期刊
CLIMATE DYNAMICS
卷 45, 期 11-12, 页码 3527-3545出版社
SPRINGER
DOI: 10.1007/s00382-015-2555-8
关键词
El Nino; El Nino Modoki; Teleconnections; Air-sea interaction; Coupled processes
资金
- MoES, Govt. of India
- NERC [NE/I022841/1] Funding Source: UKRI
- Natural Environment Research Council [NE/I022841/1] Funding Source: researchfish
The suggestion that there exist two types of El Nio in the tropical Pacific has generated a debate in the community. Applying various linear and non-linear approaches and composite analysis technique on observed and reanalyzed climate datasets primarily for the 1950-2010 period, we revisit the variability of the tropical Pacific in the light of this debate. Our objective is to examine whether the proposed El Nio Modokis need a classification distinct from canonical El Nios. Even if the distinction is subject to short data records, we demonstrate that the El Nio Modoki events indeed display a seasonal evolution and teleconnections different from the canonical El Nios, and that the distinction is not subject to inclusion of the two extreme El Nios 1982 and 1997 as canonical El Nios. We show that the El Nio Modoki events are not an artifact associated with the orthogonality constraint associated with the EOF technique. Our cluster analysis shows that evolutions of the canonical El Nio and El Nio Modokis through various seasons differ from one another. Importantly, the dynamic and thermodynamic air-sea coupling strength is distinctly different between the El Nio Modoki and the canonical El Nio events. We find that, dynamic feedback intensity is stronger for El Nio Modoki (canonical El Nio) during boreal summer (winter); though the air-sea coupling strength, a major contributor to Bjerknes feedback, is maximum for Modokis during the developing stages, it decreases thereafter. In case of thermodynamic feedback intensity, SST-wind-evaporation feedback is dominant for El Nios while SST-SHF feedback is important during El Nio Modokis. However, we find that the thermodynamic feedback values significantly differ across the flux datasets.
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