Estimating the monthly pCO₂ distribution in the North Atlantic using a self-organizing neural network

Abstract. Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO₂) for the North Atlantic on a 1° latitude by 1° longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO₂. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFS-MODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO₂ measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437 μatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 μatm, which equals to just above 3 per cent of an average pCO₂ value in the in situ dataset. The seasonal pCO₂ cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO₂ fluxes or improvement of seasonal and interannual marine CO₂ cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.