How well do global ocean approaches constrain local fCO2?

[Geoengineering News]

https://iopscience.iop.org/article/10.1088/1748-9326/ae803e

Authors: Galen A McKinley, Amanda R. Fay, Thea Hatlen Heimdal, Lauren Moseley and Abby Shaum

22 June 2026

Abstract

The ocean absorbs 29% of humanity’s annual anthropogenic carbon dioxide (CO2) emissions, and the future of climate change is strongly dependent on how this sink evolves. Marine Carbon Dioxide Removal (mCDR) approaches to enhance this sink are actively being developed. In the interest of understanding how well state-of-the-art global products and models can help to distinguish mCDR signals from the background ocean carbon sink, we use sparse independent data to quantify their local-scale (1$^{o}$x1$^{o}$, monthly) errors in CO2 fugacity (fCO2), the primary regulator of air-sea CO2 fluxes. Our 2000-2023 analysis uses 5 large regions (``superbiomes") to aggregate data across high-latitude, subtropical and equatorial zones, and we compare to 10 products and 10 models. The observed long-term trend of surface ocean CO2due to rising atmospheric CO2 is consistently estimated. At the same time, we find substantial fCO2 biases for the ensemble means: $\pm$5 $\mu$atm for the products, and from +1 to +15 $\mu$atm for the models. Across the superbiomes, ensemble-mean unbiased root-mean-square errors (uRMSEs) are 19-37 $\mu$atm for the products and 25-56 $\mu$atm for models. Individual products and models have larger errors. Seasonality is well-correlated to the independent data for the products and for the models in the subtropics, but the largest component of uRMSE also lies at this timescale. Deseasonalized variability has smaller uRMSEs and low correlations to independent data. In summary, local-scale errors in global observation-based products and models for the background ocean carbon sink are more than 1 order of magnitude larger than the local to regional-scale signals likely for open-ocean mCDR ($\sim$1 $\mu$atm). Reducing local-scale uncertainty in the ocean carbon sink will be critical if these products and models will be part of mCDR monitoring, reporting and verification systems. Improving the representation of seasonality should be a key target for improving both observation-based products and models.

Source: IOP Science