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DOI | 10.1175/JCLI-D-18-0881.1 |
A Time-Varying Causality Formalism Based on the Liang-Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems | |
Hagan, Daniel Fiifi Tawia1; Wang, Guojie1; Liang, X. San2,3; Dolman, Han A. J.4 | |
2019-11-01 | |
发表期刊 | JOURNAL OF CLIMATE
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2019 |
卷号 | 32期号:21页码:7521-7537 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Netherlands |
英文摘要 | The interaction between the land surface and the atmosphere is of significant importance in the climate system because it is a key driver of the exchanges of energy and water. Several important relations to heat waves, floods, and droughts exist that are based on the interaction of soil moisture and, for instance, air temperature and humidity. Our ability to separate the elements of this coupling, identify the exact locations where they are strongest, and quantify their strengths is, therefore, of paramount importance to their predictability. A recent rigorous causality formalism based on the Liang-Kleeman (LK) information flow theory has been shown, both theoretically and in real-world applications, to have the necessary asymmetry to infer the directionality and magnitude within geophysical interactions. However, the formalism assumes stationarity in time, whereas the interactions within the land surface and atmosphere are generally nonstationary; furthermore, it requires a sufficiently long time series to ensure statistical sufficiency. In this study, we remedy this difficulty by using the square root Kalman filter to estimate the causality based on the LK formalism to derive a time-varying form. Results show that the new formalism has similar properties compared to its time-invariant form. It is shown that it is also able to capture the time-varying causality structure within soil moisture-air temperature coupling. An advantage is that it does not require very long time series to make an accurate estimation. Applying a wavelet transform to the results also reveals the full range of temporal scales of the interactions. |
英文关键词 | Atmosphere-land interaction Feedback Soil moisture Kalman filters Time series Climate prediction |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000489017000002 |
WOS关键词 | LEAST-SQUARES METHOD ; SOIL-MOISTURE ; KALMAN FILTER ; ATMOSPHERE INTERACTIONS ; FEEDBACKS ; MODELS |
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/188108 |
专题 | 气候变化 |
作者单位 | 1.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Sch Geog Sci, Nanjing, Jiangsu, Peoples R China; 2.Nanjing Inst Meteorol, Sch Marine Sci, Nanjing, Jiangsu, Peoples R China; 3.Nanjing Inst Meteorol, Sch Atmospher Sci, Nanjing, Jiangsu, Peoples R China; 4.Free Univ Amsterdam, Fac Sci, Dept Earth Sci, Amsterdam, Netherlands |
推荐引用方式 GB/T 7714 | Hagan, Daniel Fiifi Tawia,Wang, Guojie,Liang, X. San,et al. A Time-Varying Causality Formalism Based on the Liang-Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems[J]. JOURNAL OF CLIMATE,2019,32(21):7521-7537. |
APA | Hagan, Daniel Fiifi Tawia,Wang, Guojie,Liang, X. San,&Dolman, Han A. J..(2019).A Time-Varying Causality Formalism Based on the Liang-Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems.JOURNAL OF CLIMATE,32(21),7521-7537. |
MLA | Hagan, Daniel Fiifi Tawia,et al."A Time-Varying Causality Formalism Based on the Liang-Kleeman Information Flow for Analyzing Directed Interactions in Nonstationary Climate Systems".JOURNAL OF CLIMATE 32.21(2019):7521-7537. |
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