Search Results (274)

Climate change increasingly threatens the sustainability of regional water resources; therefore, robust station-scale precipitation projections are essential for basin-level planning. This study aims to develop and evaluate a hybrid, machine-learning-based statistical downscaling framework to generate monthly precipitation projections for the 21st century in the Büyük Menderes Basin, western Türkiye, using the HadGEM3-GC31-LL global climate model from the CMIP6. Monthly observations from 23 rainfall observation stations and ERA5 reanalysis predictors were employed to train station-specific Random Forest (RF) models, with optimal predictor sets identified through a multistage selection procedure (MPSP). Coarse-resolution general circulation model (GCM) fields were harmonized with ERA5 data using a three-stage inverse distance weighting (IDW), Delta, and Variance rescaling approach. The downscaled projections were bias-corrected using Quantile Delta Mapping (QDM) to maintain the climate-change signal. The RF models exhibited strong predictive skill across most stations, with test Nash–Sutcliffe Efficiency (NSE) values ranging from 0.45 to 0.81, RSR values from 0.43 to 0.74, and PBIAS values from −21.99% to +5.29%. Future projections indicate a basin-wide drying trend under both scenarios. Relative to the baseline, mean annual precipitation is projected to decrease by approximately 12.2, 19.6, and 33.7 mm in the near (2025–2050), mid (2051–2075), and late (2076–2099) periods under SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5, a moderate greenhouse gas scenario). Under the high-emission SSP5-8.5 scenario, projected decreases are 25.2, 53.2, and 86.9 mm, respectively. Late-century reductions reach approximately 15–22% in several sub-basins. These findings indicate a substantial decline in future water availability and underscore the value of RF-based hybrid downscaling and trend-preserving bias correction for water resources planning in semi-arid Mediterranean basins. Full article

The prediction accuracy of the General Circulation Model (GCM) is influenced by the effectiveness of gravity wave activity parameterization. Although research focuses on small-scale horizontal gravity wave activity as a carrier for energy and momentum coupling between atmospheric layers, routine observations of horizontal gravity wave activity on scales less than a dozen kilometers are scarce due to limitations in observational instruments. This paper presents a method for observing small-scale horizontal gravity waves using monostatic Rayleigh lidar, along with the associated data processing workflow. The data processing results indicate that the observed gravity waves generally exhibit wavelengths less than 3 km and phase velocities less than 0.5 m/s. Furthermore, the annual variation in small-scale horizontal gravity waves displays a semi-annual oscillation (SAO), like that observed in medium- and large-scale waves. This suggests that the observed gravity waves originate from secondary gravity waves resulting from saturation dissipation or breaking. Full article

We use deep Physics-Informed Neural Networks (PINNs) to simulate stratified forced convection in plane Couette flow. This process is critical for atmospheric boundary layers (ABLs) and oceanic thermoclines under global warming. The buoyancy-augmented energy equation is solved under two boundary conditions: Isolated-Flux (single-wall heating) and Flux–Flux (symmetric dual-wall heating). Stratification is parameterized by the Richardson number $(R_i\in [-1,1\left]\right),$ representing $\pm 2 °\mathrm{C}$ thermal perturbations. We employ a decoupled model (linear velocity profile) valid for low-Re, shear-dominated flow. Consequently, this approach does not capture the full coupled dynamics where buoyancy modifies the velocity field, limiting the results to the laminar regime. Novel contribution: This is the first deep PINN to robustly converge in stiff, buoyancy-coupled flows ($\mid R_i\mid \le 1$) using residual connections, adaptive collocation, and curriculum learning—overcoming standard PINN divergence (errors $>28\%$). The model is validated against analytical ($R_i=0$) and RK4 numerical ($R_i\ne 0$) solutions, achieving $L_2$ errors $\le 0.009\%$ and $L_{\mathrm{\infty }}$ errors $\le 0.023\%$. Results show that stable stratification $(R_i>0)$ suppresses convective transport, significantly reduces local Nusselt number ($Nu$) by up to $100\%$ (driving $Nu$ towards zero at both boundaries), and induces sign reversals and gradient inversions in thermally developing regions. Conversely, destabilizing buoyancy $(R_i<0)$ enhances vertical mixing, resulting in an asymmetric response: $Nu$ increases markedly (by up to $140\%$) at the lower wall but decreases at the upper wall compared to neutral forced convection. At $5–10\times$ lower computational cost than DNS or RK4, this mesh-free PINN framework offers a scalable and energy-efficient tool for subgrid-scale parameterization in general circulation models (GCMs), supporting SDG 13 (Climate Action). Full article

Urban flooding poses an escalating threat to riverine cities in Southern Asia’s tropical regions, primarily driven by rapid urban expansion. This study develops future projections of Intensity–Duration–Frequency (IDF) curves for major urban centers in Punjab, Pakistan, utilizing downscaled satellite-derived precipitation data. Baseline IDF curves were established using historical rainfall records from multiple meteorological stations. Among eight General Circulation Models (GCMs) assessed, EC-Earth3-Veg-LR demonstrated the highest skill in capturing extreme rainfall patterns relevant to the region. Future precipitation projections from this model were downscaled using the Equidistant Quantile Matching (EQM) technique and applied to generate IDF curves under two CMIP6 scenarios: SSP2-4.5 and SSP5-8.5. The results reveal a substantial increase in extreme rainfall intensities, particularly under the SSP5-8.5 scenario, with projected 100-year return period rainfall intensities rising by approximately 30–55% across key cities. The downscaled projections reveal more pronounced variations than the raw GCM outputs, emphasizing the importance of high-resolution climate data for accurate regional hydrological risk evaluation and effective urban flood resilience planning. Full article

The field of climate modeling is undergoing a significant transformation, moving away from the traditional General Circulation Models (GCMs) and toward the use of sophisticated artificial intelligence (AI)-based prediction systems. Research has shown that AI has the potential to improve climate modeling’s regional accuracy and computing efficiency. Machine learning downscaling better captures local precipitation extremes than GCMs, while hybrid AI–physics models cut ensemble costs by reducing computational demand without sacrificing accuracy. Nevertheless, these investigations have frequently functioned in discrete settings and oversimplified situations without a thorough connection with basic physical concepts. This drawback emphasizes the necessity of a more comprehensive strategy that can handle the intricacies of climatic variability and guarantee reliable model validation. In order to assess the possibilities and challenges of hybrid models in comparison to conventional GCMs, highlighting that AI complements GCMs in regional downscaling and extremes, while GCMs provide stronger global consistency, this study synthesizes proven climate models, AI methodologies, and their accuracy in climate predictions and analyzes existing climate models to evaluate the potential and limitations of hybrid models compared to traditional GCMs. Integrated AI-driven models show notable improvements in predicting regional variations in climate and accelerating simulation processes, especially when dealing with the growing presence of extreme weather occurrences. However, it is important to have consistent datasets and open evaluation procedures in order to guarantee accuracy and deal with the difficulties that come with model benchmarking. This research highlights how crucial it is to maintain interdisciplinary cooperation in order to properly utilize what AI has to offer in climate modeling. This partnership is essential to creating more accurate and useful climate projections, which will eventually guide successful mitigation and adaptation plans for a changing global environment. In order to have a greater understanding of our climate’s future, we must keep pushing the limits of the existing modeling tools. Full article

The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model for exoplanets. The eventual model will be based on both collected observational spectra and synthetic spectral data generated by the ROCKE-3D general circulation model (GCM) developed by the climate modeling program at NASA’s Goddard Institute for Space Studies (GISS). In this initial study, spline curves are used to describe the bin heights of simulated atmospheric absorption spectra as a function of one of the values of the planetary parameters. Bayesian Adaptive Exploration is then employed to identify areas of the planetary parameter space for which more data are needed to improve the model. The resulting system will be used as a forward model so that planetary parameters can be inferred given a planet’s atmospheric absorption spectrum. This work is expected to contribute to a better understanding of exoplanetary properties and general exoplanet climates and habitability. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

Climate warming is anticipated to significantly alter the distribution and composition of plant species in the Arctic, thereby cascading through food webs and affecting both associated fauna and entire ecosystems. To elucidate the trend in plant distribution in response to climate change, we employed the MaxEnt model to project the future ranges of 25 representative Arctic and Circumpolar plant species (including grasses and shrubs). Species distribution data, in conjunction with bioclimatic variables derived from climate projections of three selected General Circulation Models (GCMs), ESM2, IPSL, and MPIE, were utilized to fit the MaxEnt models. Subsequently, we predicted the potential distributions of these species under three Shared Socioeconomic Pathways (SSPs)—SSP126, SSP245, and SSP585—across a timeline spanning 2010, 2050, 2100, 2200, 2250, and 2300 AD. Range shift indices were applied to quantify changes in plant distribution and range sizes. Our results show that the ranges of nearly all species are projected to diminish progressively over time, with a more pronounced rate of reduction under higher emission scenarios. The species are generally expected to shift northward, with the distances of these shifts positively correlated with both the time intervals from the current state and the intensity of thermal forcing associated with the SSPs. Arctic species (A_Spps) are anticipated to face higher extinction risks compared to Boreal–Arctic species (B_Spps). Additional indices, such as range gain, loss, and overlap, consistently corroborate these patterns. Notably, the peak range shift speeds differ markedly between SSP245 and SSP585, with the latter extending beyond 2100 AD. In conclusion, under all SSPs, A_Spps are generally expected to experience more significant range shifts than B_Spps. In the SSP585 scenario all species are projected to face substantial range reductions, with Arctic species being more severely affected and consequently facing the highest extinction risks. These findings provide valuable insights for developing conservation recommendations for polar plant species and have significant ecological and socioeconomic implications. Full article

Coastal regions are increasingly threatened by compound flooding due to the increasing intensities of storm surges and rainfall under climate change. However, relevant research has been limited because significant amounts of data, scenarios, and computations are often required to evaluate long-term variations in compound flood risk. In this study, a framework was proposed through efficient hydraulic simulations and a consequence-based statistical method using data projected under different general circulation models (GCMs). The analysis focuses on analyzing the interdecadal trends of compound flood risk for a coastal area in southwestern Taiwan across a baseline period and four future periods in the short-term (2021–2040), mid-term (2041–2060), mid-to-long-term (2061–2080), and long-term (2081–2100). Although discrepancies exist in the short term, the results show that the values of the annual maximum flood area exhibit an increasing pattern in the future for all GCMs by increasing about 27.8% on average at the end of the 21st century. This means that, under the same flood areas given in the baseline period, the return periods will decrease, and flood events will occur more frequently in the future. This framework can be extended to other regions to assess the impacts of compound flooding with different geographical and meteorological conditions. Full article

The evaluation of cloud distribution, properties, and their interaction with the radiation (longwave and shortwave) is of utmost importance for the proper assessment of future climate. Therefore, this study focuses on the Coupled Model Inter-Comparison Project Phase-6 (CMIP6) historical and future projections using the Shared Socio-Economic Pathways (SSPs) low- (ssp1–2.6), moderate- (ssp2–4.5), and high-emission (ssp5–8.5) scenarios along the South Asian region. For this purpose, a multi-model ensemble mean approach is employed to analyze the future projections in the low-, mid-, and high-emission scenarios. The cloud water content and cloud ice content in the CMIP6 models show an increase in upper and lower troposphere simultaneously in future projections as compared to ERA5 and historical projections. The longwave and shortwave cloud radiative effects at the top of the atmosphere are examined, as they offer a global perspective on radiation changes that influence atmospheric circulation and climate variability. The longwave cloud radiative effect (44.14 W/m²) and the shortwave cloud radiative effect (−73.43 W/m²) likely indicate an increase in cloud albedo. Similarly, there is an expansion of Hadley circulation (intensified subsidence) towards poleward, indicating the shifting of subtropical high-pressure zones, which can influence regional monsoon dynamics and cloud distributions. The impact of future projections on the tropospheric temperature (200–600 hPa) is studied, which seems to become more concentrated along the Tibetan Plateau in the moderate- and high-emission scenarios. This increase in the tropospheric temperature at 200–600 hPa reduces atmospheric stability, allowing stronger convection. Hence, the strengthening of convective activities may be favorable in future climate conditions. Thus, the correct representation of the model physics, cloud-radiative feedback, and the large-scale circulation that drives the Indian Summer Monsoon (ISM) is of critical importance in Coupled General Circulation Models (GCMs). Full article

This study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applied in atmospheric hydrology and combines the features of neural networks and fuzzy logic. This combination enables artificial intelligence (AI) to effectively represent reasoning derived from complex data and expert experience. Due to the multiple atmospheric and hydrological factors that influence rainfall, the nonlinear interrelations among them are highly intricate. Nonlinear principal component analysis can extract nonlinear features from the data, reduce dimensionality, and minimize the adverse effects of data noise and excessive input factors on soft computing, which may otherwise result in poor model performance. Ultimately, ensemble learning enhances prediction accuracy and reduces uncertainty. This study used Tamsui and Kaohsiung in Taiwan as case study locations. Historical monthly rainfall data (January 1950 to December 2005) from Tamsui Station and Kaohsiung Station of the Central Weather Administration, along with historical and varied emission scenario data (RCP 4.5 and RCP 8.5) from three AR5 GCM models (ACCESS 1.0, CSIRO-MK3.6.0, MRI-CGCM3), were used to evaluate future regional rainfall trends and uncertainties through the method proposed in this study. The research findings indicate the following: (1) Ensemble learning results demonstrate that all examined general circulation models effectively simulate historical rainfall trends. (2) The average rainfall trends under the RCP 4.5 emission scenario are generally consistent with historical rainfall trends. (3) The exceedance probabilities of future rainfall during the mid-term (2061–2080) and long-term (2081–2100) suggest that Kaohsiung may experience precipitation events with higher rainfall than historical data during dry seasons (October to April of next year), while Tamsui Station may exhibit greater variability in terms of exceedance probabilities. (4) Under both the RCP 4.5 and RCP 8.5 emission scenarios, the percentage changes in future rainfall variability at Kaohsiung Station during dry seasons are higher than those during wet seasons (May to September), indicating an increased risk of extreme precipitation events during dry seasons. Full article

Accurate runoff simulation and prediction are crucial for water resources management, especially under the impact of climate change. In this study, a multi-physics ensemble Bayesian model averaging (MPE-BMA) model is developed to improve runoff prediction accuracy by integrating a soil and water assessment tool (SWAT), hydrologiska byråns vattenbalansavdelning (HBV) model, and Bayesian model averaging (BMA) into a general framework. The MPE-BMA model integrates the strengths of the SWAT and HBV models. This approach enhances the robustness of simulation outputs and reduces uncertainties from single-model methods. MPE-BMA is subsequently employed to simulate and predict runoff for the upper reaches of Xiangxi River Basin (XXRB) in China, where four general circulation models (GCMs) and three shared socioeconomic pathways (SSP126, SSP245, and SSP585) are considered. Multiple statistical metrics (R², NSE, and RMSE) prove that the MPE-BMA model outperforms the single models of SWAT and HBV. Results reveal that higher-emission scenarios generally lead to significant decreases in runoff, particularly by the 2080s. Specifically, under SSP585, runoff is projected to decrease by approximately 4.61–12.68% by the 2040s and 5.96–11.28% by the 2080s compared to the historical period. From the perspective of monthly and seasonal runoff changes, the peak runoff is projected to shift from June to May by the 2080s. Additionally, under SSP585, spring and summer runoffs tend to significantly increase, while winter runoff decreases sharply, leading to wetter summers and drier winters. These findings underscore the importance of enhancing water use efficiency, upgrading hydropower stations, and implementing watershed management practices to ensure sustainable water resources management in the XXRB amidst climate change. Full article

This data descriptor presents δ-MedBioclim, a newly developed dataset for the Euro-Mediterranean region. This dataset applies the delta-change method by comparing the values of 25 General Circulation Models (GCMs) for the reference period (1981–2010) with their projections for future periods (2026–2050, 2051–2075, and 2076–2100) under the SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5 scenarios. These anomalies are added to two pre-existing datasets, ERA5-Land and CHELSA, yielding resolutions of 0.1° and 0.01°, respectively. Additionally, this manuscript provides a ranking of GCMs for each major river basin within the study area to guide model selection. δ-MedBioclim includes, for all the aforementioned scenarios, monthly mean temperature, total monthly precipitation, and 23 bioclimatic variables, including 9 (biorm1 to biorm9) from the Worldwide Bioclimatic Classification System (WBCS) that are not available in other databases. It also provides two bioclimatic classifications: Köppen–Geiger and WBCS. This dataset is expected to be a valuable resource for modeling the distribution of Mediterranean species and habitats, which are highly affected by climate change. Full article

The ability of a general circulation model (GCM) to capture the variability of El Niño–Southern Oscillation (ENSO) is not only a scientific issue of climate model performance, but also critical for climate change and variability impact studies. Here, we assess 48 CMIP5 GCMs for their skill in simulating ENSO interdecadal variability and its teleconnection with precipitation globally. The results show that (1) only 22 out of 48 GCMs display interdecadal variability that is similar to the observations; (2) the ensemble of the 48 GCMs captures the ENSO–precipitation teleconnection at the global scale; (3) no single GCM can capture the observed ENSO–precipitation teleconnection globally; and (4) a GCM that can realistically simulate ENSO variability does not necessarily capture the ENSO-precipitation teleconnection, and vice versa. The results could also be used by climate change impact studies to select suitable GCMs, especially for regions with a statistically significant teleconnection between ENSO and precipitation, as well as for the comparison of CMIP5 and CMIP6. Full article

The consequences of climate change in recent decades include global warming and variations in precipitation patterns. In this research, the impacts of climate change on temperature and precipitation in the east and west of Mazandaran Province, northern Iran, are examined via five GCMs (general circulation models) and two scenarios (SSP2-2.6 and SSP5-8.5) for the baseline period (2005–2023), near future period (2025–2050), and far future period (2051–2080) according to the IPCC (Intergovernmental Panel on Climate Change) Sixth Assessment Report. In the study area, four synoptic stations in the west of Mazandaran and seven stations in the east of Mazandaran are considered. The analyzed data are daily precipitation and minimum, maximum, and average temperatures. Downscaling was performed by using LARS-WG 8.0 (Long Ashton Research Station Weather Generator) software. The results revealed that the SSP5-8.5 (shared socioeconomic pathways) scenario showed better accuracy than the SSP2-2.6 scenario. In the west of Mazandaran, in the near future, the maximum temperature is projected to increase by 1.1 °C, while precipitation is projected to decrease by 26.3 mm, compared to the baseline period. In the east of Mazandaran, in the near future, the maximum temperature is projected to increase by 0.82 °C, while precipitation is expected to decrease by 7.1 mm, compared to the baseline period. In the west of Mazandaran, in the far future, the maximum temperature is projected to increase by 1.34 °C and precipitation is going to decrease by 55.7 mm, relative to the baseline period. In the east of Mazandaran, in the far future, the maximum temperature is projected to increase by 1.1 °C, while precipitation decreases by 31.3 mm, relative to the baseline period. The projected warming trends and precipitation reduction in both the east and west regions of Mazandaran Province are expected to have adverse environmental and socioeconomic implications. Full article