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

Telszewski, M., Chazottes, A., Schuster, U., Watson, A. J., Moulin, C., Bakker, D. C. E. ORCID: https://orcid.org/0000-0001-9234-5337, Gonzalez-Davila, M., Johannessen, T., Kortzinger, A., Luger, H., Olsen, A., Omar, A., Padin, X. A., Rios, A. F., Steinhoff, T., Santana-Casiano, M., Wallace, D. W. R. and Wanninkhof, R. (2009) Estimating the monthly pCO2 distribution in the north Atlantic using a self-organizing neural network. Biogeosciences, 6 (8). pp. 1405-1421.

Preview

PDF - Draft Version Available under License Creative Commons Attribution. Download (5MB) | Preview

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 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 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.

Item Type:	Article
Additional Information:	© Author(s) 2009. This work is distributed under the Creative Commons Attribution 3.0 License.
Uncontrolled Keywords:	sdg 14 - life below water ,/dk/atira/pure/sustainabledevelopmentgoals/life_below_water
Faculty \ School:	Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups:	Faculty of Science > Research Groups > Climate, Ocean and Atmospheric Sciences (former - to 2017) Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Science > Research Groups > Marine and Atmospheric Sciences (former - to 2017)
Depositing User:	Rosie Cullington
Date Deposited:	23 Feb 2011 11:53
Last Modified:	24 Sep 2024 09:26
URI:	https://ueaeprints.uea.ac.uk/id/eprint/24355
DOI:	10.5194/bg-6-1405-2009

Downloads

Actions (login required)

View Item