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A new detection and attribution method is presented and applied to the global mean surface air temperature (GSAT) from 1900 to 2014. The method aims at attributing the climate changes to the variations of greenhouse gases, anthropogenic aerosols, and natural forcings. A convolutional neural network (CNN) is trained using the simulated GSAT from historical and single‐forcing simulations of 12 climate models. Then, we perform a backward optimization with the CNN to estimate the attributable GSAT changes. Such a method does not assume additivity in the effects of the forcings. The uncertainty in the attributable GSAT is estimated by sampling different starting points from single‐forcing simulations and repeating the backward optimization. To evaluate this new method, the attributable GSAT changes are also calculated using the regularized optimal fingerprinting (ROF) method. Using synthetic non‐additive data, we first find that the neural network‐based method estimates attributable changes better than ROF. When using GSAT data from climate model, the attributable anomalies are similar for both methods, which might reflect that the influence of forcing is mainly additive for the GSAT. However, we found that the uncertainties given both methods are different. The new method presented here can be adapted and extended in future work, to investigate the non‐additive changes found at the local scale or on other physical variables. Plain Language Summary: In order to design effective adaptation policies, it is essential to have reliable estimates of the effect of anthropogenic activities on the climate. For that purpose, a new attribution method based on a neural network is designed and evaluated. The method estimates the past global mean surface air temperatures anomalies caused by the changes in the greenhouse gases concentration, the variation of anthropogenic aerosols, and the variations driven by naturally occurring phenomena. To build this estimation, the data from observations and climate models are used. This methodology is compared with another state‐of‐the‐art method. The results of both methods are evaluated and discussed. The proposed method provides better estimations in the case of large non‐additivity of the causes of climate change and can be applied to other physical variables or at the regional scale. In the case of the global mean surface air temperature, the method presented provides estimation similar to other methods. Key Points: We present a non linear method based on neural network to attribute the global mean surface air temperature variability to different forcingsWe use a convolutional neural network associated with a backward optimization to estimate the climate response to the different external forcingsThe attributable forcings are consistent with those obtained using another state‐of‐the‐art method [ABSTRACT FROM AUTHOR]

Future changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analyzing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro‐Mediterranean temperatures explain up to 60% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro‐Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro‐Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in the opposite storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that Sahel precipitation uncertainty will not be substantially reduced unless the uncertainty in the future warming of the North Atlantic and the Euro‐Mediterranean areas is constrained. Plain Language Summary: Variations in the strength of the West African Monsoon (WAM) circulation have societal impacts on around 80 million people from Senegal to Chad. However, future projections of the WAM precipitation are uncertain for the end of the 21st century because of strong differences from one climate model to another. We show here that sources of uncertainty in Sahel precipitation depend on changes in North Atlantic and Euro‐Mediterranean temperature, relative to changes in global mean surface air temperature. We show that differences in how climate models simulate the future warming over the North Atlantic and Euro‐Mediterranean area explain a large proportion of the uncertainty in precipitation change over the central Sahel. We provide a method that could be helpful at selecting models for impact studies, based on their sensitivity to climate change over the North Atlantic ocean and Mediterranean Sea. Key Points: Future changes in Sahel precipitation are uncertainChanges in North Atlantic and Euro‐Mediterranean temperatures explain up to 60% of the Sahel precipitation change uncertaintyUncertainty in changes in Sahel precipitation is associated with uncertainty in the future northward shift of the Saharan heat low [ABSTRACT FROM AUTHOR]

Biophysical effects of forest cover changes are often neglected by climate policies and recent state‐of‐the‐art climate models exhibit wide spreads in simulating the biophysical impacts of deforestation. By using the CMIP6‐LUMIP simulations, here we examined the biophysical impacts of deforestation on global temperature and attributed deforestation‐induced surface temperature change to different biophysical effects (i.e., radiative forcing, aerodynamic resistance, Bowen ratio and atmospheric feedbacks) at regional scales. Results show that models agree on the sign of temperature responses to different biophysical factors in the tropics, but exhibit wide divergence in the extratropical regions. Among the three local biophysical factors (i.e., radiative forcing, aerodynamic resistance, and Bowen ratio), aerodynamic resistance contributes largely to local surface warming in models. As the local effects rarely affect the areas away from the deforested regions, much of the modeled discrepancies result from non‐local atmospheric feedbacks in the middle and high latitudes. Our results suggest that climate responses to deforestation have a large spread in current models and highlight the need to improve our understanding and modeling of non‐local effects in the biophysical impacts of deforestation. Plain Language Summary: The Land Use Model Intercomparison Project (LUMIP) multimodel simulations with a common experimental protocol provide a great opportunity to explore the biophysical effects of land use and land cover change and allow a direct comparison of model responses to deforestation. By analyzing the CMIP6‐LUMIP multimodel simulations, we show that deforestation causes widespread summer warming in the tropics, but the simulated temperature changes in response to deforestation diverge in the middle and high latitudes. We further attributed deforestation‐induced surface temperature change to different biophysical effects (radiative forcing, aerodynamic resistance, Bowen ratio and atmospheric feedbacks). Our results indicate that atmospheric feedbacks play the dominant role in determining the temperature responses, and aerodynamic resistance also matters. The atmospheric feedbacks in the temperate and boreal regions are highly model‐dependent. Further efforts to explore the non‐local influence of temperate deforestation using state‐of‐the‐art climate models are needed. Key Points: Our study assesses different biophysical impacts of deforestation on surface temperature in CMIP6 modelsCMIP6 models show wide divergence on the surface temperature changes in the extratropical regions due to non‐local atmospheric feedbacksChanges in the aerodynamic resistance dominate the surface temperature response to local biophysical effects of deforestation [ABSTRACT FROM AUTHOR]

European climate variability is shaped by atmospheric dynamics over the North Atlantic and local processes. Better understanding their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We explore atmospheric seasonality over 1979–2017 and 1979–2100 with seasonal circulation regimes (SCRs), by clustering year‐round daily fields of Z500 from the ERA‐Interim reanalysis and 12 Coupled Model Intercomparison Project phase 5 (CMIP5) climate models (historical and RCP8.5 runs). The spatial and temporal variability of SCR structures and associated patterns of surface air temperature are investigated. Climate models have biases but reproduce structures and evolutions of SCRs similar to the reanalysis over 1979–2017: decreasing frequency of winter conditions (starting later and ending earlier in the year) and the opposite for summer conditions. These changes are stronger over 1979–2100 than over 1979–2017, associated with a large increase of North Atlantic seasonal mean Z500 and temperature. When using more SCRs (more freedom in definition of seasonality), the changes over 1979–2100 correspond to a long‐term swap between SCRs, resulting in similar structures (annual cycle and spatial patterns) relative to the evolution of seasonal mean Z500 and temperature. To understand whether the evolution of SCRs is linked to uniform warming, or to changes in circulation patterns, we remove the calendar trend in the Z500 regional average to define SCRs based on detrended data (d‐SCRs). The temporal properties of d‐SCRs appear almost constant whereas their spatial patterns change, indicating that the calendar Z500 regional trend drives the evolutions of SCRs, and that changing spatial patterns in d‐SCRs account for the heterogeneity of this trend. Our study suggests that historical winter conditions will continue to decrease in the future while historical summer conditions continue to increase. However, it also suggests that the spatial and temporal patterns of SCRs would remain similar, relatively to the year‐round Z500 increase. [ABSTRACT FROM AUTHOR]

Early summer (May–June) is the season where the surface air temperature (SAT) variability is largest and may result in extreme temperature conditions over Pakistan. Therefore, we analysed the early summer interannual SAT variability over Pakistan for the period 1981–2018 using observational dataset and model simulations. We noted that upper‐level anticyclonic circulation anomalies over Pakistan, which are associated with upper to the middle tropospheric descending motion, favour clear skies with an increase in net shortwave radiation that leads to extreme surface warming over the region. Moreover, the El Niño–Southern Oscillation (ENSO) based on Niño3.4 SST index is negatively associated with SAT anomalies over Pakistan. The cold ENSO phase favours the positive geopotential height anomalies due to the strengthening of the Walker circulation that enhance the sinking motion and result in less cloudiness leading to the extreme higher surface temperature conditions over Pakistan, while the opposite happens in the warm ENSO phase. Moreover, the Saudi‐KAU Atmospheric Global Climate Model (AGCM) simulated large‐scale patterns that are in good agreement with the observations. Sensitivity experiments with the AGCM confirm that cold SST anomalies in the ENSO region significantly favour above‐normal SAT anomalies over Pakistan, while the opposite happens in the warm ENSO phase. These results are important to understand and potentially predict regional heatwaves over the South Asian region, particularly over Pakistan. [ABSTRACT FROM AUTHOR]

Observations have shown subseasonal reversal of temperature anomalies between early and late winter over Eurasia, which is distinct from the seasonal mean condition. Based on the reanalysis data, the 1800‐year control simulation and the 40‐member ensemble simulations in 1920–2100 from the Community Earth System Model (CESM) Large Ensemble (CESM‐LE), this study reveals that the reversal of surface air temperature (SAT) anomalies between early and late winter is one of the dominant and intrinsic features of the Arctic‐Eurasian winter climate. Such a reversal is characterized by "colder Arctic, warmer Eurasia" in December (January–February) and "warmer Arctic, colder Eurasia" in January–February (December). Robust climate dynamic processes associated with the reversal of SAT anomalies, including subseasonal reversals of anomalies in the Ural blocking, midlatitude westerlies, and stratospheric polar vortex, are found in both reanalysis data and CESM simulations, indicating the important role of internal atmospheric variability. Further analysis reveals that the reversal of Ural blocking anomalies in late December can be a potential precursor for the reversal of SAT anomalies in late winter. The reversal of midlatitude westerly wind anomalies associated with the Ural blocking can affect upward propagation of planetary‐scale waves especially with wavenumber 1, subsequently promoting the contribution of stratospheric polar vortex to the reversal of SAT anomalies in late winter over the Arctic‐Eurasian regions. Such a troposphere‐stratosphere pathway triggered by the perturbation of tropospheric circulations is confirmed by the CESM‐LE simulations, and it may be useful for the prediction of subseasonal reversal of SAT anomalies. Plain Language Summary: Observations show that Eurasia may experience subseasonal reverse temperature anomalies from early to late winter. For example, the warmer‐than‐normal conditions in early winter can change to colder‐than‐normal conditions in late winter. Such a reverse temperature anomaly in Eurasia is usually concurrent with a reverse one in the Arctic. The "colder Arctic, warmer Eurasia" pattern in early winter followed by "warmer Arctic, colder Eurasia" pattern in late winter is robustly detected in climate model simulations, indicating that the subseasonal reversal of temperature anomalies is one of the intrinsic features of the Arctic‐Eurasian climate. This feature is associated with robust climate dynamic processes, including subseasonal reversals of the anomalies in the Ural blocking, the midlatitude westerly winds, and the stratospheric polar vortex. Because the Ural blocking can trigger the troposphere‐stratosphere interaction, the reversal of Ural blocking anomalies in late December can be a potential precursor for the reversal of surface air temperature anomalies in late winter. Key Points: Reversal of surface air temperature (SAT) anomalies between early and late winter is an intrinsic feature of the Arctic‐Eurasian climateThe changes of Ural blocking in late December may indicate whether the SAT in late winter will be reversed to early winterRobust climate dynamic processes associated with such a reversal of SAT are found in both reanalysis data and long‐term model simulations [ABSTRACT FROM AUTHOR]

Climate reconstructions by data assimilation need to accommodate for sensitivities to proxies and prior estimates because models are uncertain and proxies are spatiotemporally limited. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (i.e., corals, ice cores, and tree‐ring cellulose). Experiments were conducted using an offline data assimilation approach; the results showed annual variations in the global distribution of surface air temperature and precipitation amount from 850 to 2000. Standard deviations of surface air temperature and precipitation amount during the entire period differed by up to 36% due to prior estimates. Experiments with different types of proxies showed that the El Niño‐like distribution of positive anomalies in the central to eastern tropical Pacific may only be adequately reproduced in experiments with corals, and not experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55‐year Reanalysis (JRA‐55) was 0.81 at maximum. By contrast, the correlation coefficient between experiments without corals and JRA‐55 was a maximum of 0.19. Key Points: Climate reconstruction by data assimilation illustrates annual variations in surface air temperature and precipitation from 850 to 2,000Spatiotemporal differences in climate reconstruction were compared based on prior estimatesThe impact of proxies on the results of past reproduced El Niño cases from 1971 to 2000 was validated [ABSTRACT FROM AUTHOR]

In this study, a dynamically downscaled regional climate model (RegCM4.3) is used to study the Indian summer monsoon (ISM) surface air temperature over the South‐Asia CORDEX domain using six convection schemes during 1986–2010. The spatial and temporal variability of mean surface air temperature has been analysed with reference to the India Meteorological Department (IMD) analysis data using various statistical scores. The sensitivity experiments in selecting the best convective parameterized schemes have been performed in simulating the surface air temperature during the summer monsoon season (June–September) over India and its five sub‐regions such as Northwest India, Northcentral India, West Peninsular India, Eastern Peninsular India, and Southern Peninsular India. The model results show the tendency of overestimation of surface air temperature mainly in four cumulus parameterization schemes (CPSs) that is, Tiedtke, Emanuel, Mix98, and Mix99 of RegCM4.3 during the JJAS, where Grell and Kuo CPSs show better agreement with the IMD data. Overall, Grell CPS has a close resemblance to the observation data with a minimum root mean square error, mean absolute error, lowest mean absolute percentage error (MAPE), and higher correlation coefficient. The model simulated results have also been investigated further using modified Nash Sutcliffe efficiency and modified Willmott's degree of index. These analyses confirm the potentiality of the Grell CPS followed by the Kuo CPS in simulating interannual variability of the surface air temperature over Indian and its five sub‐regions. The MAPE in Grell and Kuo CPSs are 0.004 and 0.013°C during monsoon season over India, respectively. The inter‐scheme difference in simulating surface air temperature is linked with the generation of low cloud convection and warming‐induced atmospheric moisture advection in the schemes. Therefore, Emanuel, Tiedtke, and Mix98 CPSs have shown a persistent nature of overestimation in surface air temperature variability during JJAS. It is also inferred that after removing the systematic mean bias from the RegCM4.3 model simulated outputs; the skill of Emanuel, Mix98, and Mix99 could be useful over the Indian subcontinent except for the southern peninsular region. [ABSTRACT FROM AUTHOR]