气候变化领域集成服务门户(CCPortal): Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities

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DOI	10.1007/s00382-019-04865-3
	Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities
	Zhao Y.; Deng X.; Zhang S.; Liu Z.; Liu C.
发表日期	2019
ISSN	0930-7575
起始页码	5349
结束页码	5373
卷号	53 期号:2020-09-10
英文摘要	While various data assimilation algorithms based on Bayes’ theorem have been developed for state estimation, some of these algorithms have also been applied to model parameter estimation. Coupled model parameter estimation (CPE) adjusts model parameters using available observations; then, the observation-adjusted parameters can greatly mitigate the model bias, which has great potential to reduce climate drift and enhance forecast skill in coupled climate models. However, given numerous model parameters that are associated with multiple time scales, how to conduct CPE with the simultaneous estimation of multiple parameters (SEMP) is still a popular research topic. With the aid of 3 coupled models, ranging from the conceptual coupled model to the intermediate coupled circulation model, this study has developed a systematic method to implement the SEMP–CPE. Linking coupled model sensitivities with the signal-to-noise ratio of the CPE, the SEMP–CPE method uses a timescale structure with coupled model sensitivities to determine which and how many parameters are estimated simultaneously in each CPE cycle to minimize the error of the coupled model simulation. Given that in a coupled model, the timescales by which different model components sensitively respond to a parameter perturbation can be quite different due to their different variabilities in their characteristic timescales, the first part of our study series focuses on the SEMP–CPE associated with single model component sensitivities. The results show that the quality of the model state analysis (in terms of assimilation) improves with the number of parameters being estimated by the order of sensitivities until the signal-to-noise ratio reaches a low threshold. Only when the most impactful physical parameters are estimated is the error of the state estimation consistently decreasing; as well as the signal-to-noise ratio in state-parameter covariance in SEMP scheme is enhanced. While only the signal extracted from the SEMP–CPE reaches saturated, the signal-to-noise ratio in the SEMP–CPE is maximized, and the state estimation error is minimized. Otherwise, if the parameters with low sensitivities are included in the CPE, the error of the state estimation increases instead. These results provide some insight into simultaneously estimating multiple parameters in a biased coupled general circulation model that assimilates real observations, which further improves climate analysis and prediction initialization. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Coupled parameter estimation; Data assimilation; Simultaneous estimation of multiple parameters; Single model component sensitivities
语种	英语
scopus关键词	algorithm; climate modeling; climate prediction; data assimilation; parameter estimation; perturbation; sensitivity analysis; signal-to-noise ratio
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145914
作者单位	College of Automation, Harbin Engineering University, Harbin, 150001, China; Atmospheric Science Program, Department of Geography, Ohio State University, Columbus, OH 43210, United States; Physical Oceanography Laboratory/CIMST, and College of Atmosphere and Ocean, Ocean University of China, Qingdao, 266100, China; Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China
推荐引用方式 GB/T 7714	Zhao Y.,Deng X.,Zhang S.,等. Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities[J],2019,53(2020-09-10).
APA	Zhao Y.,Deng X.,Zhang S.,Liu Z.,&Liu C..(2019).Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities.Climate Dynamics,53(2020-09-10).
MLA	Zhao Y.,et al."Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities".Climate Dynamics 53.2020-09-10(2019).