Abstract
The soil in China contains an abundance of gravels, but it is poorly described in land surface models. To solve this problem, the Beijing Climate Center Atmosphere–Vegetation Interaction Model (BCC_AVIM), which is a land surface model with the gravel parameterization, is coupled to the Beijing Climate Center Climate System Model (BCC_CSM). The simulation ability of BCC_CSM for China using the gravel parameterization is evaluated by comparing the simulation results using default and new schemes with the observed data. The results show that the annual average surface temperature simulated with the new schemes is more consistent with the observation in terms of spatial distribution, and the simulation results are significantly improved, especially in summer. From the perspective of the area-averaged variables, more precipitation simulated using the default schemes is improved except for summer. The high-level and low-level wind fields simulated by BCC_CSM significantly improve the Qinghai-Tibet Plateau. In general, this gravel parameterization is more suitable for areas with high gravel content, and it improves the simulation performance of BCC_CSM in some areas of China.
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Acknowledgements
Thanks to the National Climate Center for providing the BCC model in Tianhe. We thank Nanjing Hurricane Translation for reviewing the English language quality of this paper.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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No code was developed in the current study.
Funding
This work was supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0103) and the National Natural Science Foundation of China (Grant No. 41975007, 91837208).
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Xu analyzed the data and wrote the paper; Lyu guided the research ideas; Ma, Zhang, Ma and Liu suggested improvements to the research method and program code.
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Xu, Y., Lyu, S., Ma, Y. et al. Gravel parameterization scheme and verification using BCC_CSM. Theor Appl Climatol 148, 1647–1661 (2022). https://doi.org/10.1007/s00704-022-04015-6
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DOI: https://doi.org/10.1007/s00704-022-04015-6