To achieve real-time prediction of surface ocean carbonaceous variables in the Indian Ocean, we integrate an extended-range coupled forecast system with a global ocean biogeochemistry model incorporating an explicit carbon cycle. The physical ocean state is provided by the Indian Institute of Tropical Meteorology Extended Range Prediction for Applications to Society (IITM-ERPAS) coupled model, which drives the Ocean Tracer Transport Model (OTTM) with phosphate-dependent biology and OCMIP-II-type carbonate chemistry (hereafter referred to as OTTM-ERPAS-BGC). Daily mean outputs from IITM-ERPAS are used to drive the biogeochemical simulations. Model skill is evaluated against in situ observations of surface partial pressure of CO₂ (pCO₂) and pH from the Bay of Bengal Ocean Acidification (BOBOA) mooring (90°E, 15°N) over the period 2013-2018. The model successfully reproduces observed variability, with strong and statistically significant correlations (r = 0.65 for pCO₂ and r = 0.80 for pH; p < 0.001). Based on this performance, a coherent region of influence (87.5°E-92.5°E, 12.5°N-17.5°N) is identified to further assess model skill against BOBOA time-series observations, climatological products and other derived datasets of pCO₂ and pH in terms of seasonal cycle, subseasonal variability and long-term trends. Seasonal cycle analysis reveals that the updated Takahashi climatological pCO₂ product is systematically underestimated by ∼18 ± 3 μatm compared to BOBOA observations, OTTM-ERPAS-BGC simulations and other derived products. Subseasonal variability exhibits pronounced intraseasonal signals, with significant spectral peaks at 13-41 days for pCO₂ and 17-41 days for pH, reflecting dominant intraseasonal modulation of the Bay of Bengal carbon cycle. Long-term trends indicate a robust increase in pCO₂ (2.02 μatm yr⁻¹) and a concomitant decline in pH (-0.00253 yr⁻¹), consistent with progressive acidification of the basin. The decomposition analysis demonstrates that dissolved inorganic carbon (DIC) and total alkalinity (ALK) exert dominant and complementary controls on pCO₂ and pH variability, respectively, while sea surface temperature (SST) plays a secondary role. Overall, this study demonstrates that coupling extended-range physical forecasts with ocean biogeochemical models provides a viable pathway for the operational prediction of carbon variables. Such capability is critical for the early detection of ocean acidification and hypercapnia hotspots, with significant implications for marine ecosystem management and climate services in the Indian Ocean.
Desai T. N., Valsala V., Mandal R., Ganga Bhavani I. V., Deshpande A., Chakraborty K., Joseph S. & Sahai A. K., 2026. Assessing the skill of a biogeochemical model tailored to a global coupled forecasting system: a case study for the Bay of Bengal carbon cycle. Journal of Operational Oceanography: 1-17. doi: 10.1080/1755876X.2026.2724745. Article (subscription required).
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