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The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase in greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of daily solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data, its superiority over related machine learning methods, and its ability to reproduce the daily variability. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099. [ABSTRACT FROM AUTHOR]

Bimodality in precipitation frequency distributions is often evident in atmospheric models, but rarely in observations. This study i) proposes a metric to objectively quantify the bimodality in precipitation distributions, ii) evaluates model simulations contributed to the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5), phase 6 (CMIP6), and the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project by comparing them to satellite-based and reanalysis precipitation products, and iii) investigates possible origins of bimodal precipitation distributions. Our results reveal that about 83% (20 out of 24) of CMIP5 and 70% (21 out of 30) of CMIP6 models used in this study exhibit bimodal distributions. The few DYAMOND models that use a deep convective parameterization also show bimodal distributions, while most DYAMOND models do not. Predictably, the bimodality originates from the separation of precipitation process between resolved grid-scale and parameterized subgrid-scale. However, in a larger number of models bimodality arises from the parameterized subgrid-scale convective precipitation alone. [ABSTRACT FROM AUTHOR]

This study evaluates hydrology and hydrological extremes under future climate change scenarios. The climate change scenarios were developed from multiple Global Circulation Models (GCMs), Representative Concentration Pathway (RCP) scenarios, and statistical downscaling techniques. To ensure hydrological model robustness, the Soil Water Assessment Tool (SWAT) was calibrated and validated using the Differential Split Sample Test (DSST) approach. The model was also calibrated and validated at the multi-gauges of the watershed. Future climate change scenarios revealed a reduction in precipitation (in the order of −9.1% to 4.9%) and a consistent increase in maximum temperature (0.34°C to 4.10°C) and minimum temperature (−0.15 °C to 3.7°C) in different climate model simulations. The climate change scenarios triggered a reduction of surface runoff and streamflow and a moderate increase in evapotranspiration. Future climate change scenarios projected a decrease in high flow (Q5) and low flow (Q95). A higher reduction of Q5 and annual minimum flow is also simulated in future climate scenarios, whereas an increase in annual maximum flow is simulated in climate change scenarios developed from the RCP8.5 emission scenario. The study suggests optimal water management structures which can reduce the effect of change in high and low flows. [ABSTRACT FROM AUTHOR]

Groundwater plays an important part in protecting the ecological values of the earth's systems. Assessment of groundwater (GW) recharge due to change in land use/land cover (LULC) and climate variables is essential for integrated water management and adaptation. This study is an attempt to check the combined influence of change in LULC and climate variables on GW recharge in Kallada river basin, Kerala, India. The LULC map is predicted for the year 2030 based on LULC maps during the years 1985, 1995, 2005 and 2015 using Markov chain and multilayer perceptron model. The major LULC category in the study area is plantation with an area of 797.8 km2 (49.6% in 2015) and dense forest with an area of 366.5 km2 (22.8% in 2015). Considering the period 1985–2005, a decrease of 7.43% dense forest and an increase of 2.55% built area were observed. The predicted LULC shows that there is a reduction in plantation (3.77%) and dense forest (1.28%) and increase in built-up land (4.12%) by 2030 as compared to the year 2015. The CMIP5 General Circulation Model is used for future projections of climate variables, namely daily precipitation, maximum temperature and minimum temperature under RCP 4.5 and RCP 8.5 scenarios. Multilayer perceptron neural network model was used for statistical downscaling of GCM projections. The SWAT model was used to estimate the groundwater recharge for the year 2030 using the predicted LULC map and hydro-meteorological variables. The results illustrate a significant drop in GW recharge in Kallada river basin by the year 2030. Average GW recharge in the Vettikavala and kottarakkara, which is in the western region of the basin, is reduced to 4.6% and 9.3% under RCP 4.5 and RCP 8.5 scenarios, compared to the average GW recharge in 2005, which was 24.8% of rainfall. The decline in the groundwater recharge in the western regions may be due the increase in built-up area and decrease in plantation. The proposed model results provide a reliable insight for water resources planners in conducting future research on groundwater resources. [ABSTRACT FROM AUTHOR]

The INMIO-CICE hydrodynamic model of the Caspian Sea was used to calculate equilibrium river runoff and evaporation from the sea surface over a wide range of sea levels (from –85 to +50 m a.s.l.) for different climatic conditions: the Last Glacial Maximum (about 21 ka BP) and the pre-industrial climate (~1850). Data from the INMCM4.8 climate model were used as boundary conditions. It was found out that a river runoff of about 400 km3/yr was required in the Last Glacial Maximum to maintain the sea level at 35–50 m a.s.l. corresponding to the maximum values of the Khvalynian transgression. In the Last Glacial Maximum, evaporation from the sea surface decreased by 105–175 mm (12–22%), and a precipitation layer, according to the INMCM4.8 model, decreased by 50–70 mm (15–30%). These conditions caused the equilibrium runoff to decrease by about 10–20% compared to the pre-industrial conditions. Smaller absolute and relative changes corresponded to lower sea levels. The maximum evaporation decrease occurred at 5 m a.s.l. [ABSTRACT FROM AUTHOR]

Emerging large ensemble climate datasets produced by multiple general circulation models and their downscaling products challenge the limits of hydrodynamic models because of the immense data size. To overcome this new challenge and estimate the discharge quantiles corresponding to different return periods at all river sections in an entire region, this study proposes an event-based regional approach that uses a nationwide distributed rainfall–runoff model as well as large climate projection ensembles. This approach addresses the high computational burden associated with continuous simulations and solves the problem of conventional event-based simulations serving only a single outlet of a basin. For our analysis, we extracted 372 annual maximum 48 h rainfall events that cover the entirety of Shikoku Island and its eight major river basins. Peak discharges were estimated using a 150 m resolution rainfall–runoff–inundation model. These discharges were then screened using either the peak-over-threshold (POT) method or block maxima (BM) method, and frequency curves were subsequently constructed and evaluated. The primary reason for the necessity of POT or BM was to avoid interference from extraneous low discharges. The POT-based frequency curves showed good accuracy when using peak discharges in the range of the top 10–50%, and the results remain stable within this threshold range. The BM method, employing block sizes of 2–5 years, can generate relatively accurate frequency curves, but the choice of block size introduces significant variations in results among certain basins. Generally, the accuracy of results based on the POT method surpasses that of the BM method. Considering the accuracy, computational cost, and result stability, the POT method is preferred. The error introduced by the regional approach was acceptable with more than half of the relative root-mean-square errors remaining within 10% and basically all of the results are within 20%. The results of the regional approach exhibited good accuracy across climate scenarios and provided consistent information regarding future flood quantiles. This study serves as the foundation for high-resolution future flood risk assessment. [ABSTRACT FROM AUTHOR]

Eucalyptus globulus is a species endemic to southeastern Australia. It has naturalized non-native ranges in other parts of Australia, Europe, Africa, and the western United States. This study is the first of its kind in Ethiopia to model and map the spatiotemporal distribution of the species using species distribution models (SDMs). A total of 874 occurrence records were used from the online Global Biodiversity Information Facility (GBIF) database and field observation. Three environmental variables, including terrain, climate, and soil were used to predict the species' distribution. The terrain, climate, and soil raster grids were resampled to a 200-meter resolution. The Global Circulation Model (GCM) HadGEM3-GC3.1 was used to extract future climate data. This GCM has a good match between the atmospheric and oceanic components showing little drift in its surface climate. Besides, it has the best coverage of Africa. Three climate change scenarios (SSPs 1-2.6, SSPs 2-4.5, and SSPs 5-8.5) were used for predicting suitable habitat of the species. The jackknife test was chosen to assess the importance of each environmental predictor variable. The model's performance was evaluated using the Area under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The model had excellent predictive performance with an average AUC of 0.94. Altitude, rooting conditions, slope, dry-month precipitation, and temperature seasonality are the most important environmental factors in shaping E. globulus distribution. Ethiopian highlands are predicted to be more suitable to the species, but the increase in temperature seasonality may reduce suitable habitat under the high-forcing climate change scenario. Climate change is expected to create more suitable habitats for eucalyptus in the future which may encourage plantations in potential distribution areas. Consequently, ensuring long-term forest health necessitates robust management systems prioritizing native trees and responsible grower or farmer practices. [ABSTRACT FROM AUTHOR]

Climate change is among the most pressing challenges for human advancement in the twenty-first century. Climate change is having a significant impact on the agricultural sector. The main objective of this study is to evaluate the food security status and sustainability of crop production under different climate change scenarios in the arid and semi-arid zones of Iran. To this end, the Statistical Down-Scaling Model (SDSM) and the outputs of the General Circulation Model (GCM) are employed to project future climate parameters under three climate scenarios. The study then employs the Just and Pope approach by using a panel dataset to evaluate the impact of climate change on crop production. Also, the multi-criteria decision-making (MCDM) technique is used to assess the sustainability of crop production from an economic, social, and environmental perspective in the context of climate change and baseline conditions. The results of the study show that precipitation has a significant favourable effect on crop yield in both zones. Maximum temperature is positively and significantly related to crop yield in the semi-arid area, while the relationship is negative in the arid region. Future projections reveal that under different climate scenarios, the production of irrigated wheat, dryland wheat, irrigated barley, dryland barley, and potato crops will change by a maximum of approximately 7.57%, 35.70%, 62.86%, 15.90%, and 26.88% in the semi-arid zone and − 24.07%, 29.73%, − 31.33%, − 4.80%, and 25.80% in the arid zone, respectively. The findings imply that climate change will decrease the food security index for all crops in the arid zone, while improving the situation for crops in the semi-arid region. The results also indicate that future climate change can have a significant adverse effect on economic water productivity, economic benefits, and the sustainability of strategic crop production in the arid zone compared to the semi-arid zone. Given that a large part of Iran is covered by an arid climate, designing coherent adaptation actions and mitigation policies need to be prioritized to tackle the negative impact of climate change on food production systems. [ABSTRACT FROM AUTHOR]