A common strategy for calculating the direction and rate of carbon dioxide gas (CO₂) exchange between the ocean and atmosphere relies on knowledge of the partial pressure of CO₂ in surface seawater (pCO_2(sw)), a quantity that is frequently observed by autonomous sensors on ships and moored buoys, albeit with significant spatial and temporal gaps. Here we present a monthly gridded data product of pCO_2(sw) at 0.25^∘ latitude by 0.25^∘ longitude resolution in the northeastern Pacific Ocean, centered on the California Current System (CCS) and spanning all months from January 1998 to December 2020. The data product (RFR-CCS; Sharp et al., 2022; https://doi.org/10.5281/zenodo.5523389) was created using observations from the most recent (2021) version of the Surface Ocean CO₂ Atlas (Bakker et al., 2016). These observations were fit against a variety of collocated and contemporaneous satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from sensors on moored buoys, and find that the data product effectively captures seasonal pCO_2(sw) cycles at nearshore sites. This result is notable because global gridded pCO_2(sw) products do not capture local variability effectively in this region, suggesting that RFR-CCS is a better option than regional extractions from global products to represent pCO_2(sw) in the CCS over the last 2 decades. Lessons learned from the construction of RFR-CCS provide insight into how global pCO_2(sw) products could effectively characterize seasonal variability in nearshore coastal environments. We briefly review the physical and biological processes – acting across a variety of spatial and temporal scales – that are responsible for the latitudinal and nearshore-to-offshore pCO_2(sw) gradients seen in the RFR-CCS reconstruction of pCO_2(sw). RFR-CCS will be valuable for the validation of high-resolution models, the attribution of spatiotemporal carbonate system variability to physical and biological drivers, and the quantification of multiyear trends and interannual variability of ocean acidification.

This research has been supported by grant nos. NA20OAR4320271 (CICOES) and NA19NES4320002 (CISESS) from the National Oceanic and Atmospheric Administration.