首页|CMIP6模式关于中国叶面积指数对温度和降水变化敏感性的模拟能力评估

CMIP6模式关于中国叶面积指数对温度和降水变化敏感性的模拟能力评估

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评估地球系统模式对气候和植被的模拟能力是利用地球系统模式研究植被对气候变化响应的基础.基于观测和遥感数据,本文评估了第六次国际耦合模式比较计划(CMIP6)中 18个全球耦合模式对中国生长季温度、降水和叶面积指数(Leaf Area Index,LAI)的模拟性能.我们基于多元线性回归模型定量了植被对温度、降水的敏感性,对CMIP6模式关于植被敏感性的模拟能力进行定量评估.研究结果表明:(1)大部分模式可较好地模拟生长季温度、降水和LAI的气候态空间分布特征,但普遍高估全国平均LAI,且各模式对气候和植被变化趋势的模拟结果存在较大偏差;(2)与观测数据相比,模式关于LAI对温度和降水的敏感性符号模拟能力均表现出对正值区的模拟优于对负值区的模拟,并且典型脆弱区植被敏感性大于中国区域植被敏感性,模式对植被敏感性幅度及其与气候场对应关系的模拟方面存在较大偏差;(3)基于模式在生长季的温度、降水、LAI及其敏感性方面的综合排名,四个模拟性能最佳的模式分别为CanESM5-CanOE、INM-CM5-0、IPSL-CM6-LR和MPI-ESM1-2-LR.
Evaluation of CMIP6 Models in Simulating the Sensitivity of Leaf Area Index to Temperature and Precipitation Changes over China
Evaluating climate and vegetation status in earth system models(ESMs)is fundamental to understanding climate change,terrestrial ecosystems,and the carbon cycle.Our study examines temperature,precipitation,and leaf area index(LAI)during the growing season in China,using data from eighteen ESMs part of the Sixth International Coupled Model Comparison Project(CMIP6).This analysis was underpinned by site observations and remote sensing data.We employed a multiple linear regression model to quantify the sensitivity of LAI to temperature and precipitation,aiming to evaluate the ability of the CMIP6 model to simulate the sensitivity of vegetation in geographical and climatic spaces.Ultimately,models demonstrating superior simulation performance were selected.Our results show that:(1)While most models can simulate the spatial distribution of temperature,precipitation,and LAI during the growing season,significant discrepancies are evident in their mean values and trend patterns.(2)Compared to observations,the simulation ability of LAI sensitivity to temperature and precipitation was more accurate for regions exhibiting positive sensitivity.It was observed that the sensitivity of vegetation in ecotone was greater than elsewhere in China.However,the magnitude and distribution of vegetation sensitivity across climate spaces showed considerable variance(i.e.,the corresponding relationship with the climate field).(3)After extensive evaluations,CANESM5-CanOE,INM-CM5-0,IPSL-CM6-LR,and MPI-ESM1-2-LR demonstrated the best performance on simulations of vegetation sensitivity to climate during China's growing season.

CMIP6TemperaturePrecipitationLeaf area index(LAI)Vegetation sensitivityEvaluation of historical experiment

王丹云、曾晓东、宋翔

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中国科学院大气物理研究所国际气候与环境科学中心,北京 100029

天津市气象灾害防御技术中心,天津 300074

中国科学院大学,北京 100049

南京信息工程大学气象灾害预报预警与评估协同创新中心,南京 210044

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CMIP6 温度 降水 叶面积指数 植被敏感性 历史模拟评估

国家重点研发计划项目国家重大科技基础设施项目National Key Scientific and Technological Infrastructure Project

2017YFA0604804

2024

10.3878/j.issn.1006-9895.2304.22067

大气科学
中国科学院大气物理研究所

大气科学

CSTPCD北大核心
影响因子：2.11
ISSN：1006-9895
年,卷(期)：2024.48(5)