Estimating the monthly $_pCO_2$ distribution in the North Atlantic using a self-organizing neural network

• Published in: Biogeosciences
• Main Authors: Telszewski, M., Chazottes, A., Schuster, U., Watson, A., Moulin, C., Bakker, D., González-Dávila, M., Johannessen, T., Körtzinger, A., Lüger, H., Olsen, A., Omar, A., Padin, X., Ríos, A., Steinhoff, T., Santana-Casiano, M., Wallace, D., Wanninkhof, R.
• Other Authors: School of Environmental Sciences Norwich, University of East Anglia Norwich (UEA), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X), Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université de Las Palmas de Gran Canaria Espagne (ULPGC), Geophysical Institute Bergen (GFI / BiU), University of Bergen (UiB), Leibniz Institute of Marine Science at the University of Kiel (IFM-GEOMAR), Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel (CAU), NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML), National Oceanic and Atmospheric Administration (NOAA), Bjerknes Centre for Climate Research (BCCR), Department of Biological Sciences Bergen (BIO / UiB), University of Bergen (UiB)-University of Bergen (UiB), Analytical and Marine Chemistry, Göteborgs Universitet = University of Gothenburg (GU), Consejo Superior de Investigaciones Cientificas España = Spanish National Research Council Spain (CSIC), the Spanish project ICCABA CTM2005-03893/MAR;, the Norwegian Research Council through A-CARB (178167) and CARBON-HEAT (185093), the Swedish National Space Board through RESCUE – II, European Project: 30029,CARBOOCEAN, European Project: 238366,EC:FP7:PEOPLE,FP7-PEOPLE-ITN-2008,GREENCYCLESII(2010), European Project: 693594,H2020,ERC-2015-PoC,EndoNaut(2016)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2009
• Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; Triplets; North Atlantic
• Online Access: https://hal.science/hal-03199296; https://hal.science/hal-03199296/document; https://hal.science/hal-03199296/file/bg-6-1405-2009.pdf; https://doi.org/10.5194/bg-6-1405-2009

International audience 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