The change in interannual precipitation variability(P_(IAV)),especially the part driven by El Niño–Southern Oscillation over the Pacific,has sparked worldwide concern.However,it is plagued by substantial uncerta...The change in interannual precipitation variability(P_(IAV)),especially the part driven by El Niño–Southern Oscillation over the Pacific,has sparked worldwide concern.However,it is plagued by substantial uncertainty,such as model uncertainty,internal variability,and scenario uncertainty.Single-model initial-condition large ensembles(SMILEs)and a polynomial fitting method were suggested to separate these uncertainty sources.However,the applicability of a widely used polynomial fitting method in the uncertainty separation of P_(IAV)projection remains unknown.This study compares three sources of uncertainty estimated from five SMILEs and 28 models with one ensemble member in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Results show that the internal uncertainty based on models with one ensemble member calculated using the polynomial fitting method is significantly underestimated compared to SMILEs.However,internal variability in CMIP6 as represented in the pre-industrial control run,aligns closely with SMILEs.At 1.5°C warming above the preindustrial level,internal variability dominates globally,masking the externally forced P_(IAV)signal.At 2.0°C warming,both internal and model uncertainties are significant over regions like Central Africa,the equatorial Indian Ocean,the Maritime Continent,and the Arctic,while internal variability still dominates elsewhere.In some regions,the forced signal becomes distinguishable from internal variability.This study reveals the limitations of the polynomial fitting method in separating P_(IAV)projection uncertainties and emphasizes the importance of SMILEs for accurately quantifying uncertainty sources.It also suggests that improving the intermodel agreement at warming levels of 1.5°C and 2.0°C will not substantially reduce uncertainty in most regions.展开更多
This article evaluates the performance of 20 Coupled Model Intercomparison Project phase 6(CMIP6)models in simulating temperature and precipitation over China through comparisons with gridded observation data for the ...This article evaluates the performance of 20 Coupled Model Intercomparison Project phase 6(CMIP6)models in simulating temperature and precipitation over China through comparisons with gridded observation data for the period of 1995–2014,with a focus on spatial patterns and interannual variability.The evaluations show that the CMIP6 models perform well in reproducing the climatological spatial distribution of temperature and precipitation,with better performance for temperature than for precipitation.Their interannual variability can also be reasonably captured by most models,however,poor performance is noted regarding the interannual variability of winter precipitation.Based on the comprehensive performance for the above two factors,the“highest-ranked”models are selected as an ensemble(BMME).The BMME outperforms the ensemble of all models(AMME)in simulating annual and winter temperature and precipitation,particularly for those subregions with complex terrain but it shows little improvement for summer temperature and precipitation.The AMME and BMME projections indicate annual increases for both temperature and precipitation across China by the end of the 21st century,with larger increases under the scenario of the Shared Socioeconomic Pathway 5/Representative Concentration Pathway 8.5(SSP585)than under scenario of the Shared Socioeconomic Pathway 2/Representative Concentration Pathway 4.5(SSP245).The greatest increases of annual temperature are projected for higher latitudes and higher elevations and the largest percentage-based increases in annual precipitation are projected to occur in northern and western China,especially under SSP585.However,the BMME,which generally performs better in these regions,projects lower changes in annual temperature and larger variations in annual precipitation when compared to the AMME projections.展开更多
Based on 20 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),this article explored possible reasons for differences in simulation biases and projected changes in precipitation in northern China ...Based on 20 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),this article explored possible reasons for differences in simulation biases and projected changes in precipitation in northern China among the allmodel ensemble(AMME),“highest-ranked”model ensemble(BMME),and“lowest-ranked”model ensemble(WMME),from the perspective of atmospheric circulations and moisture budgets.The results show that the BMME and AMME reproduce the East Asian winter circulations better than the WMME.Compared with the AMME and WMME,the BMME reduces the overestimation of evaporation,thereby improving the simulation of winter precipitation.The three ensemble simulated biases for the East Asian summer circulations are generally similar,characterized by a stronger zonal pressure gradient between the mid-latitudes of the North Pacific and East Asia and a northward displacement of the East Asian westerly jet.However,the simulated vertical moisture advection is improved in the BMME,contributing to the slightly higher performance of the BMME than the AMME and WMME on summer precipitation in North and Northeast China.Compared to the AMME and WMME,the BMME projects larger increases in precipitation in northern China during both seasons by the end of the 21st century under the Shared Socioeconomic Pathway 5-8.5(SSP5-8.5).One of the reasons is that the increase in evaporation projected by the BMME is larger.The projection of a greater dynamic contribution by the BMME also plays a role.In addition,larger changes in the nonlinear components in the BMME projection contribute to a larger increase in winter precipitation in northern China.展开更多
Climate condition over a region is mostly determined by the changes in precipitation,temperature and evaporation as the key climate variables.The countries belong to the Belt and Road region are subjected to face stro...Climate condition over a region is mostly determined by the changes in precipitation,temperature and evaporation as the key climate variables.The countries belong to the Belt and Road region are subjected to face strong changes in future climate.In this paper,we used five global climate models from the latest Sixth Phase of Coupled Model Intercomparison Project(CMIP6)to evaluate future climate changes under seven combined scenarios of the Shared Socioeconomic Pathways and the Representative Concentration Pathways(SSP1-1.9,SSP1-2.6,SSP2-4.5,SSP3-7.0,SSP4-3.4,SSP4-6.0 and SSP5-8.5)across the Belt and Road region.This study focuses on undertaking a climate change assessment in terms of future changes in precipitation,air temperature and actual evaporation for the three distinct periods as near-term period(2021−2040),mid-term period(2041−2060)and long-term period(2081−2100).To discern spatial structure,Köppen−Geiger Climate Classification method has been used in this study.In relative terms,the results indicate an evidence of increasing tendency in all the studied variables,where significant changes are anticipated mostly in the long-term period.In addition to,though it is projected to increase under all the SSP-RCP scenarios,greater increases will be happened under higher emission scenarios(SSP5-8.5 and SSP3-7.0).For temperature,robust increases in annual mean temperature is found to be 5.2°C under SSP3-7.0,and highest 7.0°C under SSP5-8.5 scenario relative to present day.The northern part especially Cold and Polar region will be even more warmer(+6.1°C)in the long-term(2081−2100)period under SSP5-8.5.Similarly,at the end of the twenty-first century,annual mean precipitation is inclined to increase largely with a rate of 2.1%and 2.8%per decade under SSP3-7.0 and SSP5-8.5 respectively.Spatial distribution demonstrates that the largest precipitation increases are to be pronounced in the Polar and Arid regions.Precipitation is projected to increase with response to increasing warming most of the regions.Finally,the actual evaporation is projected to increase significantly with rate of 20.3%under SSP3-7.0 and greatest 27.0%for SSP5-8.5 by the end of the century.It is important to note that the changes in evaporation respond to global mean temperature rise consistently in terms of similar spatial pattern for all the scenarios where stronger increase found in the Cold and Polar regions.The increase in precipitation is overruled by enhanced evaporation over the region.However,this study reveals that the CMIP6 models can simulate temperature better than precipitation over the Belt and Road region.Findings of this study could be the reliable basis for initiating policies against further climate induced impacts in the regional scale.展开更多
This study assesses future climate projections for the Nouhao sub-basin at three key temporal horizons(2050,2070,and 2100)under different SSP scenarios.Using recent precipitation and temperature data from 17 global CM...This study assesses future climate projections for the Nouhao sub-basin at three key temporal horizons(2050,2070,and 2100)under different SSP scenarios.Using recent precipitation and temperature data from 17 global CMIP6 models downscaled at a 0.25˚resolution,the multi-model approach was applied to capture the range of future climate changes.The CMIP6 models were validated against CHIRP and ERA5 reference datasets,demonstrating strong performance for precipitation(r=0.76,RMSE=29.14 mm,MAE=24.72 mm)and moderate accuracy for temperature(r=0.27,RMSE=0.30℃,MAE=0.26℃),with all metrics calculated at the annual timescale.Results indicate a projected 35%increase in precipitation across the basin by 2100,alongside a gradual temperature rise of 1℃to 4℃.However,the analysis reveals significant uncertainties,particularly for temperature and precipitation projections,with some individual models suggesting a slight decline in precipitation and even cooling trends over the basin.These discrepancies underscore the challenges in modelling regional climate impacts and the need for more robust projections.These findings highlight the urgency of developing basin-specific adaptation strategies,focusing on agriculture,water management,and climate resilience.Policymakers are thus provided with critical insights to guide proactive decision-making,ensuring that the basin is better prepared for future climate challenges.展开更多
Landslides pose a significant threat to both human society and environmental sustainability,yet,their spatiotemporal evolution and impacts on global scales in the context of a warming climate remain poorly understood....Landslides pose a significant threat to both human society and environmental sustainability,yet,their spatiotemporal evolution and impacts on global scales in the context of a warming climate remain poorly understood.In this study,we projected global landslide susceptibility under four shared socioeconomic pathways(SSPs)from 2021 to 2100,utilizing multiple machine learning models based on precipitation data from the Coupled Model Intercomparison Project Phase 6(CMIP6)Global Climate Models(GCMs)and static metrics.Our results indicate an overall upward trend in global landslide susceptibility under the SSPs compared to the baseline period(2001–2020),with the most significant increase of about 1%in the very far future(2081–2100)under the high emissions scenario(SSP5-8.5).Currently,approximately 13%of the world’s land area is at very high risk of landslide,mainly in the Cordillera of the Americas and the Andes in South America,the Alps in Europe,the Ethiopian Highlands in Africa,the Himalayas in Asia,and the countries of East and South-East Asia.Notably,India is the country most adversely affected by climate change,particularly during 2081–2100 under SSP3-7.0,with approximately 590 million people—23 times the global average—living in areas categorized as having very high susceptibility.展开更多
The East Asian summer monsoon （EASM） is a distinctive component of the Asian climate system and critically influences the economy and society of the region.To understand the ability of AGCMs in capturing the major f...The East Asian summer monsoon （EASM） is a distinctive component of the Asian climate system and critically influences the economy and society of the region.To understand the ability of AGCMs in capturing the major features of EASM,10 models that participated in Coupled Model Intercomparison Project/Atmospheric Model Intercomparison Project （CMIP5/AMIP）,which used observational SST and sea ice to drive AGCMs during the period 1979-2008,were evaluated by comparing with observations and AMIP Ⅱ simulations.The results indicated that the multi-model ensemble （MME） of CMIP5/AMIP captures the main characteristics of precipitation and monsoon circulation,and shows the best skill in EASM simulation,better than the AMIP Ⅱ MME.As for the Meiyu/Changma/Baiyu rainbelt,the intensity of rainfall is underestimated in all the models.The biases are caused by a weak western Pacific subtropical high （WPSH） and accompanying eastward southwesterly winds in group Ⅰ models,and by a too strong and west-extended WPSH as well as westerly winds in group Ⅱ models.Considerable systematic errors exist in the simulated seasonal migration of rainfall,and the notable northward jumps and rainfall persistence remain a challenge for all the models.However,the CMIP5/AMIP MME is skillful in simulating the western North Pacific monsoon index （WNPMI）.展开更多
Based on the historical and RCP8.5 runs of the multi-model ensemble of 32 models participating in CMIP5, the present study evaluates the formation mechanisms for the patterns of changes in equatorial Pacific SST under...Based on the historical and RCP8.5 runs of the multi-model ensemble of 32 models participating in CMIP5, the present study evaluates the formation mechanisms for the patterns of changes in equatorial Pacific SST under global warming. Two features with complex formation processes, the zonal E1 Nifio-like pattern and the meridional equatorial peak warm- ing （EPW）, are investigated. The climatological evaporation is the main contributor to the E1 Nifio-like pattern, while the ocean dynamical thermostat effect plays a comparable negative role. The cloud-shortwave-radiation-SST feedback and the weakened Walker circulation play a small positive role in the E1 Nifio-like pattern. The processes associated with ocean dynamics are confined to the equator. The climatological evaporation is also the dominant contributor to the EPW pattern, as suggested in previous studies. However, the effects of some processes are inconsistent with previous studies. For example, changes in the zonal heat advection due to the weakened Walker circulation have a remarkable positive contribution to the EPW pattern, and changes in the shortwave radiation play a negative role in the EPW pattern.展开更多
Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling.However,the simulation outcomes have be...Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling.However,the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget.Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority,and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required.Building on the Coupled Model Intercomparison Project Phase 6(CMIP6),we have transplanted a conventional ensemble learning approach,and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset,both of which have realised outstanding performances(R2>0.95,RMSE<2.12 ppbv).The conventional ensemble learning approach is computationally cheaper and results in higher overall performance,but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable.The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability,but induces heavier computational burdens.Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.展开更多
The South Asian summer monsoon（SASM） precipitation is analyzed based on reanalysis datasets and historical simulation results from 23 climate models of the Coupled Model Intercomparison Project phase 5（CMIP5）. The...The South Asian summer monsoon（SASM） precipitation is analyzed based on reanalysis datasets and historical simulation results from 23 climate models of the Coupled Model Intercomparison Project phase 5（CMIP5）. The results show that most models reproduce well the climatological pattern of SASM precipitation, but the main rainfall period lags that of the reanalysis by one month. The relationship between the simulated SASM precipitation and sea surface temperature anomalies（SSTAs） is quite similar to the reanalysis data. This is attributed to the well-reproduced Walker cell anomaly in the tropical zone. It is projected that the negative correlation between SASM precipitation and SSTAs in the eastern equatorial Pacific will weaken and even reverse to a positive one in the period 2070–2096 under the representative concentration pathway（RCP） scenario with strong external forcing（RCP8.5）, while the change of the correlation under moderate forcing（RCP4.5） still has great uncertainty.展开更多
In this paper, the features and possible causes of sea surface temperature(SST) biases over the Northwest Pacific are investigated based on a mixed-layer heat budget analysis in 21 coupled general circulation models(C...In this paper, the features and possible causes of sea surface temperature(SST) biases over the Northwest Pacific are investigated based on a mixed-layer heat budget analysis in 21 coupled general circulation models(CGCMs) from phase 5 of the Coupled Model Inter-comparison Project(CMIP5). Most CMIP5 models show cold SST biases throughout the year over the Northwest Pacific. The largest biases appear during summer, and the smallest biases occur during winter. These cold SST biases are seen at the basin scale and are mainly located in the inner region of the low and mid-latitudes. According to the mixed-layer heat budget analysis, overestimation of upward net sea surface heat fluxes associated with atmospheric processes are primarily responsible for the cold SST biases. Among the different components of surface heat fluxes, overestimated upward latent heat fluxes induced by the excessively strong surface winds contribute the most to the cold SST biases during the spring, autumn, and winter seasons. Conversely, during the summer, overestimated upward latent heat fluxes and underestimated downward solar radiations at the sea surface are equally important. Further analysis suggests that the overly strong surface winds over the Northwest Pacific during winter and spring are associated with excessive precipitation over the Maritime Continent region,whereas those occurring during summer and autumn are associated with the excessive northward extension of the intertropical convergence zone(ITCZ). The excessive precipitation over the Maritime Continent region and the biases in the simulated ITCZ induce anomalous northeasterlies, which are in favor of enhancing low-level winds over the North Pacific. The enhanced surface wind increases the sea surface evaporation, which contributes to the excessive upward latent heat fluxes. Thus, the SST over the Northwest Pacific cools.展开更多
The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credib...The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credible quantitative estimates of future climate change; however, the mismatches between the IPCC AR4 model ensembles and the observations, especially the multi-decadal variability (MDV), have cast shadows on the confidence of the model-based decadal projections of future cli mate. This paper reports an evaluation of many individual runs of AR4 models in the simulation of past global mean tempera ture. We find that most of the individual model runs fail to reproduce the MDV of past climate, which may have led to the overestimation of the projection of global warming for the next 40 years or so. Based on such an evaluation, we propose an al ternative approach, in which the MDV signal is taken into account, to project the global mean temperature for the next 40 years and obtain that the global warming during 2011–2050 could be much smaller than the AR4 projection.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.42425504).
文摘The change in interannual precipitation variability(P_(IAV)),especially the part driven by El Niño–Southern Oscillation over the Pacific,has sparked worldwide concern.However,it is plagued by substantial uncertainty,such as model uncertainty,internal variability,and scenario uncertainty.Single-model initial-condition large ensembles(SMILEs)and a polynomial fitting method were suggested to separate these uncertainty sources.However,the applicability of a widely used polynomial fitting method in the uncertainty separation of P_(IAV)projection remains unknown.This study compares three sources of uncertainty estimated from five SMILEs and 28 models with one ensemble member in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Results show that the internal uncertainty based on models with one ensemble member calculated using the polynomial fitting method is significantly underestimated compared to SMILEs.However,internal variability in CMIP6 as represented in the pre-industrial control run,aligns closely with SMILEs.At 1.5°C warming above the preindustrial level,internal variability dominates globally,masking the externally forced P_(IAV)signal.At 2.0°C warming,both internal and model uncertainties are significant over regions like Central Africa,the equatorial Indian Ocean,the Maritime Continent,and the Arctic,while internal variability still dominates elsewhere.In some regions,the forced signal becomes distinguishable from internal variability.This study reveals the limitations of the polynomial fitting method in separating P_(IAV)projection uncertainties and emphasizes the importance of SMILEs for accurately quantifying uncertainty sources.It also suggests that improving the intermodel agreement at warming levels of 1.5°C and 2.0°C will not substantially reduce uncertainty in most regions.
基金the National Key Research and Development Program of China(2018YFA0606301)the National Natural Science Foundation of China(42025502,41991285,42088101).
文摘This article evaluates the performance of 20 Coupled Model Intercomparison Project phase 6(CMIP6)models in simulating temperature and precipitation over China through comparisons with gridded observation data for the period of 1995–2014,with a focus on spatial patterns and interannual variability.The evaluations show that the CMIP6 models perform well in reproducing the climatological spatial distribution of temperature and precipitation,with better performance for temperature than for precipitation.Their interannual variability can also be reasonably captured by most models,however,poor performance is noted regarding the interannual variability of winter precipitation.Based on the comprehensive performance for the above two factors,the“highest-ranked”models are selected as an ensemble(BMME).The BMME outperforms the ensemble of all models(AMME)in simulating annual and winter temperature and precipitation,particularly for those subregions with complex terrain but it shows little improvement for summer temperature and precipitation.The AMME and BMME projections indicate annual increases for both temperature and precipitation across China by the end of the 21st century,with larger increases under the scenario of the Shared Socioeconomic Pathway 5/Representative Concentration Pathway 8.5(SSP585)than under scenario of the Shared Socioeconomic Pathway 2/Representative Concentration Pathway 4.5(SSP245).The greatest increases of annual temperature are projected for higher latitudes and higher elevations and the largest percentage-based increases in annual precipitation are projected to occur in northern and western China,especially under SSP585.However,the BMME,which generally performs better in these regions,projects lower changes in annual temperature and larger variations in annual precipitation when compared to the AMME projections.
基金jointly supported by the National Natural Science Foundation of China(Grant No.41991285)the National Key Research and Development Program of China(2017YFA0605004)the Program for Distinguished Professors of Jiangsu。
文摘Based on 20 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),this article explored possible reasons for differences in simulation biases and projected changes in precipitation in northern China among the allmodel ensemble(AMME),“highest-ranked”model ensemble(BMME),and“lowest-ranked”model ensemble(WMME),from the perspective of atmospheric circulations and moisture budgets.The results show that the BMME and AMME reproduce the East Asian winter circulations better than the WMME.Compared with the AMME and WMME,the BMME reduces the overestimation of evaporation,thereby improving the simulation of winter precipitation.The three ensemble simulated biases for the East Asian summer circulations are generally similar,characterized by a stronger zonal pressure gradient between the mid-latitudes of the North Pacific and East Asia and a northward displacement of the East Asian westerly jet.However,the simulated vertical moisture advection is improved in the BMME,contributing to the slightly higher performance of the BMME than the AMME and WMME on summer precipitation in North and Northeast China.Compared to the AMME and WMME,the BMME projects larger increases in precipitation in northern China during both seasons by the end of the 21st century under the Shared Socioeconomic Pathway 5-8.5(SSP5-8.5).One of the reasons is that the increase in evaporation projected by the BMME is larger.The projection of a greater dynamic contribution by the BMME also plays a role.In addition,larger changes in the nonlinear components in the BMME projection contribute to a larger increase in winter precipitation in northern China.
基金This study was cooperatively funded by National Key Research and Development Program of ChinaMOST(2018FY100501)The authors are thankful for the support by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX20_0957)High-level Talent Recruitment Program of the Nanjing University of Information Science and Technology(NUIST),and the vip Professor Program of the Xinjiang Institute of Ecology and Geography,CAS.The authors would like to thank the World Climate Research Program's working group on coupled modeling and European Centre for Medium-Range Weather Forecasts for producing and making available their model output.
文摘Climate condition over a region is mostly determined by the changes in precipitation,temperature and evaporation as the key climate variables.The countries belong to the Belt and Road region are subjected to face strong changes in future climate.In this paper,we used five global climate models from the latest Sixth Phase of Coupled Model Intercomparison Project(CMIP6)to evaluate future climate changes under seven combined scenarios of the Shared Socioeconomic Pathways and the Representative Concentration Pathways(SSP1-1.9,SSP1-2.6,SSP2-4.5,SSP3-7.0,SSP4-3.4,SSP4-6.0 and SSP5-8.5)across the Belt and Road region.This study focuses on undertaking a climate change assessment in terms of future changes in precipitation,air temperature and actual evaporation for the three distinct periods as near-term period(2021−2040),mid-term period(2041−2060)and long-term period(2081−2100).To discern spatial structure,Köppen−Geiger Climate Classification method has been used in this study.In relative terms,the results indicate an evidence of increasing tendency in all the studied variables,where significant changes are anticipated mostly in the long-term period.In addition to,though it is projected to increase under all the SSP-RCP scenarios,greater increases will be happened under higher emission scenarios(SSP5-8.5 and SSP3-7.0).For temperature,robust increases in annual mean temperature is found to be 5.2°C under SSP3-7.0,and highest 7.0°C under SSP5-8.5 scenario relative to present day.The northern part especially Cold and Polar region will be even more warmer(+6.1°C)in the long-term(2081−2100)period under SSP5-8.5.Similarly,at the end of the twenty-first century,annual mean precipitation is inclined to increase largely with a rate of 2.1%and 2.8%per decade under SSP3-7.0 and SSP5-8.5 respectively.Spatial distribution demonstrates that the largest precipitation increases are to be pronounced in the Polar and Arid regions.Precipitation is projected to increase with response to increasing warming most of the regions.Finally,the actual evaporation is projected to increase significantly with rate of 20.3%under SSP3-7.0 and greatest 27.0%for SSP5-8.5 by the end of the century.It is important to note that the changes in evaporation respond to global mean temperature rise consistently in terms of similar spatial pattern for all the scenarios where stronger increase found in the Cold and Polar regions.The increase in precipitation is overruled by enhanced evaporation over the region.However,this study reveals that the CMIP6 models can simulate temperature better than precipitation over the Belt and Road region.Findings of this study could be the reliable basis for initiating policies against further climate induced impacts in the regional scale.
文摘This study assesses future climate projections for the Nouhao sub-basin at three key temporal horizons(2050,2070,and 2100)under different SSP scenarios.Using recent precipitation and temperature data from 17 global CMIP6 models downscaled at a 0.25˚resolution,the multi-model approach was applied to capture the range of future climate changes.The CMIP6 models were validated against CHIRP and ERA5 reference datasets,demonstrating strong performance for precipitation(r=0.76,RMSE=29.14 mm,MAE=24.72 mm)and moderate accuracy for temperature(r=0.27,RMSE=0.30℃,MAE=0.26℃),with all metrics calculated at the annual timescale.Results indicate a projected 35%increase in precipitation across the basin by 2100,alongside a gradual temperature rise of 1℃to 4℃.However,the analysis reveals significant uncertainties,particularly for temperature and precipitation projections,with some individual models suggesting a slight decline in precipitation and even cooling trends over the basin.These discrepancies underscore the challenges in modelling regional climate impacts and the need for more robust projections.These findings highlight the urgency of developing basin-specific adaptation strategies,focusing on agriculture,water management,and climate resilience.Policymakers are thus provided with critical insights to guide proactive decision-making,ensuring that the basin is better prepared for future climate challenges.
基金supported by the project of National Natural Science Foundation of China(Grant No.42371203 and U21A2032)the project of Sichuan Provincial Science and Technology Department Program Funding(Grant No.2025YFHZ0010)the project of the Science and Technology Program of Aba City(Grant NO.R24YYJSYJ0001).
文摘Landslides pose a significant threat to both human society and environmental sustainability,yet,their spatiotemporal evolution and impacts on global scales in the context of a warming climate remain poorly understood.In this study,we projected global landslide susceptibility under four shared socioeconomic pathways(SSPs)from 2021 to 2100,utilizing multiple machine learning models based on precipitation data from the Coupled Model Intercomparison Project Phase 6(CMIP6)Global Climate Models(GCMs)and static metrics.Our results indicate an overall upward trend in global landslide susceptibility under the SSPs compared to the baseline period(2001–2020),with the most significant increase of about 1%in the very far future(2081–2100)under the high emissions scenario(SSP5-8.5).Currently,approximately 13%of the world’s land area is at very high risk of landslide,mainly in the Cordillera of the Americas and the Andes in South America,the Alps in Europe,the Ethiopian Highlands in Africa,the Himalayas in Asia,and the countries of East and South-East Asia.Notably,India is the country most adversely affected by climate change,particularly during 2081–2100 under SSP3-7.0,with approximately 590 million people—23 times the global average—living in areas categorized as having very high susceptibility.
基金supported by the National High Technology Research and Development Program of China (Grant No. 2010AA012305)the General Project of the National Natural Science Foundation of China (Grant No. 41275108)+1 种基金the National Basic Research Program of China (Grant No. 2010CB950504)the Fundamental Research Funds for the Central Universities (Grant No. 2012YBXS27)
文摘The East Asian summer monsoon （EASM） is a distinctive component of the Asian climate system and critically influences the economy and society of the region.To understand the ability of AGCMs in capturing the major features of EASM,10 models that participated in Coupled Model Intercomparison Project/Atmospheric Model Intercomparison Project （CMIP5/AMIP）,which used observational SST and sea ice to drive AGCMs during the period 1979-2008,were evaluated by comparing with observations and AMIP Ⅱ simulations.The results indicated that the multi-model ensemble （MME） of CMIP5/AMIP captures the main characteristics of precipitation and monsoon circulation,and shows the best skill in EASM simulation,better than the AMIP Ⅱ MME.As for the Meiyu/Changma/Baiyu rainbelt,the intensity of rainfall is underestimated in all the models.The biases are caused by a weak western Pacific subtropical high （WPSH） and accompanying eastward southwesterly winds in group Ⅰ models,and by a too strong and west-extended WPSH as well as westerly winds in group Ⅱ models.Considerable systematic errors exist in the simulated seasonal migration of rainfall,and the notable northward jumps and rainfall persistence remain a challenge for all the models.However,the CMIP5/AMIP MME is skillful in simulating the western North Pacific monsoon index （WNPMI）.
基金supported by the National Basic Research Program of China (Grant Nos. 2014CB953903 and 2012CB955604)the National Natural Science Foundation of China (Grant Nos. 41575088 and 41461164005)
文摘Based on the historical and RCP8.5 runs of the multi-model ensemble of 32 models participating in CMIP5, the present study evaluates the formation mechanisms for the patterns of changes in equatorial Pacific SST under global warming. Two features with complex formation processes, the zonal E1 Nifio-like pattern and the meridional equatorial peak warm- ing （EPW）, are investigated. The climatological evaporation is the main contributor to the E1 Nifio-like pattern, while the ocean dynamical thermostat effect plays a comparable negative role. The cloud-shortwave-radiation-SST feedback and the weakened Walker circulation play a small positive role in the E1 Nifio-like pattern. The processes associated with ocean dynamics are confined to the equator. The climatological evaporation is also the dominant contributor to the EPW pattern, as suggested in previous studies. However, the effects of some processes are inconsistent with previous studies. For example, changes in the zonal heat advection due to the weakened Walker circulation have a remarkable positive contribution to the EPW pattern, and changes in the shortwave radiation play a negative role in the EPW pattern.
文摘Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling.However,the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget.Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority,and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required.Building on the Coupled Model Intercomparison Project Phase 6(CMIP6),we have transplanted a conventional ensemble learning approach,and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset,both of which have realised outstanding performances(R2>0.95,RMSE<2.12 ppbv).The conventional ensemble learning approach is computationally cheaper and results in higher overall performance,but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable.The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability,but induces heavier computational burdens.Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.
基金Supported by the National(Key)Basic Research and Development(973)Program of China(2010CB950503)West Light Foundation of the Chinese Academy of Sciences(Y229D21001)National Natural Science Foundation of China(41130961)
文摘The South Asian summer monsoon（SASM） precipitation is analyzed based on reanalysis datasets and historical simulation results from 23 climate models of the Coupled Model Intercomparison Project phase 5（CMIP5）. The results show that most models reproduce well the climatological pattern of SASM precipitation, but the main rainfall period lags that of the reanalysis by one month. The relationship between the simulated SASM precipitation and sea surface temperature anomalies（SSTAs） is quite similar to the reanalysis data. This is attributed to the well-reproduced Walker cell anomaly in the tropical zone. It is projected that the negative correlation between SASM precipitation and SSTAs in the eastern equatorial Pacific will weaken and even reverse to a positive one in the period 2070–2096 under the representative concentration pathway（RCP） scenario with strong external forcing（RCP8.5）, while the change of the correlation under moderate forcing（RCP4.5） still has great uncertainty.
基金supported by the National Key Research and Development Program of China(Grant No.2017YFA0604004)the R&D Special Fund for Public Welfare Industry(Meteorology)(Grant No.GYHY201506012)
文摘In this paper, the features and possible causes of sea surface temperature(SST) biases over the Northwest Pacific are investigated based on a mixed-layer heat budget analysis in 21 coupled general circulation models(CGCMs) from phase 5 of the Coupled Model Inter-comparison Project(CMIP5). Most CMIP5 models show cold SST biases throughout the year over the Northwest Pacific. The largest biases appear during summer, and the smallest biases occur during winter. These cold SST biases are seen at the basin scale and are mainly located in the inner region of the low and mid-latitudes. According to the mixed-layer heat budget analysis, overestimation of upward net sea surface heat fluxes associated with atmospheric processes are primarily responsible for the cold SST biases. Among the different components of surface heat fluxes, overestimated upward latent heat fluxes induced by the excessively strong surface winds contribute the most to the cold SST biases during the spring, autumn, and winter seasons. Conversely, during the summer, overestimated upward latent heat fluxes and underestimated downward solar radiations at the sea surface are equally important. Further analysis suggests that the overly strong surface winds over the Northwest Pacific during winter and spring are associated with excessive precipitation over the Maritime Continent region,whereas those occurring during summer and autumn are associated with the excessive northward extension of the intertropical convergence zone(ITCZ). The excessive precipitation over the Maritime Continent region and the biases in the simulated ITCZ induce anomalous northeasterlies, which are in favor of enhancing low-level winds over the North Pacific. The enhanced surface wind increases the sea surface evaporation, which contributes to the excessive upward latent heat fluxes. Thus, the SST over the Northwest Pacific cools.
基金supported by the National Basic Research Program of Chi-na (Grant No. 2011CB952000)the National Natural Science Founda-tion of China (Grant No. 40810059003)+1 种基金Qian Cheng was partly supported by the "Strategic Priority Research Program" of the Chinese Academy of Sciences (Grant No. XDA05090103)Wu Zhaohua was supported by the Natural Science Foundation of USA (Grant No. ATM-0917743)
文摘The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credible quantitative estimates of future climate change; however, the mismatches between the IPCC AR4 model ensembles and the observations, especially the multi-decadal variability (MDV), have cast shadows on the confidence of the model-based decadal projections of future cli mate. This paper reports an evaluation of many individual runs of AR4 models in the simulation of past global mean tempera ture. We find that most of the individual model runs fail to reproduce the MDV of past climate, which may have led to the overestimation of the projection of global warming for the next 40 years or so. Based on such an evaluation, we propose an al ternative approach, in which the MDV signal is taken into account, to project the global mean temperature for the next 40 years and obtain that the global warming during 2011–2050 could be much smaller than the AR4 projection.
