Evaluation of China's Terrestrial Vegetation Carbon Stocks Simulated by the CMIP5 and CMIP6 Models during 1950-2014
The performances of the CMIP5 and CMIP6 earth system models in simulating China's terrestrial vegetation biomass were assessed using two sets of observation-based biomass carbon density data,the first to ninth(1950-2018)China Forest Resource Inventory data and a set of remote sensing land cover data.Furthermore,the impacts of different land-use harmonization(LUH)datasets on the vegetation biomass simulations of the CMIP5 and CMIP6 models were explored.Our results show that these models overestimate China's total vegetation carbon storage during 1995-2004[28.0±6.0 Pg(C)for CMIP5 and 25.3±7.7 Pg(C)for CMIP6 and 18.1 and 18.7 Pg(C)for the two sets of reference data].The spatial distributions of the vegetation carbon density simulated using the CMIP6 model are better than those by CMIP5,demonstrating improved Taylor skill score(hereinafter referred to as"TSS")values and reduced model uncertainties.During 1950-1990,both CMIP5 and CMIP6 models display that China's vegetation acts as carbon sources[-89.4 Tg(C)a-1 for CMIP5 and-58.2 Tg(C)a-1 for CMIP6].These carbon sources substantially increase to-256.6 Tg(C)a-1(CMIP5)and-171.0 Tg(C)a-1(CMIP6)in the 1980s.However,from 1990 to 2014,the CMIP5 model shows that carbon sources reduce to-48.1 Tg(C)a while the CMIP6 model indicates a shift from carbon sources to weak carbon sinks[42.8 Tg(C)a-1,P＜0.05].The differences in China's vegetation carbon between CMIP5 and CMIP6 are closely related to their different land change conditions.Compared with LUH1 used in CMIP5,the forest coverage and changes depicted in LUH2(used in CMIP6)are more consistent with the China Forest Inventory data and are closer to that since the 1980s.This study implies that LUH datasets used in CMIP exhibit a large bias for China's forest and crop trajectories over the past 65 years.Therefore,more accurate land cover datasets are essential for improving the simulation of vegetation carbon in future CMIP models.
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万方数据
