How well do global ocean approaches constrain local pCO2?

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 (1ox1o, monthly) errors. Our analysis is for 2000-2023 and uses 5 large regions (“superbiomes”) to aggregate sparse data across high-latitude, subtropical and equatorial zones, and we compare to an ensemble of 10 products and 10 models. The observed long-term trend of surface ocean CO2 due to rising atmospheric CO2 concentrations is consistently estimated. At the same time, we find substantial biases for the ensemble means: ±5µatm for the products, and ranging from +1 to +15µatm for the models. Across the superbiomes, ensemble-mean unbiased root-mean-square-errors (uRMSEs) are 19-37µatm for the products and 25-56µatm for models. Seasonality is generally 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, monthly 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 (∼1µatm). 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.