Search

In this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data. [ABSTRACT FROM AUTHOR]

The onset of periods with very high or low air temperatures has aroused general interest for a long time, which is understandable since it has many dangerous effects directly affecting humans. Such perilous climatic phenomena include exceptionally cold and exceptionally warm months, which this study investigates for all Europe and for its five physico‐geographic regions over the 190‐year period of 1831–2020. Therefore, the research in this paper includes two periods characteristic of the history of climate – the late Little Ice Age (LIA) and present‐day warming. The studies are based on average monthly air temperature values from 40 weather stations in Europe. In this paper, exceptionally cold months (ECMs) or exceptionally warm months (EWMs) are considered to have occurred when the average air temperature at a station differed from the respective long‐term average by at least two standard deviations. The highlights of the study include the identification of a drop in the number of ECMs by 20 over the entire 190‐year period, and a highly statistically significant increase in EWMs by 44 between 1980 and 2020. These changes proceeded with different intensities from one physico‐geographic region of Europe to another. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the relationships between soil moisture, cloud cover, solar radiation, air temperature and humidity, and precipitation during the warm half of the year in East‐Central Europe over the years 1971–2020. The temporal and spatial variability of these meteorological elements is presented in association with the occurrence of anticyclonic blocking events over the study area. It demonstrates that changes in soil moisture in East‐Central Europe point to the combined influence of many meteorological factors resulting from the atmospheric circulation, and are an indicator of the comprehensive relationships among those factors. The main factors affecting soil water content are precipitation and evapotranspiration, which in turn depend on air humidity, cloudiness, intensity of solar radiation and air temperature. The increase in the frequency and duration of sequences of days with blocking events in East‐Central Europe has contributed to an increased probability of longer periods with soil moisture negative anomalies. [ABSTRACT FROM AUTHOR]

This paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change. [ABSTRACT FROM AUTHOR]

Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30‐year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS‐TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30‐year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets. [ABSTRACT FROM AUTHOR]

Monitoring and quantifying high‐latitude atmospheric circulation variability and trends are important towards understanding the anatomy of extreme events and constraining their probabilities under continued Arctic change. The greater Alaska region stands out as one region of enhanced warming and environmental changes over the Arctic amplification era, which has shown proclivity to extreme events and speaks to the need to consistently monitor overlying atmospheric variability. In this paper, we describe the creation and analysis of an updated, daily mid‐tropospheric Alaska Blocking Index (ABI) time series from 1948 to 2020. Sensitivity testing and index modifications from the initial dataset are discussed, and the new ABI is evaluated over the full period and subperiods. Additionally, we assess the role of the ABI as a regional climate indicator by analysing its variability against surface air temperature and precipitation from 13 climate divisions across Alaska as well as broader‐scale hemispheric temperatures. Months of the highest ABI means mainly transpired in the most recent climatological period, 1991–2020, and most notably during the spring and autumn transition seasons, reflecting the nonstationary nature of the jet stream magnitude through time atop Alaska. ABI trends are positive across all months, seasons, and annually only in the 1991–2020 period. Contemporaneous ABI values are strongly and positively correlated with air temperature across all Alaskan climate divisions during the warm season, but negatively correlated with winter temperatures over southeastern Alaska. Meanwhile, the ABI is significantly anticorrelated with summer precipitation over Interior and southern Alaska, but only over southeastern Alaska in other seasons. The ABI is statistically differentiated from the primary modes of atmospheric variability as shown by its generally weak correlations with the Arctic Dipole and the Arctic Oscillation (r ≤ 0.40). [ABSTRACT FROM AUTHOR]

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

This paper investigated the trends and variability in warmest daily maximum (TXx) and warmest daily minimum (TNx) air temperatures, and heatwave characteristics in Turkey in May during 1950–2020. It also analysed the climatological and synoptic meteorological evolutions of heatwave (HW) observed in May 2020. Trend analysis revealed significant increasing trends in TXx and TNx series and heatwave characteristics in May since 1950. Since the mid‐1980s, a continuously increasing trend in TXx and TNx series, and the number, frequency, and magnitude of the heatwaves were observed that accelerated with the mid‐1990s. The most severe heatwave in Turkey since 1950 in May was observed in 2020. The record‐breaking daily maximum (daily minimum) air temperatures were noted at 32 (23) of 96 stations in May 2020, mainly over Turkey's western and southern parts. Record‐high maximum (minimum) air temperature was 43.2°C (31.1°C) reached for the first time in May since 1950. The generalized extreme value model applied to the May TXx and TNx series revealed that the daily air temperatures in May 2020 exceeded 100 years' return periods at many stations of Turkey. This heatwave was linked to a regionalized strong anticyclonic blocking anomaly circulation and other associated atmospheric anomalies in May 2020. This also resulted in the centring of an unprecedented subsiding, calm, and stable warm air mass over Turkey. Adiabatic warming and drying developed very likely under subsidence control because of relatively weak circulation conditions. Such circumstances resulted in extremely hotter conditions in May 2020 compared to the long‐term averages. [ABSTRACT FROM AUTHOR]

There is growing interest in the international scientific community in characterizing climate variability in Antarctica because of the continent's fundamental role in regulating the world's climate. Researchers have intensively studied the Antarctic Peninsula since the warming that began in the mid‐1950s. This was followed by a subsequent cooling period over the last decades. For this paper, using the available data, we analysed the variability in surface air temperatures at five meteorological stations located on King George Island (KGI) (a subantarctic island that is part of the South Shetland Islands); we also investigated the relationships between the air temperatures and large‐scale atmospheric patterns from 1968 to 2019. In this study we found that summer temperatures are above 0°C from December to March and close to melting temperatures (extreme values) in spring and autumn; consequently, a small increase in temperature can have a significant impact on the cryosphere. The statistical analysis of the mean temperatures confirmed a trend toward cooling during the summer and in the mean monthly maximum temperatures over the 1990s at most of the weather stations whose data we analysed. Analysing the teleconnection patterns showed that the Southern Annular Mode (SAM) had strong, direct, and positive correlations during the autumn and less strong connections in spring, winter, and on an annual scale. Furthermore, we observed a lesser influence of El Niño‐Southern Oscillation (ENSO). [ABSTRACT FROM AUTHOR]

This study investigates some of the uncertainties sources associated with the pseudo global warming (PGW) approach which was employed to project future patterns of tropical cyclones (TCs) over the Arabian Sea (AS). First, the climate variables controlling the patterns of tropical cyclones were extracted from reanalysis datasets of ERA5, ERAI, CFSR, and NCEP/NCAR. Then, each dataset was evaluated against long‐term measurements to identify the best‐performing reanalysis dataset. ERA5 showed the best performance for most of the variables. Outputs of 20 CMIP5 global climate models (GCMs) were then evaluated against the ERA5 data resulting in an ensemble of the best performing GCMs. A PGW framework was then used to project the changes in patterns of three significant historical cyclones: Gonu, Phet, and Ashobaa. In doing so, the signals of future climate variables were extracted from the GCMs ensemble to modify the initial and boundary conditions of the WRF model which was previously tuned for reproducing the historical TCs. Different tests were conducted to address the sources of uncertainty in the PGW approach, including the selection of the climate variables contributing to the computation of the signals, the selection of GCMs, and the spatial variation of signals. A considerable sensitivity of the projected track and intensity of TCs to the choice of GCMs was observed, acknowledging the importance of GCMs evaluation before calculating the signals. Moreover, it was found that among all variables, signals of sea surface temperature and air temperature have major effects on the cyclone's track and intensity. Apart from that, when the signals were applied to the domain of the WRF model uniformly, compared to applying spatially varying signals, different tracks and intensities for future TCs were also observed. Overall, the findings of this paper challenge the reliability of the projected changes in TCs patterns obtained from PGW. [ABSTRACT FROM AUTHOR]

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

This paper presents a comprehensive analysis of the effect of urbanization on the surface air temperature (SAT) from 1951 to 2018 in the Philippines. The daily minimum temperature (Tmin) and daily maximum temperature (Tmax) records from 34 meteorological stations were used to derive extreme temperature indices. These stations were then classified as urban or rural based on satellite night‐lights. The results showed a significant difference in the SAT trends between urban and rural stations, indicative of the effect of urbanization in the country. Larger and more significant warming trends were observed in indices related to Tmin than those related to Tmax. In particular, the effects of urbanization were significant in the annual index series of Tmin, diurnal temperature range, minimum Tmin, percentage of days when Tmin was less than the 10th percentile (TN10p), percentage of days when Tmin was greater than 90th percentile (TN90p), and the number of coldest nights. The effects of urbanization were not as clear on the index series of maximum Tmax (TXx), minimum Tmax (TXn), percentage of days when Tmax was less than 10th percentile (TX10p), and the number of hottest days. The effects of urbanization on the annual series of extreme temperature indices were statistically significant at the 95% confidence level, with the exception of Tmax, TXn, TXx, TX10p, and the number of hottest days. Further analysis revealed that the effect of urbanization was the greatest during the DJF (December–January–February) season. These findings serve as a baseline study that focuses on the countrywide effect of urbanization on SAT trends in the Philippines. [ABSTRACT FROM AUTHOR]

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

Several atmospheric variables are directly influenced by oceanic conditions, mainly the sea surface temperature (SST). SST trends and variability over the southwestern Atlantic (SWA) have affected the South American climate, principally the monsoonal period (austral summer). This study analysed the SST trends over the SWA for all seasons. In addition, possible large‐scale triggers of the austral summer's warmest SST over the SWA and the straight impacts of these warmest SSTs on the South American climate were investigated. The results showed a significant positive SST trend of approximately 0.02°C·year−1 over the SWA for all seasons. Since 2000, positive SST anomalies have increased in frequency in all seasons, with the seven warmest summers occurring during the 2010s. The highest SST summers over the SWA have been related to four leading causes: (i) planetary waves triggered by warm anomalies over the subtropical South Pacific; (ii) an omega‐type blocking configuration over the South Atlantic; (iii) a Southern Annular Mode positive phase; and (iv) a strengthening of the Hadley Cell, responding to warm SST over tropical North Atlantic. They all reflected on intensification of the South Atlantic Subtropical High and the western boundary current, heating the SWA. The summer's warmest SST led to positive air temperature anomalies extending in almost all of eastern South America, with significantly highest temperatures over southeastern Brazil, northern Argentina and western Paraguay. Clouds decrease strengthen the incident shortwave in southeastern Brazil, warming the region. Significant reduction of cloudiness and precipitation indicate a low performance of the South Atlantic Convergence Zone. The comparison of the warmest summers in 1980s–1990s in the SWA ratified the role of SST intensification. In conclusion, the positive SST trends over the SWA are related to the summer's warmest SST after 2000s, causing heating and dryness in eastern South America, mainly over southeastern Brazil. [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 article discusses the reasons for shortening snow cover duration in the Western Sudetes, considering local changes in: air temperature; amount and type of precipitation; sunshine duration and atmospheric circulation leading to changes in the number of days with snow cover and its depth; and its start and end dates. All factors were linked to the exposure and relief of the study area. The analysis was made for the winter seasons (Nov–Apr) 1961/1962–2020/2021. It was found that the primary reasons for the shortening of snow cover duration in the Western Sudetes are: a multi‐year increase in air temperature and sunshine duration; changes in precipitation patterns—a decrease in the proportion of solid precipitation, changes in atmospheric circulation—including an increase in anticyclonic circulation types with sunny weather, especially in April (snow cover disappears in most of the elevation profile of the Sudetes); and less cyclonic weather types. The above factors synergistically affect the lower snow depth, and fewer days with solid precipitation, which promotes its faster spring ablation. In the subsequent 30 years (climatological norms), there is a successive shortening in its duration. On the snow cover start dates, there are no clear trends in the direction and rate of change. On end dates, negative trends are observed, in most cases statistically significant. The rate of change for the end dates of snow cover is about twice as high as the start dates. The rate of decline in snow cover is higher at stations at similar altitudes with northern macro‐exposure than southern. The results correspond with other studies from Europe and the world on the earlier disappearance of snow cover. They confirm the successive global warming and shortening snow cover duration, especially evident in the last few decades. [ABSTRACT FROM AUTHOR]

The frequency and magnitude of global warming events varies greatly across different regions and countries. The climatic diversity for China and future warming features are projected across 12 climatic zones based on the ensemble of the five well‐performing high‐resolution downscaled climate models for each zone. There are warming patterns for the mean near‐surface air temperature (Tm), maximum near‐surface air temperature (Tmax), minimum near‐surface air temperature (Tmin) as well as heatwave and frost events. Under RCP4.5 and RCP8.5 scenarios, the three indices (i.e., Tm, Tmax and Tmin) countrywide are likely to increase at respective rates of 0.30–0.31 and 0.64–0.67°C·decade−1. The extent of frost event (FE) are projected to decrease at a rate of −1912 and −4442 day·km−2·decade−1 while the extent of heatwave event (HE) increase at a rate of 1116 and 3557 day·km−2·decade−1 under RCP4.5 and RCP8.5 scenarios, respectively. A higher increment in temperatures, a decreasing trend in diurnal temperature range (DTR), frost days and FE are highlighted in the western and northern zones, while these trends are opposite in southern zones. The warming is likely to get faster in the current colder zones (western and northern zones) while heatwave is more intense and severe in Jianghuai, Jianghan, the south Yangzi River, South China and Xinjiang. These potential changes indicate that adaption and mitigation strategies are necessary in response to future warming. [ABSTRACT FROM AUTHOR]

Mozambique is highly dependent on subsistence agriculture. Thus a climatology of rainfall onset and cessation is derived together with their interannual variability and trends using gridded daily data and station data. Mean onset is found to occur from early to mid‐November over southernmost regions, mid‐ to late November over central regions and late November to early December over the northern and coastal regions while cessation is from mid to late March over southern and late March to early April over most of central and northern Mozambique. The two gridded datasets showed differences (~30 days) in rainfall onset and cessation dates over southern Mozambique with standard deviations of 20–35 days. Timing of early onset over northern and central Mozambique appears associated with both zonal and meridional moisture convergence and tropical extratropical cloud bands, while timing of late onset is associated with southeast moisture fluxes from the subtropical South Indian Ocean and uplift over the landmass. Early (late) onset appears to be linked to La Niña (El Niño), particularly over central Mozambique. Rainfall cessation over most of Mozambique tends to be early (late) during El Niño (La Niña) summers. The relationship of ENSO with onset and cessation over large areas in Mozambique may help farmers and other user groups in planning their activities for the rainy season. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP. [ABSTRACT FROM AUTHOR]

A wet‐to‐dry shift in summer over the Sichuan Basin (SCB) during the late 20th century was noticed by previous studies. However, this study found that summer precipitation over SCB significantly increases during the past two decades, with an abrupt change point around 2011/2012. An anomalous cyclone over Lake Baikal, accompanied by an anticyclone over southern China, is responsible for the increase in SCB precipitation by inducing anomalous upper‐level divergence, mid‐level ascending motion and moisture convergence. Such atmospheric circulation anomalies are closely associated with the sea surface temperature (SST) warming over the Barents–Kara Sea (BKS). Observational analyses and model experiments indicate that the SST warming over the BKS warms local atmosphere and weakens meridional air temperature gradients over northern Asia and induces local anomalous easterlies, which further induce the anomalous cyclone over Lake Baikal that favours SCB precipitation. Additionally, the inhomogeneous warming over Europe increases local meridional air temperature gradients, enhances atmospheric baroclinicity and intensifies local transient eddy activities over central Europe. The intensified transient eddy activities generate negative geopotential height tendency anomalies over northern Europe, which can further excite a Rossby wave train that directly influences SCB precipitation, and can warm the atmosphere above the Kara Sea that indirectly influences SCB precipitation. This study can deepen our understanding of the interdecadal variability of summer precipitation over the SCB. [ABSTRACT FROM AUTHOR]

The set of meteorological data from the Jagiellonian University station in Kraków is one of the longest‐recorded in central Europe. Measurement records date back to 1826. Based on the average monthly values of air temperature, total cloudiness, water vapour pressure and wind speed at 1200 UTC, the UTCI values were calculated and the occurrence of cold stress (UTCI < −13°C) and heat stress (UTCI > 32°C) was estimated. The variability of the applied measures of thermal stress conditions was studied both annually and in individual seasons (MAM, JJA, SON, DJF). Statistically significant changes in the studied characteristics of the bioclimate were found. Average annual UTCI values increased at the rate of 0.27°C/10 years. In individual seasons, the trend of UTCI changes ranged from 0.10°C/10 years in summer to 0.43°C/10 years in winter. The number of days with cold stress decreased by 1.8 days/10 years and the number of days with heat stress significantly increased (0.5 days/10 years). Two subperiods with different trends of changes were determined. The first covers the years 1826–1940, and the second, 1941–2021. The former indicates a negative UTCI trend of −0.17°C/10 years, and the latter, an increasing trend of 1.09°C/10 years. The last eight decades have brought a decrease in cold days of 6.5/10 years and an increase in heat days of 2.6/10 years. [ABSTRACT FROM AUTHOR]

The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter‐comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series. [ABSTRACT FROM AUTHOR]

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

The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP. [ABSTRACT FROM AUTHOR]

The present research focuses on temperature change signals over the Carpathian Basin with a special focus on selected lowland and mountainous subregions. High‐resolution (0.11°) EURO‐ and Med‐CORDEX regional climate model (RCM) simulations of near‐surface air temperature are analysed based on raw and bias‐adjusted data. The mini‐ensemble consists of eight RCM simulations driven by five different general circulation models for the period 1976–2099 under the high‐end RCP8.5 scenario. The high‐resolution, homogenized and quality controlled CARPATCLIM was used as a reference dataset. The selected subregions cover eight municipalities located at diverse altitudes: Bratislava, Budapest, Brassov, Debrecen, Hoverla, Novi Sad, Pécs and Poprad. The following climate indices are assessed: summer days, ice days, frost days, tropical nights, the coldest day, the warmest day, the coldest night and the warmest night. In general, for the reference period (1976–2005) bias‐adjusted RCM data showed almost perfect match with observations. Accordingly, no best performing RCM is found for all indices. The ensemble mean of the bias‐adjusted RCM simulations projects an increase (decrease) of 32% and 112% (18% and 25%) in the annual number of summer days and tropical nights (frost days and ice days) for the period 2021–2050. For 2070–2099 we can expect more frequent tropical nights (about five times) with respect to the reference period and the frequency of frost days can be halved. Profound warming manifests in the increase of the warmest temperature of day of up to 2–3°C by the near future and of 5–7°C by the end of the 21st century, which means the absolute maximum temperature can reach 44–47°C for the period 2070–2099. Our results also highlight the need for bias‐adjusted data adapted by different sectors (human health, agriculture, transport, disaster management, heritage conservation) under the national adaptation strategies. [ABSTRACT FROM AUTHOR]

Snowfall and snow accumulation play a crucial role in shaping ecosystems and human activities in the Alpine region. This resource is under threat as a consequence of the visible effects of global warming, and, therefore, it appears urgent to understand how snowfall trends have changed in time and space. In this context, we recovered data from over a hundred snowfall (HN) time series covering the period 1980–2020 over the mountain region of Trentino‐South Tyrol in the northeastern Italian Alps and analysed them to understand snowfall climatology in the region, recent trends and their dependence on elevation and timing of the season. Negative, although not always statistically significant, trends were found in the lowest elevation range (0–1,000 m a.s.l.) over the whole winter season, while some positive and even significant trends were found from January to March above 2,000 m a.s.l. The intermediate elevation range (1,000–2,000 m a.s.l.) exhibits a strong variability with no clear trend. Negative and statistically significant trends were found in April for all elevations. An attribution analysis was performed using precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, such as the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) indices. The analysis shows that, overall, P is the driver that best explains the snowfall trends, but, for low elevations, especially during mid‐winter, TMEAN is more relevant. Low elevations are facing a clear decrease in HN due to a significant increase in mean temperatures, while high elevations during mid‐winter display a slight increase in HN, associated with a general increase in precipitation. NAO and AO indices exhibit no significant correlations with HN, except at the lowest elevations and at the beginning of the season. [ABSTRACT FROM AUTHOR]

In this study, we focus on the features and predictability of winter 2‐m air temperature (t2m) over China under different intensities of La Niña events. The seasonal and intraseasonal prediction skills of winter t2m over China are examined by four state‐of‐art seasonal climate forecast models. The results show that the China winter t2m anomaly is sensitive to the intensity of La Niña. Strong and moderate (S/M) La Niña events are often accompanied by cold winter over China, while a positive anomaly of mean t2m over China frequently occurs in the weak La Niña events. The intensity difference of the East Asian winter monsoon (EAWM) results in this discrepancy. All models have a slightly higher anomaly correlation coefficient (ACC) for the S/M La Niña than that for the weak La Niña in leading 1 month (LT1). Both individual models and MME exhibit higher skills of anomaly sign consistency rate (ASCR) for the S/M La Niña, which implies that models predict t2m anomaly over China more accurately in the S/M La Niña events. Furthermore, the winter t2m over China presents larger intraseasonal variability for the S/M La Niña than that for the weak La Niña. Models can capture the t2m anomaly over China more accurately when the temperature varies gently with small intraseasonal variability during winter for the S/M La Niña. [ABSTRACT FROM AUTHOR]

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

Known as the "roof of the world", 50%–56% area of the Qinghai–Tibet Plateau (QTP) is covered by seasonal frozen ground (SFG), which has an important impact on local and global climate change, terrestrial ecosystems, and regional energy and hydrological cycles. In this study, long‐term observational data of air and soil water (precipitation and soil moisture) and heat [surface air temperature (SAT) and soil temperature (ST)] at 30 meteorological stations were used to study the temporal and spatial changes of SFG and their possible causes for the central‐eastern QTP (CEQTP). The results showed that latitude and altitude are the key factors affecting the spatial distributions of seasonal freeze–thaw activities of CEQTP. The stations with deeper freeze depths and more freeze days are mainly located in high‐altitude and high‐latitude regions, and those with shallower freeze depths and fewer freeze days are mainly located in the low‐altitude and low‐latitude regions of the southern QTP. This may be the reason that latitude and altitude are the key factors determining the temperature distribution on the CEQTP. SAT, ST, precipitation, and soil moisture are all significant correlations with the freeze depth, freeze days, freeze start date (FSD) and thaw end date (TED), and the abrupt change years of them are also consistent; they are the important factors affecting the freeze–thaw changes (FTCs) of SFG. Among them, ST is the key factor influencing the FTCs of SFG, and the variations of monthly average soil temperature (MAST) at 0–320 cm depths are the inverse of those of the monthly average freeze depth and freeze days during the year. Using the MAST data at 0–320 cm depths and the 0°C ST threshold, the soil freeze–thaw processes at different depths on the CEQTP are revealed. Affected by global warming, SAT and ST at different depths on the CEQTP have shown the upward trends since the 1980s. Additionally, precipitation and soil moisture have also increased substantially, especially since the late 1990s. Enhancement of warming and wetting conditions from the land surface to the deep soil have accelerated the thawing of SFG, and led to the delay of FSD and the advance of TED, which further caused the reduction of freeze depth and freeze days of SFG on the QTP, especially since the late 1990s. [ABSTRACT FROM AUTHOR]

China's surface air temperature is increasing due to global warming, so it is interesting that how low temperatures would be changed during the growth period of winter wheat in future. We focused on the low temperatures of winter wheat from 2021 to 2050, using temperatures under the high emission scenario Representative Concentration Pathway 8.5 (RCP8.5) projected by the RegCM4.4 regional climate model. The results showed that the annual mean temperature was projected to increase by 0.42°C⋅decade−1 in the northern and by 0.35°C⋅decade−1 in the southern winter wheat region. Furthermore, the temperature was expected to increase rapidly in spring, which could advance the dates of flowering and the start of the grain‐filling period. Using the genetic parameters determined by the calibration and validation of WOFOST and bias‐corrected projected meteorological data, simulations of winter wheat growth were performed over the winter wheat region for 2021–2050. The simulated number of days to the flowering period of winter wheat for 2041–2050 was on average 6.5 days less than in 2021–2030, due to the spring warming. Because of the earlier start of the growing season, winter wheat could face negative effects by being subjected to low temperatures. Indeed, the number of low‐temperature days was projected to increase by 110% from 2041 to 2050 compared to 2021–2030, and the number of killing degree days (KDDs) is projected to increase by 120% at the same time. If the number of days to flowering did not change, the number of low‐temperature days and KDDs only changed slightly, showing that the negative influence of low temperature was mainly caused by the advancement of the flowering date. The effect of low temperature on growth was underestimated when the response of winter wheat growth to global warming was not considered. [ABSTRACT FROM AUTHOR]

Using 2001 and 2016 land use (LU) data and three developed deforestation LU scenarios, namely Deforest I, II and III, the possible impacts of the ongoing land use land cover change (LULCC) over West Africa (WA) and the uncertainty regarding the impacts on regional climate were assessed. Twelve Weather Research and Forecasting (WRF) model simulations were configured with the default and integrated MODIS satellite dataset for two sets of 3 months (December–February DJF 2011–2012 and July–September JAS 2012) over WA. Between 2001 and 2016, changes that occurred in precipitation, temperature and dew point temperature due to LULCC are mostly not significant at the 95% level with a decrease of 0.8, 0.0 and 0.1%, respectively. Deforest I, II and III caused significant changes with an increase of 44.4, 44.7, 41.5%; 0.8, 0.9, 1.1%; and 0.3, 0.1, −0.2%, respectively, for precipitation, temperature and dew point temperature. Precipitation was lower in the simulation with 2016 LU than it was with 2001 LU, which may have also been influenced by the reforestation that occurred between the periods. However, Deforest III resulted in a widespread nonsignificant rise in precipitation, as the modified areas showed a significant decrease during JAS period. Areas where forests were converted into barren lands revealed severe increase in the surface albedo which is a strong factor that its alteration is capable of influencing precipitation and other variables over WA. LULCC also altered the atmospheric column by modifying the strength of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), wind speed, and monsoonal, vertical, meridional and zonal winds. Heat related fields, such as dew point and air temperature, and moisture fields such as relative humidity were also modified by LULCC. Therefore, as WA reacts strongly to LULCC, sustainable land management practices should be encouraged to control its adverse effects. [ABSTRACT FROM AUTHOR]

High‐latitude Asia (above 60°N) is highly sensitive to global climate change. Warming has led to frequent extreme climate events and has produced important impacts on both the ecological environment and social economy. However, station observations are scarce and there is limited climate change research in the region. Reanalysis data is a potential alternative to scarce observations, but its suitability in high‐latitude Asia has not been well evaluated. In this study, we systematically evaluate the abilities of five reanalysis datasets (MERRA‐2, CFSR, ERA‐Interim, JRA‐55, and ERA5) and one gridded observation dataset (CPC) to characterize mean/extreme temperature and precipitation in high‐latitude Asia through comparison with GHCN‐D station observations. All six datasets reasonably reproduce spatial and temporal variations in mean/extreme temperature and precipitation over the region and match well with the observed climatology. Of these datasets, CPC shows the best performance for mean/extreme temperature and precipitation in high‐latitude Asia, because it is produced through interpolation of station observations. Among the five reanalysis datasets, ERA5 overall shows the best agreement with observed spatiotemporal changes in mean/extreme temperature and precipitation, although it is not superior for all variables. Temperature is generally characterized better than precipitation in these datasets. These results will be helpful for the selection and improvement of reanalyses and thus further climate research in high‐latitude Asia. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is a variable used to characterize the evaporative demand of the atmosphere and its impact on the water balance. During the last decades, significant ETo variabilities have been observed, especially at mid‐latitudes. These variabilities respond mainly to local variations in their physical drivers, such as solar radiation, vapour pressure deficit or wind speed. In this study, the annual and seasonal ETo estimates are generated using the Penman–Monteith method (FAO). Surface weather stations for the Argentine territory and reanalysis data for southern South America of the last four decades (1981–2020) are used. Contributions of both aerodynamic (ETaero) and radiative (ETrad) effects are evaluated to analyse their driving role. Significant positive ETo trends are observed from reanalysis data throughout Argentina, especially on the central east side of the Andes Mountain range with values up to 10 mm·year−1. Most of these ETo changes respond to positive trends in air temperature in the study area, while those in the central Andes also respond to negative trends in dew point temperatures. On the other hand, the increase in energy availability through positive trends in net surface radiation produced a slightly higher ETo in the northern regions of the country. Regional ETo values have shown to be more sensitive to variations in air temperature in the northeastern areas, although changes in humidity and solar radiation could also play a role. In a context of climate change, given that temperature and rainfall are expected to increase in the central and northeastern region of the country and decrease along the eastern side of the Andes Mountains in the coming decades, the characteristics observed over the 1981–2020 period are expected to intensify in the near future. [ABSTRACT FROM AUTHOR]

Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid‐changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann–Kendall test, at a significance level of 0.10, was used to determine trends and the Theil–Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December–March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0°C between the decades of 1988–1997 and 2008–2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking. [ABSTRACT FROM AUTHOR]

During the winter of 2015–2016, an extraordinary warming event occurred in the troposphere in the Arctic region. The above‐normal temperatures extended from the surface to 300 hPa in the Arctic. The number of extremely warm days derived from the surface air temperature revealed that this event was the most significant warming event since 1980. It was found that the tropospheric warming anomaly was not fully dependent on the warming anomalies at lower levels, and it formed earlier than the lower anomalies. In December 2015, there were three obvious warm centers in the troposphere in the mid‐high latitude regions in eastern North America, northern Europe, and western Asia. Albeit the strongest warming anomaly occurred in North America, the prominent warm advection to the Arctic region was transported from northern Europe during the initial stage of the Arctic mid‐upper tropospheric warming. Further investigation revealed that the teleconnection wave train stretching across North America to the North Atlantic and northern Europe contributed significantly to the existence of a poleward transport channel from northern Europe to the Arctic. The wave train spread from west to east and produced a strong southerly wind disturbance over the warm region in northern Europe. As a result, extraordinary poleward heat and water vapour transport occurred across the polar boundary causing substantial warming in the Arctic mid‐upper troposphere. During the extreme Arctic tropospheric warming event, the dynamic processes related to the heat transport over northern Europe directly contributed to the advanced rapid warming in the Arctic mid‐upper troposphere; whereas, the dynamic and thermal processes associated with the transport across the Arctic boundary jointly maintained the late stage of Arctic warming. [ABSTRACT FROM AUTHOR]

Extreme temperatures were considered natural hazards because they could increase morbidity and mortality. Understanding the extreme temperature changes at different warming levels is crucial to climate change mitigation and adoption for human health. This study projected climate change effects on the intensity, occurrence, and duration of extreme temperatures in Taiwan with 2 and 4°C global warming scenarios using the Weather Research and Forecasting model. The future climate simulations were conducted with the pseudo‐global warming approach, and the future climate changes were obtained from the ensemble mean of simulations in the Coupled Model Intercomparison Project 5. The simulated daily mean temperature increased by 1.40 and 3.09°C under 2 and 4°C global warmings. In a warming world, the daily maximum temperature was projected to increase by 1.35–3.00°C, whereas the daily minimum temperature was even higher, leading to weaker diurnal temperature variation in most regions. The simulation results show that intensified heatwaves with frequent and prolonged durations become par for the course, whereas extremely cold days disappear gradually. The occurrence of heatwaves in the future is projected to be five times that in the current climate. Comparing the global warming impacts over different land‐use types, the heatwave occurrence over urban areas rose more quickly than over other land‐use types; forests are less vulnerable to global warming. On the contrary, the changes in extremely cold days over urban areas were weaker than over other land‐use types. Overall, the effects of global warming on temperature revealed that extreme events were more severe with increased temperature than with the mean state of air temperature. Nonlinear behaviours indicated that global warming should be limited to 2°C, and the additional 2°C warming (from 2 to 4°C) should be addressed carefully. [ABSTRACT FROM AUTHOR]

Convective sea‐effect snowfall, in the form of snowbands, is observed over the northern Baltic Sea annually. Quasi‐stationary snowbands may last up to several days over the sea and, depending on the wind direction, move towards the coast. This study provides climatology of spatial and temporal occurrence of snowbands in Finland for a 48‐year period (1973–2020). We used a set of detection criteria together with ERA5 reanalysis at off‐shore areas and FMIClimGrid gridded observational data for on‐shore areas to find the days favouring snowband formation. Only those snowband days (SBD) when snow reached the Finnish coastal mainland were considered. The total annual number of SBDs in Finland varied from 6 to 40 with an average of 16. SBDs were detected most frequently over the Gulf of Bothnia near the western coast of the country. The largest increase in snow depth (SDI) during an SBD (67 cm/day) also took place on the western coast, although the long‐term mean of SDI (3–5 cm/day) was highest over the southern coast. Throughout the country, November and December showed the highest frequency of SBDs. However, between the periods 1973–1996 and 1997–2020, the seasonal cycle of SBDs shifted 1 month forward from late autumn to mid‐winter as the decrease in the number of SBDs during December as well as the increase during January and February were statistically significant in Finland. In northern Baltic Sea, long‐term increases in monthly means of sea surface temperature (SST) and air temperature at the atmospheric level of 850 hPa (T850) were in line with the decadal changes in the occurrence of SBDs. The increasing trend in SST favours the formation of snowbands but in late autumn the probability for snowband formation decreased because even larger increases in T850 resulted in diminishing differences between SST and T850. [ABSTRACT FROM AUTHOR]

Forty years (1980–2019) of reanalysis data were used to investigate climatology and trends of heat stress in the Caribbean region. Represented via the Universal Thermal Climate Index (UTCI), a multivariate thermophysiological‐relevant parameter, the highest heat stress is found to be most frequent and geographically widespread during the rainy season (August, September, and October). UTCI trends indicate an increase of more than 0.2°C·decade−1, with southern Florida and the Lesser Antilles witnessing the greatest upward rates (0.45°C·decade−1). Correlations with climate variables known to induce heat stress reveal that the increase in heat stress is driven by increases in air temperature and radiation, and decreases in wind speed. Conditions of heat danger, as depicted by the heat index (HI), have intensified since 1980 (+1.2°C) and are found to occur simultaneously to conditions of heat stress suggesting a synergy between heat illnesses and physiological responses to heat. This work also includes the analysis of the record‐breaking 2020 heat season during which the UTCI and HI achieved above average values, indicating that local populations most likely experienced heat stress and danger higher than the ones they are used to. These findings confirm the gradual intensification of heat stress in the Caribbean and aim to provide a guidance for heat‐related policies in the region. [ABSTRACT FROM AUTHOR]

Knowledge of the difference between soil and air temperatures (ΔT) is helpful to improve our understanding on the land‐atmosphere thermal interactions and temperature‐dependent soil processes. Based on 272 stations across China, this study investigated the spatiotemporal variations of the annual and seasonal ΔT (difference between soil temperature at a depth of 0.4 m and air temperature) from 1981 to 2014, and quantified the relative contributions of multiple environmental variables (snow cover, precipitation, vegetation, soil moisture, and solar radiation) to ΔT variation for the first time. Air temperature primarily controls soil temperature dynamics, but the asynchronous trends of soil and air temperatures may lead to the complexity of the land‐atmosphere relationship. Almost no apparent trends in ΔT were detected for the entire China (except in summer), but the spatial heterogeneity of trends was evident. Snow cover conditions greatly dominated the ΔT dynamics both annually and seasonally (except in summer). The relative contribution of snow cover duration to ΔT variation was significantly greater than that of mean snow depth for the entire China, but the regional differences in the contributions of the two variables were noticeable at different seasons. The greening of vegetation closely associated with the ΔT variation in annual, autumn and winter, and soil moisture exerted a great influence on summer ΔT, associated with sunshine duration (a proxy for surface solar radiation). The amount of precipitation made a slight impact on ΔT at either seasonal or annual scales. [ABSTRACT FROM AUTHOR]

Based on the long‐range correlation (LRC) of daily air temperature records in China, the simulation performance of 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models is first evaluated in this study. And then, those CMIP6 models which can reproduce LRC of the well in observed records are selected for multi‐model ensemble mean. The results indicate that multi‐model ensemble mean can significantly reduce model uncertainty in reproducing LRC characteristics of daily air temperature records. Comparing the differences of the scaling exponents of the ensemble mean temperature data between the historical well‐mixed greenhouse gas, historical natural‐only, historical anthropogenic aerosols, and historical experiments, the LRC characteristics of different temperature variables show some difference. It means that the different forcings are critical for the model to reproduce the LRC characteristics of different temperature variables well. Generally, the influence of anthropogenic forcing enhances LRC of daily maximum air temperature in parts of northern China. Therefore, anthropogenic forcing is very important for CMIP6 models to accurately reproduce the strength of LRC for observed daily air maximum temperature in northern China. [ABSTRACT FROM AUTHOR]

High‐resolution simulation is important for understanding climate change and its impact over regions with complex terrain. Northern Asia (NA) is such a region and sensitive to global warming. However, the performance of the regional model over NA is still unclear. In this study, we conducted a two‐decade‐long high‐resolution (25 km) simulation over NA using the Weather Research and Forecasting (WRF) model forced by the ERA‐Interim reanalysis and then evaluated the performance of WRF against the observations. The results show that the WRF simulation, set up with selections of the physical schemes, can reasonably reproduce the spatial and temporal features of surface air temperature and precipitation over NA. In addition, the WRF simulation can add more regional details on the climate features than the ERA‐Interim data, especially over the NA regions with complex terrain. The spatial‐pattern correlation coefficient of summer precipitation over NA between the WRF simulation and observations is higher than that of the ERA‐Interim data. WRF can reasonably reproduce the annual cycles of precipitation. Moreover, the observed year‐to‐year variations in summer surface air temperature and precipitation over most NA subregions could be generally captured by the WRF. In conclusion, although systemic model bias still exists, the evaluations in this study indicate that WRF shows reasonable performance in a high‐resolution simulation of climate over NA, consequently providing a scientific basis for future simulation studies over the region. [ABSTRACT FROM AUTHOR]

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

The present study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHI) in Singapore. Observations collected at 20 locations across the island city‐state and during 6 years make this one of the most extensive studies carried out in a tropical city. Local climate zones (LCZs), defined as urban built and rural land cover types which produce a unique air temperature response, are used to standardize intersite comparison. The results show that the choice of the rural reference can affect the night‐time UHI magnitude by up to 2°C under "ideal" (dry, clear, calm) conditions. The most frequently observed median UHI magnitude increases from 2.8 during all‐weather to 3.7°C during "ideal" conditions, respectively. A seasonality is present with lowest (highest) mean all‐weather values of ~2.0°C (~3.3°C), observed during the wet (dry) December–January (April–October) period. Mean night‐time UHI intensity across seven built‐type LCZs ranges between 1.8 and 3.5°C (2.5 and 4.3°C) for all‐weather ("ideal") conditions. Corresponding daily values are 1.1–2.3°C (1.5–2.7°C). The lowest (highest) night‐time magnitudes are associated with open low‐rise LCZ 6 (compact high‐rise LCZ 1) built type. In the middle of the day LCZ 1 can experience a cool island effect. The average UHI values presented here give an indication of the extra warmth experienced in the built‐up spaces of Singapore relative to one possible rural reference land cover type (scattered trees LCZ B). Highest daytime heat exposure is observed in LCZs 3 and 8 which are characterized by low building heights, high impervious surface fraction and lack of vegetation. The present results can be used to support the development and evaluation of urban climate models, develop urban planning policies and improve local weather forecasts and the delivery of integrated urban services. [ABSTRACT FROM AUTHOR]

The Trans‐Volcanic Axis (TVA)—comprising ridges and terrain elevations above 1,900 m—strongly affects meridional propagation of summer precipitation in western Mexico. This barrier modifies atmospheric moisture content and changes the annual precipitation cycle configuration, which distinguishes it from the precipitation cycle on both sides of the TVA. Using upper‐air observations and rainfall records, this study demonstrates that the TVA is the region where the North American monsoon (NAM) and the mid‐summer drought (MSD) influence intersections regarding precipitation. This region affects both northward extent of double precipitation maxima with the MSD and the southward extent of the NAM single precipitation peak. This study proposes that the forcing mechanism that controls the annual precipitation cycle in the TVA region relies on latent heat loss via ground cooling by reducing air temperature below dewpoint temperature in the 700–500 hPa layer, consistent with local terrain elevations. The orographic effect increases specific humidity in the atmospheric column—sufficiently large to enhance local precipitation accumulation—resulting in a mid‐summer drought intensity decline but not enough to yield a single peak in precipitation. Consequently, the TVA becomes an uncategorized region that does not display the NAM nor the characteristic features of the MSD. [ABSTRACT FROM AUTHOR]

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

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

Ongoing global warming particularly affects the coldest regions of our planet, where thunderstorm activity is considered to be the lowest. Scientific studies usually predict that lightning will become more frequent in polar areas in a warmer world. The aim of this study is to test this hypothesis and present the current knowledge on thunderstorm occurrence at high latitudes (>60°) based on SYNOP data from manned WMO stations operating from 2000 to 2019. According to this source, most thunderstorm events at high latitudes occur in summer (85%), when the air temperature ranges from 15 to 25°C (70%), during the days with positive temperature anomalies (75%) and negative sea‐level pressure anomalies (65%). The highest thunderstorm activity is observed over inland areas, especially in the European part of Russia. The changes in thunderstorm frequency are only visible at certain WMO manned stations and mostly during the summer months. The regional Kendall test revealed a statistically significant increase in the number of thunderstorm days north of 60°N in Interior Alaska, northwestern Canada, much of Siberia and European Russia. A decrease in thunderstorm frequency over a larger area was detected only on the shores of the southern Norwegian Sea, and seasonally in spring in the northern Urals. The observed trends were strongest in the Central Siberia and Interior Alaska regions, where the increase in the number of thunderstorm days exceeded 5 per decade. For the entire high‐latitude area, the change in the number of days with thunderstorms was statistically insignificant, but for stations located 250–1,000 km from the coastline, the averaged increase amounted 1 day per decade. [ABSTRACT FROM AUTHOR]

The land–atmosphere interactions play an important role in the South American climate system. Previous studies have explored the coupling between soil moisture (SM), surface heat fluxes and atmospheric variables such as temperature and precipitation; however, the SM–precipitation feedback is still debated, especially at the diurnal scale. We computed three diurnal local coupling diagnostics between spatial, temporal and heterogeneity morning SM anomalies and the occurrence of afternoon precipitation events. We analyse South America in the period October–March 1983–2012 with the ERA5 reanalysis and the RCA4 regional climate model. Two 30‐year climate simulations are performed: a control and an uncoupled simulation where the SM is prescribed with daily climatological values, limiting the SM–atmosphere interaction. Comparing both simulations we isolate the effect of SM variability, allowing us to confirm that the diagnostics are indeed showing the contribution of the SM–precipitation coupling. We test different methods of calculating the morning SM anomalies and observe that all diagnostics show some methodological sensitivity, especially the spatial and temporal diagnostics. Despite the differences, in eastern Brazil we find a preference for afternoon precipitation to develop when the soil is wetter and heterogeneous, over points that are wetter than their surroundings. Towards southeastern South America, there is a spatial and temporal preference for drier or average conditions, depending on the method, with heterogeneity preference, and the same is observed in southwestern Amazonia in RCA4. Ultimately, it is important that future studies disentangle the spatiotemporal coupling mechanisms that may act simultaneously into its distinct components to avoid erroneous assumptions and contradictory results. [ABSTRACT FROM AUTHOR]

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

The surface air temperature seasonal cycle in China has clearly changed in response to global warming, but its future projection has yet to be studied. Here, we investigate the performance and projection of the temperature seasonal cycle in China in 32 global climate models participating in the Coupled Model Intercomparison Project 6 (CMIP6). The characteristics of the temperature seasonal cycle, as represented by the amplitude and phase, are measured by the Fourier transform. The results show that the CMIP6 models can reasonably capture the observed typical temperature seasonal cycle feature with clear north–south gradients both for amplitude and phase in China. In the twenty‐first century, the models robustly project a delay in the phase across the whole country and a reduction in the amplitude in most of China relative to the reference period. Those changes are similar in the two shared socioeconomic pathway (SSP) scenarios, with overall larger magnitudes in the SSP5‐8.5 scenario than in the SSP2‐4.5 scenario. Projected amplitude changes are mainly linked to surface energy flux changes, especially latent heat flux changes. In response to future global warming, larger increases in the latent heat flux and thus stronger surface evaporative cooling lead to weaker surface warming in summer than in winter, contributing to a decrease in the amplitude of the temperature seasonal cycle in most regions of China. [ABSTRACT FROM AUTHOR]