The seasonal variability of future evapotranspiration over China during the 21st century

The evapotranspiration (ET) plays a crucial role in shaping regional climate patterns and serves as a vital indicator of ecosystem function. However, there remains a limited understanding of the seasonal variability of future ET over China and its correlation with environmental drivers. This study evaluated the skills of 27 models from the Six Phase of Coupled Model Intercomparison Project in modeling ET and the Bayesian Model Averaging (BMA) method was employed to merge monthly simulated ET based on the top five best-performing models. The seasonal changes in ET under three climate scenarios from 2030 to 2099 were analyzed based on the BMA-merged ET, which was well validated with observed ET collected from fourteen flux sites across China. Significant increasing ET over China are projected under all seasons during 2030-2099, with 0.05-0.13 mm yr-1, 0.11-0.23 mm yr-1, and 0.20-0.41 mm yr-1 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively. Relative to the historical period (1980-2014), the relative increase in ET over China is highest in winter and lowest in summer. Seasonal ET increases significantly in all seven climate sub-regions under high forcing scenario. Higher ET increase is generally found in southeastern humid regions, while lowest ET increase occurs in northwest China. At the country level, the primary factor driving ET increase during spring, summer, and autumn seasons is the increasing net radiation and warming. In contrast, ET increase during winter is influenced not only by energy factors but also by vegetation-related factors. Future seasonal ET increase is predominantly driven by increasing energy factors in the southeastern humid region and Tibetan Plateau, while seasonal ET changes in the northwest region prevailingly depend on soil moisture. Results indicate that China will experience a "wet season will get wetter, and dry season will become drier" in the 21st century with high radiation forcing scenario.

Evaluation of the effect of scenarios in the 6th report of IPCC on the prediction groundwater level using the non-linear model of the input-output time series

Due to the increase in greenhouse gases, water and climate crises, increasing population, and decreasing water resources, accurately predicting the changes in the GWL is essential for the management of water resources. For this purpose, in this research, the MIROCES2L model was used to predict the climatic parameters of Birjand Plain under three scenarios of the sixth climate change report: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The minimum temperature, maximum temperature, and precipitation parameters from these three scenarios were measured using the CMhyd model. The results indicated that the minimum and maximum temperature would generally increase in the future under the influence of climate change, but precipitation has a sinusoidal behavior and has a decreasing trend in the summer and spring seasons and an increasing trend in the winter and autumn seasons. Then, three ANN, NIO, and MLR models were employed to simulate groundwater depletion. The results indicated that the evaluation of the performance criteria of the NIO model is superior to the other two models, and it was chosen as the model for predicting groundwater depletion in the future period under the influence of climate change based on all three mentioned scenarios. The final results of this research indicated that the GWL of Birjand Plain in the future period (2024-2041) under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios would respectively decrease to 5.58m, 5.13m, and 5.38. The results of this research indicate that the need for sustainable management to conserve groundwater resources is also very important in the study area.

Warming increases the differences among spring phenology models under future climate change

Phenological models are built upon an understanding of the influence of environmental factors on plant phenology, and serve as effective tools for predicting plant phenological changes. However, the differences in phenological model predictive performance under different climate change scenarios have been rarely studied. In this study, we parameterized thirteen spring phenology models, including six one-phase models and seven two-phase models, by combining phenological observations and meteorological data. Using climatic data from two Shared Socioeconomic Pathways (SSP) scenarios, namely SSP126 (high mitigation and low emission) and SSP585 (no mitigation and high emission), we predicted spring phenology in Germany from 2021 to 2100, and compared the impacts of dormancy phases and driving factors on model predictive performance. The results showed that the average correlation coefficient between the predicted start of growing season (SOS) by the 13 models and the observed values exceeded 0.72, with the highest reaching 0.80. All models outperformed the NULL model (Mean of SOS), and the M1 model (driven by photoperiod and forcing temperature) performed the best for all the tree species. In the SSP126 scenario, the average SOS advanced initially and then gradually shifted towards a delay starting around 2070. In the SSP585 scenario, the average SOS advanced gradually at a rate of approximately 0.14 days per year. Moreover, the standard deviation of the simulated SOS by the 13 spring phenology models exhibited a significant increase at a rate of 0.04 days per year. On average, two-phase models exhibited larger standard deviations than one-phase models after approximately 2050. Models driven solely by temperature showed larger standard deviations after 2060 compared to models driven by both temperature and photoperiod. Our findings suggest investigating the release mechanisms of endodormancy phase and incorporating new insights into future phenological models to better simulate the changes in plant phenology.

Trade-Offs between Direct Emission Reduction and Intersectoral Additional Emissions: Evidence from the Electrification Transition in China's Transport Sector

Electrifying the transport sector is crucial for reducing CO2 emissions and achieving Paris Agreement targets. This largely depends on rapid decarbonization in power plants; however, we often overlook the trade-offs between reduced transportation emissions and additional energy-supply sector emissions induced by electrification. Here, we developed a framework for China's transport sector, including analyzing driving factors of historical CO2 emissions, collecting energy-related parameters of numerous vehicles based on the field- investigation, and assessing the energy-environment impacts of electrification policies with national heterogeneity. We find holistic electrification in China's transport sector will cause substantial cumulative CO2 emission reduction (2025-2075), equivalent to 19.8-42% of global annual emissions, but with a 2.2-16.1 GtCO2 net increase considering the additional emissions in energy-supply sectors. It also leads to a 5.1- to 6.7-fold increase in electricity demand, and the resulting CO2 emissions far surpass the emission reduction achieved. Only under 2 and 1.5 °C scenarios, forcing further decarbonization in the energy supply sectors, will the holistic electrification of transportation have a robust mitigation effect, -2.5 to -7.0 Gt and -6.4 to -11.3 Gt net-negative emissions, respectively. Therefore, we conclude that electrifying the transport sector cannot be a one-size-fits-all policy, requiring synergistically decarbonization efforts in the energy-supply sectors.

Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change

Climate change can alter the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In recent decades, China suffered great human and economic losses due to rainfall-induced landslides. However, how the landslide hazard situation will evolve in the future is still unclear, also because of sparse comprehensive evaluations of potential changes in landslide susceptibility and landslide occurrence frequency under climate change. This study builds upon observed and modelled rainfall data from 24 bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs), a statistical landslide susceptibility model, and empirical rainfall thresholds for landslide initiation, to evaluate changes in landslide susceptibility and landslide occurrence frequency at national-scale. Based on four Shared Socioeconomic Pathways (SSP) scenarios, changes in the rainfall regime are projected and used to evaluate subsequent alterations in landslide susceptibility and in the frequency of rainfall events exceeding empirical rainfall thresholds. In general, the results indicate that the extend of landslide susceptible terrain and the frequency of landslide-triggering rainfall will increase under climate change. Nevertheless, a closer inspection provides a spatially heterogeneous picture on how these landslide occurrence indicators may evolve across China. Until the late 21st century (2080-2099) and depending on the SSP scenarios, the mean annual precipitation is projected to increase by 13.4 % to 28.6 %, inducing an 1.3 % to 2.7 % increase in the modelled areal extent of moderately to very highly susceptible terrain. Different SSP scenarios were associated with an increase in the frequency of landslide-triggering rainfall events by 10.3 % to 19.8 % with respect to historical baseline. Spatially, the southeastern Tibetan Plateau and the Tianshan Mountains in Northwestern Basins are projected to experience the largest increase in landslide susceptibility and frequency of landslide-triggering rainfall, especially under the high emission scenarios. Adaptation and mitigation methods should be prioritized for these future landslide hotspots. This work provides a better understanding of potential impacts of climate change on landslide hazard across China and represents a first step towards national-scale quantitative landslide exposure and risk assessment under climate change.

Projecting future health burden associated with exposure to ambient PM2.5 and ozone in China under different climate scenarios

Projecting future air pollution and related health burdens remains challenging because of the complex interactions among future emissions, population, and climate change. In this study, we estimated the premature deaths attributed to ambient fine particulate matter (PM_2.5) and ozone (O₃) from 2015 to 2100 under four socioeconomic climate scenarios based on an age-stratified assessment method. We found that PM_2.5 will decrease in all shared socioeconomic pathway (SSP) scenarios and O₃ will decrease in the SSP1-2.6 and SSP2-4.5 scenarios, contributing to a decrease in premature mortality together with the declining total population in China. However, the benefits of a decline in population size and PM_2.5 and O₃ concentrations over time will be largely offset by population aging, and premature death caused by PM_2.5 and O₃ will continue to rise till 2060-2080. This impact was greater for the O₃-related deaths than those for PM_2.5. Our study highlights the importance of future prevention strategies that must jointly improve air quality and susceptibility to aging.

Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models

This study investigates changes in fine particulate matter (PM_2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM_2.5 concentrations were comparable to satellite-derived data. Overall, the PM_2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM_2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM_2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM_2.5, it may also contribute to the warming of near- and mid-future climates.

Urbanization and climate change impacts on future flood risk in the Pearl River Delta under shared socioeconomic pathways

Climate change and urbanization are converging to challenge the flood control in the Pearl River Delta (PRD) due to their adverse impacts on precipitation extremes and the urban areas environment. Previous studies have investigated temporal changes in flood risk with various single factor, few have considered the joint effects of climate change, urbanization and socio-economic development. Here, based on the representative concentration pathway (RCP) scenarios, we conducted a comprehensive assessment of future (2030-2050) flood risk over the PRD combined with a thorough investigation of climate change, urbanization and socio-economic development. Precipitation extremes were projected using the regional climate model RegCM4.6, and urbanization growth was projected based on the CA-Markov model. The economic and population development was estimated by the shared socio-economic pathways (SSPs). Flood risk mapping with different RCPs-urbanization-SSPs scenarios was developed for the PRD based on the set pair analyze theory. The results show that climate change and urbanization are expected to exacerbate flood risk in most parts of the PRD during the next few decades, concurrently with more intense extreme precipitation events. The high flood risk areas are projected mainly in the urban regions with unfavorable terrain and dense population. The highest flood risk areas are expected to increase by 8.72% and 19.80% under RCP4.5 and RCP8.5 scenarios, respectively. Reducing greenhouse gas emissions may effectively mitigate the flood risk over the PRD. This study highlight the links between flood risk and changing environment, suggesting that flood risk management and preventative actions should be included in regional adaptation strategies.

Global Socioeconomic Risk of Precipitation Extremes Under Climate Change

Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986-2005) and future periods (2016-2035 and 2046-2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5-SSP3 scenario produces the highest global population exposure for 2046-2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6-SSP1 scenario produces the highest global GDP exposure for 2046-2065, with a 5.56-fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $-days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64-77% and 78-91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.