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# Projected Potential Vegetation Change in China under the SRES A2 and B2 Scenarios

• The ability of seven global coupled ocean-atmosphere models to reproduce East Asian monthly surface temperature and precipitation climatologies during 1961--1990 is evaluated. January and July climate differences during the 2050s and 2090s relative to 1961--1990 projected by the seven-model ensemble under the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios are then briefly discussed. These projections, together with the corresponding atmospheric CO2 concentrations under the SRES A2 and B2 scenarios, are subsequently used to drive the biome model BIOME3 to simulate potential vegetation distribution in China during the 2050s and 2090s. It is revealed that potential vegetation belts during the 2050s shift northward greatly in central and eastern China compared to those during 1961--1990. In contrast, potential vegetation change is slight in western China on the whole. The spatial pattern of potential vegetation during the 2090s is generally similar to that during the 2050s, but the range of potential vegetation change against 1961--1990 is more extensive during the 2090s than the 2050s, particularly in western China. Additionally, there exists model-dependent uncertainty of potential vegetation change under the SRES A2 scenario during the 2090s, which is due to the scatter of projected climate change by the models. The projected change in potential vegetation under the SRES A2 scenario during the 2090s is attributable to surface temperature change south of 35N and to the joint changes of surface temperature, precipitation, and atmospheric CO2 concentration north of 35N.
•  [1] REN Guoyu, DING Yihui, ZHAO Zongci, ZHENG Jingyun, WU Tongwen, TANG Guoli, XU Ying, 2012: Recent Progress in Studies of Climate Change in China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 958-977.  doi: 10.1007/s00376-012-1200-2 [2] ZHOU Mengzi, WANG Huijun, 2015: Potential Impact of Future Climate Change on Crop Yield in Northeastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 889-897.  doi: 10.1007/s00376-014-4161-9 [3] DING Yihui, REN Guoyu, ZHAO Zongci, XU Ying, LUO Yong, LI Qiaoping, ZHANG Jin, 2007: Detection, Causes and Projection of Climate Change over China: An Overview of Recent Progress, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 954-971.  doi: 10.1007/s00376-007-0954-4 [4] SONG Xiang and ZENG Xiaodong*, , 2014: Investigation of Uncertainties of Establishment Schemes in Dynamic Global Vegetation Models, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 85-94.  doi: 10.1007/s00376-013-3031-1 [5] Xiaoxin WANG, Dabang JIANG, Xianmei LANG, 2018: Climate Change of 4°C Global Warming above Pre-industrial Levels, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 757-770.  doi: 10.1007/s00376-018-7160-4 [6] TIAN Di, GUO Yan*, DONG Wenjie, 2015: Future Changes and Uncertainties in Temperature and Precipitation over China Based on CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 487-496.  doi: 10.1007/s00376-014-4102-7 [7] Deliang CHEN, Christine ACHBERGER, Jouni R¨AIS¨ANEN, Cecilia HELLSTR¨OM, 2006: Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 54-60.  doi: 10.1007/s00376-006-0006-5 [8] LI Hongmei, FENG Lei, ZHOU Tianjun, 2011: Multi-model Projection of July--August Climate Extreme Changes over China under CO$_{2}$ Doubling. Part I: Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 433-447.  doi: 10.1007/s00376-010-0013-4 [9] LI Hongmei, FENG Lei, ZHOU Tianjun, 2011: Multi-Model Projection of July--August Climate Extreme Changes over China under CO2 Doubling. Part II: Temperature, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 448-463.  doi: 10.1007/s00376-010-0052-x [10] CHEN Huopo, SUN Jianqi, 2009: How the “Best” Models Project the Future Precipitation Change in China, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 773-782.  doi: 10.1007/s00376-009-8211-7 [11] Yin ZHAO, Tianjun ZHOU, Wenxia ZHANG, Jian LI, 2022: Change in Precipitation over the Tibetan Plateau Projected by Weighted CMIP6 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1133-1150.  doi: 10.1007/s00376-022-1401-2 [12] Chenxi WANG, Zhihua ZENG, Ming YING, 2020: Uncertainty in Tropical Cyclone Intensity Predictions due to Uncertainty in Initial Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 278-290.  doi: 10.1007/s00376-019-9126-6 [13] Lin WANG, Gang HUANG, Wen ZHOU, Wen CHEN, 2016: Historical Change and Future Scenarios of Sea Level Rise in Macau and Adjacent Waters, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 462-475.  doi: 10.1007/s00376-015-5047-1 [14] Guoxiong WU, Bian HE, Anmin DUAN, Yimin LIU, Wei YU, 2017: Formation and Variation of the Atmospheric Heat Source over the Tibetan Plateau and Its Climate Effects, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1169-1184.  doi: 10.1007/s00376-017-7014-5 [15] ZHANG Lixia* and ZHOU Tianjun, , 2014: An Assessment of Improvements in Global Monsoon Precipitation Simulation in FGOALS-s2, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 165-178.  doi: 10.1007/s00376-013-2164-6 [16] HAN Zuoqiang, YAN Zhongwei*, LI Zhen, LIU Weidong, and WANG Yingchun, 2014: Impact of Urbanization on Low-Temperature Precipitation in Beijing during 19602008, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 48-56.  doi: 10.1007/s00376-013-2211-3 [17] HU Shujuan, CHOU Jifan, 2004: Uncertainty of the Numerical Solution of a Nonlinear System's Long-term Behavior and Global Convergence of the Numerical Pattern, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 767-774.  doi: 10.1007/BF02916373 [18] Yuejian ZHU, 2005: Ensemble Forecast: A New Approach to Uncertainty and Predictability, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 781-788.  doi: 10.1007/BF02918678 [19] Yujie WANG, Botao ZHOU, Dahe QIN, Jia WU, Rong GAO, Lianchun SONG, 2017: Changes in Mean and Extreme Temperature and Precipitation over the Arid Region of Northwestern China: Observation and Projection, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 287-305.  doi: 10.1007/s00376-016-6160-5 [20] Ge Ling, Liang Jiaxing, Chen Yiliang, 1996: Spatial / Temporal Features of Antarctic Climate Change, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 375-382.  doi: 10.1007/BF02656854

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## Manuscript History

Manuscript revised: 10 January 2008
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Projected Potential Vegetation Change in China under the SRES A2 and B2 Scenarios

• 1. Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Abstract: The ability of seven global coupled ocean-atmosphere models to reproduce East Asian monthly surface temperature and precipitation climatologies during 1961--1990 is evaluated. January and July climate differences during the 2050s and 2090s relative to 1961--1990 projected by the seven-model ensemble under the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios are then briefly discussed. These projections, together with the corresponding atmospheric CO2 concentrations under the SRES A2 and B2 scenarios, are subsequently used to drive the biome model BIOME3 to simulate potential vegetation distribution in China during the 2050s and 2090s. It is revealed that potential vegetation belts during the 2050s shift northward greatly in central and eastern China compared to those during 1961--1990. In contrast, potential vegetation change is slight in western China on the whole. The spatial pattern of potential vegetation during the 2090s is generally similar to that during the 2050s, but the range of potential vegetation change against 1961--1990 is more extensive during the 2090s than the 2050s, particularly in western China. Additionally, there exists model-dependent uncertainty of potential vegetation change under the SRES A2 scenario during the 2090s, which is due to the scatter of projected climate change by the models. The projected change in potential vegetation under the SRES A2 scenario during the 2090s is attributable to surface temperature change south of 35N and to the joint changes of surface temperature, precipitation, and atmospheric CO2 concentration north of 35N.

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