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《大气和海洋科学快报(英文版)》 2017-02
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A new climate scenario for assessing the climate change impacts on soil moisture over the Huang-Huai-Hai Plain region of China

PENG Fei;SUN Guo-Dong;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences;Numerical Weather Prediction Center,China Meteorological Administration;College of Earth Science,University of Chinese Academy of Sciences;  
To assess the impacts of temperature and precipitation changes on surface soil moisture(SSM)in the Huang-Huai-Hai Plain(3H) region of China,the approach of conditional nonlinear optimal perturbation related to parameters(CNOP-P) and the Common Land Model are employed.Based on the CNOP-P method and climate change projections derived from 22 global climate models from CMIP5 under a moderate emissions scenario(RCP4.5),a new climate change scenario that leads to the maximal change magnitudes of SSM is acquired,referred to as the CNOP-P type temperature or precipitation change scenario.Different from the hypothesized climate change scenario,the CNOP-P-type scenario considers the variation of the temperature or precipitation variability.Under the CNOP-P-type temperature change,the SSM changes in the last year of the study period mainly fluctuate in the range from-0.014 to +0.012 m~3 m~(-3)(-5.0%to +10.0%),and from +0.005 to +0.018 m~3 m~(-3)(+1.5%to +9.6%) under the CNOP-P-type precipitation change scenario.By analyzing the difference of the SSM changes between different types of climate change scenarios,it is found that this difference associated with SSM is obvious only when precipitation changes are considered.Besides,the greater difference mainly occurs in north of 35°N,where the semi-arid zone is mainly situated.It demonstrates that,in the semi-arid region,SSM is more sensitive to the precipitation variability.Compared with precipitation variability,temperature variability seems to play little role in the variations of SSM.
【Fund】: provided by the National Natural Science Foundation of China[grant number 91437111] [grant number41375111] [grant number 40830955]
【CateGory Index】: P467
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