EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Lianyi Zhang1 and Yan Du1,2,3
Lianyi Zhang and Yan Du
  • 1South China Sea Institute of Oceanology, State Key Laboratory of Tropical Oceanography, Guangzhou, China (
  • 2University of Chinese Academic Sciences, Beijing, China (
  • 3Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China (

The El Niño-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 Indian Ocean Dipole (IOD) modes, exerting strong influences on the IO 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 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.

How to cite: Zhang, L. and Du, Y.: Revisiting ENSO impacts on the Indian Ocean SST based on a combined linear regression method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6704,, 2022.