气候变化领域集成服务门户(CCPortal): Spatial Covariance Modeling for Stochastic Subgrid-Scale Parameterizations Using Dynamic Mode Decomposition

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DOI	10.1029/2020MS002115
	Spatial Covariance Modeling for Stochastic Subgrid-Scale Parameterizations Using Dynamic Mode Decomposition
	Gugole F.; Franzke C.L.E.
发表日期	2020
ISSN	19422466
卷号	12 期号:8
英文摘要	Stochastic parameterizations are increasingly being used in climate modeling to represent subgrid-scale processes. While different parameterizations are being developed considering different aspects of the physical phenomena, less attention is given to technical and numerical aspects. In particular, empirical orthogonal functions (EOFs) are employed when a spatial structure is required. Here, we provide evidence they might not be the most suitable choice. By applying an energy-consistent parameterization to the two-layer quasi-geostrophic (QG) model, we investigate the model sensitivity to a priori assumptions made on the parameterization. In particular, we consider here two methods to prescribe the spatial covariance of the noise: first, by using climatological variability patterns provided by EOFs, and second, by using time-varying dynamics-adapted Koopman modes, approximated by dynamic mode decomposition (DMD). The performance of the two methods are analyzed through numerical simulations of the stochastic system on a coarse spatial resolution and the outcomes compared to a high-resolution simulation of the original deterministic system. The comparison reveals that the DMD-based noise covariance scheme outperforms the EOF-based one. The use of EOFs leads to a significant increase of the ensemble spread and to a meridional misplacement of the bimodal eddy kinetic energy (EKE) distribution. Conversely, using DMDs, the ensemble spread is confined, the meridional propagation of the zonal jet stream is accurately captured, and the total variance of the system is improved. Our results highlight the importance of the systematic design of stochastic parameterizations with dynamically adapted spatial correlations, rather than relying on statistical spatial patterns. ©2020. The Authors.
英文关键词	dynamic mode decomposition; dynamically adapted noise covariance; empirical orthogonal functions; Koopman operator; stochastic parameterizations
语种	英语
scopus关键词	Kinetic energy; Kinetics; Nanofluidics; Numerical methods; Orthogonal functions; Parameterization; Deterministic systems; Dynamic mode decompositions; Empirical Orthogonal Function; High resolution simulations; Spatial correlations; Sub-grid scale process; Time-varying dynamics; Variability patterns; Stochastic systems; climate modeling; correction; decomposition analysis; design; empirical orthogonal function analysis; jet stream; kinetic energy; meridional circulation; parameterization; spatial variation; stochasticity; zonal wind
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156679
作者单位	Meteorological Institute and Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany
推荐引用方式 GB/T 7714	Gugole F.,Franzke C.L.E.. Spatial Covariance Modeling for Stochastic Subgrid-Scale Parameterizations Using Dynamic Mode Decomposition[J],2020,12(8).
APA	Gugole F.,&Franzke C.L.E..(2020).Spatial Covariance Modeling for Stochastic Subgrid-Scale Parameterizations Using Dynamic Mode Decomposition.Journal of Advances in Modeling Earth Systems,12(8).
MLA	Gugole F.,et al."Spatial Covariance Modeling for Stochastic Subgrid-Scale Parameterizations Using Dynamic Mode Decomposition".Journal of Advances in Modeling Earth Systems 12.8(2020).