Abstract
El Ninois probably the most influential climate phenomenon on inter-annual time scales. It affects the global climate system and is associated with natural disasters; it has serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Ninoare still not accurate enough, at least more than half a year ahead. Here, we introduce a new forecasting index based on climate network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the Nino 3.4 region. We find that significant upward trends in our index forecast the onset of El Nino approximately 1 year ahead, and the highest peak since the end of last El Nino in our index forecasts the magnitude of the following event. We study the forecasting capability of the proposed index on several datasets, including, ERA-Interim, NCEP Reanalysis I, PCMDI-AMIP 1.1.3 and ERSST.v5.
| Original language | English |
|---|---|
| Article number | 043036 |
| Journal | New Journal of Physics |
| Volume | 20 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2018 |
Bibliographical note
Publisher Copyright:© 2018 The Author(s). Published by IOP Publishing Ltd on behalf of Deutsche Physikalische Gesellschaft.
Funding
We thank Kai Xu for helpful discussions and suggestions. We acknowledge the Israel-Italian collaborative project NECST, the Israel Science Foundation, ONR, Japan Science Foundation, BSF-NSF, and DTRA (Grant No. HDTRA-1-10-1-0014) for financial support. JF thanks the fellowship program funded by the Planning and Budgeting Committee of the Council for Higher Education of Israel.
| Funders | Funder number |
|---|---|
| BSF-NSF | HDTRA-1-10-1-0014 |
| Japan Science Foundation | |
| Office of Naval Research | |
| Seventh Framework Programme | 317532 |
| Israel Science Foundation | |
| Council for Higher Education |
Keywords
- ENSO
- climate networks
- complex systems
- dynamic networks