CMIP6模式对北半球春季积雪覆盖度的评估及预估
P468.0+25; 积雪是对气候变化响应最敏感的自然要素之一,对地表的辐射平衡和水循环有着重要影响,全球积雪覆盖面积约为46×106 km2,且98%分布在北半球,由于积雪具有独特的辐射(高表面反照率)和热(低热传导率)特性,其变化对陆地和大气之间的能量平衡和水循环过程具有重要的影响,在全球变暖背景下,近几十年来北半球积雪覆盖面积减少趋势明显,尤其春季最明显,基于观测数据评估CMIP6模式数据对于积雪覆盖面积的模拟能力,应用多模式平均评估未来时期积雪覆盖度的变化情况.本文以美国国家海洋和大气管理局/美国国家气候数据中心(NOAA/NCDC)的积雪产品为参考数据,采用泰勒技巧评分、相对偏差等方...
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| Published in | 高原气象 Vol. 43; no. 6; pp. 1397 - 1415 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
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内蒙古自治区遥感与地理信息系统重点实验室,内蒙古 呼和浩特 010022%清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京 100084
28.12.2024
内蒙古师范大学地理科学学院,内蒙古 呼和浩特 010022 华北水利电力大学水利学院,河南 郑州 450046 |
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| Online Access | Get full text |
| ISSN | 1000-0534 |
| DOI | 10.7522/j.issn.1000-0534.2024.00029 |
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| Abstract | P468.0+25; 积雪是对气候变化响应最敏感的自然要素之一,对地表的辐射平衡和水循环有着重要影响,全球积雪覆盖面积约为46×106 km2,且98%分布在北半球,由于积雪具有独特的辐射(高表面反照率)和热(低热传导率)特性,其变化对陆地和大气之间的能量平衡和水循环过程具有重要的影响,在全球变暖背景下,近几十年来北半球积雪覆盖面积减少趋势明显,尤其春季最明显,基于观测数据评估CMIP6模式数据对于积雪覆盖面积的模拟能力,应用多模式平均评估未来时期积雪覆盖度的变化情况.本文以美国国家海洋和大气管理局/美国国家气候数据中心(NOAA/NCDC)的积雪产品为参考数据,采用泰勒技巧评分、相对偏差等方法,对国际耦合模式比较计划第六阶段(CMIP6)发布的1982-2014年北半球春季积雪覆盖度(SCF)数据进行评估,并选取排名前三的模式的集合平均预估未来(2015-2099年)不同排放情景下SCF的时空变化特征.结果表明:历史时期(1982-2014年)从整体上看,积雪覆盖度呈现出高纬高,低纬低,青藏高原和亚洲东部等高海拔地区较同纬地区高的特点,北半球的积雪覆盖度呈减少趋势地区为68.37%,积雪覆盖度呈现增加趋势的区域面积占北半球总面积的31.63%,与参考数据相比,CMIP6各模式模拟北半球春季SCF在大部分地区表现为减少特征,多数CMIP6模式高估了青藏高原地区的SCF,大多模式的SCF结果呈减少趋势的地区大于参考区域,并且低估了3月、4月和5月的SCF.总体来看,各模式模拟SCF的能力存在差异,其中NorESM2-MM、CESM2、BBC-CSM2-MR、NorESM2-LM和CESM2-WACCM综合模拟能力最优,模拟能力最差的是MIROC-ES2L、MPI-ESM1-2-LR和MPI-ESM-1-2-HAM.而多模式集合平均(MME)的模拟能力在各方面都优于多数单个模式,其综合模拟能力泰勒得分与NorESM2-MM模式和CESM2-WACCM模式均为最高的0.984,在空间分布、年际变化趋势、年内变化三个方面,CMIP6各模式模拟北半球春季SCF的能力差异显著,CMIP6 MME模拟的北半球春季SCF更接近观测数据(CMIP6各模式的偏差值为-14.27%～5.96%,CMIP6 MME的偏差值为-2.3%),相对于1982-2014年参考时段,21世纪末期(2067-2099年)北半球春季SCF在大部分地区 |
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| AbstractList | P468.0+25; 积雪是对气候变化响应最敏感的自然要素之一,对地表的辐射平衡和水循环有着重要影响,全球积雪覆盖面积约为46×106 km2,且98%分布在北半球,由于积雪具有独特的辐射(高表面反照率)和热(低热传导率)特性,其变化对陆地和大气之间的能量平衡和水循环过程具有重要的影响,在全球变暖背景下,近几十年来北半球积雪覆盖面积减少趋势明显,尤其春季最明显,基于观测数据评估CMIP6模式数据对于积雪覆盖面积的模拟能力,应用多模式平均评估未来时期积雪覆盖度的变化情况.本文以美国国家海洋和大气管理局/美国国家气候数据中心(NOAA/NCDC)的积雪产品为参考数据,采用泰勒技巧评分、相对偏差等方法,对国际耦合模式比较计划第六阶段(CMIP6)发布的1982-2014年北半球春季积雪覆盖度(SCF)数据进行评估,并选取排名前三的模式的集合平均预估未来(2015-2099年)不同排放情景下SCF的时空变化特征.结果表明:历史时期(1982-2014年)从整体上看,积雪覆盖度呈现出高纬高,低纬低,青藏高原和亚洲东部等高海拔地区较同纬地区高的特点,北半球的积雪覆盖度呈减少趋势地区为68.37%,积雪覆盖度呈现增加趋势的区域面积占北半球总面积的31.63%,与参考数据相比,CMIP6各模式模拟北半球春季SCF在大部分地区表现为减少特征,多数CMIP6模式高估了青藏高原地区的SCF,大多模式的SCF结果呈减少趋势的地区大于参考区域,并且低估了3月、4月和5月的SCF.总体来看,各模式模拟SCF的能力存在差异,其中NorESM2-MM、CESM2、BBC-CSM2-MR、NorESM2-LM和CESM2-WACCM综合模拟能力最优,模拟能力最差的是MIROC-ES2L、MPI-ESM1-2-LR和MPI-ESM-1-2-HAM.而多模式集合平均(MME)的模拟能力在各方面都优于多数单个模式,其综合模拟能力泰勒得分与NorESM2-MM模式和CESM2-WACCM模式均为最高的0.984,在空间分布、年际变化趋势、年内变化三个方面,CMIP6各模式模拟北半球春季SCF的能力差异显著,CMIP6 MME模拟的北半球春季SCF更接近观测数据(CMIP6各模式的偏差值为-14.27%～5.96%,CMIP6 MME的偏差值为-2.3%),相对于1982-2014年参考时段,21世纪末期(2067-2099年)北半球春季SCF在大部分地区 |
| Abstract_FL | As one of the most sensitive natural elements in response to climate change,snow cover has a signifi-cant effect on the Earth's surface radiation balance and water cycle.The global snow cover area is approximately 46×106 km2 and 98%of the snow cover distributed in the Northern Hemisphere.Due to its distinctive radiative properties(high surface albedo)and thermal characteristics(low thermal conductivity),changes in snow cover play a crucial role in the energy balance and water cycle between land and the atmosphere.In the context of glob-al warming,the snow cover in the Northern Hemisphere has been decreasing in recent decades,especially in the spring.Therefore,the capabilities of CMIP6(Coupled Model Intercomparison Project Phase 6)data to simulate the snow cover area were evaluated based on observational data and the future changes in snow cover were also assessed using a multi-model average in this study.By using the snow cover products from the National Oceanic and Atmospheric Administration/National Climatic Data Center(NOAA/NCDC)as reference data,the Taylor skill scoring,relative deviation,and other methods were applied to evaluate the spring snow cover(SCF)data in the Northern Hemisphere from the International Coupled Model Comparison Project Phase 6(CMIP6)during 1982-2014.The ensemble average of the top three models was further selected to predict the spatiotemporal vari-ation characteristics of SCF under different emission scenarios from 2015 to 2099,providing insights into the modeling capabilities of CMIP6 and future changes in SCF.During the historical period(1982-2014),SCF was characterized by high coverage at high latitudes and low coverage at low latitudes,with high-altitude regions such as Tibetan Plateau and eastern Asia having higher snow coverage than those at the same latitudes.Overall,68.37%of the regions in the Northern Hemisphere showed a decreasing trend in SCF,while 31.63%of the re-gions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared to the reference data.In addition,most models simulated larger areas with a decreasing trend in SCF than those evaluated by the reference data and underestimated SCF in March,April,and May.Various models exhibited differing abilities to simulate SCF,with NorESM2-MM,CESM2,BBC-CSM2-MR,NorESM2-LM,and CESM2-WACCM demonstrating superior capabilities.The Multi-Model Ensemble Mean(MME)consistent-ly outperformed individual models,closely aligning with observational data.There were significant differences in the ability of the CMIP6 models to simulate the spatial distribution,inter-annual variation trends,and intra-an-nual variations of SCF in the Northern Hemisphere.At the end of the 21st-century(2067-2099),SCF in the Northern Hemisphere exhibited a decreasing trend in most areas,which intensifies with increasing emission in-tensity.The changes in SCF were relatively consistent under different emission scenarios before 2040.SCF main-tains a steady state under the SSP1-2.6 scenario,showed a slight decreasing trend under the SSP2-4.5 scenario,and showed a significant decreasing trend under the SSP5-8.5 scenario after 2040. |
| Author | 王旭蕾 萨楚拉 孙慧 孟凡浩 郭辉 罗敏 |
| AuthorAffiliation | 内蒙古师范大学地理科学学院,内蒙古 呼和浩特 010022;内蒙古自治区遥感与地理信息系统重点实验室,内蒙古 呼和浩特 010022%清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京 100084;华北水利电力大学水利学院,河南 郑州 450046 |
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| Author_FL | SA Chula GUO Hui WANG Xulei SUN Hui MENG Fanhao LUO Min |
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| Keywords | CMIP6 assessment 北半球 Northern Hemisphere snow cover fraction prediction 积雪覆盖度 评估 预估 |
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| Publisher | 内蒙古自治区遥感与地理信息系统重点实验室,内蒙古 呼和浩特 010022%清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京 100084 内蒙古师范大学地理科学学院,内蒙古 呼和浩特 010022 华北水利电力大学水利学院,河南 郑州 450046 |
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| Title | CMIP6模式对北半球春季积雪覆盖度的评估及预估 |
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