Articles | Volume 6, issue 8
Biogeosciences, 6, 1405–1421, 2009
Biogeosciences, 6, 1405–1421, 2009

  05 Aug 2009

05 Aug 2009

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

M. Telszewski1, A. Chazottes2, U. Schuster1, A. J. Watson1, C. Moulin2, D. C. E. Bakker1, M. González-Dávila3, T. Johannessen4, A. Körtzinger5, H. Lüger6, A. Olsen4,8,9, A. Omar4, X. A. Padin7, A. F. Ríos7, T. Steinhoff5, M. Santana-Casiano3, D. W. R. Wallace5, and R. Wanninkhof6 M. Telszewski et al.
  • 1School of Environmental Sciences, University of East Anglia, Norwich, UK
  • 2L'Institut Pierre-Simon Laplace/Laboratoire des Sciences du Climat et de l'Environnement, Centre National de la Recherche Scientifique – Commissariat à l'Énergie Atomique, Gif-sur-Yvette, France
  • 3Department of Marine Chemistry, Universidad de Las Palmas de Gran Canaria, Las Palmas, Gran Canaria, Spain
  • 4Geophysical Institute, University of Bergen, Bergen, Norway
  • 5Leibniz Institute of Marine Sciences, Kiel, Germany
  • 6Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Miami, Florida, USA
  • 7Instituto de Investigaciones Marinas, Consejo Superior de Investigaciones Científicas (CSIC), Vigo, Spain
  • 8Bjerknes Centre for Climate Research, UNIFOB AS, Bergen, Norway
  • 9Marine Chemistry, Departement of Chemistry, University of Göterborg, Göteborg, Sweden

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.

Final-revised paper