West Nile virus (WNV), a mosquito-borne flavivirus, has circulated in the United States since 1999. In 2025, Florida was home to 24 million people, with projected increases in population and urbanization in a changing climate. The southern house mosquito, Culex quinquefasciatus, is found in every county, and is a major vector for WNV. Describing shifting WNV transmission risk is important to inform public health and vector control planning. Using published estimates of thermal suitability of WNV transmission by Cx. quinquefasciatus, with climate models and population data, we calculated and mapped baseline and projected county-level transmission suitability and people at risk for 2000, 2030, and 2050. Five general circulation models and two mitigation scenarios (SSP2-4.5, SSP5-8.5) were used to explore future trajectories. At baseline, all 67 counties in Florida experienced 5-9 months of transmission suitability. Using the year 2000 census estimates, 2.33 million people in 2 counties experienced 9 months, and in 2030, across climate models, 8.93-12.26 million people (10-20 counties; SSP2-4.5), and 8.95-18.10 million people (11-26 counties; SSP5-8.5) are projected to experience 9 or more months of transmission suitability. In 2050, for both SSP2-4.5 and SSP5-8.5, 17.08-20.42 million people (23-26 counties), ranging to approximately 70% of the projected population of Florida will experience 9 or more months. The 10 most populated counties in 2000 are projected to experience 1-3 months of additional climate-driven transmission suitability in the future. The southern house mosquito was previously managed as a seasonal nuisance in Florida, but now represents an increasing public health exposure risk. Projections across climate trajectories underscore an increasing suitability and exposure risk for WNV in Florida, ranging as high as around 70% of the population exposed to suitable climate conditions for transmission for 9 or more months of the year in the 2050s. This means the types of operations and number of employees needed in vector control and public health will also increase.

Projection of heat-related occupational injuries under climate change and demographic scenarios in Italian cities.

Projecting Actual Evapotranspiration (AET) is critical for the survival of semi-arid Pinus sylvestris var. mongolica forests but remains difficult due to climate uncertainties. We bridged this gap by developing a hybrid framework in Inner Mongolia. We trained a Long Short-Term Memory (LSTM) network using SEBAL-derived AET as a physics-based proxy target. This approach achieved high accuracy in simulating historical dynamics (R2 = 0.926, RMSE = 13.56 mm/month). Crucially, our model relies on high-resolution data from 2021 to learn intra-annual seasonality; consequently, our projections represent responses to mean climatological shifts rather than interannual variability. To assess future risks, we drove this validated model with an ensemble of five bias-corrected CMIP6 General Circulation Models (GCMs) for the 2030–2050 period under SSP2-4.5 and SSP5-8.5 scenarios. The ensemble projections reveal a robust increase in total annual AET, driven by a predicted ‘warmer and wetter’ climate (+2.4°C temperature, +12% precipitation). However, this increase is uneven, showing a significant intensification specifically during the growing season (May–August). This seasonal spike indicates a heightened risk of rapid soil moisture depletion due to soaring atmospheric demand, paradoxically creating water stress despite higher annual rainfall. These findings challenge conventional views and highlight the urgent need for adaptive management focused on seasonal vulnerability.

Acidification reshapes plastisphere communities to sustain potassium-stimulated N2O emissions under warming.

Reliable regional climate projections are essential for adaptation in climate-sensitive regions such as Odisha, India. This study develops an integrated framework combining Expert Team on Climate Change Detection and Indices (ETCCDI)-based extremes, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Monte Carlo sensitivity analysis to evaluate and optimise 35 bias-corrected Global Climate Models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The optimised Multi-Model Ensemble (MME) reproduces historical temperature and precipitation patterns (1985-2014) with modest bias and strong seasonal agreement, and is applied to projections under SSP2-4.5 and SSP5-8.5 (2015-2100). Results indicate progressive warming: maximum and minimum temperatures increase by ~ 1.7°C under SSP2-4.5 and ~ 3.3-3.4°C under SSP5-8.5 by late century, with summer days (SU) rising by ~ 20-46days and tropical nights (TR) by up to ~ 112days. The extreme temperature range (ETR) widens significantly under SSP5-8.5. Precipitation increases are largest during winter and post-monsoon seasons, while monsoon-season changes remain small and slightly negative under SSP2-4.5. Very wet (R95pTOT) and extremely wet (R99pTOT) precipitation increase by ~ 3% and ~ 2% under SSP2-4.5, and by ~ 5% and ~ 4% under SSP5-8.5, indicating a growing contribution of extreme rainfall events to annual precipitation totals. Northern and coastal Odisha show higher precipitation increases, while inland regions experience stronger warming and enhanced heat stress. The framework provides a transparent and reproducible basis for regional climate assessment.

Dimethylsulfide (DMS; CH3SCH3) from marine phytoplankton is a major source of atmospheric sulfur 1. Its oxidation products include sulfuric acid (SA; H2SO4) and methanesulfonic acid (MSA; CH3SO3H), which has a higher yield than SA below 10 °C 2. Whereas SA is known to drive the formation of new particles 3, which may subsequently grow and act as cloud condensation nuclei (CCN), the role of MSA remains unclear 4. Here, in experiments performed under atmospheric conditions at the CERN CLOUD (Cosmics Leaving OUtdoor Droplets) chamber, we show that MSA nucleates together with ammonia (NH3) below -10 °C, at rates comparable to SA-NH3. Moreover, MSA and SA nucleate synergistically below -10 °C, forming multi-acid molecular clusters with NH3. Even at ultra-low NH3 levels, MSA drives particle growth at or near the kinetic limit below 9 °C and above 40 % relative humidity (RH). Since MSA and SA generally coexist at similar concentrations in cool marine regions, our findings indicate that nucleation rates may be accelerated up to tenfold and growth rates up to twofold compared with SA-NH3 alone. Our global model simulations indicate that MSA can enhance CCN concentrations, especially in polar regions. We propose that MSA might be an important driver of biogenic particles in cool, pristine marine regions of both the present-day and pre-industrial atmospheres, and yet is unaccounted for in global climate models 5.

Egypt possesses substantial potential for renewable energy generation, prompting heavy national investments to increase the share of wind power in its overall energy portfolio. Consequently, it is crucial to evaluate the long-term vulnerability of future wind energy production to climate change. This study fills a critical gap in regional climate-energy modelling by providing a novel quantification of turbine-specific capacity ratios across four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Through a comparative assessment of 23 CMIP6 Global Climate Models (GCMs), EC-Earth3-Veg, EC-Earth3, and CESM2-WACCM were identified as the most reliable models against historical ERA5-Land data using the Kling-Gupta Efficiency (KGE) metric, followed by Quantile Mapping for bias correction of both historical and future scenarios. Evaluating nine wind turbine models (T1-T9) revealed that T1 and T2 maintained the highest historical capacity ratios, peaking at 68.0-76.5% and 59.5-68.0%, respectively. By 2100, meteorological projections indicate a regional warming trend coupled with a decrease in mean wind speed; notably, the high-emission SSP5-8.5 scenario projects the highest mean temperature (28°C) and lowest mean wind speed (3.8m/s). Despite these declines, future projections for T1 and T2 indicate resilient power generation and localized increases in strategic locations, such as Ras Ghareb and southern Egypt, particularly under the SSP2-4.5 scenario. Ultimately, these findings provide essential data-driven insights for energy planners to optimize turbine selection and site development, ensuring the long-term resilience of Egypt's wind energy infrastructure.

ABSTRACTThe rapid spread of Toxomerus floralis (Fabricius, 1798) (Diptera: Syrphidae) within the Afrotropical region is described. We characterise and compare the climatic niches of T. floralis in its native (Southern North America, Central America and South America) and invaded (Afrotropical Region) range to assess the potential for further expansion across Africa and beyond, and included future global climate models and socioeconomic pathways as projections. Occurrence data for native and invaded ranges were obtained from field sampling by authors, major collections of Afrotropical Syrphidae, collections records and occurrence data from the Global Biodiversity Information Facility (GBIF), including iNaturalist data. Single and ensemble species distribution modelling was performed utilising the ‘biomod2’ package, and the ‘ecospat’ package was used to determine the Continuous Boyce Index and niche dynamics of the species. Current and global climate models (GCM) Worldclim 2.1 data were used as environmental variables. Ensemble models showed high predictive accuracy (native: TSS = 0.824, CBI Rs = 0.982; expanded: TSS = 0.805, CBI Rs = 0.997), Bio18 and Bio2 Worldclim 2.1 variables proving the most important predictors. Niche dynamics showed primarily niche conservatism (76.4%) as well as a degree of expansion (23.6%, p = 0.048). Models predict high probability of further spread throughout Africa, with potential expansion into other Ecoregions. Future climate projections suggest continued range expansion through 2100 under most scenarios. The species' potential distribution shows spatial overlap with its larval host plants, however, since host distributions were not integrated into the modelling framework, the relative roles of climatic and biotic factors in limiting distribution cannot be directly evaluated from current analyses. It is predicted that T. floralis will invade the tropical regions of Asia and Australia in the near future. Citizen science data proved invaluable for tracking the expansion of T. floralis, highlighting the value of such platforms for monitoring non‐native species expansions.

Cryptosporidium is an important cause of moderate-to-severe diarrhea (MSD) among children under five, with the highest burden in Sub-Saharan Africa (SSA). Cryptosporidium is highly climate sensitive, yet there has been no published literature projecting the incidence of Cryptosporidium under climate change in SSA where vulnerability to climate change is highest. Utilizing monthly case counts of Cryptosporidium from two case-control studies in The Gambia, Kenya, and Mali, we modeled the relationship between monthly temperature, precipitation, and vulnerability factors with Cryptosporidium incidence by study site using Bayesian Network models. Global climate models were then used to project temperature and precipitation at each site in 2040 and 2055 under two emission scenarios. Future Cryptosporidium incidence was estimated based on projected climate and future vulnerability scenarios. Global climate models predict increasing temperatures at all sites and increasing rainfall in Kenya under all scenarios. Incidence of Cryptosporidium per 10,000 children is predicted to change by -16.93 (range: -24.84 - 8.92), -4.34 (range: -24.50 - 9.70), and 24.81 (range: 10.41 - 44.06) % in Kenya, Mali, and The Gambia, respectively from baseline (2007 to 2015) to 2055 under high carbon emissions. These estimates are influenced by future vulnerability levels. The effect of climate change on Cryptosporidium incidence will be location and climate specific, however whether or not vulnerability factors remain stable will have a strong influence on what future Cryptosporidium looks like. Prioritizing development in areas with the highest burden of climate-sensitive health outcomes can mitigate the impact of climate change.

Abstract Tropical high clouds are the cloud type that contributes most to the uncertainty in climate sensitivity, as their feedback on global warming remains poorly constrained. One reason for this is that global circulation models (GCMs) do not resolve the full spectrum of high clouds, ranging from thin cirrus to thick deep convective clouds. We use a set of storm-resolving aquaplanet simulations that resolve this spectrum to investigate how high clouds with different thicknesses contribute to the total high-cloud feedback. We find that the total feedback is positive, arising from all high clouds remaining at fixed temperatures and from an intensification of the diurnal cycle of deep convection. Fixed high-cloud temperatures require an interactive representation of ozone. Prescribing ozone, as often done in GCMs, results in an unrealistic warming of high clouds and hence a less positive feedback. Thick clouds produced by deep convection partly shift from daytime to nighttime in response to surface warming. This reduces sunlight reflection and results in a previously unrecognised positive feedback. The reduction of high clouds in response to warming, assumed to produce a negative feedback, affects thin (warming) and thick (cooling) clouds equally in our simulations, resulting in a near-neutral feedback.

The climate system's nonlinear dynamics is influenced by various external forcings and internal feedbacks, which can give rise to regional and even global tipping points that may lead to significant, potentially irreversible changes. Palaeoclimatic records reveal that Earth's climate has shifted between distinct equilibria, including a 'hothouse Earth' state with temperatures about 10K higher than at present. However, a specific mechanism for a sudden tipping to an alternate stable state, several degrees warmer than the present climate, has yet to be presented. We introduce a temperature-carbon-vegetation (TCV) model comprising an energy balance model (EBM) of global temperature, coupled with global terrestrial and ocean CO2 dynamics, and with vegetation ecosystem change. Our model exhibits a new tipping mechanism that leads to a hothouse Earth under a high-emission scenario. Its simulations align with both observations and Intergovernmental Panel on Climate Change (IPCC)-class global climate models (GCMs) prior to tipping. The two processes that produce global tipping are: (i) temperature-albedo feedback owing to darkening of the terrestrial cryosphere by glacial microalgae and (ii) limits to vegetation adaptation that lead to reduced carbon absorption. This article is part of the theme issue 'Critical transitions and intelligent control in complex systems'.

The Somali upwelling is the strongest upwelling region globally during its seasonal peak. The intense productivity that occurs during the southwest monsoon (May-September) sustains artisanal and industrial fisheries. However, due to its complex structure and seasonality, and the typically coarse spatial resolution of global climate models, understanding its future fate remains a challenge. Using a high-resolution future climate projection and a size-spectrum model (in the absence of reliable fish catch data), we identify key climate stressors and projected changes in higher trophic levels to understand potential future impacts on Somali fisheries. Overall, the productivity generated by the Somali upwelling is projected to decline by the end of the century. Our results show that the inner coastal zone may experience elevated productivity, potentially due to changes in the prevailing winds and Somali Current. This may indicate potential climate refugia and minimal impacts to the artisanal fishing fleet. However, further offshore in the Great Whirl region dominated by small pelagic fish, there is a projected decline in productivity and biomass, which may impact the industrial fishing fleets that may target this area in future. To overcome challenges in understanding the fate of global upwelling systems, high-resolution models must be employed to more accurately simulate individual systems.

Spatial fields in the Earth and environmental sciences are often available at multiple scales or resolutions. While coarse-scale data (e.g., from global circulation models) are often abundant, they lack the local detail provided by fine-scale data (e.g., from regional climate models), which are typically computationally expensive to generate. Statistical downscaling and multi-scale data fusion address this challenge by predicting high-resolution fields from low-resolution or related inputs. We propose a highly scalable Bayesian approach that can learn the joint non-Gaussian distribution and nonlinear dependence structure of nonstationary spatial fields across multiple scales from a small number of training samples. Our method employs scale-aware autoregressive Gaussian processes with suitably chosen regularization-inducing priors to model the conditional distribution of fine-scale fields given coarse-scale data. Exploiting conjugacy, the integrated likelihood is available in closed form, enabling efficient parameter optimization via stochastic gradient descent. Once trained, the method provides a closed-form characterization of the posterior distribution of fine-scale fields given coarse-scale inputs. In numerical comparisons, we demonstrate that our approach substantially outperforms existing methods and effectively characterizes and simulates fine-scale climate behavior based on output from coarse global circulation models.

Artificial intelligence has advanced global weather forecasting, outperforming traditional numerical models in both accuracy and computational efficiency. Nevertheless, extending predictions beyond subseasonal timescales requires the development of deep learning (DL)–based ocean-atmosphere coupled models that can realistically simulate complex oceanic responses to atmospheric forcing. This study presents KIST-Ocean, a DL-based global three-dimensional ocean general circulation model. Comprehensive evaluations demonstrate the model’s robust ocean simulation skill and efficiency. Moreover, it reproduces ocean responses, such as Kelvin and Rossby wave propagation, and vertical motions induced by wind stress curl, demonstrating its ability to represent key atmospherically forced ocean dynamics underlying climate phenomena, including the El Niño–Southern Oscillation. These findings reinforce confidence in DL-based global weather and climate models by demonstrating their capacity to capture essential ocean-atmosphere relationships. Building on this foundation, the present study paves the way for extending DL-based modeling frameworks toward integrated Earth system simulations, thereby offering substantial potential for advancing long-range climate prediction capabilities.

This study examined historical trends and variability, as well as future projections of rainfall and temperature under different emission scenarios in Bale Mountains National Park (BMNP), southeastern Ethiopia. For the historical analysis, thirty years (1994-2023) of daily rainfall and temperature data from eight meteorological stations within and surrounding the park were obtained from the National Meteorology Institute of Ethiopia. Future climate conditions were projected using an ensemble of eight Global Climate Models (ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, INM-CM4-8, MIROC-ES2L, MPI-ESM1-2-LR, NorESM2-MM, and MRI-ESM2-0) from CMIP6 under three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), covering the near future (2021-2040), mid-century (2041-2070), and end of the century (2071-2099). Statistical methods, including descriptive statistics, spatial interpolation, the Mann-Kendall trend test, and Sen's slope estimator, were used to evaluate spatiotemporal patterns of historical climate variables. In addition, decadal deviations from long-term means were analyzed using the Inverse Distance Weighting (IDW) technique in ArcGIS 10.8 to identify climate shifts. The Delta method was applied to downscale Global Climate Model outputs for improved regional relevance. Results revealed strong spatial variability in rainfall influenced by elevation and orographic effects, with higher precipitation observed in elevated areas. Despite noticeable year-to-year fluctuations, most stations showed declining rainfall trends, particularly after 2000, indicating a shift toward drier conditions. Over the last three decades (1994-2023), the annual mean rainfall decreased by about 100mm, while the maximum and minimum temperatures increased by approximately 1.6°C and 1.7°C, respectively, between the first decade (1994-2003) and the last decade (2014-2023). Future projections indicate increases in both rainfall and temperature relative to the 1985-2014 baseline period. The most pronounced changes are expected under the high-emission scenario (SSP5-8.5) by 2071-2099, with rainfall increasing by about 265mm and temperatures rising by up to 3.9°C. These findings highlight the urgency of sustained climate monitoring and adaptive conservation strategies.

Projected Increases in Heat-Related Emergency Department Imaging Utilization Under Climate Change Scenarios.

Wildfire risk is increasing in the eastern U.S., yet spatial and climate-driven assessments remain limited. This study evaluates climate change impacts on wildfire risk in the Upper James Watershed (UJW) using baseline (2000-2019), near-future (2021-2040), and far-future (2061-2080) projections from a 12-model CMIP6 global climate model (GCM) ensemble under SSP2-4.5 and SSP5-8.5. A wildfire risk model was developed in ArcGIS Pro using nine key factors and validated with MODIS hotspot data, showing good agreement between modeled risk patterns and observed fire occurrences (NOF = 0.21, RMSE = 4.20, MAE = 3.37). Baseline risk was primarily driven by land cover, fire-lookout visibility, annual precipitation, population density, and aspect. Baseline maps classified the UJW as very low (21%), low (60%), medium (18.77%), and high risk (0.23%), with high-risk zones concentrated in the southwestern and northeastern regions. Climate projections indicate increased precipitation (up to 8.78%) and rising temperatures (maximum 24.58%; minimum 92.11%), leading to a 519.75% expansion of high-risk areas and > 65% growth in medium-risk zones under SSP5-8.5 by the far-future period, particularly in autumn and spring, while winter risk declines across all scenarios. Jefferson National Forest shows moderate risk increases, whereas Moores Creek, Douthat, and Lake Robertson parks experience substantial growth, with Lake Robertson's risk doubling. This study fills a critical regional gap and supports climate-adaptive wildfire planning by enabling targeted risk prioritization, improved resource allocation, and enhanced long-term preparedness in vulnerable eastern U.S. landscapes.

Marine radionuclides in climate change studies: Pacific Ocean and marginal seas.

Global warming has become an increasingly urgent issue that must be addressed within a limited timeframe, as the debate over the overshoot pathway intensifies. Existing climate and socio-economic models inform our understanding but are too complex for timely action and non-expert use. To address these challenges, our stochastic framework leverages a one-box Ornstein-Uhlenbeck model with colored-noise forcing to characterize variance scaling in detrended temperatures and links this behavior to an empirical Hurst coefficient. The observed weakening of anti-persistence, as summarized by this coefficient, is then used to quantify changes in temperature variance relative to the pre-industrial era. Our empirical Hurst maps reveal patterns consistent with findings from global climate models, highlighting greater changes in temperature variance in equatorial regions, such as southeastern Amazonia and western Indonesia, than in high-latitude regions. Furthermore, seasonal analyses of African temperature records reveal pronounced heterogeneity in variance changes, identifying vulnerable regions that are masked when variability is assessed at annual timescales. By linking our results to climate solutions that consider fairness in burden-sharing and unequal risk-bearing capacity, we anticipate that the empirical Hurst coefficient will support more equitable and effective climate action.

Climate change threatens nitrogen cycling in agricultural ecosystems. Optimizing sowing dates and nitrogen management for maize–soybean intercropping is critical for sustainable production in the North China Plain (NCP). Using a calibrated Agricultural Production Systems Simulator (APSIM) model driven by three representative global climate models (GCMs) selected from 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, we evaluated management strategies under two Shared Socioeconomic Pathway scenarios (SSP2-4.5 and SSP5-8.5) across three climatic zones for near-term (2030–2059) and long-term (2070–2099) periods. Under SSP5-8.5, warming was 1.8–2.2 times greater than under SSP2-4.5, nitrate nitrogen (NO3−-N) leaching increased by 12.1%, and nitrate storage in the 100–150 cm soil layer rose by 53.4% in Zone III. Biological nitrogen fixation contributed 20.1–29.1% of soybean nitrogen uptake under low nitrogen and 14.9–23.4% under medium nitrogen. Optimal strategies were identified: sowing on 7 June (S3) with medium nitrogen (220.8 kg N ha−1) under SSP2-4.5, and advancing sowing to 28 May (S2) with medium nitrogen under SSP5-8.5 to alleviate heat stress. This study reveals a climate-driven “earlier supply–shortened demand–concentrated leaching” mismatch, providing adaptive management guidance for maize–soybean intercropping systems in the NCP.