Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐Chem
Abstract 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 sens...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2017-03-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/2016MS000863 |
| _version_ | 1797736036960305152 |
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| author | Jian Sun Joshua S. Fu John Drake Jean‐Francois Lamarque Simone Tilmes Francis Vitt |
| author_facet | Jian Sun Joshua S. Fu John Drake Jean‐Francois Lamarque Simone Tilmes Francis Vitt |
| author_sort | Jian Sun |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-12T13:07:46Z |
| format | Article |
| id | doaj.art-ddf4cb0116e946c990d391b42ee0aaaa |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-03-12T13:07:46Z |
| publishDate | 2017-03-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-ddf4cb0116e946c990d391b42ee0aaaa2023-08-28T13:36:50ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662017-03-019148250010.1002/2016MS000863Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐ChemJian Sun0Joshua S. Fu1John Drake2Jean‐Francois Lamarque3Simone Tilmes4Francis Vitt5Department of Civil and Environmental EngineeringUniversity of TennesseeKnoxville Tennessee USADepartment of Civil and Environmental EngineeringUniversity of TennesseeKnoxville Tennessee USADepartment of Civil and Environmental EngineeringUniversity of TennesseeKnoxville Tennessee USANational Center for Atmospheric ResearchBoulder Colorado USANational Center for Atmospheric ResearchBoulder Colorado USANational Center for Atmospheric ResearchBoulder Colorado USAAbstract 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.https://doi.org/10.1002/2016MS000863second‐order Rosenbrock solverfirst‐order implicit solvertime stepozonecomputational performance |
| spellingShingle | Jian Sun Joshua S. Fu John Drake Jean‐Francois Lamarque Simone Tilmes Francis Vitt Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐Chem Journal of Advances in Modeling Earth Systems second‐order Rosenbrock solver first‐order implicit solver time step ozone computational performance |
| title | Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐Chem |
| title_full | Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐Chem |
| title_fullStr | Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐Chem |
| title_full_unstemmed | Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐Chem |
| title_short | Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order Rosenbrock solver in CAM4‐Chem |
| title_sort | improvement of the prediction of surface ozone concentration over conterminous u s by a computationally efficient second order rosenbrock solver in cam4 chem |
| topic | second‐order Rosenbrock solver first‐order implicit solver time step ozone computational performance |
| url | https://doi.org/10.1002/2016MS000863 |
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