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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]

In the current context of climate change, the increase in minimum temperatures recorded in recent decades has received special scientific attention due to its importance for the proper night's sleep and health of the population, among other considerations. One of the main indicators usually considered refers to the frequency of tropical nights (≥20°C), which have begun to become widespread in regions hitherto excluded from this type of event. This paper analyses tropical nights in Spain between 1970 and 2023, addressing their mean annual occurrence, their intensity, their monthly distribution and the average number of consecutive tropical nights recorded, as well as their relationship with relative humidity. The results, based on the analysis of 75 homogenized series located in 44 different provinces, allow us to differentiate seven large areas based on the minimum temperature in which there has been an increase in the frequency and intensity of tropical nights, which progressively extend the season in which they can appear. Similarly, a generalized increase in the maximum number of consecutive nights thermally above 20°C was identified, in addition to the presence of high relative humidity in coastal areas during these episodes. [ABSTRACT FROM AUTHOR]

Precipitation is a highly variable meteorological element. Similarly, to other meteorological elements (e.g. temperature), the changes in the measurement practices or in the environment can cause inhomogeneities in the precipitation data series. Therefore, homogenization of precipitation data series is necessary before studying the long‐term climate change. In this paper we present the main features of the MASH procedure, which we use to produce homogenized climate datasets for Hungary at the Climate Department of the Hungarian Meteorological Service (OMSZ). Due to the increasing number of discontinued precipitation stations, the number of missing data has increased significantly; therefore the station networks used for precipitation homogenization have been completely renewed. With this renewal, the percentage of missing data has been minimized, the number of data series has been increased and a denser station network is now used in mountainous areas where the spatial variability of precipitation is the highest. As a result, a new, more homogeneous, representative precipitation database of Hungary from 1901 to the present has been created. In the paper, we also examine the main characteristics of the detected inhomogeneities, with examples of the main types of inhomogeneities for some stations. [ABSTRACT FROM AUTHOR]

Precipitation is a highly variable meteorological element. Similarly, to other meteorological elements (e.g. temperature), the changes in the measurement practices or in the environment can cause inhomogeneities in the precipitation data series. Therefore, homogenization of precipitation data series is necessary before studying the long‐term climate change. In this paper we present the main features of the MASH procedure, which we use to produce homogenized climate datasets for Hungary at the Climate Department of the Hungarian Meteorological Service (OMSZ). Due to the increasing number of discontinued precipitation stations, the number of missing data has increased significantly; therefore the station networks used for precipitation homogenization have been completely renewed. With this renewal, the percentage of missing data has been minimized, the number of data series has been increased and a denser station network is now used in mountainous areas where the spatial variability of precipitation is the highest. As a result, a new, more homogeneous, representative precipitation database of Hungary from 1901 to the present has been created. In the paper, we also examine the main characteristics of the detected inhomogeneities, with examples of the main types of inhomogeneities for some stations. [ABSTRACT FROM AUTHOR]

Adverse climate changes in Pakistan undermine the water resources and give rise to transient and permanent droughts in different parts of the country. The occurrence of drought cannot be prevented, but its detrimental impacts can be brought down by proper pre‐drought planning. Effective planning owes to using scientific and probabilistic approaches. In this paper, the interarrival time of drought for seven operational drought indices in Pakistan is modelled by using the modified distribution of convolution derived from the Bivariate Affine Linear Exponential (BALE) distribution. A new stochastic variable, interarrival time of drought, is generated from the data of drought indices from 1951 to 2016 used in Pakistan by applying the theory of runs. The proposed model identifies Palmer drought severity index (PDSI) and Reconnaissance drought index (RDI) as the most appropriate meteorological indices for the under‐study country. The quantiles of the model are computed that provide information about the interarrival time of drought and help to anticipate this phenomenon. In addition, return periods for all the seven indices are calculated to report the frequency and duration of drought over a certain period. Finally, some recommendations for the National Drought Monitoring Centre (NDMC) and Pakistan Council of Research in Water Resources (PCRWR) are stated for better future planning to avoid the adverse impacts of drought. [ABSTRACT FROM AUTHOR]

The Caspian Sea level (CSL) is strongly sensitive to climate‐induced changes in its water balance components, especially evaporation from its surface as the main expenditure component of the Caspian Sea's (CS) water budget. Projecting evaporation and determining associated uncertainties obtained from such studies are critical for reliably predicting future CSL fluctuations and developing mitigation and adaptation strategies. This paper studies the projected changes in evaporation from the CS using 18 global climate models (GCMs) from the latest Coupled Model Intercomparison Project phase 6 (CMIP6) and Meyer's semi‐empirical formula. Future evaporation projections are constructed employing a weighted combination of the top‐ranked GCMs, including all ensemble members of selected individual models in a probabilistic framework. This study estimates late 21st century median evaporation of 945, 1016, 1105 and 1173 mm under the low‐emission, medium‐emission, medium‐to‐high emission and high‐emission scenarios, respectively. The weighted multimodel ensemble suggests CS's annual mean evaporation is projected to substantially increase by 3.9%, 13.2%, 20% and 27.9% under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5, respectively, for the late 21st century against the reference period. According to the spatial distribution of evaporation, arid eastern regions of CS experience higher evaporation than semi‐arid western and southwestern temperate regions. Additionally, the northwestern continental regions experience the least evaporation over the CS. [ABSTRACT FROM AUTHOR]

Drought seriously endangers the development of agro‐pastoral ecotone in arid and semi‐arid regions. However, drought evolution characteristics of semi‐arid sandy grassland and its drivers are not well understood in the complex climate change context. Thus, in this paper, the revised standardized precipitation evaporation indices on one‐month (SPEI‐1) and growing‐season (SPEI‐6) scale, flash drought and dry spells (DS) were calculated as drought indicators, and the temporal variabilities and meteorological drivers of drought in Horqin Sandy Land during the growing period (April–September) from 2007 to 2021 were studied. The results showed that SPEI‐1 and SPEI‐6 exhibited negative trends and periodic change. This drought aggravation was manifested in increased grades and numbers of drought months. Flash droughts can occur every month during the growing season. And its occurrence was not only positively correlated with all drought events in frequency but also synchronized in time. The increased number of DS showed an asymmetrical shift towards 7‐day‐and‐longer DS. SPEI sensitivity to precipitation (P) was noticeably higher than to atmospheric evapotranspiration demand (AED) at both monthly and growing season scales. SPEI sensitivity to AED was modulated by P amounts, which was higher during low P period. Apart from the amounts of P and AED, their temporal variability also strongly determined how they affect the drought severity. P was also the main driver for flash drought and long DS, but AED with larger amount and higher variability is the reason for their aggravation. Our results demonstrate the complexity of drought intensification in semi‐arid sandy grasslands and highlight the important role of AED anomalies in it. [ABSTRACT FROM AUTHOR]

Sea ice is an important and complex component of the Earth system and is considered a sensitive indicator of climate change. The seasonal sea ice cycle regulates the exchange of heat and salinity, altering the energy balance between high and low latitudes as well as the ocean and atmospheric circulation. The accurate representation of Antarctic sea ice has been considered a hot topic in the climate modelling community and lacks conclusive answers. In this paper, we evaluated the ability of 11 climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) to simulate the sea ice seasonal cycle in Antarctica in terms of area (SIA) and concentration (SIC), as well as the improvements in the most recent models' version, submitted to CMIP6. The results indicated that all models are able to accurately capture the seasonal cycle of the Antarctic SIA, with the minimum (maximum) occurring in February (September). In the Weddell Sea, Amundsen Sea, Bellingshausen Sea, and the Ross Sea, the simulated sea ice concentration revealed a large and systematic bias in February when compared to observations. In September, a large and systematic bias was found nearby the Southern Ocean's northern limit in the Polar Front. Several CMIP6 models exhibited slight improvements on the SIA and SIC estimate over the previous version (CMIP5). All models indicated a significant sea ice loss in the coming years as a response to CO2 forcing. Despite the advancements in the sea ice representation, our findings show that the models are still unable to accurately represent the regional sea ice changes [ABSTRACT FROM AUTHOR]

Sea ice is an important and complex component of the Earth system and is considered a sensitive indicator of climate change. The seasonal sea ice cycle regulates the exchange of heat and salinity, altering the energy balance between high and low latitudes as well as the ocean and atmospheric circulation. The accurate representation of Antarctic sea ice has been considered a hot topic in the climate modelling community and lacks conclusive answers. In this paper, we evaluated the ability of 11 climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) to simulate the sea ice seasonal cycle in Antarctica in terms of area (SIA) and concentration (SIC), as well as the improvements in the most recent models' version, submitted to CMIP6. The results indicated that all models are able to accurately capture the seasonal cycle of the Antarctic SIA, with the minimum (maximum) occurring in February (September). In the Weddell Sea, Amundsen Sea, Bellingshausen Sea, and the Ross Sea, the simulated sea ice concentration revealed a large and systematic bias in February when compared to observations. In September, a large and systematic bias was found nearby the Southern Ocean's northern limit in the Polar Front. Several CMIP6 models exhibited slight improvements on the SIA and SIC estimate over the previous version (CMIP5). All models indicated a significant sea ice loss in the coming years as a response to CO2 forcing. Despite the advancements in the sea ice representation, our findings show that the models are still unable to accurately represent the regional sea ice changes [ABSTRACT FROM AUTHOR]

As a crucial component of the climate system, snow cover plays an important role on surface energy budgets, hydrological cycles and socioeconomic development. This paper investigated the spatiotemporal patterns of snow depth across the Qinghai Plateau (QP) during 1980–2018 based on passive microwave (PMW) satellite observation and reanalysis products. The study identified the relationships between the changes in PMW snow depth and topographic features and revealed the impact of climatic variables (air temperature, precipitation and wind speed) and large‐scale atmospheric circulations on observed snow depth variability. The results show that the spatial pattern of snow depth climatology is similar in all datasets, except in terms of magnitude, with high values in the southern and southeastern parts of the QP and low values in the eastern and northwestern parts. Average snow depth correlates positively with precipitation, elevation and slope, and negatively with air temperature and wind speed. The long‐term trends in snow depth vary with the season and the datasets. The PMW snow depth across the QP shows a significant annual (−0.125 cm·decades−1, p <.10) and spring (−0.184 cm·decades−1, p <.05) negative trend, while snow depth for ERA5 and MERRA2 does not present significant trends. Air temperature dominates total snow depth variation over the QP, explaining 43.85, 24.88 and 47.28% of annual, winter and spring PMW snow depth variations, which significantly affects snow depth variations in most parts of the QP, yet the effects of precipitation and wind speed on snow depth variation exhibit significant regional differences, and atmospheric circulations (e.g., AMO) also have a remarkable controlling effect on some localized areas. [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]

Soil respiration is the primary efflux of carbon dioxide (CO2) in the terrestrial ecosystem. The soil of alpine grassland on the Tibetan Plateau (TP) is rich in soil organic matter, which may release more carbon dioxide as the climate warming. However, due to the limited observations here, there are still deficiencies in understanding the response of soil respiration to climate change, especially the difference between permafrost and non‐permafrost regions. In this paper, we investigate the climatology and trend of soil respiration on the TP from 1979 to 2018, using the Community Land Model version 4.5 (CLM4.5) forced by a suite of high‐resolution atmosphere dataset. Evaluation results show that the land surface model could properly reproduce permafrost extent, and capture the spatial pattern of soil temperature, soil moisture, leaf area index (LAI), and soil respiration. For the whole TP, we find that the spatial pattern for both climatology and trends of soil respiration are correlated with LAI significantly and positively. In addition to the effects of vegetation, precipitation was more correlated with soil respiration than temperature among climatic variables in recent decades. For permafrost and non‐permafrost regions, climate change affects soil respiration in different ways. In permafrost areas, precipitation plays a more important role than temperature. Conversely, in non‐permafrost regions, temperature has a more pronounced effect on soil respiration. The results of this study provide valuable information for predicting greenhouse gas emissions and understanding the carbon cycle on the TP. [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]

Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%). [ABSTRACT FROM AUTHOR]

The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC‐2) and this is the last of three papers aimed to evaluate and intercompare five empirical/statistical downscaling (ESD) methods developed at AEMET: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, in order to decide which methods and under what configurations are more suitable for that purpose. Following the framework established by the EU COST Action VALUE, in this experiment we test the transferability of these methods to future climate conditions with the use of regional climate models (RCMs) as pseudo observations. We evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation, over mainland Spain and the Balearic Islands, analysed by season. For maximum/minimum temperatures all methods display certain transferability issues, being remarkable for Support Vector Machines and Kernel Ridge Regression. For precipitation all methods appear to suffer from transferability difficulties as well, although conclusions are not as clear as for temperature, probably due to the fact that precipitation does not present such a marked signal of change. This study has revealed how an analysis over a historical period is not enough to fully evaluate ESD methods, so we propose that some type of analysis of transferability should be added in a standard procedure of a complete evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel graph‐based method to classify the regions with similar climate in a local area. We refer our proposed method as graph partition based method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state‐of‐the‐art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state‐of‐the‐art climate classification techniques. The climate data are collected from 24 synoptic stations in Fars province in southern Iran. The data include seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes. To the best of our knowledge, our proposed method is the first graph‐based climate classification system. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [ABSTRACT FROM AUTHOR]