Work

An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning

Year: 2025

Type: data-paper

Abstract: Accurate estimation of surface ocean p CO 2 is crucial for understanding the ocean’s role in the global carbon cycle and its response to climate change. In this study, we employ a machine learning algorithm to correct the deviations in high-resolution (1/12°) model simulations of surface p CO 2 from the INCOIS-BIO-ROMS model ( p CO 2 model ) for the period 1980–2019, using available observations ( p CO 2 obs ). We train the XGBoost model to generate spatio-temporal deviations ( p CO 2 obs − p CO 2 model ) of p CO 2 model . The interannually and climatologically varying deviations are then added back to the original model separately, which results in an improved surface p CO 2 data product. A comparison of our surface p CO 2 data product with moored observations, gridded SOCAT, CMEMS-LSCE-FFNN, and OceanSODA demonstrates an improvement by approximately 40% ± 3.31% in RMSE. Further analysis reveals that adding climatological deviations to p CO 2 model results in greater improvements than adding interannual deviations. This analysis underscores the ability of machine learning algorithms to enhance the accuracy of model-simulated surface p CO 2 outputs.

Source: Scientific Data

DOI: 10.1038/s41597-025-04914-z

Authors: Prasanna Kanti Ghoshal, A.P. Joshi, Kunal Chakraborty

Institutions: Indian National Centre for Ocean Information Services, Kerala University of Fisheries and Ocean Studies, Ministry of Earth Sciences

Language: English

Topic: Marine and coastal ecosystems

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,490