Impacts of different types of ENSO on the interannual seesaw between the Somali and the maritime continent cross-equatorial flows

Chen Li, Jing Jia Luo, Shuanglin Li

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)


The impacts of different types of El Niño-Southern Oscillation (ENSO) on the interannual negative correlation (seesaw) between the Somali cross-equatorial flow (CEF) and the Maritime Continent (MC) CEF during boreal summer (June-August) are investigated using the ECMWF twentieth-century reanalysis (ERA-20C) dataset and numerical experiments with a global atmospheric model [the Met Office Unified Model global atmosphere, version 6 (UM-GA6)]. The results suggest that ENSO plays a prominent role in governing the CEF-seesaw relation. A high positive correlation (0.86) exists between the MC CEF and Niño-3.4 index and also in the case of eastern Pacific (EP) El Niño, central Pacific (CP) El Niño, EP La Niña, and CP La Niña events. In contrast, a negative correlation (-0.35) exists between the Somali CEF and Niño-3.4 index, and this negative relation is significant only in the EP El Niño years. Further, the variation of the MC CEF is highly correlated with the local north-south sea surface temperature (SST) gradient, while the variation of the Somali CEF displays little relation with the local SST gradient. The Somali CEF may be remotely influenced by ENSO. The model results confirm that the EP El Niño plays a major role in causing the weakened Somali CEF via modifying the Walker cell. However, the impact of the EP El Niño on the Somali CEF differs with different seasonal background. It is also found that the interannual CEF seesaw displays a multidecadal change before and after the 1950s, which is linked with the multidecadal strengthening of the intensity of the EP ENSO.

Original languageEnglish
Pages (from-to)2621-2638
Number of pages18
JournalJournal of Climate
Issue number7
Publication statusPublished - 1 Apr 2017
Externally publishedYes


  • ENSO
  • General circulation models
  • Interannual variability

Cite this