Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1007/s00382-018-4168-5 |
On climate prediction: how much can we expect from climate memory? | |
Yuan, Naiming1,2; Huang, Yan3; Duan, Jianping1; Zhu, Congwen4; Xoplaki, Elena2; Luterbacher, Juerg2,5 | |
2019 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 52页码:855-864 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Germany |
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 epsilon(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 epsilon(t), which is an important quantity that determines climate predictive skills. |
英文关键词 | Long-term climate memory Climate predictability Climate prediction Fractional integral statistical model |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000460619200049 |
WOS关键词 | SURFACE AIR-TEMPERATURE ; LONG-TERM-MEMORY ; SCALING BEHAVIORS ; VARIABILITY ; WEATHER ; FORECASTS ; PRECIPITATION ; MACROWEATHER ; OSCILLATION ; UNCERTAINTY |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/36189 |
专题 | 气候变化 |
作者单位 | 1.Chinese Acad Sci, Inst Atmospher Phys, CAS Key Lab Reg Climate Environm Temperate East A, Beijing 100029, Peoples R China; 2.Justus Liebig Univ Giessen, Dept Geog Climatol Climate Dynam & Climate Change, Senckenbergstr 1, D-35390 Giessen, Germany; 3.CMA, CMA Publ Meteorol Serv Ctr, Beijing 100081, Peoples R China; 4.Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China; 5.Justus Liebig Univ Giessen, Ctr Int Dev & Environm Res, Senckenbergstr 3, D-35390 Giessen, Germany |
推荐引用方式 GB/T 7714 | Yuan, Naiming,Huang, Yan,Duan, Jianping,et al. On climate prediction: how much can we expect from climate memory?[J]. CLIMATE DYNAMICS,2019,52:855-864. |
APA | Yuan, Naiming,Huang, Yan,Duan, Jianping,Zhu, Congwen,Xoplaki, Elena,&Luterbacher, Juerg.(2019).On climate prediction: how much can we expect from climate memory?.CLIMATE DYNAMICS,52,855-864. |
MLA | Yuan, Naiming,et al."On climate prediction: how much can we expect from climate memory?".CLIMATE DYNAMICS 52(2019):855-864. |
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