Shared Socioeconomic Pathways Research Articles

Analyzing inflow to Faye reservoir sensitivity to climate change using CMIP6 and random forest algorithm

ABSTRACT In the era of Climate Change and Climate Variability (CC and CV), renewable energy sources such as Hydropower (HP) have a significant role to play in mitigation. However, inflow to reservoir which is the key fuel for HP generation is vulnerable to CC and CV. Thus, there is a need to investigate the potential impacts of CC and CV on HP systems in the future. This study attempts to assess the potential impacts of CC and CV on the Faye reservoir inflow using the Random Forest (RF) algorithm. For this purpose, bias-adjusted precipitation and temperature data of thirteen climate model outputs and their ensemble mean from Coupled Model Inter-comparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways scenarios (SSP1-2.6; SSP2-4.5 and SSP5-8.5) were used as predictors. The potential changes in reservoir inflows were evaluated in the near (2025–2049), mid (2050–2074) and far (2075–2099) futures relative to the reference period (1990–2014). The results show the good performance of the RF algorithm in simulating reservoir inflows with Cor > 0.6 for all models. The annual inflows to the Faye reservoir are noted to increase in the future compared to the reference period despite the potential decrease in future precipitation probably due to land use/cover change. For the ensemble mean of models, this projected increase is estimated to around 16%, 23% and 10%, respectively under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios for all projection periods. The largest annual increase is noted under the SSP2-4.5 scenario while the lowest increase is noted under the SSP5-8.5 scenario for all projection periods. This study could help the small dam managers better consider the implications of CC and CV on inflow management.

Risk assessment of the jhumias in eastern Himalayan region: An IPCC framework approach

Climate change is intricately linked with amplifying existing risk across multiple sectors, and assessment of the indigenous farmers practising jhum (jhumias) is highly necessary for developing adaptation strategies in the biogeographically-diverse northeast Himalayan region. The current study estimates the Shared Socio-Economic Pathway 2 (SSP2) – 4.5 hazard scenario for the year 2040–2059 using Coupled Model Inter-comparison Project 6 (CIMP6) (mean projection) and Multi-Model Ensemble. The remaining risk dimensions (exposure and vulnerability) were derived from household survey data involving 450 households from 18 villages representing six districts, three states, and four watershed basins. This framework used an indicator approach for defining risk dimensions and a spatial approach for the final risk presentation and classification. Principal Component Analysis (PCA) was used to select and group the indicators. All chosen indicators underwent varimax rotation, and factors with eigenvalues greater than one were considered for grouping into components. Different weights for each indicator were defined using the Shanon entropy method. Climate change risk was found to be highest in Khudengthabi village in Chandel district, followed by Sangshak village in Ukhrul district. Lack of land ownership, low yield, no alternative livelihood option, an insufficient market, and unavailability of labour force are some of the major factors contributing to higher risk in the area. The study recommends skill development and alternative livelihood programmes with a new education policy that encourages the dropout youth to continue in schools and colleges. Subsequent policy initiatives should prioritize villages, indicators and components with high index scores to optimize resource allocation and facilitate quicker adaptation.

Response of Species to the Impact of Climate Change in the Gum Arabic Belt, Sudan: A Case Study in Acacia senegal

Sustainable management strategies of trees are important for indigenous agroforestry plant species, such as Acacia. Senegal (A. Senegal), due to the impacts of rapid population growth, land use and climate change. The objective of this investigation was to predict the spatio-temporal distribution of A.senegal in the Gum Arabic belt in Sudan in current (1985–2000) and future climate scenarios (2021–2100). Bioclimatic data was used for modeling purposes utilizing Maxent, with the assessment of model precision conducted through the utilization of the Area Under the Curve (AUC) and shown a high goodness-of-it (AUC=0.905±0.003 ). Significant differences were shown in species distribution between current and future periods under selected Shared Socioeconomic Pathways (SSPs) of SSP2-4.5 and SSP5-8.5, climate scenario. Our findings indicated that the main predictors influence the distribution of the species were precipitation of wettest quarter and maximum temperature of warmest month. Under the current potential distribution (25.4%), it is projected that Acacia Senegal would expand 36.2%-87.7% (SSP2-4.5) and 38.9-42.5% (SSP5-8.5). It is expected that A.cacia Senegal will create new environments suitable for it due to expected climate changes. Hence, the research necessitates the formulation of a strategic plan aimed to rehabilitation plantations of Acacia senegal and cultivation these species within existing and prospective habitats conducive to their existence.

Climate variability impacts on runoff projection in the 21st century based on the applicability assessment of multiple GCMs: A case study of the Lushi Basin, China

In this study, we initiatively employed the improved Secure Hash Algorithm (SHA) to evaluate and compare the competencies of multiple Coupled Model Intercomparison Project Phase Six (CMIP6) General Circulation Models (GCMs). The selected GCM outputs were bias-corrected by the non-parametric quantile mapping (QM) method. The calibrated and validated data were then used to run the XAJ (Xin’anjiang) model to assess long-term climate variation impacts on runoff under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), where the results were compared with the baseline period (1976–2000). Overall, the CNRM-CM6-1-HR, FIO-ESM-2–0, INM-CM5-0 and NorESM2-MM models reconstructed better spatio-temporal evolutionary trends related to historical precipitation and evaporation, and were applied as selected GCMs for reliable runoff projection applications. The intensification amplitudes of precipitation and evaporation produced by selected GCMs gradually spread from SSP1-2.6 to SSP5-8.5 throughout the 21st century. Compared to the baseline period (257 m3/s), the extreme streamflow during the wet periods is likely to increase for SSP5-8.5 (288 ∼ 298 m3/s), SSP3-7.0 (276 ∼ 294 m3/s), and SSP2-4.5 (222 ∼ 280 m3/s), whereas SSP1-2.6 decreases to varying degrees (178 ∼ 255 m3/s). Meanwhile, during the dry periods, the extreme streamflow is projected to decrease by 28 ∼ 73 m3/s (SSP5-8.5), 32 ∼ 38 m3/s (SSP3-7.0), 8 ∼ 31 m3/s (SSP2-4.5), and 5 ∼ 33 m3/s (SSP1-2.6) from historical levels (62 m3/s). The overwhelming majority of increasing trends in the wet periods and vastly decreasing trends in the dry seasons indicate that the basin will face some floods and water shortage risks in the mid to late 21st century. The findings broaden the efficacious applications of SHA in the performance selection of GCMs and QM method in bias correction. The conclusions provide a reference for flood and drought mitigation planning and water resources policymaking in the Yellow River Basin.

Urbanization impact on meteorological condition and O3 concentration under past and future climates scenarios over the Greater Bay Area in Southern China

Cities face twin challenges of changing climate and urban heat island (UHI) effect, with the environmental implications of urbanization still unclear. This study employs the Weather Research and Forecasting (WRF) model to examine the impacts of urbanization in the Greater Bay Area (GBA) during warm and cold seasons in past (2000) and near-future (2030) scenarios [i.e., under shared socioeconomic pathways (SSP): SSP1-26, SSP2-45, and SSP5-85]. Datasets from the World Urban Database and Access Portal Tools (WUDAPT) were incorporated into the WRF model to improve the representation of urbanization effects on weather patterns. Our findings indicate that urban expansion significantly increases urban temperatures and decreases wind speed across selected climate change scenarios. The spatially averaged urban temperatures in the fine resolution domain for 2030 could rise by 0.9 °C (warm season) and 2.4 °C (cold season), respectively, and urban wind speed decreases by ∼ 3 m/s under the SSP5-85 scenario. In cold season, the UHI effect could last over 20 h, while urban area may experience cooler nighttime temperatures than rural areas in warm season. Moreover, the future scenario predicts higher daytime O3 levels due to the warming effects of climate change and urbanization, but lower nighttime levels in warm season, attributed to intensified south-easterly sea breeze and background winds, when compared to the past scenario. This study highlights the importance of incorporating urbanization and climate change in future urban atmospheric environment studies, and underscores that urban climate change adaptation and mitigation should consider extra impacts on built-environment caused by urbanization.

Wide range of possible trajectories of North Atlantic climate in a warming world.

Decadal variability in the North Atlantic Ocean impacts regional and global climate, yet changes in internal decadal variability under anthropogenic radiative forcing remain largely unexplored. Here we use the Community Earth System Model 2 Large Ensemble under historical and the Shared Socioeconomic Pathway 3-7.0 future radiative forcing scenarios and show that the ensemble spread in northern North Atlantic sea surface temperature (SST) more than doubles during the mid-twenty-first century, highlighting an exceptionally wide range of possible climate states. Furthermore, there are strikingly distinct trajectories in these SSTs, arising from differences in the North Atlantic deep convection among ensemble members starting by 2030. We propose that these are stochastically triggered and subsequently amplified by positive feedbacks involving coupled ocean-atmosphere-sea ice interactions. Freshwater forcing associated with global warming seems necessary for activating these feedbacks, accentuating the impact of external forcing on internal variability. Further investigation on seven additional large ensembles affirms the robustness of our findings. By monitoring these mechanisms in real time and extending dynamical model predictions after positive feedbacks activate, we may achieve skillful long-lead North Atlantic decadal predictions that are effective for multiple decades.

Amplified seasonality in western Europe in a warmer world.

Documenting the seasonal temperature cycle constitutes an essential step toward mitigating risks associated with extreme weather events in a future warmer world. The mid-Piacenzian Warm Period (mPWP), 3.3 to 3.0 million years ago, featured global temperatures approximately 3°C above preindustrial levels. It represents an ideal period for directed paleoclimate reconstructions equivalent to model projections for 2100 under moderate Shared Socioeconomic Pathway SSP2-4.5. Here, seasonal clumped isotope analyses of fossil mollusk shells from the North Sea are presented to test Pliocene Model Intercomparison Project 2 outcomes. Joint data and model evidence reveals enhanced summer warming (+4.3° ± 1.0°C) compared to winter (+2.5° ± 1.5°C) during the mPWP, equivalent to SSP2-4.5 outcomes for future climate. We show that Arctic amplification of global warming weakens mid-latitude summer circulation while intensifying seasonal contrast in temperature and precipitation, leading to an increased risk of summer heat waves and other extreme weather events in Europe's future.

How to quantify the regional effects of ocean temperature rise due to climate change: implications of Octopus maya ecophysiology on food security of the Yucatan shelf artisanal fishermen

The vital survival, maturation, and reproduction rates of Octopus maya were formulated according to the thermal preferences in each stage (juvenile and adult) and the bottom temperature of the Yucatan shelf projected from different shared socioeconomic pathways (SSPs): SSP1-5, to describe the species population growth in shallow waters. The dispersion of each individual in the population and the spread of the offshore population were incorporated with an integrodifference equation. For each SSP, the food security of the artisanal fishermen in the Western, Central, and Eastern Yucatan shelf region was analyzed based on the proposed availability, access, and utilization indices of Octopus maya as food. The analysis was complemented with an average monthly protein and income poverty indicators that this species’ shallow water fishery would have the capacity to supply by the year 2100. Although the proportion of the legally O. maya catchable population may be favored with the gradual increase in temperature in the coming years, this increase may not translate into greater food security for the artisanal fishermen who catch this species in shallow waters. Moreover, this fishery alone may not have the capacity to supply the average annual intake of 10.6 kg of protein per person recommended and maintain fishermen and their families above the income poverty line by 2100.

Communicating climate futures: a multi-country study of how the media portray the IPCC scenarios in the 2021/2 Working Group reports

The way governments and policy makers think about climate futures has a wide-ranging impact on how they formulate policy and plan for climate change impacts. In the lead-up to the Sixth Assessment Report (AR6), the IPCC adopted a new scenarios framework that aimed to provide a fuller picture of the interacting elements and policy choices that affect climate change. However, these scenarios, known as Shared Socioeconomic Pathways (SSPs), are complex and difficult to communicate. Most audiences, including policy makers, receive much of their information about climate change from mainstream media, and particularly online news sites. We therefore examined the five most popular online news sites in the UK and the USA, five popular English-language news sites in India, English-language news media from a wide range of African countries, and the Reuters News agency. Based on manual content analysis to assess 252 articles, we identify several important findings, amongst them: in all countries, the media provide little detailed explanation of how scenarios are developed, very little mention of SSPs, and virtually no detailed explanations of them; generally, journalists use the words ‘projections’, ‘futures’, and ‘pathways’ when talking about the IPCC scenarios, although some usage of ‘predictions’ or ‘forecasts’ is apparent; contrary to previous research, there were very few doomsday narratives such as ‘only 12 years to act’. We conclude by drawing out some implications for more effective communication of the IPCC scenarios.

Impacts of Climate Change and Adaptation Strategies for Rainfed Barley Production in the Almería Province, Spain

Mediterranean water-stressed areas face significant challenges from higher temperatures and increasingly severe droughts. We assess the effect of climate change on rainfed barley production in the aridity-prone province of Almería, Spain, using the FAO AquaCrop model. We focus on rainfed barley growth by the mid-century (2041–2070) and end-century (2071–2100) time periods, using three Shared Socio-economic Pathway (SSP)-based scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Using the paired t-test, Spearman and Pearson correlation coefficient, Root Mean Squared Error, and relative Root Mean Squared Error, we verified AquaCrop’s ability to capture local multi-year trends (9 or more years) using standard barley crop parameters, without local recalibration. Starting with a reference Initial Soil Water Content (ISWC), different soil water contents within barley rooting depth were modelled to account for decreases in soil water availability. We then evaluated the efficiency of different climate adaptation strategies: irrigation, mulching, and changing sowing dates. We show average yield changes of +14% to −44.8% (mid-century) and +12% to −55.1% (end-century), with ISWC being the main factor determining yields. Irrigation increases yields by 21.1%, utilizing just 3% of Almería’s superficial water resources. Mulches improve irrigated yield performances by 6.9% while reducing irrigation needs by 40%. Changing sowing dates does not consistently improve yields. We demonstrate that regardless of the scenario used, climate adaptation of field barley production in Almería should prioritize limiting soil water loss by combining irrigation with mulching. This would enable farmers in Almería’s northern communities to maintain their livelihoods, reducing the province’s reliance on horticulture while continuing to contribute to food security goals.

Exploring the Impact of Climate Change on Water Resources for Vegetation Covers in Extremadura (Spain)

Mediterranean areas will likely undergo climate shifts in the near future that modify the water resources for vegetation. However, in some regions of southwestern Spain, such as Extremadura, the impact of different future scenarios on the water resources for vegetation has not been studied extensively. This study focused on the quantification and spatial distribution of water resources for vegetation covers in Extremadura and analyzed the impact of future climate change scenarios on those water resources. For this, five downscaled global climate models from Coupled Model Intercomparison Project phase 6 (CMIP6) were used in four future periods (from 2021 to 2100) following two Shared Socioeconomic Pathways (SSP-2.45 and SSP-5.85). These projections were compared with a historical baseline period (1970–2000) to obtain the variation of water resources. The results showed decreases in the water resources for all the scenarios and periods analyzed compared to those observed in the historical baseline period. The smallest decreases were noted over 2041–2060 for SSP2-4.5, with almost 74% of the region decreasing between 15 and 18% (with an average of 16.4%). The greatest decreases were over 2081–2100 for SSP5-8.5, in which 90% of the region displayed water resource declines of greater than 50%. In this last situation, the three more widespread vegetation covers (agrosilvopastoral systems of dehesas, grasslands, and crops) underwent similar declines of around 55% of their water resources (from ≈203 to ≈93 mm), while the fourth widely spread vegetation cover, forests, declined by 49% (from ≈261 to ≈133 mm). If any of these future projections occur, the decline in water resources could modify the forest composition and structure of these water-dependent ecosystems, compromising their maintenance and ecological, cultural, and economic functions.

Assessing CESM2 Clouds and Their Response to Climate Change Using Cloud Regimes

Abstract The Community Earth System Model, version 2 (CESM2), has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k-means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late twenty-first century in four shared socioeconomic pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

Compound successive dry-hot and wet extremes in China with global warming and urbanization

Global warming has accelerated the interregional hydrological cycle, resulting in a significant increase in the frequency and intensity of extreme events worldwide. These events often involve a combination of spatial and temporal factors, giving rise to compound events. Among them are rapid transitions from dry and hot conditions to wet (DHW) events, which can have more severe impacts on human societies and ecosystems than individual extreme events. Urbanization not only heightens the likelihood of disasters but also exacerbates the exposure of affected populations. However, there has been insufficient attention given to understanding the connections between these successive compound events and the heightened risks posed by the increasing urbanization. In this study, we used bias-corrected daily precipitation and maximum temperature data simulated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, along with historical and future daily runoff data simulated by the Variable Infiltration Capacity (VIC) model. We systematically investigated the spatiotemporal changes (i.e., frequency, duration, intensity, and compound probability) in DHW events in China during the upper historical period (1979–2014) and under two medium and high emission scenarios (2015–2100) − Shared Socioeconomic Pathways (SSP245 and SSP585). Furthermore, we examined how extreme runoff responds to variations in maximum temperature and daily precipitation during DHW events. We also evaluated the potential population and urban exposure to DHW events using dynamic future population and urban expansion data to assess the potential risks. Our results indicate that the multi-model ensemble predicts varying spatial patterns of future DHW events under SSP245 and SSP585 scenarios. The frequency and duration of these events are expected to decrease by approximately 20–25% in the Northwest region under both scenarios. At the same time, the middle and lower plains of the Yangtze and Yellow River Basins experience increased occurrence, broader geographical impact, and higher DHW event intensity alongside urban expansion and population growth in these regions. Specifically, event intensity is anticipated to increase by a factor of approximately 7–11. Temporally, we expect short-duration, high-intensity DHW events to occur in the Yangtze and Yellow River basins around 2023, 2038, and 2058, respectively. The primary driver for the future rise in population exposure to DHW events is the expected increase in event frequency in the middle and lower plains of the Yangtze and Yellow River basins, concurrent with population growth in these regions. Under the medium emission scenario, the total exposed population to DHW events in China is predicted to be 1.61 times higher than the historical baseline. In contrast, the high-emission scenario estimates a total population of 2.41 times higher than the historical baseline period. Most of the DHW events occurred in regions that exhibit a positive dependence between high temperature and high runoff events, serving as the primary driver of DHW events. Urbanization has a positive impact on DHW events, with the effect under high emissions being approximately 30% higher than during the historical base period. Additionally, it is indicated that future DHW events will exhibit higher sensitivity to global warming, with intensity projected to increase approximately fourfold and the exposed population to rise by about 1.5 times for every 1 °C of global warming. This research enhances our understanding of forecasting future compound hydrometeorological extreme events under different scenarios and provides insight into the role of climate change and urbanization in shaping these events in China.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia

Water erosion has emerged as a significant global environmental challenge, particularly in South Asia, recognized as one of the regions hardest to hit globally by climate change. This study aims to identify the potential erosion hotspots in South Asia and to assess areas under risk in the future due to climate and land use change scenarios. We utilized the Revised Universal Soil Loss Equation (RUSLE), an empirical erosion model, to estimate erosion for both historical and future periods, considering two distinct Shared Socio-economic Pathways (SSP126 and SSP585). The baseline model predicts a mean water erosion rate of 10.6 t. ha−1. year−1 in South Asia, with more than 30 t. ha−1. year−1 recorded for 6.3 % of the total land area. Notably, Bangladesh and Nepal exhibit significantly higher erosion rates than other South Asian countries. Further, our projection indicates an + 10 % increase in erosion rates during the near future (2021–2040) under SSP585 compared to the historical estimates. A substantial rise of approximately + 31 % and + 39 % in water erosion rates in South Asia is anticipated during the far future periods of 2061–2080 and 2081–2100 under SSP585. At the country level, Bangladesh (+42 %), Bhutan (+108 %), and Nepal (+70 %) are expected to experience substantial increases in erosion rates during the 2081–2100 period, with India projecting a + 38 % increase. Additionally, a significant + 35 % expansion in the spatial extent of high erosion areas (>30 t. ha−1. year−1) is foreseen in 2081–2100 under SSP585. Furthermore, under the low climate change scenario (SSP126), the impact on the erosion rates is minimal, while under SSP585, a substantial increase in erosion rates is anticipated. Regions such as Southern India, the arid and semi-arid areas near the India-Pakistan border, and the Himalayan foothills are identified as hotspots for significant changes in erosion rates. Acknowledging a certain level of uncertainty, our study provides valuable insights into erosion risk areas in South Asia and their plausible future impacts due to climate and land use changes. This information can assist policymakers in South Asian countries in developing national-level strategies for soil conservation and climate resilience.

Precipitation Seasonality Amplifies as Earth Warms

AbstractPrecipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world.

Marginal land in China suitable for bioenergy crops under diverse socioeconomic and climate scenarios from 2020 to 2100

ABSTRACT Maximizing the development of renewable energy plays a critical role in mitigating the climate crisis. Marginal land provides space for the development of biomass energy; however, it remains unclear how the amount and spatial distribution of marginal land that is suitable for energy crop development will change in the future. Here, we project energy marginal land changes in China following the shared socioeconomic pathway (SSP) and/or representative concentration path (RCP). We provide datasets of marginal land, agriculturally suitable land, and potentially suitable for energy crops under historical scenarios and six future scenarios (i.e. SSP1–1.9, SSP1–2.6, SSP4–3.4, SSP2–4.5, SSP4–6.0, and SSP3–7.0) for the period 2020–2100, with a spatial resolution of 5 km. Under the six scenarios, from 2020–2100, the area of suitable marginal land ranged from 1.90–16.28 (Jatropha curcas L.) to 37.37–73.97 (Panicum virgatum L.) (×104 km2), depending on the choice of energy crops and climate scenario. Based on the growing suitability of eight important bioenergy crops—Ricinus communis L., Saccharum officinarum L., Pistacia chinensis Bunge, Panicum virgatum L., Jatropha curcas L., Miscanthus giganteus J., Manihot esculenta Crantz, and Sorghum bicolor Moench—our dataset can be used to identify suitable locations for specific energy crops. This new synthetic dataset could support the development of multiscenario-based solutions related to carbon neutrality, ecosystem services, and energy transition.

Northeast Atlantic fish stock productivity hindcasts and forecasts from a Bayesian framework reveal pronounced climate‐induced dynamics

AbstractClimate change continues to exert pressure on ocean ecosystems. The fisheries‐related responses, such as altered body growth, recruitment and spatial distribution of the targeted stock(s), have generally been reasonably well investigated. Nevertheless, there are still important knowledge gaps in how biophysical drivers impact stock productivity and thereby sustainable harvest levels. Considering this, we investigated 26 fish stocks in the Northeast Atlantic, a region characterized by accelerated climate change effects and a diverse set of fisheries. A novel, stepwise, Bayesian framework to quantify stock productivity was established to identify shared trends and project future patterns, aiming at determining essential baselines for adaptive fishery management in the face of climate change. Despite variation among large marine ecosystems and stocks, an overall declining trend in productivity over the past four decades was observed, especially in high‐latitude areas. These hindcast results were mainly attributed to higher temperatures posing negative effects on productivity, which was dependent on the stock's thermal preference. Contrastingly, the proxy for food availability – gross secondary production – exhibited less consistent impacts. In the forecast, the applied Shared Socio‐economic Pathways (SSPs) indicated that most stocks are likely to encounter adverse effects, with the worst cases expected to occur under SSP2‐4.5 in the 2050s, and under SSP5‐8.5 in the 2090s. Thus, this study generally not only supports earlier climate vulnerability assessments (‘scorings’) of experts but also provides evidence for revised directional effects under climate change, underlining the complexity of processes affecting stock productivity.

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

Probabilistic scenario-based assessment of national food security risks with application to Egypt and Ethiopia

Abstract This study presents a novel approach to assessing food security risks at the national level, employing a probabilistic scenario-based framework that integrates both Shared Socio-economic Pathways and Representative Concentration Pathways. This innovative method allows each scenario, encompassing socio-economic, and climate factors, to be treated as a model capable of generating diverse trajectories. This approach offers a more dynamic understanding of food security risks under varying future conditions. The paper details the methodologies employed, showcasing their applicability through a focused analysis of food security challenges in Egypt and Ethiopia, and underscores the importance of considering a spectrum of socio-economic and climatic factors in national food security assessments.