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DOI	10.1007/s00382-018-4168-5
	On climate prediction: how much can we expect from climate memory?
	Yuan N.; Huang Y.; Duan J.; Zhu C.; Xoplaki E.; Luterbacher J.
发表日期	2019
ISSN	0930-7575
起始页码	855
结束页码	864
卷号	52 期号:2020-01-02
英文摘要	Slowing variability in climate system is an important source of climate predictability. However, it is still challenging for current dynamical models to fully capture the variability as well as its impacts on future climate. In this study, instead of simulating the internal multi-scale oscillations in dynamical models, we discussed the effects of internal variability in terms of climate memory. By decomposing climate state x(t) at a certain time point t into memory part M(t) and non-memory part ε(t) , climate memory effects from the past 30 years on climate prediction are quantified. For variables with strong climate memory, high variance (over 20 %) in x(t) is explained by the memory part M(t), and the effects of climate memory are non-negligible for most climate variables, but the precipitation. Regarding of multi-steps climate prediction, a power law decay of the explained variance was found, indicating long-lasting climate memory effects. The explained variances by climate memory can remain to be higher than 10% for more than 10 time steps. Accordingly, past climate conditions can affect both short (monthly) and long-term (interannual, decadal, or even multidecadal) climate predictions. With the memory part M(t) precisely calculated from Fractional Integral Statistical Model, one only needs to focus on the non-memory part ε(t) , which is an important quantity that determines climate predictive skills. © 2018, The Author(s).
英文关键词	Climate predictability; Climate prediction; Fractional integral statistical model; Long-term climate memory
语种	英语
scopus关键词	climate conditions; climate prediction; numerical model; statistical analysis
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/146611
作者单位	CAS Key Laboratory of Regional Climate Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus-Liebig University Giessen, Senckenbergstr. 1, Giessen, 35390, Germany; CMA Public Meteorological Service Centre, CMA, Beijing, 100081, China; Chinese Academy of Meteorological Science, Beijing, 100081, China; Centre for International Development and Environmental Research, Justus-Liebig University Giessen, Senckenbergstr. 3, Giessen, D-35390, Germany
推荐引用方式 GB/T 7714	Yuan N.,Huang Y.,Duan J.,et al. On climate prediction: how much can we expect from climate memory?[J],2019,52(2020-01-02).
APA	Yuan N.,Huang Y.,Duan J.,Zhu C.,Xoplaki E.,&Luterbacher J..(2019).On climate prediction: how much can we expect from climate memory?.Climate Dynamics,52(2020-01-02).
MLA	Yuan N.,et al."On climate prediction: how much can we expect from climate memory?".Climate Dynamics 52.2020-01-02(2019).