to estimate circulation-related trends. Its simplicity and transparency is an attraction of our method. An additional attraction is that it uses only observational data, with no reliance on climate model data. However, in considering only a single dominant pattern of circulation variability, it potentially neglects contributions from other patterns. Hence, as noted in the main text, it may provide a lower bound on the estimated dynamical contribution to warming trends. . CMIP6 and DAMIP simulations. We investigated the impacts of anthropogenic forcings on regional warming trends using multimodel simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) 36 , including both historical all forcing simulations (referred to as ALL: driven with changes in all anthropogenic and natural forcings) during 1979–2014, extended to 2020 using the SSP2-4.5 future scenario simulations and single forcing simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) 37 with the SSP2-4.5 future scenario for 2015–2020. Single forcing experiments include greenhouse gases (GHG) only (driven with changes in well-mixed greenhouse gas concentrations only), anthropogenic aerosol (AER) only (driven with changes in anthropogenic aerosol emissions), and natural forcing (NAT) only (driven with changes in natural forcings only) simulations during 1979–2020 which were designed to estimate the contributions of different anthropogenic and natural forcings to observed global and regional climate changes. We selected thirteen models that have all the variables needed for all historical and single forcing simulations (Supplementary Table 1). They are the Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model (ACCESS-CM2) 53 , the Australian Community Climate and Earth System Simulator Earth System Model version 1.5 (ACCESS-ESM1-5) 54 , the Beijing Climate Center Climate System Model (BCC-CSM2-MR) 55 , the Canadian Earth System Model version 5 (CanESM5) 56 , the National Center for Atmospheric Research Community Earth System Model Version 2 (CESM2) 57 , the sixth generation Centre National de Recherches Météorologique Coupled Model (CNRM-CM6-1) 58 , the GFDL's Earth System Model Version 4 (GFDL-ESM4) 59 , the Goddard Insitute for Space Studies climate model (GISS-E2-1-G) 60 , the Hadley Centre Global Environment Model version 3 (HadGEM3-GC31-LL) 61 , the Institute Pierre-Simon Laplace Climate Model (IPSL-CM6A-LR) 62 , the Model for Interdisciplinary Research on Climate version 6 (MIROC6) 63 , the Meteorological Research Institute Earth System Model (MRI-ESM2-0) 64 and the second version of the coupled Norwegian Earth System Model (NorESM2) 65 . We downloaded monthly mean variables from these simulations. Model simulations were interpolated to a common grid with a horizontal resolution of a resolution of 1.875° longitude by 1.25° latitude before the analysis. We used monthly mean data to construct winter (December, January, February) and summer (June, July, August) means and analysed model simulated warming trends over the period 1979–2020 (42 years) in model simulations. We calculated trends for each member of model experiments with different forcings, performed similar analysis for each ensemble member to separate the role of atmospheric circulation for model simulated warming trend and the residual in CMIP6 historical simulations, constructed ensemble mean trends for different forcing experiments for each model, and then constructed the multimodel mean (MMM) by averaging 13 model results (i.e. giving equal weight to each model) for different forcing simulations. The MMM trends in NAT simulations are based on 12 models since some variables in GISS-E2-1-G NAT simulations are not available in the database. The robustness of multimodel simulations was assessed if 80% of models gave the same sign of trends in ALL, GHG, AER, and NAT simulations. Before we investigated and quantified the role of atmospheric circulations on European warming trends in CMIP6 historical simulations, we evaluated model simulated SAT, circulation index variability, and spatial patterns of atmospheric circulation associated with European SAT interannual variability with detrended data to demonstrate that many features of variabilities in SAT and circulation and their associations shown in observations are realistically reproduced by CMIP6 historical ensemble simulations. We calculated circulation index trends for each model and ensemble member for both winter and summer from the CMIP6 historical simulations during 1979–2020, and constructed probability density functions for these index trends. Then, we constructed multimodel mean (MMM) warming trends due to changes in atmospheric circulation in CMIP6 historical simulations and MMM residual warming in the same way as we did for the reanalysis data sets and compared model results with the results based on the reanalyses. The 95% confidence interval (CI) for the multi-data set mean or multimodel mean regional warming trend is estimated by assuming trends are standard normal distributions and the 95% CI is 1.96 \(\sigma /\surd N\) where \(\sigma\) is standard deviation of regional warming trends among data sets or different models and N is the number of data sets or models. We also estimated contributions of atmospheric circulation to excess European warming based on ERA5 during 1979–2020 and results are very similar to those during 1979–2022 (Table 2 ). Therefore, trends based on observations/reanalyses were calculated during 1979–2022 and were used in all figures and supplementary figures. Note that trends in observations/reanalyses and model simulations were given by trends per decade to make it easy to compare them. Calculating Probability Density Function (PDF) of circulation trends in Fig. 6 a, b. We calculated circulation trends for two reanalyses and for each ensemble member of CMIP6 ALL forcing simulations for all models. We divided the ranges (-1.5 to 1.5 hPa decade-1) for NAO index and ranges (-0.10 to 0.10 x100m decade-1) for geopotential height index into 20 equal bins and plotted probability of circulation trends in these bins. Figure 6 a, b demonstrates that the circulation trends based on two reanalyses are outside the ranges of those based on CMIP6 multimodel ensemble simulations. Data availability ERA5 reanalysis is available at https://climate.copernicus.eu/climate-reanalysis . JRA55 reanalysis is available at https://jra.kishou.go.jp/JRA-55/index_en.html and is downloaded from https://rda.ucar.edu/datasets/ds628.1/ . HadCRUT5 data set is available at https://www.metoffice.gov.uk/hadobs/hadcrut5/ . GISSTEMP v4 is available at at https://data.giss.nasa.gov/gistemp/ . NOAAGlobalTemp is available at https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp . BEST is available at https://berkeleyearth.org/data/ . The CMIP6 and DAMIP simulations analyzed in this study are versions archived at the Centre for Environmental Data Analysis (CEDA) and they are available at https://help.ceda.ac.uk/article/4801-cmip6-data . Code availability All relevant codes used in this work are available, upon request, from the corresponding author B.D. Declarations Data availability ERA5 reanalysis is available at https://climate.copernicus.eu/climate-reanalysis. JRA55 reanalysis is available at https://jra.kishou.go.jp/JRA-55/index_en.html and is downloaded from https://rda.ucar.edu/datasets/ds628.1/. HadCRUT5 data set is available at https://www.metoffice.gov.uk/hadobs/hadcrut5/. GISSTEMP v4 is available at at https://data.giss.nasa.gov/gistemp/. NOAAGlobalTemp is available at https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp. BEST is available at https://berkeleyearth.org/data/. The CMIP6 and DAMIP simulations analyzed in this study are versions archived at the Centre for Environmental Data Analysis (CEDA) and they are available at https://help.ceda.ac.uk/article/4801-cmip6-data. Code availability All relevant codes used in this work are available, upon request, from the corresponding author B.D. Acknowledgements This work was supported by the Natural Environment Research Council (NERC) Climate Change in the Arctic-North Atlantic Region and Impacts on the UK (CANARI) project (NE/W004981/1) and the Towards an Integrated Capability to Explain and Predict Regional Climate Changes (EXPECT) project by the European Union's Horizon Europe research and innovation programme under grant agreement no.101137656. BD and RS are supported by the UK National Centre for Atmospheric Science, funded by the Natural Environment Research Council. We acknowledge the World Climate Research Programme, which through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. Author contributions BD and RS designed research. BD carried out analysis. BD and RS worked together on the interpretation of the results and wrote the paper. 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Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci. Model Dev. (2019). https://doi.org/10.5194/gmd-12-2727-2019 Yukimoto, S. et al. The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: description and basic evaluation of the physical component. J. Meteorol. Soc. Jpn 97, 931–965 (2019). Seland, Ø. et al. Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations. Geoscientific Model Development, 13 (12), 6165–6200 (2020). Tables Table 1 Linear trends of SAT indices and the 95% confidence interval (CI) in observations/reanalyses and CMIP6 (DAMIP) simulations . Multi-data mean linear SAT trends ( o C decade − 1 ) based on four observations and ERA5 reanalysis during 1979–2022 and 1979–2020, and CMIP6 (DAMIP) multi-model ensembles during 1979–2020 over Europe, Global, and their differences (Eu-GL, excess European warming) and uncertainty. SUM is the sum of model responses to GHG, AER, and NAT forcings. See Methods for details. Obs (79 − 22) Obs (79 − 20) CMIP6 GHG AER NAT SUM DJF Europe 0.52 ± 0.015 0.54 ± 0.016 0.45 ± 0.101 0.32 ± 0.083 0.024 ± 0.076 0.062 ± 0.085 0.41 Global 0.17 ± 0.008 0.18 ± 0.008 0.25 ± 0.035 0.24 ± 0.025 -0.014 ± 0.020 0.020 ± 0.007 0.25 Eu-GL 0.35 ± 0.016 0.37 ± 0.017 0.20 ± 0.087 0.085 ± 0.081 0.038 ± 0.062 0.042 ± 0.084 0.17 JJA Europe 0.48 ± 0.014 0.45 ± 0.014 0.51 ± 0.068 0.26 ± 0.045 0.16 ± 0.052 0.044 ± 0.021 0.46 Global 0.17 ± 0.006 0.17 ± 0.006 0.25 ± 0.032 0.21 ± 0.024 0.008 ± 0.017 0.023 ± 0.010 0.24 Eu-GL 0.31 ± 0.017 0.28 ± 0.017 0.26 ± 0.049 0.05 ± 0.035 0.15 ± 0.042 0.021 ± 0.021 0.22 Table 2 Linear trends SAT, SAT trend due to circulation. Linear SAT trends ( o C decade − 1 ) in ERA5 reanalysis during 1979–2022 and 1979–2020, CMIP6 multi-model ensemble mean trend and the 95% confidence interval (CI) during 1979–2020 over Europe (Total), and relative contributions of changes in circulations to the trends over Europe (Circ), residual trends (Residual), differences in trends between Europe and global (Eu-GL, excess European warming), percentage contributions of circulation induced SAT change to SAT trends over Europe (Circ/Eu) and to excess European warming (Circ/(Eu-GL). See Methods for details. Europe (Total) Europe (Circ) Europe (Residual) Global Eu-GL Eu/GL Circ/Eu Circ/ (Eu-GL) DJF ERA5 (79 − 22) 0.54 0.14 0.40 0.19 0.35 2.8 26% 40% ERA5 (79 − 20) 0.56 0.17 0.39 0.19 0.37 2.9 30% 46% CMIP6 0.45± 0.101 0.01 ± 0.024 0.44 ± 0.096 0.25 ± 0.035 0.20 ± 0.087 1.8 2% 5% JJA ERA5 (79 − 22) 0.48 0.09 0.39 0.17 0.31 2.9 19% 29% ERA5 (79 − 20) 0.45 0.08 0.37 0.16 0.29 2.8 18% 28% CMIP6 0.51 ± 0.068 -0.01 ± 0.013 0.52± 0.062 0.25± 0.032 0.26 ± 0.049 2.0 -2% -3.8% Additional Declarations (Not answered) Supplementary Files DongSuttonEuroenhancedwarmingSI.docx Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: revise 16 Jul, 2024 Review # 3 received at journal 09 Jul, 2024 Review # 1 received at journal 03 Jul, 2024 Review # 2 received at journal 01 Jul, 2024 Reviewer # 3 agreed at journal 12 Jun, 2024 Reviewer # 2 agreed at journal 11 Jun, 2024 Reviewer # 1 agreed at journal 11 Jun, 2024 Reviewers invited by journal 06 Jun, 2024 Editor assigned by journal 05 Jun, 2024 Submission checks completed at journal 04 Jun, 2024 First submitted to journal 03 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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(a, c, e) \u0026nbsp;DJF and (b, d, f) JJA during 1979-2022 based on ERA5 reanalysis. Trends that are significantly different from 0 at the 10% level using the Mann-Kendall test are dotted in (a, b). (g, h) Time series of area averaged SAT anomalies over Europe (full lines) in DJF over the region (45\u003csup\u003eo\u003c/sup\u003eN-65\u003csup\u003eo\u003c/sup\u003eN, 10\u003csup\u003eo\u003c/sup\u003eW-60\u003csup\u003eo\u003c/sup\u003eE, black box in a, c, e), in JJA over the region (35\u003csup\u003eo\u003c/sup\u003eN-65\u003csup\u003eo\u003c/sup\u003eN, 10\u003csup\u003eo\u003c/sup\u003eW-60\u003csup\u003eo\u003c/sup\u003eE, black box in b, d, f), and corresponding global averaged anomalies (dotted lines) during 1979-2022 based on four observational data sets and ERA5 reanalysis.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/7453860749965e12f2b17c3d.png"},{"id":58718182,"identity":"73a65c4f-251e-4675-9f66-e2dfcc7a909d","added_by":"auto","created_at":"2024-06-20 08:24:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear trends of SAT, atmospheric circulation, regression patterns and circulation induced SAT trends in DJF\u003c/strong\u003e \u003cstrong\u003ebased on ERA5 reanalysis. \u003c/strong\u003e(a, b, c)\u003cstrong\u003e \u003c/strong\u003eLinear trends of SAT (\u003csup\u003eo\u003c/sup\u003eC decade-1), zonal wind (m s\u003csup\u003e-1\u003c/sup\u003e d2g andecade\u003csup\u003e-1\u003c/sup\u003e), and SLP (hPa decade\u003csup\u003e-1\u003c/sup\u003e). (d, e, f) Regression patterns of SAT (\u003csup\u003eo\u003c/sup\u003eC), zonal wind (m s\u003csup\u003e-1\u003c/sup\u003e) at 500 hPa, and SLP (hPa) in DJF to the normalized time series of European SAT index which is defined as the area averaged SAT over the region (45\u003csup\u003eo\u003c/sup\u003eN-65\u003csup\u003eo\u003c/sup\u003eN, 10\u003csup\u003eo\u003c/sup\u003eW-60\u003csup\u003eo\u003c/sup\u003eE, black box in a, d, g, h) in DJF. (g, h) Linear trends of SAT due to circulation changes (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e) and SAT residual trend (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e). (i) Time series (i) of SAT anomalies (total, part due to circulation change, and the residual) over Europe and linear trends (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e; numbers in top right). The circulation index used for decomposition of SAT trends is the NAO index, defined as the difference of area averaged SLP over the southern box (36\u003csup\u003eo\u003c/sup\u003eN-38\u003csup\u003eo\u003c/sup\u003eN, 28\u003csup\u003eo\u003c/sup\u003eW-22\u003csup\u003eo\u003c/sup\u003eW) around Azores and the northern box (64\u003csup\u003eo\u003c/sup\u003eN-66\u003csup\u003eo\u003c/sup\u003eN, 26\u003csup\u003eo\u003c/sup\u003eW-21\u003csup\u003eo\u003c/sup\u003eW) around Iceland (see Methods). The global mean is removed in panels (a-h). Note that the removal of global mean has a very little impact on panels (b-g). The black box in panels (a, d, g, h) outlines the region that is used to define SAT index over Europe in DJF. The black box in (b) and (e) is used to define a zonal jet index over the North Atlantic jet exit region to test the sensitivity of the SAT trend decomposition to an alternative choice for the circulation index and results are shown in Supplementary Information Figure S2.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/0758d5a064fab2e667567eb5.png"},{"id":58717610,"identity":"d6571e41-8e85-4852-97c0-32612285088a","added_by":"auto","created_at":"2024-06-20 08:16:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":135367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear trends of SAT, atmospheric circulation, regression patterns and circulation induced SAT trends in JJA\u003c/strong\u003e \u003cstrong\u003ebased on ERA5 reanalysis. \u003c/strong\u003e\u0026nbsp;(a, b, c) Linear trends of SAT (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e), zonal wind (m s\u003csup\u003e-1\u003c/sup\u003e decade\u003csup\u003e-1\u003c/sup\u003e) and geopotential height (m decade\u003csup\u003e-1\u003c/sup\u003e) at 500 hPa. (d, e, f) Regression patterns of SAT (\u003csup\u003eo\u003c/sup\u003eC), zonal wind (m s\u003csup\u003e-1\u003c/sup\u003e) and geopotential height (m) at 500 hPa in JJA to the normalized time series of the European SAT index which is defined as area averaged SAT over the region (35\u003csup\u003eo\u003c/sup\u003eN-65\u003csup\u003eo\u003c/sup\u003eN, 10\u003csup\u003eo\u003c/sup\u003eW-60\u003csup\u003eo\u003c/sup\u003eE, black box in a, d, g, h) in JJA. (g, h) \u0026nbsp;SAT trends due to circulation changes (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e) and SAT residual trend (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e).\u0026nbsp; (i) Time series of SAT anomalies (total, part due to circulation change, and the residual) over Europe and linear trends (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e; numbers in top right). The circulation index used for decomposition of SAT trends is the height index, which is defined as the area averaged geopotential height over the region (40\u003csup\u003eo\u003c/sup\u003eN-65\u003csup\u003eo\u003c/sup\u003eN, 5\u003csup\u003eo\u003c/sup\u003e-50\u003csup\u003eo\u003c/sup\u003eE, black box in c and f) with zonal averaged value over the same latitude band removed (see Methods). The global mean is removed in panels (a-h). Note that the removal of global mean has a very little impact on panels (b-g). The black box in panels (a, d, g, h) outlines the region that is used to define SAT index over Europe in JJA. The red box in (b) and (e) is used to define a zonal jet index over the Mediterranean to test the sensitivity of SAT trend decomposition to an alternative choice for the circulation index and results are shown in Supplementary Information Figure S4.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/c40d7c39312ef8593424f886.png"},{"id":58717608,"identity":"173db722-2eb1-4fb9-bc95-a14452d69edb","added_by":"auto","created_at":"2024-06-20 08:16:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe multimodel mean (MMM)\u003c/strong\u003e \u003cstrong\u003elinear trends of SAT in winter (December, January, February) and summer (June, July, August) during 1979-2020 in CMIP6 (DAMIP) simulations.\u003c/strong\u003e (a, b, c, d) Response to different forcings (ALL, GHG, AER, NAT) in DJF and (e, f, g, h) in JJA. Dots indicate regions where 80% of models have the same sign of trend. Local SAT trend ratio relative to global mean trend in DJF (i) and JJA (j). The details for analyses of model simulations are in Methods.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/fd46504a5b457df249de2257.png"},{"id":58717611,"identity":"575a4c24-164b-4cfa-a53a-b0f07870a167","added_by":"auto","created_at":"2024-06-20 08:16:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear trends of SAT indices and the 95% confidence interval (CI) in winter (December, January, February) and summer (June, July, August) in observations/reanalyses and CMIP6 (DAMIP) simulations. \u003c/strong\u003e(a, c) SAT (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e) over Europe and\u0026nbsp; (b, d) SAT trend difference between Europe and global mean in observations (blue bar) during 1979-2022 and model simulations (red bar) during 1979-2020 based on CMIP6/DAMIP model simulations in response to different forcings. (a, b) for DJF and (c, d) for JJA. ObsRes is the SAT trend residual with the circulation induced trend based on ERA5 reanalysis removed from the total SAT trend (see methods). SUM is the sum of model responses to GHG, AER, and NAT forcings. All-SUM is the difference between the response in ALL forcing and SUM. The details are in Methods.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/5b61a169f48f1fe171efe2c1.png"},{"id":58717606,"identity":"97874168-01ee-4ae1-adc6-e21787c0efd6","added_by":"auto","created_at":"2024-06-20 08:16:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear trends of atmospheric circulation indices, circulation induced SAT change, and SAT residual in winter (December, January, February) and summer (June, July, August) in reanalyses and CMIP6 (DAMIP) simulations\u003c/strong\u003e. (a, b) Probability density function of NAO index trend (hPa decade\u003csup\u003e-1\u003c/sup\u003e) in DJF and height index trends (100m decade\u003csup\u003e-1\u003c/sup\u003e) in JJA based on CMIP6 historical (ALL forcing) ensemble simulations during 1979-2020. Corresponding trends during 1979-2022 based on two reanalysis data sets are plotted as vertical lines (red for ERA5 and black for JRA55). (c, d) The multimodel mean SAT trends (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e-1\u003c/sup\u003e) due to changes in circulation in DJF and JJA during 1979-2020. (e, f) SAT trend residuals during in DJF and JJA. The global mean SAT trend is removed in panels (c-f). Note that the removal of global mean has a very little impact on panels (c-d). Dots indicate regions where 80% models have the same sign of trend. The details for analyses of model simulations are in Methods.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/2cd2a51098c8ea38f13404ae.png"},{"id":75597689,"identity":"091428c3-eb59-4a2c-9666-146bc154bd32","added_by":"auto","created_at":"2025-02-06 08:13:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1944158,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/f922dcaf-2459-40b3-8068-2159f3117bf9.pdf"},{"id":58717612,"identity":"3e099117-2c2c-4256-9a30-475d0ae302dd","added_by":"auto","created_at":"2024-06-20 08:16:33","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":3676007,"visible":true,"origin":"","legend":"","description":"","filename":"DongSuttonEuroenhancedwarmingSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-4523385/v1/0bc64bef6c662d80bb5c4392.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Anthropogenic influence on excess warming in Europe during recent decades","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTrends in surface air temperature (SAT) during recent decades exhibit a non-uniform pattern with amplified warming over Europe in both winter and summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). Indeed, Europe has been the fastest warming continent on the Earth, warming more than twice as fast as the global mean over recent decades (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, f), consistent with previous studies\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. The rapid warming in summer is associated with increases in intense and longer-lasting extreme heatwaves, sometimes coupled with droughts, and has led to Europe being identified as a heatwave hotspot\u003csup\u003e4\u0026ndash;11\u003c/sup\u003e. Meanwhile, the warming in winter is associated extreme rainfall events, increased runoff, and risk of flooding, related to changes in atmospheric circulation and precipitation characteristics\u003csup\u003e7, 10, 11\u0026ndash;14\u003c/sup\u003e. In view of these changes in climate extremes and their impacts, further understanding European excess warming, including whether it may continue in the future, is an important challenge. For the purposes of this study, we define excess European warming as the difference between the rate of European regional warming and the rate of global warming.\u003c/p\u003e \u003cp\u003eRegional warming on decadal-multidecadal scales is influenced by global and regional drivers, such as radiative forcings from anthropogenic greenhouse gases (GHGs)\u003csup\u003e1, 15\u0026ndash;16\u003c/sup\u003e, anthropogenic aerosol emissions (AER)\u003csup\u003e16, 17\u0026ndash;19\u003c/sup\u003e, changes in solar activity and large-scale volcanic eruptions\u003csup\u003e20\u003c/sup\u003e, and by modes of internal climate variability, such as the North Atlantic Oscillation (NAO)\u003csup\u003e21\u0026ndash;25\u003c/sup\u003e, and Atlantic Multidecadal Variability (AMV)\u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. Previous studies have demonstrated the important influence of atmospheric circulation on warming over Europe in both winter and summer\u003csup\u003e1, 21, 23\u0026ndash;25, 29\u003c/sup\u003e and for understanding trends in hot extremes in Europe\u003csup\u003e8, 11, 30\u0026ndash;31\u003c/sup\u003e. However, it remains unclear whether the relevant changes in atmospheric circulation are the result of natural climate variability or are responses to natural or anthropogenic external forcings\u003csup\u003e11, 32\u003c/sup\u003e. Furthermore, past studies concluded that previous generations of CMIP3 and CMIP5 models\u003csup\u003e33\u003c/sup\u003e tended to underestimate the observed warming trend in summer over Western Europe, linked in part to a failure to simulate observed atmospheric circulation changes over the Atlantic sector\u003csup\u003e1, 34\u0026ndash;35\u003c/sup\u003e. Given the outstanding knowledge gaps, there is a clear need to investigate further, with a new generation of climate models, the drivers and underlying mechanisms that may be responsible for the observed excess warming in Europe, and the extent to which these mechanisms may be accurately simulated in that latest models.\u003c/p\u003e \u003cp\u003eIn this study, we first analyze observational and reanalysis data sets to quantify regional warming trends over Europe in boreal winter and summer - including the excess regional warming relative to the global mean - focusing on the period 1979\u0026ndash;2022. Next, we investigate the contribution of atmospheric circulation to excess European warming using a simple regression-based method. Lastly, we use multimodel simulations from the coupled model intercomparison project phase 6 (CMIP6)\u003csup\u003e36\u003c/sup\u003e and detection and attribution model intercomparison project (DAMIP) single forcing experiments\u003csup\u003e37\u003c/sup\u003e to investigate the drivers and physical processes that contribute to excess European warming (see Methods).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExcess warming over Europe in observations and reanalyses\u003c/h2\u003e \u003cp\u003eThe spatial patterns of surface air temperature (SAT) trends based on ERA5 reanalysis during last four decades show relatively large warming trends over Europe in both winter and summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b) with excess local warming relative to global means (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec-d). The linear trends of SAT over Europe are around three times higher than the trends in global mean SAT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee-f). Four observational datasets and the ERA5 reanalysis show very consistent interannual variability of area averaged SAT over Europe and the globe, and also show robust linear trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg-h). The multi-dataset mean trends of area averaged SAT over Europe during 1979\u0026ndash;2022 are 0.52\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.015 and 0.48\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.014\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;1\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) corresponding to total changes of 2.29\u0026plusmn;0.065 and 2.10\u0026plusmn;0.063\u003csup\u003eo\u003c/sup\u003eC) in winter and summer respectively, and these trends are 3.1 and 2.8 times higher than the corresponding global mean trends (0.17\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.008 and 0.17\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.006\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;1\u003c/sup\u003e, corresponding to total changes of 0.76\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.018 and 0.75\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.013\u003csup\u003eo\u003c/sup\u003eC).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstimating the contribution of atmospheric circulation to excess European warming\u003c/h3\u003e\n\u003cp\u003eAssociated with the excess European warming in winter, atmospheric circulation trends based on ERA5 show a spatial pattern over the North Atlantic sector which projects onto a positive NAO pattern in SLP, accompanied by a strengthening and eastward extension of the North Atlantic jet (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c). The circulation trends based on the JRA55 reanalysis show very similar features (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To quantify the contribution of atmospheric circulation trends to regional warming, we employ a simple regression-based method (see Methods). Specifically, we use the relationship between atmospheric circulation and SAT on interannual timescales to estimate the contribution from atmospheric circulation to the multidecadal warming trend. This approach is appropriate because the dominant patterns of atmospheric circulation are very similar on the different timescales. However, as it considers only one pattern of circulation, it may be considered to provide a lower bound on the circulation-related warming trend.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-f show regression patterns for detrended winter SAT, 500hPa zonal wind and SLP onto a detrended index of European SAT, thus highlighting interannual relationships. The patterns indicate that interannual warming over Europe is associated with a positive NAO phase and enhanced westerlies over the North Atlantic. As the circulation patterns are similar to those associated with the multi-decadal trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c), we use a NAO index to quantify the contribution of atmospheric circulation change to excess warming over Europe (see Methods). This estimated contribution is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg, with the residual warming shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh. For the European area average, the results suggest that \u003cem\u003e40% of the excess warming (26% of the total warming) in winter is explained by atmospheric circulation\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To test robustness of this result to an alternative choice for the circulation index, we repeated the analysis using an index of zonal wind at 500 hPa, averaged over the North Atlantic jet exit region (45\u003csup\u003eo\u003c/sup\u003eN-60\u003csup\u003eo\u003c/sup\u003eN, 50\u003csup\u003eo\u003c/sup\u003eW-20\u003csup\u003eo\u003c/sup\u003eE). The results show that 37% of the excess warming (24% of the total warming) is explained by atmospheric circulation (Supplementary Fig. S2). These numbers are less than those obtained using the NAO index, which suggests that the NAO index is a better choice for a single index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg, h, i, Supplementary Fig. S2g, h, i).\u003c/p\u003e \u003cp\u003eAssociated with excess warming trends over Europe in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), atmospheric circulation trends based on ERA5 reanalysis show an anomalous low over the North Atlantic, accompanied by a southward displacement of the North Atlantic jet, and an anomalous high over Europe, accompanied by a dipole structure of zonal wind trends over the European sector with a weakening of Mediterranean jet and increased westerlies to the north (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c). Regression patterns of detrended circulation interannual variability onto a detrended SAT index over Europe show that warm summers are associated with an anomalous high over Europe, associated with a dipole pattern in zonal winds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-f). This pattern of circulation anomalies over Europe is similar to that associated with the multi-decadal warming trend, although the pattern of anomalies over the North Atlantic is quite different. Focussing on Europe, we define a summer circulation index as the area averaged geopotential height over Europe (The trend in zonally averaged geopotential height over the same latitude band is first removed - see Methods). Following the same procedure as for winter, we use this index to quantify the contribution of atmospheric circulation change to excess summer warming over Europe. This estimated contribution is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg, with the residual warming shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh. For the European area average, the results suggest that \u003cem\u003e29% of the excess warming (19% of the total warming) in summer is explained by atmospheric circulation\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo test the robustness of this result to an alternative choice for the circulation index, we repeated the analysis using an index of zonal wind at 500 hPa, averaged over the Mediterranean (35\u003csup\u003eo\u003c/sup\u003eN-50\u003csup\u003eo\u003c/sup\u003eN, 0\u003csup\u003eo\u003c/sup\u003e-50\u003csup\u003eo\u003c/sup\u003eE) in summer. The results suggest that that 23% of the excess warming (15% of the total warming) is explained by atmospheric circulation (Supplementary Fig. S4). As was the case for winter, these numbers are again less than those obtained using the initial index, which suggests that geopotential height index is a better choice for summer.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAttribution of the observed warming trends\u003c/h2\u003e \u003cp\u003eTo investigate drivers of the observed excess warming over Europe we analyse multi-model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6)\u003csup\u003e36\u003c/sup\u003e, including both historical all forcing simulations (ALL) and single forcing simulations from the Detection and Attribution Model Intercomparison Project (DAMIP)\u003csup\u003e37\u003c/sup\u003e for the period 1979\u0026ndash;2020 (see Methods, Supplementary Table\u0026nbsp;1). Similar to features seen in observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b), Multi-model mean (MMM) responses to ALL forcing changes show large warming trends over the continents in the northern hemisphere in both winter and summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, Supplementary Fig. S5a, b). However, the model simulated warming over the continents is more uniform than is seen in observations. As a result, \u003cem\u003ethe ratio of European warming to global mean warming in the ALL simulations gives values of 2-2.5 (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei, j\u003cem\u003e), which are 20\u0026ndash;30% lower than the observed values\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo identify the contribution of greenhouse gas (GHG), anthropogenic aerosols (AER) and natural (NAT) forcings to the excess warming over Europe, we use DAMIP single forcing experiments\u003csup\u003e37\u003c/sup\u003e. MMM results indicate a widespread more or less uniform warming of SAT over Eurasian continent in response to GHG forcing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, f, Supplementary Fig. S5b, f). By contrast AER simulations show spatially inhomogeneous trends of SAT in summer, characterized by enhanced warming over mid-high latitude Eurasia and reduced warming over tropical Africa, and South and East Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, Supplementary Fig. S5g). These results strongly suggest that AER forcing changes have been an important driver of the observed excess warming over Europe in summer over recent decades, consistent with other recent studies\u003csup\u003e16\u0026ndash;19\u003c/sup\u003e. AER induced warming trends in winter are weak over the Eurasian continent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, Supplementary Fig. S5c). NAT forcing induced SAT trends are generally weak and not consistent among different models in both winter and summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, h, Supplementary Fig. S5d, h).\u003c/p\u003e \u003cp\u003eQuantitative comparisons between the SAT trends over Europe in reanalyses and model responses to different forcings are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The MMM surface warming in winter over Europe in response to ALL forcings during 1979\u0026ndash;2020 exhibits a trend of 0.4\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(5\\pm 0.101\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;1\u003c/sup\u003e (see Methods), which is slightly weaker (by about 13%) than the mean value based on the four observations and ERA5 reanalysis (0.52\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm 0.015\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) during 1979\u0026ndash;2022. However, the MMM global warming is about 47% stronger than that based on observations (0.25 vs 0.17 \u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;1\u003c/sup\u003e). This is consistent with higher climate sensitivity in CMIP6 models by comparison with CMIP5 models \u003csup\u003e38\u0026ndash;40\u003c/sup\u003e. As a result, \u003cem\u003ethe excess warming relative to global mean observed on Europe is severely underestimated (by about 43%) in the MMM\u003c/em\u003e. Responses to different forcings show that the absolute and excess winter warming over Europe in the models is predominantly due to GHG forcings, with AER and NAT forcings making weak contributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summer, the MMM simulated warming trend of 0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.068\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in response to ALL forcing changes is slightly stronger (by about 6%) than the mean value based on observations (0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014 \u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). However, similar to winter, the MMM global warming in summer is about 47% stronger than was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As a result, the excess European warming relative to the global mean is underestimated by 16% in CMIP6 MMM simulations. The largest contribution to excess European warming in the model simulations is from AER forcing, with an additional smaller contribution from GHG forcing. The responses to NAT forcing are generally weak and make a negligible contribution to the excess European warming.\u003c/p\u003e \u003cp\u003eAn additional feature to note in the model simulations is that the sum of MMM European warming trends in response to GHG, AER, and NAT is about 10% weaker in summer and winter than the trends in response to ALL forcings, while the sum of global mean trends is very close to those in response to ALL forcing in both seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three factors may be important for explaining this discrepancy. First, land use and land cover change (included in ALL) might have played a role in local warming over Europe\u003csup\u003e41\u0026ndash;42\u003c/sup\u003e. Secondly, there could be nonlinear interactions between the responses to different forcings\u003csup\u003e43\u0026ndash;44\u003c/sup\u003e. A third possibility is that the discrepancy may be attributable to internal variability. Understanding these contributions is an important area for future research.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe contribution of atmospheric circulation to excess European warming in CMIP6 models\u003c/h3\u003e\n\u003cp\u003eWe now investigate the contribution of atmospheric circulation to excess European warming in the CMIP6 models. First, we evaluate model simulated interannual variability of SAT and atmospheric circulation over Europe in CMIP6 historical simulations for the period 1979\u0026ndash;2020. The standard deviations of circulation indices and European SAT indices show some spread among models and ensemble members but the observed values lie within model range in both seasons (Supplementary Fig. S6. In winter, the MMM SAT interannual variability over Europe is very close to that observed while the MMM NAO interannual variability is slightly lower that the observed value. In summer, interannual variability of European SAT in most models and ensemble members has a similar value to that observed, while interannual variability of the geopotential height index is higher than is observed in some models and ensemble members. The MMM regression patterns of circulation to European SAT in model historical simulations in both seasons show very similar characteristics (Supplementary Fig. S7) in comparison with those based on ERA5 reanalysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, e, f, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, e, f). These similar circulation patterns associated with European SAT variability in model simulations and reanalyses indicate a feasibility to investigate the role of atmospheric circulation trends in European warming trends in the CMIP6 model historical simulations.\u003c/p\u003e \u003cp\u003eComparison between the observed trends in circulation indices and the trend distributions derived from the CMIP6 ALL simulations show observed trends in both summer and winter are outside the ranges of those in model simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, see Methods). For winter, this result is consistent with the findings of ref 32. For summer, the result is in line with the findings of ref 11, who focussed however on heat extremes rather than mean warming. The MMM circulation trends in winter in ALL simulations project onto the positive NAO phase with increased westerlies downstream of the North Atlantic jet, predominantly due to the response to GHG, but with very weak magnitude (Supplementary Fig. S8a, b) in comparison with the trends seen in reanalyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb, c). Circulation responses to individual forcings are also very weak (Supplementary Fig. S8). The MMM summer geopotential height trends in ALL do not show the observed increase in height over Europe, but responses to AER forcing show a zonal dipole pattern which has some similarities to the observed trend, albeit with a magnitude about 10% of that based on reanalyses (Supplementary Fig. S9, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Supplementary Fig. S3c).\u003c/p\u003e \u003cp\u003eAn important consequence of the very weak forced circulation trends seen in the CMIP6 simulations is that the estimated atmospheric circulation contribution to excess European warming is also very small (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, d, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In winter, atmospheric circulation changes explain 5% of the excess European warming (2% of the total warming) in CMIP6 simulations, which is far less than 40% of the excess warming (26% of the total warming) estimated from the ERA5 reanalysis. In summer, atmospheric circulation changes in CMIP6 MMM simulations play a weak damping role for European warming (-3.8% for excess European warming and \u0026minus;\u0026thinsp;2% for the total European warming) since the MMM gives a negative geopotential height anomaly over Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Supplementary Fig. S9a), which is in contrast to the positive contribution to excess European warming (by 29%) and total European warming (by 19%) estimated from the ERA5 reanalysis. Therefore, \u003cem\u003ethe excess European warming seen in the CMIP6 MMM simulations is almost entirely \u0026ldquo;thermodynamic\u0026rdquo; rather than \u0026ldquo;dynamic\u0026rdquo;, in both winter and summer\u003c/em\u003e. This observation invites us to look again at Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Alongside the observed trends, this figure also shows an estimate of the thermodynamic component of observed warming (computed from the residual difference between the actual observed warming and the estimated circulation-related contribution). As the CMIP6 MMM simulations fail to capture the circulation-related contribution, it is arguably more appropriate to compare these model results with the observed estimate for the thermodynamic contribution to warming. If we focus on excess European warming (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, d) we see that the trends found in the ALL simulations compare in magnitude extremely well with the estimated thermodynamic contribution in both winter and summer. This suggests that the models may be simulating well this thermodynamic contribution to excess European warming, whilst failing to simulate the dynamic contribution.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we have investigated the causes of \u0026ldquo;excess\u0026rdquo; European warming in recent decades, this being a measure of the extent to which Europe has warmed more rapidly than the global mean in both winter and summer. Expressed as a ratio, European temperatures warmed around three times faster, in both seasons, than global mean temperatures. We have shown that this excess warming has both dynamic and thermodynamic contributions. The dynamic contributions are related to trends in atmospheric circulation and, by using a simple regression-based method, we have estimated that these trends can account for about 40% (26%) and 29% (19%) of the excess (total) European warming in winter and summer respectively. These numbers are likely to be a lower bound, as they are based on the contribution of a single dominant pattern of atmospheric variability. Refs 11 and 30 reported estimates of the dynamic contribution to trends in summer heat extremes over Western Europe using different approaches. Their estimates of 24% for the trend over 1950\u0026ndash;2022 (Ref 11) and 33% for the trend over 1979\u0026ndash;2021 (Ref 30) are similar to our value of 26% for the mean summer warming.\u003c/p\u003e \u003cp\u003eAnalyses of CMIP6 MMM simulations show that models reproduce the magnitude of observed European warming quite well. These results suggest that CMIP6 models, unlike previous generations of CMIP3 and CMIP5 models\u003csup\u003e1, 35\u003c/sup\u003e do not underestimate European warming in winter and summer; however, they get the right answer for the wrong reasons. As a result, the ratio of European warming to global mean warming in the CMIP6 MMM simulations gives values of 2-2.5, which are 20\u0026ndash;30% lower than the observed values. The CMIP6 MMM simulations underestimate the magnitude of excess European warming in both winter (by about 43%) and summer (by about 16%), but reproduce the magnitude of thermodynamic excess warming very well in both seasons. However, CMIP6 models do not capture the magnitude of the dynamic warming as a forced response in either season, and the observed circulation trends lie outside the ranges of those in CMIP6 ALL forcing simulations.\u003c/p\u003e \u003cp\u003eThe weakness compared to observations of the regional circulation trends simulated in CMIP6 models has been highlighted in a number of recent studies\u003csup\u003e1, 11, 30, 32\u003c/sup\u003e. For example, Ref 32 showed that the observed wintertime North Atlantic jet strengthening during 1951\u0026ndash;2020 is greater than in any of the 303 simulations from 44 CMIP6 climate models. Ref 30 showed that observed heat extreme trends in summer over Western Europe during 1979\u0026ndash;2021 stand out above the 95th percentile of trend distribution simulated by CMIP6 multimodel ensembles. And Ref 11 showed that none of 170 CMIP6 ensemble simulations exhibited a trend in a circulation\u0026ndash;induced heat extremes over Western Europe as large as was observed during 1950\u0026ndash;2022. These findings are also in line with previous studies which focused on related aspects of European (or wider) climate change\u003csup\u003e11, 16, 24\u0026ndash;25, 30, 32, 44\u0026ndash;45\u003c/sup\u003e and suggest \u0026ndash; as also highlighted in these previous studies \u0026ndash; either that the circulation response to external forcings is underestimated in the CMIP6 MMM or that low frequency internal variability is underestimated, or both. In either case, these findings imply substantial uncertainty concerning the future rate of European warming, and particularly the dynamical component. However, the situation regarding the thermodynamic contribution to excess European warming looks better. For this component, the CMIP6 MMM agrees well with the observational estimate for both winter and summer. Furthermore, the CMIP6 DAMIP simulations provide quantitative estimates of the contributions from different forcings, identifying GHG forcing as the dominant contribution to the excess warming in winter, but AER forcing as the dominant contribution to the excess warming in summer. Our findings are consistent with previous studies\u003csup\u003e16, 19\u003c/sup\u003e that have highlighted the importance of AER forcing for understanding summer warming in Europe, but also extend these results by quantifying the contributions of different forcings using DAMIP results and by considering excess warming in both winter and summer. These findings have implications for projections. In particular, the large AER contribution to the excess warming in summer is unlikely to be continued in future as it arises from declines in aerosol precursor emissions since the 1970s, which are not expected to continue in future. Thus \u0026ndash; if other influences are equal \u0026ndash; we should expect excess warming in summer to be lower (perhaps by as much as 60% of the thermodynamic contribution) in future decades than in the recent past. A reduction of the AER contribution would also reduce excess warming in winter, but by a much smaller percentage (up to 20% of the thermodynamic contribution).\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eReanalysis and observational data sets.\u003c/b\u003e The reanalysis datasets used in this study are monthly mean zonal winds on pressure levels and surface air temperature (SAT) from the new state-of-the-art climate reanalysis of the European Centre for Medium Range Weather Forecast (ERA5)\u003csup\u003e46\u003c/sup\u003e, and the Japanese 55-year Reanalysis Project (JRA55) reanalysis\u003csup\u003e47\u003c/sup\u003e during 1979\u0026ndash;2022. The observed monthly mean SAT data sets used are the HadCRUT5 dataset\u003csup\u003e48\u003c/sup\u003e, the NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)\u003csup\u003e49\u003c/sup\u003e, the GISS Surface Temperature Analysis version 4 (GISTEMP v4)\u003csup\u003e50\u003c/sup\u003e, and Berkeley Earth Surface Temperatures (BEST)\u003csup\u003e51\u003c/sup\u003e. These reanalysis and observational data sets were interpolated to a common grid with a horizontal resolution of 1.875\u0026deg; longitude by 1.25\u0026deg; latitude. We used monthly mean data to construct boreal winter (December, January, February, DJF) and summer (June, July, August, JJA) means and investigated the trends of surface warming over Europe averaged over the region (45\u0026deg;N-65\u0026deg;N, 10\u0026deg;W-60\u0026deg;E) in winter and over the region (35\u003csup\u003eo\u003c/sup\u003eN-65\u003csup\u003eo\u003c/sup\u003eN, 10\u003csup\u003eo\u003c/sup\u003eW-60\u003csup\u003eo\u003c/sup\u003eE) in summer. DJF in a year is the mean of December in the previous year, January and February in the current year. We compared these regional warming trends with trends of global means to quantify excess warming over Europe in two seasons. The significance of warming trends at each grid point and the area averaged temperature was tested by the Mann-Kendall nonparametric method. The multi-data mean warming trend was the arithmetic mean of trends based on four observation data sets and ERA5 reanalysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEstimating the contribution of atmospheric circulation to excess European warming.\u003c/b\u003e To quantify the influence of atmospheric circulation change on regional warming trends in observations/reanalyses, we first regressed the detrended atmospheric circulation variables in winter and summer seasons to the detrended surface air temperature index averaged over the region (45\u0026deg;N-65\u0026deg;N, 10\u0026deg;W-60\u0026deg;E) for winter and over the region (35\u0026deg;N-65\u0026deg;N, 10\u0026deg;W-60\u0026deg;E) for summer, where the trends show excess warming compared to the corresponding trends of global means. As discussed in the main text, since the dominant patterns of circulation associated with interannual variability are similar to those associated with the multi-decadal (1979\u0026ndash;2022) trends in both seasons (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), we next defined a circulation index to quantify the variability in the relevant patterns. For winter, we used an NAO index defined as the difference of area averaged SLP over a southern box (36\u0026deg;N-38\u0026deg;N, 28\u0026deg;W-22\u0026deg;W) around Azores and a northern box (64\u0026deg;N-66\u0026deg;N, 26\u0026deg;W-21\u0026deg;W) around Iceland. For summer, we used an index of area averaged geopotential height over the region (40\u0026deg;N-65\u0026deg;N, 5\u0026deg;-50\u0026deg;E); in this case we subtracted the trend in the zonally averaged value over the same latitude band to remove the global warming influence on geopotential height.\u003c/p\u003e \u003cp\u003eIn a third step, we regressed detrended SAT interannual variations during 1979\u0026ndash;2022 onto the detrended circulation indices in winter and summer to obtain spatial patterns of SAT associated with circulation index variability. We then estimated the circulation-related SAT trend by scaling these interannual patterns in proportion to the magnitude of the trend in the appropriate circulation index (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). The difference between the raw SAT trends and the circulation-related part provides a residual warming pattern (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh), which we interpret over Europe as being primarily related to thermodynamic processes.\u003c/p\u003e \u003cp\u003eIn mathematical terms, the regression of detrended SAT onto the detrended circulation index may be expressed as:\u003c/p\u003e \u003cp\u003eSAT\u003csub\u003edetrended\u003c/sub\u003e = a + b x CI \u003csub\u003edetrended\u003c/sub\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ewhere SAT\u003csub\u003edetrended\u003c/sub\u003e and CI\u003csub\u003edetrended\u003c/sub\u003e are detrended surface air temperature and circulation indices respectively, b is the estimated slope and a is the estimated intercept. Then, we rescaled SAT variability using the raw circulation index (CI\u003csub\u003eraw\u003c/sub\u003e):\u003c/p\u003e \u003cp\u003eSAT\u003csub\u003eCI\u003c/sub\u003e = a + b x CI\u003csub\u003eraw\u003c/sub\u003e (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ewhere SAT\u003csub\u003eCI\u003c/sub\u003e is total SAT variation (including trend) resulting from circulation variability. The trend in SAT\u003csub\u003eCI\u003c/sub\u003e is the estimated dynamical contribution to warming at each grid point (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), and area averaged trends of SAT\u003csub\u003eCI\u003c/sub\u003e provide estimates of the regional warming trend attributable to changes in circulation.\u003c/p\u003e \u003cp\u003eWe repeated our calculations using the ERA5 reanalysis and JRA55 reanalysis and obtained very similar results. We also explored the sensitivity to alternative choices for the circulation indices, namely a zonal wind circulation index at 500 hPa averaged over the North Atlantic jet exit region (45\u003csup\u003eo\u003c/sup\u003eN-60\u003csup\u003eo\u003c/sup\u003eN, 50\u003csup\u003eo\u003c/sup\u003eW-20\u003csup\u003eo\u003c/sup\u003eE) in winter, and averaged over the Mediterranean (35\u003csup\u003eo\u003c/sup\u003eN-50\u003csup\u003eo\u003c/sup\u003eN, 0\u003csup\u003eo\u003c/sup\u003e-50\u003csup\u003eo\u003c/sup\u003eE) in summer and results. As described in the main text, the results were similar to those obtained with our original indices (Supplementary Fig. S2 and S4).\u003c/p\u003e \u003cp\u003eWe note that our method is significantly simpler to some alternative methods\u003csup\u003e11, 30\u003c/sup\u003e that have been used in the literature to estimate circulation-related trends. Its simplicity and transparency is an attraction of our method. An additional attraction is that it uses only observational data, with no reliance on climate model data. However, in considering only a single dominant pattern of circulation variability, it potentially neglects contributions from other patterns. Hence, as noted in the main text, it may provide a lower bound on the estimated dynamical contribution to warming trends.\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCMIP6 and DAMIP simulations.\u003c/b\u003e We investigated the impacts of anthropogenic forcings on regional warming trends using multimodel simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6)\u003csup\u003e36\u003c/sup\u003e, including both historical all forcing simulations (referred to as ALL: driven with changes in all anthropogenic and natural forcings) during 1979\u0026ndash;2014, extended to 2020 using the SSP2-4.5 future scenario simulations and single forcing simulations from the Detection and Attribution Model Intercomparison Project (DAMIP)\u003csup\u003e37\u003c/sup\u003e with the SSP2-4.5 future scenario for 2015\u0026ndash;2020. Single forcing experiments include greenhouse gases (GHG) only (driven with changes in well-mixed greenhouse gas concentrations only), anthropogenic aerosol (AER) only (driven with changes in anthropogenic aerosol emissions), and natural forcing (NAT) only (driven with changes in natural forcings only) simulations during 1979\u0026ndash;2020 which were designed to estimate the contributions of different anthropogenic and natural forcings to observed global and regional climate changes. We selected thirteen models that have all the variables needed for all historical and single forcing simulations (Supplementary Table\u0026nbsp;1). They are the Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model (ACCESS-CM2)\u003csup\u003e53\u003c/sup\u003e, the Australian Community Climate and Earth System Simulator Earth System Model version 1.5 (ACCESS-ESM1-5)\u003csup\u003e54\u003c/sup\u003e, the Beijing Climate Center Climate System Model (BCC-CSM2-MR)\u003csup\u003e55\u003c/sup\u003e, the Canadian Earth System Model version 5 (CanESM5)\u003csup\u003e56\u003c/sup\u003e, the National Center for Atmospheric Research Community Earth System Model Version 2 (CESM2)\u003csup\u003e57\u003c/sup\u003e, the sixth generation Centre National de Recherches M\u0026eacute;t\u0026eacute;orologique Coupled Model (CNRM-CM6-1)\u003csup\u003e58\u003c/sup\u003e, the GFDL's Earth System Model Version 4 (GFDL-ESM4)\u003csup\u003e59\u003c/sup\u003e, the Goddard Insitute for Space Studies climate model (GISS-E2-1-G)\u003csup\u003e60\u003c/sup\u003e, the Hadley Centre Global Environment Model version 3 (HadGEM3-GC31-LL)\u003csup\u003e61\u003c/sup\u003e, the Institute Pierre-Simon Laplace Climate Model (IPSL-CM6A-LR)\u003csup\u003e62\u003c/sup\u003e, the Model for Interdisciplinary Research on Climate version 6 (MIROC6)\u003csup\u003e63\u003c/sup\u003e, the Meteorological Research Institute Earth System Model (MRI-ESM2-0)\u003csup\u003e64\u003c/sup\u003e and the second version of the coupled Norwegian Earth System Model (NorESM2)\u003csup\u003e65\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe downloaded monthly mean variables from these simulations. Model simulations were interpolated to a common grid with a horizontal resolution of a resolution of 1.875\u0026deg; longitude by 1.25\u0026deg; latitude before the analysis. We used monthly mean data to construct winter (December, January, February) and summer (June, July, August) means and analysed model simulated warming trends over the period 1979\u0026ndash;2020 (42 years) in model simulations.\u003c/p\u003e \u003cp\u003eWe calculated trends for each member of model experiments with different forcings, performed similar analysis for each ensemble member to separate the role of atmospheric circulation for model simulated warming trend and the residual in CMIP6 historical simulations, constructed ensemble mean trends for different forcing experiments for each model, and then constructed the multimodel mean (MMM) by averaging 13 model results (i.e. giving equal weight to each model) for different forcing simulations. The MMM trends in NAT simulations are based on 12 models since some variables in GISS-E2-1-G NAT simulations are not available in the database. The robustness of multimodel simulations was assessed if 80% of models gave the same sign of trends in ALL, GHG, AER, and NAT simulations.\u003c/p\u003e \u003cp\u003eBefore we investigated and quantified the role of atmospheric circulations on European warming trends in CMIP6 historical simulations, we evaluated model simulated SAT, circulation index variability, and spatial patterns of atmospheric circulation associated with European SAT interannual variability with detrended data to demonstrate that many features of variabilities in SAT and circulation and their associations shown in observations are realistically reproduced by CMIP6 historical ensemble simulations. We calculated circulation index trends for each model and ensemble member for both winter and summer from the CMIP6 historical simulations during 1979\u0026ndash;2020, and constructed probability density functions for these index trends. Then, we constructed multimodel mean (MMM) warming trends due to changes in atmospheric circulation in CMIP6 historical simulations and MMM residual warming in the same way as we did for the reanalysis data sets and compared model results with the results based on the reanalyses. The 95% confidence interval (CI) for the multi-data set mean or multimodel mean regional warming trend is estimated by assuming trends are standard normal distributions and the 95% CI is 1.96 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma /\\surd N\\)\u003c/span\u003e\u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma\\)\u003c/span\u003e\u003c/span\u003e is standard deviation of regional warming trends among data sets or different models and N is the number of data sets or models.\u003c/p\u003e \u003cp\u003eWe also estimated contributions of atmospheric circulation to excess European warming based on ERA5 during 1979\u0026ndash;2020 and results are very similar to those during 1979\u0026ndash;2022 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, trends based on observations/reanalyses were calculated during 1979\u0026ndash;2022 and were used in all figures and supplementary figures. Note that trends in observations/reanalyses and model simulations were given by trends per decade to make it easy to compare them.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCalculating Probability Density Function (PDF) of circulation trends in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b. We calculated circulation trends for two reanalyses and for each ensemble member of CMIP6 ALL forcing simulations for all models. We divided the ranges (-1.5 to 1.5 hPa decade-1) for NAO index and ranges (-0.10 to 0.10 x100m decade-1) for geopotential height index into 20 equal bins and plotted probability of circulation trends in these bins. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b demonstrates that the circulation trends based on two reanalyses are outside the ranges of those based on CMIP6 multimodel ensemble simulations.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eERA5 reanalysis is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climate.copernicus.eu/climate-reanalysis\u003c/span\u003e\u003cspan address=\"https://climate.copernicus.eu/climate-reanalysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. JRA55 reanalysis is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jra.kishou.go.jp/JRA-55/index_en.html\u003c/span\u003e\u003cspan address=\"https://jra.kishou.go.jp/JRA-55/index_en.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and is downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rda.ucar.edu/datasets/ds628.1/\u003c/span\u003e\u003cspan address=\"https://rda.ucar.edu/datasets/ds628.1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. HadCRUT5 data set is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metoffice.gov.uk/hadobs/hadcrut5/\u003c/span\u003e\u003cspan address=\"https://www.metoffice.gov.uk/hadobs/hadcrut5/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. GISSTEMP v4 is available at at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.giss.nasa.gov/gistemp/\u003c/span\u003e\u003cspan address=\"https://data.giss.nasa.gov/gistemp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. NOAAGlobalTemp is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp\u003c/span\u003e\u003cspan address=\"https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. BEST is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://berkeleyearth.org/data/\u003c/span\u003e\u003cspan address=\"https://berkeleyearth.org/data/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The CMIP6 and DAMIP simulations analyzed in this study are versions archived at the Centre for Environmental Data Analysis (CEDA) and they are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://help.ceda.ac.uk/article/4801-cmip6-data\u003c/span\u003e\u003cspan address=\"https://help.ceda.ac.uk/article/4801-cmip6-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eAll relevant codes used in this work are available, upon request, from the corresponding author B.D.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eERA5 reanalysis is available at https://climate.copernicus.eu/climate-reanalysis. JRA55 reanalysis is available at https://jra.kishou.go.jp/JRA-55/index_en.html and is downloaded from https://rda.ucar.edu/datasets/ds628.1/. HadCRUT5 data set is available at https://www.metoffice.gov.uk/hadobs/hadcrut5/. GISSTEMP v4 is available at at https://data.giss.nasa.gov/gistemp/. NOAAGlobalTemp is available at https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp. BEST is available at https://berkeleyearth.org/data/. The CMIP6 and DAMIP simulations analyzed in this study are versions archived at the Centre for Environmental Data Analysis (CEDA) and they are available at https://help.ceda.ac.uk/article/4801-cmip6-data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant codes used in this work are available, upon\u0026nbsp;request, from the corresponding author B.D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Environment Research Council (NERC)\u0026nbsp;Climate Change in the Arctic-North Atlantic Region and Impacts on the UK\u0026nbsp;(CANARI) project (NE/W004981/1) and the\u0026nbsp;Towards an Integrated Capability to Explain and Predict Regional Climate Changes (EXPECT) project by the European Union\u0026apos;s Horizon Europe research and innovation programme under grant agreement no.101137656.\u0026nbsp;\u0026nbsp;BD and RS are supported by the UK National Centre for Atmospheric Science, funded by the Natural Environment Research Council. We acknowledge the World Climate Research Programme, which through its Working Group on Coupled Modelling, coordinated and promoted CMIP6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBD and RS designed research. BD carried out analysis. BD and RS worked together on the interpretation of the results and wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVan Oldenborgh, G. J. et al. Western Europe is warming much faster than expected. Clim. 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Geoscientific Model Development, \u003cem\u003e13\u003c/em\u003e(12), 6165\u0026ndash;6200 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eLinear trends of SAT indices and the 95% confidence interval (CI) in observations/reanalyses and CMIP6 (DAMIP) simulations\u003c/b\u003e. Multi-data mean linear SAT trends (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) based on four observations and ERA5 reanalysis during 1979\u0026ndash;2022 and 1979\u0026ndash;2020, and CMIP6 (DAMIP) multi-model ensembles during 1979\u0026ndash;2020 over Europe, Global, and their differences (Eu-GL, excess European warming) and uncertainty. SUM is the sum of model responses to GHG, AER, and NAT forcings. See Methods for details.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs (79\u0026thinsp;\u0026minus;\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObs (79\u0026thinsp;\u0026minus;\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCMIP6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGHG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSUM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDJF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.024\u0026thinsp;\u0026plusmn;\u0026thinsp;0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.062\u0026thinsp;\u0026plusmn;\u0026thinsp;0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.014\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEu-GL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.085\u0026thinsp;\u0026plusmn;\u0026thinsp;0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.038\u0026thinsp;\u0026plusmn;\u0026thinsp;0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.042\u0026thinsp;\u0026plusmn;\u0026thinsp;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eJJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.044\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.023\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEu-GL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.021\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eLinear trends SAT, SAT trend due to circulation.\u003c/b\u003e Linear SAT trends (\u003csup\u003eo\u003c/sup\u003eC decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in ERA5 reanalysis during 1979\u0026ndash;2022 and 1979\u0026ndash;2020, CMIP6 multi-model ensemble mean trend and the 95% confidence interval (CI) during 1979\u0026ndash;2020 over Europe (Total), and relative contributions of changes in circulations to the trends over Europe (Circ), residual trends (Residual), differences in trends between Europe and global (Eu-GL, excess European warming), percentage contributions of circulation induced SAT change to SAT trends over Europe (Circ/Eu) and to excess European warming (Circ/(Eu-GL). See Methods for details.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope (Total)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEurope (Circ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEurope (Residual)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEu-GL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEu/GL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCirc/Eu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCirc/\u003c/p\u003e \u003cp\u003e(Eu-GL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDJF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERA5\u003c/p\u003e \u003cp\u003e(79\u0026thinsp;\u0026minus;\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERA5\u003c/p\u003e \u003cp\u003e(79\u0026thinsp;\u0026minus;\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026plusmn;\u003c/p\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eJJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERA5\u003c/p\u003e \u003cp\u003e(79\u0026thinsp;\u0026minus;\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERA5\u003c/p\u003e \u003cp\u003e(79\u0026thinsp;\u0026minus;\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e 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Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4523385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4523385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOver the period 1979\u0026ndash;2022, European surface air temperatures warmed around three times faster than global mean temperatures in both winter and summer. Here we define \u0026ldquo;excess\u0026rdquo; European warming as the difference between the rate of European regional warming and the rate of global warming and investigate the causes. We estimate that about 40% (in winter) and 29% (in summer) of excess European warming is \u0026ldquo;dynamical\u0026rdquo; - attributable to changes in atmospheric circulation. We show that the rate of European warming simulated in CMIP6 models compares well with the observations, but only because these models warm too fast in the global mean; excess European warming is underestimated, particularly in winter. The CMIP6 models simulate well the magnitude of the thermodynamic component of excess European warming since 1979 in both winter and summer, but do not simulate the dynamical contribution. The models suggest greenhouse gas induced warming made the largest contribution to excess thermodynamic warming in winter, whereas changes in anthropogenic aerosols made the largest contribution in summer. They also imply a substantially reduced future rate of excess European warming in summer. However, the failure of current models to simulate observed circulation trends also implies large uncertainty in future rates of European warming.\u003c/p\u003e","manuscriptTitle":"Anthropogenic influence on excess warming in Europe during recent decades","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 08:16:26","doi":"10.21203/rs.3.rs-4523385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-07-16T09:10:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-09T08:49:37+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-03T21:06:56+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-01T10:28:57+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-12T11:47:26+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-11T14:47:05+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-11T12:41:41+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-06-06T09:38:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-05T06:29:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-04T14:04:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Climate and Atmospheric Science","date":"2024-06-03T17:17:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04fafab2-e006-4fff-98cc-f96b6e7aabb1","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32898589,"name":"Earth and environmental sciences/Climate sciences/Climate change/Attribution"},{"id":32898590,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics"}],"tags":[],"updatedAt":"2025-02-06T08:13:30+00:00","versionOfRecord":{"articleIdentity":"rs-4523385","link":"https://doi.org/10.1038/s41612-025-00930-3","journal":{"identity":"npj-climate-and-atmospheric-science","isVorOnly":false,"title":"npj Climate and Atmospheric Science"},"publishedOn":"2025-02-05 05:00:00","publishedOnDateReadable":"February 5th, 2025"},"versionCreatedAt":"2024-06-20 08:16:26","video":"","vorDoi":"10.1038/s41612-025-00930-3","vorDoiUrl":"https://doi.org/10.1038/s41612-025-00930-3","workflowStages":[]},"version":"v1","identity":"rs-4523385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4523385","identity":"rs-4523385","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}