Including high-frequency variability in coastal ocean acidification projections

Abstract. Assessing the impacts of anthropogenic ocean acidification requires knowledge of present-day and future environmental conditions. Here, we present a simple model for upwelling margins that projects anthropogenic acidification trajectories by combining high-temporal-resolution sensor data, hydrographic surveys for source water characterization, empirical relationships of the CO₂ system, and the atmospheric CO₂ record. This model characterizes CO₂ variability on timescales ranging from hours (e.g., tidal) to months (e.g., seasonal), bridging a critical knowledge gap in ocean acidification research. The amount of anthropogenic carbon in a given water mass is dependent on the age; therefore a density–age relationship was derived for the study region and then combined with the 2013 Intergovernmental Panel on Climate Change CO₂ emission scenarios to add density-dependent anthropogenic carbon to the sensor time series. The model was applied to time series from autonomous pH sensors deployed in the surf zone, kelp forest, submarine canyon edge, and shelf break in the upper 100 m of the Southern California Bight. All habitats were within 5 km of one another, and exhibited unique, habitat-specific CO₂ variability signatures and acidification trajectories, demonstrating the importance of making projections in the context of habitat-specific CO₂ signatures. In general, both the mean and range of pCO₂ increase in the future, with the greatest increase in both magnitude and range occurring in the deeper habitats due to reduced buffering capacity. On the other hand, the saturation state of aragonite (Ω_Ar) decreased in both magnitude and range. This approach can be applied to the entire California Current System, and upwelling margins in general, where sensor and complementary hydrographic data are available.