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Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. The main aim of this paper was to determine changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 and to identify changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI). Cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020. While a decrease in the number of months with SPEI-1 below − 1 was observed in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. This is an important finding for the agriculture or forestry because it affects work management and planning. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. The increase in drought intensity was not spatially consistent over the cluster area, mostly reaching a slight decline of about − 0.1 to − 0.3, which means slight intensification of drought periods in 1991–2020. [ABSTRACT FROM AUTHOR]

İstanbul is the largest city located in the Mediterranean Basin and has a medium to high risk of climate change and future climate risks. Changes in temperature and other weather variables have had significant impacts on İstanbul. In this context, there is a need for studies on the issues of climate monitoring and climate change vulnerability to reduce the adverse impacts. The aim of this study is to investigate the temperature trends of synoptic weather station in İstanbul Atatürk Airport between 1973 and 2023 to have a general idea about how the temperature has changed over the last half-century and to establish statistically whether a trend is significant or not. The values of minimum (Tmin), maximum (Tmax) and mean (Tmean) temperature related parameters were estimated. Annual, monthly and seasonal temperature trends are also analyzed. The findings of this study indicate a significant (p < 0.001) rise in the mean air temperature (Tmean) of İstanbul over the past 51 years (1973–2023), with an annual warming trend of 0.06 °C. The strongest increasing trend in seasonal mean air temperatures has been observed in the summer season, with an increase of 0.08 °C per year. The trend analysis also shows a statistically non-significant increase in yearly average minimum temperature (Tmin) between 1973 and 2023, with a rate of 0.04 °C per year. However, the annual maximum temperature (Tmax) has shown no changes. [ABSTRACT FROM AUTHOR]

The complex meso- and microclimatic heterogeneity inherent to mountainous regions, driven by both topographic and biotic factors, and the lack of observations, poses significant challenges to using climate models to predict and understand impacts at various scales. We present here a six-year dataset (2017–2022) of continuous climatic measurements collected at five elevations from 983 m to 2705 m above sea level in the Val Mazia - Matschertal valley in the Italian Alps. The measurements include the air temperature, relative humidity, wind speed and direction, solar radiation, soil properties, precipitation, and snow height. Collected within the European Long-Term Ecological Research program (LTER), this dataset is freely available in an open access repository. The time series may be valuable for the validation of regional climate models, atmospheric exchange modelling, and providing support for hydrological models and remote sensing products in mountain environments. Additionally, our data may be useful for research on the influence of elevation on ecological processes such as vegetation growth, plant composition, and soil biology. Beyond its utility in advancing such fundamental research, meteorological monitoring data contribute to informed socio-political decisions on climate adaptation strategies, land management, and water resource planning, enhancing the safety and resilience of mountain communities and biodiversity. [ABSTRACT FROM AUTHOR]

Predictive estimates of changes in the climatological boundary of the permafrost zone as a function of the average annual air temperature in the Bolshezemelskaya tundra under various global economy scenarios until the middle of the XXI century have been obtained. The permafrost climatological boundary shift in the northeasterly direction observed in the period from 1950 to 2010 was determined from the threshold average annual air temperature. According to the adjusted scenario forecasts obtained using the climate model, it will continue in the coming decades under any global economy scenario and is an inevitable consequence of the anthropogenic influence on the climate. The study results are important for assessing the prospects and development of a long-term observation network created to monitor the permafrost state and greenhouse gas fluxes in the Russian Federation. [ABSTRACT FROM AUTHOR]

Several developing countries in Asia are relying on agricultural practices in which climate change is a challenging concern. The trends change in atmospheric parameters and vegetation indices are questioned and vague. Consequently, the trend variations of the environmental characteristics were introduced in the first decade of 21 century to better understand the trend changes and envisage the relationship between the atmospheric parameters and the vegetation cover. The Moderate Resolution Imaging Spectroradiometer and the Atmospheric Infrared Sounder provide several atmospheric parameters, e.g., Air Temperature, Aerosol Optical Depth (AOD), and Carbon Dioxide (CO2). In addition, NDVI was estimated and aimed at the role of the interactions among the atmospheric parameters based on statistical analysis. The results showed that AOD and NDVI were air temperature dependent; meanwhile, AOD, NDVI, and air temperature were exchanging and influencing one another. Furthermore, the average minimum air temperature of the region increased during this period and carbon dioxide was a time-dependant parameter that was rising at a steep slope, which may lead to environmental crises. The trend analysis outlines what policies, programs, and procedures should be used to balance the ecosystem and mitigate the climatic negative effects. The Current research strengthens on the social awareness and the international cooperation to reduce socio-economic and political harm and loss in the face of climate change. [ABSTRACT FROM AUTHOR]

Primary production in the Sea of Okhotsk is largely supported by dissolved iron (dFe) transported by the Amur river, indicating the importance of dFe discharge from terrestrial environments. However, little is known about the mechanisms of dFe discharge into the Amur river, especially in terms of long-term change in dFe concentration. In the Amur river, extreme increase in dFe concentration was observed between 1995 and 1997, the cause of which remains unclear. As a cause of this iron anomaly, we considered the impact of permafrost degradation. To link the permafrost degradation to long-term variation in dFe concentration, we examined the changes in annual air temperature (Ta), accumulated temperature (AT), and net precipitation for three regions (northeast, south, and northwest) of the basin between 1960 and 2006. Ta and AT were relatively high in one out of every few years, and were especially high during 1988–1990 continuously. Net precipitation in late summer (July to September) has increased since 1977 and has stayed positive until 2006 throughout the basin. Most importantly, we found significant correlations between Ta and late summer dFe concentration with a 7-year lag (r = 0.54–0.69, p < 0.01), which indicate a close relationship between high Ta in year Y and increased late summer dFe concentration in year Y + 7. This correlation was the strongest in northeastern Amur basin where permafrost coverage is the highest. Similar 7-year lag correlation was also found between AT in the northeastern basin and late summer dFe concentration (r = 0.51, p < 0.01). Based on our findings, we propose the following hypothesis as a cause of iron anomaly. (1) Increased net precipitation since 1977 has increased soil moisture, which created suitable conditions for microbial dFe generation; (2) permafrost degradation during the warm years of 1988–1990 promoted iron bioavailability and led to the intensive dFe generation in the deeper part of the active layer; and (3) dFe took approximately 7 years to reach the rivers and extremely increased dFe concentration during 1995–1997. This is the first study to suggest the time-lagged impact of permafrost degradation on iron biogeochemistry in the Amur river basin. [ABSTRACT FROM AUTHOR]