How the CMIP6 climate models project the historical terrestrial GPP in China. (2nd September 2022)

• Record Type: Journal Article
• Authors: Zhang, Chi; Qi, Wei; Dong, Jinwei; Deng, Yu
• Is Part Of: International journal of climatology. Volume 42:Number 16(2022)
• Page Start: 9449
• Page End: 9461
• Publication Date: 2022-09-02
• Subjects: climate change -- climate model -- CMIP6 -- evaluation -- GPP; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.7834
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: Gross primary production (GPP) is an important indicator that measures the carbon uptake by vegetation through photosynthesis. How the latest climate models project GPP is critical for climate change evaluation and ecosystem prediction. This study compares the historical runs of seven climate models joining CMIP6 with an observation‐based dataset from 1980 to 2013 in China. It is found that BCC‐CSM2‐MR and MPI‐ESM1‐2‐HR from Beijing Climate Center and the Max Planck Institute give the best estimation of climatological GPP at both regional and national scales. MPI‐ESM1‐2‐HR performs much better than others in characterizing the spatial structure in regions other than the temperate continental, while CMCC‐CM2‐SR5 from Italy performs the best in the temperate monsoonal. No climate model can capture well the GPP interannual variation even over one climate zone. BCC‐CSM2‐MR is a not‐too‐bad choice as it provides the most positively and significantly correlated GPP grids with observations. Further analyses reveal that BCC‐CSM2‐MR and CMCC‐CM2‐SR5 can well capture ecosystem response to climate over regions except for the Tibetan Plateau. With the response parameters and the observational climate, the two climate models can simply rebuild the GPP variabilities as the observational. Over the Tibetan Plateau, all climate models produce spuriously too large precipitation, which turns precipitation from the most confining into no longer significantly influential to the ecosystem. It highlights the urgency to improve the modelling of the Plateau climate and the corresponding ecosystem‐climate feedbacks. Abstract : The CMIP6 climate models' performance in simulating historical GPP in China is systematically evaluated. BCC‐CSM2‐MR (China) and MPI‐ESM1‐2‐HR (Germany) give the best estimation in climatological GPP. MPI‐ESM1‐2‐HR performs best in characterizing the spatial structure, while no model can capture well the GPP interannual variation. The response of ecosystem to climate is well represented in BCC‐CSM2‐MR and CMCC‐CM2‐SR5 (Italy). With regressed model parameters and observational climate, the two models can simply rebuild the GPP variabilities as the observational.