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Revisiting ENSO impacts on the Indian Ocean SST based on a combined linear regression method

Acta Oceanologica Sinica 2021年第5期40卷 47-57页

作者： Lianyi Zhang Yan Du Tomoki Tozuka Shoichiro Kido State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China University of Chinese Academy of Sciences Beijing 100049China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou 511458China Department of Earth and Planetary Science Graduate School of ScienceThe University of TokyoTokyo 113-0033Japan Application Laboratory(APL) Research Institute for Value-Added-Information Generation(VAiG)Japan Agency for Marine-Earth Science and TechnologyYokohama 236-0001Japan

The El Nino-Southern Oscillation(ENSO)has great impacts on the Indian Ocean sea surface temperature(SST).In fact,two major modes of the Indian Ocean SST namely the Indian Ocean Basin(IOB)and the Indian Ocean Dipole(IOD)modes,exerting strong influences on the Indian Ocean rim countries,are both influenced by the ENSO.Based on a combined linear regression method,this study quantifies the ENSO impacts on the IOB and the IOD during ENSO concurrent,developing,and decaying stages.After removing the ENSO impacts,the spring peak of the IOB disappears along with significant decrease in number of events,while the number of events is only slightly reduced and the autumn peak remains for the IOD.By isolating the ENSO impacts during each stage,this study reveals that the leading impacts of ENSO contribute to the IOD development,while the delayed impacts facilitate the IOD phase switch and prompt the IOB development.Besides,the decadal variations of ENSO impacts are various during each stage and over different regions.These imply that merely removing the concurrent ENSO impacts would not be sufficient to investigate intrinsic climate variability of the Indian Ocean,and the present method may be useful to study climate variabilities independent of ENSO.

关键词： Indian Ocean ENSO sea surface temperature climate modes combined linear regression

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