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Projected Land Evaporation and Its Response to Vegetation Greening Over China Under Multiple Scenarios in the CMIP6 Models
Author(s) -
Lu Jiao,
Wang Guojie,
Li Shijie,
Feng Aiqing,
Zhan Mingyue,
Jiang Tong,
Su Buda,
Wang Yanjun
Publication year - 2021
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1029/2021jg006327
Subject(s) - environmental science , vegetation (pathology) , climate change , precipitation , greening , radiative forcing , climatology , greenhouse gas , climate model , water cycle , forcing (mathematics) , coupled model intercomparison project , atmospheric sciences , meteorology , geography , ecology , geology , medicine , pathology , biology
Land evaporation (ET) is of great significance in climate change research, water resource management, and numerical weather forecasting. In this study, Ridge Regression Method and Sensitivity Analysis Methods have been used to study the projected land evaporation changes over China, and its response to vegetation greening under low (Shared Socioeconomic Pathway [SSP]1‐2.6), medium (SSP2‐4.5), and high (SSP5‐8.5) forcing scenarios during 2020–2099, based on 16 of the latest generation of Earth System Models (ESMs) taking part in the Coupled Models Intercomparison Project Phase 6. Land evaporation is projected to significantly increase under all climate change scenarios, especially in the southern China where there is a humid climate. The influencing factors, including precipitation, air temperature, solar radiation, and leaf area index (LAI), are analyzed; LAI is indicated to dominate the interannual variations of land ET, contributing over 40% of the interannual variance in the warming climates. However, the sensitivity of land ET to vegetation greening is found to generally reduce along with the increasing radiation forcing levels. Such a reduced sensitivity is particularly true when making intermodel comparisons, possibly due to the uncertainties of vegetation parameterizations in different models. This study reveals the response of land ET to vegetation greening under multiple climate change scenarios, which may help to understand the essential role of vegetation in water cycle and provide support for future water resource management.