Highlights
- A Multiparametric Linear Regression (MLR) model was developed using in-situ and satellite observations to accurately estimate pCO2 in the NIO region.
- Validation of the MLR model showed significant low errors (MRE = 0.08, MNB = 0.013, RMSE = 7.26 μatm) and a high correlation coefficient (R2 = 0.96), demonstrating superior performance.
- The interannual (2012-2022) variability of pCO2 in the NIO region shows an increasing trend.
- The study reveals seasonal variability in pCO2 in the NIO, peaking pre and post-monsoon, influencing marine ecosystems.
Abstract
The partial pressure of carbon dioxide (pCO2) in the North Indian Ocean (NIO) undergoes significant variations due to factors such as biological activity, ocean circulation patterns, and atmospheric influences. Understanding these variations is crucial for assessing the ocean role in the global carbon cycle and their impact on climate change. Estimating pCO2 through in-situ platforms is challenging due to the time-consuming, expensive, and complex nature of water sample collection, particularly under rough oceanic conditions. Conversely, remote sensing technology offers high spatiotemporal resolution data over extensive synoptic scales, making it a valuable tool for pCO2 estimation. Current models for estimating pCO2 in the NIO region are limited due to the improper selection of model parameters and the scarcity of in-situ measurements, highlighting the need for a more accurate approach. This study develops a Multiparametric Linear Regression (MLR) method, integrating satellite and in-situ observations of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. To develop and validate this model, in-situ data were sourced from the Global Ocean Data Analysis Project (GLODAP). Validation results showed that the proposed MLR approach outperformed existing global models, achieving low mean relative error (MRE = 0.08), mean normalized bias (MNB = 0.013), and root mean square error (RMSE = 7.26 μatm), with a high correlation coefficient (R2 = 0.96). This study has the potential to improve understanding of carbon dynamics in the NIO region and its contribution to the global carbon cycle. The pCO2 maps generated in this study improve climate modeling and monitoring, supporting predictions and mitigation efforts. This accurate model also aids policy-making, environmental management, and ecological assessments.
Shaik I., Fida Fathima M. P., Nagamani P. V., Yadav S., Behera S., Manmode Y. & Srinivasa Rao G., 2025. Satellite-derived ocean color data for monitoring pCO2 dynamics in the North Indian Ocean. Dynamics of Atmospheres and Oceans: 101534. doi: 10.1016/j.dynatmoce.2025.101534. Article (subscription required).
