Shared Socioeconomic Pathways Research Articles

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.

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.

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 2020–2100

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.

Prediction of the Potential Distribution of Teinopalpus aureus Mell, 1923 (Lepidoptera, Papilionidae) in China Using Habitat Suitability Models

The Golden Kaiser-I-Hind (Teinopalpus aureus Mell, 1923) is the only butterfly among Class I national protected animals in China and is known as the national butterfly. In this study, by accurately predicting the suitable habitat in China under current and future climate scenarios, the potential distribution area of T. aureus was defined, providing a theoretical basis for conservation and management. Based on species distribution records, we utilized the Biomod2 platform to combine climate data from the BCC-CSM2-MR climate model, future shared socio-economic pathways, and altitude data. The potential distribution areas of T. aureus in the current (1970–2000s) and future SSP1_2.6 and SSP5_8.5 climate scenarios in China in 2041–2060 (2050s), 2061–2080 (2070s), and 2081–2100 (2090s) were predicted. The AUC and TSS values of the combined model based on five algorithms were greater than those of the single models, and the AUC value of the receiver operating characteristic curve was 0.990, indicating that the model had high reliability and accuracy. The screening of environmental variables showed that the habitat area of T. aureus in China was mainly affected by annual precipitation, precipitation in the driest month, the lowest temperature in the coldest month, temperature seasonality, elevation, and other factors. Under the current circumstances, the habitat area of T. aureus was mainly located in southern China, including Fujian, Guangdong, Guangxi, Hainan, Zhejiang, Yunnan, Guizhou, Hunan, Taiwan, and other provinces. The suitable area is approximately 138.95 × 104 km2; among them, the highly suitable area of 34.43 × 104 km2 is a priority area in urgent need of protection. Under both SSP1_2.6 and SSP5_8.5, the population centroid tended to shift southward in the 2050s and 2070s, and began to migrate northeast in the 2090s. Temperature, rainfall, and altitude influenced the distribution of T. aureus. In the two climate scenarios, the habitat area of T. aureus declined to different degrees, and the reduction was most obvious in the SSP5_8.5 scenario; climate was the most likely environmental variable to cause a change in the geographical distribution. Climate change will significantly affect the evolution and potential distribution of T. aureus in China and will increase the risk of extinction. Accordingly, it is necessary to strengthen protection and to implement active and effective measures to reduce the negative impact of climate change on T. aureus.

Coupling and coordination of rainfall erosivity and aerosols in the Pearl River Basin under multiple shared socio-economic pathways

Coupling and coordination of rainfall erosivity and aerosols in the Pearl River Basin under multiple shared socio-economic pathways

Evaluation and projection of the summer precipitation recycling over the Tibetan plateau based on CMIP6 GCMs

AbstractBased on ERA5 and 23 CMIP6 models under three Shared Socioeconomic Pathways (SSPs), we assess the performance of CMIP6 models in reproducing the summer precipitation, evaporation and precipitation recycling ratio (PRR) over the Tibetan plateau (TP) during 1981–2014. We also project future changes of the summer precipitation, evaporation, PRR and moisture transport over the TP during 2021–2100. CMIP6 models and their ensemble mean to reproduce the temporal and spatial characteristics of precipitation, evaporation, PRR and moisture transport over the TP. And the performance in simulating precipitation is better than that in simulating evaporation and PRR. The precipitation and evaporation under the three SSPs will increase, with the significant upward trends of 0.84%/decade ~2.79%/decade and 0.80%/decade ~2.14%/decade, respectively. However, the PRR will increase slightly (0.13%/decade) and then marginally reduce (−0.22%/decade). In the late 21st century, regional mean summer precipitation, evaporation and PRR over the TP in SSP5‐8.5 will change by about 20.13%, 17.14%, and −0.77%, respectively. From spatial distribution, precipitation will increase in most parts of the TP in the 21st century, especially in the western part of the plateau, reaching more than 50% in of the late 21st century, the area of PRR decreased gradually expanded, with the maximum reduction (15%–20%) of PRR in the western region. It indicates that more precipitation in the western of the plateau depends on the external moisture transport. The PRR in the southern part of the plateau will increase, with a maximum of more than 20%, which indicates the contribution of the external water vapour transport to the southern precipitation will decrease, and that of the internal cycle (local evaporation) to the local precipitation will increase. In conclusion, the increase of the regional mean precipitation over the TP in the near and mid‐21st century is mainly due to the local evaporation, while the increase of the summer precipitation in the late 21st century is due to both the local evaporation and the external water vapour advection, it shows that the precipitation generated by the inner circulation is becoming weaker and more dependent on the external water vapour transport.

Future Climate Change Impacts on Rice in Uttar Pradesh, India's Most Populous Agrarian State

AbstractUttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs.

Unstable interdecadal relationship between the Pacific Decadal Oscillation and eastern China summer precipitation in simulated warm interglacial epochs and twenty-first century

The modern connection between the Pacific Decadal Oscillation (PDO) and eastern China summer precipitation (ECSP) at the decadal scale has been extensively studied. However, whether this correlation is stable during the warmer backgrounds in the past and future remains unclear. Clarifying this issue may yield meaningful insights into past PDO reconstruction and decadal climate projection in eastern China (EC). Based on the FGOALS-f3-L, which performs best in reproducing the observed PDO pattern and the decadal PDO–ECSP relationship among 10 models under the framework of the coupled Model Intercomparison Project phase 6 (CMIP6), we find an unstable relationship between the PDO and ECSP at the decadal scale during the last Interglacial (LIG), mid-Holocene, and the twenty-first century under the Shared Socioeconomic Pathway (SSP) SSP5–8.5 scenario. The PDO-related ECSP roughly shows a west–east dipole pattern with excessive precipitation in the east during the LIG, whereas the mid-Holocene witnesses a generally decreased ECSP, except for South and Northeast China. Under the SSP5–8.5 scenario, the PDO-related decadal ECSP exhibits excessive precipitation in the west but a deficit in the east, especially in South and Northeast China. This instability is ascribed to changes in the vertical motions and moisture divergence associated with various configurations of large-scale atmospheric circulations from the lower to upper troposphere. Furthermore, these diverse atmospheric circulation patterns relate to the variations in the convective heating over the western North Pacific and the propagation of extratropical Rossby waves, which are connected with the different PDO patterns and the mean state differences in the westerly jet stream locations during the past and future warm periods

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.

Predicting Current and Future Habitat Suitability of an Endemic Species Using Data-Fusion Approach: Responses to Climate Change

Fritillaria imperialis L., an indicator plant species in Iran, is facing threats and its population has declined in recent years. To provide insights into the drivers affecting its loss, this research aims to identify the effects of three groups of factors, including climate, soil, and physiographic variables, on the current and future spatial distributions of F. imperialis. For this purpose, we used five machine learning algorithms as well as an ensemble forecasting of species distribution approach to explain the geographical distributions of the species as a function of these factors. In addition, we used two shared socio-economic pathways scenarios – SSP 1-2.6 and SSP 5-8.5 – to project the future distributions of F. imperialis in 2030, 2050, 2070, and 2090. Based on evaluation indices, area under the ROC curve (AUC) and true skill statistic (TSS), the Random Forest (RF) model generated the strongest prediction of the distribution of F. imperialis (TSS>0.9 and AUC>0.9). No significant difference observed among the three datasets (climate-only variables, climate + physiography variables, and climate + physiography + soil variables) in terms of AUC values. In models using climate + physiography + soil datasets, soil electrical conductivity, clay, and pH emerged as the most important variables affecting the growth and development of F. imperialis while climate factors played a lesser role in its distribution. Future projections revealed different patterns when using the optimistic (SSP 1-2.6) and pessimistic (SSP 5-8.5) socio-economic pathway scenarios and either the climate only or climate + physiography models. The climate + physiography + soil model produced similar prediction patterns for the scenarios. The climate-only models predicted larger areas suitable for crown imperial in the future than did the climate + physiography + soil model. These results emphasize the consideration of factors beyond climate scenarios when modeling biological responses to global warming and regional climate change.

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 south 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.

Anthropogenic influence on the extremely low September sea ice and hot summer of 2020 over the arctic and its future risk of occurrence

During 2020, the Arctic is marked by extremely low sea ice coverage and hot climate. September Sea Ice Extent (SIE) was about 2.3 million km2 below the 1979–2014 mean and the 2nd lowest on the 1979–2020 record, while regional summer (June–August, JJA) mean 2 m air temperature (TAS) was about 1.3 °C above the 1979–2014 mean and was the hottest on record at the time. Locally, September Sea Ice Concentration (SIC) was approximately 70% lower and JJA TAS can be as much as 6.0 °C higher than the 1979–2014 mean. Although the proximate cause for the extreme event was the continuously favorable atmospheric circulation patterns, wind conditions and ice-albedo feedback, the main objective of this paper is probabilistic extreme event attribution studies to assess the anthropogenic influence. Based on the CMIP6 multi-model ensemble products, modeled long-term trends of Arctic sea ice and TAS are consistent with observed trends when including anthropogenic forcing or greenhouse gas (GHG) forcing, while cannot exhibit observed trends with only aerosol or natural forcing. Further analysis reveals that human influence including GHG forcing has substantially increased the probability of occurrence of the 2020-like extreme events, which are rare in aerosol-only or natural-only forcing. The frequencies of 2020-like low SIC increase by 19 times with all forcing and 16 times with GHG forcing than with natural forcing. Future climate simulations under different Shared Socioeconomic Pathway (SSP) scenarios of SSP126, SSP245 and SSP585 show that the 2020-like extreme event that is currently considered rare is projected to become the norm and almost occur 1-in-1 year beyond 2041–2060. The probabilities will be approximately in the range of 0.85–1.00 for SIC and 0.76–0.99 for TAS from low emission of SSP126 to high emission of SSP585.

Changes in Future Rainfall over Southeast Asia Using the CMIP6 Multi-model Ensemble

A multi-model ensemble from the new CMIP6 models was utilized to determine the future changes in precipitation over Southeast Asia (SEA; longitude: 90°E–140°E, latitude: 15°S–30°N). The changes are computed for the three (3) future time slices (2021–2040, 2041–2060, and 2081–2100) under four (4) different scenarios based on the Shared Socioeconomic Pathways (SSPs): 1-2.6, 2-4.5, 3-7.0, and 5-8.5. Our results indicate that future rainfall in the SEA-averaged region could increase by about 4%, 5%, 6%, and 9% towards the end of the century relative to the present-day average (1995–2014) under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively. Among all scenarios, SSP3-7.0 widely shows remarkably dry conditions whereas SSP5-8.5 suggests extremely wet conditions on different time scales. A clear dissociation of wet and dry areas is expected in the far-term period (2081–2100). Changes in the annual cycle indicate that monsoon rainfall could experience significant increases. The study also emphasizes the importance of moisture flux convergence (MFC) in determining precipitation patterns across different seasons and regions. The results suggest that MFC plays a crucial role in the projected increase or decrease of rainfall in SEA regions. Spatial correlation of future global mean temperature (GMT) and rainfall have a high positive (negative) correlation in the north (south) latitudes. Changes in rainfall are found to be sensitive to GMT. The responses to future rainfall changes per degree Celsius of warming are at the rate of 8.9%, 6.3%, 3.6%, and 2.7% under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively.

Climate change impairs the effects of vegetation improvement on soil erosion control in the Qinghai-Tibetan Plateau

Water erosion is a wide ecosystem degradation threat for the Qinghai-Tibetan Plateau (QTP) region and surrounding areas. Climate wetting and vegetation greening in the QTP could have complex impacts on water erosion. However, there is limited information about the erosion dynamics under climate change and the effects of vegetation improvement on erosion control in this alpine region. By calibrating the Revised Universal Soil Loss Equation (RUSLE) model, we estimated water erosion in the QTP and simulated future changes using different Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP) scenarios. We showed intensified erosion and that the benefits of vegetation improvement to erosion control was impaired under climate change. Under the SSP585 scenario, annual water erosion in the QTP was predicted to increase by 0.35 t∙ha−1∙yr−1 and be dominated by an accelerative rise of rainfall erosivity (R factor of RUSLE). Compared with historical observations, the C factor of RUSLE under the SSP585 scenario was believed to reduce by 4.35 % on average during the 2081–2100 period, but predicted soil loss will increase by more than 90 %. Although more considerable vegetation improvement could occur under high climate forcing, there may also be more intensified rainfall erosivity and water erosion across the QTP than those under SSP126 and SSP245 scenarios. Our results suggest that mitigating climate change rather than vegetation improvement is essential to address climate-dominated ecosystem degradation threats such as water erosion in the QTP.

Increasing Risk of a “Hot Eastern‐Pluvial Western” Asia

AbstractObservations have revealed that the deadly “hot eastern‐pluvial western” climate pattern in Asia in summer 2022 is by no means an exception. In recent decade this pattern is becoming much more frequent owing to the notable westward extension and enhancement of the western Pacific subtropical high (WPSH). The anomalously enhanced WPSH fills the gap in geopotential heights between the Arabian Sea‐Iranian Plateau and the western Pacific, produces a heat dome over eastern Asia and intensifies the moisture convergence over western Asia by changing the water vapor transportation path. Projections of 19 CMIP6 models show that both the temperature in eastern Asia and precipitation in western Asia will remarkably increase under Shared Socioeconomic Pathway (SSP) 2‐4.5, SSP 3‐7.0, and SSP 5‐8.5 scenarios, with temperature anomalies 0.8°C, 1.5°C, and 2.4°C higher and the percentages of precipitation anomaly 6.3%, 9.4%, and 12.5% larger than the reference period in the near, middle, and long terms under the moderate emission scenario (SSP2‐4.5). Therefore, the “hot eastern‐pluvial western” Asia is becoming a new normal and exacerbating the risk of compound disasters.

Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble

AbstractSea surface temperature (SST) and sea surface air temperature (SSAT) are commonly used as proxies for investigating the impact of climate change on oceans. These variables have been warming since pre-industrial times and are expected to continue to warm in the future under all Shared Socioeconomic Pathways (SSPs). However, they are warming in a spatially heterogeneous way, even with some cooling spots. In this work, we provide a general overview on the regional scaling of SST and SSAT with global warming, based on a 26-member CMIP6 ensemble. We utilize the global warming level (GWL) as a climate change dimension to analyze scaling patterns between sea temperature anomalies and the corresponding GWLs during the 21st century. This analysis is conducted globally, regionally, and on grid-point basis. The results show that SST and SSAT scale linearly with GWL at global scale, with scaling factors $$\beta $$ β = 0.71 ± 0.001 K/K and $$\beta $$ β = 0.86 ± 0.001 K/K, respectively. These results are robust, showing only minor differences between seasons, SSPs, and horizontal model resolutions. However, large differences emerge at regional scale, and the scaling of the two temperatures are strongly influenced by sea-ice. The lowest values are obtained for the Southern Ocean region, $$\beta $$ β = 0.54 ± 0.005 K/K, projecting that the mean SST will increase only half as fast as the global mean temperature. These results provide valuable insight for refining the ocean IPCC reference regions, considering spatial homogeneity in terms of the regional response to global warming. A refinement of six ocean reference regions has been proposed.