Projecting Three‐Dimensional Ocean Thermohaline Structure in the North Indian Ocean From the Satellite Sea Surface Data Based on a Variational Method

Abstract

Monsoonal climate dominates the Northern Indian Ocean (NIO; Schott & McCreary, 2001). The salinity distribution of NIO is controlled by local evaporation, precipitation, runoff, and dynamical processes in the ocean. The two major basins that characterize the NIO are the Arabian Sea (AS) in the west with high salinity surface water and the Bay of Bengal (BoB) with the significantly low salinity surface water in the east. In the AS, during the winter monsoon, the cold and dry northeast monsoon winds, combined with Ekman pumping, cause subduction of high-salinity surface waters in the interior northern AS. This generates the widespread AS Water salinity maximum just underneath the mixed layer (ML) (Schott & Fischer, 2000; Schott & McCreary, 2001). Some of the high salinity water will overflow from the Red Sea and the Persian Gulf (Tomczak & Godfrey, 2003). In the BoB, a large quantity of fresh water from river runoff and precipitation cause strong stratification in the upper ocean. The current along India's east coast, known as the East India Coastal Current, is seasonally reversed. Its poleward phase is most developed in March and April, while the equatorial phase prevails as the southwest monsoon recedes (Shetye et al., 1996). These characteristics will lead to great seasonal changes in the upper water mass of NIO. Therefore, it is considerably difficult to estimate the thermohaline structure in the upper NIO. Satellite sea surface observations such as sea surface height (SSH), temperature (SST), and salinity (SSS) are now routinely available on a global scale. These well-sampled data have provided us an unprecedented opportunity to understand ocean circulations and mesoscale processes (e.g.,

Three-Dimensional Thermohaline Structure Reconstruction in the Northwest Pacific Ocean from HY-2 Satellite Data Based on a Variational Method

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“…How to effectively combine two kinds of data to construct a reasonable three-dimensional thermohaline fields is important to undersea navigation and economics. Several sorts of methods have been applied to reconstruct the subsurface structures, including statistical analysis [1, 2], the dynamical [3,4], and the variational methods [5,6,7].…”

Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application

“…The ocean observations used in this study included SLA, SST, SSS, and in situ observations. The satellite SLA data were delayed time and gridded maps of sea level anomaly (MSLA) from Copernicus Marine Environment Monitoring Service (CMEMS) with a horizontal resolution of 0.25°; the satellite SST data were acquired from the gridded product released by United Kingdom Meteorological Office (UKMO) with a horizontal resolution of 1/20°and were interpolated to SLA gridded points to maintain a consistent horizontal resolution (Good et al, 2020); the satellite SSS data were obtained from the Soil Moisture Active Passion (SMAP) with the same spatial and temporal resolutions as the SST and SLA data (He et al, 2021). All of the above satellite products had a temporal resolution of one day.…”

Estimating Three‐Dimensional Structures of Eddy in the South Indian Ocean From the Satellite Observations Based on the isQG Method

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“…Zhang et al, 2016). Despite the increase of in situ observations over the past two decades (Brett et al, 2020;Z. He et al, 2021;Riser et al, 2016), historical data can only support an understanding of the general 3D structure of eddies (D. Dong et al, 2017;Y.…”