 |
| Titel |
Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network |
| VerfasserIn |
M. Telszewski, A. Chazottes, U. Schuster, A. J. Watson, C. Moulin, D. C. E. Bakker, M. González-Dávila, T. Johannessen, A. Körtzinger, H. Lüger, A. Olsen, A. Omar, X. A. Padin, A. F. Ríos, T. Steinhoff, M. Santana-Casiano, D. W. R. Wallace, R. Wanninkhof |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 6, no. 8 ; Nr. 6, no. 8 (2009-08-05), S.1405-1421 |
| Datensatznummer |
250003938
|
| Publikation (Nr.) |
 copernicus.org/bg-6-1405-2009.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|