Cite as:
Yuan, N.; Fu, Z. &amp; 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="" target="_blank"></a>.

Resource Description

Title: Extracting climate memory using fractional integrated statistical model: A new perspective on climate prediction
F2Fdw ID: 70
Publication Date: 2014-10-10
License and Usage Rights: FACE2FACE data user agreement.
Resource Owner(s):
Individual: Yuan, Naiming
Individual: Fu, Zuntao
Individual: Liu, Shida
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.
| climate memory | FISM |
Literature type specific fields:
Journal: Scientific Reports
Volume: 4
Page Range: 06577
Metadata Provider:
Individual: Yuan, Naiming
Online Distribution:
Download File:

Quick search

  • Publications:
  • Datasets: