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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]

Here, we introduce the concept of "outdoor days" to describe how climate change can affect quality of life for different communities and individuals. An outdoor day is characterized by moderate temperature, neither too cold nor too hot, allowing most people to enjoy outdoor activities. The number of "outdoor days" is a nonlinear function of the daily surface air temperature. If the latter falls within a specific range describing assumed thermal comfort conditions, then we assign that day as an "outdoor day." Using this function, we describe climate change impacts on temperature differently, compared to other studies which often describe these impacts in terms of the linear averaging of daily surface air temperature. The introduction of this new concept offers another way for communicating how climate change may impact the quality of life for individuals who usually plan their outdoor activities based on how local weather conditions compare to their preferred levels of thermal comfort. Based on our analysis of regional variations in "outdoor days," we present observational and modeling evidence of a north–south disparity in climate change impacts. Under high-emission scenarios, CMIP5 and CMIP6 models project fewer "outdoor days" for people living in developing countries, primarily located in low-latitude regions. Meanwhile, developed countries in mid- and high-latitude regions could gain more "outdoor days," redistributed across seasons. Significance Statement: We introduce a novel concept: outdoor days, characterizing surface air temperature conditions that allow for outdoor activities, such as walking, jogging, and cycling, by most people. We project that under high-emission scenarios of anthropogenic greenhouse gases, a north–south disparity of climate change risk will be enhanced considerably toward the end of this century due to more frequent outdoor days in the wealthy Global North and less frequent outdoor days in the deprived Global South. [ABSTRACT FROM AUTHOR]

Mars atmospheric dynamics are crucial for understanding its climate and weather patterns, especially over plateaus. Previous studies have explored localized atmospheric heating mechanisms over Mars plateaus only to a little extent. The local atmospheric heating dynamics over the Tharsis plateau, especially during global dust storms (GDSs), have not been quantitatively analyzed before. Based on reanalysis datasets, our analysis reveals that the central highlands of Tharsis experience ~130 K diurnal temperature fluctuations, driven by intense daytime convective activity. Surface temperature and near-surface air temperatures show fluctuations approximately 25 K and 20 K higher than those at similar latitudes, respectively. We quantify a super-adiabatic lapse rate around noon that suggests strong atmospheric instability, previously unquantified in this region. By dusk, the atmosphere stabilizes, presenting a homogenized condition. At aphelion, sensible heating and adiabatic terms control the atmospheric heating, while, at perihelion, radiative and sensible heating predominate. Notably, the onset of GDS significantly alters this dynamic, reducing the ground–air temperature gap from 17 K to 5 K and enhancing diabatic heating (adiabatic cooling) in the mid-to-lower (mid-to-upper) troposphere, with increases in radiative components up to 60 W/m2. [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]

Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers. [ABSTRACT FROM AUTHOR]

An accurate simulation and projection of future warming are needed for a proper policy response to expected climate change. We examine the simulations of the mean global and Arctic surface air temperatures by the CMIP6 (Climate Models Intercomparison Project phase 6) climate models. Most models overestimate the observed mean global warming. Only seven out of 19 models considered simulate global warming that is within ±15% of the observed warming between the average of the 2014–2023 and 1961–1990 reference period. Ten models overestimate global warming by more than 15% and only one of the models underestimates it by more than 15%. Arctic warming is simulated by the CMIP6 climate models much better than the mean global warming. The reason is an equal spread of over and underestimates of Arctic warming by the models, while most of the models overestimate the mean global warming. Eight models are within ±15% of the observed Arctic warming. Only three models are accurate within ±15% for both mean global and Arctic temperature simulations. [ABSTRACT FROM AUTHOR]

In order to evaluate the simulation effect of the model on the dynamic characteristics of the climate system, the approximate entropy method was used to analyze the complexity of surface air temperature (SAT) based on observational data (CN05.1) and the model-simulated data by the Beijing Climate Central Climate Model (BCC-CSM2-MR) in this study. We investigated whether the simulated climate model data exhibited complexity characteristics similar to real-world meteorological elements. In this scenario, we could assess the climate model's performance by comparing the complexity of observed and simulated data. The results indicate that the BCC-CSM2-MR model can accurately reflect the complexity of SAT in China. When considering the entire year, the model exhibited greater complexity in the simulated SAT than the observations in most regions of China, indicating an overestimation of complexity by the model. When examining different seasons, the model's complexity was similar to the observations in spring, summer, and winter, indicating a better performance during these seasons. In contrast, the simulated performance is poor during autumn, mainly due to an overestimation of complexity in Qinghai, south-central Xinjiang, and western Xizang. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) directly heats the middle tropospheric atmosphere, and accurate simulation of its surface temperature is of great concern for improving climatic prediction and projection capabilities, but climate models always exhibit a cold bias. Based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and in-situ observations during 1981–2014, this study elucidates the impact of the snow overestimation on the temperature simulation over the TP in CMIP6 from the perspective of local radiation processes and atmospheric circulation. On the one hand, more snow in the CMIP6 models not only directly cools the surface more, but also makes the surface receive less shortwave radiation due to the higher surface albedo, and thus has lower ground surface temperature (GST), and the more snow/albedo-low temperature process is particularly evident in the westerly region due to more uncertainty at high elevations. This process contributes 87% to the annual GST cold bias. Lower GST corresponds to less latent heat transfer and thereby lower surface air temperature (SAT). In addition, the more snow in the CMIP6 models leads to the weaker the South Asian summer monsoon and the westerlies, and brings less warm and moist air (less integrated water vapor flux), as well as less clear-sky downward longwave radiation (less water vapor amount and lower tropospheric air temperature) to the TP (contributing 58% to the annual GST cold bias). These processes will result in less both precipitation and surface latent heat loss, which offsets a 35% annual GST cold bias. Besides, the physical mechanism of snow on GST and SAT differs with season over the westerly and monsoon regions of the TP. The research highlights the importance of topography and snow parameterization schemes for optimizing CMIP6 models. [ABSTRACT FROM AUTHOR]

Frequent occurrences of extreme heats in recent years pose a threat to the local society, economy, and ecosystem in Southern China. Understanding the process and predictability of these heat events is crucial for scientific research and policy-making. The long-term variation characteristics and the corresponding driving factors of high-temperature days (HTDs) and surface air temperature (SAT) in midsummer in Southern China were analyzed by observed data of meteorological stations and atmospheric reanalysis data. The results reveal an abrupt rise in HTDs around the early 2000s during the period of 1979–2021, rather than a sustained increase. Prior to and after this mutation, no clear trend is observed. The changes in SAT align closely with the variations in HTDs. The abrupt increase in solar radiation, resulting from a decrease in total cloud cover, is identified as the direct cause of the SAT change. In the decade following the abrupt change point, significant anomalous descent motion and low-level divergent winds over Southern China are observed, creating favorable climatic conditions for the rise in SAT. The primary driving factor behind these circulation changes is identified as the Pacific interdecadal oscillation, which shifted from a positive to a negative phase during the same period. This shift led to the strengthening of the Walker circulation and the formation of an anomalous anticyclone over the northwest Pacific. It is worth noting that current mainstream climate dynamical models failed to capture the abrupt rise in SAT, instead exhibiting a sustained increasing trend that compromised the accuracy of the forecast. This discrepancy may be attributed to the models' slow response to rapid changes in sea surface temperature. [ABSTRACT FROM AUTHOR]

Summer surface air temperature (SAT) variability over Northern Hemisphere (NH) continents can profoundly impact human society, yet its seasonal prediction remains challenging, partly due to the limited prediction skill of dynamical models, especially over extratropical and high-latitude areas. Previous research has defined five indices associated with different atmospheric circulation patterns, which have important contributions to variations of summer SAT. This study further establishes a physically based empirical model (P–E model) using the Bayesian dynamic linear model method for the prediction of the indices, and uses the predicted indices to reconstruct the summer SAT anomaly field. Results show that the P–E model can reasonably well predict the five indices during 1950–2021. Combining this with the linear trend, the total summer SAT anomaly is also reconstructed. The high cross-validated hindcast skill for the period of 1950–2021 and independent forecast skill of 2022 indicate that the summer SAT over NH continents can be reasonably predicted by the P–E model. [ABSTRACT FROM AUTHOR]

The impact of Northern Hemisphere stratospheric final warming (SFW) events on surface air temperature (SAT) in April and May is examined based on the fifth-generation reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5) and historical simulations from the Coupled Model Intercomparison Project Phases 6 (CMIP6). In ERA5, early SFWs occur in mid-March and lead to colder SAT in the Arctic, North Atlantic, and the Eurasian hinterland and warmer SAT in the Black Sea–eastern Europe region in April–May, while the anomalous SAT related to late SFWs that occur in early May is nearly the opposite. In addition, the effect of SFWs on SAT over the North Atlantic and Eurasia is more significant in April (May) in early (late) SFW years. The main pathway that SFW affects SAT over North Atlantic and Eurasia is through a tropospheric quasi-barotropic wave train, successively exhibiting anomalous high (low) centers above warmer (colder) SAT. This wave train seems associated with the Northern Annular Mode at 200 hPa in April, indicating its crucial role in linking SFWs and anomalous SAT. Generally, CMIP6 models can well reproduce the onset process of early and late SFWs but fail to capture its impact on spring SAT because of the underrepresentation of the associated Eurasian wave train due to ensemble means. The intercomparison among the models reveals that, models with realistic anomalous tropospheric wave train over Eurasia also simulate better surface impact of SFWs. [ABSTRACT FROM AUTHOR]

Strengthened by polar amplification, Arctic warming provides direct evidence for global climate change. This analysis shows how Arctic surface air temperature (SAT) extremes have changed throughout time. Using ERA5, we demonstrate a pan-Arctic (>60°N) significant upward SAT trend of +0.62°C decade−1 since 1979. Due to this warming, the warmest days of each month in the 1980s to 1990s would be considered average today, while the present coldest days would be regarded as normal in the 1980s to 1990s. Over 1979–2021, there was a 2°C (or 7%) reduction of pan-Arctic SAT seasonal cycle, which resulted in warming of the cold SAT extremes by a factor of 2 relative to the SAT trend and dampened trends of the warm SAT extremes by roughly 25%. Since 1979, autumn has seen the strongest increasing trends in daily maximum and minimum temperatures, as well as counts of days with SAT above the 90th percentile and decreasing trends in counts of days with SAT below the 10th percentile, consistent with rapid Arctic sea ice decline and enhanced air–ocean heat fluxes. The modulated SAT seasonal signal has a significant impact on the timing of extremely strong monthly cold and warm spells. The dampening of the SAT seasonal fluctuations is likely to continue to increase as more sea ice melts and upper-ocean warming persists. As a result, the Arctic winter cold SAT extremes may continue to exhibit a faster rate of change than that of the summer warm SAT extremes as the Arctic continues to warm. Significance Statement: As a result of global warming, the Arctic Ocean's sea ice is receding, exposing more and more areas to air–sea interactions. This reduces the range of seasonal changes in Arctic surface air temperatures (SAT). Since 1979, the reduced seasonal SAT signal has decreased the trend of warm SAT extremes by 25% over the background warming trend and doubled the trend of cold SAT extremes relative to SAT trends. A substantial number of warm and cold spells would not have been identified as exceptional if the reduction of the Arctic SAT seasonal amplitudes had not been taken into account. As the Arctic continues to warm and sea ice continues to diminish, seasonal SAT fluctuations will become more dampened, with the rate of decreasing winter SAT extremes exceeding the rate of increasing summer SAT extremes. [ABSTRACT FROM AUTHOR]

A thermodynamic energy budget analysis is applied to the lowest model level of the ERA5 dataset to investigate the mechanisms that drive the growth and decay of extreme positive surface air temperature (SAT) events. Regional and seasonal variation of the mechanisms are investigated. For each grid point on Earth's surface, a separate composite analysis is performed for extreme SAT events, which are days when temperature anomaly exceeds the 95th percentile. Among the dynamical terms, horizontal temperature advection of the climatological temperature by the anomalous wind dominates SAT anomaly growth over the extratropics, while nonlinear horizontal temperature advection is a major factor over high-latitude regions and the adiabatic warming is important over major mountainous regions. During the decay period, advection of the climatological temperature by the anomalous wind sustains the warming while nonlinear advection becomes the dominant decay mechanism. Among diabatic heating processes, vertical mixing contributes to the SAT anomaly growth over most locations while longwave radiative cooling hinders SAT anomaly growth, especially over the ocean. However, over arid regions during summer, longwave heating largely contributes to SAT anomaly growth while the vertical mixing dampens the SAT anomaly growth. During the decay period, both longwave cooling and vertical mixing contribute to SAT anomaly decay with more pronounced effects over the ocean and land, respectively. These regional and seasonal characteristics of the processes that drive extreme SAT events can serve as a benchmark for understanding the future behavior of extreme weather. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people's livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/minimum temperatures at 0.1° spatial resolution over the QTP. The projections are based on the output from seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for historical (1979–2013) and projected (2015–2100) climates across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). An updated nonstationary cumulative distribution function matching method (called CNCDFm) was used to remove model systemic bias. We verify the necessity of taking into account altitude in downscaling processes and the validity of nonstationary bias correction. Compared to the historical period, the climate in the QTP in the 21st century is warmer (1.2–5.1 °C, for maximum surface temperature) and wetter (3.9–26.8%) according to the corrected GCM projection. For precipitation, the Indus River (IDR), Tarim River (TMR), Inner of Qiangtang Basin (IQTB), Yarlung Zangbo (YLZBR), and Qaidam Basin (QDB) showed growth well above the global average across high radiative forcing scenarios, which could have a profound impact on the regional hydrological cycle. However, there is great uncertainty in precipitation prediction, which is demonstrated by a very low signal-to-noise ratio (SNR) and a large difference between Bayesian model averaging (BMA) and multi-model averages (MMAs). This bias-corrected dataset is available for climate change impact research in the QTP at the subregion scale. [ABSTRACT FROM AUTHOR]

Land snow is considered one of the important Earth system elements altering sub-seasonal to seasonal (S2S) atmospheric variability and predictability. However, the causal relationship in the snow–atmosphere interaction and its impact on S2S predictability are still not clearly understood. In this study, we investigated the sub-monthly causal relationship between observed snow cover (SC) and surface air temperature (SAT) in the Northern Hemisphere. We used Liang–Kleeman information flow analysis to scrutinise the direction of causation and identify "cold spots" where SC conditions actively influence SAT on a sub-monthly timescale. The cold spots were identified by geographical location and season: North Eurasia in September and October; East Siberia in October and May; Canada in November; East Asia in November and March; Central Asia in October and November; and East Europe in March. Results based on snow water equivalent instead of SC also confirmed the cold spots identified in SC. Furthermore, we evaluated the SC–SAT causal relation in operational S2S prediction models. The results indicated that the S2S models underestimate the SC influence on SAT to greater or lesser degrees, implying the deficiencies in the models. This study emphasises the importance of faithfully reproducing the SC effect on SAT in S2S models for further possible improvements in sub-seasonal prediction skill. The findings renew a fundamental understanding of the snow–atmosphere interaction and sub-seasonal predictability arising from land snow conditions. [ABSTRACT FROM AUTHOR]

The slowly varying soil temperature can exert local and nonlocal influences on regional climate system, and may thus provide a critical source of subseasonal-to-seasonal climate prediction. In this study, we identify that soil temperature in May over the key region of Central Asia (42 °N–50 °N, 62 °E–80°E, KRCA) from Noah, Mosaic, CLM and ERA-interim datasets is closely linked to variations of the surface air temperature, daily maximum temperature and hot days over Northeastern China in summer (June–July–August), with correlation coefficients of regional average detrended time series ranging from 0.42 to 0.54, and all significant at the 99% confidence level for the period of 1979–2018. The possible physical mechanism behind the substantial downstream impacts of soil temperature over Central Asia are explored via diagnostical analysis combined with regional climate model experiments. Warmer soil temperature in May over the KRCA can keep to the summer and cause positive anomalies of geopotential height in summer over Northeastern China through the Rossby wave propagation, and associated stronger subsidence warming, less cloud cover, more solar radiation reaching the surface, higher planetary boundary layer, and stronger thermal advection at 850 hPa, which provide favorable conditions for warmer surface air temperature particularly in the daytime as well as more hot days. This study further reveals that soil temperature over Central Asia in May makes important contribution to prediction of summer surface air temperature, daily maximum temperature and hot days over Northeastern China in terms of regional average time series and spatial patterns. Our findings highlight the previously-unknown substantial role of antecedent soil temperature condition over Central Asia for summer surface air temperature variation and prediction over Northeastern China. [ABSTRACT FROM AUTHOR]

This study investigates the evolution of an extreme anomalous anticyclone (AA) event over Northeast Asia, which was one of the dominant circulation systems responsible for the catastrophic extreme precipitation event in July 2021 in Henan, and further explores the significant impact of this AA on surface temperatures beneath it. The results indicate that this AA event over Northeast Asia was unprecedented in terms of intensity and duration. The AA was very persistent and extremely strong for 10 consecutive days from 13 to 22 July 2021. This long-lived and unprecedented AA led to the persistence of warmer surface temperatures beyond the temporal span of the pronounced 500-hPa anticyclonic signature as the surface air temperatures over land in Northeast Asia remained extremely warm through 29 July 2021. Moreover, the sea surface temperatures in the Sea of Japan/East Sea were extremely high for 30 consecutive days from 13 July to 11 August 2021, persisting well after the weakening or departure of this AA. These results emphasize the extreme nature of this AA over Northeast Asia in July 2021 and its role in multiple extreme climate events, even over remote regions. Furthermore, possible reasons for this long-lasting AA are explored, and it is suggested to be a byproduct of a teleconnection pattern over extratropical Eurasia during the first half of its life cycle, and of the Pacific–Japan teleconnection pattern during the latter half. [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]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

To better understand the contributions of various external factors to past and future changes in global and regional climate, this study investigates the impacts of natural and anthropogenic forcings on historical and future changes in global land surface air temperature (GLSAT) using model simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results show that the anthropogenic forcing (ANT) can be robustly detected and separated from the response to the natural external forcing (NAT) since the 1970s. The observed warming changes since the 1950s are primarily attributed to the GHG forcing. ANT contributes a robust warming trend of 0.1–0.2 °C per decade for global landmass during 1951–2020 and cumulative warming by 2011–2020 (relative to 1901–1930) of 1.0–1.6 °C. These attributable warmings largely encompass the observed warming trend of ~ 0.18 °C per decade in 1951–2012 and the observed warming of 1.59 °C by 2011–2020 (relative to 1850–1900) for global landmass reported in IPCC AR5 and AR6, respectively. The anthropogenic warming is projected to increase by 3–6 °C for most global landmass under the SSP2-4.5 scenario, especially in the high latitudes Northern Hemisphere by the late twenty-first century, along with an increase in the mean and widespread flattening of the probability distribution functions (PDFs). The anthropogenic aerosol (AA) cooling effect is projected to decrease only modestly, from 0.7 °C in 2011-20 to 0.6 °C by the late 21st century, for the SSP2-4.5 scenario. [ABSTRACT FROM AUTHOR]

This study identifies a significantly positive relationship between summer surface air temperature (SAT) anomalies over two remote regions in the Eurasian continent and North America during the period 1979–2021 on the interannual time scale. The former region includes the East European Plain and the West Siberian Plain, and the latter region includes central and eastern North America. The regionally averaged summer SAT anomalies show a correlation coefficient of 0.66 between these two regions, which is significant at the 99% confidence level. This intercontinental SAT relationship can be explained by a wavelike pattern of circulation anomalies, which is the leading mode of upper-tropospheric circulation anomalies over the middle and high latitudes of the Northern Hemisphere in summer. Further analysis suggests that the sea surface temperature (SST) anomalies over the Pacific and North Atlantic in the preceding spring, being coupled with the leading mode of atmospheric circulation anomalies over the Pacific–Atlantic sector, persist into summer and affect the SATs in the two remote regions, resulting in the intercontinental SAT connection. Significance Statement: Summer surface air temperature (SAT) has profound effects on public health and agricultural production. Here we find a significantly positive relationship between interannual variations of summer SATs over two remote regions, one in the Eurasian continent and the other in North America. This intercontinental relationship in SATs can be explained as a result of atmosphere–ocean coupling over the Pacific and North Atlantic in the preceding spring. The result is likely to be a critical implication for the seasonal forecast of SAT variations over the two regions. In addition, the concurrence of higher or lower temperatures in these two regions may have impacts on global grain production, since these two regions include many major grain-producing areas in the world. [ABSTRACT FROM AUTHOR]

The surface and air temperature gradient (TS00 − Tair) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in TS00 − Tair from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that TS00 − Tair reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of TS00 − Tair has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (TS05), to calculate TS05 − Tair, we find that TS00 − Tair and TS05 − Tair have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of TS00 − Tair has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to Tair increasing at a higher rate than TS00 during the nighttime. The occurrence frequency of near-surface stable conditions (TS00 − Tair < 0) increases significantly, and the frequency of unstable conditions (TS00 − Tair > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of TS00 − Tair tends to decrease while the TS00 − Tair tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how TS00 − Tair responds to warming. Significance Statement: The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient. [ABSTRACT FROM AUTHOR]

Using the observational and reanalysis datasets during the period of 1979–2018, this study examines the modulation effects of the Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) on the interdecadal change in summer surface air temperature (SAT) over the eastern Tibetan Plateau (TP). The results show that the eastern TP has experienced a remarkable warming in boreal summer since 1997. In addition, the warming is associated with simultaneously enhanced westward water vapor transport and the northward shift of the subtropical westerly jet (SWJ) over the northeast of the TP. Further research indicates that both water vapor transport and the SWJ are modulated by the PDO and AMO on interdecadal time scales. Since the late 1990s, the PDO has changed from the positive to negative phase, while the AMO has changed from the negative to positive phase. The change in the out-of-phase combination of the PDO and AMO has resulted in a northward shift of the SWJ at 200 hPa near the northeast of the TP through the Rossby wave train originating from the northwestern Atlantic. In addition, the PDO and AMO could both affect the Rossby wave train. From the northwestern Atlantic to Eurasia, the wave train may be dominated by the PDO, while the wave train near the TP is dominated by the AMO. The anomalous anticyclone over the northeastern TP is part of the Rossby wave train, causing the northward shift of the SWJ, favoring the intrusion of the water vapor from the eastern China into the eastern TP, thus leading to the TP warming. Significance Statement: It is understood that the Tibetan Plateau (TP) may be among the most sensitive regions to the global climate change. In addition, this region has warmed very rapidly in the latter half of the twentieth century. Based on the statistical analysis and dynamic diagnostics, the influences of interdecadal variations of atmospheric circulations on summer surface air temperature (SAT) over the eastern TP were analyzed for the years 1979 to 2018. We found that the eastern TP experienced an increase in summer SAT. In addition, the interdecadal change in SAT over the eastern TP is strongly affected by the water vapor transport and the subtropical westerly jet. Further analyses show that the PDO and AMO were the key factors influencing summer SAT over the eastern TP on the interdecadal time scales. The PDO and AMO could both affect the Rossby wave train. From the northwest Atlantic to Eurasia, the wave train may be dominated by the PDO, while the wave train near the TP is dominated by the AMO. In addition, the northwest Atlantic SSTs, which are dominated by the AMO, could also affect the SAT over the eastern TP by the Rossby wave train. [ABSTRACT FROM AUTHOR]

Modern climate change and variability of the natural environment have a significant impact on the quality of life and health of the population, as well as on the development of the recreational and touristic industry in resort cities. Based on the long-term observations (1993–2023), the changes in the following climatic characteristics have been analyzed: surface air temperature, total precipitation, and current microbioclimatic parameters of the resort city of Kislovodsk. It has been found for the past 30 years that the average annual temperature in Kislovodsk according to the scale of balneological significance can be identified as favorable, and the average annual minimum and maximum temperatures can be characterized as the most favorable. The statistical parameters of the analyzed characteristics and their extremes are presented. The microbioclimatic potential of the central part of the city is characterized by an increased level of natural air ionization and high purity of the surface atmosphere in the areas that do not experience high anthropogenic load. [ABSTRACT FROM AUTHOR]

The frequency and duration of observed concurrent hot and dry events (HDEs) over China during the growing season (April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China (SEC), northern China (NC), and northeastern China (NEC). The frequency of HDEs averaged over China in the present day (PD, 1994–2011) is double that in the early period (EP, 1964–81); the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model (MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes, suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas (GHG) concentrations dominates the simulated decadal changes, increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol (AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Skill decreases in real-time seasonal climate prediction due to decadal variability" published in the journal Climate Dynamics. The correction updates the captions for Figures 7 and 9 in the article. Figure 7 shows the statistical significance of the hindcast and real-time forecast of seasonal mean surface air temperature, while Figure 9 displays the hindcast and real-time forecast of surface air temperature over the Yangtze River basin. The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

• 23 synoptic stations network is used to evaluate five temperature datasets in Ghana. • The assessment is carried out from multiple perspectives, including climatology. • Multiple performance metrics are used to assess the robustness of the datasets. • Using CRU, CPC, and TerraClimate in Ghana is most recommended. Global temperature datasets have become increasingly accessible in recent years, yet their performance in regions with sparse data remains a subject that requires thorough investigation, especially in areas where validation is essential. This study addresses this urgency, given the significant impact of the increase in global temperature, as highlighted in the IPCC's 6th Assessment Report, on regional and local levels. In this research, we evaluated air temperature datasets from Climatic Research Unit (CRU TS4.05), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), Climate Prediction Centre (CPC), European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-5), and TerraClimate (hereinafter referred to as Gridded Temperature Datasets - GTDs). Our assessment involved comparing and validating these datasets against reference ground observation data from 23 synoptic stations, spanning 40 years from 1981 to 2020, to ensure robustness in regions with sparse data. We conducted a spatio-temporal investigation to explore temperature patterns, and evaluated their capacity to reproduce inter-annual and intra-seasonal variability. To assess dataset performance, we employed a group of statistical metrics, including root-mean-square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS), and correlation coefficient (R). The GTDs consistently captured the annual climatological cycle distribution over Ghana. The correlation coefficient values significantly varied across the 23 synoptic stations and three climatic zones. At the annual level, CPC exhibited R values ranging from 0.53 to 0.93, while CRU ranged from 0.48 to 0.88. The bias for CPC and CRU spanned from 2.40 to −0.35 °C and 2.07 to −0.91 °C, respectively. Overall, CRU, CPC, and TerraClimate demonstrated the most consistent near-surface temperature patterns over Ghana, outperforming other datasets. Based on our findings, we recommend these datasets for model evaluation and assessment in Ghana, given their strong performance in capturing temperature variations. [ABSTRACT FROM AUTHOR]

El Niño is known to affect Arctic temperature. However, the robustness of the observed relationship between El Niño and Arctic temperature remains debated. Here we reexamine the impacts of El Niño on the Arctic temperature in boreal winter [December–February (DJF)] using reanalysis datasets and atmospheric model experiments. This study shows that extreme El Niño events are accompanied by significant boreal winter cooling over northeastern Canada and Greenland (NECG), while moderate eastern Pacific (MEP) El Niño events are accompanied by significant boreal winter warming in this region. For central Pacific (CP) El Niño events, a cold signal appears in NECG, but with no statistical significance. During extreme El Niño winters, a positive Pacific–North America (PNA)-like pattern is seen in the Pacific, and anomalously negative 200-hPa geopotential height (Z200) strengthening occurs over NECG, which is a response to anomalous strong wave activity originating in the tropical Pacific. El Niño–induced circulation anomalies can further induce NECG cooling via cold temperature advection and decreased downward longwave radiation. In contrast, for the MEP El Niño, the subtropical jet extends zonally from the North Pacific to the North Atlantic, which is accompanied by increased baroclinicity anomalies and favors the propagation of synoptic eddies into the Atlantic, leading to a negative North Atlantic Oscillation (NAO)-like pattern. This in turn could further enhance the positive Z200 anomalies over NECG, resulting in anomalous warming in NECG through warm temperature advection and enhanced downward longwave radiation. A series of atmospheric model experiments simulates the observed circulation changes and associated warming over NECG. Significance Statement: This work investigates the different impacts of the three El Niño types on regional Arctic wintertime temperature anomalies based on observations and model experiments. The impacts of El Niño events on northeastern Canada and Greenland temperatures during boreal winter show distinct differences between extreme El Niño and moderate EP El Niño events. These distinct differences can be attributed to the different atmospheric circulation patterns induced by different SST patterns, which can lead to warm (cold) temperature advection and enhanced (decreased) downward longwave radiation. These results highlight the different impacts of extreme and moderate EP El Niños on Arctic temperatures and provide an improved understanding of the impact of El Niños on the Arctic climate. [ABSTRACT FROM AUTHOR]

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS's ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales. [ABSTRACT FROM AUTHOR]

We investigate the interannual variability of surface air temperature in the twenty-first century using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model data under three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Relative to 1961−2014, the interannual variability of annual temperature is projected to increase by 6%, 6%, and 12% globally in the 2051–2100 period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, with an average increase of 10–22% in low latitudes and decrease of 3–28% in high-latitude oceans. The spatial pattern of wintertime variability changes is mainly determined by variations in the meridional temperature gradient. Sea ice losses and variations in snow extent in the mid to high latitudes are both related with large variability decreases according to analysis of the underlying surface. In South America and South Africa, the variability enhancement is related to the adjustment of the wet–dry status, while that in South Asia and central Africa depends more on the positive longwave radiative effect due to the increase in clouds. In tropical oceans, the increases in sea surface temperature variabilities and air–sea interactions dominate the enhanced surface air temperature variability. [ABSTRACT FROM AUTHOR]

The relationship between Arctic Oscillation (AO) and surface air temperature (SAT) over the Tibetan Plateau (TP) has been long debated, particularly in boreal summer. Based on the statistical analysis, this study reveals a significant interdecadal shift in the relationship between summer AO and the first mode of the summer SAT (SAT_EOF1) over the eastern TP in recent decades. Specifically, during 1979–1998, the correlation between summer AO and SAT_EOF1 was weak and insignificant, but during 1999–2018, it was strong and significant. Changes in the AO-related regional atmospheric circulations are responsible for this enhanced AO-SAT_EOF1 connection. During the latter period, the anomalous anticyclone related to AO has extended further westward from eastern China to the eastern TP, leading to the increased water vapor transport and prevalent descending over the eastern TP. As a result, there has been a decrease in cloud cover over the eastern TP, allowing more solar radiation to reach the ground surface and thus contributing to the enhanced AO-SAT_EOF1 linkage. Further analysis indicates that the phase transition of the Pacific decadal oscillation (PDO) is a crucial factor in regulating the AO-SAT_EOF1 linkage. Since the late 1990s, the PDO has transitioned from a positive to a negative phase, playing a role in the westward extension of the AO-related anomalous anticyclone over the eastern side of the TP through the Pacific-East Asian (PEA) teleconnection. Finally, the model simulation results provide conclusive evidence that the phase transition of the PDO contributes to the unstable AO-SAT_EOF1 connection. [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]

Studies of variations in the parameters of various geophysical fields during the preparation of destructive earthquakes that occurred in Turkey in February 2023 with magnitudes of 6 ≤ M ≤ 7.8 are carried out using satellite data. It has been found that anomalies of these parameters manifested themselves from 34 to 25 days before the earthquakes as a sharp decrease in the values of relative humidity (RHS) and outgoing longwave radiation (OLR), as well as in an increase in the density of local lineaments. An increase in the surface skin temperature (SST), surface air temperature (SAT), RHS, and OLR, as well as in the values of the aerosol optical depth (AOD) and ionospheric total electron content (TEC), was revealed 19–9 days before the analyzed seismic events. In the period from 5 to 2 days before these earthquakes, a decrease in the SST, SAT, the flux of OLR, and the ionospheric TEC, as well as an increase in RHS and in the length of the secants of the rose diagrams of regional lineaments, were recorded. Quantitative characteristics of these anomalies are determined. [ABSTRACT FROM AUTHOR]

It has been well known that the preceding winter ENSO affects the atmospheric convection over the tropical western North Pacific (WNP) in summer, which has important impacts on Asian climate. However, more than half of the interannual variance in tropical WNP convection cannot be explained by ENSO. This study separates the WNP convection into two components, namely, independent of and dependent on the preceding winter ENSO, and compares the anomalies associated with these two components. The linear regression results indicate that the independent convection suppression corresponds to significant cyclonic anomalies over East Asia in both the lower and upper troposphere, and correspondingly a southward displacement of upper-tropospheric East Asian westerly jet. By contrast, these circulation anomalies are weakened for the dependent convection suppression, which is more closely related to the lower-tropospheric cyclonic anomalies over the Indian Ocean. Accordingly, the independent and dependent components exert distinct impacts on rainfall and temperature in Asia. Specifically, the independent suppression corresponds to more significantly enhanced rainfall in subtropical East Asia compared with the dependent one. Moreover, there are colder surface air temperatures in the midlatitude East Asia for the independent suppression and warmer temperatures in South and Southeast Asia for the dependent suppression. Further analyses suggest that the circulation and climate anomalies for the independent component are mainly contributed by July and August, while those for the dependent component become weak from June to August. These results can be helpful for a better understanding of summer Asian climate variability and predictability. [ABSTRACT FROM AUTHOR]

Forcing pathways between urban surfaces (impervious and pervious pavers) and near-surface air temperature were measured and investigated with a network of multiple sensors. Utilizing field data measured between April 2021 and May 2022, and assuming that the influential variables follow the basic heat-transfer energy-balance equations, multiple regression-based statistical models were built to predict the surface temperature and near-surface air temperature (0.05 m, 0.5 m, 1 m, 2 m, and 3 m) of one impervious paver site and one pervious paver site in Taipei City, Taiwan. Evaporative cooling was found to be more influential on the pervious paver with a statistically significant influence on the microclimate up to 1.8 m (and up to 0.7 m for the impervious paver), using in situ data with an ambient air temperature higher than 24 °C. The surface temperature is mainly affected by solar shortwave radiation and ambient air temperature. As for near-surface air temperature, ambient air temperature is the most influential factor, followed by surface temperature. The importance of surface temperature indicates the influence of upwelling longwave radiation on the microclimate. The predictive equations show that pervious surfaces can help cities with hot and humid climates fight the changing climate in the future. [ABSTRACT FROM AUTHOR]

The Daxing'anling forest region represents a crucial forestry hub in China and confronts some of the nation's most severe freezing–thawing hazards. This study delved into the temporal trends and spatial distributions of various parameters related to freezing and thawing, including air temperature, ground surface temperature, freezing index, thawing index, and freezing–thawing frequency. Furthermore, this study assessed and delineated freezing–thawing hazards within the research area. The findings revealed a rapid increase in air temperature and ground surface temperature within the Daxing'anling forest region yet a lower rate of increase in ground surface temperature compared to Northeast China. Latitude had the strongest influence on mean annual air temperature, mean annual ground surface temperature, air freezing index, air thawing index, ground surface freezing index, ground surface thawing index, air freezing–thawing frequency, and ground surface freezing–thawing frequency, followed by longitude and elevation. Overall, freezing index, and air freezing–thawing frequency increased from south to north, whereas mean annual air temperature, mean annual ground surface temperature, air thawing index, ground surface thawing index, and ground surface freezing–thawing frequency decreased from south to north. The assessment outcomes underscore the importance of closely monitoring freezing–thawing hazards in regions north of the 50th parallel. [ABSTRACT FROM AUTHOR]

Higher temperature is one of the key reasons for thermal distresses in asphalt pavement. It also adds to the Urban Heat Island (UHI) effect as it influences the near-surface air temperature. To design cooler pavements, phase change materials (PCMs) can be incorporated into asphalt pavements. However, considering the negative effects of PCM leakage on the physical and rheological properties of asphalt binder, a cent-percent effective core-shell encapsulated PCM suitable for asphalt pavement applications was developed under this study. With the incorporation of two organic mixture (OM) based PCMs, i.e., OM-35 and OM-42, a peak decrease in the pavement surface temperature of 3.05 °C and 4.36 °C, respectively were observed under the field condition. Furthermore, from the long-term thermal performance assessment, it was found that the magnitude of temperature reduction depends on the phase change temperature and latent heat of PCM. The season of occurrence of the peak temperature reduction depends on the phase change temperature of the PCM. Due to PCM solidification, the increase in night-time pavement surface temperature was observed to be about half of that during day time. Further, the statistical analysis reveals that the decrease in pavement surface temperature due to PCM incorporation is significant and consistent. [ABSTRACT FROM AUTHOR]

An enhanced Warm Arctic—Cold Eurasia (WACE) pattern has been a notable feature in recent winters of the Northern Hemisphere. However, divergent results between model and observational studies of the WACE still remain. This study evaluates the performance of 39 climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the WACE pattern in winter of 1980–2014 and explores the key factors causing the differences in the simulation capability among the models. The results show that the multimodel ensemble (MME) can better simulate the spatial distribution of the WACE pattern than most single models. Models that can/cannot simulate both the climatology and the standard deviation of the Eurasian winter surface air temperature well, especially the latter, usually can/cannot simulate the WACE pattern well. This mainly results from the different abilities of the models to simulate the range and intensity of the warm anomaly in the Barents Sea—Kara seas (BKS) region. Further analysis shows that a good performance of the models in the BKS area is usually related to their ability to simulate location and persistence of Ural blocking (UB), which can transport heat to the BKS region, causing the warm Arctic, and strengthen the westerly trough downstream, cooling central Eurasia. Therefore, simulation of UB is key and significantly affects the model's performance in simulating the WACE. [ABSTRACT FROM AUTHOR]

A strong coherence of the summer (June–August) surface air temperature (SAT) anomalies exists over eastern Eurasia (EE) and central Eurasia (CE) in association with a remarkable intraseasonal (10–90-day) variability. The intraseasonal SAT over Eurasia shows a negative correlation between EE and CE, which is closely related to an intraseasonal wave train at the upper troposphere over the mid–high latitudes of Eurasia. The wave train propagates eastward and results in the intraseasonal variation of SAT in a seesaw pattern. The column-integrated temperature budget suggests that both the horizontal and vertical advection are important for the intraseasonal SAT dipole. More specifically, the climatological westerly, intraseasonal meridional wind anomaly, and the anomalous vertical motion play dominant roles in perturbing temperature over the CE and EE regions, while the diabatic heating is a negative feedback. The propagation of the Rossby wave train in the upper troposphere helps to maintain the circulation anomalies associated with the SAT seesaw. The leading modes of meridional wind anomalies at 200 hPa over the Eurasian high latitudes resemble the intraseasonal wave train, indicating that the wave train associated with the SAT seesaw is an atmospheric intrinsic mode over the mid–high latitudes. This study also investigates the impact of the intraseasonal SAT seesaw on regional heat wave events. It is suggested that the intraseasonal SAT seesaw has a major influence on the variability of heat wave events over both EE and CE, which may provide great implications for regional subseasonal forecasts of extreme weather events. [ABSTRACT FROM AUTHOR]

For all the benefits of a compost barn system to be achieved, the management, especially with the bedding, must be done correctly. One should consider the requirements regarding the revolving of the material, use of good quality organic matter, suitable ventilation, and an ideal animal stocking rate, so that the composting occurs efficiently. Given this, this work aimed to evaluate the spatial variability of thermal characteristics of rice straw bedding, using values of temperature (surface and internal) and humidity. The temperature and moisture values of the litter were recorded at intervals during the morning shift, from 6:00 am to 7:00 am and from 8:00 am to 9:00 am, and during the afternoon shift, from 3:00 pm to 4:00 pm and from 5:00 pm to 6:00 pm, considering the management of the turn-over, that is, before and after the movement of the litter. Litter variables were collected at points distributed on a 39-point grid. There were no differences between litter surface temperatures before and after turning over in the morning shift, but in the afternoon shift, the surface temperature was lower (P < 0.05) before, when compared to after turning. The internal temperature and litter moisture were higher (P < 0.05) before revolving in both shifts. Spatial dependence was verified (IDE) for the variables studied. Based on the geostatistical analysis, it is concluded that the IDE was rated as strong for surface and internal temperatures, which demonstrates heterogeneity among the points evaluated and a large spatial variability regarding bed temperatures. In the case of litter moisture, the greatest variability occurred in the morning period. The litter surface temperature values did not exceed the ambient air temperature values. [ABSTRACT FROM AUTHOR]

This study investigates the influence of the Atlantic multidecadal oscillation (AMO) on the multidecadal variability of winter surface air temperature in arid central Asia (ACASAT). Apart from a long-term warming trend, the observational analysis shows that the winter ACASAT exhibits a significant multidecadal variability, which is characterized by antiphase fluctuations with the AMO. The mechanism for this negative correlation between the AMO and the winter ACASAT is explored from the aspect of wave teleconnection. The AMO provides energy for the Scandinavian teleconnection pattern at middle and low altitudes by regulating the high-altitude wave train over the middle and high latitudes of Eurasia, and thus has an impact on the remote climate in arid central Asia. Results from the linear baroclinic model (LBM) provide evidence for the linkage between the AMO and the Scandinavian teleconnection pattern. When the AMO is in its warm periods, the Scandinavian teleconnection pattern is in a positive phase, which further makes the cold air from the northeast strengthen, leading to the anomalously colder surface air temperature in arid central Asia. Based on the relationship that the North Atlantic Oscillation (NAO) leads the AMO by 15–20 years, it is further found that there is a leading relationship between the NAO and the winter ACASAT via the AMO. On this basis, an empirical model using the NAO as a predictor was established to predict the ACASAT, and the empirical model shows good hindcast performance. Results from the model show that the winter ACASAT will continue to rise in the next 10 years and decline after 2030. [ABSTRACT FROM AUTHOR]

Physically based observational constraint methods can effectively reduce uncertainty in global warming projections but have not been widely applied at regional scales. We first develop and apply multivariate linear regression models for constraining projections of surface air temperature averaged over subcontinental regions in the extratropical Northern Hemisphere, based on a set of potential constraints including climatological metrics derived from tropical and subtropical low-level cloud and global average past warming trend, as well as a set of regional climate metrics previously used in the literature. We evaluate the performance of the multivariate linear regression models based on cross-validated tests using output from phases 5 and 6 of the Coupled Model Intercomparison Projects (CMIP). We find that linear regression models using global-scale low-cloud metrics alone perform more robustly than linear regression models using the past global mean warming trend or regional climate metrics as constraints. These results, while favoring global constraints over the set of regional constraints considered, do not preclude the existence of even better regional constraints for particular regions. Through model-based cross-validation, the projections constrained using low-level cloud metrics exhibit more accurate best estimate projections, narrower uncertainty ranges, and more reliable uncertainty estimates in most Northern Hemisphere regions when compared with unconstrained projections. Application of the approach to climate projections based on both Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 using observed low-cloud metrics results in considerably narrower 5%–95% uncertainty ranges of twenty-first-century warming over subcontinental Northern Hemisphere land regions compared to unconstrained projections. [ABSTRACT FROM AUTHOR]

This study examines seasonal and annual mean temperature changes from 1970 to 2014. Climatologically, the June–July–August season exhibited the highest (27 °C) mean temperatures over the country, followed by March–April–May (MAM; 20.2 °C), September–October–November (SON; 19.1 °C), and December–January–February (DJF; 8.8 °C), while the annual mean was 18.8 °C. The southern region exhibited higher mean temperatures than the northern region for the DJF, MAM, JJA, SON, and annual timescales during the study period. The seasonal trend across the country was highest in MAM (0.027 °C/year), followed by SON (0.025 °C/year), DJF (0.023 °C/year), and JJA (0.016 °C/year). The interannual trend increased significantly at 0.023 °C/year across the country. Across the north, MAM showed the highest increase (nonsignificant) in trends at 0.025 °C/year, followed by a significant increase during SON (0.018 °C/year), DJF (0.017 °C/year), and JJA (0.010 °C/year), while annual trends were the lowest (0.017 °C/year). Examination of abrupt change over Pakistan showed nonsignificant change during JJA, while MAM, JJA, SON, and annual timescales demonstrated significant positive and negative changes. Decadal anomalies showed long-term positive tendencies in DJF (since 1990–2014) temperature, followed by JJA, SON, annual, and MAM timescales (2000–2014). In conclusion, the observed changes in temperature were significantly robust and promise increasing signal patterns in all time scales. [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]

This work involves calculations of climatic trends of anomalies in daily minimum, maximum, and average air temperatures based on the quantile regression method (QRM), which allows one to estimate trends in detail for any quantile in the range of quantile values from 0 to 1. Based on the QRM climate trend calculations detailed for different quantiles of trends in daily air temperature anomalies, clustering of more than 1400 meteorological stations of Russia is performed. Clustering is carried out in the multidimensional space, the formation of which takes into account seasonal peculiarities of the QRM trends of anomalies for three types of daily temperatures (daily minimum, maximum, and average temperatures) and features of the QRM trends in different parts of the quantile range. Twelve clusters of weather stations have been distinguished in the created multidimensional space using the k-means method. The stations that are included in each of the distinguished clusters are similar in terms of manifestation of the QRM trends of temperature. Despite the absence of characteristics of the geographical location of the observation stations among the variables of the multidimensional space, the stations within each of the twelve distinguished clusters are situated geographically quite compactly. The geographical distribution of stations assigned to different clusters is demonstrated and discussed. Based on the results of clustering, some features of quantile trends of temperature anomalies of specific seasons within the groups of stations assigned to individual clusters are described. Differences in manifestation of quantile trends between 12 clusters of Russian stations distinguished based on QRM quantile trends are obvious. At the same time, however, significant similarities can be observed between some individual pairs of clusters. The approaches and results of this work can be used to improve the climatic zoning of the Russian territory, which seems to be very relevant for the preparation and implementation of regional plans of adaptation to climate changes. The results can also be used for solving various applied climatology problems based on calculations of quantiles of different meteorological parameters. [ABSTRACT FROM AUTHOR]

Prolonged low temperature significantly threatens transportation and planting in populous areas and is closely linked to slowly varying forcing such as sea surface temperature (SST). In this study, we reveal the impact of the north–south dipole pattern in extra-tropical North Atlantic SST (DNA) anomaly on surface air temperature (SAT) over southern China in February. The positive phase of the DNA is favorable for a positive North Atlantic Oscillation-like pattern at the troposphere via synoptic eddy vorticity forcing and triggers a southeastward-propagating Rossby wave train associated with the deepened East Asian trough and the strengthened Siberian high, which are conducive to enhancement of the East Asian winter monsoon. A stronger monsoon leads to lower-than-normal SAT over southern China by intensifying southward cold air outbreaks. Considerably, the extremely low temperature over southern China in February 2022 is explainable by this physical mechanism. Furthermore, experiments with a linear baroclinic model verify that the proposed downstream propagating Rossby wave train is mainly associated with the midlatitude North Atlantic SST anomaly. These results emphasize that the extra-tropical North Atlantic SST anomaly should be focused when interpreting and predicting the late winter SAT over southern China. [ABSTRACT FROM AUTHOR]

Higher temperature is one of the key reasons for thermal distresses in asphalt pavement. It also adds to the Urban Heat Island (UHI) effect as it influences the near-surface air temperature. To design cooler pavements, phase change materials (PCMs) can be incorporated into asphalt pavements. However, considering the negative effects of PCM leakage on the physical and rheological properties of asphalt binder, a cent-percent effective core-shell encapsulated PCM suitable for asphalt pavement applications was developed under this study. With the incorporation of two organic mixture (OM) based PCMs, i.e., OM-35 and OM-42, a peak decrease in the pavement surface temperature of 3.05 °C and 4.36 °C, respectively were observed under the field condition. Furthermore, from the long-term thermal performance assessment, it was found that the magnitude of temperature reduction depends on the phase change temperature and latent heat of PCM. The season of occurrence of the peak temperature reduction depends on the phase change temperature of the PCM. Due to PCM solidification, the increase in night-time pavement surface temperature was observed to be about half of that during day time. Further, the statistical analysis reveals that the decrease in pavement surface temperature due to PCM incorporation is significant and consistent. [ABSTRACT FROM AUTHOR]