| Abstract: | Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution (30 m) global land cover dataset (GlobeLand30) produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model (BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the GlobeLand30 data in the model. First, the GlobeLand30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type (PFT) data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution GlobeLand30 land cover type and area percentage with the coarser model grid resolutions globally. The GlobeLand30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies (lakes and wetlands). The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the GlobeLand30-based data were used in the BCC_CSM atmosphere model. The results suggest that the GlobeLand30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations. |
