Abstract
The hypothesis that regional characteristics of the East Asian summer monsoon (EASM) result from the presence of nonlinear coupled features that modulate the seasonal circulation and rainfall at the intraseasonal time scale is advanced in this study. To examine this hypothesis, the authors undertake the analysis of daily EASM variability using a nonlinear multivariate data classifying algorithm known as self-organizing mapping (SOM). On the basis of various SOM node analyses, four major intraseasonal phases of the EASM are identified. The first node describes a circulation state corresponding to weak tropical and subtropical pressure systems, strong upper-level jets, weakened monsoonal winds, and cyclonic upper-level vorticity. This mode, related to large rainfall anomalies in southeast China and southern Japan, is identified as the mei-yu-baiu phase. The second node represents a distinct circulation state corresponding to a strengthened subtropical high, monsoonal winds, and anticyclonic upper-level vorticity in southeast Korea, which is identified as the changma phase. The third node is related to copious rain over Korea following changma, which we name the postchangma phase. The fourth node is situated diagonally opposite the changma mode. Because Korea experiences a dry spell associated with this SOM node, it is referred to as the dry-spell phase. The authors also demonstrate that a strong modulation of the changma and dry-spell phases on interannual time scales occurs during El Niño and La Niñ a years. Results imply that the key to predictability of the EASM on interannual time scales may lie with analysis and exploitation of its nonlinear characteristics. © 2012 American Meteorological Society.
| Original language | English |
|---|---|
| Pages (from-to) | 6975-6988 |
| Journal | Journal of Climate |
| Volume | 25 |
| Issue number | 20 |
| DOIs | |
| Publication status | Published - Oct 2012 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- ENSO
- Intraseasonal variability
- Neural networks
- Nonlinear dynamics
- Rainfall
- Summer/warm season
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