Understanding climate modes’ impact on the Indian Ocean decadal upwelling variability

Abstract

<p align="justify">This study explores the spatial pattern and climate modes’ impact on the Indian Ocean decadal upwelling variability by using observational dataset, Static Linear Regression Model (SLM) and Bayesian Dynamic Linear Model (BDLM). Our analysis shows that the Indian Ocean decadal upwellings averaged in the Eastern and Western Indian Ocean (EIO and WIO) regions are positively correlated. Moreover, the BDLM that represents the temporal modulations of the El Niño and Southern Ocean (ENSO) and Indian Ocean Dipole (IOD) impacts, reproduces the time series of the EIO and WIO upwellings more realistically than a conventional SLM does. BD<span style="font-family: 'Times New Roman';">L</span>M simulations further suggest that in both EIO and WIO, IOD is more important than ENSO impact. The time-varying regression coefficients in BDLM indicate that the observed shift of the IOD impact on the EIO upwelling around 1985 is mainly associated with the changes of <span style="font-family: 'Times New Roman';">alongshore </span>wind stress forcing <span style="font-family: 'Times New Roman';">and </span>the sensitivity of the upper <span style="font-family: 'Times New Roman';">ocean </span>temperature <span style="font-family: 'Times New Roman';">in the </span>E<span style="font-family: 'Times New Roman';">IO</span> through the surface warming tendency and the enhanced ocean stratification. This suggests that climate models need to consider the time-varying impact of different climate modes in order to simulate the Indian Ocean dynamics correctly.<strong></strong></p>