Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.展开更多
Objective:This study aimed to analyze the temporal trends in cancer mortality in China from 2013-2021 and project the future trends through 2030.Methods:This study was based on the China Causes of Death Surveillance D...Objective:This study aimed to analyze the temporal trends in cancer mortality in China from 2013-2021 and project the future trends through 2030.Methods:This study was based on the China Causes of Death Surveillance Dataset,which covers 2.37 billion person-years.Age-standardized mortality rates(ASMRs)were calculated using Segi’s world standard population and the trends were evaluated via Joinpoint regression.Bayesian age-period-cohort models were used for mortality projections.Contributions of demographic changes(population size and age structure)and risk factors to the mortality burden were quantified using the decomposition analysis.Results:The combined ASMRs for all cancers decreased annually by 2.3%,driven by significant declines in esophageal(4.8%),stomach(4.5%),and liver cancers(2.7%).In contrast,the pancreatic and prostate cancer ASMRs increased by 2.0% and 3.4% annually,respectively.Urban areas demonstrated a more rapid decline in the combined ASMRs for all cancers[average annual percent change(AAPC)=-3.0% in urban areas vs.-2.0% in rural areas],highlighting persistent disparities.Population aging contributed 20%-50% to death increases between 2013 and 2021.The combined ASMRs for all cancers,like the findings of temporal trend analyses,will continue to decrease and the regional(urban and rural)difference is projected to simulate that of the temporal trend through 2030.In fact,cancer deaths are projected to reach 2.4 million by 2030.Conclusions:The cancer burden in China is facing the dual challenges of population aging and urban-rural disparities.It is necessary to prioritize rural screening,control risk factors,such as smoking and diet,and integrate more efficacious cancer prevention and control programmes into the policy to reduce mortality in the future.展开更多
Estimation and attribution of evapotranspiration(ET)and its components under changing environment is still a challenge but is essential for understanding the mechanisms of water and energy transfer for regional water ...Estimation and attribution of evapotranspiration(ET)and its components under changing environment is still a challenge but is essential for understanding the mechanisms of water and energy transfer for regional water resources management.In this study,an improved hydrological model is developed to estimate evapotranspiration and its components,i.e.,evaporation(E)and transpiration(T)by integrated the advantages of hydrological modeling constrained by water balance and the water-carbon close relationships.Results show that the improved hydrological model could captures ET and its components well in the study region.During the past years,annual ET and E increase obviously about 2.40 and 1.42 mm/a,particularly in spring and summer accounting for 90%.T shows less increasement and mainly increases in spring while it decreases in summer.Precipitation is the dominant factor and contributes 74.1%and 90.0%increases of annual ET and E,while the attribution of T changes is more complex by coupling of the positive effects of precipitation,rising temperature and interactive influences,the negative effects of solar diming and elevated CO_(2).In the future,ET and its components tend to increase under most of the Shared Socioeconomic Pathways(SSP)scenarios except for T decreases under the very high emissions scenario(SSP5-8.5)based on the projections.From seasonal perspective,the changes of ET and the components are mainly in spring and summer accounting for 75%,while more slight changes are found in autumn and winter.This study highlights the effectiveness of estimating ET and its components by improving hydrological models within water-carbon coupling relationships,and more complex mechanisms of transpiration changes than evapotranspiration and evaporation changes under the interactive effects of climate variability and vegetation dynamics.Besides,decision makers should pay attention to the more increases in the undesirable E than desirable T.展开更多
Understanding trends in rainfall and temperature projections is critical for assessing climate change impacts,managing water resources,mitigating disaster risks,and guiding sustainable agricultural and infrastructure ...Understanding trends in rainfall and temperature projections is critical for assessing climate change impacts,managing water resources,mitigating disaster risks,and guiding sustainable agricultural and infrastructure planning.This study investigates projected changes in temperature and rainfall in the Upper Bernam River Basin(UBRB),Malaysia,using ten Global Climate Models(GCMs)from CMIP6 across four scenarios(SSP126,SSP245,SSP370,and SSP585).Downscaling was conducted with the Climate-Smart Decision Support System(CSDSS)for the baseline period(1985-2014)and for future periods:2020s,2040s,2060s,and 2080s.Results indicate a consistent warming trend,with maximum temperatures projected to increase from 1.4℃(2020s,SSP126)to 4.66℃(2080s,SSP585),and minimum temperatures from 1.97℃ to 5.70℃ over the same period and scenarios.Rainfall projections reveal high variability and inter-scenario uncertainty,with average monthly rainfall changes ranging from−17.6%(2020s,SSP585)to+6.6%(2080s,SSP370).Extremes analysis shows intensifying wet and dry spells,with 95th percentile rainfall rising to 7.87% and significant increases in 90th percentile temperatures,reaching nearly 20%under SSP585 by 2080s.Seasonal shifts include reduced rainfall from January to April and potential increases in main-season(July-August)flooding.These findings highlight the importance of adaptive strategies such as flood control,off-season(January-June)water storage,and climate-resilient infrastructure.The study underscores inter-scenario uncertainties and provides critical insights for climate-resilient water resource planning and disaster risk mitigation in UBRB.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
基金supported by the CAMS Innovation Fund for Medical Sciences(Grant No.2021-I2M-1-011)the Capital’s Funds for Health Improvement and Research(Grant No.CFH2024-2G-40214).
文摘Objective:This study aimed to analyze the temporal trends in cancer mortality in China from 2013-2021 and project the future trends through 2030.Methods:This study was based on the China Causes of Death Surveillance Dataset,which covers 2.37 billion person-years.Age-standardized mortality rates(ASMRs)were calculated using Segi’s world standard population and the trends were evaluated via Joinpoint regression.Bayesian age-period-cohort models were used for mortality projections.Contributions of demographic changes(population size and age structure)and risk factors to the mortality burden were quantified using the decomposition analysis.Results:The combined ASMRs for all cancers decreased annually by 2.3%,driven by significant declines in esophageal(4.8%),stomach(4.5%),and liver cancers(2.7%).In contrast,the pancreatic and prostate cancer ASMRs increased by 2.0% and 3.4% annually,respectively.Urban areas demonstrated a more rapid decline in the combined ASMRs for all cancers[average annual percent change(AAPC)=-3.0% in urban areas vs.-2.0% in rural areas],highlighting persistent disparities.Population aging contributed 20%-50% to death increases between 2013 and 2021.The combined ASMRs for all cancers,like the findings of temporal trend analyses,will continue to decrease and the regional(urban and rural)difference is projected to simulate that of the temporal trend through 2030.In fact,cancer deaths are projected to reach 2.4 million by 2030.Conclusions:The cancer burden in China is facing the dual challenges of population aging and urban-rural disparities.It is necessary to prioritize rural screening,control risk factors,such as smoking and diet,and integrate more efficacious cancer prevention and control programmes into the policy to reduce mortality in the future.
基金supported by the Chongqing Natural Science Foundation Innovation-Driven Development Joint Funds(No.CSTB2025NSCQ-LZX0055)the Youth Innovation Promotion Association,CAS(No.2021385)+4 种基金the Fundamental Research Funds for the Central Universities of South-Central Minzu University(No.CZQ24028)the Hubei Provincial Natural Science Foundation of China(No.2023AFB782)the Program of China Scholarship Council(No.202407780001)the National Natural Science Foundation of China(No.51809008)the Fund for Academic Innovation Teams of South-Central Minzu University(No.XTZ24019).
文摘Estimation and attribution of evapotranspiration(ET)and its components under changing environment is still a challenge but is essential for understanding the mechanisms of water and energy transfer for regional water resources management.In this study,an improved hydrological model is developed to estimate evapotranspiration and its components,i.e.,evaporation(E)and transpiration(T)by integrated the advantages of hydrological modeling constrained by water balance and the water-carbon close relationships.Results show that the improved hydrological model could captures ET and its components well in the study region.During the past years,annual ET and E increase obviously about 2.40 and 1.42 mm/a,particularly in spring and summer accounting for 90%.T shows less increasement and mainly increases in spring while it decreases in summer.Precipitation is the dominant factor and contributes 74.1%and 90.0%increases of annual ET and E,while the attribution of T changes is more complex by coupling of the positive effects of precipitation,rising temperature and interactive influences,the negative effects of solar diming and elevated CO_(2).In the future,ET and its components tend to increase under most of the Shared Socioeconomic Pathways(SSP)scenarios except for T decreases under the very high emissions scenario(SSP5-8.5)based on the projections.From seasonal perspective,the changes of ET and the components are mainly in spring and summer accounting for 75%,while more slight changes are found in autumn and winter.This study highlights the effectiveness of estimating ET and its components by improving hydrological models within water-carbon coupling relationships,and more complex mechanisms of transpiration changes than evapotranspiration and evaporation changes under the interactive effects of climate variability and vegetation dynamics.Besides,decision makers should pay attention to the more increases in the undesirable E than desirable T.
基金funded by the Petroleum Technology Development Fund(PTDF),the Federal Republic of Nigeria(Grant No.PTDF/ED/OSS/PHD/MDZ/1726/20).
文摘Understanding trends in rainfall and temperature projections is critical for assessing climate change impacts,managing water resources,mitigating disaster risks,and guiding sustainable agricultural and infrastructure planning.This study investigates projected changes in temperature and rainfall in the Upper Bernam River Basin(UBRB),Malaysia,using ten Global Climate Models(GCMs)from CMIP6 across four scenarios(SSP126,SSP245,SSP370,and SSP585).Downscaling was conducted with the Climate-Smart Decision Support System(CSDSS)for the baseline period(1985-2014)and for future periods:2020s,2040s,2060s,and 2080s.Results indicate a consistent warming trend,with maximum temperatures projected to increase from 1.4℃(2020s,SSP126)to 4.66℃(2080s,SSP585),and minimum temperatures from 1.97℃ to 5.70℃ over the same period and scenarios.Rainfall projections reveal high variability and inter-scenario uncertainty,with average monthly rainfall changes ranging from−17.6%(2020s,SSP585)to+6.6%(2080s,SSP370).Extremes analysis shows intensifying wet and dry spells,with 95th percentile rainfall rising to 7.87% and significant increases in 90th percentile temperatures,reaching nearly 20%under SSP585 by 2080s.Seasonal shifts include reduced rainfall from January to April and potential increases in main-season(July-August)flooding.These findings highlight the importance of adaptive strategies such as flood control,off-season(January-June)water storage,and climate-resilient infrastructure.The study underscores inter-scenario uncertainties and provides critical insights for climate-resilient water resource planning and disaster risk mitigation in UBRB.
