Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second‐order R osenbrock solver in CAM 4‐ C hem

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.