Impact of Physics Parameterization Ordering in a Global Atmosphere Model. (23rd February 2018)

• Record Type: Journal Article
• Authors: Donahue, Aaron S.; Caldwell, Peter M.
• Is Part Of: Journal of advances in modeling earth systems. Volume 10:Number 2(2018)
• Page Start: 481
• Page End: 499
• Publication Date: 2018-02-23
• Subjects: global climate models -- sequential time splitting -- net climate feedback -- physics parameterizations; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1002/2017MS001067
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid‐scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effect of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k ‐means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution. Key Points: The order in which physics processes are called in atmospheric models has a significant impact on the model behavior Process order can have a substantial impact on climate sensitivity Atmospheric physical processes are applied sequentially and the model state changes greatly from process to process