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The purpose of this study is to analyze the spatiotemporal variations in drought in Serbia over recent decades using the standardized precipitation evapotranspiration index (SPEI). The analysis is performed for time series of 1-, 3-, 6-, and 12-month SPEI indices (SPEI-01, SPEI-03, SPEI-06 and SPEI-12, respectively) calculated using the E -OBS gridded dataset with a horizontal resolution of 0.1° for the period of 1950–2022. In general, it was found that negative SPEI trends prevailed in Serbia for all months and all analyzed SPEI timescales, indicating a more frequent appearance of droughts in recent decades. Moreover, for a large majority of month-timescale combinations, the area with a negative trend was larger than the area with a positive trend. For several reasons, we focused on the SPEI for August. First, the SPEI for this month shows one of the most pronounced negative trends, which is rather consistent over different timescales and consistent in terms of the area over which the trend was found to be significant. Statistically significant negative trends of SPEI-03, −06 and − 12 for August were found over 11, 17 and 38% of the country area, respectively. To better understand the trends and changes in the SPEI for August, focusing on extreme and severe drought events, an innovative trend analysis (ITA) was applied. It was found that the occurrence of both extreme and severe SPEI drought categories has doubled over the last thirty years in comparison with the previous thirty-year period. Additionally, more frequent transitions from neutral and wet categories to dry categories were observed through an application of the Markov chain. Second, the special focus on SPEI-03, −06 and − 12 for August was motivated by the fact that we found a significant correlation between these index values and the annual maize production in Serbia, indicating that the SPEI for August can be useful for the assessment of drought impact on agriculture. To better understand interannual variability and long-term trends, this paper also explores the relations between the SPEI over Serbia and large-scale circulation patterns, together with general global and continental warming trends. Our findings indicate that drought changes over recent decades may be driven by the interplay of long-term warming and large-scale circulation patterns. In the context of climate change, especially climate projections, Serbia is situated in a region for which climate projections of mean precipitation change demonstrate weak signals and often with conflicting signs of change within multimodel ensembles. From this point of view, our results can be seen as a contribution to a better understanding of changes in extremes, in our case droughts, that can be hidden by inconclusive changes in the mean values. Finally, due to the similar situation in terms of precipitation and drought trends and changes, our results may be relevant for a wider region, and not only for Serbia. • For Serbia negative trends in SPEI index, for different months and timescales, are dominant. • Occurrence of extreme drought categories has doubled over the last decades. • Transitions from neutral and wet categories to dry categories are more frequent. • These changes are driven by long-term warming and large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Most studies on the relationship between global warming and El Niño-Southern Oscillation (ENSO) rely on simulation experiments, but they have produced contradictory results, which cannot guarantee the reliability of the experimental findings. To address this challenge, we approached the issue from the perspective of objective ENSO and global warming signals. The effects of global warming on ENSO are not limited to the Earth's surface, but also present in the atmosphere. While objective monitoring indices exist for surface global warming and ENSO signals, objective ENSO and global warming signals in the atmosphere are difficult to obtain. To obtain high-precision ENSO and global warming signals in the atmosphere, we propose using the strategy of ICA (Independent Component Analysis) combined with a non-parametric method. The results of the objective ENSO and global warming signals suggest that the global warming signal leads the ENSO signal by approximately three months and exhibits a clear positive correlation with the ENSO signal, especially the occurrence of strong ENSO events is closely related to global warming, indicating that global warming promotes the occurrence of ENSO events. Under the influence of global warming, the frequency and intensity of ENSO increase, the characteristics and complexity of ENSO atmospheric teleconnections may be altered, and there is a greater tendency for more destructive El Niño events to occur. Since the beginning of the 21st century, the probability of ENSO event has increased significantly. Furthermore, moderate or strong ENSO events tend to develop earlier, and extreme ENSO events not only develop earlier but also prolong their decay phase, leading to an extended lifespan of ENSO events. This paper investigates the effect of global warming on ENSO from the perspectives of objective ENSO and global warming signals, which well supplements the objective factual basis for the previous simulation experimental studies. • Most simulation studies on the relationship between global warming and ENSO cannot guarantee the authenticity ofresults • We investigated the relationship between global warming and ENSO from the perspective of objective signals. • To obtain ENSO and global warming signals, we proposed the strategy of ICA combined with a non-parametric method. • The results suggest that the global warming signal exhibits a clear positive correlation with the ENSO signal. • Under influence of global warming, frequency, lifespan, intensity and atmospheric teleconnections of ENSO may be altered [ABSTRACT FROM AUTHOR]

Climate classifications based on temperature and precipitation measurements are increasingly being used for environmental and climate change studies. Using three classification methods (Köppen, Extended Köppen, and Holdridge) and one observational dataset for present climate (CRU, Climate Research Unit), we show that GCMs have bridged the gap that led to the emergence of RCMs thirty years ago, as GCMs can now provide global climate classifications whose accuracy and precision are comparable to those of regional outputs of the RCMs. Projections of high-resolution GCMs for future climates under the assumptions of three Representative Concentration Pathways (RCP26, RCP45 and RCP85) can therefore be used as a primary source for climate change and global warming studies at high resolution. This paper provides comprehensive, model-derived climate classifications for the entire planet, using RCMs and two GCMs for present and future climate-change scenarios, and discusses how well the models actually represent the climates of the world when compared with reference, ground validation data. It turns out that both GCMs and RCMs appear still limited to provide practical estimates of the world climates even for present climate conditions. The modeling of precipitation remains the Achilles' heel of models and thus of multidimensional indices, which are very sensitive to this variable. The conclusion is that model outputs at regional scale need to be taken with extreme caution without venturing into informing policies presenting potentially large societal impacts. Nonetheless, the role of models as privileged tools to advance our scientific knowledge of the Earth's system remains undisputed. • A systematic analysis of climate classifications with RCMs and two high resolution GCMs is presented. • Three classification methods are used to illustrate the differences between models and observations. • Current high-resolution GCMs can provide climate classifications with the same accuracy as those from RCMs. • Uncertainties in modeling precipitation persist for large parts of the world. [ABSTRACT FROM AUTHOR]

Abstract Climate classifications of climate models' outputs have been used to assess environmental changes but systematic analyses of the differences between models, scenarios and classification methods are scarce. Here, the results of applying the most commonly used climate classifications to the outputs of 47 Global Climate Models (GCM) of different physical parameterizations and varied grid size are presented. The extent and intensity of changes for present climate, three different Representative Pathways Scenarios (RCP26, RCP45 and RCP85) and three increasingly-fine classification methods show that there is a consensus between models, and that climate classifications are indeed useful tools to translate physical climatology variables into environmental changes. The main conclusions are that climate classifications can indeed be used to gauge model performance at several grid sizes and that the classification method does not decisively affects the potential global changes in future climates under increasing greenhouse gas emissions. The analyses also reveal that there are several uncertainties that are not attributable to model grid size or to limitations in the reference datasets but more likely to deficiencies in the physics of the models. Highlights • This paper provides a systematic analysis of climate classifications for present and future climates. • Several classifications methods are used to illustrate the consensus between models and observations. • Overall, global models show consistent changes but uncertainties persist with large differences in individual model skill. [ABSTRACT FROM AUTHOR]

Regional precipitation patterns may change in a warmer climate, thereby increasing flood and drought risks. In this paper, annual, annual maximum, intense, heavy, moderate, light, and trace precipitation are employed as indicators to assess changes in precipitation patterns under two scenarios in which the global mean temperature increases by 1.5 °C and 2.0 °C relative to pre-industrial levels using the regional climate model COSMO-CLM (CCLM). The results show that annual precipitation in China will be approximately 2.5% higher under 1.5 °C warming relative to the present-day baseline (1980–2009), although it will decrease by approximately 4.0% under an additional 0.5 °C increase in global mean temperature. This trend is spatially consistent for regions with annual precipitation of 400–800 mm, which has experienced a drying trend during the past half century; thus, limiting global warming to 1.5 °C may mitigate these drying conditions. The annual maximum precipitation continues to increase from present day levels to the 2.0 °C warming scenario. Relative to the baseline period, the frequency of trace and light precipitation days exhibits a negative trend, while that of moderate, heavy, and intense precipitation days has a positive trend under the 1.5 °C warming scenario. For the 2.0 °C warming world, the frequency of days is projected to decrease for all precipitation categories, although the intensity of intense precipitation increases. Spatially, a decrease in the number of precipitation days is expected to continue in central and northern China, where a drying trend has persisted over the past half century. Southeastern China, which already suffers greatly from flooding, is expected to face more heavy and intense precipitation with an additional 0.5 °C increase in global mean temperature. Meanwhile, the intensity of intense precipitation is expected to increase in northern China, and the contribution of light and moderate precipitation to the annual precipitation is expected to decrease in southeastern China. Therefore, flood risk in northern China and drought risk in southern China should draw more attention for a global air temperature increase from 1.5 °C to 2.0 °C. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of bias-corrected Flexible Global Ocean-Atmosphere-Land System-g3 (FGOALS-g3) model products and then detects changes in extreme precipitation (EP) in the Tienshan Mountains, Central Asia (TMCA), as reflected by 25 EP indices. The reliability of the FGOALS-g3 model outputs is assessed systematically based on multiple statistical indicators against multi-source precipitation datasets and the bias-corrected FGOALS-g3 model products are applied to project EP variations under different global warming levels. Using the geographical detector method, a novel statistical method for detecting spatial heterogeneity and elucidating the underlying causes, the explanatory power of 20 atmospheric circulation factors related to EP is examined. The findings indicate that while the FGOALS-g3 products can detect the spatial pattern of multi-year average precipitation in the TMCA, there is an obvious overestimation in magnitude, especially in the West and Middle Tienshan Mountains. These biases are significantly reduced, however, after downscaling and bias correction. Compared with the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, the grid-by-grid error in bias-corrected FGOALS-g3 simulated mean precipitation is between −7.64% and 10.95%. In terms of EP, the corrected FGOALS-g3 products not only reproduce the spatial distribution, but also reasonably simulate their magnitudes, with some overestimation in light EP and underestimation in heavy EP. Overall, across the historical period, EP has increased. The intensity and frequency of EP are projected to generally increase under different scenarios. At 1.5 °C warming levels, annual total precipitation in wet days (PRCP) increases by 5.74% (7.74%) under the SSP245 (SSP585). Additionally, as an EP becomes rarer, its rate of change rises. The main driving factors in EP are detected to be 30 hPa zonal wind (30ZW), relative number of sunspots (SF), south Asian summer monsoon (SAM), sea surface temperature anomaly in the region of 5°S-5°N, 170°-120°W (NINO 3.4), and mean surface temperature (T). [Display omitted] • The local intensity scaling method is an excellent choice for correcting precipitation bias in model products. • The intensity and frequency of extreme precipitation (EP) are projected to increase further. • The rate of change in EP increases with the rareness of events. • The main driving factors associated with EP are 30ZW, SF, SAM, NINO3.4, and T. [ABSTRACT FROM AUTHOR]

This paper evaluates changes in precipitation in the upper the Euphrates–Tigris basin enclosing the whole area of Southeastern Anatolian Project (GAP), which is the largest investment for regional development in the history of Turkey and the fourth largest irrigation project in the world. For this purpose, the analyses are based on new data sequences formed by summing up monthly precipitation of 19 stations in the study area at seven different time scales. The Mann–Kendal (MK) and Theil–Sen Slope estimator (TSE) statistics to detect the direction and magnitude of an available monotonic trend in any given data, respectively, were used for the statistical analysis of the new data sequences. Besides, the sequential version of Mann–Kendal rank test (SVMK) to detect the approximate starting point of a trend was also applied the data set of the stations with significant monotonic trend. The monotonic trend was detected at 63% of the stations (12 of 19 stations) for the different time scales at 5% significance level. The stations with positive and negative or insignificant precipitation trends were scattered in the different parts of the basin. The significant trends were found in six stations with respect to the critical value (± 2.33) from the standard normal distribution for one-sided hypothesis at 1% significance level. The TSE results for 12 stations with monotonic trend at different time scales varied from − 2.36 mm to 2.51 mm per year. Of the six stations, the maximum decrease (− 72.5%) and increase (166.9%) in the percent change (PC) were determined in Agri and Bitlis stations, respectively. The approximate starting points of upward and downward trend for Kilis, Sirnak, Agri, Elazig, Bitlis and Siirt stations were at about 1980, 1979, 1987, 1986, 1984 and 1965 with respect to the SVMK test, respectively. According to the results obtained from the previous studies, the river basin is affected by the North Atlantic Oscillation (NAO) and global warming. Therefore, it is very likely that the NAO and global warming have been contributing to changes in precipitation of the river basin. [ABSTRACT FROM AUTHOR]

Abstract: This paper is largely concerned with research focused on, but not restricted to, aspects of Marine Cloud Brightening (MCB), one of several geo-engineering ideas for reducing the amount of sunlight arriving at the Earth's surface, thereby compensating for global warming resulting from fossil-fuel burning. Predominant attention is given to the development of techniques for generating sprays of sub-micrometer salt particles that can enter marine stratocumulus clouds and increase their albedo, thus producing a cooling. Generation of sub-micrometer salt particles by spraying salt solutions at supercritical conditions is described, along with a description of the apparatus used. Log-normal particle size distributions having median diameters of 32 to 286nm, with GSDs (Geometric Standard Deviations) around 2, were generated by two variations on the technique. [Copyright &y& Elsevier]

Abstract: The Arctic Oscillation (AO) has been found in previous studies to be a major synoptic factor affecting the climate of many regions in the high and mid-latitudes. This paper demonstrates the physical process by which the AO affects the climate of the Eastern Mediterranean basin, with a focus on precipitation in Israel as a case study. It is shown that a trend of increasing AO is associated with a substantial decrease of winter precipitation from the Iberian Peninsula, though Italy, Greece, Turkey and Cyprus, as well as Lebanon, Syria and also the northern parts of Israel. Winter rain is slightly increased in the southern coast of the eastern half of the Mediterranean Sea. The immediate meteorological causes are shown to be a larger northerly component of the flow over the Mediterranean Sea, associated with a decreasing relative humidity and stability, except over the southern coast, where the air mass has the longest track over the relatively warm water. We suggest here that the observed changes in air flow that drives the precipitation trends can be explained by shifts in the AO that can be partially explained by increasing greenhouses gases. Results from the IPCC multi climate models show that the AO will continue to increase during the 21st century. This increase may lead to a continuation of the trends discussed here. The importance of the analysis provided here is in pointing out the possibility that processes that have been predicted by global warming and changes in global circulation have already started to affect precipitation and major water resources in the Mediterranean basin. [Copyright &y& Elsevier]

Regional Climate Models (RCMs) emerged 30 years ago as a transient tool to provide detailed estimates of meteorological parameters (temperature, precipitation, humidity, wind, and others) for regional applications. Their dynamic downscaling approach was intended to fill the gap between the global but coarse estimates of Global Climate/Circulation Models (GCMs), which typically had a 2.5° resolution, and practical requirements such as estimating precipitation for hydrologic operations in small basins under conditions of increased greenhouse gas emissions. Over the three decades, RCMs provided data to inform policies and helped to increase knowledge of the present climate and the impacts of global warming at regional level. This paper describes the major achievements of RCMs, critically reviewing the main issues and limitations that have been featured in the literature. It puts forward a controversial claim aimed at starting a debate in the climate community, namely, that the cycle of RCM research has reached an end for informing policies. This is because these models have recently been superseded for that purpose by high-resolution GCMs and Earth System Models (ESM). • The paper reviews the main issues and achievements of RCMs since its inception 30 years ago. • Current high-resolution GCMs may supersede RCMs for informing policies. • RCMs may still be useful for benchmarking new parameterizations and rapid prototyping. [ABSTRACT FROM AUTHOR]

Major floods in Spain in September 9–13, 2019 resulted in seven casualties and massive losses to agriculture, property and infrastructure. This paper investigates the utility of satellite data to: (1) characterize the event when input into a hydrological model, and to provide an accurate picture of the evolution of the floods; and (2) inform meteorologists in real time in order to complement model forecasts. It is shown that the precipitation estimates from the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO, available since 2014) and the merged satellite estimates provide an extraordinary improvement over previous technologies to monitor severe hydrometeorological episodes in near real time. In spite of known biases and errors, these new satellite precipitation estimates can be of broad practical interest to deal with emergencies and long-term readiness, especially for semi-arid areas potentially affected by ongoing global warming. Comparisons of satellite data of the September event with model outputs and more direct observations such as rain gauges and ground radars reinforce the idea that satellites are fundamental for an appropriate management of hydrometeorological events. • The IMERG product is useful for monitoring severe storms in the Mediterranean. • Integration of satellite precipitation data into models can save lives and assists planning. • The integration of satellite estimates with numerical forecasts seems unavoidable. • More research on the effects of deep convection over land is required. [ABSTRACT FROM AUTHOR]