Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3975-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-3975-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 May 2024
Development and technical paper |
| 15 May 2024
| 15 May 2024
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Jiaxu Guo
College of Computer Science and Technology, Jilin University, Changchun, China
National Supercomputing Center in Wuxi, Wuxi, China
Juepeng Zheng
CORRESPONDING AUTHOR
School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China
Yidan Xu
National Meteorological Information Centre, CMA Meteorological Data Centre, Beijing, China
Haohuan Fu
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Ministry of Education Key Laboratory for Earth System Modeling and the Department of Earth System Science, Tsinghua University, Beijing, China
National Supercomputing Center in Wuxi, Wuxi, China
Wei Xue
Department of Computer Science and Technology, Tsinghua University, Beijing, China
National Supercomputing Center in Wuxi, Wuxi, China
Lanning Wang
Faculty of Geographical Science, Beijing Normal University, Beijing, China
National Supercomputing Center in Wuxi, Wuxi, China
Lin Gan
Department of Computer Science and Technology, Tsinghua University, Beijing, China
National Supercomputing Center in Wuxi, Wuxi, China
Department of Computer Science and Technology, Tsinghua University, Beijing, China
School of Software, Shandong University, Jinan, China
National Supercomputing Center in Wuxi, Wuxi, China
Wubing Wan
Department of Computer Science and Technology, Tsinghua University, Beijing, China
National Supercomputing Center in Wuxi, Wuxi, China
Xianwei Wu
College of Computer Science and Technology, Jilin University, Changchun, China
National Supercomputing Center in Wuxi, Wuxi, China
Zhitao Zhang
College of Geoexploration Science and Technology, Jilin University, Changchun, China
Liang Hu
CORRESPONDING AUTHOR
College of Computer Science and Technology, Jilin University, Changchun, China
Gaochao Xu
College of Computer Science and Technology, Jilin University, Changchun, China
Xilong Che
CORRESPONDING AUTHOR
College of Computer Science and Technology, Jilin University, Changchun, China
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Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
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Hui Wang, Qizhong Wu, Alex B. Guenther, Xiaochun Yang, Lanning Wang, Tang Xiao, Jie Li, Jinming Feng, Qi Xu, and Huaqiong Cheng
Atmos. Chem. Phys., 21, 4825–4848, https://doi.org/10.5194/acp-21-4825-2021, https://doi.org/10.5194/acp-21-4825-2021, 2021
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We assessed the influence of the greening trend on BVOC emission in China. The comparison among different scenarios showed that vegetation changes resulting from land cover management are the main driver of BVOC emission change in China. Climate variability contributed significantly to interannual variations but not much to the long-term trend during the study period.
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Few studies have investigated the effects of initial conditions on the simulation or prediction of PM2.5 concentrations. Here, sensitivity experiments are used to explore the effects of three initial mechanisms (clean, restart, and continuous) and emissions in Xi’an in December 2016. According to this work, if the restart mechanism cannot be used due to computing resource and storage space limitations when forecasting PM2.5 concentrations, a spin-up time of at least 27 h is needed.
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Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model...
