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CMIP5和CMIP6模式对亚洲中高纬地区植被的模拟及未来预估

宋仁杰 濮烨 魏江峰

大气科学学报2025,Vol.48Issue(5):777-791,15.
大气科学学报2025,Vol.48Issue(5):777-791,15.DOI:10.13878/j.cnki.dqkxxb.20240428001

CMIP5和CMIP6模式对亚洲中高纬地区植被的模拟及未来预估

Simulating and projecting future vegetation dynamics in the mid-to-high latitudes of Asia using CMIP5 and CMIP6 models

宋仁杰 1濮烨 2魏江峰1

作者信息

  • 1. 南京信息工程大学气候系统预测与变化应对全国重点实验室/气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心,江苏南京 210044||南京信息工程大学大气科学学院,江苏南京 210044
  • 2. 南京信息工程大学气候系统预测与变化应对全国重点实验室/气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心,江苏南京 210044||南京信息工程大学大气科学学院,江苏南京 210044||甘肃省酒泉市气象局,甘肃酒泉 735000
  • 折叠

摘要

Abstract

Vegetation plays a critical role in regulating the water cycle and mediating carbon fluxes within the climate system.It responds rapidly to climate change and is highly sensitive to climate variability.In the context of accelerating global warming,particularly in the mid-to-high latitudes of Asia where warming rates are 2-3 times the global average-vegetation dynamics are expected to undergo significant changes.However,substantial uncertainties persist in projecting future vegetation changes in this region due to model biases,limitations in the spatiotemporal resolution and consistency of satellite remote sensing datasets,and variations in parametrizations of vegetation-climate feedbacks across models.This study integrates three independent satellite-based leaf area in-dex(LAI)datasets-GLOBMAP(Version 3),GIMMS LAI3g,and GLASS-with climate and vegetation out-puts from 15 CMIP5 and 19 CMIP6 models.Using a multi-model ensemble mean(MME)framework,we sys-tematically evaluate historical and projected vegetation characteristics in the mid-to-high latitudes of Asia. Analysis of the satellite datasets reveals that regions with sparse vegetation exhibit higher interannual varia-bility,while dense vegetated regions show more pronounced increasing trends in LAI.Areas of high MME mean LAI,variability,and seasonal amplitude are primarily located in woodland regions at elevations below 1 200 m.Among the datasets,GLOBMAP and GLASS exhibit stronger mutual consistency.The MME approach involves simulation performance by mitigating nonlinearities in individual model outputs.Evaluation of historical simula-tions indicates that both CMIP5 and CMIP6 models perform best in reproducing surface air temperature,with CMIP6 models demonstrating superior accuracy overall.CMIP6 also partially corrects the overestimation of LAI seen CMIP5 simulations.Ensemble simulations(MME)outperform individual modes in reproducing historical vegetation dynamics. Future projections under both low-and high-emission scenarios(RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5,respectively)show consistent increases in LAI mean values,interannual variability,and seasonal ampli-tude,with larger changes under high-emission scenarios.Regions with higher baseline vegetation cover are pro-jected to experience greater LAI increases.While spatial patterns of change vary,the greatest increases are pro-jected in high-LAI regions,high-latitude zones,and East Asia.Notably,LAI increases during the warm season are more pronounced than those in the cold season,indicating enhanced seasonal growth dynamics under future warming. This study enhances our understanding of vegetation-climate interactions in complex mid-to-high latitude e-cosystems which provides key insights into model performance,vegetation sensitivity,and carbon cycle feed-backs.These findings offer a scientific basis for improving ecosystem modeling and informing regional climate adaptation and carbon management strategies.

关键词

亚洲中高纬/植被/叶面积指数/耦合模式比较计划

Key words

mid-to-high latitudes of Asia/vegetation dynamics/leaf area index(LAI)/Coupled Model Inter-comparison Project

引用本文复制引用

宋仁杰,濮烨,魏江峰..CMIP5和CMIP6模式对亚洲中高纬地区植被的模拟及未来预估[J].大气科学学报,2025,48(5):777-791,15.

基金项目

国家自然科学基金项目(41991285) (41991285)

大气科学学报

OA北大核心

1674-7097

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