DOI: 10.1002/2016MS000863
Scopus记录号: 2-s2.0-85013449871
论文题名: Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second-order Rosenbrock solver in CAM4-Chem
作者: Sun J ; , Fu J ; S ; , Drake J ; , Lamarque J ; -F ; , Tilmes S ; , Vitt F
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 1 起始页码: 482
结束页码: 500
语种: 英语
英文关键词: Climate models
; Computational chemistry
; Computational efficiency
; Economic and social effects
; Jacobian matrices
; Numerical methods
; Chemistry-climate models
; Computational performance
; Computationally efficient
; Implicit solvers
; Ozone concentration
; Rosenbrock
; Surface ozone concentrations
; Time step
; Ozone
; atmospheric chemistry
; climate modeling
; computer simulation
; concentration (composition)
; error analysis
; mathematical analysis
; ozone
; prediction
; sensitivity analysis
; trade-off
; Asia
; Europe
; United States
英文摘要: The global chemistry-climate model (CAM4-Chem) overestimates the surface ozone concentration over the conterminous U.S. (CONUS). Reasons for this positive bias include emission, meteorology, chemical mechanism, and solver. In this study, we explore the last possibility by examining the sensitivity to the numerical methods for solving the chemistry equations. A second-order Rosenbrock (ROS-2) solver is implemented in CAM4-Chem to examine its influence on the surface ozone concentration and the computational performance of the chemistry program. Results show that under the same time step size (1800 s), statistically significant reduction of positive bias is achieved by the ROS-2 solver. The improvement is as large as 5.2 ppb in Eastern U.S. during summer season. The ROS-2 solver is shown to reduce the positive bias in Europe and Asia as well, indicating the lower surface ozone concentration over the CONUS predicted by the ROS-2 solver is not a trade-off consequence with increasing the ozone concentration at other global regions. In addition, by refining the time step size to 180 s, the first-order implicit solver does not provide statistically significant improvement of surface ozone concentration. It reveals that the better prediction from the ROS-2 solver is not only due to its accuracy but also due to its suitability for stiff chemistry equations. As an added benefit, the computation cost of the ROS-2 solver is almost half of first-order implicit solver. The improved computational efficiency of the ROS-2 solver is due to the reuse of the Jacobian matrix and lower upper (LU) factorization during its multistage calculation. © 2017. The Authors.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75817
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, United States; Climate Change Science Institute and Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States; National Center for Atmospheric Research, Boulder, CO, United States
Recommended Citation:
Sun J,, Fu J,S,et al. Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second-order Rosenbrock solver in CAM4-Chem[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(1)