Abstract

The Euromediterranean area is a key region in which the link between atmospheric moisture transport and hydrometeorological extremes concerns. Atmospheric rivers, the main moisture transport mechanism affecting the region, play a notable role in extreme precipitation there. Moreover, moisture transport deficits from two of the major oceanic moisture sources of the planet, the North Atlantic Ocean and the Mediterranean Sea, are strongly related to drought occurrence in that region. Here, we examine the projected changes in these relationships with global warming, under a high-emission scenario (shared socioeconomic pathway 5–8.5). Here we show that, for the mid-21st century, a moderate increase in the influence of moisture transport on winter precipitation maxima is projected, in line with its increasing concurrence with atmospheric rivers. A stronger increase is estimated for the relationship between moisture transport deficits and drought occurrence, for which probabilities between two and three times greater than those observed in the present climate are obtained for the mid- and end-21st century. This highlights the increasing importance of moisture transport from the ocean in future droughts in the region, especially in the context of reduced local moisture inputs from terrestrial evaporation as a consequence of drier soil.

Abstract

Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.

Abstract

Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.

Abstract

In this research, the impact of climate change on annual maximum daily precipitation (AMP) during the period 2024-2050 and the evaluation of flood risk in Ilam province have been investigated using the outputs of CMIP6 models. After identifying the top-performing CMIP6 models, the annual maximum daily precipitation for return periods of 2, 10, 100, and 1000 years was determined based on fitting 65 probability distributions, considering the 1992-2018 observational period and future periods (SSP1-2.6 and SSP5-8.5). The study also integrates hazard, vulnerability, and exposure components to assess flood risk at various return periods (2, 10, 100, and 1000 years). Vulnerability and exposure assessment involved the selection of indicators such as hamlet density (HD), land use (LU), population density (PD), land cover (LC), SAVI vegetation index, digital elevation model (DEM), slope, soil erodibility (SE), drainage density (DD), and distance from drainage (DFD). The AHP-Entropy weight method was employed to determine the relative importance of each component. The results indicated that changes in annual maximum daily precipitation and flood risk under the SSP1 scenario did not differ significantly from the observational period, exhibiting similar trends and patterns. However, conditions under the SSP5 scenario differed, showing significant fluctuations in annual maximum daily precipitation, particularly for the 1000-year return period, resulting in increased high-risk areas. For instance, in the SSP5-8.5 scenario, the moderate-risk area for the 1000-year return period expanded from 7% to over 13%, and a new high-risk classification arose, covering 5% of the province’s area, which is unprecedented in the other scenarios.

Abstract

In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities in the nonstationarity R criterion compared to others. Although according to the ranking of the raw and bias-corrected outputs of CMIP6 GCMs for Iran, the NorESM2-MM, AWI-ESM-1-1-LR, and MPI-ESM1-2-LR models are consistently among the top six ranked models for precipitation in both raw and corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, and IITM-ESM are consistently among the top six models for both the raw and bias-corrected outputs of CMIP6 GCMs. The Bias correction methods, including quantile mapping and linear scaling, integrated with Bayesian model averaging, were applied. While quantile mapping demonstrates superior performance and less disparity than linear scaling, it proves ineffective for correcting biases at stations with bias nonstationarity over time. The RMSE for monthly precipitation ranges from almost 0 to 200 mm, with a large RMSE value related to the high precipitation stations, and the monthly temperature exhibits a range of 0 to 4 °C. The use of a multi-model ensemble improves accuracy compared to individual models, resulting in a reduction in the differences between the minimum and maximum RMSE values from 178.6 to 91.0. Additionally, the range for mean absolute error decreases from 126.9 to 93.3, and the difference in the correlation coefficient narrows from 0.9 to 0.42. Averaging models after bias correction prevents significant fluctuations while maintaining higher accuracy, in contrast to the second method, which involves bias-correcting models after averaging.