DOI: 10.1002/2017MS001067
Scopus记录号: 2-s2.0-85042371982
论文题名: Impact of Physics Parameterization Ordering in a Global Atmosphere Model
作者: Donahue A ; S ; , Caldwell P ; M
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2018
卷: 10, 期: 2 起始页码: 481
结束页码: 499
语种: 英语
英文关键词: Earth (planet)
; Feedback
; Macros
; Parameterization
; Climate feedbacks
; Coupled Model Intercomparison Project
; General circulation model
; Global climate model
; Spatial and temporal scale
; Sub-grid scale process
; Time splitting
; Weather and climate models
; Climate models
; atmospheric modeling
; climate feedback
; climate modeling
; CMIP
; convection
; parameterization
; spatiotemporal analysis
英文摘要: 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. © 2018. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75646
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: Lawrence Livermore National Laboratory, Livermore, CA, United States
Recommended Citation:
Donahue A,S,, Caldwell P,et al. Impact of Physics Parameterization Ordering in a Global Atmosphere Model[J]. Journal of Advances in Modeling Earth Systems,2018-01-01,10(2)