气候变化领域集成服务门户(CCPortal): Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition

CCPortal

DOI	10.1007/s00382-020-05444-7
	Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition
	Nian D.; Yuan N.; Ying K.; Liu G.; Fu Z.; Qi Y.; Franzke C.L.E.
发表日期	2020
ISSN	0930-7575
起始页码	3239
结束页码	3252
卷号	55
英文摘要	It is well recognized that climate predictability has three origins: (i) climate memory (inertia of the climate system) that accumulated from the historical conditions, (ii) responses to external forcings, and (iii) dynamical interactions of multiple processes in the climate system. However, how to systematically identify these predictable sources is still an open question. Here, we combine a recently developed Fractional Integral Statistical Model (FISM) with a Variance Decomposition Method (VDM), to systematically estimate the potential sources of predictability. With FISM, one can extract the memory component from the considered variable. For the residual parts, VDM can then be applied to extract the slow varying covariance matrix, which contains signals related to external forcings and dynamical interactions of multiple processes in climate. To demonstrate the feasibility of this new method, we analyzed the seasonal predictability in observational monthly surface air temperatures over China from 1960 to 2017. It is found that the climate memory component contributes a large portion of the seasonal predictability in the temperature records. After removing the memory component, the residual predictability stems mainly from teleconnections, i.e., in summer the residual predictability is closely related to sea surface temperature anomalies (SSTA) in the eastern tropical Pacific and the northern Indian Ocean. Our results offer the potential of more skillful seasonal predictions compared with the results obtained using FISM or VDM alone. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Long-term memory; Seasonal potential predictability; Seasonal predictability sources; Variance decomposition
语种	英语
scopus关键词	air temperature; climate prediction; climate variation; covariance analysis; decomposition analysis; matrix; numerical model; seasonal variation; variance analysis; China
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/145253
作者单位	Lab for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China; School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China; CAS Key Laboratory of Regional Climate Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; Chinese Academy of Meteorological Sciences, Beijing, 100081, China; Meteorological Institute, and Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany
推荐引用方式 GB/T 7714	Nian D.,Yuan N.,Ying K.,et al. Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition[J],2020,55.
APA	Nian D..,Yuan N..,Ying K..,Liu G..,Fu Z..,...&Franzke C.L.E..(2020).Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition.Climate Dynamics,55.
MLA	Nian D.,et al."Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition".Climate Dynamics 55(2020).