气候变化领域集成服务门户(CCPortal): A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

CCPortal

DOI	10.1175/JCLI-D-19-0769.1
	A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability
	Foster D.; Comeau D.; Urban N.M.
发表日期	2020
ISSN	0894-8755
起始页码	6065
结束页码	6081
卷号	33 期号:14
英文摘要	Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies-Typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation-dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-Type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes.We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model's ability to capture variability of SST anomalies. © 2020 American Meteorological Society. All rights reserved.
英文关键词	Atmospheric temperature; Bayesian networks; Climate models; Inference engines; Inverse problems; Maximum likelihood estimation; Oceanography; Stochastic models; Stochastic systems; Submarine geophysics; Surface properties; Surface waters; Fluctuation dissipation theories; High-dimensional problems; Maximum likelihood estimate; Methodological studies; Point estimation method; Sea surface temperature (SST); Sea surface temperature variability; Spatial and temporal scale; Parameter estimation; Bayesian analysis; decadal variation; linearity; parameter estimation; prediction; regional climate; sea surface temperature; spatiotemporal analysis
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/171226
作者单位	Oregon State University, Corvallis, OR, United States; Computational Physics and Methods (CCS-2), Los Alamos National Laboratory, Los Alamos, NM, United States
推荐引用方式 GB/T 7714	Foster D.,Comeau D.,Urban N.M.. A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability[J],2020,33(14).
APA	Foster D.,Comeau D.,&Urban N.M..(2020).A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability.Journal of Climate,33(14).
MLA	Foster D.,et al."A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability".Journal of Climate 33.14(2020).