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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Cited articles
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Schanen, D. P., Meyer, N. R., and Craig, C.: Unified parameterization of the planetary boundary layer and shallow convection with a higher-order turbulence closure in the Community Atmosphere Model: single-column experiments, Geosci. Model Dev., 5, 1407–1423, https://doi.org/10.5194/gmd-5-1407-2012, 2012. a
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and Schanen, D. P.: Higher-Order Turbulence Closure and Its Impact on Climate Simulations in the Community Atmosphere Model, J. Climate, 26, 9655–9676, 2013. a
Bogenschutz, P. A., Tang, S., Caldwell, P. M., Xie, S., Lin, W., and Chen, Y.-S.: The E3SM version 1 single-column model, Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, 2020. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Caflisch, R. E.: Monte carlo and quasi-monte carlo methods, Acta Numer., 7, 1–49, 1998. a
Dalbey, K. R., Eldred, M. S., Geraci, G., Jakeman, J. D., Maupin, K. A., Monschke, J. A., Seidl, D. T., Tran, A., Menhorn, F., and Zeng, X.: Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 Theory Manual, https://doi.org/10.2172/1868423, 2021. a
Fu, H., Liao, J., Yang, J., Wang, L., Song, Z., Huang, X., Yang, C., Xue, W., Liu, F., Qiao, F., Zhao, W., Yin, X., Hou, C., Zhang, C., Ge, W., Zhang, J., Wang, Y., Zhou, C., and Yang, G.: The Sunway TaihuLight supercomputer: system and applications, Science China Information Sciences, 59, 072001, https://doi.org/10.1007/s11432-016-5588-7, 2016. a
Gettelman, A., Morrison, H., and Ghan, S. J.: A New Two-Moment Bulk Stratiform Cloud Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part II: Single-Column and Global Results, J. Climate, 21, 3660–3679, 2008. a
Gettelman, A., Truesdale, J. E., Bacmeister, J. T., Caldwell, P. M., Neale, R. B., Bogenschutz, P. A., and Simpson, I. R.: The Single Column Atmosphere Model Version 6 (SCAM6): Not a Scam but a Tool for Model Evaluation and Development, J. Adv. Model. Earth Sy., 11, 1381–1401, https://doi.org/10.1029/2018MS001578, 2019. a, b
Guo, J.: LB-SCAM: a learning-based SCAM tuner, figshare [code], https://doi.org/10.6084/m9.figshare.21407109.v9, 2024a. a
Guo, J.: LB-SCAM: a learning-based SCAM tuner, figshare [data set], https://doi.org/10.6084/m9.figshare.25808251.v1, 2024b. a
Guo, J., Xu, Y., Fu, H., Xue, W., Gan, L., Tan, M., Wu, T., Shen, Y., Wu, X., Hu, L., and Che, X.: GEO-WMS: an improved approach to geoscientific workflow management system on HPC, CCF Trans. High Perform. Comput., 5, 360–373, https://doi.org/10.1007/S42514-022-00131-X, 2023. a
Harada, M.: GSA: Stata module to perform generalized sensitivity analysis, Statistical Software Components, https://EconPapers.repec.org/RePEc:boc:bocode:s457497 (last access: 27 January 2024), 2012. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013. a
Kennedy, J. and Eberhart, R.: Particle swarm optimization, Proceedings of ICNN'95 – International Conference on Neural Networks, Perth, WA, Australia, 1995, 1942–1948 Vol. 4, https://doi.org/10.1109/ICNN.1995.488968, 2002. a
May, P. T., Mather, J. H., Vaughan, G., Bower, K. N., Jakob, C., Mcfarquhar, G. M., and Mace, G. G.: The tropical warm pool international cloud experiment, B. Am. Meteorol. Soc., 89, 629–646, https://doi.org/10.1175/BAMS-89-5-629, 2008. a
McKay, M. D., Beckman, R. J., and Conover, W. J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 42, 55–61, 2000. a
Mirjalili, S. and Lewis, A.: The Whale Optimization Algorithm, Adv. Eng. Softw., 95, 51–67, https://doi.org/10.1016/j.advengsoft.2016.01.008, 2016. a
Mitchell, M.: An Introduction to Genetic Algorithms, ISBN: 978-0-262-63185-3, https://doi.org/10.7551/mitpress/3927.001.0001, 1996. a
Morris, M. D.: Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161–174, https://doi.org/10.1080/00401706.1991.10484804, 1991. a, b, c
NCAR: CESM Models, NCAR [code], http://www.cesm.ucar.edu/models/cesm1.2/ (last access: 2 February 2024), 2018. a
Neale, R. B., Chen, C.-C., Gettelman, A., Lauritzen, P. H., Park, S., Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J.-F., Mills, M., Tilmes, S., Morrison, H., Cameron-Smith, P., Collins, W. D., Iacono, M. J., Easter, R. C., Liu, X., Ghan, S. J., Rasch, P. J., and Taylor M. A.: Description of the NCAR community atmosphere model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR, 1, 1–12, https://doi.org/10.5065/wgtk-4g06, 2010. a
Nelder, J. A. and Wedderburn, R. W.: Generalized linear models, J. Roy. Stat. Soc. Ser. A, 135, 370–384, 1972. a
Park, S.: A Unified Convection Scheme (UNICON). Part I: Formulation, J.e Atmos. Sci., 71, 3902–3930, 2014. a
Pathak, R., Dasari, H. P., El Mohtar, S., Subramanian, A. C., Sahany, S., Mishra, S. K., Knio, O., and Hoteit, I.: Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6), Front. Climate, 3, 670740, https://doi.org/10.3389/fclim.2021.670740, 2021. a, b, c
Qian, Y., Yan, H., Hou, Z., Johannesson, G., Klein, S., Lucas, D., Neale, R., Rasch, P., Swiler, L., Tannahill, J., Wang, H., Wang, M., and Zhao, C.: Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5, J. Adv. Model. Earth Sy., 7, 382–411, https://doi.org/10.1002/2014MS000354, 2015. a
Saltelli, A.: Making best use of model evaluations to compute sensitivity indices, Comput. Phys. Commun., 145, 280–297, 2002. a
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global sensitivity analysis: the primer, John Wiley & Sons, https://doi.org/10.1002/9780470725184, 2008. a, b
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., and Tarantola, S.: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259–270, 2010. a
Schober, P., Boer, C., and Schwarte, L. A.: Correlation Coefficients: Appropriate Use and Interpretation, Anesth. Analg., 126, 1763–1768, 2018. a
Shi, L., Copot, C., and Vanlanduit, S.: Evaluating Dropout Placements in Bayesian Regression Resnet, J. Artif. Intell. Soft Comput. Res., 12, 61–73, https://doi.org/10.2478/jaiscr-2022-0005, 2022. a
Sobol, I. M.: Sensitivity analysis for non-linear mathematical models, Mathematical Modelling and Computational Experiment, 1, 407–414, 1993. a
Storn, R. and Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 341–359, 1997. a
Tang, J., Deng, C., and Huang, G.-B.: Extreme Learning Machine for Multilayer Perceptron, IEEE T. Neur. Net. Lear., 27, 809–821, https://doi.org/10.1109/TNNLS.2015.2424995, 2016. a
Thompson, R. M., Payne, S. W., Recker, E., and Reed, R. J.: Structure and Properties of Synoptic-Scale Wave Disturbances in the Intertropical Convergence Zone of the Eastern Atlantic, J. Atmos., 36, 53–72, 1979. a
Tong, C.: Problem Solving Environment for Uncertainty Analysis and Design Exploration, 1–37, https://doi.org/10.1007/978-3-319-11259-6_53-1, 2016. a
Wang, X., Yan, X., and Ma, Y.: Research on User Consumption Behavior Prediction Based on Improved XGBoost Algorithm, 2018 IEEE International Conference on Big Data (Big Data) 4169–4175, https://doi.org/10.1109/BigData.2018.8622235, 2018. a
Webster, P. J. and Lukas, R.: TOGA COARE: the coupled ocean-atmosphere response experiment, B. Am. Meteorol. Soc., 73, 1377–1416, 1992. a
Yan, J., Chen, J., and Xu, J.: Analysis of Renting Office Information Based on Univariate Linear Regression Model, in: International Conference on Electrical And Control Engineering (ICECE 2015), Adv Sci Res Ctr, ISBN 978-1-60595-238-3, international Conference on Electrical and Control Engineering (ICECE), Guilin, Peoples R China, 18–19 April 2015, 780–784, 2015. a
Yang, B., Qian, Y., Lin, G., Leung, L. R., Rasch, P. J., Zhang, G. J., Mcfarlane, S. A., Zhao, C., Zhang, Y., and Wang, H.: Uncertainty quantification and parameter tuning in the CAM5 Zhang‐McFarlane convection scheme and impact of improved convection on the global circulation and climate, J. Geophys. Res.-Atmos., 118, 395–415, 2013. a, b
Zhang, G. J.: Sensitivity of climate simulation to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model, Atmos. Ocean, 33, 407–446, https://doi.org/10.1080/07055900.1995.9649539, 1995. a
Zhang, T., Zhang, M., Lin, W., Lin, Y., Xue, W., Yu, H., He, J., Xin, X., Ma, H.-Y., Xie, S., and Zheng, W.: Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method, Geosci. Model Dev., 11, 5189–5201, https://doi.org/10.5194/gmd-11-5189-2018, 2018. a
Zou, L., Qian, Y., Zhou, T., and Yang, B.: Parameter Tuning and Calibration of RegCM3 with MIT – Emanuel Cumulus Parameterization Scheme over CORDEX East Asia Domain, J. Climate, 27, 7687–7701, https://doi.org/10.1175/JCLI-D-14-00229.1, 2014. a
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...
