Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling

Journal of Advances in Modeling Earth Systems

Xubin Zeng¹  Christopher J. Vogl²  Carol S. Woodward²  David J. Gardner²  Hui Wan³  Philip J. Rasch³  Panos Stinis³  Vincent E. Larson³  Balwinder Singh³  Shixuan Zhang³ 

[1] Department of Hydrology and Atmospheric Sciences University of Arizona Tucson AZ USA;Lawrence Livermore National Laboratory Livermore CA USA;Pacific Northwest National Laboratory Richland WA USA;

关键词: atmospheric model;    time stepping;    convergence;    parameterization;   

DOI  :  10.1029/2019MS001982

来源: DOAJ

【 摘 要 】

Abstract Convergence testing is a common practice in the development of dynamical cores of atmospheric models but is not as often exercised for the parameterization of subgrid physics. An earlier study revealed that the stratiform cloud parameterizations in several predecessors of the Energy Exascale Earth System Model (E3SM) showed strong time step sensitivity and slower‐than‐expected convergence when the model's time step was systematically refined. In this work, a simplified atmosphere model is configured that consists of the spectral‐element dynamical core of the E3SM atmosphere model coupled with a large‐scale condensation parameterization based on commonly used assumptions. This simplified model also resembles E3SM and its predecessors in the numerical implementation of process coupling and shows poor time step convergence in short ensemble tests. We present a formal error analysis to reveal the expected time step convergence rate and the conditions for obtaining such convergence. Numerical experiments are conducted to investigate the root causes of convergence problems. We show that revisions in the process coupling and closure assumption help to improve convergence in short simulations using the simplified model; the same revisions applied to a full atmosphere model lead to significant changes in the simulated long‐term climate. This work demonstrates that causes of convergence issues in atmospheric simulations can be understood by combining analyses from physical and mathematical perspectives. Addressing convergence issues can help to obtain a discrete model that is more consistent with the intended representation of the physical phenomena.

【 授权许可】

Unknown