This study analyzes the forest flammability hazard in the south of Tyumen Oblast (Western Siberia, Russia) and identifies variation patterns in fire areas depending on weather and climate characteristics in 2008-2023. Using correlation analysis, we proved that the area of forest fires is primarily affected by maximum temperature, relative air humidity, and the amount of precipitation, as well as by global climate change associated with an increase in carbon dioxide in the atmosphere and the maximum height of snow cover. As a rule, a year before the period of severe forest fires in the south of Tyumen Oblast, the height of snow cover is insignificant, which leads to insufficient soil moisture in the following spring, less or no time for the vegetation to enter the vegetative phase, and the forest leaf floor remaining dry and easily flammable, which contributes to an increase in the fire area. According to the estimates of the CMIP6 project climate models under the SSP2-4.5 scenario, by the end of the 21st century, a gradual increase in the number of summer temperatures above 35 degrees C is expected, whereas the extreme SSP5-8.5 scenario forecasts the tripling in the number of such hot days. The forecast shows an increase of fire hazardous conditions in the south of Tyumen Oblast by the late 21st century, which should be taken into account in the territory's economic development.

Black carbon (BC) is one of the major aerosol components with relatively high implications on climatic patterns through its radiative forcing (RF). South Asia has recently experienced an increased concentration of pollution; however, relatively fewer studies have been carried out on long-term assessment of BC and its implications. The present study analyzed the long-term concentration of BC in selected urban locations over South Asia using the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The study employed statistical analysis, including linear regression techniques, to assess the long-term concentration of BC. The results show that a rapid increase of BC is observed over most urban locations of South Asia with the predominance in winter and hence requires strict regional control measures to reduce the excess concentration of BC in the atmosphere. High concentration of BC in winter is attributed to anthropogenic activities and changes in meteorological conditions that enhance the accumulation of pollutants in the atmosphere. The relationship of BC with cloud top temperature and cloud effective radius demonstrates the direct and indirect effect of BC on cloud properties in this region. The RF results reveal that aerosol optical depth has positive aerosol RF in the atmosphere and negative RF at the top of the atmosphere (TOA) as well as at the bottom of the atmosphere (BOA). Negative RF at the TOA indicates less forcing efficiency due to fewer BC aerosols. On the other hand, averaging aerosol RF within the atmosphere reveals positive forcing, which suggests the efficiency force exerted by BC aerosols after absorbing solar radiation.

Water temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non-extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro-meteorological drivers differ from those of non-extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co-occurrence analysis to examine the relationship between water temperature extremes and hydro-climatic extremes. Our results show that mean water temperature has increased by +0.38 +/- 0.14 ${+}0.38\pm 0.14$degrees C per decade, leading to more extreme events at high elevations in spring and summer. While non-extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March-30th June) in all 22 years. We tested for long-term trend significance with the Mann-Kendall test and trend magnitude with Theil-Sen's slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%-8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Ground temperature's sensitivity to climate change has garnered attention. This study aimed to monitor and analyze temporal trends and estimate Active Layer Thickness from a monitoring point at Fildes Peninsula, King George Island, in Antarctica. Quality control and consistency analysis were performed on the data. Methods such as serial autocorrelation, Mann-Kendall, Sen-Slope, Pettitt, and regression analysis tests were applied. Spearman's correlation examined the relationship between air temperature and ground depths. The active layer thickness was estimated using the maximum monthly temperature, and the permafrost lower limit used the minimum monthly temperature. Significant summer seasonal trends were observed with Mann- Kendall tau, positive Sen-Slope, and Pettitt slope at depths of 67.5 and 83.5 cm. The regression analysis was significant and positive for all ground depths and in different seasons. The highest correlation (r=0.82) between air temperature and surface ground depth was found. Freezing prevailed at all depths during 2008-2018. The average Active Layer Thickness (ALT) was 92.61 cm. Temperature is difficult to monitor, and its estimation is still complex. However, it stands out as a fundamental element for studies that refer to the impacts of climate change

The extreme floods of recent years underline the urgency of studying long-term changes of floods and their driving processes. This paper reports results on this issue obtained within the framework of subproject 6 of the DFG research group SPATE (Space-Time Dynamics of Extreme Floods). The analyses use an extensive dataset of flood observations at rivers and complementary information to determine and explain significant changes in flood probabilities. The data show that the flood-rich periods of the last 500 years in Europe have been significantly colder than usual. Over the last 60 years, the number of flood-rich periods in north-western Europe has increased. This increase is due to more intense precipitation. In medium-sized and large catchments of southern and eastern Europe, on the other hand, lower soil moisture and less snow cover have led to decreasing flood probabilities. These results are intended as a basis for more reliable design flood estimates in a changing world.

Tree-ring width chronologies are a critically important material to reconstruct past precipitation variability on the northeastern Tibetan Plateau (NTP). However, temperature signals are often encoded in these chronologies, which complicate the precipitation reconstructions and should be carefully assessed. Here, a dataset of 487 Qilian juniper (Juniperus przewalskii Kom.) tree-ring width series from 16 sites on the NTP were collected to investigate the influence of different temperature signals on the precipitation reconstructions. Correlation analysis showed that all tree-ring series recorded similar precipitation information, but had positive (p 0.05, Group1), weak (p 0.05, Group2), and negative (p < 0.05, Group3) correlations with temperature, respectively. In view of this, all tree-ring series were divided into three groups to develop chronologies to reconstruct local precipitation. During the calibration period of 1957?2011 CE, the Group1 reconstruction had the fastest uptrend, which almost overlapped the observed precipitation; the Group2 reconstruction showed a slower uptrend, whereas the Group3 reconstruction lacked an uptrend. As a result, we get different results when the reconstructions were used to assess the current precipitation status over the past millennium. The Group1 (Group2) reconstructions showed that the recent 20 (10) years were the highest precipitation period over the past millennium, whereas the Group3 reconstruction did not capture this phenomenon. Therefore, we caution that the temperature effects should be evaluated carefully before tree-ring width chronologies being employed to study past precipitation variability.

Aerosol-cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX-GDDP, a multimodel ensemble of high-resolution (0.25 degrees) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX-GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REMADE) or the direct and indirect (REMALL) aerosol effects. Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REMALL simulated future CWD and CDD changes are mostly opposite to those in REMADE, indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models. We investigate the impacts of aerosol on India's wet and dry climate. High-resolution downscaled CMIP5 models were used to calculate extreme indices like CDD (consecutive dry days), CWD (consecutive wet days), SDII (precipitation intensity). From the group of 22 models, 12 reliable models were chosen based on their fidelity to the observations. Amongst the reliable models, certain models incorporate only aerosol-radiation interaction (REMADE), while others have both aerosol-radiation and aerosol-cloud interaction (REMALL). We found that the simulated trends in the REMAll were similar to the observed trends. In the current period (1975-2005), the aerosol-cloud interactions led to the reduction in rainfall (both frequency and intensity wise) and enhanced the dry days, however in the future projections, the reduction in aerosol emissions leads to a wetter climate (increase in wet days and rainfall intensity) over India.

The Qinghai-Tibetan Plateau (Q-TP hereafter) has experienced dramatic warming in recent decades, resulting in severe effects on the ecosystems and downstream. However, none of previous studies investigated elevationdependency temperature trend with the high resolution over the long-term period. Based on monthly temperature dataset with 0.1 developed by generative adversarial network, elevation-dependency temperature trend over the Q-TP and 5 climate zones (humid, humid-semihumid, semihumid, semiarid, arid region) during 1901-1946, 1946-1965, 1965-1997 and 1997-2015 are investigated. Snow cover (SNC), high cloud cover (HCC), middle cloud cover (MCC), specific humidity (SHUM) and soil moisture content (SOILM) are introduced to analyze possible mechanism. There are 4 cases of elevation-dependency temperature trend, which are positive/negative elevation-dependency warming (EDW+/EDW-) and cooling (EDC+/EDC-). These patterns (EDW+, EDW-, EDC+ and EDC-) are identified as warming/cooling trends that become stronger/weaker with increasing elevation. EDW- signal is found during 1901-1946 due to the influence of SOILM. The most prominent EDW- signal occurs over the arid region. EDC+/- is presented during 1946-1965 under the dominance of SHUM and SOILM. The stronger EDC signal is shown over the arid and semiarid region than over the humidsemihumid region. The subtle EDW+ signal is shown over the semihumid, semiarid and arid regions during 1965-1997 when SOILM has a relatively large contribution to temperature trend. The robust EDW+ signal is exhibited from 1997 to 2015 when SNC plays a vital role in regulating the temperature change. There is a more significant EDW+ over the humid, humid-semihumid, and semihumid regions than that over the semiarid and arid regions during this period. Above all, SNC, SHUM and SOILM are found to be the primary contributors to elevation-dependency temperature trend. SOILM and SHUM are associated with hydrological effects and control temperature variations over the Q-TP during 1901-1997. SNC is related to snow/ice-albedo feedback and dominates temperature variations over the Q-TP during 1997-2015.

For the period 2001-2020, the interannual variability of the normalized difference vegetation index (NDVI) is investigated in connection to Indian summer monsoon rainfall (ISMR). According to Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data, the ISMR and the vegetative activity of the Indo-Gangetic plain (IGP) in the month of January show a significant negative association. We hypothesized that the January vegetation state affects the ISMR via a delayed hydrological response, in which the wet soil moisture anomaly formed throughout the winter to accommodate the water needs of intensive farming influences the ISMR. The soil moisture anomalies developed in the winter, particularly in the root zone, persisted throughout the summer. Evaporative cooling triggered by increasing soil moisture lowers the summer surface temperature across the IGP. The weakening of monsoon circulation as a result of the reduced intensity of land-sea temperature contrast led in rainfall suppression. Further investigation shows that moisture transport has increased significantly over the past two decades as a result of increasing westerly over the Arabian Sea, promoting rainfall over India. Agriculture activities, on the other hand, have resulted in greater vegetation in India's northwest and IGP during the last two decades, which has a detrimental impact on rainfall processes. Rainfall appears to have been trendless during the last two decades as a result of these competing influences. With a lead time of 5 months, this association between January's vegetation and ISMR could be one of the potential predictors of seasonal rainfall variability.