Application and impacts of the GlobeLand30 land cover dataset on the Beijing Climate Center Climate Model

9th Symposium of the International Society for Digital Earth

地球科学;计算机科学

Shi, X.^1 ; Nie, S.^1 ; Ju, W.^2 ; Yu, L.^3,4

National Climate Center, Beijing, China^1

Nanjing University, Nanjing, China^2

Ministry of Education, Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China^3

Joint Center for Global Change Studies, Beijing, China^4

关键词: Climate simulation;    Climate system model;    Component model;    Higher resolution;    Northern Hemispheres;    Satellite remote sensing;    Southern Hemisphere;    Spatial features;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/34/1/012032/pdf
DOI  :  10.1088/1755-1315/34/1/012032

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Land cover (LC) is a necessary and important input variable of the land surface and climate model, and has significant impacts on climate and climate changes. In this paper, the new higher-resolution global LC dataset, GlobeLand30, was employed in the Beijing Climate Center Climate System Model (BCC-CSM) to investigate LC impacts on the land surface and climate via simulation experiments. The strategy for connecting the new LC dataset and model was to merge the GlobeLand30 data with other satellite remote sensing datasets to enlarge the plant function types (PFT) fitted for the BCC-CSM. The area-weighted up-scaling approach was used to aggregate the 30m-resolution GlobeLand30 data onto the coarser model grids and derive PFT as well as percentage information. The LC datasets of GlobeLand30 and the original BCC-CSM had generally consistent spatial features but with significant differences. Numerical simulations with these two LC datasets were conducted and compared to present the effects of the new GlobeLand30 data on the climate. Results show that with the new LC data products, several model biases between simulations and observations in the BCC climate model with original LC datasets were effectively reduced, including 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. Therefore, the GlobeLand30 data are suitable for use in the BCC-CSM component models and can improve the performance of climate simulations.