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Changes in the soil freeze–thaw status will inevitably affect the thermal conditions and properties of the Tibetan Plateau (TP), thereby affecting its upper atmosphere, and further afield in East Asia and even globally. In this study, using the soil temperature simulated by the Community Land Model Version 5.0 (CLM5.0), the timing and duration of the soil freeze–thaw status were divided into freeze start‐date, freeze end‐date and freeze duration. Then, using linear trend estimation, correlation analysis and other methods, the changes in the spatiotemporal distribution of the timing and duration of the soil freeze–thaw status from 1979 to 2018 over the TP were analysed, and the relationships between them and surface temperature, altitude and latitude were analysed. The results obtained were as follows: (1) The soil temperature simulated by CLM5.0 can reasonably reproduce the seasonal changes in multilayer soil temperature, and correlated well with observations. Simulated by CLM5.0 of the time and duration of the soil freeze–thaw status also correlated well with observations. (2) The spatial distribution of the soil freeze–thaw status is characterized by a trend of delayed freezing, advanced thawing and shortened freeze duration from northwest to southeast over the TP. From 1979 to 2018, the freeze start‐date postponed by 7.3 days and became delayed at a rate of 1.9 days per decade, while the freeze end‐date advanced by 6.4 days at a rate of 1.7 days per decade, and the freeze duration shortened by 13.7 days at a rate of 3.6 days per decade. The timing and duration of the soil freeze–thaw status vary across different regions of the TP. The freeze start‐date in all areas of the TP has been delayed in the past 39 years. Except for the subcold zone and arid regions of the TP, the freeze end‐date has occurred earlier and the freeze duration has shortened, with the most significant changes in the subcold zone and humid regions, while the freeze end‐date has advanced at a rate of 3.6 days per decade and the freeze duration has shortened at a rate of 6.3 days per decade. (3) The timing and duration of the soil freeze–thaw status are significantly correlated with surface air temperature, elevation and latitude, exceeding the 99% confidence level. The correlation between the timing and duration of the soil freeze–thaw status and surface temperature is strongest, followed by altitude, and correlation with latitude is weaker. The correlation between surface air temperature and the timing and duration of the soil freeze–thaw status in the western TP is stronger than that in the eastern TP. The rate of change in the soil freeze–thaw status increases with altitude to 3000 m above sea level, while this rate decreases with elevation above 3000 m. The rate of change in the soil freeze–thaw status is greatest at 29°N, while the rate of delay in the freeze start‐date is minimal at 33°N and the rates of advancement in the freeze end‐date and shortening of the freeze duration are minimal at 35°N. [ABSTRACT FROM AUTHOR]

Over recent decades, the southeastern United States (Southeast) has become increasingly well represented by the terrestrial climate proxy record. However, while the paleo proxy records capture the region's hydroclimatic history over the last several centuries, the understanding of near surface air temperature variability is confined to the comparatively shorter observational period (1895‐present). Here, we detail the application of blue intensity (BI) methods on a network of tree‐ring collections and examine their utility for producing robust paleotemperature estimates. Results indicate that maximum latewood BI (LWBI) chronologies exhibit positive and temporally stable correlations (r = 0.28–0.54, p < 0.01) with summer maximum temperatures. As such, we use a network of LWBI chronologies to reconstruct August‐September average maximum temperatures for the Southeast spanning the period 1760–2010 CE. Our work demonstrates the utility of applying novel dendrochronological techniques to improve the understanding of the multi‐centennial temperature history of the Southeast. Plain Language Summary: Tree‐ring data are important sources of paleoclimate information, which allow for the longer‐term evaluation of modern climate values and trends. Compared to much of North America, the Southeastern United States (Southeast) contains substantially fewer paleoclimate records from tree rings, and no estimates of past temperature variability which extend before the observational period. Employing a recently developed technique, which uses light reflectance properties of wood to obtain a representative metric of tree‐ring density, we develop a network of temperature‐sensitive tree‐ring records across the Southeast. These records enable us to reconstruct late summer maximum temperatures across the region spanning the period 1760–2023 CE. As few ground‐based, pre‐instrumental temperature records previously existed for this region, our reconstruction allows for an improved understanding of the region's multi‐centennial climatic history. Key Points: Maximum latewood blue intensity from tree rings can effectively be used to develop paleotemperature estimates for the southeastern USThe fidelity of tree‐ring density parameters for paleoclimate reconstruction are influenced by disturbance regimes and microsite conditionsCompared to the last 260 years, regional 20th‐century maximum late summer temperatures are not characterized by unprecedented positive trend [ABSTRACT FROM AUTHOR]

This study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe‐shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30‐year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO‐related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO‐related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO‐related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO‐related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO‐related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections. [ABSTRACT FROM AUTHOR]

The sporadic E‐layer (Es) exhibits unique opportunity for exploring the coupling from lower to upper atmosphere. It was found that the East Asia is with the highest intensity and occurrence probability of Es. By using the long‐term data of 21 ionosonde stations in China and Japan over the past 60 yrs, this paper explores the probable control on the Es layer from the lower layers. It is found that the intensity of the Es layer is strongly correlated with the surface atmospheric temperature, terrain, and land‐sea boundary. The correlation coefficient of the intensity of Es with surface temperature is as high as 0.8204, while that with the terrain and land‐sea boundary is up to 0.6668. Based on the coupling between the lower and upper atmosphere, this paper reveals the probable controls from lower layers on the intensity of the Es in East Asia. Plain Language Summary: As an occasional ionized layer of the ionosphere, the sporadic E‐layer (Es) exhibits unique opportunity for exploring the coupling from lower to upper atmosphere. Despite extensive research on both behavior and mechanism of the Es, none have adequately explained the strong inhomogeneous of its global morphology. It is found in fact that the East Asia is with the highest intensity and occurrence probability of Es. By using the long‐term data of 21 ionosondes in China and Japan over the past 60 yrs, this letter explores the formation mechanism of the strongest Es regions in the world. It is found that the intensity of the Es layer is strongly correlated with the surface atmospheric temperature, terrain, and land‐sea boundary. The correlation coefficient between the intensity of Es and surface temperature is highest, while that with the terrain and land‐sea boundary is also high. By analyzing the data as long as six decades, this letter reveals the probable controls from lower layers on the intensity of the Es in East Asia. Key Points: By analyzing the six‐decade data of 21 ionosondes in East Asia, it reveals probable controls on the strongest Es layer from lower layersFor the first time, it is found that the intensity of Es layer is highly correlated with the surface atmospheric temperatureAn interesting coupling from lower to upper atmosphere may be found by taking advantage of observations longer than half of a century [ABSTRACT FROM AUTHOR]

Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications. [ABSTRACT FROM AUTHOR]

The out‐of‐phase mode of winter surface air temperature anomalies (SATAs) over northern Central Asia (NCA; 45°–65°N, 40°–100°E) between January and February is investigated in this study. This mode corresponds to warm (cold) SATAs in January (February) over NCA and is mainly modulated by the enhanced tropical convection anomalies over the Maritime Continent in previous late December, associated with MJO phase 4. These tropical convection anomalies can induce anomalous tropospheric Rossby‐wave sources over the North Pacific in late December. The eastward‐propagating Rossby‐wave train, triggered by these anomalous Rossby‐wave sources, can cause negative and positive tropospheric geopotential height anomalies over the Greenland–Scandinavia region and NCA in the following early–mid‐January, subsequently leading to warm SATAs over NCA in January. The negative geopotential height anomalies over the Greenland–Scandinavia region in early–mid‐January can trigger upward‐propagating wave activity fluxes (WAFs) into the stratosphere, resulting in negative stratospheric geopotential height anomalies in late January–early February. These stratospheric anomalies, by triggering downward‐propagating WAFs, can in turn lead to positive tropospheric geopotential height anomalies over the Greenland–Scandinavia region in early February. These anomalies over the Greenland–Scandinavia region can maintain themselves in the following mid‐ and late February by feedback of anomalous storm tracks, and cause negative geopotential height anomalies and subsequently cold SATAs over NCA in February by triggered southeastward‐propagating Rossby‐wave train. [ABSTRACT FROM AUTHOR]

A significant and striking seesaw pattern of winter surface air temperature (SAT) has emerged, featuring pronounced warming Arctic and cooling Eurasian (referred to as WACE). This study investigates the subseasonal SAT modes across the mid‐ and high‐latitudes of Eurasia and their possible mechanisms based on daily reanalysis data from 1979 to 2022. Our results reveal that Eurasian winter SAT exhibits two distinct subseasonal modes, characterized by a correlated southeastward propagation of temperature and geopotential height anomalies (GHAs) in the middle and lower troposphere. Notably, the subseasonal SAT anomalies with eight phases constrained by the hydrostatic equilibrium, originate from the GHAs in the Arctic stratosphere and then transfer to the East Asia. The sixth phase of the transmitted subseasonal SAT mode is proved to be the key transition phase from the WACE pattern to its counterpart. Further analysis indicates that the strength of the transmitted subseasonal SAT mode is controlled by the tripolar sea surface turbulent heat flux anomalies over the north Atlantic. Plain Language Summary: Over recent decades, Earth's changing climate has brought about some significant temperature anomalies, particularly in the Arctic and mid‐latitude regions. This has led to severe socio‐ecological consequences, including increased damage to our ecosystems. A noticeable pattern in winter temperature is the warm Arctic‐cold Eurasian mode, which has been observed on multiple time scales. Here we show, on the subseasonal time scale, during the winter in Eurasia, there are two different patterns of temperature changes, and they move together toward the southeast in the troposphere. One of these patterns, called WACE, goes through a repeating cycle from the Arctic to East Asia. We specifically looked at the reversal phase of this cycle, where we found that changes in the stratosphere above the Arctic region kickstart the process, and then the turbulent heat near the surface contribute to the changes in the polar region by triggering a Rossby wave. Key Points: The detailed southeastward propagation of a transmitted subseasonal Eurasia winter surface air temperature (SAT) mode is revealedThe subseasonal SAT mode originates from the geopotential height anomaly in Arctic stratosphere constrained by the hydrostatic equilibriumThe strength of the subseasonal SAT mode is controlled by the tripolar sea surface turbulent heat flux anomalies over the North Atlantic [ABSTRACT FROM AUTHOR]

Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i‐GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i‐GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i‐GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model‐simulated forced SAT trend patterns. Plain Language Summary: When comparing model simulations of Earth's recent warming to real‐world observations, differences may arise from several factors. Two important factors are the model errors in the simulated response to increased greenhouse gases, and natural fluctuations within the climate system that produced discrepancies between observations and models. Thus, quantifying the role of these natural fluctuations is important for the assessment of model‐observation differences. Previous studies have shown that natural climate variability has depressed global warming and enhanced Arctic warming. By compositing the multi‐decadal trend patterns from CMIP6 simulations in which natural variability warms the Arctic but has a global cooling effect, we find that the majority of these model simulations also produce enhanced warming in the Barents and Kara Seas and cooling in the Tropical Eastern Pacific and Southern Ocean due to natural variability. Since these are the exact features imprinted on observed surface temperature changes over 1980–2022, our work suggests that natural variability is an important component of several noteworthy differences between models and observations. Key Points: Internal variability has enhanced Arctic warming but suppressed global warming over 1980–2022This manifestation of internal variability is rare in model simulations but has a robust global surface air temperature (SAT) trend patternThis internal SAT pattern features warming in the Barents and Kara Sea and cooling of the Tropical Eastern Pacific and Southern Ocean [ABSTRACT FROM AUTHOR]

Under global warming, the summer surface air temperature (SAT) change signal in the northern mid‐latitudes is generally the most significant, while the SAT change in North China (NC) shows distinct local characteristics. Using simulations from the Coupled Model Intercomparison Project Phase 6 and observation from Berkeley Earth Surface Temperature, this study reveals an unusually weak signal of summer SAT change in NC since the 1960s. This uniquely weak signal can be attributed to the small net contribution of external forcings, mainly from anthropogenic greenhouse gases (GHG) and aerosols (AA). Compared to other land regions in the same latitudinal band, the GHG‐induced warming was weaker and AA‐induced cooling was stronger in NC, resulting in the weakest SAT change signal and thus the lowest signal‐to‐noise ratio. This weaker GHG‐induced warming plays a more important role in the SAT change difference between NC and other land regions in the same latitudinal zone. Under different emission scenarios in the future, the signal‐to‐noise ratio in NC will become as large as those in the other land regions in the same latitudinal band and the northern hemisphere, which is partly due to the smaller relative differences in the SAT change signal between NC and the other two regions. The projections of SAT change indicate that climate change in NC will probably become more violent and vulnerable. [ABSTRACT FROM AUTHOR]

This study focuses on the interannual variability of winter mean surface air temperature (SAT) and subseasonal SAT reversal in North China, which have profound impacts on national life and economic activities. The analysis explores the temporal and spatial distribution characteristics of these variations and their relationship with the Eurasian Zonal Circulation (EZ). The findings reveal distinct interdecadal changes in North China's winter mean SAT, along with a general subseasonal reversal of cold and warm conditions. In comparison to the North Atlantic oscillation/Arctic Oscillation, the EZ has a more significant influence on the interannual variability of winter and subseasonal reversals. Strong EZ years are associated with the weakening of the Siberian High and the Ural Blocking High, causing the East Asian Trough to move eastward, which leads to the retention of cold air from the north and increased SAT in North China. Conversely, weak EZ years exhibit opposite circulation patterns. The winter mean EZ is influenced by preceding autumn Arctic sea ice concentrations (SIC). Reduced autumn SIC results in decreased heat flux and reduced eddy energy, leading to substantial attenuation of the winter storm track across Scandinavia and Western Asia. This attenuation, mediated through the interaction between eddies and mean flow, triggers a slowdown of Eurasian zonal circulation and intensification of the Siberian High. These conditions favor a positive phase of the winter Scandinavia pattern, facilitating the transport of cold Arctic air into North China. Likewise, subseasonal reversals in SIC influence corresponding alterations in the EZ. Plain Language Summary: The winter surface air temperatures (SAT) in North China exhibit notable interdecadal variations and distinct intra‐seasonal changes. These intra‐seasonal variations lead to subseasonal reversals, where early winter may be unusually warm, followed by later winter turning unusually cold, or vice versa. These subseasonal SAT fluctuations are influenced by atmospheric circulation anomalies. In this study, we investigate the spatial and temporal patterns of the mean winter SAT and subseasonal SAT reversals in North China, focusing on their interannual variability. We emphasize the significant impact of anomalous zonal circulation over the middle and high latitudes in Eurasia and explore the relative influence of the Arctic as a boundary forcing factor in circulation anomalies. Key Points: The winter surface air temperature (SAT) and subseasonal SAT reversal in North China have noteworthy interannual variationsThe Eurasian Zonal Circulation (EZ) strongly influences winter SAT and subseasonal reversal variabilityThe physical mechanisms linking autumn Arctic sea ice concentrations to EZ is examined [ABSTRACT FROM AUTHOR]

The relative importance of oceanic modes to global surface air temperature (SAT) and terrestrial precipitation during 1934–2020 was investigated using a variety of statistical and dynamical system methods. Through singular spectrum analysis, we present the distribution of decadal (10–20‐year), multidecadal (20–50‐year), and secular (>50‐year) variabilities of global SAT and terrestrial precipitation anomalies. Three sea surface temperature modes were identified by singular value decomposition that affect the low‐frequency variabilities of global SAT and terrestrial precipitation anomalies—namely, global warming (GW), the Interdecadal Pacific Oscillation (IPO), and the Atlantic Multidecadal Oscillation (AMO). The relative importance GW, the IPO, and the AMO in SAT and terrestrial precipitation was obtained through a normalized multivariate information flow analysis. Besides a high information flow percentage (60%) from GW to global SAT, especially over the northern Indian Ocean and tropical West Pacific, information flow from the AMO to the northwestern Pacific was also found. In terms of terrestrial precipitation, a large area with a wet trend was found over Eurasia at mid‐to‐high latitudes, and this trend was especially remarkable in the boreal winter half‐year (November–April), as compared with that in the boreal summer half‐year (May–October). By employing artificial neutral networks with a self‐organized map to cluster the key patterns of vertically integrated water vapour flux, we found that the synoptic circulation related to the wet trend is characterized by westerly flow that transports water vapour from the northeastern Atlantic to Eurasia, which is favourable for precipitation there both in the boreal winter and summer half‐year. [ABSTRACT FROM AUTHOR]

Indian subcontinent witnessed a rise in surface air temperature (SAT) in recent decades, during the summer months of March, April and May. The monsoon core region (MCR) of India experiences a hot and humid climate, with temperatures typically highest in May and June before the onset of the monsoon. Global climate model (GCM) simulations of SAT are very much essential to understand the future climate of Indian MCR. Biases in GCMs simulations are due to insufficient knowledge of parameterizations and various assumptions that are made to simulate the complex interactions between land, ocean and atmosphere. The objective of this study is to correct the bias in the Coupled Model Intercomparison Project Phase 6 (CMIP6)–GCM simulations of SAT during March, April and May months over MCR for the historical period 1985–2014 and shared socio‐economic pathways (SSPs) SSP2‐4.5 and SSP5‐8.5 for the period 2015–2100. SAT dataset of fifth‐generation reanalysis (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used as reference dataset to perform bias correction for the historical period. Preliminary investigation of both SAT datasets has shown that there exists considerable warm bias (1.47°C) over the MCR. Bias correction is performed using a one‐dimensional convolutional neural network (CNN‐1D) and a convolutional long short‐term memory network (CNN‐LSTM) deep learning algorithm. The performance of these algorithms is evaluated with the statistical metrics such as root‐mean‐square error (RMSE), normalized root‐mean‐square error, Nash–Sutcliffe efficiency, mean absolute error, percent bias, correlation coefficient and dynamic time warping. RMSE and percent bias were decreased to 0.35°C and 0.8% with CNN‐LSTM algorithm. The CNN‐LSTM algorithm also preserves the year‐to‐year variability of SAT. Hence, CNN‐LSTM algorithm is found to be suitable for the bias correction of GCM simulations of SAT with encouraging results. [ABSTRACT FROM AUTHOR]

The surface air temperature (SAT) trend on the Tibetan Plateau (TP) was 3.45°C 100 years−1 from 1961 to 2014. The multi‐model ensemble (MME) of 33 coupled models participated in the Coupled Model Intercomparison Project phase six (CMIP6) was about 1°C 100 years−1 lower than the observation. Although MME generally shows better skill in reproducing the distribution of SAT trend over TP than most of the CMIP6 models, its performance is greatly degraded by a small group of models, about 12% on average, with large biases. In this paper, the constrained multi‐model ensemble (CMME) based on a certain observation‐based threshold is used to constrain future projections of the SAT trend over TP. Compared with the MME results, the improvements in CMME are mainly over the eastern plateau in historical simulation and are relative to the reduction of the model biases to carbon dioxide (CO2) forcing. Under the high‐emission SSP5‐8.5 scenario, SAT increases significantly over the entire TP. The constraint of CMME on the MME is mainly over the eastern plateau with a difference of 0.5°C 100 years−1, about 6% of the MME results. Under the intermediate‐emission scenario SSP2‐4.5, the effect of CMME is relatively smaller, but the corresponding spatial distribution is similar to that under the SSP5‐8.5 scenario. The CMIP6 models tend to underestimate the warming trend projections over the water source regions in the northeastern plateau and should be noticed. Key Points: The Coupled Model Intercomparison Project phase six model ensemble in surface air temperature trend reproduction on the Tibetan Plateau are greatly degraded by a small group of models with large biasesThe impact of constrained ensemble (CMME) is mainly over the eastern plateau in relative to the reduction of model biases to CO2 forcingThe CMME results indicate the underestimated warming projections in multi‐model ensemble over the northeastern plateau [ABSTRACT FROM AUTHOR]

The Paris Agreement proposed two global warming levels relative to the preindustrial period, with the ideal objective of 1.5°C warming and an upper boundary of 2.0 °C. However, the years when the temperature will first reach 1.5 or 2.0°C vary in different regions. Therefore, climate extremes and their population exposure are still not clear at regional or global warming levels. This study investigated climate extremes in China at different Chinese and global warming with 16 CMIP6 models under the middle SSP245 scenario. In China, the year when 1.5°C warming is projected to occur is 2020 and 2035 for 2.0°C warming. These values are more than 10 years earlier than the corresponding global warming levels of 2030 and 2049. Population exposure to percentile‐based heat extremes at global 1.5°C warming is projected to greatly increase relative to those when 1.5°C warming occurs in China due to increases in climate extremes, and exposure to absolute heat extreme indices is projected to decrease from Chinese to global 2.0°C warming under the joint impacts of increases in extremes and population decreases. Furthermore, from Chinese to global 1.5°C warming, about 344 million people will experience increased exposure to heat, wet and dry extremes, and around 468 and 371 million people will be affected by increased exposure to heat–wet and heat–dry extremes, respectively. Thus, a more adaptive strategy should be proposed to cope with the future possible natural hazards caused by heat–wet and heat–dry extremes. Plain Language Summary: In general, heat extremes are projected to increase in a warmer climate. However, changes in surface air temperature will vary among different regions at regional and global warming levels which might impact future climate extremes and their population exposure. The heat extremes, heavy precipitation amount and consecutive days with no precipitation, and population exposure to these extremes were investigated at global and Chinese 1.5 or 2.0°C warming levels. The years when 1.5 and 2.0°C warming will occur in China are more than 10 years earlier than those of global warming levels, respectively. About 344 million people will be involved in increased exposure to above three types of extremes moving from Chinese to global 1.5°C warming. Thus, more adaptive strategies should be established to cope with the possible increased risks in the future. Key Points: Warming by 1.5 and 2.0°C will occur more than 10 years earlier in China compared with global warming levelsPopulation exposure to percentile‐based heat extremes in China is projected to greatly increase from Chinese to global 1.5°C warmingAbout 400 million people will experience increased exposure to heat–wet and heat–dry extremes from Chinese to global 1.5°C warming [ABSTRACT FROM AUTHOR]

Based on the ERA5 reanalysis since the 1950s, this study investigates the coherent inter‐decadal variations of the winter continental surface air temperature (SAT) between the Eurasian (EUA) and the North American continents in the past 70 years. The SAT variation over the two continents can be characterized with five typical periods successively from a 'both‐warmer' (1950s–1960s), to a 'both‐colder' (1960s–1980s), and then a 'EUA‐warmer' (1980s–1990s) followed by another 'both‐warmer' (1990s–2000s) and a 'EUA‐colder' (2000s–2010s) period. Such periodic variation of the SAT is closely coupled with changes of the low isentropic‐level meridional mass transport (IMMT), as manifested by the coupling relationship between the midlatitude SAT and the net IMMT out of the polar circle, in terms of their zonal patterns and geographical patterns. The SAT variation can be well reconstructed with the first two leading maximum covariance analysis (MCA) modes between the SAT and IMMT. The first leading MCA mode (MCA1) mainly dominates a 'EUA‐warmer/colder' pattern, while the second (MCA2) dominates the 'both‐warmer/colder' pattern. There exists a significant lead–lag correlation between the MCA1 and MCA2, which determines the changing SAT pattern in the five successive periods. Furthermore, the MCA1 and MCA2, especially their lead–lag coupling after the 1975 is significantly associated with the Pacific decadal oscillation (PDO) which dominates the much‐intensified MCA1 and the related 'EUA‐warmer/colder' SAT pattern since then. The Atlantic multi‐decadal oscillation mainly dominates the third leading MCA mode with it's effects on the SAT over Europe coincidently to amplify the PDO‐related SAT changes. [ABSTRACT FROM AUTHOR]

Six gridded temperature datasets (T‐datasets) were evaluated for the first time over the South American continent, through the case study of Bolivia, by comparing them with temperature records acquired from 82 meteorological stations spanning the 1995–2010 period. The comparisons were carried out at the daily time step considering different seasons (annual scale, austral summer and austral winter) and regions (Amazon, La Plata and Altiplano basins). Overall, the climate hazards group infrared temperature with stations (CHIRTS) and the climate prediction centre (CPC) T‐datasets provided the most reliable mean daily temperature (Tmean) and also described well the temporal variability of minimum and maximum daily temperature estimates (Tn and Tx). Tmean, Tn and Tx trends were analysed over the 1983–2016 period to observe temperature temporal evolution across the three regions. Despite some general agreements between the trends (Tmean, Tx and Tn), large discrepancies are also observed. It was found that CPC overestimates and CHIRTS underestimates mean temperature trends and that CPC (CHIRTS) was better than CHIRTS (CPC) to estimate Tx (Tn) trends, both in magnitude and space. Furthermore, opposing trends (i.e., warming and cooling) are described by CPC and CHIRTS for some specific regions, which call into question their reliability for such analyses. These findings highlight the need to validate gridded temperature products with reliable ground data for the regions under study, particularly if they have a wide elevation range. [ABSTRACT FROM AUTHOR]

The CMIP6 projections constitute the basis of our latest understanding of the climate response to anthropogenic forcing. However, there is still considerable uncertainty in the projections, especially at the regional scale. One way to constrain the uncertainty is by comparing the models historical climate change signals against observations and investigate the physical reasons for divergences. Here, we assess the signal‐to‐noise ratio (S/N) of surface air temperature (SAT), precipitation (PREC) and soil moisture (SM) over Europe for a set of CMIP6 historical simulations and compare them against the E‐OBS observational product and the ERA5 reanalysis. We found considerable divergences between the CMIP6 ensemble mean S/N and that of E‐OBS and ERA5, as well as between ERA5 and E‐OBS. The latter indicates that the S/N is affected by data coverage. We show that the differences among model signals are associated with different atmospheric circulation responses. We also investigate the potential relationships between the models' signals and climatological biases, and we found evidence that the models with a warm climatological bias in southern Europe tend to have smaller SAT signals (warm less). Finally, we found no apparent relationship between SM biases and the warming signal, suggesting that the mechanism by which SM–atmosphere interactions affect climate variability does not explain the mean changes. However, there is a tendency for models with higher SM to dry faster than models with lower SM. [ABSTRACT FROM AUTHOR]

Based on the daily average temperature data of 2374 stations in China from 1993 to 2022 and the monthly average temperature data of European Centre for Medium‐Range Weather Forecasts (ECMWF) System5 in winter, this article analyses the intraseasonal variations of winter surface air temperature (SAT) over China by using seasonal empirical orthogonal function decomposition (S‐EOF) and compare the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S‐EOF1) is characterized by out‐of‐phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño‐Southern Oscillation (CP ENSO) can impact S‐EOF1. The second mode (S‐EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai‐Tibet Plateau's variation, while the other areas are characterized by the out‐of‐phase change in SATA. S‐EOF2 is closely related to Arctic oscillation (AO). The third mode is characterized by alternating changes over 3 months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S‐EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well. [ABSTRACT FROM AUTHOR]

The systematic simulated cold biases in models for surface air temperature (SAT) over China have been under discussion for a long time, but the related attribution is still unclear. In this study, we investigate the main contributors and relevant physical processes of SAT biases over China based on 31 models participating in the sixth phase of the Coupled Model Intercomparison Project from a surface energy budget perspective. On annual and seasonal scales, less downward clear‐sky longwave radiation in the models, which is due to the underestimated tropospheric temperature and water vapor amount, is the main cause of cold biases over the Tibetan Plateau, Tarim Basin, Sichuan Basin, and most of eastern China. Meanwhile, the simulation biases in surface albedo and sensible heat flux induce cold biases depending on regions and seasons. In winter and spring, the overestimated surface albedo in association with more snow cover leads to prevalent cold biases over the western Tibetan Plateau and Northeast China. Excessive sensible heat release caused by the overestimated ground–air temperature difference facilitates SAT underestimation in the Sichuan Basin during summer and autumn. In addition, the cold biases in winter and spring are larger than those in summer and autumn, due to the stronger snow cover overestimation and tropospheric temperature underestimation. Plain Language Summary: The global climate model is a complex system used for describing the atmosphere, land surface, ocean, sea ice components, and their interactions. It has been widely applied to investigate climate change and its associated mechanisms. However, there are still deficiencies in current models for surface air temperature (SAT) simulations. The underlying cause for insufficient simulation is a hot topic of research. In past decades, many studies have shown prevalent SAT cold biases over China in most models, while the related mechanisms remain unknown. Based on the state‐of‐the‐art models, we investigate the causes of the cold biases over China from the perspective of a surface energy budget. The results indicate that the downward clear‐sky longwave radiation simulation error due to the bias of atmospheric states is the primary reason, and the cold biases induced by surface albedo and sensible heat flux vary among regions and seasons. Key Points: We investigate the reasons for the simulated surface air temperature cold biases in sixth phase of the Coupled Model Intercomparison Project models over ChinaLess downward clear‐sky longwave radiation due to underestimated tropospheric temperature and water vapor amount is the main causeCold biases induced by surface albedo and sensible heat flux vary among regions and seasons [ABSTRACT FROM AUTHOR]

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]

The dominant pattern of Northern Hemisphere extratropical climate variability is the Northern Annular Mode (NAM), which in wintertime represents the coupling of the stratospheric and tropospheric circulations. The internal variability associated with the NAM has been shown to give rise to substantial uncertainty in climate projections of regional surface air temperatures (SATs), but it's unclear how much of this variability arises from stratosphere‐troposphere coupling processes. Here, using three large‐ensemble coupled climate model simulations from 1850 to 2100 with varying fidelity of stratosphere‐troposphere coupling processes, we demonstrate that regional SAT variability across timescales is amplified when the tropospheric NAM is coupled to the stratospheric NAM. Moreover, models that lack adequate simulation of stratosphere‐troposphere coupling processes may not capture the magnitude of uncertainty in near‐term SAT projections associated with this coupling. Plain Language Summary: Large‐scale atmospheric circulation variability drives substantial variation in regional surface air temperatures (SATs), so that even on 30–40 year timescales local cooling trends are possible even as global‐mean temperatures increase. We provide evidence that when the stratospheric mean flow is coupled to the tropospheric mean flow, the decadal variability of SATs is amplified in some regions. Some models do not represent well the observed coupling of the stratosphere to the troposphere, and these models may underestimate decadal variability associated with this coupling. Key Points: The Northern Annular Mode (NAM) exhibits decadal variability that can accelerate or slow the rate of anthropogenic regional surface warmingAmplified decadal variability of extratropical surface temperatures occurs when the NAM is coupled from the stratosphere to the troposphereModels that fail to adequately capture vertical coupling may underestimate the magnitude of decadal variations in surface air temperatures [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]

In 2021/2022 winter, an intraseasonal surface air temperature (SAT) reversal occurred in China, shifting from regional warm to cold phase in late January 2022. The intraseasonal variations of winter SAT in China are tightly connected with 500 hPa potential height. In particular, the Arctic Oscillation (AO) positive phase is diagnosed to be contributing to the SAT reversal. Under its influence, the intraseasonal build‐up and collapse of blockings over Urals Mountain led to the cold surges in February 2022. Seasonal prediction systems (SPSs) such as NCEP_CFSv2, EC_SYSTEM5 and BCC_CSM1.1 are assessed to have poor predictability in this SAT reversal and similar historical events, owing to not only the poor skills in winter SAT predictions but also the integral inertia in SPSs. Prediction skill only increases as the forecasting time approaches, owing to initial values. Besides, the SPSs can simulate the physical process of the influence on SAT from circulation. The poor predictability is attributed to the poor prediction skills in mid–high latitude circulation pattern and potential height over China. [ABSTRACT FROM AUTHOR]

Quantitative assessment of urbanization effect on surface air temperature (SAT) change provides crucial basis for formal detection and attribution analyses of climate change. However, debates about urbanization‐related warming bias in documented regional SAT trend still persist, mainly due to different determination of rural stations. Here the urbanization effect on SAT change over the Beijing–Tianjin–Hebei region in China during 1980–2019 is estimated through three kinds of ways (i.e., comparisons between urban and rural stations [arithmetically station‐averaged], urban‐dominated and rural‐dominated patches [patch‐weighted mean], and realistic urban and rural areas [area‐weighted mean]). The last method explicitly takes urban and rural land cover fractions into account when calculating urban/rural and regional mean SAT trends. Urbanization‐induced warming in the annual mean SAT change of urban stations (areas) through the three ways are estimated as 0.159°C, 0.195°C, and 0.138°C per decade, respectively. And urbanization effect on regional averaged annual mean SAT calculated by patch‐weighted and area‐weighted methods are 0.113°C and 0.050°C per decade, respectively, which account for 33.8% and 14.8% of the total regional warming. The urbanization effect on observed SAT change estimated by considering realistic urban/rural land cover proportions is much lower than traditional station‐unweighted way. [ABSTRACT FROM AUTHOR]

The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment. Plain Language Summary: In 2021, the IPCC reported a decrease in the near‐surface diurnal air temperature range (DTR) since the 1950s. However, using the in‐situ surface air temperature observations, the global DTR trend was found to reverse after the 1980s, as daily maximum air temperature increased faster than the daily minimum air temperature did between 1980 and 2021. The observed results for 1980–2014 were used to assess the historical simulations within CMIP6. Models generally depicted similar spatial variability as observed results but high variation existed between models. Most of the models did not capture the reversal of the global DTR trend and underestimated regional results. To narrow down the uncertainty and produce a complete global land DTR estimation, we applied the emergent constraint approach by combining observation data and model results. The global DTR trend from 1980 to 2014 was 0.063 ± 0.012°C decade−1. The constraining data was also used at a regional scale. It was shown that DTR trends for North America retained high uncertainty (−0.011 ± 0.026°C decade−1), while Europe and Asia showed reduced uncertainty with increasing DTR. Key Points: The up‐to‐date Hadley Center Integrated Surface Database (HadISD) in‐situ data reveals a reversed global diurnal air temperature range (DTR) trend, increasing for 1980–2021The observed spatial patterns are partially captured but the reversal trends are not shown by CMIP6 modelsThe emergent constraint for CMIP6 models with HadISD estimates increasing DTR at the global scale [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]

Land–atmosphere coupling (LAC) is a critical process in the climate system, and has great effects on surface air temperature over the humid–dry transition zones. Using ERA5/ERA5‐land reanalysis datasets, we show that the summer temperature anomalies over Northwest China (NWC), known as one of the most important LAC hotpots in China, are significantly correlated with the LAC strength during 1981–2020. The stronger the coupling strength between air temperature and land surface, the more pronounced decrease in air temperature and extreme hot days over NWC. Among the four pathways of land–air temperature coupling, including soil moisture (SM)/soil temperature–latent heat flux/sensible heat flux–air temperature coupling, SM–sensible heat flux–air temperature pathway corresponds most closely with the temperature changes in summer. By analyzing the possible local and non‐local physical links in this relationship, we show that the wave trains composed of the intensification of Ural ridge and Central–Asia trough stimulate a meridional fluctuation of westerly airflow over the mid–high latitudes of Eurasia. Large moisture, therefore, converges into NWC, leading to excessive precipitation and deficient incident solar radiation reaching the land surface. In such case, wetter and cooler soil states accelerate the cooling of the environment through feedbacks between the lands, land–atmosphere exchanging fluxes and air temperature locally, accompanied by a significant enhancement in LAC strength. Our study provides insight into the physical processes causing variations in summer temperature over a typical LAC hotspot and suggests that the role of land surface–atmosphere interactions needs to be considered when studying temperature extremes under climate change. Plain Language Summary: There exist strong interactions between land surface and air temperature over Northwest China (NWC) in summer. In the meantime, NWC region (especially Xinjiang province) has undergone severe hot extremes recently and suffered great loss of life and property. In this case, this study is presented to investigate the relationship of variation in surface air temperature with the land–air temperature coupling strength using reanalysis‐based datasets. We revealed that the stronger the coupling strength, the more pronounced decrease in air temperature and extreme hot days there. Both local and non‐local factors contribute to this process. The large‐scale anomalous Arctic Oscillation–East Atlantic/West Russia‐like patterns are conducive to extra water vapor convergence in southern Xinjiang, accompanied by reduced net solar radiation. Such changes further result in significant variations in land states, and local land–air temperature interactions had also been exacerbated, collectively leading to air temperature decreases. These findings are expected to deepen our understanding of the variation of surface air temperature and related extreme temperature events in NWC. Key Points: The stronger the land–atmosphere coupling (LAC), the more decrease of air temperature and extreme hot days (HDs) during summer in Northwest China (NWC)Anomalous circulation patterns correlate with the LAC strength and air temperature variations in NWCThe local land–air temperature interactions enhance the impacts from non‐local atmospheric forcings [ABSTRACT FROM AUTHOR]

At most latitudes, the seasonal cycle of zonal‐mean surface air temperature is notably asymmetric: the length of the warming season is not equal to the length of the cooling season. The asymmetry varies spatially, with the cooling season being ∼40 days shorter than the warming season in the subtropics and the warming season being ∼100 days shorter than the cooling season at the poles. Furthermore, the asymmetry differs between the Northern Hemisphere and the Southern Hemisphere. Here, we show that these observed features are broadly captured in a simple model for the evolution of temperature forced by realistic insolation. The model suggests that Earth's orbital eccentricity largely determines the hemispheric contrast, and obliquity broadly dictates the meridional structure. Clouds, atmospheric heat flux convergence, and time‐invariant effective surface heat capacity have minimal impacts on seasonal asymmetry. This simple, first‐order picture has been absent from previous discussions of the surface temperature seasonal cycle. Plain Language Summary: Away from the equator, most places on Earth experience a noticeable seasonal cycle in surface air temperature. The seasonal cycle of temperature governs many aspects of human and ecosystem behavior. Observations show that, at most latitudes, the length of the warming season is not equal to the length of the cooling season, that is, the seasonal cycle is asymmetric. For example, at 25°N, the average warming season is 35 days longer than the cooling season. Here, we look at how this asymmetry varies with distance from the equator. We find that the asymmetry can largely be explained as a result of variations in the amount of radiation from the sun that reaches Earth, which are determined by Earth's orbit. Key Points: The seasonal cycle in zonal‐mean surface air temperature is asymmetricThe warming period tends to be shorter than 6 months in high latitudes and longer than 6 months in low latitudesThe asymmetry can largely be explained by variations in top‐of‐atmosphere insolation, dictated by Earth's orbit [ABSTRACT FROM AUTHOR]

This study investigates the intermodel spread of changes in the Eurasian winter surface air temperature (SAT) based on Coupled Model Intercomparison Project 6 (CMIP6) and explains it from the perspective of circulation under shared socioeconomic pathway 2–4.5. Results show that the leading intermodel spread of Eurasian SAT change, derived from the intermodel empirical orthogonal function analysis (EOF), is characterized by a warming pattern of SAT change. This warming pattern is associated with the changes in the weakened Siberian High and Aleutian Low, the weakened subtropical jet, the poleward polar jet, and the tilting trough. The combination of these circulations has prevented the intrusion of the cold air to the south and therefore results in the warming SAT change pattern. Their relative contribution weights of the weakened Siberian High and Aleutian Low, the tilting East Asian trough, the weakened subtropical jet, and the poleward polar jet are 12.73%, 20.43%, 30.24%, 20.96%, and 15.64%, respectively. All these changes may be traced to the warm sea surface temperature (SST) changes in the western North Pacific and tropical Atlantic Ocean, via the meridional temperature gradient and wave trains, respectively. These warmings would be the potential metrics for reducing the intermodel uncertainties of the winter SAT projection at the end of the 21st century over the Eurasian continent. Plain Language Summary: There are still large intermodel uncertainties in the projection of the winter SAT over the Eurasian continent. In this study, the intermodel EOF is utilized to derive the intermodel spread in the winter SAT change at the end of the 21st century over the Eurasian continent. The leading mode is a warming pattern over Eurasia, which is closely related to the large‐scale circulations, including the changes in the weakened Siberian High and Aleutian Low, the tilt of the East Asian trough, the weakened western subtropical jet, and the northward shift of the eastern polar front jet. All these changes may be traced to the warming in the western North Pacific and the tropical Atlantic. Our findings provide a perspective to improve the projection of Eurasian winter SAT. Key Points: The leading intermodel spread in winter surface air temperature change at the end of the 21st century over the Eurasian continent is a warming pattern from the CMIP6 modelsThe large‐circulation changes in the sea‐level pressure, the tilting trough, and jets are highly associated with this intermodel spreadAll these changes might be traced to the warm SST changes in the western North Pacific and the tropical Atlantic [ABSTRACT FROM AUTHOR]

This study analyses the spring climate anomalies under different configurations of western North Pacific anticyclone (WNPA) and Siberian high (SH). Compared to the WNPA alone with increased precipitation over southern China, the out‐of‐phase configuration (strong‐WNPA–weak‐SH) can enhance the WNPA‐related southwesterlies over eastern China, favouring increased precipitation with larger amplitude over southern China and southern Northeast China. Differently, the in‐phase configuration (strong‐WNPA–strong‐SH) decreases and confines WNPA‐related southwesterlies over southern China, causing decreased precipitation over eastern Southwest China; additionally, the WNPA‐related southwesterlies converge with SH‐related northwesterlies over the lower reaches of the Yangtze River, causing increased precipitation there. For surface air temperature, the WNPA alone induces warming over western Southwest China through changing heat flux, while the SH alone induces cooling over northern China through changing temperature advection, and over Northwest and southern China through changing heat flux. In the out‐of‐phase configuration, warming expands into Northern China due to the cooperation of weakening SH. In the in‐phase configuration, SH‐related cooling mainly occurs to the north of the Yangtze River because of the obstruction of WNPA. Further analysis indicates the eastern Pacific (EP) and central Pacific (CP) El Niño‐Southern Oscillation (ENSO), European low and Ural high contribute to the different configurations of WNPA and SH. The EP ENSO‐related zonal overturning circulation intensifies the WNPA and European low‐induced warm advection weakens the SH, resulting in the out‐of‐phase configuration. The CP ENSO‐related zonal overturning circulation intensifies the WNPA with a northeast–southwest tilt and Ural high‐induced cold advection intensifies SH, resulting in the in‐phase configuration. [ABSTRACT FROM AUTHOR]

Clear‐sky radiances (CSRs) derived from observations made by imager sensors on board geostationary satellites are widely used in most operational numerical weather prediction systems. CSRs have data on tropospheric water vapour and temperatures, and the products at water vapour bands are generally assimilated into global data assimilation systems. In another band, known as the CO2 band (13.3–13.4 μm), CSRs are not used widely yet, despite having a wealth of information about temperatures in the mid‐ and low troposphere. This is mainly because of the high surface sensitivity of this band, which makes it difficult to accurately simulate brightness temperatures when there are non‐negligible errors in the surface parameters in the models. This article quantitatively investigated the surface sensitivities of CO2 and water vapour bands, which have the sensitivity under dry atmospheric conditions, and developed retrieval of land surface temperature (LST) from window band CSRs to obtain a more accurate simulated brightness temperature. Additionally, it was discovered that the retrieved LST outperformed that from the model in short‐range forecasts for the low‐water‐vapour band (7.3 μm) CSR data assimilation, and throughout the forecasting period, especially in the tropics, for the CO2 band CSR data assimilation. We also examined an unexpected improvement in the low troposphere in the model's LST trials, and we concluded that it was related to the relationship between the LST and atmospheric temperature biases. [ABSTRACT FROM AUTHOR]

This study explores the reason why strong upward anomalous surface turbulent heat fluxes (STHFs) over the Barents and Kara Seas (BKS) occur for a period of only a few days after wind‐driven sea ice loss, even though anomalously low sea ice persists for more than one month. Composite analysis with ERA5 reanalysis data reveals that the sea ice decline coincides with the poleward advection of warm, moist air on the eastern flank of a synoptic‐scale surface low. This results in the anomalous surface air temperature (SAT) exceeding the anomalous skin temperature (SKT) and a downward anomalous STHF. As the surface low propagates eastward, the wind direction changes, resulting in the advection of cold, dry air, the anomalous SKT exceeding the anomalous SAT and a brief period with a strong upward anomalous STHF. This period of strong upward anomalous STHF is cut short, as the surface low propagates southeastward out of the BKS. The eastward propagation of the surface low is crucial, as it allows for northward driving of sea ice to be followed by cold advection and a strong upward anomalous STHF. These results indicate that when wind‐driven sea ice motion exposes the ocean to the atmosphere, except for a brief episode, the reduction of sea ice does not coincide with an increase in a strong upward anomalous STHF. [ABSTRACT FROM AUTHOR]

The regional amplitude and phase characteristics of surface (2 m) air temperature (SAT) annual cycle (AC) over the Northern Hemisphere land area are analysed by the clustering method for the period 1953–2018. The five clusters are found in the nonlinear changes of AC amplitude and the six clusters are found in the AC phase. The spatial distributions of clusters are not the same for amplitude and phase, but the similarity is that both the amplitude and phase have remarkably different trends in the mainland of Eurasia and North America, even though these two regions locate over the similar latitudinal zone. Descending trends of AC amplitude are found for the whole period 1953–2018 in most areas, except in and around Greenland. However, when checking the nonlinear variation and making a piece‐wise linear fit, the abnormal increasing trend (stronger seasonality) appears in the major part of Eurasia after the year 1990. The comparisons of surface radiation fluxes in Eurasia and North America indicate that the long‐term reduction of the reflection and absorption of clouds and aerosols with the declined surface albedo after 1980–1985 may lead to the amplitude amplification after 1990 in Eurasia. In contrast, the variation of surface radiation in North America remains unchanged, where the decreasing trend of amplitude may result from other causes. The overall phase trend is increasing (toward earlier season), but in North America after 1990, the phase shift becomes delayed. The phase variations are correlated to North Atlantic Oscillation (NAO) cold season index over almost all regions, especially in Europe. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) has warmed at a rate twice the global average and presents unique warming patterns in surface temperature changes. However, key characteristics of glacier surface heatwave duration and intensity over the TP during the present extreme warming period are still unknown. In this study, we show that surface temperatures in glacial regions of the TP (0.37 ± 0.10°C per decade) have increased faster than those in non‐glacial areas (0.29 ± 0.05°C per decade) between 2001 and 2020. Moreover, the duration (5.3 ± 3.2 days per decade) and cumulative intensity (24.9 ± 16.3 days °C per decade) of glacier surface heatwaves have increased significantly during autumn. Our results demonstrate an elevation dependence to these key warming characteristics, which we also suggest are associated with extreme glacier mass loss. Here, we highlight potential threats to the sustainability of glacier water resources and increasing risk of glacier related hazards at the "roof of the world." Plain Language Summary: The Tibetan Plateau, commonly referred to as "the roof of the world," has experienced substantial warming during the past 50 years, at a rate twice that of the global average. Previous studies in this climate sensitive environment have primarily focused on air temperature changes measured from a limited number of ground‐based observational stations, as well as from a number of satellite‐derived land surface temperature products. However, the spatiotemporal characteristics of glacier surface heatwaves—periods of extreme warm land surface temperatures—are yet to be explored. In this study, using satellite‐derived land surface temperature data, we investigated temperature changes across the Tibetan Plateau, and critically explored the occurrence of thermal extreme events of glacier surface temperatures from 2001 to 2020. We show that glacial regions have experienced faster surface warming than non‐glacial regions since 2001. Our results also suggest higher surface temperature trends and increases in heatwave intensity and duration during autumn, along with a clear elevation dependence, which is likely due to decreased albedo. Glaciers with extreme high mass loss were highly associated with increases in glacier heatwave duration and intensity. We highlight the implications of glacier heatwave threats to water resources and hazard risk. Key Points: Glacial areas experienced two times higher mean warming rates than non‐glacial areas in autumnThe duration and cumulative intensity of glacier surface heatwaves increased significantly during autumn, likely due to decreased albedoIncreases in glacier surface heatwave duration and cumulative intensity are associated with extreme high glacier mass loss [ABSTRACT FROM AUTHOR]

Temperatures near 0°C represent a critical threshold for many environmental processes and socio‐economic activities. This study examines surface air temperatures (T) near 0°C (−2°C ≤ T ≤ 2°C) across much of southern Canada over a 13 year period (October 2000–September 2013). It utilized hourly data from 39 weather stations and from 4‐km resolution Weather Research and Forecasting model simulations that were both a retrospective simulation as well as a pseudo‐global warming simulation applicable near the end of the 21st century. Average annual occurrences of near‐0°C conditions increase by a relatively small amount of 5.1% from 985 hr in the current climate to 1,035 hr within the future one. Near‐0°C occurrences with precipitation vary from <5% to approximately 50% of these values. Near‐0°C occurrences are sometimes higher than values of neighboring temperatures. These near‐0°C peaks in temperature distributions can occur in both the current and future climate, in only one, or in neither. Only 4.3% of southern Canada is not associated with a near‐0°C peak and 65.8% is associated with a near‐0°C peak in both climates. It is inferred that latent heat exchanges from the melting and freezing of, for example, precipitation and the snowpack contribute significantly to some of these findings. Plain Language Summary: Our changing climate is spurring the development of huge efforts to improve resiliency. For many regions of the world, these efforts must account for potential changes in near‐0°C conditions within which both melting and freezing can occur and the accompanying latent heat exchanges can push air temperature toward 0°C. This article focuses on the occurrence of near‐0°C surface temperatures across southern Canada through an examination of observational and model information including projections in a future warmer (average 6.1°C increase) climate near the end of the 21st century. Average annual occurrences of near‐0°C conditions increase by a relatively small amount of 5.1% in the future climate and highest values continue to be along the Pacific coast or within the Western Cordillera and lowest values continue to be within central and northern areas. Near‐0°C occurrences are often higher than those of neighboring temperatures in the present climate and some of these elevated occurrences persist into the future one despite dramatic warming. It is inferred that latent heat exchanges from the melting and freezing of precipitation and snowcover contribute to these findings. Key Points: Near‐0°C surface air temperatures were examined over southern Canada using retrospective and pseudo‐global warming simulationsOverall average occurrences increase slightly and their spatial patterns are largely maintained in the warmer climateNear‐0°C occurrences sometimes exceed those of neighboring temperatures and this feature often persists despite dramatic warming [ABSTRACT FROM AUTHOR]

Surface air temperatures are significant indicators of environmental and climatic change that affect a diverse set of physical systems including permafrost. Most temperature products, such as gridded or reanalysis data, are still at a relatively low spatial resolution, limiting the ability to simulate heterogeneous permafrost changes and leading to large uncertainties. Here we apply a downscaling method based on elevation to obtain high‐resolution surface air temperatures from the sixth Coupled Model Intercomparison Project in Northern Hemisphere permafrost regions. Root‐mean‐square errors and mean absolute errors after downscaling are reduced by 34 and 37%, respectively, relative to meteorological site data and gridded observations from the Climatic Research Unit. Compared to the downscaled surface air temperature data, nondownscaled model projections overestimate by 0.12–0.39°C in the discontinuous, isolated, and sporadic permafrost regions and underestimate up to 0.18°C in the continuous permafrost region under different emission scenarios. The warming rates in Northern Hemisphere permafrost regions were 0.093°C/10 year during the historical (1850–2014) period and are projected to be 0.22°C/10 year for SSP1‐2.6, 0.48°C/10 year for SSP2‐4.5, 0.75°C/10 year for SSP3‐7.0, and 0.95°C/10 year for SSP5‐8.5 during 2015–2100, which is 1.4–1.6 times the warming of nonpermafrost regions. Warming rates in high latitudes are 1.2–1.7 times higher than those in high‐elevation regions. Continuous permafrost regions' warming will be 1.2–1.4 times higher than in other permafrost regions. For permafrost with high ground ice content, warming will be 1.1 times greater than in permafrost regions with medium or low ground ice content. [ABSTRACT FROM AUTHOR]

Atmosphere and surface properties are routinely retrieved from satellite measurements and extensively used in weather forecast and climate analysis. Satellite missions dedicated to fill the gap of far‐infrared (far‐IR) observations are scheduled to be launched this decade. To explore mid‐infrared (mid‐IR) and far‐IR joint retrievals for the future far‐IR satellite missions, this study uses an optimal‐estimation‐based method to retrieve atmospheric specific humidity and temperature profiles, surface skin temperature, and surface spectral emissivity from the Infrared Interferometer Sounder‐D (IRIS‐D) measurements in 1970, the only existing spaceborne far‐IR spectral observations with global coverage. Based on a set of criteria, two cases in the Arctic, which are most likely under clear‐sky conditions, are chosen for the retrieval experiments. Information content analysis suggests that the retrieved surface skin temperature and the mid‐IR surface spectral emissivity are highly sensitive to the true values while the retrieval estimates of far‐IR surface emissivity are subject to the variation of water vapor abundance. Results show that radiances based on the retrieved state variables are more consistent with the IRIS‐D observations compared to those based on the reanalysis data. Retrieval estimates of the state variables along with retrieval uncertainties generally fall within reasonable ranges. The relative uncertainties of retrieved state variables decrease compared to the a priori relative uncertainties. In addition, the necessity to retrieve surface emissivity is corroborated by a parallel retrieval experiment assuming a blackbody surface emissivity that has revealed significant distortions of retrieved specific humidity and temperature profiles in the Arctic lower troposphere. Key Points: Atmospheric profiles and surface properties are simultaneously retrieved from satellite observations made 50 years agoCompared to reanalysis data, the retrieval estimates produce radiances which are more consistent with the observationsRetrievals of humidity and temperature profiles in the lower troposphere can be considerably affected by the surface spectral emissivity [ABSTRACT FROM AUTHOR]

Estimates of global mean near‐surface air temperature (global SAT) for the Cenozoic era rely largely on paleo‐proxy data of deep‐sea temperature (DST), with the assumption that changes in global SAT covary with changes in the global mean deep‐sea temperature (global DST) and global mean sea‐surface temperature (global SST). We tested the validity of this assumption by analyzing the relationship between global SST, SAT, and DST using 25 different model simulations from the Deep‐Time Model Intercomparison Project simulating the early Eocene Climatic Optimum (EECO) with varying CO2 levels. Similar to the modern situation, we find limited spatial variability in DST, indicating that local DST estimates can be regarded as a first order representative of global DST. In line with previously assumed relationships, linear regression analysis indicates that both global DST and SAT respond stronger to changes in atmospheric CO2 than global SST by a similar factor. Consequently, this model‐based analysis validates the assumption that changes in global DST can be used to estimate changes in global SAT during the early Cenozoic. Paleo‐proxy estimates of global DST, SST, and SAT during EECO show the best fit with model simulations with a 1,680 ppm atmospheric CO2 level. This matches paleo‐proxies of EECO atmospheric CO2, indicating a good fit between models and proxy‐data. Plain Language Summary: The global mean surface temperature is a commonly used indicator to measure global climate change. Our understanding of how the global surface temperature has changed in the last 66 Myr is mainly based on the assumption that changes in the deep‐sea temperature (DST) reflect changes at the surface well. Here, we test this idea by using climate model simulations of a hothouse period, the early Eocene Climate Optimum (53–49 Myr ago). We find that changes in the global DST indeed correlate well with temperature changes at the surface. We find the best fit between the models and data from the early Eocene for atmospheric CO2 levels at around 1,680 ppm. Key Points: In early Eocene model simulations (Deep‐Time Model Intercomparison Project [DeepMIP]), global mean deep‐sea, and surface temperatures are equally sensitive to atmospheric CO2 changesModel‐simulated deep‐sea temperatures show limited spatial variability, making local estimates generally representative of the global meanThe model simulations with a CO2 forcing of 1,680 ppm match paleo‐proxies of global mean deep‐sea, sea‐surface, and surface temperature [ABSTRACT FROM AUTHOR]

The Mars Environmental Dynamics Analyzer instrument on Mars 2020 has five Atmospheric Temperature Sensors at two altitudes (0.84 and 1.45 m) plus a Thermal InfraRed Sensor that measures temperatures on the surface and at ∼40 m. We analyze the measurements from these sensors to describe the evolution of temperatures in Jezero up to mission sol 400 (solar longitude LS = 13°–203°). The diurnal thermal cycle is characterized by a daytime convective period and a nocturnal stable atmosphere with a variable thermal inversion. We find a linear relationship between the daytime temperature fluctuations and the vertical thermal gradient with temperature fluctuations that peak at noon with typical values of 2.5 K at 1.45 m. In the late afternoon (∼17:00 Local True Solar Time), the atmosphere becomes vertically isothermal with vanishing fluctuations. We observe very small seasonal changes in air temperatures during the period analyzed. This is related to small changes in solar irradiation and dust opacity. However, we find significant changes in surface temperatures that are related to the variety of thermal inertias of the terrains explored along the traverse of Perseverance. These changes strongly influence the vertical thermal gradient, breaking the nighttime thermal inversion over terrains of high thermal inertia. We explore possible detections of atmospheric tides on near‐surface temperatures and we examine variations in temperatures over timescales of a few sols that could be indicative of atmospheric waves affecting near‐surface temperatures. We also discuss temperatures during a regional dust storm at LS = 153°–156° that simultaneously warmed the near surface atmosphere while cooling the surface. Plain Language Summary: The Mars Environmental Dynamics Analyzer instrument on the Mars 2020 Perseverance rover records temperatures in Jezero at four altitudes from the surface to 40 m. We describe the evolution of temperatures over the first 400 Martian days of the mission from northern spring to early autumn. Diurnal temperatures show an unstable convective regime during the daytime and a stable atmosphere at night. Daytime convection produces thermal fluctuations that peak at noon with typical values of 2.5 K at 1.45 m. These thermal fluctuations vanish in the late afternoon when an isothermal atmosphere is observed from the surface up to 40 m. We also find a linear relationship between the daytime temperature fluctuations and the thermal gradient between the surface and the atmosphere. We find very little seasonal change in air temperatures. However, the thermal inertia of the terrain affects surface temperatures, and the nighttime thermal inversion breaks over terrains with high thermal inertia. We investigate the possible detection in near surface temperatures of thermal tides and atmospheric waves. Finally, we show the thermal response of the surface and the atmosphere during the passage of a regional dust storm with a simultaneous cooling of the surface and warming of the near‐surface atmosphere. Key Points: Surface and atmospheric temperatures at Jezero show small seasonal variations from Spring to early Autumn that agree with modelsThe intensity of daytime convective fluctuations is correlated with vertical thermal gradients, which depend on the surface thermal inertiaA dust storm significantly heated the lower atmosphere while cooling the surface indicating strong radiative effects of low altitude dust [ABSTRACT FROM AUTHOR]

Based on inferences from proxy records the Miocene (23.03–5.33 Ma) was a time of amplified polar warmth compared to today. However, it remains a challenge to simulate a warm Miocene climate and pronounced polar warmth at reconstructed Miocene CO2 concentrations. Using a state‐of‐the‐art Earth‐System‐Model, we implement a high‐resolution paleobathymetry and simulate Miocene climate at different atmospheric CO2 concentrations. We estimate global mean surface warming of +3.1°C relative to the preindustrial at a CO2 level of 450 ppm. An increase of atmospheric CO2 from 280 to 450 ppm provides an individual warming of ∼1.4°C, which is as strong as all other Miocene forcing contributions combined. Substantial changes in surface albedo are vital to explain Miocene surface warming. Simulated surface temperatures fit well with proxy reconstructions at low‐ to mid‐latitudes. The high latitude cooling bias becomes less pronounced for higher atmospheric CO2 concentrations. At such CO2 levels simulated Miocene climate shows a reduced polar amplification, linked to a breakdown of seasonality in the Arctic Ocean. A pronounced warming in boreal fall is detected for a CO2 increase from 280 to 450 ppm, in comparison to weaker warming for CO2 changes from 450 to 720 ppm. Moreover, a pronounced warming in winter is detected for a CO2 increase from 450 to 720 ppm, in contrast to a moderate summer temperature increase, which is accompanied by a strong sea‐ice concentration decline that promotes cloud formation in summer via enhanced moisture availability. As a consequence planetary albedo increases and dampens the temperature response to CO2 forcing at a warmer Miocene background climate. Key Points: At a CO2 level of 450 ppm, a Miocene simulation shows a global mean surface warming of +3.1°C relative to the preindustrial stateAtmospheric CO2 increase from 280 to 450 ppm causes a warming of ∼1.4°C, which is as strong as all other forcing factors combinedAt higher atmospheric CO2 levels, the Miocene climate shows a reduced polar amplification linked to a breakdown of seasonality in the Arctic [ABSTRACT FROM AUTHOR]

Diurnal ground surface and air temperatures (Tg, Ta) and the five major surface energy budget fluxes are displayed as derived from M2020 mission observations and from column model simulations in two extreme cases (low and high diurnal Tg‐variation) along the Perseverance rover track in the Jezero crater. In both cases the fluxes and Tg are well modeled when using diurnally variable apparent ground thermal inertia I derived via a Fourier series method from the hourly observations. Hence the measurements, the diagnostic method and the model results are consistent with high‐ and low‐I nonhomogeneous terrain in the field‐of‐view (FOV) of the thermal infrared and solar sensors. In contrast less extreme values of I consistent with THEMIS retrievals are necessary for good simulations of observed Ta. We deduce that the measured Tg for the small ∼3 m2 FOV may not always be representative for the larger region around the rover, which controls the near‐surface atmospheric temperature profile. Plain Language Summary: We present comparisons of hourly surface and air temperatures and solar and thermal (atmospheric) radiation as measured by Perseverance during quite low and quite high noon temperature. We also compare the observed values to those produced by a numerical model. It appears that the model can produce excellent simulations of radiation and the ground surface temperature, if the small measurement spot for the latter is assumed to be a thermally extreme and nonhomogeneous mixture of sand and rocks (models usually assume homogeneous ground). Less extreme soil properties, such as measured by satellites, are needed instead for good air temperature predictions at 1.5 m height, as air temperatures are controlled by larger areas of surface temperatures around the rover. These results are important to better interpret local measurements by Perseverance and to provide ground‐truth to satellite observations with a much greater spatial resolution. Key Points: MEDA‐observed radiative fluxes and ground temperatures Tg are compared to model simulations during weak and strong diurnal variation in TgRadiation and Tg are best modeled with use of diurnally variable apparent thermal inertiasAir temperatures are best modeled with less extreme area‐averaged thermal inertias [ABSTRACT FROM AUTHOR]

Many studies have examined the influence of global climate change and regional human activities on the climate of arid Central Asia, but their separate contributions have rarely been reported due to lack of a comprehensive description of regional human activities, especially the irrigation process, in the model. In this study, we conducted two sets of 40‐year continuous simulations with and without human activities to distinguish the effects of regional human activities and global climate change on the surface air temperature (T2), humidity (Q2) and precipitation of a typical mountain‐oasis‐desert system in northern Tianshan by using a modified WRF‐Noah model with irrigation processes. The results indicated that the annual average T2, Q2 and convective precipitation during the past 40 years increased by 0.393°C, 0.063 g/kg and 0.014 mm/d, respectively, and the annual average large‐scale precipitation and total annual average precipitation decreased by approximately −0.017 and −0.003 mm/d, respectively. Regional human activities contributed 52.63% and 50.38% of the change in large‐scale precipitation and total precipitation, respectively, while global climate change accounted for more than 80% of the change in temperature and humidity. Among the human activities, irrigation had the largest impact on T2/Q2 over the local irrigation region and on precipitation in the upstream Tianshan mountainous area. However, for the whole region, the increase in the green vegetation fraction was the core factor that impacted the regional climate. Our results can deepen the understanding of the attribution and mechanism of climate change and provide water and heat change references for other mountain‐oasis‐desert systems in arid endorheic regions. Plain Language Summary: We counted the separate contributions of global climate change and regional human activities on the surface air temperature (T2), humidity (Q2) and precipitation of arid Central Asia by difference of two sets of 40‐year continuous simulations with and without human activities. The results indicated that regional human activities contributed 52.63% and 50.38% of the change in large‐scale precipitation and total precipitation, respectively, while global climate change accounted for more than 80% of the change in temperature and humidity. Among the human activities, irrigation had the largest impact on T2/Q2 over the local irrigation region and on precipitation in the upstream Tianshan mountainous area. But the vegetation greening was main reason for the changes of regional climate for the whole region. Our results can deepen the understanding of the attribution and mechanism of climate change and provide water and heat change references for other mountain‐oasis‐desert systems in arid endorheic regions. Key Points: The attribution and mechanism of regional climate change in a typical mountain‐oasis‐desert system of arid Central Asia were exploredGlobal climate change dominated regional temperature change while regional human activities dominated regional precipitation changeThe increase in the green vegetation fraction was the core factor that impacted regional climate [ABSTRACT FROM AUTHOR]

Globally, a variety of numerical weather prediction (NWP) models are employed by national agencies. However, the derived outputs from these models are generally inadequate for precise cultivation management due to bias error. This study uses observational data acquired over several months to develop an equation for correcting surface air temperature (SAT) error in NWP model outputs. With the proposed method, model outputs are converted into potential temperature to cancel the influence of differences between computational elevation and real elevation. The potential temperature errors in the NWP model outputs are corrected using a linear regression equation in which a radiative cooling scale (RCS) serves as the independent variable. The RCS is computed using air temperature observed at a nearby meteorological observation station, together with upper‐level pressure data from the NWP model. Versions of the correction models are developed for specific situations: day, night, and upper‐level meteorology, classified into six groups. In an application of the proposed method, the root mean square error (RMSE) of the corrected SAT output from a global spectral model operated by the Japan Meteorological Agency (JMA) was 1.2–1.6 K; the RMSE of the corrected SAT output from a nonhydrostatic model operated by the JMA was 1.2–1.5 K. The proposed method corrected the NWP model SAT output errors to a level of accuracy similar to that of data corrected using the Kalman filtering technique without the requirement for constant observation at the target site. [ABSTRACT FROM AUTHOR]

In situ measurements are the most important basis of obtaining precise meteorological datasets. However, it is difficult to accurately extrapolate the plausible meteorological field in certain regions based on in situ measurements alone, especially in areas with complex topography like the Tibetan Plateau (TP). Gridded products, remote sensing, and data assimilation technique overcome this problem but they have their own weaknesses, such as low resolution, huge computation, and time‐consuming. Here we applied the state‐of‐the‐art generative adversarial networks (GAN) in image inpainting to construct the surface temperature over the TP combined with high‐resolution China Meteorological Forcing Dataset (CMFD) air temperature product and in situ observations. In this study, the surface air temperature at 2 m dataset with grids of 0.1° over the TP from 1979 to 2020 based on GAN‐based model have been generated, which can capture the spatial patterns and trends of surface air temperature over the TP. Moreover, the GAN‐based daily temperature has higher correlation coefficients (0.99) and lower root‐mean‐square errors (1.06°C) than CMFD (0.98, 1.43°C) and reanalysis products when compared with in situ observations. Finally, the temperature fields over the TP from 2019 to 2020 is constructed, whereas CMFD is only available from 1979 to 2018. The results reflect the advantages of GAN on big‐data computation and fully utilizing effective information over the TP, and GAN‐based temperature dataset can be used to detect climate change over the region. This study demonstrated the potential and applicability of artificial intelligence for developing and extending practicable high‐resolution meteorological dataset over regions with sparse stations and rugged topography. [ABSTRACT FROM AUTHOR]

Arctic warming is of growing interest because it could affect midlatitude climates. Even though numerous studies have paid attention to the relationship between Arctic warming and the midlatitude climate in winter, the influence of Arctic warming on the summer conditions has not been examined in depth. Here we identify a significant relationship between East Asia hot summers and Arctic warming over the Barents and Kara seas. To represent Arctic warming, we define a combined index by simply differencing the normalized surface air temperature and sea‐ice indices. We found that the combined index in July has significantly high correlations with the temperatures in China, Korea, and Japan. While the teleconnection mechanism is not completely revealed, it is evident that warming over the Barents and Kara seas is accompanied by the local development of an anomalous anticyclone and downstream wave trains, which yield a strong anticyclonic circulation over East Asia and the North Pacific. The anticyclonic circulation persists and intensifies until August, which leads to hot summer conditions in East Asia with warm advection and enhanced shortwave radiation. Our results suggest that Arctic climate conditions can be used as a useful precursor of East Asia temperature variations even in summer. [ABSTRACT FROM AUTHOR]

The rise in the Earth's global average surface temperature and the increase of extreme weather events have been the focus of scientific discussion in the last decades. Extreme heat combined with other environmental extremes, like high concentrations of air pollution, may induce health problems—especially in socioeconomically vulnerable populations. Spring in South America requires particular attention due to its association with hot, dry weather and air pollution in most parts of the continent. This paper intends to better comprehend the behaviour of heatwaves during the austral winter–spring transition. We propose identifying the spatial coverage, frequency and intensity of heatwaves in homogeneous areas of maximum air temperature near the surface (Tmax). We employed cluster analyses during the period between July and October for 41 years (1979–2019) through the ERA5 reanalysis. Homogeneous Tmax areas in South America were defined by cluster analyses that indicated three homogeneous Tmax regions, as follows: a larger area of the continent including the tropical region (Area 1), eastern and southeastern South America (Area 2) and southernmost and steep areas in the Andes (Area 3). The heatwave events identified via ERA5 reanalysis were classified according to their intensity (intense, moderate and weak events). Spatial frequency and trend analyses were also performed regarding the intensity and persistence of heatwave episodes. These methods allowed the identification of the behavioural aspect of heatwaves spanning the last four decades. The Mann–Kendall statistical test (MK) was applied in order to analyse the heatwave trend with a statistical significance level of 5%. A total of 191 heatwave episodes were identified. Of this total, 47.12, 35.60 and 17.28% of episodes occurred in Areas 2, 3 and 1, respectively. The hotter area extending from northeast to southwest in central South America stood out by its largest frequencies of intense heatwave episodes. Across the continent there was a significant increase in the intensity and persistence of heatwaves over the period of 1979 through to 2019. [ABSTRACT FROM AUTHOR]

Geographical distributions of trends in climate indices with statistical significance in Japan are investigated at each of the 51 observation stations for the recent 120 years. We employed a comprehensive set of the 70 climate indices on an annual timescale to detect climate changes and to facilitate climate information for a variety of sectors, which are developed by expert team on sector‐specific indices in the World Meteorological Organization. The rising trends have regional differences such as the annual mean of daily maximum and minimum surface air temperatures in the Kanto‐Koushin region. The statistically significant increasing trends in three daily heavy precipitation‐relevant climate indices, R95p, R99p, and RX1day, are seen in Hokuriku, Shikoku, the western part of Northern and Southern Kyusyu regions, and isolated stations in the other regions. Rotational empirical orthogonal function analysis of R99p revealed different features of statistically significant modes suggesting the trends are attributed to changes in different atmospheric phenomena as well as those of warm spell duration index. The number of the stations with statistically significant trends varies with an index. Indies relevant to daily minimum surface air temperatures have statistically significant trends at most of the 51 stations. Consecutive dry days are the most sensitive to climate changes for the 120 years among the indices relevant to precipitation in this analysis. The indices relevant to heavy precipitation are not always been most sensitive to climate change detections in Japan. The geographical distributions of the percentage of the number of indices with statistically significant changes to the total number of the stations provide information about stations sensitive to climate changes. The highest sensitivity is seen at Kumamoto. These results directly provide detection of climate changes and show the potentials of which index is sensitive to ongoing climate changes from the viewpoint of geographical distributions. [ABSTRACT FROM AUTHOR]

The two dominant patterns of the winter (December–February) intraseasonal surface air temperature (SAT) over central Asia is derived by empirical orthogonal function analysis, with the first (mode 1) featuring a regional monopole and the second (mode 2) featuring a northwest–southeast‐orientated dipole. Mode 1 is characterized with warmer (colder) Arctic and colder (warmer) Eurasia along with an anomalous high (low) over the Ural Mountains, while mode 2 is characterized with northwest–southeast dipole over Eurasia along with wave train‐like atmospheric circulation anomalies from the western Atlantic to Asia. Whether the two intraseasonal modes are modulated by lower‐boundary forcing is then investigated, and the results suggest that mode 1 is significantly modulated by sea ice concentration over the Greenland Sea, but by central‐Pacific ENSO for mode 2. Finally, the underlying mechanisms are diagnosed, and the results suggest that the diabatic heating related with decreased sea ice in Greenland Sea induce a positive height anomaly over Urals which favours the formation of Ural blocking and therefore tends to intensify the occurrence of mode 1. Also, central‐Pacific El Niño excites the Pacific–North American teleconnection, which alters North Atlantic subtropical jet extension, favours intensified energy conversion to intraseasonal wave train and subsequently intensifies the occurrence of mode 2. This study highlights the joint role of Arctic sea ice and the SST in the tropical central Pacific. [ABSTRACT FROM AUTHOR]

In recent decades, global warming has been an indisputable fact. The increase in extreme events accompanied by the rise in temperature has huge influences on many aspects of the society and economy globally. As a result, forecasting how global temperatures will change and evolve in the future is crucial, but it is restricted by the accuracy and uncertainty of model simulations. Based on ensemble empirical mode decomposition (EEMD) method, this study first evaluates the performance of the CMIP6 models in simulating different timescale components including high‐frequency components (HFC), low‐frequency components (LFC), and secular trend (ST) of surface air temperature (SAT). The results show that the performance of the CMIP6 models in simulating the ST is better. Following the observation constraint method's correction of CMIP6 model simulations, we investigate the evolution characteristics of future temperature secular trends under different Shared Socioeconomic Pathway (SSP) scenarios. The results show that under SSP245 scenario, the rise of SAT first appeared in northwest and east North America and eastern Europe. The spatial distribution of evolution characteristic under the SSP585 scenario is similar to that of the SSP245 scenario but with a larger magnitude. However, more pronounced differences appear in warming rate, which shows a downward trend over time under the SSP245 scenario and an upward trend with time under the SSP585 scenario. [ABSTRACT FROM AUTHOR]

This study examined the relationship between heatwaves in Korea and blocking in the Sea of Okhotsk (OK) region, which significantly influences the Korean climate in summer. According to the analysis, negative correlations were observed between the blocking frequency in the OK region and heatwave days (HWD) and the surface air temperature (SAT) in Korea over the past four decades (1979–2018). These negative correlations suggest that when the blocking frequency in the OK region increases in summer, the HWD and SAT decrease in Korea. This study selected 10 years of high blocking frequency years (high BF years) and 10 years of low blocking frequency years (low BF years) and analysed the differences between the two groups to explain the negative relationship between the HWD in Korea and the blocking frequency in the OK region. The difference between the two groups revealed strong anomalous anticyclonic and cyclonic circulations that developed in the north Siberia region and northeast Australia (NEA), respectively, throughout all layers in the troposphere. The large anomalous anticyclonic pattern in the North Siberia region was related to the blocking high, while the anomalous cyclonic pattern that developed in NEA was related to the East Asian summer monsoon (EASM) front. The difference in the sea surface temperature (SST) between the two groups was also analysed. In the North Atlantic Ocean, a tripole structure appeared with a cold anomaly in the high latitudes, a warm anomaly in the mid‐latitudes and a cold anomaly again in the low latitudes, which is a typical spatial distribution of the SST anomaly related to the positive North Atlantic Oscillation (NAO) phase. [ABSTRACT FROM AUTHOR]