Low Emission Scenarios Research Articles

Ticks jump in a warmer world: Global distribution shifts of main pathogenic ticks are associated with future climate change.

In recent decades, the threats of ticks and tick-borne diseases (TBDs) increased extensively with environmental change, urbanization, and rapidly changing interactions between human and animals. However, large-scale distribution of tick and TBD risks as well as their relationship with environmental change remain inadequately unclear. Here, we first proposed a "tick-pathogen-habitat-human" model to project the global potential distribution of main pathogenic ticks using a total of 70,714 occurrence records. Meanwhile, the effects of ecological factors and socio-economic factors driving the distribution pattern were evaluated. Based on this, the risk distribution of TBDs was projected by large-scale "tick-pathogen-disease" analysis. Furthermore, the distribution shifts of tick suitability were projected under different shared socio-economic pathways in the future. Our findings demonstrate that warm temperate countries (e.g., the United States, China and European countries) in the Northern Hemisphere represent significant high risk regions for ticks and TBDs. Specifically, solar radiation of January emerges as the main decisive factor determining the risk distribution pattern. Future shifts of tick suitability showed decrease trend under low greenhouse gas emission scenarios but increase trend under high scenarios. These suitability shifts were significantly correlated with future temperature- (9 species) and precipitation- (19 species) related factors. Collectively, in this study we first shaped the global risk distribution of main ticks and TBDs as well as tick suitability shifts correlated with future global climate change, which will provide helpful references for disease prevention and administration. The methods proposed here will also shed light on other emerging and recurrent zoonotic diseases (e.g., COVID-19, monkeypox) in the future.

Mixed responses among closely related Neotropical butterflies under extreme climate and land cover changes

Abstract Although the Neotropics harbour almost half of the world's butterfly species, there are few assessments regarding the potential future suitability of habitats for this important bioindicator in the region. Here, we test if butterfly species restricted to the Amazonia rainforest will be more affected by environmental change than closely related, more widespread species. We compiled 1149 individually checked observation records of three closely related species pairs with distinct distribution patterns (Amazonian‐restricted and widely distributed in the Neotropics). Using MaxEnt species distribution models, we projected the future habitat suitability (2050 and 2100) under low (SSP126) and high (SSP585) greenhouse gas emission scenarios and also considering reduced forested cover. Estimated models showed that in the low‐emission scenario, most species will potentially expand their suitable habitats and distributions by 3.7%–11.4% in 2100. Contrastingly, in the high‐emission scenario, most species will potentially lose habitat and distribution ranges by 9.6%–49.7% in 2100, regardless of their original range extent or phylogenetic relatedness. Only one widespread generalist species might benefit from this scenario in 2100, increasing its suitable habitat area by 30%. Both Amazonian range‐restricted and more widely distributed Neotropical butterflies may be equally negatively affected by global change in high‐emission scenarios. The fact that only one generalist species might expand its suitable habitat suggests a butterfly fauna homogenisation in the Neotropics. This prospect requires further testing to consider whether global change will affect not only restricted but generalist species as well.

Predicting changes in agricultural yields under climate change scenarios and their implications for global food security

Climate change has direct impacts on current and future agricultural productivity. Statistical meta-analysis models can be used to generate expectations of crop yield responses to climatic factors by pooling data from controlled experiments. However, methodological challenges in performing these meta-analyses, together with combined uncertainty from various sources, make it difficult to validate model results. We present updates to published estimates of crop yield responses to projected temperature, precipitation, and CO2 patterns and show that mixed effects models perform better than pooled OLS models on root mean squared error (RMSE) and explained deviance, despite the common usage of pooled OLS in previous meta-analyses. Based on our analysis, the use of pooled OLS may underestimate yield losses. We also use a block-bootstrapping approach to quantify uncertainty across multiple dimensions, including modeler choices, climate projections from the sixth Coupled Model Intercomparison Project (CMIP6), and emissions scenarios from Shared Socioeconomic Pathways (SSP). Our estimates show projected yield responses of − 22% (maize), − 9% (rice), − 15% (soy), and − 14% (wheat) from 2015 to 2080–2100 under the business-as-usual scenario of SSP5–8.5, which reduce to − 3.8%, − 2.7%, 1.4%, and − 1.5% respectively under the lower emissions scenario of SSP1–2.6. Without mitigation and adaptation, countries in South Asia, sub-Saharan Africa, North America, and Oceania could become at risk of being unable to meet national calorie demand by the end of the century under the most severe emissions scenario.

Reduced aerosols and intensified summertime rainfall in India during the pandemic suggest potentially more amplified precipitation in the future

Abstract Aerosol pollution is anticipated to decrease in the future, yet the associated effects of reduced aerosol loading on precipitation remain insufficiently explored. Widespread reductions in anthropogenic emission during COVID-19 lockdowns offer a unique opportunity to understand precipitation responses to changes in anthropogenic aerosols. Based on observations and regional and global climate-chemistry coupled model simulations, we attribute unprecedented precipitation in India during the 2021 lockdown to decreased aerosol levels due to emission reductions. Reduced aerosol loading leads to a northward shift of the subtropical westerly jet, which induces a westward movement of the subtropical southern branch trough and negative sea-level pressure anomalies over the eastern Arabian Sea. This shift facilitates greater water vapor transport from surrounding oceans to land, increasing precipitation in India by approximately 24.2% in May according to WRF-Chem simulations and by 28.5% over the entire lockdown period according to CESM2 simulations. Future projections under the lower aerosol emission scenario indicate an additional enhancement in monsoon precipitation in India. Our findings highlight the complex interplay between aerosol emissions and hydrometeorological dynamics, with implications for understanding future precipitation changes and providing theoretical reference for water resource management.

Impact of Climate Change on the Habitat Distribution of Decapterus macarellus in the South China Sea

This study examines the potential distribution of Mackerel scad (Decapterus macarellus) in the South China Sea under future climate scenarios (SSP 1.26, SSP 2.45, SSP 5.85) using an ensemble species distribution model (SDM). Key environmental variables included sea surface salinity (SSS), sea surface height (SSH), sea surface temperature (SST), mixed-layer depth (MLD), chlorophyll-a concentration (CHL), and sea-bottom temperature (SBT). Results show that SST and MLD are the primary drivers of habitat suitability, with current suitable habitats concentrated in the northern offshore areas. Projections for the 2050s and 2090s indicate a reduction in suitable habitats, particularly under high-emission scenarios, with more gradual reductions under low-emission scenarios. Habitat loss is most pronounced in the northern South China Sea, while the central region is projected to see an expansion of suitable habitats. These findings highlight the climate impact on D. macarellus distribution and inform sustainable management strategies for the species in the region.

Climate Change Impacts on the Potential Distribution of Camellia japonica in South Korea under SSP Scenarios

Climate change presents significant challenges to the habitat suitability of Camellia japonica, a key species in East Asian ecosystems. This study evaluated the potential impacts of climate change on the distribution of Camellia japonica in South Korea using species distribution models (SDMs) and shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Eleven SDMs, including an ensemble model weighted mean (EMW), were applied to predict habitat suitability from 2010 to 2070. Model performance was assessed using receiver operating characteristics curve (ROC), true skill statistics (TSS), and Kappa statistics, with EMW achieving the highest accuracy (ROC mean, 0.941), validating its reliability for integrated predictions. Bioclimatic variables, specifically mean diurnal range (BIO2) and annual precipitation (BIO12), were identified as the most influential factors driving habitat suitability. Under low-emission scenarios (SSP1-2.6 and SSP2-4.5), Camellia japonica habitats remained stable or showed slight expansion, maintaining suitability across coastline and central inland regions. Under high-emission scenarios (SSP3-7.0 and SSP5-8.5), Its habitat is expected to expand more rapidly and extensively across the eastern coastline and inland regions. Habitat shifts towards higher altitudes were observed across all scenarios, suggesting potential refugia in mountainous regions. This study highlights the vulnerability of Camellia japonica to climate change and the critical need for emission reduction policies to mitigate habitat loss. The findings provide actionable insights for conservation planning, emphasizing adaptive management strategies, such as preserving high-altitude refuges and restoring lowland habitats. Future research should incorporate additional ecological factors and higher-resolution datasets to refine predictions and guide sustainable biodiversity conservation efforts.

Assessment of extreme climate stress across China’s maize harvest region in CMIP6 simulations

Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China’s maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change.

Impact of Climate Change on Spatiotemporal Patterns of Snow Hydrology: Conceptual Frameworks, Machine learning versus Nested Model

Snow accumulation in mountainous watersheds plays a paramount role in the hydrological cycle and environmental stability. In the current research, three different snow modeling approaches including conceptual models, Random Forest (RF) and their nested (Nested-RF) model have been implemented to project climate change. Also, three distinct calibration scenarios employing Snow Cover Area (SCA) and Snow Water Equivalent (SWE) have been used. These scenarios involve using SCA and SWE data individually or combined to calibrate both conceptual snowpack models and machine learning methods. Considering comparative analysis in the mountainous and complex topographic area of Tashk-Bakhtegan watershed as a case study, the Guo model (calibrated using both SCA and SWE data) achieves the highest prediction accuracy in predicting SCA/SWE using conceptual method. Considering various statistics, RF using the same calibration scenario demonstrates high accuracy in estimating SWE and shows promising results in detecting SCA. Considering the feature importance analysis and statistics of the RF and Nested-RF models indicate that the feeding conceptual outputs as extra information into the Nested-RF had minimal influence and even slightly reduced accuracy. Future projections using RF indicate significant reductions in mean annually SCA/SWE, especially under SSP8.5, with a 28.4% decrease in SCA and a reduction in SWE to 13.62 mm from a historical baseline of 19.4 mm. Lower emission scenarios (SSP2.6 and SSP4.5) show less drastic declines. While minor differences were observed between the best conceptual model and the RF approach in capturing spatiotemporal and annual snow patterns, these discrepancies did not reach statistical significance.

Impacts of Sea Level Rise on Danish Coastal Wetlands - a GIS-based Analysis.

Intergovernmental Panel on Climate Change (IPCC) scenarios run by an ensemble of models developed by the Coupled Model Intercomparison Project (CMIP) projects an average sea level rise (SLRs) of 0.6 to 1.2 m for the low and high emission scenarios (SSP1-1.9, SSP5-8.5), during the next century (IPCC 2021). The coastal zone will experience an increase in the flooding of terrestrial habitats and the depth of marine productive areas, with potential negative consequences for these ecosystems. The coast in Denmark is highly modified due to anthropogenic uses. Dikes, dams, and other coastal infrastructure are widespread, causing a coastal squeeze that prevents natural coastal development and inland migration of coastlines. We performed a national-scale analysis on the impacts of mean sea level rise (MSLR) in 2070 and 2120, and a 1 in 10-year storm surge water level (10SS) in 2120 MSLR for the Danish coast. Our study shows extensive permanent flooding of coastal habitats (~14%), whereas only 1.6% of urban areas will be flooded. Finally, very large agricultural areas (~191,000 ha) will be frequently flooded by 10SS if no extra protective measures are planned. With the present coastal protection structures, key habitats will be affected by permanent flooding or coastal squeeze while even larger extents will be subjected to intermittent marine flooding. About 45% (199 km2) of all Danish coastal wetlands will be permanently flooded by 2120, while areas occupied by forest, lakes and freshwater wetlands will be more frequently flooded by marine water. This study highlights the importance of including coastal habitats as dynamic elements in climate adaptation plans. Conservation and restoration of key habitats such as coastal wetlands should be prioritized in management plans. If Denmark does not change its current priorities, it may face the complete loss of coastal wetlands habitat in the 22nd century.

Climate-driven distributional shifts in Chocó endemic birds of southwest Colombia

IntroductionClimate change poses a significant threat to bird communities, especially forest-dwelling and narrowly distributed species, which are expected to experience severe range contractions and higher extinction risks compared to widely distributed and open-area species. The Chocó region in southwestern Colombia, known for its rich bird endemism, is particularly vulnerable.MethodsWe analyzed potential distribution shifts for 27 endemic and near-endemic bird species in the Chocó region using eBird occurrence records and climate projections. We modeled species distributions under low and high greenhouse gas emission scenarios for 2050 and 2070, comparing these projected distributions to current forested and protected areas to evaluate future conservation needs.ResultsOur findings indicate that nearly all species are projected to lose climate-suitable areas under at least one future scenario, resulting in a regional decline in species richness. Changes in species richness are most pronounced near the Colombia-Ecuador border, suggesting a shift to higher elevations. Notably, the Scarlet-and-white Tanager (Chrysothlypis salmoni) is predicted to suffer the greatest losses in climate-suitable area, both within protected and forested regions.DiscussionThese results highlight the urgency of expanding the protected area network and conserving key forested regions to help species adapt to climate change. By providing projected distribution maps and potential range shifts, our study underscores the importance of modeling future distributions to support conservation strategies for at-risk species and the ecological services they provide in tropical montane regions.

Prediction of the Future Evolution Trends of Prunus sibirica in China Based on the Key Climate Factors Using MaxEnt Modeling.

Mountain apricot (Prunus sibirica) is an important fruit tree variety, and has a wide range of planting and application value in China and even the world. However, the current research on the suitable distribution area of P. sibirica is still inconclusive. In this study, we retrieved distribution data for P. sibirica in China from the Global Biodiversity Information Facility (GBIF), and identified six key environmental factors influencing its distribution through cluster analysis. Using these six selected climate factors and P. sibirica distribution points in China, we applied the maximum entropy model (MaxEnt) to evaluate 1160 candidate models for parameter optimization. The final results predict the potential distribution of P. sibirica under the current climate as well as two future climate scenarios (SSPs126 and SSPs585). This study shows that the model optimized with six key climate factors (AUC = 0.897, TSS = 0.658) outperforms the full model using nineteen climate factors (AUC = 0.894, TSS = 0.592). Under the high-emission scenario (SSPs585), the highly suitable habitat for P. sibirica is expected to gradually shrink towards the southeast and northwest, while expanding in the northeast and southwest. After the 2050s, highly suitable habitats are projected to completely disappear in Shandong, while new suitable areas may emerge in Tibet. Additionally, the total area of suitable habitat is projected to increase in the future, with a more significant expansion under the high-emission scenario (SSPs585) compared to the low-emission scenario (SSPs126) (7.33% vs. 0.16%). Seasonal changes in precipitation are identified as the most influential factor in driving the distribution of P. sibirica.

Predicting the Global Distribution of Gryllus bimaculatus Under Climate Change: Implications for Biodiversity and Animal Feed Production

The potential range and distribution of insects are greatly impacted by climate change. This study evaluates the potential global shifts in the range of Gryllus bimaculatus (Orthoptera: Gryllidae) under several climate change scenarios. The Global Biodiversity Information Facility provided the location data for G. bimaculatus, which included nineteen bioclimatic layers (bio01–bio19), elevation data from the WorldClim database, and land cover data. For the near future (2021–2040) and far future (2081–2100) under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) and the Institute Pierre-Simon Laplace Coupled Model Intercomparison Project (IPSL-CM6A-LR) were used. Assessing habitat gain, loss, and stability for G. bimaculatus under potential scenarios was part of the evaluation analysis. The results showed that the main environmental parameters affecting the distribution of G. bimaculatus were mean temperature of the driest quarter, mean diurnal temperature range, isothermality, and seasonal precipitation. Since birds, small mammals, and other insectivorous insects rely on G. bimaculatus and other cricket species as their primary food supply, habitat loss necessitates management attention to the effects on the food web. The spread of G. bimaculatus as a sentinel species in the food chain and its use in animal feeds are both impacted by habitat loss and gain.

Climate change and wind energy potential in South America

Wind energy is crucial in mitigating greenhouse gas emissions and combating global warming. However, the economic viability of wind energy projects is tied to wind resources, which may be affected by climate change. The objective of this work is to investigate the evolution of wind energy resources in South America using the most up-to-date climate-change scenarios: the Shared Socioeconomic Pathways. Three scenarios are considered, corresponding to low, medium and high emissions pathways. A multi-model ensemble is constructed with Global Climate Models selected for their accuracy in the study region in a historical period. Results indicate that east of the Andes, in Brazil, Paraguay and Venezuela, substantial increases in average wind power density are expected, surpassing 100 % in certain areas and in the most pessimistic scenario (the fossil-fuelled development pathway). By contrast, significant reductions (up to ~50 %) are projected for certain areas west of the Andes: South Chile, Peru and the waters off West Colombia. South Chile has the windiest climate in the continent, which makes this reduction due to climate change all the more important. Even under the low-emissions scenario, the projected evolution in wind resources is relevant, which highlights the necessity of considering climate change in wind energy planning.

Accelerated Historical and Future Warming in the Middle East and North Africa

AbstractThe present study assesses the climate of the Middle East and North Africa (MENA) in detail, focusing on the historical and future warming trends in the region. The assessment incorporates data from observations, reanalyses, and statistically downscaled climate models from the Coupled Model Intercomparison Project Phases 5 and 6. Since the pre‐industrial era, the observed average climate in the MENA has warmed by 1.5°C and is on the brink of exceeding 2°C. The reanalysis data suggest that the regional warming over some MENA sub‐regions is three times faster than the global average. By the end of the 21st century, the Arabian Peninsula is projected to warm to 2.6°C ± 0.57°C and 7.6°C ± 1.53°C under low and high greenhouse gas emission scenarios, respectively. Distinct warming hotspots emerge over the Arabian Peninsula and Algeria in summer and over Mauritania in West Africa and the Elburz Mountains in Iran in winter. The summer hotspot over the Arabian Peninsula has already warmed by more than 2°C and can potentially warm to approximately 9°C under the high‐emission scenario. As global warming progresses to 1.5, 2.0, 3.0, and 4.0°C, the average temperature over the MENA land is projected to increase by 2.3°C ± 0.18°C, 3.0°C ± 0.22°C, 4.6°C ± 0.26°C, and 6.1°C ± 0.31°C, respectively. The 1.5, 2.0, 3.0, and 4.0°C warming levels over the MENA are expected to predate those of the global mean by two or three decades. Natural climate fluctuations also significantly influence the region's warming, contributing to temperature extremes.

Evaluating Wheat Cultivation Potential in Ethiopia Under the Current and Future Climate Change Scenarios

Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate.

Projected changes of Greenland’s periphery glaciers and ice caps

Abstract Rapid global warming has caused drastic mass loss in Greenland’s peripheral glaciers and ice caps (GICs), contributing to a rise of global sea levels. To better understand future changes under different emission scenarios, we used the Open Global Glacier Model to simulate glacier dynamics and runoff changes from 2015 to 2100. The results show that their area and volume will decrease by 38.88% (SSP1-2.6) to 60.84% (SSP5-8.5) and 47.56% (SSP1-2.6) to 67.10% (SSP5-8.5) by 2100, with regions that have larger glacierized areas and are farther from the ocean experiencing less volume loss. Meanwhile, the predicted surface mass balance of Greenland’s peripheral GICs in 2100 is −0.58 ± 0.92, −1.18 ± 1.13, −2.04 ± 0.79 and −3.16 ± 0.96 m w.e. a−1 under four emission scenarios. The runoff under higher emission scenarios is larger than that under lower emission scenarios, with peak water occurring later in regions that have larger glacierized areas and are farther from the ocean.

Projecting atmospheric N2O rise until the end of the 21st century: an Earth System Model study

Abstract Nitrous Oxide (N2O) is a potent greenhouse gas with a centennial-scale lifetime that contributes significantly to global warming. It is emitted from natural and anthropogenic sources. In nature, N2O is released mainly from nitrification and denitrification from the ocean and terrestrial systems. The use of agricultural fertilizers has significantly increased the emission of N2O in the past century. Here we present, to our knowledge, the first coupled ocean and terrestrial N2O modules within an Earth System Model. The coupled modules were used to simulate the six Shared Socioeconomic Pathways (SSPs) scenarios with available nitrogen fertilizer inputs. Our results are compared to projections of atmospheric N2O concentrations used for SSPs scenario experiments. Additionally, an extra set of simulations were prescribed with emulated N2O concentrations available as input in Shared Socioeconomic Pathways scenarios. We report four main drivers for terrestrial N2O uncertainties: atmospheric temperature, agricultural fertilizer input, soil denitrification and agricultural model dynamics. We project an atmospheric N2O concentration range from 401 to 418 ppb in six SSPs simulations with a robust lack of sensitivity to equilibrium climate sensitivity. We found a large difference between our low emission scenarios N2O concentrations by 2100 compared to the concentration provided for SSPs experiments. This divergence is likely explained by strong mitigation assumptions that were not accounted for in this study, which would require a substantial decrease of agricultural N2O emissions. The coupled model and the simulations prescribed with N2O concentrations showed a difference between −0.02 and 0.09 ∘C by 2100. Our model simulation shows a lack of sensitivity to climate mitigation efforts projecting similar N2O concentration in low and high mitigation scenarios, that could indicate the need of further development of agricultural model dynamics. Further improvements in Earth system models should focus on the impact of oxygen decline on N2O dynamics in the ocean and the representation of anaerobic soils and agricultural dynamics on land, including mitigation methods on nitrogen fertilizers.

Global Spatial Projections of Forest Soil Respiration and Associated Uncertainties

The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can be biased; therefore, Ra and Rh should be predicted separately to improve prediction accuracy. In this study, we used the SRDB_V5 database and the random forest model to analyze the uncertainty in predicting Rs using a single global model (SGM) and Ra/Rh using a specific categorical model (SCM) and predicted the spatial dynamics of the distribution pattern of forest Ra, Rh, and Rs in the future under the two different climate patterns. The results show that Rs is higher under tropical and inland climatic conditions, while Rh fluctuates less than Ra and Rs. In addition, the SCM predictions better capture key environmental factors and are more consistent with actual data. In the SSP585 (high emissions) scenario, Rs is projected to increase by 19.59 percent, while in the SSP126 (low emissions) scenario, Rs increases by only 3.76 percent over 80 years, which underlines the need for SCM in future projections.

Differential flood insurance participation and housing market trajectories under future coastal flooding in the United States

Communities respond to flooding events based upon risk perceptions and available adaptive behaviors (e.g., emigrating, purchasing insurance, constructing levees). Across the United States, sea level rise, intensifying storm-surges, and extreme rainfall may alter human-flood dynamics. Here, we use calibrated Socio-Environmental models of contiguous US coastal census tracts and two shared socio-economic pathways (SSP245 and SSP585). We project that by 2100, total flood insurance claims will increase by +25% to +130% under low (SSP245) and high (SSP585) emissions scenarios, respectively. The increase in flood insurance claims will impact mainly socially vulnerable communities. Further, we project that active NFIP policies will increase from +30% under low emission scenario to +60% under high emission scenario. Our finding also suggests the growing debt of the National Flood Insurance Program under higher emissions. Raising the water elevation threshold for coastal flooding by +1 meter via levees may reduce future surge-related losses by 95% and 40% under low and high emission scenarios and stabilize housing markets. Our future projections of flood insurance claims, policy coverage, and the impact of water elevation serve as credible hypotheses for the evolution of human flood dynamics under climate change. They can inform national flood policy and future research.

Mapping Climatic Regions of the Cerrado: General Patterns and Future Change

ABSTRACTThe Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region.