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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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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 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]

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]

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]

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]

Using the daily 2 m maximum temperature (Tmax), 2 m minimum temperature (Tmin) and cloud cover data measured at ground sites of the China Meteorological Administration in North China from 2000 to 2017, this study investigates the influence of clouds on the daily temperature range (DTR) defined as the difference between Tmax and Tmin. As expected, the cloud cover shows the similar averaged spatial distribution and monthly variation with Tmin. Surprisingly, it also shows the similar average spatial distribution and monthly variation with Tmax, suggesting the more important roles of regions (latitude) and seasons associated with the variations of land surface temperature, which is further related to solar radiation absorbed and surface heat capacity. By comparing monthly variations of temperature between cloudy and clear skies, we find that clouds can weaken Tmax and increase Tmin, and thus decrease DTR. As a result, the spatial distribution of DTR is opposite to the cloud cover. The clouds have relatively stronger impact on Tmin and DTR over mountain region, which is most likely caused by the stronger longwave cloud radiative forcing associated with higher cloud tops over the mountain region. [ABSTRACT FROM AUTHOR]

This study investigates the spontaneous self‐aggregation of convection in non‐rotating Radiative‐Convective Equilibrium (RCE) simulations performed by the CNRM‐CM6‐1 General Circulation Model within the framework of the RCE Model Intercomparison Project (RCEMIP). In this model, the level of convection self‐aggregation at equilibrium, as quantified by metrics based on moisture or moist static energy, strongly increases with sea surface temperature (SST). As it gets warmer, the troposphere gets drier, high cloud cover diminishes in dry regions, the top of high cloud rises and their thickness increases in moist regions, and low‐cloud cover increases. At high SSTs, the large‐scale circulation exhibits a shallow component, stronger than its deep counterpart. The transition toward self‐aggregation has a similar first 20‐day phase for all SSTs within the 295–305 K range. It primarily involves radiative positive feedback processes. Then, for SSTs above approximately 298 K, a new, slower, transition toward higher levels of self‐aggregation occurs. It is concomitant with a shift from a top‐heavy to a more bottom‐heavy large‐scale circulation, a strengthening of the shallow circulation and a reduced mobility of convective aggregates. This second transition is mostly driven by the dry regions, still involves longwave radiative positive feedbacks, but also advective positive feedbacks in the driest regions. It is argued that boundary‐layer radiative cooling difference between moist and dry regions, which is stronger at high SSTs, is instrumental in this second phase of self‐aggregation. The sensitivity of deep convection to environmental dry air also likely acts as a positive feedback on the system. Plain Language Summary: In idealized configurations of the Earth, convective clouds can spontaneously organize into large clusters: this is convective self‐aggregation. We investigate the sensitivity of this process to surface temperature in the atmospheric component of the state‐of‐the‐art global climate model CNRM‐CM6‐1. For surface temperatures spanning a typical range of tropical conditions (295–305 K), the model exhibits an aggregated state when equilibrium is reached. As the surface gets warmer, convection is more aggregated, the troposphere gets drier, high clouds get less frequent in dry regions and low‐cloud cover increases. When starting from homogeneous conditions, an initial rapid phase of self‐aggregation occurs in all experimented sea surface temperatures. Radiative processes are instrumental in leading to self‐aggregation. For warm surface temperature above approximately 298 K, a second, slower, transition occurs and leads to higher levels of self‐aggregation. It is associated with an adjustment of the large‐scale circulation, in which shallow circulations in the lower troposphere (surface‐700 hPa) and between dry and moist regions strengthen. The radiative loss of energy within the boundary layer, and its unbalanced state between dry and moist regions after the initial transition is argued to be the main process at play. Key Points: The sensitivity to surface temperature of convective self‐aggregation under radiative‐convective equilibrium is documented in CNRM‐CM6‐1Increase of self‐aggregation with surface temperature is associated to an efficient shallow circulation between dry and moist regionsA second slower transition toward self‐aggregation at high surface temperature is primarily driven by radiative processes in dry regions [ABSTRACT FROM AUTHOR]

This study investigates the relative contributions of internal variability and external forcing to multidecadal variations in the winter (December–February) East Asian averaged surface air temperature (EASAT) since 1880 by analyzing three observational datasets and large ensemble simulations from 12 coupled models. The results indicate that the internal variability plays a dominant role in the EASAT multidecadal variations. Specifically, the Atlantic Multidecadal Oscillation (AMO) affects the EASAT via an atmospheric teleconnection across the Eurasian continent, and the correlation coefficient between internally generated EASAT variation and the AMO index is −0.49. In comparison, the external forcings only modulate the observed EASAT variations due to offsetting effects of the external forcing agents. That is, the multidecadal variations in the greenhouse gas concentrations and anthropogenic/volcanic aerosols make positive and negative contributions, respectively. Our new finding is conducive to EASAT predictions in the following decades due to the predictability of AMO and anthropogenic forcings. Plain Language Summary: Internal variability is one of the most important sources of uncertainty in predicting the East Asian winter temperature in future decades. It is thus essential to address associated reasons for the internally generated East Asian temperature variability. This study tries to separate internal variability from the externally forced climate response for the winter East Asian averaged surface air temperature (EASAT) based on three observational datasets and large ensemble simulations from 12 latest coupled models. We find that the internal variability explains 59% of the total EASAT multidecadal variations, in which the Atlantic Multidecadal Oscillation (AMO) plays a vital role. The external forcings play the secondary role. This means that accurate prediction of the AMO would be helpful for predicting internally generated EASAT variations at the multidecadal scale. Key Points: Internal variability has played a dominant role in the multidecadal variations of winter temperature averaged over East Asia since 1880The Atlantic Multidecadal Oscillation plays a vital role in internally generated East Asian winter temperature variationsChanges in greenhouse gas concentrations and anthropogenic/volcanic aerosols make positive and negative contributions, respectively [ABSTRACT FROM AUTHOR]

Irrigation cools near surface air temperature by increasing evapotranspiration from wetter soil. However, elevated evapotranspiration can also increase atmospheric albedo and enhance the local greenhouse effect via increased atmospheric water vapor. Their net effects on daily air temperature remains controversial. Here we show that in several considered regions, Northwest India and Central Valley of California, irrigation could result in warmer air temperature if night‐time warming is stronger than daytime cooling by irrigation. During the daytime, air temperature reduces through evaporative cooling and reduced solar radiation from increased atmospheric albedo outweighing the local greenhouse effect. At night‐time, the increased atmospheric water vapor by irrigation tends to make a stronger local greenhouse effect that increases night‐time temperature. Our results highlight the possible increase of air temperature by irrigation and the importance of considering sub‐daily processes when assessing the impact of irrigation on daily air temperature and temperature related socioeconomic phenomena. Plain Language Summary: Irrigation can influence air temperature in two contrasting ways. Evaporative cooling from wetter soil can reduce air temperature. However, evaporated water is a greenhouse gas, which can increase air temperature. The impact of these two influences differs for daytime and night‐time air temperature. Here, we examine the impact of irrigation on daytime and night‐time air temperature under two different assumptions. Our simulations show that the warming influence of irrigation on night‐time temperature could be greater than the cooling influence on daytime temperature in several considered regions, Northwest India and Central Valley of California. Our results underline that the impact of irrigation is a complex interplay of evaporative cooling and greenhouse warming and still needs to be further investigated. Key Points: Irrigation may result in warmer night‐time temperature by enhancing the local greenhouse effectThe temperature response of local irrigation can be influenced by irrigation practices over nearby areasHighlight the importance of water vapor as a greenhouse gas in climate change [ABSTRACT FROM AUTHOR]

The last millennium (LM, 1000–1850 AD) is crucial for studying historical climate change on decadal to multidecadal timescales. The summer surface air temperature (SAT) evolutions on regional scales (e.g. over China) are more uncertain than the globe/Northern Hemisphere, especially in response to external forcing factors and internal climate variability. Here, we provide one‐signal (full‐forcing) fingerprints of summer SAT in China derived from three large ensemble model archives with a multi‐proxy reconstruction during the LM, Little Ice Age (LIA, 1451–1850 AD), and Medieval Climate Anomaly (MCA, 1000–1250 AD), respectively. Our results show that (a) SATs in the northeast, southeast, northwest, and Tibetan Plateau (TP) regions of China show evident decreasing trends during the LM. External forcing response from all model archives agrees with the regional SAT reconstruction but underestimates variability in northwest China at the multidecadal timescale. (b) During the LIA, the summer regional SAT exhibits a cold condition in the reconstruction and simulations, especially in the northeast and northwest regions of China. External forcing responses in most model archives are the dominant factor on multidecadal SAT evolutions in the southeast, northeast, and TP regions of China and decadal SAT evolutions in northwest China. (c) During the MCA, detection and attribution of SAT shows that internal climate variability dominates in southeast, northeast, and TP regions of China, but external forcing dominates in northwest China at decadal to multidecadal timescales. These results contribute to a better understanding of the causes and mechanisms of regional climate change. [ABSTRACT FROM AUTHOR]

We present a near surface air temperature (NSAT) fused data product over the contiguous United States using Level 2 data from the Atmospheric Infrared Sounder, on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar‐orbiting Partnership satellite. We create the fused product using Spatial Statistical Data Fusion, a procedure for fusing multiple data sets by modeling spatial dependence in the data, along with ground station data from NOAA's Integrated Surface Database (ISD) which is used to estimate bias and variance in the input satellite data sets. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We provide detailed validation using withheld ISD data and comparison with ERA5‐Land reanalysis. The fused gridded product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. Over the domain of our study, the fused product decreases daytime bias magnitude by 1.7 and 0.5 K, nighttime bias magnitude by 1.5 and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input data sets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple data sets estimating the same quantity. Finally, because our product reduces bias, it produces long‐term data sets across multi‐instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end. Plain Language Summary: We have used a data fusion technique called spatial statistical data fusion (SSDF) to create an improved near surface air temperature (NSAT) data set by fusing two separate satellite data sets. NSAT is important for a variety of applications, such as drought, wildfire, and extreme heat research and prediction. The two input NSAT data sets come from the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding suite on the Suomi National Polar‐orbiting Partnership satellite. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We also performed a detailed validation using withheld reference data (which was not included in the bias‐correction data) and comparison with ERA5‐Land reanalysis. The new fused product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. SSDF is computationally fast and generalizable, capable of data fusion from multiple data sets so long as they estimate the same quantity. Finally, because our product reduces bias, it provides a means of creating high‐quality continuous long‐term data sets across the years, as individual satellite missions and their data records begin and end. Key Points: We demonstrate spatial statistical fusion for Level 2 remote sensing data sets which estimate the same observableWe introduce a new daily and nightly fused near‐surface air temperature product from satellite hyperspectral sounders over contiguous United StatesThe fused product decreases bias and RMSE by 1 K and 25% respectively relative to input data sets, averaged over the domain of the study [ABSTRACT FROM AUTHOR]

Australia experienced record‐breaking wildfires during 2019–2020, which emitted large amounts of carbonaceous aerosols (CAs) to the atmosphere. In this study, we explored the atmospheric dynamics and thermodynamic mechanism of the long‐range transport of CAs during November 2019–February 2020. The results indicate that the emitted CAs had a ternary advective spreading pattern across the Pacific Ocean in the Southern Hemisphere (SH), influenced mainly by the westerlies and anticyclonic systems. Upward vertical motion over the convergence zone and heat released from smoke pyro‐cumulonimbus clouds and strong light absorption of CAs provided strong vertical lift as the predominant injection pathway of the CAs into the stratosphere in the central Pacific Ocean in the SH. The abundant tropospheric CAs induced atmospheric heating (and potentially surface cooling), with increasing air temperature and decreasing of surface temperature. The anomalous thermal structure led to changes in atmospheric circulation, which weakened the upward vertical motion. Plain Language Summary: During the summer of 2019–2020, Australia suffered unprecedented wildfires. These wildfires emitted large amounts of pollutants into the free atmosphere. The pollutants are mainly composed of carbonaceous aerosols (CAs). Under the effects of meteorological conditions, the CAs experienced a long‐range spread expanding from the western Pacific Ocean to the eastern Pacific Ocean even arriving at the Atlantic Ocean. During the transportation, the CAs were accumulated over the central Pacific Ocean under the trapping effect of the anticyclone and then exhibited significant lift owing to upward motion over the convergence zone between westerlies and anticyclone and heat buoyancy released from smoke pyro‐cumulonimbus clouds and strong light absorption of CAs. As a response, the strong absorption of CAs resulted in the warming of the atmosphere and cooling of the surface, which enhanced atmospheric stratification and constrained the development of atmospheric instability. Thus, the upward vertical motion was weakened. Key Points: The westerlies and anticyclonic system dominated the advective spread of carbonaceous aerosols (CAs)The upward vertical motion, heat released from smoke pyro‐cumulonimbus clouds, and strong CAs light absorption contributed to CAs liftThe CAs caused warming of air temperature and cooling of surface temperature, which weakened the upward vertical motion [ABSTRACT FROM AUTHOR]

The diversity of El Niño events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) type. While the remote impacts, that is, teleconnections, of EP and CP events have been studied for different regions individually, a global picture of their structure is still lacking. Here, we use Forman‐Ricci curvature applied on climate networks constructed from surface air temperature data to distinguish regional links from teleconnections. Our results confirm that both El Niño types influence the teleconnection patterns, however, with different spatial manifestations. Our analysis suggests that EP El Niños alter the general circulation which changes the teleconnection structure to primarily tropical teleconnections. In contrast, the teleconnection pattern of CP El Niños show only subtle changes to normal conditions. Moreover, this work identifies the dynamics of the Eastern Pacific as a proxy for the remote impact of both El Niño types. Plain Language Summary: El Niño events, characterized by anomalous sea surface temperatures (SSTs) in the Tropical Pacific, come in two flavors; Eastern Pacific (EP) and Central Pacific (CP) types, depending on the longitudinal location of the strongest SST anomalies. Their remote impacts, known as teleconnections, differ. Although there are many studies investigating teleconnections of EP and CP events for individual target regions, a global analysis of the spatial distribution of their teleconnections is still lacking. In this study, we use the theory of complex networks to study EP and CP El Niño teleconnections. We construct "climate networks" from global surface air temperature data and use the notion of "curvature" of a network link to uncover their spatial organization. We show that the most negatively curved links highlight important teleconnection patterns that differ depending on the El Niño type. EP events change the teleconnection structure to the tropics while CP and Normal year conditions reveal teleconnections to all latitudes. Interestingly, the Central Pacific does not show many teleconnections, even during CP El Niño events which we attribute to the varying location of warm water anomalies in the Central Pacific. The Eastern Pacific changes more consistently allowing identifying remote impacts of both El Niños types. Key Points: Ricci curvature of boreal winter climate networks reveals long‐range teleconnection structureEastern Pacific (EP) El Niños show primarily teleconnections in tropical while Central Pacific El Niños teleconnections on all latitudesThe EP contains robust teleconnections for both El Niño types [ABSTRACT FROM AUTHOR]

Long‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured. Plain Language Summary: Long‐term changes in surface temperature due to climate change are usually studied using near‐surface air temperatures (T2m) from weather stations. However, these data are not available everywhere and changes in T2m are poorly understood in some regions (e.g., parts of Africa). This study assesses the potential for satellite‐observed land surface temperatures (LST) to help fill these gaps. The study finds that LST and T2m provide very similar information on surface temperatures and therefore LST can provide useful information where T2m data are unavailable. However, some of the LST data sets are found to be "unstable" and suffer from jumps and changes over time that result from for example, changes in satellite instrument or a change in the orbit of the satellite platform. These unstable LST data sets cannot be used to measure changes in surface temperature. However, the study finds that for "stable" LST data sets, which do not suffer from these non‐climatic jumps and changes, the changes in LST over time are very similar to, or even the same as, the changes in T2m. Therefore, this work suggests that satellite LST data could be used to measure changes in surface temperature where there are not enough stations to make these measurements using T2m. Key Points: The stability of six satellite land surface temperature data sets is assessed using homogenized station air temperatures over EuropeOnly two data sets are stable; the other four show non‐climatic discontinuities associated with a change in sensor and/or drift over timeSignificant trends in land surface temperature of ∼0.65 K/decade over Europe are found for one of the stable data sets over 2002–2018 [ABSTRACT FROM AUTHOR]

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

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

We investigated interdecadal relationship changes between the Arctic oscillation (AO) and Aleutian low (AL) for the period 1980–2021 along with the linkage on surface air temperature (SAT) over Eastern Eurasia during the boreal winter (December–January–February). The AO–AL relationship has weakened since the mid‐1990s. To explore a possible explanation, we compared atmospheric fields for two contrasting periods, one when the AO–AL relationship is the strongest (1986–2006) and one when it is the weakest (2000–2020). In both periods, atmospheric circulation over the Arctic region for the two AO–AL in‐phase relationships (i.e., either +AO/+AL or –AO/−AL) evolves with negative AO‐like patterns, contributing to a weakening AO–AL relationship in concurrent with a cool SAT in Eastern Eurasia. We found that the correlation between AL and SAT over Eastern Eurasia decreases, while the AO and SAT correlation remains unchanged. This implies that low‐frequency atmospheric circulation changes such as those of the AO and AL should be considered when studying SAT changes in Eastern Eurasia. [ABSTRACT FROM AUTHOR]

This paper uses monthly surface air temperature (SAT) data from the Climatic Research Unit dataset and the Coupled Model Intercomparison Project phase 6 multimodel ensemble simulations to investigate the variations in the annual‐mean and four seasons SAT across the Eurasian and North Africa landmasses and the possible causes. The SAT over multiple timescales has significant seasonal and regional differences. The trend, variance of decadal variation (DV), and variance of interannual variability (IV) show that the differences between Siberia and mid‐low latitudes are much larger in winter and spring than in summer and autumn. The SAT exhibits a robust warming trend over Siberia in winter (0.21–0.42°C·decade−1) than over mid–low latitudes in summer (<0.07°C·decade−1). The cause is the combined diversity effects of greenhouse gases (GHG) and anthropogenic aerosols (AA) in four seasons over different regions. The warming trend of Siberia in winter is affected by the combination of strong warming effect by GHG and the weak cooling effect by AA, and the trend of mid–low latitudes in summer is opposite. The leading modes of the DV show a consistent spatial pattern over the whole Eurasia in four seasons while the IV show a meridional dipole pattern across 40°N except in summer. The internal climate variability is the main driver of the SAT IV and DV, but the effect of anthropogenic activity and natural forcing may reinforce the IV and DV in some regions. The combined role of GHG and AA forcings can significantly enlarge the DV in many parts of Eurasia, and the AA and natural forcing have significant effect on the IV in the high latitudes. [ABSTRACT FROM AUTHOR]

The impact of the quasi‐biennial oscillation (QBO) on the surface air temperature (SAT) in the Northern Hemisphere extratropics is investigated. It is found that the QBO, defined as 70‐hPa zonal wind in the deep tropics, is negatively correlated with the SAT over the western North Pacific in February and March. Cold temperature anomaly appears during the QBO westerly phase. Such relationship is likely mediated by the subtropical jet. During the QBO westerly phase, a horseshoe‐shaped zonal wind anomaly forms in the upper troposphere and lower stratosphere and is connected to the equatorward shift of the Asia‐Pacific jet. This equatorward jet shift is accompanied by a cyclonic circulation anomaly in the subtropical North Pacific and an anticyclonic circulation anomaly over northern Eurasia in the troposphere. The resultant temperature advection brings cold air to East Asia and the western North Pacific. This regional downward coupling in February and March, which is not sensitive to El Niño‐Southern Oscillation, has become statistically significant in recent decades. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) index is often characterized by independent positive and negative NAO events with a characteristic spatial pattern and a typical lifetime of around 1 week. These events are separated by periods of near‐neutral NAO conditions. Here, we challenge this view by showing in reanalysis and observed data that the strength and spatial shape of NAO events depends on the NAO index prior to the window of 1 week and this dependency is most pronounced for negative NAO events. The influence is seen in the mean sea level pressure, and in other important features, including blocking frequency and jet stream characteristics, and also in air surface temperature and precipitation in parts of Europe. This new appreciation is important for efforts to improve methods for subseasonal‐to‐seasonal predictions of NAO. Plain Language Summary: Northern European winters differ. Some are mild and moist with prevailing westerly winds and some are cold and dry, dominated by the Siberian air masses from East. When the Northern European winters are mild, the winter in Greenland is cold, and vice versa. This is the North Atlantic Oscillation (NAO) and its companion, the Europe–Greenland temperature seesaw. They were described in qualitative terms already in the late eighteenth century. Nowadays, the NAO is often described as a series of independent positive or negative events with a duration of around 1 week with intermittent periods of near‐neutral conditions and variable duration. The net result of these events determines the NAO strength of the winter. We show that this picture is too simplified because the NAO outside the time horizon of 1 week influences the strength of the individual NAO events. This is most pronounced for the negative events where this influence can both distort and amplify or diminish the pattern of the event. Also the typical temperatures and precipitation patterns associated with NAO are influenced significantly by this effect. One important consequence for our discovery is the improved prospects of doing subseasonal‐to‐seasonal predictions. Key Points: The daily North Atlantic Oscillation (NAO) index consists of positive and negative events of duration around 1 week separated by periods of near‐neutral conditionsThe strength and spatial shape of negative NAO events depends on NAO‐index values prior to the eventBlocking frequencies, jet strength and position, and surface temperature and precipitation have similar dependencies [ABSTRACT FROM AUTHOR]

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

Southeast Asian (SEA) river basins, particularly Huang He, Yangtze and Mekong river basins, are home for large share of global population. The region is experiencing rapid socio‐economic development, urbanization and industrialization. Using Community Earth System Model large ensemble data, seasonal mean surface air temperature (SAT) in summer is projected to increase by ~6°C under Representative Concentration Pathway (RCP) 8.5 scenario. As a consequence, in the long term (2071–2100), record‐breaking extreme hot and precipitation events are projected to increase by ~90–100% and ~ 60–70%, respectively. The climate mean SAT will increase by 4 ~ 5.5°C by the end of 21st century. Extreme events occur due to the combined influence of external forcing factors and internal variability. The relative strength of both the factors are estimated by signal‐to‐noise ratio (SNR), that is, the ratio of external forcings and internal variability. Until 2000/1998/2000, SNR < 1 over Huang He/Yangtze/Mekong river basins, that is, internal variability dominates over external forcings, while from 2001/1999/2001 onwards, SNR > 1, that is, external forcings strongly override the internal climate variability. Interestingly, the SAT variability at 50th (mean state) and 95th (record‐breaking hot/wet extreme) percentile will decrease significantly as climate warms in the future. It contradicts the existing hypothesis that as the climate warms in the future, variability will increase and weather will get more volatile everywhere. Interestingly, in our analysis, SAT variability appears to decrease over SEA river basins, which is opposite to the current understanding. It makes the result a novel one. We analyse the temperature–precipitation relationship under RCP8.5 scenario, which displays a peak‐shaped structure, that is, precipitation is increasing at low–mid‐range of temperature rise, while it decreases at higher temperatures. [ABSTRACT FROM AUTHOR]

An important metric for communicating the context and impact of climate change is regionally or nationally averaged temperature anomalies. Quantifying the associated uncertainty adds confidence in the reporting of temperature records and trends. For area‐averaged temperature anomalies derived from point‐source observations we model the uncertainty by a three‐step process: (1) observation random error; (2) spatial analysis grid point uncertainty; and (3) area‐average uncertainty. Each step should reflect the method used to calculate the area average. For Australian area‐average temperature anomalies, the observation random error is modelled as the sum of the sampling uncertainty and the uncertainty in the adjustment process. Daily maximum and minimum temperature time series from the 104 non‐urban locations of the Australian Climate Observation Reference Network—Surface Air Temperature (ACORN‐SAT) version 2 dataset for 1981–2010 are used to estimate the sampling uncertainty, based on predicted location pair zero‐distance correlations having values of less than 1.0. ACORN‐SAT version 2 breakpoint information is used to estimate the uncertainty from the selection of reference stations used in the detection and homogenization processes. Monthly area‐average temperature uncertainty is estimated for the 1910–2018 period from Monte Carlo simulations using the observation uncertainty and the available ACORN‐SAT network for each month. The uncertainty in annual area‐average temperature anomalies is then obtained from the monthly uncertainties and inter‐month correlations modelled with a periodic auto‐regressive model. The ±2σ annual mean temperature area‐average uncertainty is ±0.084°C in 2018 and ± 0.176°C in 1910. The larger value in 1910 is primarily due to decreased ACORN‐SAT coverage over areas of inland Australia. The Australian annual mean temperature change and associated uncertainty from ACORN‐SAT version 2 over 1910–2018 is found to be 1.42 ± 0.28°C. This is consistent with global air temperature warming by around 1°C since 1850. [ABSTRACT FROM AUTHOR]