2000年后全球气温的增温率显著下降,全球进入变暖减缓期。本文基于CRU（Climatic Research Unit）观测资料,分析讨论了2000年后全球及欧亚中高纬度地区全球变暖的减缓特征,评估了CMIP5（Coupled Model Intercomparison Project Phase 5...2000年后全球气温的增温率显著下降,全球进入变暖减缓期。本文基于CRU（Climatic Research Unit）观测资料,分析讨论了2000年后全球及欧亚中高纬度地区全球变暖的减缓特征,评估了CMIP5（Coupled Model Intercomparison Project Phase 5）试验多模式对全球变暖减缓的模拟及未来气温变化预估。结果表明,2000年后全球陆地平均地面气温的增温率大幅下降至0.14°C（10 a）-1,仅为1976~1999年加速期增温率的一半。全球陆地13个区域中有9个地区的增温率小于2000年前,4个地区甚至出现了降温。其中以欧亚中高纬地区最为特殊。加速期（1976~1999年）增温率达到0.50°C（10 a）-1,为全球陆地最大,2000年后陡降至-0.17°C（10 a）-1,为全球最强降温区,为全球变暖的减缓贡献了49.13%。并且具有显著的季节依赖,减缓期冬季增温率下降了-2.68°C（10a）-1,而秋季升高了0.86°C（10 a）-1,呈现反位相变化特征。CMIP5多模式计划中仅BCC-CSM1.1在RCP2.6情景下和MRI-ESM1模式在RCP8.5下的模拟较好地预估了全球及欧亚中高纬地区在2000年后增温率的下降以及欧亚中高纬秋、冬温度的反位相变化特征。BCC-CSM1.1在RCP2.6情景下预估欧亚中高纬地区2012年后温度距平保持在1.2°C左右,2020年后跃至2°C附近振荡。而MRI-ESM1在RCP8.5情景下预估的欧亚中高纬度温度在2030年前一直维持几乎为零的增温率,之后迅速升高。展开更多
利用第五次耦合模式比较计划（Phase 5 of Coupled Model Intercomparison Project,CMIP5）提供的30个全球气候模式模拟的1961~2005年的夏季逐月环流场资料及同期NCEP再分析资料,引入泰勒图及各种评估指标,探讨全球气候模式对东亚夏季...利用第五次耦合模式比较计划（Phase 5 of Coupled Model Intercomparison Project,CMIP5）提供的30个全球气候模式模拟的1961~2005年的夏季逐月环流场资料及同期NCEP再分析资料,引入泰勒图及各种评估指标,探讨全球气候模式对东亚夏季平均大气环流场的模拟能力,寻求具有较好东亚夏季环流场模拟能力的气候模式。结果表明：1）全球气候模式能够模拟出东亚夏季平均大气环流的基本特征,CMIP5模式的总体模拟能力较第三次耦合模式比较计划（CMIP3）有较大程度的提高,如CMIP5模式对东亚大部分地区夏季海平面气压（Sea Level Pressure,SLP）场的模拟偏差在6 h Pa以内。2）模式对不同层次环流场的模拟能力存在差异,500 h Pa高度场的模拟能力最强,其次为100 h Pa高度场、850 h Pa风场,SLP场最弱;对东亚夏季主要环流系统的模拟对比发现,模式对印度热低压及东伸槽强度指数的模拟能力最好。3）综合CMIP5模式对东亚夏季各层次平均环流场以及主要环流系统的模拟能力,发现模拟较好的5个模式为CESM1-CAM5、MPI-ESM-MR、MPI-ESM-LR、MPI-ESM-P和Can ESM2。4）相对于单一模式,多模式集合平均（MME）模拟能力较强,但较优选的前5个模式集合平均的模拟能力弱。展开更多
The abilities of 12 earth system models（ESMs） from the Coupled Model Intercomparison Project Phase5（CMIP5） to reproduce satellite-derived vegetation biological variables over the Tibetan Plateau（TP） were examine...The abilities of 12 earth system models（ESMs） from the Coupled Model Intercomparison Project Phase5（CMIP5） to reproduce satellite-derived vegetation biological variables over the Tibetan Plateau（TP） were examined.The results show that most of the models tend to overestimate the observed leaf area index（LAI）and vegetation carbon above the ground,with the possible reasons being overestimation of photosynthesis and precipitation.The model simulations show a consistent increasing trend with observed LAI over most of the TP during the reference period of 1986-2005,while they fail to reproduce the downward trend around the headstream of the Yellow River shown in the observation due to their coarse resolutions.Three of the models：CCSM4,CESM1-BGC,and NorESM1-ME,which share the same vegetation model,show some common strengths and weaknesses in their simulations according to our analysis.The model ensemble indicates a reasonable spatial distribution but overestimated land coverage,with a significant decreasing trend（-1.48%per decade） for tree coverage and a slight increasing trend（0.58%per decade） for bare ground during the period 1950-2005.No significant sign of variation is found for grass.To quantify the relative performance of the models in representing the observed mean state,seasonal cycle,and interannual variability,a model ranking method was performed with respect to simulated LAI.INMCM4,bcc-csm-1.1m,MPI-ESM-LR,IPSL CM5A-LR,HadGEM2-ES,and CCSM4 were ranked as the best six models in reproducing vegetation dynamics among the 12 models.展开更多
Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduct...Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.展开更多
采用线性回归法对长江源区2011—2021年土壤水分含量年际变化趋势进行分析,采用t检验法对长江源区2011—2021年平均气温和降水量变化与土壤水分含量变化间的相关性分析,并采用CMIP5全球气候模型的3种情景(RCP2.6、RCP4.5、RCP8.5)下耦合...采用线性回归法对长江源区2011—2021年土壤水分含量年际变化趋势进行分析,采用t检验法对长江源区2011—2021年平均气温和降水量变化与土壤水分含量变化间的相关性分析,并采用CMIP5全球气候模型的3种情景(RCP2.6、RCP4.5、RCP8.5)下耦合SWAT(Soil and Water Assessment Tool)水文模型,预测长江源区未来(2022—2100年)土壤水分年际、年内变化趋势.结果表明,长江源区2011—2021年土壤水分整体呈减少趋势,年平均气温和降水量与土壤水分变化具有明显的相关性(P<0.05). 3种RCPs气候情景下,21世纪末期(2081—2090年)土壤水分含量较21世纪中期(2041—2050年)减少,4—9月土壤水分占全年土壤水分占比较21世纪中期降低.土壤水分年际间波动较大,在50%~500%之间变动,土壤水分年内分布不均匀,1—5月土壤水分增加,6—12月土壤水分递减,1—2月土壤水分变化趋势相对平稳,年内各月份土壤水分含量差别较大.在3种RCPs气候情景下,长江源区未来土壤水分存在明显减少趋势,应加强长江源区土壤水系保护.展开更多
文摘利用第五次耦合模式比较计划（Phase 5 of Coupled Model Intercomparison Project,CMIP5）提供的30个全球气候模式模拟的1961~2005年的夏季逐月环流场资料及同期NCEP再分析资料,引入泰勒图及各种评估指标,探讨全球气候模式对东亚夏季平均大气环流场的模拟能力,寻求具有较好东亚夏季环流场模拟能力的气候模式。结果表明：1）全球气候模式能够模拟出东亚夏季平均大气环流的基本特征,CMIP5模式的总体模拟能力较第三次耦合模式比较计划（CMIP3）有较大程度的提高,如CMIP5模式对东亚大部分地区夏季海平面气压（Sea Level Pressure,SLP）场的模拟偏差在6 h Pa以内。2）模式对不同层次环流场的模拟能力存在差异,500 h Pa高度场的模拟能力最强,其次为100 h Pa高度场、850 h Pa风场,SLP场最弱;对东亚夏季主要环流系统的模拟对比发现,模式对印度热低压及东伸槽强度指数的模拟能力最好。3）综合CMIP5模式对东亚夏季各层次平均环流场以及主要环流系统的模拟能力,发现模拟较好的5个模式为CESM1-CAM5、MPI-ESM-MR、MPI-ESM-LR、MPI-ESM-P和Can ESM2。4）相对于单一模式,多模式集合平均（MME）模拟能力较强,但较优选的前5个模式集合平均的模拟能力弱。
基金Supported by the National Basic Research and Development (973) Program of China(2010CB950503 and 2013CB956004)Research Fund for Climate Change of the China Meteorological Administration(CCSF201403)
文摘The abilities of 12 earth system models（ESMs） from the Coupled Model Intercomparison Project Phase5（CMIP5） to reproduce satellite-derived vegetation biological variables over the Tibetan Plateau（TP） were examined.The results show that most of the models tend to overestimate the observed leaf area index（LAI）and vegetation carbon above the ground,with the possible reasons being overestimation of photosynthesis and precipitation.The model simulations show a consistent increasing trend with observed LAI over most of the TP during the reference period of 1986-2005,while they fail to reproduce the downward trend around the headstream of the Yellow River shown in the observation due to their coarse resolutions.Three of the models：CCSM4,CESM1-BGC,and NorESM1-ME,which share the same vegetation model,show some common strengths and weaknesses in their simulations according to our analysis.The model ensemble indicates a reasonable spatial distribution but overestimated land coverage,with a significant decreasing trend（-1.48%per decade） for tree coverage and a slight increasing trend（0.58%per decade） for bare ground during the period 1950-2005.No significant sign of variation is found for grass.To quantify the relative performance of the models in representing the observed mean state,seasonal cycle,and interannual variability,a model ranking method was performed with respect to simulated LAI.INMCM4,bcc-csm-1.1m,MPI-ESM-LR,IPSL CM5A-LR,HadGEM2-ES,and CCSM4 were ranked as the best six models in reproducing vegetation dynamics among the 12 models.
基金Supported by the National Key Research and Development Program(2017YFA0603503)National Natural Science Foundation of China(41605057 and 41661144009)
文摘Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.
文摘采用线性回归法对长江源区2011—2021年土壤水分含量年际变化趋势进行分析,采用t检验法对长江源区2011—2021年平均气温和降水量变化与土壤水分含量变化间的相关性分析,并采用CMIP5全球气候模型的3种情景(RCP2.6、RCP4.5、RCP8.5)下耦合SWAT(Soil and Water Assessment Tool)水文模型,预测长江源区未来(2022—2100年)土壤水分年际、年内变化趋势.结果表明,长江源区2011—2021年土壤水分整体呈减少趋势,年平均气温和降水量与土壤水分变化具有明显的相关性(P<0.05). 3种RCPs气候情景下,21世纪末期(2081—2090年)土壤水分含量较21世纪中期(2041—2050年)减少,4—9月土壤水分占全年土壤水分占比较21世纪中期降低.土壤水分年际间波动较大,在50%~500%之间变动,土壤水分年内分布不均匀,1—5月土壤水分增加,6—12月土壤水分递减,1—2月土壤水分变化趋势相对平稳,年内各月份土壤水分含量差别较大.在3种RCPs气候情景下,长江源区未来土壤水分存在明显减少趋势,应加强长江源区土壤水系保护.
