CMIP6模式对北半球春季积雪覆盖度的评估及预估  

Simulation and Prediction of Spring Snow Cover inNorthern Hemisphere by CMIP6 Model

作　　者：王旭蕾 孙慧 郭辉[3,4] 萨楚拉 孟凡浩[1,2] 罗敏 WANG Xulei;SUN Hui;GUO Hui;SA Chula;MENG Fanhao;LUO Min(College of Geographical Science,Inner Mongolia Normal University,Hohhot 010022,Inner Mongolia,China;Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems,Inner Mongolia Normal University,Hohhot 010022,Inner Mongolia,China;State Key Laboratory of Hydroscience and Engineering,Department of Hydraulic Engineering,Tsinghua University,Beijing 100084,China;Department of Water Conservancy Engineering,North China University of Water Conservancy and Electric Power,Zhengzhou 450046,Henan,China)

机构地区：[1]内蒙古师范大学地理科学学院,内蒙古呼和浩特010022 [2]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 [3]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 [4]华北水利电力大学水利学院,河南郑州450046

出　　处：《高原气象》2024年第6期1397-1415,共19页Plateau Meteorology

基　　金：内蒙古自治区自然科学基金项目(2022MS04004);国家自然科学基金项目(42101030,42261079,42361024);内蒙古自治区重点研发和成果转化计划项目(2022YFDZ0061);内蒙古师范大学基本科研业务费专项资金(2023JBPT004)。

摘　　要：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46×10~6 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MPIESM-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As one of the most sensitive natural elements in response to climate change,snow cover has a significant 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 global 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 variation 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 regions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared

关 键 词：积雪覆盖度 北半球 CMIP6 评估 预估 

分 类 号：P468.025[天文地球—大气科学及气象学]