Shared Socioeconomic Pathways Research Articles

Global Future Climate Signal by Latitudes Using CMIP6 GCMs

AbstractThis study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes.

The Advancement in Spring Vegetation Phenology in the Northern Hemisphere Will Reverse After 2060 Under Future Moderate Warming Scenarios

AbstractGlobal warming has largely advanced spring vegetation phenology, which has subsequently affected terrestrial carbon and water cycles. However, further shifts in vegetation phenology under future climate change remain unclear. We estimated the start of the growing season (SOS) by applying multiple extraction methods based on the NDVI3g data set, and then parameterized and evaluated 11 spring vegetation phenology models that included chilling, forcing, and the photoperiod. Based on scenario data from three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) derived from eight climate models, future vegetation phenology was predicted using the phenology models. Results showed that all the phenology models performed better than the NULL model (mean of the SOS), with the performance of one‐phase models broadly matching that of two‐phase models, although the best models varied by vegetation type. The spatial pattern of simulated SOS was similar among the models, and it explained >75% of the variation. Based on the mean predicted SOS, we found that spring vegetation phenology will continue to advance under strong warming conditions (SSP245 and SSP585), but that the trend of advance will reverse at around 2060 under the SSP126 scenario. The continued trend in SOS advance is likely related to rapid forcing fulfillment under stronger warming conditions. However, under moderate warming, chilling might be reduced and it might require longer to compensate for higher forcing, which ultimately would result in SOS delay. Our findings highlight that trends will likely change under different warming conditions, potentially causing widespread impact on species interaction, biodiversity, and ecosystem function.

Future land use simulation model-based landscape ecological risk prediction under the localized shared socioeconomic pathways in the Xiangjiang River Basin, China.

Landscape ecological risk (LER) is an effective index to identify regional ecological risk and measure regional ecological security. The localized shared socioeconomic pathways (LSSPs) can provide multi-scenario parameters of social and economic development for LER research. The research of LER under LSSPs is of scientific significance and practical value in curbing the breeding and spread of LER risk areas. In this study, land-cover raster files from 2010 to 2020 were used as the foundational data. Future land use simulation (FLUS), regression, and Markov chain models were used to predict the land cover patterns under the five LSSP scenarios in the Xiangjiang River Basin (XJRB) in 2030. Thus, an evaluation model was established, and the LER of the watershed was evaluated. We found that the rate of land cover change (LCC) in the XJRB between 2010 and 2020 had a higher intensity (increasing at an average of 18.89% per decade) than that projected under the LSSPs for 2020-2030 (averaging an increase of 8.58% per decade). Among the growth rates of all land use types in the XJRB, that of urban land was the highest (33.3%). From 2010 to 2030, the LER in the XJRB was classified as lower risk (33.73%), lowest risk (33.11%), and moderate risk (24.13%) for each decade. Finally, the LER exhibited significant heterogeneity among different scenarios. Specifically, the percentages of regions characterized by the highest (9.77%) and higher LER (9.75%) were notably higher than those in the remaining scenarios. The higher-level risk area under the localized SSP1 demonstrated a clear spatial reduction compared to those of the other four scenarios. In addition, in order to facilitate the differential management and control of LER by relevant departments, risk zoning was carried out at the county level according to the prediction results of LER. And we got three types of risk management regions for the XJRB under the LSSPs.

Increased population exposures to extreme precipitation in Central Asia under 1.5℃ and 2℃ global warming scenarios

The increase in extreme precipitation (EP) may pose a serious threat to the health and safety of population in arid and semi-arid regions. The current research on the impact of EP on population in Central Asia (CA) is insufficient and there is an urgent need for a comprehensive assessment. Hence, we opted for precipitation and temperature data under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) from ten Global Climate Models (GCMs), which were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). Simultaneously, integrating population data in 2020 and 2050 (SSP2 and SSP5), we investigated the future changes in EP and population exposure in CA under 1.5°C and 2°C global warming scenarios (GWSs). Our analysis indicates that EP in CA is projected to increase with global warming. Under the SSP5-8.5, the maximum daily precipitation (Rx1day) exhibits an average response rate to global warming of 3.58 %/K (1.99 %/K – 4.06 %/K). With rising temperatures, an increasing number of areas and populations in CA will be impacted by EP, especially in the Fergana valley. Approximately 25% of the population (land area) in CA is exposed to Rx1day with increases of more than 8.31% (9.32%) under 1.5°C GWS and 14.18% (13.25%) under 2°C GWS. Controlling temperature rise can be effective in reducing population exposures to EP. For instance, limiting the temperature increase to 1.5°C instead of 2°C results in a 2.79% (1.75%–4.59%) reduction in population exposure to Rx1day. Finally, we found that climate change serves as the predominant factor influencing the population exposure to EP, while the role of population redistribution, although relatively minor, should not be disregarded. Particularly for prolonged drought, the role of population redistribution manifests negatively.

Bio‐ORACLE v3.0. Pushing marine data layers to the CMIP6 Earth System Models of climate change research

AbstractMotivationImpacts of climate change on marine biodiversity are often projected with species distribution modelling using standardized data layers representing physical, chemical and biological conditions of the global ocean. Yet, the available data layers (1) have not been updated to incorporate data of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), which comprise the Shared Socioeconomic Pathway (SSP) scenarios; (2) consider a limited number of Earth System Models (ESMs), and (3) miss important variables expected to influence future biodiversity distributions. These limitations might undermine biodiversity impact assessments, by failing to integrate them within the context of the most up‐to‐date climate change projections, raising the uncertainty in estimates and misinterpreting the exposure of biodiversity to extreme conditions. Here, we provide a significant update of Bio‐ORACLE, extending biologically relevant data layers from present‐day conditions to the end of the 21st century Shared Socioeconomic Pathway scenarios based on a multi‐model ensemble with data from CMIP6. Alongside, we provide R and Python packages for seamless integration in modelling workflows. The data layers aim to enhance the understanding of the potential impacts of climate change on biodiversity and to support well‐informed research, conservation and management.Main Types of Variable ContainedSurface and benthic layers for, chlorophyll‐a, diffuse attenuation coefficient, dissolved iron, dissolved oxygen, nitrate, ocean temperature, pH, phosphate, photosynthetic active radiation, total phytoplankton, total cloud fraction, salinity, silicate, sea‐water direction, sea‐water velocity, topographic slope, topographic aspect, terrain ruggedness index, topographic position index and bathymetry, and surface layers for air temperature, mixed layer depth, sea‐ice cover and sea‐ice thickness.Spatial Location and GrainGlobal at 0.05° resolution.Time Period and GrainDecadal from present‐day to the end of the 21st century (2000–2100).Major Taxa and Level of MeasurementMarine biodiversity associated with surface and epibenthic habitats.Software FormatA package of functions developed for Python and R software.

Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models

Study regionGanjiang River catchment, China. Study focusQuantifying the effects of the impacts climate change on streamflow is of great importance for regional water resources management. In this study, four LSTM-based models, i.e., LSTM, Stack-LSTM, Bi-LSTM and CNN-LSTM, were constructed to assess hydrological changes under future climate change based on the bias-corrected Coupled Model Intercomparison Project Phase 6 meteorological data from four shared socioeconomic pathways (SSPs). New hydrological insights for the study region(1) The Bi-LSTM achieves the best streamflow simulation performance during the validation period of 2006–2016, followed by the LSTM, Satck-LSTM and CNN-LSTM. The climate will get warmer and wetter in future scenarios with mean annual precipitation and temperature increasing by 3.0–6.2% and 8.3–13.4% (compared to baseline period), respectively. (2) The predicted streamflow will have a decrease of 1.5–16.5% during 2026–2075 under all SSP scenarios. The results of LSTM, Stack-LSTM, and Bi-LSTM models show a decrease of 0.27–50% in November to March, indicating that the dry season will become even drier in the future. These results reveal that the hydrological regime of the Ganjiang River is likely to change and will be characterized by greater seasonal uncertainty and more potential for extreme events due to significant warming and wetting over the future periods.

Dynamics of mitigation measures in urbanizing Africa: Challenges and opportunities for future climate change

Africa stands at a critical crossroads, grappling with the complex interplay between global climate targets and urbanization dynamics. The rapid growth of urban populations coupled with unsustainable urban planning practices exacerbates inherent conflicts, impeding our collective efforts to mitigate carbon emissions. In this context, we systematically decompose the key demographic, economic, and technological drivers of emissions, evaluating their contributions and spatial variations within African city boundaries from 2000 to 2020. By integrating three shared socioeconomic pathways (i.e., SSP119, SSP245, SSP434) and two urbanization strategies (i.e., linear and urbanization-led), we aim to assess the disparities in carbon mitigation outcomes that correspond to varying urban development trajectories by the year 2060, all while adhering to the same climate targets. Our findings reveal that the pursuit of urbanization may result in a substantial reduction in carbon mitigation efforts, with potential reductions ranging from 34% to a staggering 95%. In the "Middle Road" scenario (SSP434), we posit that land productivity must witness an increase of 26%–46% to attain mitigation outcomes comparable to those achieved through a linear trajectory. This implies that the concurrent efforts to urbanize and mitigate carbon emissions may narrow disparities among cities, limiting diversity of existing mitigation measures. The proactive planning of sustainable urban clusters and the provision of localized subsidies for clean energy are imperative to fortify urban climate resilience.

Sediment records and multi-media transfer and fate of polycyclic aromatic hydrocarbons in Dianchi Lake over the past 100 years

This study analyzed the fate and transfer of PAHs through multiple media in Dianchi Lake and the sediment records of polycyclic aromatic hydrocarbons (PAHs) in sediments. The model simulation results showed the simulated concentrations to be in good agreement with the measured values. The concentration and total amount of PAHs were the highest in the sediment, which is an important sink for PAHs in lake systems. The main transport direction of the PAHs in the three phases was from the atmosphere to the water to the sediment. Advection and emission inputs were the primary input pathways for PAHs in the atmosphere, and advection output was the main removal path. The main input sources pathways for PAHs in sediments was the sedimentation of particulate matter in water, with diffusion and degradation being the primary methods of removal. Advection and atmospheric dry and wet deposition were the main sources of PAHs in water, with particulate deposition being the primary removal process. The range of PAHs was 336–3520 ng/g in sediments in Dianchi Lake, with an average of 1569 ng/g, peaking in 2012. Among the predicted future PAH concentrations in the sediments under the five shared socioeconomic pathways (SSPs), the lowest PAH concentration was found under SSP3 from 2025 to 2035, whereas the lowest PAH concentration was found under SSP4 from 2035 to 2050. In the sustainable path of SSP1, although the concentration of PAH pollutants showed an increasing trend in the short term, the increase in PAHs concentration in sediments slowed in the long run.

Land suitability analysis for turmeric crop for humid tropical Kerala, India, under current and future climate scenarios using advanced geospatial techniques.

Turmeric cultivation primarily thrives in India, followed by Bangladesh, Cambodia, Thailand, China, Malaysia, Indonesia and the Philippines. India leads globally in both area and production of turmeric. Despite this, there is a recognized gap in research regarding the impact of climate change on site suitability of turmeric. The primary objective of the present study was to evaluate both the present and future suitability of turmeric cultivation within the humid tropical region of Kerala, India, by employing advanced geospatial techniques. The research utilized meteorological data from the Indian Meteorological Department for the period of 1986-2020 as historical data and projected future data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Four climatic scenarios of shared socioeconomic pathway (SSP) from the Intergovernmental Panel on Climate Change AR6 model of MIROC6 for the year 2050 (SSP 1-2.6, SSP 2-4.5, SSP 3-7.0 and SSP 5-8.5) were used. The results showed that suitable area for turmeric cultivation is declining in future scenario and this decline can be primarily attributed to fluctuations in temperature and an anticipated increase in rainfall in the year 2050. Notable changes in the spatial distribution of suitable areas over time were observed through the application of geographic information system (GIS) techniques. Importantly, as per the suitability criteria provided by ICAR-National Bureau of Soil Survey and Land Use Planning (ICAR-NBSS & LUP), all the districts in Kerala exhibited moderately suitable conditions for turmeric cultivation. With the GIS tools, the study identified highly suitable, moderately suitable, marginally suitable and not suitable areas of turmeric cultivation in Kerala. Presently 28% of area falls under highly suitable, 41% of area falls under moderately suitable and 11% falls under not suitable for turmeric cultivation. However, considering the projected scenarios for 2050 under the SSP framework, there will be a significant decrease in highly suitable area by 19% under SSP 5-8.5. This reduction in area will have an impact on the productivity of the crop as a result of changes in temperature and rainfall patterns. The outcome of the present research suggests that the state of Kerala needs to implement suitable climate change adaptation and management strategies for sustaining the turmeric cultivation. Additionally, the present study includes a discussion on potential management strategies to address the challenges posed by changing climatic conditions for optimizing turmeric production in the region. © 2024 Society of Chemical Industry.

Predicting environmental impacts of smallholder wheat production by coupling life cycle assessment and machine learning

Wheat grain production is a vital component of the food supply produced by smallholder farms but faces significant threats from climate change. This study evaluated eight environmental impacts of wheat production using life cycle assessment based on survey data from 274 households, then built random forest models with 21 input features to contrast the environmental responses of different farming practices across three shared socioeconomic pathways (SSPs), spanning from 2024 to 2100. The results indicate significant environmental repercussions. Compared to the baseline period of 2018–2020, a similar upward trend in environmental impacts is observed, showing an average annual growth rate of 5.88 % (ranging from 0.45 to 18.56 %) under the sustainable pathway (SSP119) scenario; 5.90 % (ranging from 1.00 to 18.15 %) for the intermediate development pathway (SSP245); and 6.22 % (ranging from 1.16 to 17.74 %) under the rapid economic development pathway (SSP585). Variation in rainfall is identified as the primary driving factor of the increased environmental impacts, whereas its relationship with rising temperatures is not significant. The results suggest adopting farming practices as a vital strategy for smallholder farms to mitigate climate change impacts. Emphasizing appropriate fertilizer application and straw recycling can significantly reduce the environmental footprint of wheat production. Standardized fertilization could reduce the environmental impact index by 11.10 to 47.83 %, while straw recycling might decrease respiratory inorganics and photochemical oxidant formation potential by over 40 %. Combined, these approaches could lower the impact index by 12.31 to 63.38 %. The findings highlight the importance of adopting enhanced farming practices within smallholder farming systems in the context of climate change. Spotlights•China's smallholder wheat farming is increasingly exposed to climate threats, risking resource depletion and pollution•Increasing eco-impacts of smallholder wheat farming could be mitigated by regulated fertilizer use and straw recycling•Life cycle assessment combined with machine learning targets future environmental impacts in dynamic climate scenarios•The study offers actionable policy advice for sustainable smallholder wheat farming practices•Future work may probe machine learning's role in forecasting smallholder production behaviors and farming decision aid

Efficiency analysis of best management practices under climate change conditions in the So-okcheon watershed, South Korea

Best management practices (BMPs) are widely applied to address water quality degradation issues attributed to non-point source pollutants. The objective of this study was to assess the efficiency of two types of BMPs, vegetation filter strips (VFS) and wetlands, in reducing total nitrogen (TN) and total phosphorus (TP) in a watershed, and to investigate whether the efficiency of the two BMPs is maintained under future climate scenarios by employing the Soil and Water Assessment Tool (SWAT). A set of parameters was calibrated using the SWAT Calibration and Uncertainty Program (SWAT-CUP) to ensure acceptable simulation results. Three BMP scenarios were developed by applying VFS and wetlands individually and in combination. Projected climate data from 18 global circulation models under Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were used to depict climate change conditions. VFS demonstrated 18.2% and 22.9% greater reduction efficiency for TN and TP, respectively, relative to wetlands. When the two BMPs were simultaneously applied, the reduction efficiency was even greater than that of single-BMP implementation (by 7.4% for TN and 6.8% for TP compared to VFS alone and by 25.5% for TN and 29.7% for TP compared to wetlands alone). To assess the effect of climate change, the model simulated results for the period of 2021–2,100. The differences in efficiency between the combined BMP scenario and the individual BMPs increased with greater intensity of climate change, especially in the distant future. Therefore, this study supports the effectiveness of nutrient pollution control by applying multiple BMPs rather than by applying individual BMPs. Furthermore, this research underscores the adaptability and reliability of natural-based solutions in mitigating non-point source pollution in a changing climate, which is essential for effective ecological restoration in complex urban-agricultural landscapes. The study provides valuable insights for watershed managers and policymakers seeking effective strategies to combat nutrient pollution in the face of a changing climate within the unique landscape of South Korea.

Climate change impact on small mammals from two Neotropical hotspots

Abstract Current climate change represents the unprecedented change in weather patterns across global and regional scales over a short period due to human activity. It is expected to drive changes in species distributions across the globe. Our goal here was to analyse (i) how climate change can impact the future distribution of species in two Neotropical hotspots, and (ii) how future distribution can impact the threatened status of species. We used ecological niche modelling to estimate suitable areas in the present and four future climate change scenarios (SSPs) for 40 species of nonvolant small mammals endemic to the Atlantic Forest and Cerrado biomes of South America. We also inferred species’ future threat levels according to the IUCN’s A3 criteria using future relative area estimates. We found that species will gradually lose more area in all future scenarios, from the most optimistic Shared Socio-economic Pathway (SSP1-2.6) to the most pessimistic (SSP5-8.5) scenario. Species endemic to the Cerrado will see a greater area reduction than species endemic to the Atlantic Forest. Consequently, the Cerrado will have more threatened species than the Atlantic Forest. Species that occupy both biomes will lose proportionally less area than those endemic to the Cerrado but more area than those endemic to the Atlantic Forest. In conclusion, small mammal species will, in general, be negatively impacted by climate change. However, the degree of impact depends on the trajectory of greenhouse gas concentrations and where the species live.

Effect of climate change on soil erosion rate in a tropical Indian catchment

Abstract This study analyzes the variations in soil erosion rate in a tropical catchment in India using the empirical Revised Universal Soil Loss Equation (RUSLE) model integrated with climate change scenarios from an ensemble of global climate models (GCMs) included in the NEX Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 dataset for Shared Socioeconomic Pathways (SSP) 126 and SSP 585. A set of seven GCMs are initially selected. Based on their ability to simulate the rainfall for the current scenario, the PROMETHEE-II method is used to rank the GCMs and the top four best-performing models are used for further analysis. Soil erosion rates projected for the future climate scenarios are compared with the current scenario. In the near future and in the middle of the century, soil erosion rates under the SSP 126 scenario are projected to be higher than that under the SSP 585 scenario. The ensemble average soil erosion rate is projected to increase by 15.41%–25.94% toward the end of the century for different emission scenarios, and the areas susceptible to high and very high soil erosion rates are projected to increase to 40.3%.

Climate change causes spatial shifts in the productivity of agricultural long-term field experiments

Long-term field experiments (LTE) are highly valuable infrastructures in agricultural- and soil sciences for understanding the long-term impacts of climate and management practices. While they are designed to run under constant conditions, climate change is expected to affect site conditions considerably. This needs to be quantified when interpreting experimental results and when redesigning the experimental setup. One way to achieve this is by utilizing vegetation growth and carbon dynamics, specifically the Net Primary Productivity (NPP), as a spatially explicit indicator. NPP facilitates the assessment and interpretation of yield performance in LTEs under future climatic conditions. Our study estimated the changes in NPP for 271 LTE sites in Germany, comparing a baseline (2000–2020) with two scenarios (2081–2100) that were based on the Shared Socioeconomic Pathways (SSPs) (SSP245) and SSP585) by the Intergovernmental Panel on Climate Change (IPCC). We used the NASA-CASA biogeochemical model to calculate NPP in baseline and IPCC scenarios using Germany as a test case. LTEs were grouped by land use (crop types) and soil information (soil type, texture), drawing on the geodata infrastructure “BonaRes Repository”. The total annual terrestrial NPP for the baseline was calculated as 202.4 Mt C (sum of forests, grasslands, and arable lands) in Germany, while total NPP was up to 56.0 Mt C for different land use types. For both scenarios, NPP was projected to increase in LTEs located in southern Germany, indicating increased crop productivity, while a decrease was projected for the central Germany. The decrease in NPP of numerous LTEs in central Germany was estimated to extend to the LTEs in the eastern part corresponding to the worst-case scenario SSP585. Explicitly, the use of the multi-model ensemble mean as the climate driver in modelling may overestimate projected NPP by reducing inter-annual variability, highlighting the importance of methodological choices for accurate future projections. Besides, the results indicated that poor soils are projected to experience a further decline in productivity, primarily attributed to escalating water scarcity. Conversely, soils with high quality are likely to witness enhanced productivity, largely driven by the extension of the growing seasons. The outcomes of this study provide a basis for considering the future conditions of German LTEs and facilitate distinguishing between the effects of climate change and the impact of agricultural management on productivity at the regional level. These outputs enable planning and developing research strategies for selecting future LTE sites and redesigning existing or newly planned experiments. Moreover, the integrated modelling framework presented here highlights the potential of LTE data for large-scale modelling studies of ecosystem functions.

Suitable areas for temperate fruit trees in a Brazilian hotspot area: Changes driven by new IPCC scenarios

Chilling accumulation and sufficient water availability are mandatory requirements for most temperate fruit trees (TFT) productivity. Given the strong dependence of these crops on air temperature and water availability, it is imperative to analyze the impacts of climate change on TFT, especially in areas considered persistent climate change hotspots. This study analyzed the impacts of climate change on suitable areas for planting TFT in a Brazilian state considered a hotspot for future climate changes - Minas Gerais, using an Agroclimatic Zoning (AZ) method, and outlined adaptation measures for the sustainable cultivation of the main TFT under future climate scenarios. Daily near-surface air temperature (minimum and maximum) and precipitation data were used from 10 Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), in two Shared Socioeconomic Pathways scenarios (SSP2–4.5 and SSP5–8.5). The AZ method was applied for the current climate simulations (1995–2014), as well as for projections by the near (2041–2060) and far future (2081 – 2100) climate conditions considering two base temperature values (Tb = 7ºC and 13ºC). Given intense and persistent increases in air temperature, water deficit, and reduced chilling accumulation, TFT plantations will be restricted and economically unfeasible for about 69% (SSP2.4–5) to 86% (SSP5.8–5) of Minas Gerais in the far future for both Tb values. Only ∼6% (near future) and ∼2% (far future) of the study area located in a small portion of the Southern region of Minas Gerais will be suitable for TFT plantations. However, the future of TFT plantations in places classified as regular will depend on adopting adaptation measures to reduce vulnerability to these crops, including low-chill genotypes selection, improvements in breeding programs, using strategies to break dormancy artificially, and intensifying irrigation with less water-demanding techniques.

Assessing Hydropower Potential under Shared Socioeconomic Pathways Scenarios Using Integrated Assessment Modelling

The world is facing a global sustainability crisis affecting environmental systems and society. Addressing these issues requires a multi-dimensional approach that can integrate energy, water, and environment Systems, as well as provide scientific policy advice. In this study, an updated version of an Integrated Assessment Model (IAM) was used, together with new data compatible with Shared Socioeconomic Pathways (SSPs) projections, to significantly improve the work developed before. SSP climate data (temperature, precipitation, and total radiative forcing) and socioeconomic data (population and GDP) were loaded into the IAM, together with different scenario parameters. By analyzing varying socioeconomic scenarios, mitigation efforts, and adaptation strategies, this study assesses their impact on primary energy demand and, consequently, their impact on hydropower potential production. Our results show diverse energy paths, strongly dependent on the future scenario. Energy demand could increase up to 160%; however, several projections foresee a decline in hydropower production to minus 46% due to both climate change and socioeconomic transformation. Our findings highlight the importance of considering a range of potential future scenarios in energy planning and policy development. The varied outcomes across the considered scenarios emphasize the need for flexibility in strategies to accommodate for uncertainties and address the challenges posed by divergent trajectories in hydropower use and renewable energy shares.

Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback

Abstract. The Greenland Ice Sheet is a key contributor to sea level rise. By melting, the ice sheet thins, inducing higher surface melt due to lower surface elevations, accelerating the melt coming from global warming. This process is called the melt–elevation feedback and can be considered by using two types of models: either (1) atmospheric models, which can represent the surface mass balance (SMB), or SMB estimates resulting from simpler models such as positive degree day models or (2) ice sheet models representing the surface elevation evolution. The latter ones do not represent the surface mass balance explicitly as well as polar-oriented climate models. A new coupling between the MAR (Modèle Atmosphérique Régional) regional climate model and the PISM (Parallel Ice Sheet Model) ice sheet model is presented here following the CESM2 (Community Earth System Model; SSP5-8.5, Shared Socioeconomic Pathway) scenario until 2100 at the MAR lateral boundaries. The coupling is extended to 2200 with a stabilised climate (+7 ∘C compared to 1961–1990) by randomly sampling the last 10 years of CESM2 to force MAR and reaches a sea level rise contribution of 64 cm. The fully coupled simulation is compared to a one-way experiment where surface topography remains fixed in MAR. However, the surface mass balance is corrected for the melt–elevation feedback when interpolated on the PISM grid by using surface mass balance vertical gradients as a function of local elevation variations (offline correction). This method is often used to represent the melt–elevation feedback and prevents a coupling which is too expensive in computation time. In the fully coupled MAR simulation, the ice sheet morphology evolution (changing slope and reducing the orographic barrier) induces changes in local atmospheric patterns. More specifically, wind regimes are modified, as well as temperature lapse rates, influencing the melt rate through modification of sensible heat fluxes at the ice sheet margins. We highlight mitigation of the melt lapse rate on the margins by modifying the surface morphology. The lapse rates considered by the offline correction are no longer valid at the ice sheet margins. If used (one-way simulation), this correction implies an overestimation of the sea level rise contribution of 2.5 %. The mitigation of the melt lapse rate on the margins can only be corrected by using a full coupling between an ice sheet model and an atmospheric model.

Current and future potential distribution of Culex (Melanoconion) (Diptera: Culicidae) of public health interest in the Neotropics.

Anthropogenic activities are altering ecosystem stability and climate worldwide, which is disturbing and shifting arbovirus vector distributions. Although the overall geographic range of some epidemiologically important species is recognized, the spatiotemporal variation for other species in the context of climate change remains poorly understood. Here we predict the current potential distribution of 9 species of Culex (Melanoconion) based on an ecological niche modeling (ENM) approach and assess spatiotemporal variation in future climate change in the Neotropics. The most important environmental predictors were the mean temperature of the warmest season (27 °C), precipitation during the driest month (50 mm), and precipitation during the warmest season (>200 mm). The best current model for each species was transferred to the future general circulation model IPSL-CM6A-LR, using 2 shared socioeconomic pathway scenarios (ssp1-2.6, ssp5-8.5). Under both scenarios of climatic change, an expansion of suitable areas can be observed followed by a strong reduction for the medium-long future under the worst scenario. The multivariate environmental similarity surface analysis indicated future novel climates outside the current range. However, none of the species would occur in those areas. Even if many challenges remain in improving methods for forecasting species responses to global climate change and arbovirus transmission, ENM has strong potential to be applied to the geographic characterization of these systems. Our study can be used for the monitoring of Culex (Melanoconion) species populations and their associated arboviruses, contributing to develop region-specific public health surveillance programs.

Assessing climate change impacts on public health in Haiti: a comprehensive study of disease distribution, modeling, and adaptation strategies

This study examines the relationship between climate change and public health in Haiti, a country already facing socioeconomic challenges. The well-being of Haiti’s vulnerable population is expected to be further affected by climate change, leading to an increase in vector-borne, water-borne, and heat-related diseases. As one of the most vulnerable countries to climate change effects, Haiti is currently experiencing an increase in vector-borne diseases such as malaria, dengue, and chikungunya, as well as water-borne diseases and emerging zoonotic outbreaks. This study aims to improve planning, decision-making, and responses to public health challenges by utilizing health data, climatic information, and impact models. The methodology involves the creation of a comprehensive climate and health database to uncover detailed spatial-temporal relationships on a national scale. By evaluating disease indicators from historical periods (1950-2014) and future projections (2015-2100) using the Shared Socio-Economic Pathways (SSPs) from the multi-model ensemble mean of the CMIP6 models, target diseases, including malaria, meningitis, dengue, and heat-sensitive chronic diseases are assessed. Our results highlight a decrease in rainfall and a strong increase in temperatures, especially within western Haiti under the extreme SSP585 scenario. The ability of the impact models to simulate the seasonality and spatial distribution of malaria incidence, dengue and heatwaves was performed. The analysis of risks related to climate-sensitive diseases’ climatic parameters shows that Haiti’s west and central regions are mostly exposed to vector-borne and water-borne diseases. Models predict a decrease in malaria cases due to climate change with hot temperatures and a decline in rainfall, while dengue transmission patterns may undergo changes. These findings will inform the implementation of context-specific early-warning systems and adaptation strategies for climate-sensitive diseases while acknowledging the challenges of integrating climate-altered data into health policies.

Climate Change Threatens the Habitat of Pinus massoniana in China

Pinus massoniana Lamb. is one of the main timber tree species. There is a large artificial planting area in South China, and this tree has important economic and ecological value. In this research, we built a comprehensive habitat suitability model based on 115 current data and 22 environmental variables to analyze the potential suitable habitat distribution of this species. Future climate change scenarios were defined as four shared socioeconomic pathways (SSPs): SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, SSP 5–8.5) and four periods (including 2021–2040, 2041–2060, 2061–2080, and 2081–2100) based on nine global circulation model datasets. To fully consider the potential distribution of P. massoniana under specific climate change conditions and soil conditions, we constructed an ensemble model using four commonly used model algorithms. The results indicated that the current suitable habitat for P. massoniana covers approximately 1.10 × 106 km2 in southeastern China. In the future, the model results showed that under different climate change scenarios and at different times, the change in suitable habitat for P. massoniana varied; moreover, under moderate climate change scenarios, the average temperature decreased by less than 3 °C and the suitable habitat area decreased slightly, with an area larger than 0.95 × 106 km2. However, under intense warming scenarios, for which the average temperature increased above 3 °C, the suitable habitat for P. massoniana decreased. In the most severe warming scenario, the suitable habitat area for P. massoniana was reduced to 44% of the base climate conditions with severe habitat fragmentation, which should be fully considered in future planting initiatives and plant protection.