A sea-level monopole in the equatorial Indian Ocean

Abstract

In this study, we show the relationship between sea-level anomalies (SLA) and upper-ocean parameters in the Equatorial Indian Ocean (EIO). This work also focuses on the variability of SLA obtained from satellite altimeter data in different spatial and temporal scales and its relationship with computed ocean heat content (OHC), dynamic height (DH), and thermocline depth (20°C isotherm: D20) during 1993-2015. SLA showed low Pearson's correlation coefficient (CC) with upper-ocean parameters over central EIO resembling a "Monopole" pattern. The Array for Real-time Geostrophic Oceanography (ARGO) in situ profile data in the central EIO also confirmed this. SLA over this monopole showed low correlations with all parameters as compared with eastern and western EIO. These findings show a clear signature of a persisting sea-level monopole in the central EIO. Oscillating SLA over western and eastern EIO during summer and winter monsoon months is found to be responsible for locking this monopole in the central EIO.

Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India

“…The long-term rise in SLA over these regions implies that the thermocline became deeper, leading to an increase in SST. Thus, the total upper ocean heat content/capacity is increasing over this region 36 , 122 . SWIO region also shows the most considerable interannual and intra-annual variability of upper ocean parameters among other parts of the global tropical oceans due to its connection with coupled phenomena such as summer monsoon, IOD, ENSO 49 , 123 – 126 .…”

“…SWIO region also shows the most considerable interannual and intra-annual variability of upper ocean parameters among other parts of the global tropical oceans due to its connection with coupled phenomena such as summer monsoon, IOD, ENSO 49 , 123 – 126 . Both SSTA and MSLA along the monsoonal low-level jet path in the SWIO show a substantial change from summer half-year (April–September) to winter half-year (October–March) 36 , 127 , 128 . Thus, a slight change in the magnitude of these parameters over SWIO during antecedent months could lead to changes in the upper ocean heat capacitance which will further tend to significant changes in air-sea interactions from the ocean surface to the lower troposphere, cloud cover, variability in radiation, and turbulent fluxes over this region.…”

“…Thus, many past studies concentrated only on relationships between pre-monsoon SSTA over the Indian Ocean region and AISMR seasonal variability and forecasting 28 – 31 . As the AISMR is a coupled atmosphere–ocean phenomenon, the role of air-sea interactions over the southwestern and equatorial Indian Ocean is the key to the better understanding and forecasting of its magnitude and variability over India 34 – 36 . Shankar and Shetye 37 have suggested that the interdecadal variability of sea level at Mumbai mimicked the variability in rainfall over the Indian subcontinent.…”

“…Thus, a few studies have focused on the relationship between sea level variations and subsurface ocean variability over the Indian Ocean region and AISMR 34 , 35 . Some of these studies stressed the use of new statistical techniques and parameters, such as the strength of the winds, ocean heat content and ocean-integrated subsurface temperatures in the seasonal forecasting 35 , 36 . Distinct impacts of short- and long-time fluctuations of the Indian Ocean surface wind fields, particularly over the SWIO, also led to changes in the rainfall over homogeneous regions of India 38 .…”

Does the El Niño-Southern Oscillation Impact on the Indian Summer Monsoon 1-Dimensional?

“…While summer monsoon is a coupled atmosphere-ocean phenomenon, recent studies emphasised the role of air-sea interactions over southwestern and equatorial Indian Ocean as the key for the better understanding and forecasting of magnitude and variability of summer monsoon over India (Ali et al, 2015;Venugopal et al, 2018;Thandlam et al, 2020). Some of these studies stressed using new statistical techniques and parameters such as the strength of the winds, ocean heat content and ocean integrated subsurface temperatures in the seasonal forecasting of the summer monsoon (Yangxing et al, 2020; Venugopal et al, 2018).…”