Estimating the monthly $_pCO_2$ distribution in the North Atlantic using a self-organizing neural network
Résumé
Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide ($_pCO_2$) 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 $_pCO_2$. 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 $_pCO_2$ measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437 $\mu$atm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 $\mu$atm, which equals to just above 3 per cent of an average $_pCO_2$ value in the in situ dataset. The seasonal $_pCO_2$ 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
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