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More realistic land-use and vegetation parameters in a regional climate model reduce model biases over China

Journal article
Authors J. J. Gou
F. Wang
K. Jin
X. M. Mu
Deliang Chen
Published in International Journal of Climatology
Volume 39
Issue 12
Pages 4825-4837
ISSN 0899-8418
Publication year 2019
Published at Department of Earth Sciences
Pages 4825-4837
Language en
Keywords China, CLM, improved climate simulations, plant functional type, RegCM, cover changes, regcm4, temperature, rainfall, impacts, dataset, Meteorology & Atmospheric Sciences
Subject categories Earth and Related Environmental Sciences


Accurate vegetation cover data are important for realistic simulation of regional climate. The default vegetation parameters from Global Land Cover 2000, currently incorporated into global climate models and used in regional climate model RegCM, are not realistic for China, which may have contributed to serious bias in surface climate simulation. In this study, a new set of vegetation parameters considering the Plant Functional Type (PFT) fractions and the corresponding monthly leaf area index (PFT_LAI), were developed based on the land cover and MODIS LAI data sets. The regional climate model RegCM4.5 coupled with the land surface model CLM4.5 were utilized to test the performance of the new vegetation parameters by comparing simulations with observations using different surface parameters. The surface energy balance was analysed to examine the effects of changed vegetation parameters on regional climate. The results showed that the new parameters were more accurate than the GLC2000 parameters when describing the distribution of crops, grassland, and forests over China. The improved vegetation parameters reduced model biases for winter air temperature and precipitation over southern China by 0.9 degrees C and 8%, respectively, and reduced the winter temperature and summer precipitation biases over northeastern China by approximately 0.7 degrees C and 8%, respectively. More accurate surface albedo are the main reasons for reductions in model bias. However, certain biases, such as the cold and dry bias over the Tibetan Plateau, still remained in the simulation results using our new vegetation data.

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