利用高分辨率区域气候模式CCLM(COSMO Model in Climate Mode)对云南省气温和降水的模拟资料,采用多种评价指标,对比分析了试验期(1961~2005年)的模拟结果与同期25个气象站的观测值,并对RCP4.5情景下的近期(2030~2040年)气温可能变化趋...利用高分辨率区域气候模式CCLM(COSMO Model in Climate Mode)对云南省气温和降水的模拟资料,采用多种评价指标,对比分析了试验期(1961~2005年)的模拟结果与同期25个气象站的观测值,并对RCP4.5情景下的近期(2030~2040年)气温可能变化趋势进行了预估。结果表明:(1)CCLM区域气候模式能够较好地模拟云南省气温的演变趋势,而对降水的模拟能力相对较弱;(2)RCP4.5情景下,年平均气温(T)、最高气温(Tmax)和最低气温(Tmi n)将呈现一致上升趋势,2030~2040年比基准期1986~2005年上升幅度均为1.2℃;(3)2030~2040年,云南省暖事件发生的可能性将增加,冷事件可能有所减少。展开更多
The outputs of 17 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed to investigate the temporal and spatial features of 2.0°C warming of the surface temperature over the globe and C...The outputs of 17 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed to investigate the temporal and spatial features of 2.0°C warming of the surface temperature over the globe and China under the Representative Concentration Pathways (RCP) 4.5 scenario. The simulations of the period 1860-1899 in the "historical" experiment are chosen as the baseline. The simulations for the 21st century in the RCP4.5 experiment are chosen as the future project. The multi-model ensemble mean (MME) shows that the global mean temperature would cross the 2.0°C warming threshold in 2047. Warming in most of the models would cross the threshold during 2030-2060. For local warming, high-latitude areas in the Northern Hemisphere show the fastest warming over the globe. Land areas warm substantially faster than the oceans. Most of the southern oceans would not exceed the 2.0°C warming threshold within the 21st century. Over China, surface warming is substantially faster than the global mean. The area-averaged warming would cross the 2.0°C threshold in 2034. Locally, Northwest China shows the fastest warming trend, followed by Central North China and Northeast China. Central China, East China, and South China are the last to cross the 2.0°C warming threshold. The diversity of the models is also estimated in this study. Generally, the spread among the models increases with time, and there is smaller spread among the models for the areas with the faster warming.展开更多
利用全球气候模式BCC_CSM1.1(Beijing Climate Center Climate System Model version 1.1),耦合区域气候模式Reg CM4(Regional Climate Model version 4)输出的1961-1990年(基准时段)气候模拟数据,并根据同期实测资料,确定模拟值和实测...利用全球气候模式BCC_CSM1.1(Beijing Climate Center Climate System Model version 1.1),耦合区域气候模式Reg CM4(Regional Climate Model version 4)输出的1961-1990年(基准时段)气候模拟数据,并根据同期实测资料,确定模拟值和实测值之间的非线性传递函数与方差订正参数,构建气候模拟数据的误差订正模型。利用1991-2005年(验证时段)模拟数据与实测资料验证该模型的有效性,并对RCP(Representative Concentration Pathway)情景下2021-2050年(未来时段)气候模拟数据进行订正,同时通过潜力衰减方法预估未来江苏冬小麦气候生产潜力格局。结果表明:将气候模拟数据订正方法应用到作物气候生产潜力预估是有效的。以均值传递函数和方差信息建立的模型可以较好订正江苏逐日气候模拟数据。订正后的秋冬季气温、辐射量、蒸散量和冬春季降水量模拟偏差明显减小。在此基础上研究发现,冬小麦的成熟期在RCP4.5和RCP8.5情景下介于153~175和153~174,较基准时段均明显提前。两种情景下冬小麦气候生产潜力分别介于10335~14368kg·hm^(-2)和9991~13708kg·hm^(-2),较基准时段呈下降趋势。其变异系数分别介于7.6%~14.6%和7.5%~13.6%,较基准时段呈增大趋势,表明江苏冬小麦气候生产潜力总体趋于不稳定。未来时段,徐州中北部、连云港东北部、宿迁西部以及盐城东南部冬小麦在RCP4.5和RCP8.5情景下可以保持相对较高的生产潜力(≥12501kg·hm^(-2)),该省应确保这些地区的冬小麦种植用地。研究建议,作物气候生产潜力预估应考虑利用研究区实测资料对气候模拟数据进行订正,以提高预估可信度。展开更多
文摘利用高分辨率区域气候模式CCLM(COSMO Model in Climate Mode)对云南省气温和降水的模拟资料,采用多种评价指标,对比分析了试验期(1961~2005年)的模拟结果与同期25个气象站的观测值,并对RCP4.5情景下的近期(2030~2040年)气温可能变化趋势进行了预估。结果表明:(1)CCLM区域气候模式能够较好地模拟云南省气温的演变趋势,而对降水的模拟能力相对较弱;(2)RCP4.5情景下,年平均气温(T)、最高气温(Tmax)和最低气温(Tmi n)将呈现一致上升趋势,2030~2040年比基准期1986~2005年上升幅度均为1.2℃;(3)2030~2040年,云南省暖事件发生的可能性将增加,冷事件可能有所减少。
基金supported by the National Basic Research Program of China(Grant No.2009CB421407)
文摘The outputs of 17 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed to investigate the temporal and spatial features of 2.0°C warming of the surface temperature over the globe and China under the Representative Concentration Pathways (RCP) 4.5 scenario. The simulations of the period 1860-1899 in the "historical" experiment are chosen as the baseline. The simulations for the 21st century in the RCP4.5 experiment are chosen as the future project. The multi-model ensemble mean (MME) shows that the global mean temperature would cross the 2.0°C warming threshold in 2047. Warming in most of the models would cross the threshold during 2030-2060. For local warming, high-latitude areas in the Northern Hemisphere show the fastest warming over the globe. Land areas warm substantially faster than the oceans. Most of the southern oceans would not exceed the 2.0°C warming threshold within the 21st century. Over China, surface warming is substantially faster than the global mean. The area-averaged warming would cross the 2.0°C threshold in 2034. Locally, Northwest China shows the fastest warming trend, followed by Central North China and Northeast China. Central China, East China, and South China are the last to cross the 2.0°C warming threshold. The diversity of the models is also estimated in this study. Generally, the spread among the models increases with time, and there is smaller spread among the models for the areas with the faster warming.
文摘利用全球气候模式BCC_CSM1.1(Beijing Climate Center Climate System Model version 1.1),耦合区域气候模式Reg CM4(Regional Climate Model version 4)输出的1961-1990年(基准时段)气候模拟数据,并根据同期实测资料,确定模拟值和实测值之间的非线性传递函数与方差订正参数,构建气候模拟数据的误差订正模型。利用1991-2005年(验证时段)模拟数据与实测资料验证该模型的有效性,并对RCP(Representative Concentration Pathway)情景下2021-2050年(未来时段)气候模拟数据进行订正,同时通过潜力衰减方法预估未来江苏冬小麦气候生产潜力格局。结果表明:将气候模拟数据订正方法应用到作物气候生产潜力预估是有效的。以均值传递函数和方差信息建立的模型可以较好订正江苏逐日气候模拟数据。订正后的秋冬季气温、辐射量、蒸散量和冬春季降水量模拟偏差明显减小。在此基础上研究发现,冬小麦的成熟期在RCP4.5和RCP8.5情景下介于153~175和153~174,较基准时段均明显提前。两种情景下冬小麦气候生产潜力分别介于10335~14368kg·hm^(-2)和9991~13708kg·hm^(-2),较基准时段呈下降趋势。其变异系数分别介于7.6%~14.6%和7.5%~13.6%,较基准时段呈增大趋势,表明江苏冬小麦气候生产潜力总体趋于不稳定。未来时段,徐州中北部、连云港东北部、宿迁西部以及盐城东南部冬小麦在RCP4.5和RCP8.5情景下可以保持相对较高的生产潜力(≥12501kg·hm^(-2)),该省应确保这些地区的冬小麦种植用地。研究建议,作物气候生产潜力预估应考虑利用研究区实测资料对气候模拟数据进行订正,以提高预估可信度。
