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This study examines the multi-decadal variability and trends of surface air temperature and precipitation in the Indian Cardamom Hills (ICH), a degraded tropical rainforest area unique for cardamom cultivation. Utilizing observed long-term climatic data (1958–2017), statistical methods such as the Mann–Kendall test (MKT), Sen’s Slope Estimator (SSE), and Incremental Trend Analysis (ITA) were applied to assess the impact of surface air temperature, rainfall, and the number of rainy days on cardamom yield. The analysis revealed a significant decline in annual rainfall by approximately 13.62 mm per year, with pronounced seasonal declines 0.87 mm for winter, 12.33 mm for pre-monsoon, 24.93 mm for southwest monsoon, and 18.10 mm for post-monsoon. Simultaneously, the number of rainy days dropped by nearly 19.75 days over the 40-year period. A noticeable increase in decadal minimum and average temperatures was observed, highlighting potential adverse effects on cardamom yield and irrigation water resources. The findings suggest that excessive rainfall during the southwest monsoon negatively correlates with cardamom yield, while slightly warmer temperatures show a weak positive correlation. The study also emphasizes the need for adaptive agricultural practices and climate-resilient policies to mitigate the effects of changing climatic conditions on cardamom production. This research contributes valuable insights for farmers and other stakeholders as well as policymakers aiming to ensure sustainable cardamom cultivation amidst climate change. [ABSTRACT FROM AUTHOR]

We examine daily surface air temperatures (SAT) in the Arctic under global warming, synthesizing changes in mean temperature, variability, seasonality, and extremes based on five Earth system model large ensembles from the Coupled Model Intercomparison Project Phase 6. Our analysis shows that the distribution of daily Arctic SAT changes substantially, with Arctic mean temperatures being distinguishable from pre‐industrial levels on 84% and 97% of days at 1.5 and 2°C of global warming, respectively, and on virtually every day at 3°C of global warming. This shift is primarily due to the rapid rise in average temperature resulting from Arctic amplification and is exacerbated by a decrease in the variability of daily Arctic SAT of approximately 8.5% per degree of global warming. The changes in mean temperature and variability are more pronounced in the cold seasons than in summer, resulting in a weakened and shifted seasonal cycle of Arctic SAT. Moreover, the intensity and frequency of warm and cold extreme events change to varying degrees. The hottest days warm slightly more, while the coldest days warm 4–5 times more than the global average temperature, making extreme cold events rare. Changes in local SAT vary regionally across the Arctic and are most significant in areas of sea‐ice loss. Our findings underscore the Arctic's amplified sensitivity to global warming and emphasize the urgent need to limit global warming to mitigate impacts on human and natural systems. Plain Language Summary: The Arctic warms about three times faster than the globe on average, impacting ecosystems and human societies profoundly. While such average warming is scientifically interesting, it is the changes in local temperatures on a daily basis that humans will be experiencing. Based on climate model simulations, we therefore examine how these daily temperatures in the Arctic change with global warming. We find that the distribution of daily Arctic temperatures shifts dramatically under global warming, barely overlapping with pre‐industrial temperatures. This shift is mainly due to the amplified mean warming of the Arctic and is exacerbated by the lower fluctuations in daily temperatures with global warming. The changes in mean temperature and variability are more pronounced in winter, leading to shifting seasons and a decreasing temperature contrast between Arctic summers and winters. The intensity and frequency of warm and cold extreme events are changing. In particular, the coldest days warm 4–5 times more than the global average temperature, making extreme cold events rare. These changes vary across the Arctic, with areas losing sea ice experiencing the most significant shifts. This study emphasizes the urgent need to reduce global warming to protect Arctic ecosystems and communities. Key Points: The distribution of daily Arctic temperatures shifts drastically under global warming, barely overlapping with pre‐industrial temperaturesThis is due to amplified Arctic warming, along with a reduction in the variability of daily temperaturesThe hottest days warm slightly more, while the coldest days warm 4–5 times more than the global mean temperature [ABSTRACT FROM AUTHOR]

The atmospheric temperature change associated with rapid sea ice loss events over the Barents–Kara Seas (BKS) is explored. Rapid sea ice loss events are defined as periods with a tendency for sea ice concentration averaged over BKS to be below the fifth percentile of the probability density function distribution during the winters of 1979–2020 for at least three consecutive days. Composite analysis shows that there is a significantly positive bottom‐amplified temperature anomaly ahead of the rapid sea ice loss, which is closely associated with a wave train composed of a Ural blocking and an upstream positive phase of the North Atlantic Oscillation. This structure is favorable for the transport of warm and moist air into the BKS, and therefore, the tropospheric temperature, including the surface air temperature, increases through horizontal warm‐temperature advection, specifically through warm advection of the climatological temperature by the anomalous wind. The cooling over the BKS due to the adiabatic effect and vertical mixing opposes the horizontal warm‐temperature advection above and below about 900 hPa, respectively. However, an increase in skin temperature prominently results from enhanced downward long‐wave radiation, which is also the main contributor to the rapid sea ice loss. [ABSTRACT FROM AUTHOR]

The faster warming for Arctic Ocean surface air temperature (SAT) relative to that at lower latitude is connected with various processes, including local radiation feedback, poleward oceanic and atmospheric heat transport. It is unclear how combinations of different low‐frequency internal climate modes influence Arctic amplification on the decadal timescale. Here, the decadal Arctic SAT variation, its connection with the Atlantic meridional overturning circulation (AMOC) and possible underlying mechanisms, are investigated based on several independent observational proxies, pre‐industrial experiments, and historical large ensembles of two CMIP6 models. Our study suggests that AMOC and Arctic SAT vary in phase on the decadal timescale, whereas this relationship is insignificant at the interannual timescale. Further analysis shows that the AMOC accompanied with cross‐basin oceanic water/heat transport between Atlantic and Arctic would alter air–sea interface exchange over the melting ice regions, and then amplified poleward atmospheric heat and moisture transports. The resulting enhanced downward longwave radiation ultimately warms the Arctic SAT. Additionally, the decadal‐scale North Pacific Oscillation (NPO) can modulate the relationship between AMOC and Arctic SAT by influencing poleward moisture transport and cross‐basin circulation. Specifically, the phase shift of combined NPO and AMOC can contribute 14%–41% covariance relationship between AMOC and Arctic SAT. Our study provides potential sources for predicting the Arctic climate and constraining its uncertainty in future projections. Plain Language Summary: The Arctic has warmed faster than lower latitudes and is capable of influencing weather and climate. Previous studies suggest that Atlantic meridional overturning circulation (AMOC) can induce warming over the Arctic region, however, the underlying mechanism for AMOC influencing Arctic SAT and whether their relationship is stable are still unclear. In this study, we have identified the relationship between the AMOC and Arctic SAT during 1950–2014 on the decadal scale. Our results demonstrate that AMOC changes in phase with the Arctic SAT, which is dominated by surface downward longwave radiation resulting from poleward moisture and heat transport, and air‐ice‐sea interaction associated with AMOC. Additionally, the North Pacific Oscillation (NPO) can modulate the correlation between AMOC and Arctic SAT by influencing the meridional eddy advection of water vapor and cross‐basin processes. Specifically, the phase shift of combined NPO and AMOC can contribute 14%–41% covariance relationship between AMOC and Arctic SAT. Key Points: The Atlantic meridional overturning circulation (AMOC) change in phase with the Arctic surface air temperature (SAT) during 1950–2014 on a decadal timescale in multiple observationsDecadal Arctic SAT change associated with AMOC is dominated by surface downward longwave radiation, evaporation and poleward moisture transportThe North Pacific Oscillation accompanied with the cross‐basin processes modulates the relationship between AMOC and Arctic SAT [ABSTRACT FROM AUTHOR]

The paper justifies the possibility of improving the dynamical-statistical method of predicting monthly average surface air temperature through the application more advanced hydrodynamic models and statistical methods for operational use by the Hydrometeorological Center of Russia. A technology for monthly forecasting of surface air temperature anomalies is presented. The technology is based on using both the results of an improved 15-day medium-range weather element forecast scheme and the outcome of integrating the SL-AV model over a 16–30 day interval. Quality assessments of the forecasts obtained in real-time mode for 326 stations located in Russia are provided. The advantages of the proposed approach, particularly evident in cases of significant air temperature anomalies, are demonstrated. The obtained results are expected to be used in the technology for issuing long-range forecasts by the Hydrometeorological Center of Russia. [ABSTRACT FROM AUTHOR]

This study investigated the influence of interannual variations in tropical Indian Ocean tripole (IOT) on the surface air temperature (SAT) over the western Tibetan Plateau (TP) during boreal summer. During the positive phase of the IOT, two northward cross-equatorial airflows are induced over the tropical eastern and western Indian Ocean. These airflows reinforce the ascending motion over the southern tropical Asia (15°–25°N, 80°–125°E), increasing local precipitation, as confirmed by observations and simulations by the Community Atmosphere Model. The upper-level Asian Continental Meridional Teleconnection (ACMT) pattern is excited by the latent heat released from precipitation and transmits signals from the southern tropical Asia to the western TP, leading to the positive geopotential height anomalies and anomalous anticyclones over there. Upper-level circulation anomalies over the western TP enhance atmospheric thickness through adiabatic processes, consequently elevating local SAT. The ACMT associated with precipitation anomalies thus serves as an atmospheric bridge connecting the IOT and the SAT variations over the western TP. [ABSTRACT FROM AUTHOR]

The Siberian High (SH) over central Asia can affect East Asian weather and climate, and its formation is primarily due to the uplift of the Tibetan and Mongolian Plateaus (TP and MP). However, it is unclear whether these plateaus can modulate the synoptic (2–7 days) evolution of the SH and the subsequent temperature over East Asia during the boreal winter. Using model simulations with and without the plateau topography north of the Himalaya, we show that the synoptic evolution of the winter SH is accompanied by an anomalous tropospheric transient wave train that migrates eastward to the MP over central Siberia before the SH intensification, which is then forced to propagate southeastward along the northeastern flank of the TP after the SH peaks. This results in subsequent cold anomalies from the north to the south of East Asia and thus increases synoptic variability of winter surface air temperature (Tas) over South China. However, the plateau uplift weakens this synoptic wave train due to the inefficiency of energy conversion from the mean flow to the transient flow, thus partly limiting the increase of the synoptic variability over South China. Further analyses showed that the synoptic evolution of the SH and its associated temperature anomalies is mainly caused and maintained by the anomalous cold advection due to the southeastward propagating northerly wind anomaly and the increased winter mean temperature gradient in the lower troposphere due to the northern plateau uplift. Our findings suggest that the TP and MP uplifts north of the Himalaya can affect both the winter mean state and synoptic-scale variability over East Asia and thus play a crucial role in the emergence of frequent cold air outbreaks from the higher latitudes toward South China. [ABSTRACT FROM AUTHOR]

Abstract This study examines the multi-decadal variability and trends of surface air temperature and precipitation in the Indian Cardamom Hills (ICH), a degraded tropical rainforest area unique for cardamom cultivation. Utilizing observed long-term climatic data (1958–2017), statistical methods such as the Mann–Kendall test (MKT), Sen’s Slope Estimator (SSE), and Incremental Trend Analysis (ITA) were applied to assess the impact of surface air temperature, rainfall, and the number of rainy days on cardamom yield. The analysis revealed a significant decline in annual rainfall by approximately 13.62 mm per year, with pronounced seasonal declines 0.87 mm for winter, 12.33 mm for pre-monsoon, 24.93 mm for southwest monsoon, and 18.10 mm for post-monsoon. Simultaneously, the number of rainy days dropped by nearly 19.75 days over the 40-year period. A noticeable increase in decadal minimum and average temperatures was observed, highlighting potential adverse effects on cardamom yield and irrigation water resources. The findings suggest that excessive rainfall during the southwest monsoon negatively correlates with cardamom yield, while slightly warmer temperatures show a weak positive correlation. The study also emphasizes the need for adaptive agricultural practices and climate-resilient policies to mitigate the effects of changing climatic conditions on cardamom production. This research contributes valuable insights for farmers and other stakeholders as well as policymakers aiming to ensure sustainable cardamom cultivation amidst climate change.

Surface‐based temperature inversions (SBIs) are frequent and strong in valleys of north‐central Yukon, which drive annual average surface lapse rates that are strongly inverted (≤1.19°C 100 m−1) within the first 100 to 150 vertical meters. This study aims to test the relationship of SBI breakup with local microclimate factors determining the influence on surface lapse rate breakup patterns. A field study was conducted using elevational transect analysis. SBI breakup in the non‐winter season had a strong relationship between the diurnal cycle of increased solar radiation, warming of the surface, and development of atmospheric turbulence in the form of increased wind speed. SBIs during these non‐winter seasons had a peak breakup time of 4 to 8 h following sunrise. Wintertime SBIs often persisted past the diurnal cycle and broke up independent of solar radiation, contributing significantly to the inverted average surface lapse rates. Mechanisms of SBI breakup during the winter are likely more synoptic in nature and one possible mechanism could be shallow cold air flows. Current classification of SBI duration as persistent or transient was reviewed and adjusted, and SBIs with a duration of 18 to 22 h were removed due to the breakup of those events being related to the diurnal cycle. Key messages: Non‐winter surface‐based temperature inversions (SBIs) primarily dissipate in association with the diurnal cycle of increased solar radiation warming the surface.Wintertime SBI breakup is not driven primarily by the same diurnal cycle, as solar radiation alone is not sufficient, leading to semi‐permanent wintertime SBIs.SBIs that are 18 to 22 h in duration were frequently still broken up diurnally, and therefore the persistent SBI event duration classification should be revised to ≥24 h. [ABSTRACT FROM AUTHOR]

This study reveals the simulation results of a regional ocean-atmosphere coupled model (Coupled Ocean-Atmosphere-Wave Sediment Transport Modeling System, COAWST) for the Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia Phase II, which covers a period of 21 years (1989–2009) at a horizontal resolution of 25 km. The coupled simulation (CPL) and the uncoupled atmospheric simulation (UNCPL) are compared with observations to assess their capability of capturing the climatology characteristics over East Asia. The simulated sea surface temperature (SST) in CPL is generally overestimated (underestimated) in summer (winter) over most CORDEX-EA-II regions. Both CPL and UNCPL can capture the spatial patterns of seasonal air temperature over East Asia well. However, they both simulate cold biases of the winter temperature over most regions of China. CPL can reduce the cold biases over certain regions, which may be related to the increased cloud cover reflecting more longwave radiation to the ground. Additionally, CPL improves the simulation results of summer precipitation, diminishing the dry biases over northern Bay of Bengal and South China Sea. This improvement may be related to the better description of moisture transportation and precipitable water. Furthermore, The CPL experiment can better simulate the inter-annual variability of summer precipitation and the relationship between SST and precipitation, especially over the South China Sea and Philippines Sea. CPL also captures the northward propagation and amplitude of boreal summer intra-seasonal oscillation precipitation anomalies more accurately. These findings highlight the importance of ocean-atmosphere coupling processes to simulate multiscale climate changes in the CORDEX-EA-II regions. Key Points: • The COAWST regional coupled model enhances the simulation of surface air temperature and precipitation climatology over CORDEX East Asia. • The coupled model effectively represents the inter-annual variability of summer precipitation over most western Northern Pacific regions. • The coupled model provides a more accurate depiction of the northward propagation and amplitude of BSISO precipitation anomalies. [ABSTRACT FROM AUTHOR]

Surface air temperature (SAT) affects both natural systems and human activities, impacting health, agriculture, energy demand, and so on. To investigate and analyze SAT over the region of interest, it is crucial to choose suitable climate models. The study commenced with the evaluation of 42 Coupled Model Intercomparison Project phase 6 (CMIP6) models' simulations of SAT over India for annual and all four seasons (summer, southwest monsoon, northeast monsoon, and winter) during the historical period 1985 to 2014 with respect to the gridded SAT datasets obtained from the India Meteorological Department (IMD). Multi Model Mean (MMM) of 42 models was included in the evaluation. The evaluation was performed with various statistical metrics such as root mean squared error (RMSE), mean bias error (MBE), correlation coefficient (R), mean squared error (MAE), Taylor skill score (TSS), Brier skill score (BSS), and Interannual variability skill score (IVSS). By the method of estimating Comprehensive Rating Index (CRI), the top-ranking models were identified to be CMCC-CM2-SR5 for the annual and summer season, MIROC6 for the winter season, ACCESS-ESM-1-5 for the southwest monsoon, and NorESM2-LM for northeast monsoon. The novelty of this study lies in the approach of identifying the best ensemble. For each season, statistical metric-wise top-ranked models were picked to develop the best ensemble. Again, the overall ranking of the models along with the best ensemble for each season is determined by estimating CRI. It was observed that for all seasons, the best ensemble falls within the top 3 models' category. Future projections of SAT under four shared socio-economic pathways (SSP-2.6, 4.5, 7.0, and 8.5) were also analyzed with the best ensemble obtained for each season. The results convey that, the country will witness, especially during the summer season, there will be a 1.160 °C, 1.288 °C and 2.368 °C rise in the mean SAT between historical (1985–2014) and near future (2021–2040), near and mid future (2041–2060), mid and far future (2081–2100) if the pathway, SSP5-8.5 is followed. [ABSTRACT FROM AUTHOR]

The influence of northern polar vortex in the stratosphere (SPV) in December‐January on Asia's surface air temperature (SAT) in February has been examined using reanalysis data sets and a barotropic model. An out‐of‐phase interannual linkage between the SPV in December‐January and SAT in February during 1979–2022 has been observed, that is, a strong (weak) SPV corresponds to a cooling (warming) over Asia. Approximately 25% of the SAT over Asia in February can be explained by the SPV in December‐January. This relationship between the SPV and SAT is independent of the Arctic Oscillation. The influence of the SPV on SAT over Asia cannot be solely explained by radiative processes, but is instead related to circulation anomalies in the troposphere. A stronger SPV tends to result in negative geopotential height anomalies with cyclonic circulation over Asia. The SPV‐related geopotential height over Asia is accompanied by a weakened teleconnection pattern between the North Atlantic and Asia, with three centers from the northeastern Atlantic‐eastern Europe‐Asia, and fewer stationary waves propagated from North Atlantic into Asia. These anomalous circulation patterns and anomalous northerly wind over Central Asia in February are beneficial to the colder air transportation from the higher latitudes to Asia, facilitating a surface cooling over Asia. Our results shed light on the interannual linkage between SPV and SAT over Asia, suggesting that the SPV in December‐January could be considered as a new predicator of SAT in February over Asia. Plain Language Summary: Scientists and the public have become increasingly aware of the significant influences of the stratosphere on tropospheric weather and climate. While numerous studies have focused on the crucial roles of stratospheric anomalies in shaping surface air temperature (SAT) anomalies over Europe and North America, relatively little attention has been given to understanding the potential impacts of stratospheric polar vortex over Arctic (SPV) on SAT over Asia. In this study, we found a robust and out‐of‐phase linkage between the SPV in December‐January and SAT in February over Asia during 1979–2022. A stronger SPV tends to induce an anomalous cyclonic circulation characterized by negative geopotential height, facilitating the transport of colder air from higher latitudes to Asia, thereby contributing a cooling effect on the surface. Our findings suggest that the December‐January SPV can serve as a predictor for SAT in February over Asia. Key Points: A strong (weak) SPV in December‐January tends to result in a cooling (warming) over Asia in February on interannual timescalesThe out‐of‐phase linkage between December‐January SPV and February surface air temperature (SAT) is independent of AO in FebruaryThe influence of SPV on Asia SAT is related to the tropospheric circulation anomalies via dynamic process, rather than radiative process [ABSTRACT FROM AUTHOR]

The atmospheric circulation significantly influences the snowpack over mid-high-latitude Eurasia. This study examines the characteristics of the leading subseasonal variability mode of boreal winter sea level pressure (SLP) with 20-80-day period and its relationship with snowpack over mid-high-latitude Eurasia, using the fifth generation of European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data and different snowpack datasets. The SLP leading mode, characterized by a monopole pattern with a strong surface anomalous high centered near the Ural Mountains, exhibits a barotropic structure and extends from the surface to the tropopause. Above SLP and geopotential height anomalies propagate southeastward from the Barents-Kara Sea to East Asia. This leading SLP mode contributes to surface air temperature (SAT) and snowfall circulation anomalies over mid-high-latitude Eurasia. The latter two both directly influence on snowpack anomalies in situ. Over high latitude region, snowfall circulation anomaly is the dominant factor to control the snow depth anomaly. Over middle latitude region, both SAT and snowfall circulation anomalies lead to the snowpack anomaly. Furthermore, the response of snow depth to the leading subseaonal SLP mode occurs 2–5 days earlier than the response of snow cover to the same mode over middle latitude region. In addition, it is suggested that the Arctic Oscillation (AO), East Atlantic/West Russia (EAWR) and Polar/Eurasia (PEU) pattern may contribute to the development of the leading SLP mode and subsequently influence snowpack anomalies. [ABSTRACT FROM AUTHOR]

The increasing concentration of atmospheric CO2 since the Industrial Revolution has affected surface air temperature. However, the impact of the spatial distribution of atmospheric CO2 concentration on surface air temperature biases remains highly unclear. By incorporating the spatial distribution of satellite-derived atmospheric CO2 concentration in the Beijing Normal University Earth System Model, this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere (NH) under historical conditions from 1976–2005. In comparison with the increase in surface temperature simulated using a uniform distribution of CO2, simulation with a nonuniform distribution of CO2 produced better agreement with the Climatic Research Unit (CRU) data in the NH under the historical condition relative to the baseline over the period 1901–30. Hemispheric June–July–August (JJA) surface air temperature increased by 1.28°C ± 0.29°C in simulations with a uniform distribution of CO2, by 1.00°C ± 0.24°C in simulations with a non-uniform distribution of CO2, and by 0.24°C in the CRU data. The decrease in downward shortwave radiation in the non-uniform CO2 simulation was primarily attributable to reduced warming in Eurasia, combined with feedbacks resulting from increased leaf area index (LAI) and latent heat fluxes. These effects were more pronounced in the non-uniform CO2 simulation compared to the uniform CO2 simulation. Results indicate that consideration of the spatial distribution of CO2 concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models. [ABSTRACT FROM AUTHOR]

Previous research has shown that land surface thermal anomalies in West Asia (WA) can impact regional and global climate, particularly affecting China through the eastward propagation of wave trains. However, the factors driving these anomalies in WA have not been extensively studied. Based on the observation data, this work focuses on examining the impacts of Eurasian winter snow cover on winter surface air temperature (SAT) variability over WA from 1978/1979 to 2017/2018 and explores the underlying physical mechanisms. The results indicate that a crucial snow anomaly area extending from the Baltic Sea to eastern Ural significantly influences the winter SAT anomaly in WA. An anomalous increase (decrease) in winter snow cover in this key area corresponds to the anomalously warmer (cooler) SAT in WA. This relationship is primarily driven by the albedo effects of snow cover, where more (less) snow cover induces cooling (warming) of the overlying air, altering upper-level geopotential height and influencing the intensity, duration, and frequency of local blocking events. Additionally, changes in the air temperature above the key area modify the meridional temperature gradient (MTG) between high and low latitudes, affecting the mean zonal flow in the midlatitude. Diagnosis of the thermodynamic energy equation for SAT reveals that the combined effects of variations in blocking events in high latitudes and mean zonal flow in midlatitudes alter the advection of climatological temperature by anomalous winds, which is caused by the anomalous increase (decrease) of snow cover in the key area. Consequently, this leads to changes in cold advection transported to WA, contributing to the occurrence of a warmer (colder) SAT over WA in winter. [ABSTRACT FROM AUTHOR]

Abstract We examine daily surface air temperatures (SAT) in the Arctic under global warming, synthesizing changes in mean temperature, variability, seasonality, and extremes based on five Earth system model large ensembles from the Coupled Model Intercomparison Project Phase 6. Our analysis shows that the distribution of daily Arctic SAT changes substantially, with Arctic mean temperatures being distinguishable from pre‐industrial levels on 84% and 97% of days at 1.5 and 2°C of global warming, respectively, and on virtually every day at 3°C of global warming. This shift is primarily due to the rapid rise in average temperature resulting from Arctic amplification and is exacerbated by a decrease in the variability of daily Arctic SAT of approximately 8.5% per degree of global warming. The changes in mean temperature and variability are more pronounced in the cold seasons than in summer, resulting in a weakened and shifted seasonal cycle of Arctic SAT. Moreover, the intensity and frequency of warm and cold extreme events change to varying degrees. The hottest days warm slightly more, while the coldest days warm 4–5 times more than the global average temperature, making extreme cold events rare. Changes in local SAT vary regionally across the Arctic and are most significant in areas of sea‐ice loss. Our findings underscore the Arctic's amplified sensitivity to global warming and emphasize the urgent need to limit global warming to mitigate impacts on human and natural systems.

This study used 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze the mean and extreme projection precipitation during the Indian summer monsoon (ISM) season, i.e., June to September (JJAS). A continuous rise in precipitation and its associated unpredictability were predicted by global climate models using the CMIP6. The extreme values are estimated on the basis of generalized extreme value (GEV) distribution. Four socioeconomic paths are used as SSP126, SSP245, SSP370 and SSP585 to understand the impact of low-emission to high-emission scenarios. In all the scenarios, it is seen that all the models predict a significant rise in JJAS mean and extreme precipitation. Moreover, a strong correlation is obtained between the historical (1995–2014) and near-emission future simulation (2021–2040), mid-emission future simulation (2041–2060), high-emission future simulations (2081–2100) of air temperature and precipitation. In extreme emission scenarios, ensemble mean of CMIP6 models shows an increasing amplitude in surface air temperature and precipitation over India in the near future (2021–2040), mid future (2041–2060) and the end of the century (2081–2100). The CMIP6 simulations mainly support the results of all models, but they also demonstrate improved robustness across models. HIGHLIGHTS Projection of mean and extreme precipitation over India using CMIP6 models during JJAS season.; A generalized extreme values distribution is utilized to estimate the climate extreme.; Four socioeconomic paths are used as SSP126, SSP245, SSP370, and SSP585 to understand the impact of low-emission to high-emission scenarios over precipitation.; A strong association is observed between surface air temperature and precipitation over India.; Extreme precipitation may rise up to 80% over India in the SSP585 scenario.;

Recognizing the relationship between gross primary production (GPP) and precipitation in eastern China, the East Asian Summer Monsoon (EASM) plays a crucial role in shaping GPP. Despite confirmation of the strong link between EASM and GPP, there remains a notable research gap in understanding the specific impact of the EASM on GPP in different regions of eastern China. Here we used simulations from Trends in Net Land–Atmosphere Carbon Exchanges (TRENDY) models from 1951 to 2010 and divided eastern China into five subregions for the study. We also used the New East Asian Summer Monsoon Index (NEWI) as a quantitative metric to distinguish between periods of strong and weak EASM. Building on this, this study aims to investigate the response of GPP in different subregions of eastern China. Regionally, under strengthened EASM years (1954, 1957, 1965, 1969, 1977, 1980, 1983, 1987, 1993 and 1998), East China experienced the most pronounced increase in GPP at 12 ± 21 (mean ± 1 sigma) gC m−2 mon−1 compared to the weak EASM years (1958, 1961, 1972, 1973,1978, 1981, 1985, 1994, 1997 and 2004). In contrast, Southwest China showed a decline in GPP at −4 ± 10 gC m−2 mon−1. Moreover, GPP also increased in Northeast and North China when EASM strengthened, while South China showed a decline in GPP. This indicated that GPP changed with monsoon intensity. According to the mechanism analysis, during strong EASM, there was intense moisture convergence through alterations in the atmospheric circulation field over East China and abundant precipitation, which further contributed to the increase in GPP. Downward solar radiation in Southwest China decreased with EASM enhancement, which suppressed GPP and hindered vegetation growth. Overall, the results highlight the importance of accurately predicting the impact of different EASM intensities of regional carbon fluxes.

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]

Detecting changes in surface air temperature in mid- and low-altitude mountainous regions is essential for a comprehensive understanding of warming trend with altitude. We use daily surface air temperature data from 64 meteorological stations in Wuyi Mountains and its adjacent regions to analyze the spatio-temporal patterns of temperature change. The results show that Wuyi Mountains have experienced significant warming from 1961 to 2018. The warming trend of the mean temperature is 0.20°C/decade, the maximum temperature is 0.17°C/decade, and the minimum temperature is 0.26°C/decade. In 1961–1990, more than 63% of the stations showed a decreasing trend in annual mean temperature, mainly because the maximum temperature decreased during this period. However, in 1971–2000, 1981–2010 and 1991–2018, the maximum, minimum and mean temperatures increased. The fastest increasing trend of mean temperature occurred in the southeastern coastal plains, the quickest increasing trend of maximum temperature occurred in the northwestern mountainous region, and the increase of minimum temperature occurred faster in the southeastern coastal and northwestern mountainous regions than that in the central area. Meanwhile, this study suggests that elevation does not affect warming in the Wuyi Mountains. These results are beneficial for understanding climate change in humid subtropical middle and low mountains. [ABSTRACT FROM AUTHOR]

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation. [ABSTRACT FROM AUTHOR]

This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test were employed to identify the change cycle and abrupt change year of the boundary layer height. The Empirical Orthogonal Function (EOF) method was utilized to determine the spatial distribution of the boundary layer height, and the RF method was used to establish the relationship between the ABLH and influencing factors. The results demonstrated that the highest values of ABLH (over 1900 m) were observed in the middle parts of the study area in June, and the ABLH exhibited a significant increase over the TB throughout the study period. Abrupt changes in the ABLH were also identified in 2004, as well as in 2-, 5-, 9-, and 15-year changing cycles. The first EOF ABLH mode indicated that the middle and northeast regions are relatively high ABLH areas within the study area. Additionally, the monthly variations in ABLH show a moderately positive correlation with air temperature, while exhibiting a negative correlation with air pressure and relative humidity. [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]

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]

Influence of land-surface memory effects over the Tibetan Plateau on the Asian summer monsoon has long been studied, but not quantified because of the difficulty of extracting only these effects from observational data. This study examines the impact of spring land-surface conditions, including surface air temperature (SAT) and snow cover, over the Tibetan Plateau and its surrounding regions on the early stage of the Asian summer monsoon using large-ensemble experiments produced by an atmospheric global climate model (AGCM). The results show that the above-normal SAT over and around the Tibetan Plateau in May can enhance June monsoon circulation without the sea surface temperature (SST) forcing. The physical mechanism behind this involves warmed surface air over the plateau enhancing the north–south SAT gradient, leading to strengthening the Asian summer monsoon circulation and precipitation. Inter-ensemble correlation analysis indicated that SAT warming over the plateau is clearly associated with upper-tropospheric anti-cyclonic circulation anomalies in May. Also, although winter land-surface signals do not persist until late spring, SAT anomalies in March tend to persist until May, explaining 10–20% of the total SAT variance in May. Interestingly, the spring SAT impacts on June monsoon circulations vary interannually, indicating that the contribution of the land–atmosphere (L–A) coupling process to interannual monsoon variability differs from year to year. Active L–A coupling years tend to coincide with cooler SSTs with anomalous low-level divergence and upper-level convergence over and around the Maritime continent, implying that SST forcing is associated with the L–A coupling strength. Specifically, in May of the developing stage of monsoon circulation, the weak Walker circulation over the equatorial and northern Indian Ocean and undeveloped monsoon circulation are suitable for the L–A coupling to influence the development of the monsoon circulation effectively. However, the well-known oceanic events, such as El Ni n ~ o, are found to be less connected with the interannual L–A coupling variations. [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]

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]

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]

The importance of the Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO) in influencing zonally asymmetric changes in Antarctic surface air temperature (SAT) has been established. However, previous studies have primarily concentrated on examining the combined impact of the contrasting phases of the AMO and IPO, which have been dominant since the advent of satellite observations in 1979. This study utilizes long-term reanalysis data to investigate the impact of four combinations of +AMO+IPO, −AMO−IPO, +AMO−IPO, and −AMO+IPO on Antarctic SAT over the past 115 years. The +AMO phase is characterized by a spatial mean temperature amplitude of up to 0.5°C over the North Atlantic Ocean, accompanied by positive sea surface temperature (SST) anomalies in the tropical eastern Pacific and negative SST anomalies in the extratropical-mid-latitude western Pacific, which are indicative of the +IPO phase. The Antarctic SAT exhibits contrasting spatial patterns during the +AMO+IPO and +AMO−IPO periods. However, during the −AMO+IPO period, apart from the Antarctic Peninsula and the vicinity of the Weddell Sea, the entire Antarctic region experiences a warming trend. The most pronounced signal in the SAT anomalies is observed during the austral autumn, whereas the combination of −AMO and −IPO exhibits the smallest magnitude across all the combinations. The wavetrain excited by the SST anomalies associated with the AMO and IPO induces upper-level and surface atmospheric circulation anomalies, which alter the SAT anomalies. Furthermore, downward longwave radiation anomalies related to anomalous cloud cover play a crucial role. In the future, if the phases of AMO and IPO were to reverse (AMO transitioning to a negative phase and IPO transitioning to a positive phase), Antarctica could potentially face more pronounced warming and accelerated melting compared to the current observations. [ABSTRACT FROM AUTHOR]

In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring. [ABSTRACT FROM AUTHOR]

It is known that different relationships exist between the strength and displacement of the stratospheric polar vortex (SPV), and the surface air temperature (SAT) patterns in Eurasia and North America, but the mechanisms behind these relationships remain unclear, especially on an interannual timescale. Based on empirical orthogonal function (EOF) analysis using NCEP reanalysis data over 1958–2018, this study attempts to ascertain the relationship between the SPV intensity and displacement over the Arctic and the SATs in the midlatitudes of the Northern Hemisphere. Our results indicate that a strengthened SPV corresponds to an SAT increase in Eurasia and a decrease in eastern North America and Greenland. When the SPV is shifted towards Eurasia, however, a corresponding SAT increase occurs in both North America and Eurasia, with a larger increase in North America than in Eurasia. Specifically, a strengthened SPV tends to correspond to a positive North Atlantic Oscillation-like circulation in the troposphere with negative geopotential height (GH) anomalies in Greenland and eastern North American continent and positive GH anomalies to the north of 45°N in Eurasia, which corresponds to lower SATs in North America than in Eurasia. However, when the SPV shifted towards Eurasia, it was accompanied by a positive Pacific/North American-like pattern with a deepened Aleutian low, which corresponds to the increasing SATs in North America. These tropospheric circulation changes are related to the response of tropospheric planetary wave activity to the SPV. A strengthened SPV corresponds to the weakening of tropospheric planetary wave-1 waves, which is accompanied by a negative GH in North America but a positive GH in Eurasia. If the SPV shifted towards Eurasia, the tropospheric planetary wave-1 (-2) waves strengthened (weakened), and the combined effects of the planetary wave-1 and wave-2 waves would cause positive GH anomalies in both Eurasia and North America. [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]

Previous studies have reported significant uncertainty in the relationship between El Niño-Southern Oscillation (ENSO) and the winter surface air temperature (SAT) over central North America (NA). This paper uses reanalysis data and statistical methods to investigate the possible modulation effect of the Victoria mode (VM) during the preceding spring on the winter ENSO–SAT relationship. The correlation between ENSO and the winter SAT over central NA was found to be significantly positive during the positive VM phase. However, this significant correlation is almost absent during the negative VM phase. Further analysis revealed that the zonal location of the sea surface temperature anomalies (SSTA) over the tropical Pacific is an essential aspect of this VM modulation. During the positive VM phase, the dominant positive SSTA in the central-eastern tropical Pacific contribute to the formation of the negative phase of the North Pacific Oscillation (NPO)-like teleconnection, which further enhances the anomalous southerly winds and SAT over central NA. In contrast, during the negative VM phase, the Aleutian low-like atmospheric response induced by the central tropical Pacific SSTA is limited to the central North Pacific, which has a limited effect on the variation of the central NA SAT. The VM modulation mechanism is shown to be well reproduced by historical simulations of the Coupled Model Intercomparison Project Phase 6. Our results suggest that, despite the uncertainty associated with the ENSO–SAT connection, ENSO may provide prediction skills for the winter SAT over central NA during the positive VM phase. [ABSTRACT FROM AUTHOR]

Assumptions of linear lapse rates in regions prone to surface-based inversions (SBIs) can generate biases in the prediction of surface air temperature. Although studies of Arctic inversions are common, few regional studies of their characteristics exist in high-latitude regions with mountainous topography. To address this gap, vertical atmospheric temperature profiles for five sites in northwestern Canada were analysed using archived radiosonde data from 1990 to 2016. We present monthly, seasonal, and annual SBI characteristics including the occurrence of transient and persistent SBIs. A novel metric, surface-based inversion impact (SBIimp), was developed by combining the traditional inversion characteristics of depth, strength, and frequency, and was used to quantify the impact of SBIs on cooling the surface air temperature. SBIimp values of >5 °C yr−1 and ∼10 °C winter−1 occur locally. A weak linear relationship between sea ice coverage in the Beaufort Sea and SBIimp manifests across parts of the study area, though this relationship does not persist after detrending the datasets. Topographic analysis of areas surrounding each radiosonde location reveals highly variable SBIimp in complex mountain areas and more consistent SBIimp across areas of low relief. Our results can help interpret the role of inversions in climatic conditions maintaining cryospheric elements such as permafrost.

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 surface air temperature and precipitation, the current study examines the climate fluctuations over Sistan-and-Baluchestan Province, Iran's second-largest province. This area suffers from insufficient direct observations and a lack of climatic investigation. Three datasets were utilized including in situ data, gridded data (1984–2013), outputs of historical runs (during 1984–2013), and projections under the SSP5-8.5, SSP3-7.0, and SSP1-2.6 scenarios (in 2020–2049) of twenty-six Global Climate Models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP6). The models' performance has been evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in Multi-Criteria Decision Making (MCDM) technique including eight metrics in both seasonal and annual scales. The surface air temperature showed an increasing trend in seasonal and annual scales during 1984–2013, while the monthly precipitation trend increased for September-October-November and decreased for the other seasons and annual scale during 1984–2013. The top-ranked models for simulating surface air temperature (precipitation) were CESM2 (GFDL-ESM4), IPSL-CM6A-LR (UKESM1-0-LL), ACCESS-CM2 (GFDL-ESM4), and MIROC-ES2L (MPI-ESM1-2-LR) models in DJF, MAM, JJA, and SON seasons, respectively, while ACCESS-CM2 (CNRM-CM6-1-HR) model outperformed others in annual scale. Bias-corrected outputs of the top-ranked CMIP6 GCMs showed an increasing trend for surface air temperature in all seasons (from a 0.7 K increase in December-January-February season under SSP3-7.0 scenario to a 2.5 K increase in June-July-August season under SSP5-8.5 scenario) for period 2020–2049, comparing with that in 1984–2013 period. Bias-corrected monthly precipitation projected by top-ranked CMIP6 GCMs indicated both increasing and decreasing trends depending on selected season and scenario. This varied from a 5 mm/month decrease within December-January-February season under SSP5-8.5 scenario to a 13 mm/month increase during the March-April-May season under SSP1-2.6 scenario in 2020-2050, comparing with that from previous years. [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]

This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test were employed to identify the change cycle and abrupt change year of the boundary layer height. The Empirical Orthogonal Function (EOF) method was utilized to determine the spatial distribution of the boundary layer height, and the RF method was used to establish the relationship between the ABLH and influencing factors. The results demonstrated that the highest values of ABLH (over 1900 m) were observed in the middle parts of the study area in June, and the ABLH exhibited a significant increase over the TB throughout the study period. Abrupt changes in the ABLH were also identified in 2004, as well as in 2-, 5-, 9-, and 15-year changing cycles. The first EOF ABLH mode indicated that the middle and northeast regions are relatively high ABLH areas within the study area. Additionally, the monthly variations in ABLH show a moderately positive correlation with air temperature, while exhibiting a negative correlation with air pressure and relative humidity.

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation.

The Tibetan Plateau (TP) is an important component of the global climate system, while the characteristics of its climate are poorly represented in most regional climate models at coarse resolutions. In this study, a 20-year (2000–2019) dynamical downscaling simulation at the gray-zone resolution (9 km) using the WRF model driven by the ERA5 reanalysis is conducted over the TP. Based on comparisons against in-situ observations and the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, the assessment of basic climate variables, such as near-surface air temperature (T2m) and precipitation, is performed to evaluate the model's performance and understand its added value better. Results show that both WRF and ERA5 can successfully reproduce the spatial patterns of annual mean and seasonal mean surface air temperature. However, significant cold and wet biases are found especially over the southeastern TP in ERA5, which are greatly improved in WRF with reduced RMSEs. Not only the climatological characteristics, but also the inter-annual variability and seasonal variation of T2m and precipitation are well captured by WRF which reduces the cold and wet biases especially in winter and summer compared to ERA5, respectively. Besides, at daily scale, the overestimation of precipitation in WRF and ERA5 is mainly caused by the overestimated precipitation frequency when precipitation intensity changed slightly. Furthermore, WRF outperforms ERA5 in capturing the diurnal variation of precipitation with more realistic peak time in all sub-regions over the TP. Further investigation into the mechanism of model bias reveals that less simulated snow cover fraction plays a crucial role in increasing the surface net energy by affecting surface albedo over the southeastern TP in WRF, leading to higher T2m. In addition, less water vapor transport from the southern boundary of TP leads to reduced wet bias in WRF, indicating that the added value in dynamical downscaling at gray-zone resolution is obtained by representing water vapor transport more realistically. [ABSTRACT FROM AUTHOR]

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

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

As one of the most significant circulation systems over the Northern Hemisphere in the cold season, the East Asian winter monsoon (EAWM) has been broadly investigated from the seasonal-mean perspective, while subseasonal variations in the EAWM still remain ambiguous. Based on Season-reliant Empirical Orthogonal Function (S-EOF) analysis, this study shows that the subseasonal strength reversal of the EAWM (SR-EAWM), featuring a weaker (or stronger) EAWM in early winter (December) and a stronger (or weaker) EAWM in late winter (January–February), is a distinct leading mode of the month-to-month variation of the EAWM. The weak-to-strong SR-EAWM is characterized by an anomalous low over Eurasia and a weakened East Asian major trough (EAT) in early winter, with an intensified Siberian High and a deepened EAT in late winter. The SR-EAWM is preceded by surface air temperature anomalies over Davis Strait (DST) and those over central-eastern North America (CENAT) in September–October. The DST mainly influences the SR-EAWM in early winter through a "sea ice bridge" of the November Baffin Bay sea ice concentration anomaly (BBSIC). The BBSIC could intensify the DST in December by altering surface heat flux, thus exciting a downstream atmospheric response and modulating the strength of the EAT in early winter. The CENAT affects the SR-EAWM in late winter by inducing an "ocean bridge" of the western North Atlantic sea surface temperature anomaly (WNASST). The WNASST can persist into late winter and then significantly affects the SR-EAWM by regulating Eurasian circulation anomalies and the downstream EAT. The bridge roles of the BBSIC and WNASST can be further verified by a linear baroclinic model. Finally, two physical-empirical models are established using the DST/BBSIC and the CENAT indices, respectively. Both exhibit promising prediction skills. The results highlight that the DST, BBSIC, and CENAT are crucial predictability sources for the SR-EAWM. [ABSTRACT FROM AUTHOR]