Representative Concentration Pathways Research Articles

Seafloor marine heatwaves outpace surface events in the future on the northwestern European shelf

Abstract. Marine heatwaves are becoming increasingly frequent across the world's oceans. As a result, there are growing impacts on marine ecosystems due to temperatures exceeding the thermal niche and historical exposure of many species. Anticipating the future frequency and severity of marine heatwaves is necessary. Here, we provide the first projections of future marine heatwaves for the sea surface and seafloor across the northwestern European shelf, which is a critically important marine ecosystem. We use an ensemble of five dynamically downscaled hydrodynamic models under the high-emission scenario Representative Concentration Pathway 8.5 (RCP 8.5). Heatwaves were defined as events lasting at least 5 d where temperatures exceed the 90th percentile of a historical baseline period. The frequency of marine heatwaves at the surface and seafloor is projected to increase significantly during the 21st century under RCP 8.5, with most of the year being projected to be under heatwave conditions by the end of the century. Critically, we find that marine heatwaves are projected to increase in frequency to a greater extent at the seafloor compared to at the sea surface due to their lower levels of natural temperature variation. Similarly, we find that the severity of summer heatwaves at the surface is projected to be lower than that of heatwaves during the rest of the year due to lower climatological variations in temperature outside the summer. The impacts of marine heatwaves in shelf seas are therefore likely to be much more complex than previously thought.

Disentangling the Source of Uncertainty in Monthly Streamflow Predictions: A Case Study of Riu Mannu di Narcao Basin, Sardinia Region, Italy

This study quantifies the uncertainty in monthly streamflow predictions under future climate scenarios in two periods (near and far future) for the Riu Mannu di Narcao basin in Sardinia, Italy. The sources of uncertainty include the hydrological model structure, model parameters, and variability in climatic inputs derived from global and regional climate models (GCM-RCM coupling) and representative concentration pathways (RCPs). Three conceptual and lumped hydrological models (GR3M, ABCD, and IHACRES) were combined with four climate models and two RCPs (RCP 4.5 and RCP 8.5) to assess future streamflow. Monte Carlo simulations were performed to evaluate parameter uncertainty, and the analysis of variance (ANOVA) method was applied to quantify the different sources of uncertainty. The results reveal that, as a single source, GCM-RCM coupling is the largest contributor, accounting for 47.32% (54.64%) of total near (far) future monthly streamflow projection uncertainties, followed by the hydrological model structure at 16.02% (21.09%), RCP scenarios at 15.35% (8.54%), and parameter uncertainty at 0.79% (1.39%). A consistent decline in median monthly streamflow is projected, especially during winter months (December to February), raising a concern about water availability in the region. Our study quantified different sources of uncertainty in monthly streamflow predictions under climate change, disentangling the roles of the hydrological model, model parameters, climate model, and climate scenario for reliable future streamflow projections.

High resolution multiple scenario simulations of future extreme sea levels in hong kong and socioeconomic risks

Extreme sea levels (ESLs) induce significant risks to Hong Kong. With climate change, the risks will be amplified, while existing studies have not provided fine-grained simulations of Hong Kong’s future ESL exposure and its socio-economic risks. Employing a GIS-based coastal flood inundation model, this research integrates a high-resolution Digital Terrain Model (DTM) with datasets of ESL forecasts, demography, economy, infrastructure, and land use to estimate the future ESL risks posed on Hong Kong for 2050 and 2100 under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5. The projections indicate that, under RCP4.5, over 27.66% of the population and 39.52% of the Gross Domestic Product (GDP) will be exposed to ESL after 2050. Under RCP8.5 scenario, the exposed population may surpass 31.21%, with economic exposure estimated at 40.98% of GDP after 2050. Under both RCPs, Hong Kong’s ESL-threatened area may range from 8.23 to 11.41%, exposing over 16.09% of the infrastructure to ESL after 2050. Regions in the northwestern Yuen Long, Tuen Mun River Estuary, Rambler Channel coast, Victoria Harbor coast, Shing Mun River, and Tai Po Waterfront have particularly high ESL risks. The findings highlight the need for resilient infrastructure to counteract the long-term risks ESL poses in Hong Kong.

Impact of Climate Change on Hydrological Extremes (Floods and Droughts) in the Upper Blue Nile Basin

Abstract Understanding the impacts of climate change on hydrology is crucial for addressing water-related challenges such as water scarcity, flooding, and drought. Measurements provide evidence of climate change and global warming. As a downstream country, Egypt is highly dependent on the water resources of the Nile, where the Blue Nile’s share is the largest of the inflow at Aswan. In the Blue Nile River basin, with some exceptions, most studies generally address two periods in the 21st century for climate change impact evaluation: mid-century (2050s) and end of century (2080s). For these future horizons, the projections of precipitation changes, in previous research, remain uncertain with some indicating increased flow and others predicting decreases. This research assesses climate change impacts on Upper Blue Nile flows using high-resolution data from the COordinated Regional Climate Downscaling Experiment (CORDEX) for the African domain, incorporating multiple Global Circulation Models (GCMs) and Regional Climate Models (RCMs). Hydrological modeling is a critical step in projecting climate change impacts on the Upper Blue Nile flows; thus, it is important that the model be compatible with changing climate parameters. Therefore, the Surface Controlled Hydrologic Model (SCHydro) was modified to account for temperature changes when estimating losses. Results showed both increasing and decreasing trends in Blue Nile flows, depending on the models used. Additionally, the probabilities of flood and drought periods were found to be nearly equal through 2100. These findings highlight the need for adaptable water management strategies to address uncertainties and potential extremes in the basin. Graphical Abstract This graphic abstract presents a detailed analysis of the impact of climate change on hydrology in the Upper Blue Nile Basin, highlighting methodology, results, and conclusions. The methodology integrates climate projections from three Global Climate Models (GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) from the CORDEX-Africa dataset, bias corrected using the Delta Change Factor method for improved accuracy. the Surface Controlled Hydrologic Model (SCHydro) was modified to consider the change in temperature in determining the losses. The results indicate a significant shift in seasonal discharge patterns, with reduced wet-season flows and increased dry-season flows, exacerbating water availability challenges. The tables and graphs illustrate changes in flow frequency and intensity, revealing an increased occurrence of extreme hydrological conditions. These shifts emphasize the need for adaptive water management strategies to mitigate future risks of floods and droughts. The conclusion stresses the urgency for policymakers and water resource managers to develop sustainable approaches to balance water distribution, ensure agricultural stability, and enhance resilience amidst changing climate conditions.

Integrative electricity need prediction utilizing enhanced squeeze net and flexible Coyote optimization algorithm A combination of RCPs SSPs and socioeconomic

Rising global temperatures and frequent extreme heat waves have caused changes in people’s energy consumption patterns, leading to a significant impact on electricity consumption. Accurate forecasts of electricity consumption can provide scientific support for the enhancement of power supply services. Precise prediction of power consumption is critical for effective resource management. This research has demonstrated a new approach to forecasting that combines social and economic variables with climate factors using an advanced meta-heuristic algorithm to improve the accuracy and efficiency of the compact network. The effectiveness of the compact network is increased by using the Flexible Coyote Optimization Algorithm (FCOA), which plays a vital role in optimizing parameters and accelerating convergence. This study used the predictive capabilities of common socio-economic pathways (SSP) and representative concentration pathways (RCPs) to simulate influencing factors. Squeeze Net carefully learns complex data patterns to produce reliable predictions, while FCOA focuses on improving the prediction framework. This study has culminated in the creation of a comprehensive forecasting model for electricity demand. Through rigorous testing and comparative analysis, this model has been proven to outperform traditional forecasting methods in terms of accuracy and efficiency.

Heat, health, and habitats: analyzing the intersecting risks of climate and demographic shifts in Austrian districts

The impact of hot weather on health outcomes of a population is mediated by a variety of factors, including its age profile and local green infrastructure. The combination of warming due to climate change and demographic aging suggests that heat-related health outcomes will deteriorate in the coming decades. Here, we measure the relationship between weekly all-cause mortality and heat days in Austrian districts using a panel data set covering . An additional day reaching is associated with a increase in mortality per 1, 000 inhabitants during summer. This association is increased by approximately in districts with a two standard deviation above average share of the population over 65. Using Representative Concentration Pathways (RCP) projections of heat days and demographics in 2050, we observe that districts will have elderly populations and heat days standard deviations above the current mean in just 25 years. This predicts a drastic increase in heat-related mortality. At the same time, district green scores, measured using meter resolution satellite images of residential areas, significantly moderate the relationship between heat and mortality. Thus, although local policies likely cannot reverse warming or demographic trends, they can take measures to mediate the health consequences of these growing risks, which are highly heterogeneous across regions, even in Austria.

Modeling Streamflow Response to Climate Scenarios in Data-Scarce Mediterranean Catchment: The Medjerda in Northern Tunisia

This study aimed to evaluate the performance and robustness of the GR2m “Génie Rural à 2 paramètres au pas du temps Mensuel” rainfall–runoff model for simulating streamflow under past and future hydrometeorological shifts in the Medjerda, a data-scarce Mediterranean catchment in northern Tunisia characterized by limited hydrometeorological records and high climate variability. The evaluation was conducted across three subcatchments characterized by contrasting climatic conditions and representing the hydrometeorological pattern of the Medjerda catchment. To assess the model’s robustness, a calibration–validation process was applied. This method alternated between dry and wet periods and evaluated model performance through various criteria. Subsequently, GR2m was adopted to simulate projected discharge, using projections from the “Model for Interdisciplinary Research on Climate 5” (MIROC5) under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Standardized climate indices (SCIs) were employed to assess climate change impacts. The results demonstrate that GR2m performs well in simulating streamflow across different climatic conditions within the Medjerda catchment and maintains satisfactory performance when calibrated over a non-stationary climate period. The findings indicate a continuous decline in projected runoff and suggest a significant increase in extreme drought events.

A novel framework for analyzing rainy season dynamics in semi-arid environments: a case study in the Peruvian Rio Santa basin

Abstract. In semi-arid regions, the timing and duration of the rainy season determines plant water availability, which directly impacts food security. Rainy season metrics, which aim to define and, in some cases, predict the onset and end of seasonal rains, can support agricultural planning, such as scheduling planting dates and managing water resources. However, these metrics based on precipitation time series do not always accurately reflect plant water availability, and the variety of available metrics can complicate the selection of the most suitable one. Furthermore, a metric's ability to capture observed vegetation variability can indicate its applicability over larger spatial or temporal scales. This study introduces a new bucket-type metric that incorporates a simplified water balance, accounts for both accumulation and storage, and also takes interannual legacy effects into account. We evaluate its performance against seven commonly used rainy season metrics, both calibrated and uncalibrated, using 18 years of the satellite-derived Normalized Difference Vegetation Index (NDVI) from the semi-arid Rio Santa basin in the Peruvian Andes. Our results demonstrate that calibrating metrics using vegetation data significantly enhances their ability to capture rainy season dynamics, with the bucket metric outperforming others in both accuracy and robustness. Furthermore, we examine the sensitivity of all metrics to variations in rainfall intensity and frequency under future climate scenarios, using a previously published high-resolution dataset specifically designed for the Rio Santa basin which provides historical (1981–2018) rainfall data and future projections (2019–2100) based on 30 statistically downscaled CMIP5 models for the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios, respectively. While most rainy season metrics exhibit expected correlations in response to climatic changes, some established metrics display physically inconsistent behavior due to methodological artifacts, highlighting their limitations in assessing hydroclimatic changes. In addition to the sensitivity analysis, we evaluate long-term trends in rainy season characteristics. Statistically downscaled CMIP5 ensemble projections for the future period suggest only a slight delay in the rainy season end, with no consistent trends in onset timing. Instead, interannual variability and ensemble spread remain the dominant influences. Our findings emphasize the need for careful calibration of metrics across diverse climate scenarios and different locations to ensure their reliability for agricultural planning, policymaking, and climate adaptation strategies. By providing a novel framework for evaluating rainfall metrics, this study offers a scalable approach that can be readily applied to other semi-arid regions.

Future Scenarios of Global Fisheries and Ocean Alkalinity Enhancement Under Socio‐Economic and Climate Pathways

Abstract Achieving global climate goals while ensuring food security in a changing climate presents significant challenges, particularly when relying solely on land‐based solutions. Covering over 70% of the Earth's surface, the ocean remains an underutilized resource for climate mitigation. Ocean alkalinity enhancement (OAE) is one such strategy, designed to strengthen the ocean's natural carbon sink, reduce atmospheric CO2, and mitigate ocean acidification. However, its implications for fisheries, critical for food security and livelihoods, remain uncertain. This study examines the interplay between global fisheries, OAE, and different future socioeconomic and climatic conditions, using the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways framework. We explore how global fisheries and OAE could evolve under three combined scenarios: SSP1‐2.6 (sustainability‐focused), SSP3‐7.0 (regional rivalry), and SSP5‐8.5 (high fossil fuel dependency). By integrating ecological, economic, societal, and technological perspectives, we develop scenario narratives and quantify key bio‐economic parameters, including technological progress, fishing costs, fisheries management, marine aquaculture, and ecosystem carrying capacity. High‐emission (SSP5‐8.5) and fragmented development (SSP3‐7.0) scenarios present significant barriers to the coexistence of OAE and fisheries, whereas sustainability‐focused pathways (SSP1‐2.6) offer the most favorable conditions for their alignment. Successfully integrating OAE with fisheries management will likely depend on technological advancements, international cooperation, and socio‐economic developments. These scenarios are aligned with those used in model‐based scenario studies conducted under the frameworks of the Intergovernmental Panel on Climate Change and the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), providing a shared foundation for future work.

Impact assessment of climate change and adaptation measures through different RCPs scenarios using CERES-Wheat model for wheat yield under different agroclimatic zones of Punjab, India

The Climate change impact under Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) was studied in Punjab state conditions. The study area includes three locations for which the bias corrected temperature and rainfall meteorological data for Ensemble model was collected and used as input in calibrated and validated DSSAT CERES-Wheat model. The model run for the assessment of yield for two wheat cultivars (HD 2967 and PBW 725) using deviation from the baseline period (2010-2021) for a 70 years (2025-2095) time period in near (2025-2055) and far (2066-2095) future scenarios. The current dates of sowing observed a yield decline at different agroclimatic zones for near and far future under four scenarios as agroclimatic zone II Ballowal Saunkhri (4-37%and 0.6- 35%) agroclimatic zone III Amritsar (0.4-32% and 0.3 to 38%) and Ludhiana 0.65-32% and 0.32-38%). The major decline in yield was indicated under high emission scenario i.e. RCP 8.5 during the far future whereas RCP 2.6 indicated low decline in wheat yield during both near and far future. The declination in yield at different locations indicated a requirement of optimized sowing window under different future scenarios. The results for optimized sowing window showed that adjusting in the sowing dates of all the three locations at last week of November for both the wheat cultivars to get maximum yield.

Climate Change Impact on Human-Rodent Interfaces: Modeling Junin Virus Reservoir Shifts.

The drylands vesper mouse (Calomys musculinus) is the primary host for Junin mammarenavirus (JUNV), the etiological agent of Argentine hemorrhagic fever in humans. We assessed the potential distribution of C. musculinus and identified disease transmission hotspots under current climatic conditions and projected future scenarios, including severe (Representative Concentration Pathway 8.5) and intermediate (Representative Concentration Pathway 4.5) climate change scenarios in 2050 and 2070. Utilizing tree-based machine learning algorithms, we modeled C. musculinus distribution by incorporating bioclimatic and landscape predictors. The model showed strong performance, achieving F-scores between 80.22 and 83.09%. Key predictors indicated that C. musculinus prefers warm temperatures, moderate annual precipitation, low precipitation variability, and low pasture coverage. Under the severe climate change scenario, suitable areas for the rodent and hotspots for potential disease decreased. The intermediate scenario showed an expansion in C. musculinus distribution alongside increased potential hotspot zones. Despite the complexity of ecological systems and the limitations of the model, our findings offer a framework for preventive measures and ecological studies in regions prone to the expansion of C. musculinus and in hotspots for disease transmission driven by climate change.

Influences of ocean warming on sperm viability and oxidative status of the Brazilian reef-building coral Mussismilia braziliensis (Verrill, 1868).

Influences of ocean warming on sperm viability and oxidative status of the Brazilian reef-building coral Mussismilia braziliensis (Verrill, 1868).

Dependence of the Abundance of Reed Glass-Winged Cicadas (Pentastiridius leporinus (Linnaeus, 1761)) on Weather and Climate in the Upper Rhine Valley, Southwest Germany

The planthopper Pentastiridius leporinus, commonly called reed glass-winged cicada, transmits the pathogens “Candidatus Arsenophonus phytopathogenicus” and “Candidatus Phytoplasma solani”, which are infesting sugar beet and, most recently, also potato in the Upper Rhine valley area of Germany. They cause the “Syndrome Basses Richesses” associated with reduced yield and sugar content in sugar beet, leading to substantial monetary losses to farmers in the region. No effective solutions exist currently. This study uses statistical models to understand to what extent the abundance of cicadas depends on climate regions during the vegetation period (April–October). We further investigated what influence temperature and precipitation have on the abundance of the cicadas in sugar beet fields. Furthermore, we investigated the possible impacts of future climate on cicada abundance. Also, 22 °C and 8 mm/day were found to be the optimal temperature and precipitation conditions for peak male cicada flight activity, while 28 °C and 8 mm/day were the optimum for females. By the end of the 21st century, daily male cicada abundance is projected to increase significantly under the worst-case high greenhouse gas emission scenario RCP8.5 (RCP-Representative Concentration Pathways), with confidence intervals suggesting a possible 5–15-fold increase compared to current levels. In contrast, under the low-emission scenario RCP2.6, male cicada populations are projected to be 60–70% lower than RCP8.5. An understanding of the influence of changing temperature and precipitation conditions is crucial for predicting the spread of this pest to different regions of Germany and other European countries.

Effects of projected wave climate changes on the sizing and performance of OWCs: a focus on the Atlantic North African and European coastal waters

Accurate estimates of the annual energy production achievable by a given wave energy converter are essential for a robust assessment of the associated levelized cost of energy, a key factor in investment decision-making. Inaccurate productivity estimates can arise - among other factors - from uncertainties in evaluating the available wave energy resource. The Climate Data Store of the Copernicus Climate Change Service delivers projections of the wave climate along the 20 m bathymetric contours of the whole European coastlines, covering the period 2040-2100, under two Representative Concentration Pathway scenarios(RCP4.5 and RCP8.5). This work addresses the effect of such long-term wave climate changes on the optimal sizing and performances of an Oscillating Water Column wave energy converter intended for installation along the North African and European Atlantic coastline. The capture width ratio of the device under different wave conditions is computed using an empirical model capable of predicting the device performance with acceptable accuracy and limited computational time. The results show that the optimal geometry of the OWC varies significantly in the different geographical locations and that the long-term changes in the wave energy resource could cause a slight modification of the optimal geometry in each potential installation site.

Southeast Asia’s Extreme Precipitation Response to Solar Radiation Management with GLENS Simulations

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days (R20mm), maximum 5-day precipitation (Rx5day), consecutive dry days (CDD), and consecutive wet days (CWD)—relative to historical (1980–2009) and Representative Concentration Pathway 8.5 (RCP8.5) baselines. The results reveal that SRM induces highly heterogeneous precipitation responses across the region. While SRM increases rainfall frequency in parts of Indonesia, it reduces the number of wet days and lengthens dry spells over Vietnam, Thailand, and the Philippines. Spatial variations are also observed in changes to heavy precipitation days and multi-day rainfall events, with potential implications for flood and drought risks. These findings highlight the complex trade-offs in hydrological responses under SRM deployment, with important considerations for agriculture, water resource management, and climate adaptation strategies in Southeast Asia.

Predicting Nitrous Oxide Emissions from China’s Upland Fields Under Climate Change Scenarios with Machine Learning

Upland fields are a significant source of N2O emissions. Thus, an accurate estimation of these emissions is essential. This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N2O emissions from Chinese upland fields. The upland crops considered in this study covered food crops, oil crops, cash crops, sugar crops, fruits, and vegetables, excluding flooded rice. Comparative analysis revealed that the RF algorithm performed the best, with the highest R2 at 0.66 and the lowest root mean square error at 0.008 kg N2O ha−1 day−1. The application rate of mineral nitrogen fertilizers, mean temperature during the growing season, and soil organic carbon content were the key driving factors in the N2O emission model. Utilizing the RF model, total N2O emissions from Chinese upland fields in 2020 were estimated at 183 Gg. Future projections under Representative Concentration Pathway (RCP) scenarios indicated a 2.80–5.92% increase in national N2O emissions by 2050 compared to 2020. The scenario analysis demonstrated that the proposed nitrogen reduction strategies fail to counteract climate-driven emission amplification. Under the combined scenarios of RCP8.5 and nitrogen reduction strategies, a net 4% increase in national N2O emissions was projected, highlighting the complex interplay between anthropogenic interventions and climate feedback mechanisms. This study proposes that future attention should be paid to the development of nitrogen optimization strategies under the impact of climate change.

Biological control under climate change: Distribution patterns of the South American fruit fly, Anastrepha fraterculus and two of its parasitoids in the Americas.

Climate change affects the distribution of insects, such as pests and parasitoids. Species Distribution Models (SDMs) have been developed to determine distribution patterns and risk areas for pests and biological control agents under different climate change scenarios. The South American fruit fly, Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae), is an important pest of cultivated fruits throughout the Americas that can be controlled by natural enemies, such as the native parasitoid Doryctobracon areolatus (Szépligeti) (Hymenoptera: Braconidae) and the introduced parasitoid Diachasmimorpha longicaudata (Ashmead) (Hymenoptera: Braconidae). However, the control efficacy and parasitism performance of these organisms could be affected by changing environmental conditions. SDMs were conducted using Random Forest to predict suitable areas for the establishment of A. fraterculus, D. areolatus, and D. longicaudata under different climate scenarios or Representative Concentration Pathways (SSPs) (SSP 2-4.5 and 5-8.8) in two different periods (2021-2040 and 2041-2060). Our results predicted an increase in suitable areas for A. fraterculus in the Americas, especially in some South American countries such as Colombia and Brazil. Moreover, the projected distribution of these species is intricately linked to the regional climatic patterns. Temperate and tropical areas were more suitable for the establishment of A. fraterculus; D. areolatus was better suited to temperate climates; while tropical climates were more suitable for D. longicaudata. Suitable areas for the establishment of both parasitoid species were predicted to increase in future climate scenarios, with D. longicaudata having a greater geographical expansion than D. areolatus. These parasitoids could be used as biocontrol agents in almost all areas suitable for the establishment of A. fraterculus.

European Temperature Extremes Under Different AMOC Scenarios in the Community Earth System Model

Abstract Recent simulations using the Community Earth System Model (CESM) indicate that a tipping event of the Atlantic Meridional Overturning Circulation (AMOC) would cause Europe to cool by several degrees. This AMOC tipping event was found under constant pre‐industrial greenhouse gas forcing, while global warming likely limits this AMOC‐induced cooling response. Here, we quantify the European temperature responses under different AMOC regimes and climate change scenarios. A strongly reduced AMOC state and intermediate global warming (C, Representative Concentration Pathway 4.5) has a profound cooling effect on Northwestern Europe with more intense cold extremes. The largest temperature responses are found during the winter months and these responses are strongly influenced by the North Atlantic sea‐ice extent. Enhanced North Atlantic storm track activity under an AMOC collapse results in substantially larger day‐to‐day temperature fluctuations. We conclude that the (far) future European temperatures are dependent on both the AMOC strength and the emission scenario.

Does climate change influence the spread of malaria in Benin? Insights from ecological niche modeling for surveillance efforts.

Malaria is a severe and endemic disease, remaining one of the most prevalent tropical illnesses and a leading cause of death among children aged <5 y. Anopheles gambiae, the primary vector of malaria in Benin, plays a critical role in its transmission. This study aims to contribute to the health protection of populations in Benin by assessing the risk of vector-borne diseases, particularly malaria, in the context of climate change. Using the Maxent algorithm for ecological niche modeling, we mapped the distribution of A. gambiae, a highly effective vector of Plasmodium parasites. Our findings revealed that high-risk areas for malaria cover nearly all departments of Benin, with the majority of southern departments-Mono, Littoral, Couffo, Ouémé, Plateau and Zou-identified as high-risk zones. Projections for 2055 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 climate scenarios indicate a significant expansion of high-risk areas, extending to Collines and parts of Donga, Borgou and Atacora. Climate change is expected to exacerbate the spread of A. gambiae, increasing the disease risk across the country. These results are crucial for guiding policymakers in Benin to mitigate the current impact of malaria and implement preventative measures to address future risks.

Assessing the impact of climate change on solar energy production in Italy

This study assesses climate change’s impact on solar energy production in Italy until 2100, focusing on solar radiation, temperature, and photovoltaic (PV) energy production through capacity factor. Regional climate models (RCMs) coming from the European branch of the Coordinated Regional Climate Downscaling Experiment (Euro-CORDEX), which incorporate time-evolving aerosols, are utilized for accurate future solar radiation trend estimations under various scenarios of greenhouse gas concentration evolution—the so-called Representative Concentration Pathway (RCP). Bias correction, employing the third edition of the Surface Solar Radiation Data Set—Heliosat (SARAH-3) and the MEteorological Reanalysis Italian DAtaset (MERIDA) for temperature data, enhances the capacity factor accuracy. Solar radiation exhibits a slight decline under the most optimistic emission scenario RCP 2.6, but a significant increase under other RCPs, particularly in central Italy’s mountains, with the Alps showing an opposite trend, especially under RCP 8.5. The temperature is projected to rise, particularly under RCP 4.5 and RCP 8.5, potentially affecting production efficiency and snow cover in the Alps. The decrease in snow cover may affect the diffuse component of solar radiation with a subsequent decrease predicted by Euro-CORDEX RCMs. Trend analysis reveals significant PV production decreases under RCP 8.5, especially in the Alps, due to reduced solar radiation. Despite the increase in solar radiation, most of Italy experiences decreased PV production due to rising temperatures, potentially reducing solar panel efficiency. RCP 4.5 and RCP 2.6 scenarios exhibit less pronounced capacity factor decreases, with RCP 2.6 showing the lowest climate signal magnitude. Seasonal cycle analysis reveals variations primarily linked to changes in solar radiation throughout the year. RCP 8.5 shows significant winter production decreases, followed by slight summer increases dampened by rising temperatures. RCP 4.5 exhibits similar characteristics, with a milder winter decrease and stable production in other months, while RCP 2.6 shows a slight spring increase and generally stable production throughout the year.