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2 results on '"Santana-Casiano, M."'

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

Author

Telszewski, A. Chazottes, Schuster, U., Watson, A. J., Moulin, C., Bakker, D. C. E., González-Dávila, M., Johannessen, T., Körtzinger, Arne, Lüger, H., Olsen, A., Omar, A., Padin, X. A., Ríos, A. F., Steinhoff, Tobias, Santana-Casiano, M., Wallace, Douglas W.R., and Wanninkhof, R.

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.

Published
2009

2. Estimating the monthly pCO2 distribution in the north Atlantic using a self-organizing neural network

Author

Telszewski, M., Chazottes, A., Schuster, U., Watson, Aj, Moulin, C., Bakker, Dce, Gonzalez-Davila, M., Johannessen, T., Kortzinger, A., Luger, H., Are Olsen, Omar, A., Padin, Xa, Rios, Af, Steinhoff, T., Santana-Casiano, M., Wallace, Dwr, Wanninkhof, R., 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)-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)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), 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), Kiel University, 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 (GU), Spanish National Research Council (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), École normale supérieure - Paris (ENS Paris), 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)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment
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.

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