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Long-term responses of phytoplankton biomass to the ocean surface variables in the central parts of the Arabian Sea and the Bay of Bengal

Karnan Chinnadurai, Gautham Sreedharan, Sandhya Soolamkandath Variem, Shahin Shajahan

Oceanologia

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2024

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No. 66 (3)

art. no. 66303

EN

The northern Indian Ocean has been warming steadily for over a half-century, especially the north-western Indian Ocean. It is widely reported that the increasing sea surface temperature in the global oceans decreases phytoplankton biomass and productivity. The impacts of long-term variations in the sea surface properties on the phytoplankton biomass (chlorophyll a) are least studied in the northern Indian Ocean. In this study, we have retrieved satellite, model, and ARGO float data sets to investigate the long-term variations in the distributions and trends of major oceanic variables for a better understanding of the respective changes that occurred in chlorophyll a concentration in the central regions of the Arabian Sea (AS) and the Bay of Bengal (BB). We have selected variables such as sea surface temperature (SST), sea surface salinity (SSS), photosynthetically available radiation (PAR), euphotic depth (ZEU), mixed layer depth (MLD), wind speed, mean sea level anomaly (MSLA), surface currents, etc., to relate with chlorophyll a. We found significant increasing trends in SST and positive-MSLA in both basins, and the chlorophyll a was decreased in the AS but contrastingly increased in the BB. Further data analysis revealed the possible reasons, such as seasonal changes in mean sea level anomaly and meridional currents, for the increasing trend of chlorophyll a in the central Bay of Bengal. The northward flow of the meridional currents during the southwest monsoon (SWM), transports the nutrient-rich water from the coastal upwelling zone of the southwest coast of India to the southern and central BB, and enhances chlorophyll a. Contrastingly, the southward flow of low-saline and nutrient-depleted Bay of Bengal water reduces the chlorophyll a. In addition, the large area of cold-core eddies found during the NEM enhanced the chlorophyll a in the central BB. Though contrasting trends between both basins in chlorophyll a distribution were found, the mean concentration of chlorophyll a in the northern Indian Ocean decreases. The present study signifies the importance of monsoon currents and eddies in regulating the chlorophyll a biomass and primary productivity in the AS and BB.

2

Modeling total surface current in the Persian Gulf and the Oman Sea by combination of geodetic and hydrographic observations and assimilation with in situ current meter data

Pirooznia Mahmoud, Naeeni Mehdi Raoofian, Tourian Mohammad J.

Acta Geophysica

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2023

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Vol. 71, no. 6

2839--2863

EN

Surface currents in oceanic environment are of vital importance from economical, biological and environmental aspects. Modelling ocean currents has generally been performed using numerical ocean circulation models as a solution to initial-boundary value problems in oceanic domain. Due to lack of knowledge about model parameters as well as initial and boundary values, they need to be externally calibrated for accurate local and regional applications. In this study, an alternative approach is proposed to incorporate spaceborne geodetic observations as well as hydrographic data to estimate the total surface current in the Persian Gulf and the Oman Sea. Being the data-driven approach, the method is comparable to numerical ocean models and regionally it is more accurate and simpler in application. The proposed method focuses on the computation of dynamic topography (DT) by least squares variance component estimation combining two different schemes. They are (1) DT estimation via direct observations of sea surface height from satellite altimetry and (2) steric and non-steric modeling of sea level anomaly using temperature and salinity data for the steric component; and Gravity Recovery and Climate Experiment observations for the non-steric component. Ultimately, the total surface current is obtained by computing the horizontal gradient of DT using geostrophic equation and adding the components of the Ekman current. Moreover, the estimated total surface current is further improved by assimilating with in situ current meter data using 3D-Variational data assimilation method and it is validated against two control stations. This assimilation leads to improvement of about 3 to 15 cm/s in total surface current computed using geostrophic equation and Ekman current. Besides, to illustrate the significance of the proposed approach, the estimated total surface current is externally validated and compared with the output of Copernicus Marine Environment Monitoring Service (CMEMS), as a numerical ocean model developed for oceanographic applications. Our comparison reveals that the proposed method is more accurate and reliable than CMEMS products. As for the circulation and current pattern, the estimated surface velocities reveal the existence of eddies in the region of the Persian Gulf and the Oman Sea, indicating the occurrence of cyclonic and anti-cyclonic circulations. Moreover, they elucidate that the velocities are lower in spring and summer and higher in autumn and winter.

3

Investigating the Role of Atmospheric Variables on Sea Level Variations in the Eastern Central Red Sea Using an Artificial Neural Network Approach

Zubier Khalid M., Eyouni Lina S.

Oceanologia

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2020

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No. 62 (3)

267--290

EN

Atmospheric variables play a major role in sea level variations in the eastern central Red Sea, where the role of tides is limited to 20% or less. Extensive analysis of daily-averaged residual sea level and atmospheric variables (atmospheric pressure, air temperature, wind stress components, and evaporation rate) indicated that sea level variations in the eastern central Red Sea are mainly contributed to by the seasonal and weather-band variations in the utilized atmospheric variables. The Non-linear Auto-Regressive Network with eXogenous inputs (NARX), a type of Artificial Neural Network (ANN), was applied to investigate the role of the atmospheric variables on the sea level variations at the eastern central Red Sea. Forced by time-delayed daily-averaged observations of atmospheric variables and residual sea level, the constructed NARX-based model showed high performance in predicting the one-step-ahead residual sea level. The high performance indicated that the constructed model was able to efficiently recognize the role played by the atmospheric variables on the residual sea level variations. Further investigations, using the constructed NARX-based model, revealed the seasonal variation in the role of the atmospheric variables. The study also revealed that the role played by some of the atmospheric variables, on sea level variations, could be masked by the role of one or more of the other atmospheric variables. The obtained results clearly demonstrated that this neurocomputing (NARX) approach is effective in investigating the individual and combined role of the atmospheric variables on residual sea level variations.