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Updated: Jul 11, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
Published on: June 13, 2020
Spatio-temporal variability of surface chlorophyll and pCO2 over the tropical Indian Ocean and its long-term trend
Sachiko Mohanty1, Biswarup Bhattacharya1, Charu Singh1
1Indian Institute of Remote Sensing, ISRO, Dehradun 248001, Uttarakhand, India.
Abstract:
Under the global climate change scenario, ever-increasing ocean temperatures and elevated carbon dioxide (CO2) concentration levels in the ocean and atmosphere significantly affect ocean biogeochemistry. The spatiotemporal distribution of chlorophyll and partial pressure of CO2 (pCO2) over the Indian Ocean (IO) are investigated using satellite, in-situ, and simulations from eleven Coupled Model Inter-comparison Project phase 6 models. All the models show negative bias near the Somali region over the Arabian Sea (AS) during the southwest monsoon season. The CESMs and CanESM5 models show relatively less bias over the tropical IO except for the AS in all seasons compared to other models. The annual cycle of pCO2 shows a bimodal characteristic, with the first peak in May and the other in October over the northern IO, which the CanESM5, IPSL, and MPIs models reasonably well capture. All the models produce the phase of the annual cycle of pCO2 reasonably well for the southern IO. The pCO2 distribution and its trend decomposition are estimated using the multimodel mean from the CanESM5, IPSL-CM6A-LR, and MPIs models, suggesting that the increase in dissolved inorganic carbon is the dominant factor that contributes to about 70 % of the rise in the total pCO2 trend, and the total alkalinity and sea surface temperature have a secondary role. Region-wise analysis manifests that the southern IO and AS exhibit maximum susceptibility to the long-term variations in the pCO2 trend caused by the changes in dissolved inorganic carbon levels in the ocean. The study discusses substantive factors leading to the variability in the biogeochemical properties of the IO as simulated by these models and, thus, the possibilities for future improvements.
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