Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic?

The aragonite saturation state (ΩAr) in the subpolar North Atlantic was derived using new regional empirical algorithms. These multiple regression algorithms were developed using the bin-averaged GLODAPv2 data of commonly observed oceanographic variables [temperature (T), salinity (S), pressure (P), oxygen (O2), nitrate (NO3-), phosphate (PO43-), silicate (Si(OH)4), and pH]. Five of these variables are also frequently observed using autonomous platforms, which means they are widely available. The algorithms were validated against independent shipboard data from the OVIDE2012 cruise. It was also applied to time series observations of T, S, P, and O2 from the K1 mooring (56.5°N, 52.6°W) to reconstruct for the first time the seasonal variability of ΩAr. Our study suggests: (i) linear regression algorithms based on bin-averaged carbonate system data can successfully estimate ΩAr in our study domain over the 0–3,500 m depth range (R2 = 0.985, RMSE = 0.044); (ii) that ΩAr also can be adequately estimated from solely non-carbonate observations (R2 = 0.969, RMSE = 0.063) and autonomous sensor variables (R2 = 0.978, RMSE = 0.053). Validation with independent OVIDE2012 data further suggests that; (iii) both algorithms, non-carbonate (MEF = 0.929) and autonomous sensors (MEF = 0.995) have excellent predictive skill over the 0–3,500 depth range; (iv) that in deep waters (>500 m) observations of T, S, and O2 may be sufficient predictors of ΩAr (MEF = 0.913); and (iv) the importance of adding pH sensors on autonomous platforms in the euphotic and remineralization zone (