Yuan, N.; Fu, Z. & Liu, S. (2014): <b>Extracting climate memory using fractional integrated statistical model: A new perspective on climate prediction</b>. <i>Scientific Reports</i> <b>4</b>, 06577<br>DOI: <a href="http://dx.doi.org/10.1038/srep06577" target="_blank">http://dx.doi.org/10.1038/srep06577</a>.
Resource Description
Title:
Extracting climate memory using fractional integrated statistical model: A new perspective on climate prediction
Long term memory (LTM) in climate variability is studied by means of fractional integral techniques. By<br/>
using a recently developed model, Fractional Integral Statistical Model (FISM), we in this report proposed a<br/>
new method, with which one can estimate the long-lasting influences of historical climate states on the<br/>
present time quantitatively, and further extract the influence as climate memory signals. To show the<br/>
usability of this method, two examples, the Northern Hemisphere monthly Temperature Anomalies<br/>
(NHTA) and the Pacific Decadal Oscillation index (PDO), are analyzed in this study. We find the climate<br/>
memory signals indeed can be extracted and the whole variations can be further decomposed into two parts:<br/>
the cumulative climate memory (CCM) and the weather-scale excitation (WSE). The stronger LTM is, the<br/>
larger proportion the climate memory signals will account for in the whole variations. With the climate<br/>
memory signals extracted, one can at least determine on what basis the considered time series will continue<br/>
to change. Therefore, this report provides a new perspective on climate prediction.
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