A regression modeling approach for studying carbonate system variability in the northern Gulf of Alaska

The northern Gulf of Alaska (GOA) shelf experiences carbonate system variability on seasonal and annual time scales, but little information exists to resolve higher frequency variability in this region. To resolve this variability using platforms-of-opportunity, we present multiple linear regression (MLR) models constructed from hydrographic data collected along the Northeast Pacific Global Ocean Ecosystems Dynamics (GLOBEC) Seward Line. The empirical algorithms predict dissolved inorganic carbon (DIC) and total alkalinity (TA) using observations of nitrate (NO3), temperature, salinity and pressure from the surface to 500 m, with R2s > 0.97 and RMSE values of 11 µmol kg−1 for DIC and 9 µmol kg−1 for TA. We applied these relationships to high-resolution NO3 data sets collected during a novel 20 h glider flight and a GLOBEC mesoscale SeaSoar survey. Results from the glider flight demonstrated time/space along-isopycnal variability of aragonite saturations (Ωarag) associated with a dicothermal layer (a cold near-surface layer found in high latitude oceans) that rivaled changes seen vertically through the thermocline. The SeaSoar survey captured the uplift to <100 m of dense, high-pCO2 waters at the shelf break that had been forced by the passage of a Yakutat eddy. During this event, the aragonite saturation horizon (depth where Ωarag = 1) shoaled to a previously unseen depth in the northern GOA. This work is similar to recent studies aimed at predicting the carbonate system in continental margin settings, albeit demonstrates that a NO3-based approach can be applied to high-latitude data collected from platforms capable of high-frequency measurements.

Evans W., Mathis J. T., Winsor P., Statscewich H. & Whitledge T. E., in press. A regression modeling approach for studying carbonate system variability in the northern Gulf of Alaska. Journal of Geophysical Research: Oceans. Article (subscription required).

  • Reset


OA-ICC Highlights

%d bloggers like this: