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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]

We describe the bias adjustment and downscaling to a resolution of 1 × 1 km of daily temperature, precipitation and solar radiation of a multimodel ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs with the local observational data set BhutanClim for Bhutan and an analysis of the results for 1996–2100. The GCMs were selected based on their ability to reproduce local temporal and spatial precipitation patterns, resulting in a total of 14 models for each shared socioeconomic pathway (SSP). The results are shown for five global warming levels (GWLs) 1.5, 2, 3, 4 and 5°C, providing insight into the projected impacts of climate change at different warming levels. The daily temperature and precipitation data are publicly available, which is of great benefit for climate impact studies, such as ecological, hydrological or infrastructure‐related studies. In this paper, we discuss showcase climate indicators. Peak‐over‐threshold climate indicators such as hot days above 30°C are relevant for human health. In the capital city of Thimphu, these are currently rare, at 4 days per year, but might increase to more than 80 for GWL5.0. Precipitation increases in the summer months, but slightly decreases in the drier winter months. Accordingly, the 3‐month SPEI drought index is projected to increase in summer, but strongly decreases in the winter months, mostly pronounced in February. The increasing severity of late‐winter and spring droughts might have a negative impact on vegetation, wildfires and water supply. [ABSTRACT FROM AUTHOR]

Northwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty. [ABSTRACT FROM AUTHOR]

ABSTRACT Changes in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios. [ABSTRACT FROM AUTHOR]

This paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name "Benova" to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend‐like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided. [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]

Since their introduction 40 years ago, worldwide interest in urban climatic map (UCMap) studies has grown. Today, there are over 15 countries around the world processing their own climatic maps, developing urban climatic guidelines, and implementing mitigation measures for local planning practices. Facing the global issue of climate change, it is also necessary to include the changing climatic considerations holistically and strategically in the planning process, and to update city plans. This paper reviews progress in UCMap studies. The latest concepts, key methodologies, selected parameters, map structure, and the procedures of making UCMaps are described in the paper. The mitigation measures inspired by these studies and the associated urban climatic planning recommendations are also examined. More than 30 relevant studies around the world have been cited, and both significant developments and existing problems are discussed. The thermal environment and air ventilation condition within the urban canopy layer (UCL) of the city are important in the analytical processes of the climatic-environmental evaluation. Possible mitigation measures and planned actions include decreasing anthropogenic heat release, improving air ventilation at the pedestrian level, providing more shaded areas, increasing greenery, creating air paths, and controlling building morphologies. Further developments have and will continue to focus on the spatial analysis of human thermal comfort in urban outdoor environments and on the impacts and adaptations of climate change. Mapmakers must continue to share lessons and experiences with city planners and policy makers, especially in the rapidly expanding cities of developing countries and regions. Copyright © 2010 Royal Meteorological Society [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]

As a dynamical system approaches its critical threshold, the probability density distribution of the system will change significantly. Therefore, it is possible to present an early warning signal based on the changing skewness before reaching the critical threshold. Based on a zero‐dimensional climate model and several typical fold models, this paper systematically studies the influence of noise and missing data on the performance of the skewness coefficient as an early warning signal of an abrupt climate change. The results in three types of fold models show that the skewness coefficient has anti‐noise ability to some extent, but strong noise will significantly reduce the magnitude of the skewness coefficient and the time for early warning will also be shortened. In some cases, strong noise even will lead to the result that the skewness does not work in warning an impending abrupt change. However, the influence of strong noise on skewness is insignificant in the zero‐dimensional climate model. Therefore, the influence of strong noise needs to be considered in the practical application of the skewness coefficient as an early warning signal of an abrupt change. In addition, the results of all the models also indicate that different degrees of the missing data have no statistically significant effect on the warning performance of the skewness coefficient, even when the length of the missing data is up to 20% of the total sample size used in the present paper. [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]

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]

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]

Accurate estimation of ET0, and determination of its trend/variability and sensitivity to changes in climate variables is essential at regional scale for different applications. These are attempted for India by considering 18 homogeneous ET0 regions formed in companion paper. Food and Agriculture Organization (FAO) recommends Penman–Monteith (PM) equation for ET0 estimation, which requires information on several climate variables. The equation cannot be used in data‐sparse areas where information/forecasts on one or more required climate variables is unavailable/unreliable. To address this, relevance vector regression (RVR) relationships are developed in this paper for the 18 homogeneous regions to arrive at FAO‐PM estimate of ET0 from subsets of its predictor climate variables, which could be typically expected in data‐sparse areas. The developed relationships are shown to be better in arriving at ET0 estimates when compared to multiple linear regression (MLR) relationships and three widely used empirical equations (Hargreaves, Mcguinness–Bordne, Priestly–Taylor). Regional trend analysis revealed that ET0 is significantly decreasing (increasing) in most regions located in north (south) India. Sensitivity of ET0 and surface runoff to changes in their predictor climate variables is determined for each region by considering third‐order Taylor series approximation of their functional relationships. Key climate variable(s) that govern ET0 changes in each region are identified. Results indicate that at annual scale solar radiation and relative humidity govern ET0 changes in regions located in south and north‐east India, whereas wind speed followed by relative humidity mostly influence the ET0 changes over other regions. Existence of evaporation paradox in four regions and validity of Bouchet's complementary relationship between ET0, actual evapotranspiration (ETa), and precipitation in 11 regions is also established. Significant divergence in trend of ET0 and ETa was evident in north‐east India. [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]

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]

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]

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]

Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [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]

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]

There is a large discrepancy between studies on how the Indian Ocean Dipole (IOD) is defined. Indeed, the definition of historical events varies substantially, resulting in conflicting classifications, hampering hydroclimatic impact assessment of this phenomenon. Therefore, this paper aims to compare the various methods that have previously been adopted to define IOD events and demonstrate an alternative method that specifically captures different "flavours" of IOD. It is shown that the choice of sea surface temperature (SST) data set and climatological baseline has significant impact on the resulting classification of IOD events. Furthermore, not all events display the traditional dipole signature pattern of warming/cooling occurring at opposite poles. Rather, some events could be considered "pseudo dipoles," whereby the SST anomalies in the two poles are not of opposite sign. Importantly, the different types of IOD (i.e., "pseudo and true") are associated with varying impacts on rainfall for surrounding regions. To overcome these issues, this study treats Indian Ocean SST anomalies as a continuum of a finite maximum number of statistically distinguishable patterns using a neural network‐based cluster analysis. Ten individual SST patterns were identified with distinct rainfall teleconnections. In particular, four strong IOD patterns (two positive and two negative) were identified, alongside three pseudo dipoles representing anomalously warm conditions across the entire Indian Ocean Basin, with a SST gradient towards the east (i.e., a pseudo negative dipole) or west (i.e., a pseudo positive dipole). The occurrence of each of the 10 self‐organizing mapping (SOM) patterns is shown to be variable over the past 130 years, with some types more common during certain phases of the El Niño/Southern Oscillation or during the first (or second) half of the 20th century. The implications of these findings are particularly significant for attribution studies, development of proxy climate reconstructions, climate model evaluation and future climate change assessment. The Indian Ocean Dipole (IOD) is known to impact hydroclimate across many Indian Ocean rim counties. However, there is a growing body of research that confirms the IOD is a complex phenomenon with multiple variants, which cannot be captured by a simple index such as the dipole mode index (DMI). This paper uses a novel technique (SOM) to identify 10 individual flavours of the IOD with distinct rainfall teleconnections. Figure shows 10 different Indian Ocean SST "types" derived using SOM. Note that numbers in brackets show the number of occurrences of each type. [ABSTRACT FROM AUTHOR]

In recent years, numerous studies have investigated the relationship of extreme precipitation intensities with temperature derived from a binning method using present‐day weather data. The aim of this approach is that these dependencies, or so‐called apparent scaling rates, provide insights on how precipitation extremes could evolve in the future climate. In a recent paper, Bao et al. showed that there is a large discrepancy between the present‐day apparent scaling rates—showing a negative dependency of daily rainfall extremes on temperature for Darwin—and the climate change response showing a substantial increase in daily rainfall extremes. Their results provide compelling evidence that it may not be straightforward to predict the climate change response from the temperature scaling. We agree with this statement, but disagree with the physical explanation put forward in that study and think that moisture limitations are the most likely cause. Therefore, we advocate the use of dew point temperatures in scaling studies as a much more direct proxy of the absolute humidity of the air in which precipitation extremes develop, which we think is physically more consistent with the underlying mechanism causing the increase in precipitation extremes with global warming. [ABSTRACT FROM AUTHOR]

Climate change has caused an increased occurrence of heat waves. As a result of rising temperatures, implications for health and the environment have been more frequently reported. Outdoor labour activities deserve special attention, as is the case with agricultural and construction workers exposed to extreme weather conditions, including intense heat. This paper presents an overview of heat stress conditions in Brazil from 1961 to 2010. It also presents computer‐simulated projections of heat stress conditions up to the late 21st century. The proposed climate analysis drew on historical weather data obtained from national weather stations and on reanalysis data, in addition to future projections with the ETA (regarding the model's unique vertical coordinate) regional forecast model. The projections took into consideration two Representative Concentration Pathways (RCP)—the 4.5 and 8.5 climate scenarios, namely, moderate and high emissions scenarios, respectively. Heat stress was inferred based on the wet‐bulb globe temperature (WBGT) index. The results of this climate analysis show that Brazilian outdoor workers have been exposed to an increasing level of heat stress. These results suggest that future changes in the regional climate may increase the probability of heat stress situations in the next decades, with expectations of WBGT values greater than those observed in the baseline period (1961–1990). In terms of spatial distribution, the Brazilian western and northern regions experienced more critical heat stress conditions with higher WBGT values. As a response to the increased frequency trends of hot periods in tropical areas, urgent measures should be taken to review public policies in Brazil. Such policies should include actions towards better working conditions, technological development to improve outdoor labour activities, and employment legislation reviews to mitigate heat impacts on occupational health. [ABSTRACT FROM AUTHOR]

This study examines the problematic impact of selecting a different base period (colder 1961–1990 vs. warmer 1988–2017), on the trend magnitude of widely used percentile‐based extreme temperature indices (e.g., warm/cold spells, warm/cold days and nights). The percentile‐based indices are part of a core set of indices (27 in total) that have become a common standard for monitoring climate change, as recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices were designed to be comparable across regions provided that similar analyses are employed. Unfortunately, the use of different base periods and periods of interest to explore local and global climate change undermines the comparability of findings across regions. When utilizing "day‐count" indices with fixed thresholds, the use of different base/reference periods changes the intercept without influencing the slope (for a given period length). However, this assertion does not hold with percentile‐based indices. Our analyses show that percentile‐based temperature indices (e.g., days with temperature below the 10th or above the 90th percentiles) are particularly susceptible to the problematic use of different base periods. Hence, using percentile‐based indices may have adverse effects on researchers' conclusions. The current paper reports the results of a comparative study that used different base periods for the most commonly used percentile‐based extreme temperature indices. It was found that the (negative) trend magnitude of the cold percentile‐based indices (frequency of cold days and nights and cold spells) is strongly amplified while the (positive) trend magnitude of the warm indices (frequency of warm days and nights and warm spells) is dramatically diminished when percentiles were derived from a base period that included records from the last two decades (e.g., 1981–2010, 1988–2017). These features are even more pronounced when the study period covers only the last 30–40 years. [ABSTRACT FROM AUTHOR]

Data recovery and climate reconstruction are an important support for climate change research, as they provide information from periods and areas with sparse meteorological networks. Various sources are currently in use for obtaining valuable evidence of past climate, such as ship logs, diaries, books, monastery documents. This study exploits newspaper reports in order to enrich the historical meteorological information over the territory of Romania, from the last two decades of the 19th century. At that time, the area belonged to the Austro‐Hungarian Empire and Kingdom of Romania, and the meteorological services from Budapest (since 1870) and Bucharest (since 1884) were the providers of official meteorological information. The digital archive of three newspapers (România Liberă, Gazeta de Transilvania and Foaia Poporului) was investigated and the meteorological information was extracted and aggregated into a database containing 2,132 unique entries clearly referenced in time and space. Each entry represents a meteorological event and several associated characteristics, such as date, location, impact and source. A verification procedure, consisting of comparisons with available measurements from the nearby weather stations and with a reanalysis dataset, was applied in order to validate the entries. The results show that the meteorological information was often present in the newspapers of the epoch. Some climatic features could be retrieved such as seasonality of extreme events, temperature and precipitation characteristics. This paper demonstrates the potential of the collected information to further enhance the understanding of climate change, impacts and climate perception at the end of the 19th century in Romania. [ABSTRACT FROM AUTHOR]

Soil moisture and soil temperature, reflecting a synthetic climate regime, are vitally important for climate change assessments and adaption. As historical in situ measurements of soil states are extremely scarce and spatially uneven, reanalysis products play an increasingly important role in filling these gaps. The focus of this paper is on water–heat covariations in reanalysis products and a joint evaluation of soil moisture and soil temperature in five widely used atmospheric and land reanalyses is presented using in situ observations from 25 networks during various periods from 1979 to 2017. At the network scale, the five reanalyses show statistically significant correlations with observations, and the European Centre for Medium‐Range Weather Forecasts ERA5 shows higher skills than the other four products and a significant improvement over its predecessor. The National Centers for Environmental Prediction Climate Forecast System Reanalysis performs better in terms of long‐term trends. The most skilful signals in the five reanalyses are the seasonal cycles, with correlation coefficients of over 0.9. However, long‐term trends are substantially weaker than the observed trends and still tend to perform poorly over the high latitudes during cold seasons. Soil temperature reanalyses show even better skills, with mean correlation coefficients over 0.9 between anomalies; ERA5 shows enhanced annual ranges toward the high latitudes and altitudes. A joint evaluation of soil temperature and soil moisture showed physically consistent water–heat covariations in the soil in conjunction with atmospheric fluxes during the growing season over the Northern Hemisphere. This report suggests a good future for reanalysis products and their potential role in land surface climate change assessments. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are models fitted to a series of observed weather data in order to generate long synthetic series of these data, which is important for risk assessment. WGs are also among the main downscaling tools for climate change impact assessments. Despite their many capabilities, WGs cannot properly reproduce low‐frequency variability (LFV). Moreover, when using WGs for the downscaling of future climate scenarios, a change in rainfall occurrence will have unintended effects on secondary variables such as minimum temperature (Tmin) and maximum temperature (Tmax). This paper presents an approach based on the Quantile Perturbation Method for correcting the LFV of rainfall outputs pertaining to WGs by perturbing the monthly means of the daily generated series. A multivariate monthly generator was used to improve LFV in Tmin and Tmax. An advantage of the proposed method is the preservation of the correlation between daily and monthly timescales as well as the cross‐correlation between variables during the LFV correction. This method further reduces the unintended effects of changes in rainfall occurrence on the secondary variables during downscaling. After applying the method to the LARS‐WG model, the performance of the modified model was tested both directly and indirectly. Concerning the direct validation, the statistical characteristics of the simulated and observed weather data related to the four stations were compared over a diverse range of climatic conditions. Regarding the indirect validation, a comparison was made between the statistical characteristics of the runoffs simulated with a daily hydrologic model fed with the generated and observed weather data. The results showed that the method improved the performance of LARS‐WG regarding the reproduction of some of the observed weather characteristics, particularly LFV. The method was also shown to be well capable of reducing the unintended effects of the changes in rainfall occurrence on secondary variables during downscaling. [ABSTRACT FROM AUTHOR]

In this paper, we used a Bayesian model averaging (BMA) approach to analyse the changes in rainfall extremes in the periods 2041–2070 and 2071–2099 over northeast Bangladesh as a consequence of climate change. Climate change over this region could potentially impact agricultural production, water resources management, and the overall economy of the country. We used six regional climate models (RCMs) over the Coordinated Regional Downscaling Experiment South Asia domain. We used one medium stabilization scenario (RCP4.5) and one high‐emission scenario (RCP8.5) for projecting the extreme rainfall indices. A multi‐model ensemble mean was generated using the BMA approach. The BMA mean is a weighted average related to each RCM's predictive skill during the training period. Most of the rainfall extremes are expected to increase in both pre‐monsoon (March–May) and monsoon (June–September) seasons in the future compared with baseline (1976–2005). The average pre‐monsoon rainfall of the study area is projected to increase by 12.93 and 18.42% under RCP4.5 and 18.18 and 23.85% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. The average monsoon rainfall of the study area is projected to increase by 4.96 and 2.27% under RCP4.5 and 6.56 and 6.40% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. All the extreme indices except consecutive wet day are expected to change significantly at the 95% confidence level during the pre‐monsoon season. The study area will potentially be subjected to more frequent floods in the future both in pre‐monsoon and monsoon seasons as a consequence of climate change. Notably, the intensity and the magnitude of flash flooding in the pre‐monsoon season are expected to increase more in the future because the increase in extreme indices is more significant during that season. [ABSTRACT FROM AUTHOR]

Brazilian Cerrado biome is the largest and richest tropical savanna in the world. In order to understand the effects of climate changes on the hydrology of the Cerrado basins, this paper investigates the hydrological impacts of climate change throughout the 21st century under different emissions scenarios on the streamflow and on the droughts in the Sono, Manuel Alves da Natividade and Palma basins, located in the Brazilian Cerrado. For this purpose, the SWAT hydrological model driven by the downscaling of the HadGEM2‐ES and MIROC5 global climate models associated with RCP4.5 and RCP8.5 scenarios were used in three time slices (2011–2040, 2041–2070 and 2071–2099). The Standardized Precipitation Index (SPI) and the Standardized Streamflow Index (SSFI) were used to characterize droughts. In general, the results showed that the duration, intensity and frequency of the meteorological and hydrological droughts are expected to increase during the future periods. However, the hydrological droughts are projected to be larger than the meteorological droughts. Reductions in the streamflow are indicated in all future time slices and under both RCPs, especially, during dry periods, which may cause negative impacts in the ecological functions of the Cerrado biome, risk for reduction of the recharge of aquifers and risk for the electric energy production in northern Brazil. [ABSTRACT FROM AUTHOR]

This paper investigates the time trend coefficients in the temperatures in 48 US states using monthly data from January 1895 to December 2017, as well as in their anomalies with respect to the base period 1901–2000. For this purpose, we use techniques based on fractional integration, which is a more general approach than the standard methods used in the literature based on integer differentiation. The results indicate that with the exception of 10 states, in the remaining 38, the temperature anomalies have increased across time, with the increase being higher than the one expected under the other more standard approaches. The highest increases correspond to New Jersey and Rhode Island, with an increase of approximately 2.9°C over the last 100 years. [ABSTRACT FROM AUTHOR]

The primary objective of this paper was to determine the circulation conditions of the occurrence of cold spells in Poland and Germany. The observed inconsiderable warming slightly contributed to a change in the number of cold days and cold spells. The occurrence of cold spells in Poland and Germany was related to considerably more active and intensive pressure systems than on average in the winter season in the studied years. A strong high pressure system was located over much of the continent, blocking westerly circulation. During the occurrence of cold spells, negative anomalies of isobaric surface heights were recorded with the greatest anomalies being in the 300–250 hPa heights. The highest air temperature anomalies were recorded on the lowest isobaric levels, where they amounted to <−10°C. On average, anomalies on the 300 hPa isobaric level began developing approximately 9 days before the beginning of cold spells over the study area. The decay of cold spells was not related to rapid exchange of air masses, but rather to gradual weakening of anomalies in the upper troposphere, and their shifting westwards. [ABSTRACT FROM AUTHOR]

Climate change is the result of complex interactions between a wide array of climatic variables. Over a long period of time, climatic patterns can shift, possibly multiple times. Abrupt shifts that occur over a relatively short period of time are known as change points. During these intervals, different climatic variables may undergo dramatic shifts posing serious consequences for many biological and physical systems. In this paper, we discuss a Bayesian algorithm for detecting the location of change points in time series data. In particular, we utilize our method to analyse five different global surface temperature anomaly data sets, as well as temperature records associated with land, ocean, and different zonal bands in an attempt to identify common features of these data sets. Change points in the five global records were detected between 1902–1917, 1936–1945, and 1963–1976, but do these change points also manifest themselves across other parts of the globe? Our analysis indicates that the timing of change points is consistent from one record to the next, but not all change points appear in all records. In particular, there were more change points in the ocean than on land, and also more change points in southern latitudes than in northern latitudes. [ABSTRACT FROM AUTHOR]

Hoar frost, one of the most common types of road slipperiness, can reduce road surface friction thereby adversely affecting traffic safety. The two main causes of hoar frost are warm air advection and radiative cooling. This paper investigates the impact of a changing climate on the formation of hoar frost in Sweden during the winter months due to warm air advection and radiative cooling. The analysis is based on in situ observations from 244 stations in the Swedish Road Weather Information System. The results show that in northern Sweden, hoar frost was mainly caused by warm air advection, while radiative cooling was the main cause of hoar frost in southern Sweden. Given the increase in the air temperature over land due to climate change during the period 2000–2016, the relative frequency of hoar frost due to warm air advection has significantly decreased, while the relative frequency of hoar frost due to radiative cooling has significantly increased. Further analysis showed that the weakened temperature gradient between land and ocean, which leads to weakened warm air advection, is the main cause of the changes. Locations of in situ RWIS stations and their respective clusters (triangles), and the centres of geostrophic wind calculated for the three regions (stars). The background shows the four traditional Swedish climate zones as shown in Wallman (2004). [ABSTRACT FROM AUTHOR]

As solar energy will be an increasingly important renewable energy source in future years, the study of how climate change affects both its temporal and spatial variability is very relevant. In this paper, we study future changes of the solar radiation resource in the Iberian Peninsula (IP) through a set of simulations from ESCENA project (generation of regionalized scenarios of climate change in Spain with high‐resolution models) until mid‐century. The evaluation of the simulations against observations indicates contrasting biases for the different regional climate models (RCMs) in terms of solar irradiation amount and its inter‐annual variability. We propose a diagnostic for the quality of solar energy resource, in which the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) in the IP is well‐captured by the RCMs in general terms. The analysis of scenarios indicates a future increase in solar irradiation, although not all scenarios agree in the geographical distribution of this increase. The quality of solar energy resource is projected to increase, mostly due to a decrease in variability. This is an important result, as a more stable inter‐annual resource should decrease the need for backup sources and also reduce inter‐annual electricity price variations. Finally, results from a first approximation to the issue of the ability of solar energy to cover power demand peaks in summer show important differences between regions of the IP. However, the spatially averaged correlation of solar irradiation and summer surface temperatures for the whole IP is rather high, which is a positive result as the strong interconnections of the power grid within the IP could allow a distribution of solar power surpluses in certain regions for such high‐temperature episodes. Due to the importance of solar energy in the future we study future changes of the solar radiation over Iberian Peninsula through the simulations from ESCENA project. To evaluate the quality of solar energy resource, the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) is captured by the models. The analysis of scenarios indicates a future increase in solar irradiation amount and variability, although not all scenarios agree in this respect. [ABSTRACT FROM AUTHOR]

Growing season length (GSL) indices derived from surface air temperature are frequently used in climate monitoring applications. The widely used Expert Team on Climate Change Detection and Indices (ETCCDI) definition aims to give a broadly applicable measure of the GSL that is indicative of the duration of the mild part of the year. In this paper long‐term trends in that index are compared with an alternative measure calculated using a time series decomposition technique (empirical ensemble mode decomposition [EEMD]). It is demonstrated that the ETCCDI index departs from the mild‐season definition as its start and end dates are determined by temperature events operating within the synoptic timescale; this raises the inter‐annual variance of the index. The EEMD‐derived index provides a less noisy and more realistic index of the GSL by filtering out the synoptic‐scale variance and capturing the annual‐cycle and longer timescale variability. Long‐term trends in the GSL are comparable between the two indices, with an average increase in length of around 5 days/decade observed for the period 1965–2016. However, the results using the EEMD index display a more coherent picture of significant trends than has been previously observed. Furthermore, the EEMD‐derived growing season parameters are more closely related to variations in seasonal‐mean hemispheric‐scale atmospheric circulation patterns, with around 57% of the inter‐annual variation in the start of the growing season being connected to the North Atlantic Oscillation and East Atlantic patterns, and around 55% of variation in the end of the growing season being associated with East Atlantic/west Russia‐type patterns. Long‐term trends and inter‐annual variability of the thermal growing season length across Europe are compared by comparing two indices: the ETCCDI definition and a new index calculated using a time series decomposition technique (EEMD). Long‐term trends are comparable between the two indices. However, the EEMD index displays a much more coherent picture of significant trends than has been previously observed, and a much closer relationship to hemispheric modes of atmospheric circulation variability. [ABSTRACT FROM AUTHOR]

The paper tests if the two La Niña (LN) types can be distinguished from each other using their climate impacts on northeastern Brazil (NEB). To this end, all LN events during the 1901–2010 period followed by a wet or dry rainy season (from February to April) in NEB are classified into two categories: WET‐LN and DRY‐LN. The global and regional anomalous atmospheric circulation patterns and the rainfall anomaly patterns in South America associated with the two cases are analysed. The WET‐LN and DRY‐LN events present, respectively, the eastern Pacific LN and central Pacific LN sea surface temperature (SST) anomaly features in the tropical Pacific. On the other hand, the WET‐LN features an interhemispheric SST dipole pattern in the tropical Atlantic, and the DRY‐LN, colder‐than‐normal surface waters in the tropical South Atlantic (TSA) and equatorial Atlantic during MAM(+1). The two analysed cases show regional differential circulation patterns in all seasons. The anomalous wetness over northern and northwestern South America occurs for the WET‐LN type during JJA(0)–DJF(+1) and for the DRY‐LN type, during DJF(+1)–MAM(+1). The anomalous dryness over SESA is more evident for the WET‐LN during JJA(0) and MAM(+1), and for the DRY‐LN, during SON(0). Anomalous dryness occurs in central and eastern South America noted during JJA(0) and DJF(+1) for both cases analysed. The precipitation anomalies in northern South America during DJF(+1) are stronger and more extensive for the DRY‐LN than for the WET‐LN events due to the action of both the anomalous (double) Walker and Hadley cells for the DRY‐LN, in contrast with the exclusive action of an anomalous Walker cell for the WET‐LN case. Also, a double Walker cell drives the dry–wet dipole between northern South America and NEB during MAM(+1) for the DRY‐LN. Our results might be useful mainly for climate monitoring purposes. The two La Niña (LN) types were distinguished from each other using their climate impacts on northeastern Brazil (NEB) and classified in two categories WET‐LN (LN events followed by a wet rainy season in NEB) and DRY‐LN (LN events followed by a dry rainy season in NEB) that present, respectively, the eastern Pacific LN and central Pacific LN sea surface temperature (SST) anomaly features in the tropical Pacific. The illustration shows the LN‐related SST and precipitation anomaly patterns evolutions associated with the two LN types. [ABSTRACT FROM AUTHOR]

There is an urgent need to understand and predict how extreme precipitation events (EPEs) will change at high latitudes, both for local climate change adaptation plans and risk mitigation and as a potential proxy "anticipating" the impact of climate change elsewhere in the world. This paper illustrates that a combination of non‐stationary modelling approaches can be adopted to evaluate trends in EPEs under uncertainty. A large database of daily rainfall events from 281 sparsely distributed weather stations in Finland between 1961 and 2016 was analysed. Among the tested methods, Poisson distributions provided a powerful method to evaluate the impacts of multiple physical covariates, including temperature and atmospheric circulation patterns (ACPs), on the resulting trends. The analysis demonstrates that non‐stationarity is statistically valid for the majority of observations, independently of their location in the country and the season of the year. However, subsampling can severely hinder the statistical validity of the trends, which can be easily confused with random noise and therefore complicate the decision‐making processes regarding long‐term planning. Scaling effects have a strong impact on the estimates of non‐stationary parameters, as homogenizing the data in space and time reduces the statistical validity of the trends. Trends in EPE statistics (mean, 90 and 99% percentiles) and best‐fitted Generalized Pareto parameters in the tails of the distributions appear to be stronger when approaching the Polar region (Lapland) than away from it, consistent with the Arctic amplification of climate change. ACPs are key covariates in physically explaining these trends. In particular, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) can explain statistically significant increases in extreme precipitation in Lapland, Bothnian and South regions of Finland, particularly during summer and fall seasons. Non‐stationarity is statistically valid in the majority of observations but masked by subsampling and spatio‐temporal heterogeneity. Trends on extremes and non‐stationary generalized Pareto parameters stronger when approaching the Polar region (Lapland). Artic Oscillation (AO) and North Atlantic Oscillation (NAO) can explain statistically significant trends. [ABSTRACT FROM AUTHOR]

In the paper we focus on the notes of botanist and meteorologist Gottfried Reyger on the weather conditions in Gdańsk, Poland. In our estimation, this chronicle, covering the period from December 1721 to June 1786 (the longest weather chronicle recorded by a single person), is a reliable source of information with the potential to contribute to the understanding of climate variability in 18th‐century Gdańsk, the significant old Hanseatic city in northern Poland. The temperature and precipitation series for the period 1721–1786 were indexed against contemporary conditions using the calculated percentages of cold, normal and warm decades. The climate in the 18th century in Gdańsk was colder than nowadays; the springs were much cooler, as were the winters, while the summers were warmer. A few relatively cold periods and several warm periods were identified. The first half of the analysed period was very humid, whereas the second was quite dry. The reconstructed indexed data series were compared to annual and seasonal mean values of air temperature and precipitation derived from regular instrumental observations made in Gdańsk since 1739. The linear regression method was applied to calibrate the reconstructed index series against anomalies from the 1961–1990 average. The correlation coefficient, standard error of estimate and the root‐mean‐square error statistics were used to diagnose the quality of calibration and verification. The highest correlation between the temperature indices and the observational series was found for winter. The reconstructed series for precipitation has a smaller variability than in reality. The reconstructed values for the years prior to 1739 indicate cold years for 1725–1732 and a wet period in the 1730s. The chronicle of botanist and meteorologist Gottfried Reyger, covering the period 1721–1786, characterizes the thermal and precipitational conditions in Gdańsk, Poland. The series were indexed against contemporary conditions, compared with instrumental data and reconstructed for early non‐instrumental period. The 18th‐century Gdańsk was colder than nowadays, particularly on case of springs, while the summers were warmer. The first decades of the period were very humid, whereas the latter ones quite dry. In the figure, annual air temperature (left panel) and precipitation (right panel) reconstruction for Gdańsk for the period 1722–1785 presented in the context of observational series, 1739–1785 for air temperature and 1739–1772 for precipitation. Solid line shows observational data; dotted line shows reconstructed series. [ABSTRACT FROM AUTHOR]

Model‐based sensitivity experiments are a widely used method for studying climate change attributions. In traditional climate sensitivity studies, "climate drift" occurs because of the accumulation of model errors during long‐term integrations, both in "actual" and "hypothetical" states. A more accurate piecewise‐integration method is used to reduce the model errors, dividing the continuous simulation into a series of sequential short‐term simulations. The model fields are updated at the end of each subinterval with analysis data for the "actual" state run, and with the sum of the analysis data and the difference between "hypothetical" and "actual" states for the "hypothetical" state run. This paper conducts sensitivity experiments with the continuous‐integration method and the piecewise‐integration method to evaluate the impacts of the central and eastern Pacific Ocean sea surface temperature anomalies (SSTAs) on the severe drought that occurred in southwestern China (SWC) in the winter of 2009/2010. The results show the following. (a) Model errors produced by the piecewise‐integration method for the actual state simulation are significantly less than those produced by the continuous‐integration method. (b) The intensity of the drought is significantly overestimated in continuous‐integration experiments. However, the drought can be accurately simulated in its spatial distribution and intensity via the piecewise‐integration method. Thus, climate sensitivity to changes in external forcing can be studied with greater credibility. (c) The warm central Pacific Ocean SSTAs may be the main cause of the drought over SWC. Adequate precipitation occurred over SWC when the centre of the SSTAs shifted to the east. (d) Warm SSTAs over the central Pacific Ocean influenced precipitation over SWC by weakening the water vapour transport branch from South China Sea to SWC. With a deficit of water vapour, the pronounced subsidence and warm temperatures were the main dynamic and thermodynamic factors that caused and maintained the drought. Model‐based sensitivity experiment is a method of studying climate change attributions. In traditional climate sensitivity studies, "climate drift" occurs both in "actual" and "hypothetical" states. Therefore, the scientific value of climate sensitivity experiments is questioned. In this study, piecewise‐integration method (shown in the figure) is used to significantly reduce model errors caused by inaccuracies of analysis fields, to improve the accuracies of climate sensitivity simulations and to evaluate the impacts of the Pacific Ocean SSTAs on a drought. [ABSTRACT FROM AUTHOR]

Montane climates are shaped by high spatial variability that depends on net radiation and synoptic weather, and much on elevation and topographic features of terrain. We measured near‐ground meteorological variables using a weather station network during 14 months, in a valley‐shaped basin in southeast Brazil, to address the spatiotemporal climate variability at the meso‐γ atmospheric scale. The terrestrial climatic gradients were evident in the cross‐valley direction of the basin, where the valley tended to warm/wet up at day and cool/dry down at night relatively from the slopes. The temperature at noon showed high variability and decreased at a mean gradients of about −0.7 °C (100 m)−1. The nocturnal air temperature increased with height up to a maximum at about 200 m (the thermal belt), and showed seasonal rates higher/less in dry/wet season of +1.1/+0.2 °C (100 m)−1 over the full altitude. The vapour pressure decreased from the valley bottom upwards, in general below −0.5 hPa (100 m)−1, whereas the wind speed increased at a rate of 0.9 m s−1 (100 m)−1. We noted significant differential warming along the valley and mountain sides. The middle valley was circumstantially colder at night and warmer at daytime, relatively to the upper catchment, under mean magnitudes below 1.0 °C. The west slope at upper catchment was slightly warmer at night, and colder in the afternoon, at magnitudes below 0.5 °C, highlighting the control of hills' aspect at daytime, and the sheltering to flow aloft in east side. The cross‐valley gradients appeared to be well associated with local circulation, where downslope wind and positive temperature gradients, as well as upslope wind and negative temperature gradients strictly coexisted during the morning. The terrestrial gradients and the thermal circulation were in general dampened by cloudiness and mechanical mixing. This paper presents the spatiotemporal variability of near‐surface temperature, specific humidity and wind across the landscape of a mesoscale basin in southeast Brazil. We show the daily and seasonal variation of climatic gradient across the catchment and the characteristics of the thermally driven wind circulation and highlighted the influence of the topography and synoptic weather conditions. [ABSTRACT FROM AUTHOR]

This paper compares extreme value statistics of daily maximum 10 m wind speed in winter simulated by the regional climate model COSMO‐CLM at a horizontal resolution of 15 km (C15) with the reanalyses ERA‐Interim and Arctic System reanalysis (ASR version 1 and 2) and with a satellite data set (CCMPv2). Our C15 simulation (1979/1980–2015/2016, November–April) is thereby the longest high‐resolution simulation available for the Arctic. The results show that the extreme wind speeds tend to increase over the ocean with increasing the horizontal model resolution. A horizontal resolution of ≤15 km is required to sufficiently capture all extreme wind characteristics, in particular for the tip jets and barrier winds over the Irminger Sea and in the Denmark Strait, and for the low‐level jets in the Nares Strait. Of almost equal importance are physical parameterizations of surface fluxes and of turbulence. Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. State‐of‐the‐art global climate models and reanalyses (e.g., ERA‐Interim) underestimate the extreme near‐surface wind speeds in the Arctic and around Greenland. Regional climate models, for example, ASR/WRF or COSMO‐CLM, resolve the mesoscale wind systems that frequently produce extreme winds, which we estimated i.a. as 10‐year return levels (see the figure). Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. [ABSTRACT FROM AUTHOR]

Recent studies have focused on identifying the spatial coverage of heavy rainfall associated with tropical cyclones (TCs) and their remnants (TC‐events) throughout the southwestern United States from eastern North Pacific storms. Yet little is known about the larger spatial contribution of these storms among extreme floods over the western United States. This paper examines the spatial and fractional contribution of 103 TC‐events among annual maximum streamflow at 1,429 long‐term U.S. Geological Survey (USGS) streamgages with at least 30 years of daily discharge data over the 1958–2010 water years. The highest fractional contributions to annual daily maxima by these storms (~5–14%) are found in southern California, Arizona, southernmost Nevada and Utah, southern and western New Mexico, central Colorado, and Texas. While nearly 32% of the streamgages have at least one annual maximum that was generated by a TC‐event, TC‐events play a limited role in contributing to the upper tail of the flood peak distribution across the western United States. [ABSTRACT FROM AUTHOR]

The precipitation over arid regions may be low, but its environmental impacts can be very dramatic. The decadal variability in precipitation can also effectively disturb climatic environmental regimes. To advance the understanding of precipitation variability over arid regions, this paper focuses on decadal variability in summer precipitation over arid northwest China and its associated atmospheric circulations, using observation‐based data and atmospheric reanalysis during the period 1961–2016. The results show that climatologically, precipitation over this region has a strong seasonality and that 60.2% falls in summer. The summer precipitation increased 15.5 mm, accounting for 52.4% of the total annual increase in this part of China from 1961 to 2016. Decadal changes in summer precipitation occurred in approximately 1991 with a north–south difference structure modulated by large‐scale circulation anomalies over the Indian Ocean and central Asia as well as a shift in water vapour transport. In a positive phase, anticyclonic circulation anomalies occupy the Indian Ocean with cyclonic anomalies over central Asia, favouring stronger northwards moisture transport towards northwest China, and vice versa. Moreover, the significantly enhanced northwards water vapour transport from low‐latitude seas is also critical to the decadal changes approximately in 1991 summer precipitation in arid northwest China. These findings contribute to the understanding of decadal precipitation variability over arid regions, especially the increasing influence of low‐latitude circulations and moisture transport processes. [ABSTRACT FROM AUTHOR]

There are two aims of this paper: First, the relationship between daily and hourly precipitation extremes and air temperature is investigated for the validity of Clausius–Clapeyron (C–C) scaling; second, the impact of the atmospheric circulation and transport of humidity on the extreme precipitation events occurrence in Poland is analysed based on data obtained from 26 stations across Poland in the period 1951–2015. It was shown that daily‐scale extreme rainfall is less sensitive to changes in local atmospheric temperature but the hourly intensities reveal a similar behaviour at all stations showing a robust signal over the entire country. Regarding the impact of the large‐scale atmospheric circulation, three types were distinguished in cool part of the year: one connected with the positive phase of the North Atlantic Oscillation (NAO), second with a low‐pressure zone in the north and high‐pressure in the south of Europe and the northern Atlantic and third with a low over central Europe. Each time high precipitation in Poland was connected with the advection of humid air from the western sectors (SW, W or NW). In the warm part of the year three types were distinguished too, Poland was always in the scope of low‐pressure systems with the advection of warm air from the southern sectors or when the warm air from the south meets the colder air from the north. Similar localizations of low centres have been found in the case of local‐scale atmospheric circulation types during events with extreme precipitation. Both in cool and warm part of the year such events are connected with the increase of water vapour in the air. In cool season the wet and warm air masses inflow to Poland the most often from Atlantic, in the warm season—from Mediterranean and/or Black Seas. [ABSTRACT FROM AUTHOR]

In this paper we analyse the relationship between the mean and extreme precipitation for five stations in Hungary and the circulation types at the 500 hPa level, for a 52‐year period, 1958–2010. We used daily rainfall data for each station and a calendar of the daily circulation types at the 500 hPa geopotential height for Hungary based on an automatic classification scheme. A trend analysis showed that, in general, the mean and extreme precipitation rarely exhibit significant trends, similarly to previous studies. The only exception was found for the number of annual and summer rainy days in Budapest with significant negative trends (p = .05), as well as the number of days with rainfall amounts greater than the 95% probability level for Debrecen with a significant positive trend. It was found that the circulation types that are mainly responsible for the largest precipitation amounts overall show (in some cases significant) negative trends, as well as the precipitation amounts that are associated with them (Cwsw, Cse, Cwnw). In most cases, the circulation types that are prevailing during the extreme precipitation events are associated with western or almost‐western atmospheric circulations (Cwsw, Ase) and Mediterranean‐ or Atlantic‐origin depressions. [ABSTRACT FROM AUTHOR]

Previous studies have reported that much of the surface wind speed (SWS) over the mid‐latitudes of the northern hemisphere has declined. However, very few studies have investigated the relatively recent phenomenon of wind recovery. Based on 68 wind data series, this paper examines changes in wind speed in northwest China between 1969 and 2015. In 1992, following a decade of sharply decreasing at a rate of 0.036 m s−1 a−1 (p < .05), the SWS began to significant increase at a rate of 0.004 m s−1 a−1. The specific reasons for this increase are as follows: (a) The decrease in SWS during the pre‐1992 slowdown period is the result of declining wind speeds in spring and summer, whereas increases in wind speed during the post‐1992 recovery period are caused by increased wind in winter. (b) The number of days featuring strongly varying wind speeds has changed. Specifically, the number of days above 2 m s−1 all show a significant decreasing trend from 1969 to 1992, whereas the number of days of 1–3 m s−1 shows a significant upward trend from 1993 to 2015. (c) Stations located between 1,000 and 1,500 m.a.s.l. (meter above sea level) are more sensitive to climate change than those at other altitudes, which shows the biggest decline (increase) trend during wind speed slowdown (recovery) period. These stations could potentially act as climatic indicators to predict future wind speed changes. SWS in northwest China have been affected by changes both in large‐scale atmospheric circulation and in regional warming. Surface pressure gradient variations between high‐ and low‐latitude regions may be important contributors to wind speed changes under asymmetric warming. However, urbanization is only moderately responsible for trend changes in SWS. [ABSTRACT FROM AUTHOR]