A global surface ocean fCO2 climatology based on a feed-forward neural network

Metadata Label Value
Author(s) Zeng, J., Nojiri, Y., Landschützer, P., Telszewski, M., Nakaoka, S.
Publication Type Journal Items, Publication Status: Published
Full Text Search SFX for a Full-Text version of this document
Import to Mendeley Log in to provide feedback

Detailed Information

Metadata Field Content
Title A global surface ocean fCO2 climatology based on a feed-forward neural network
Author(s) Zeng, J.
Nojiri, Y.
Landschützer, P.
Telszewski, M.
Nakaoka, S.
Journal or Series Title Journal of atmospheric and oceanic technology
Volume Number 31
Issue Number 8
Start Page 1838
End Page 1849
ISSN 0739-0572
1520-0426
Publisher American Meteorological Society
Publication Place Boston, MA
Publication Date 2014-08
Keyword(s) Fluxes
Carbon dioxide
Climatology
Neural networks
Oceanic variability
Abstract A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO2 (fCO2) on a 1° × 1° spatial resolution. Using 127 880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO2 Atlas version 2.0 (Bakker et al.), the model yields a global mean fCO2 increase rate of 1.50 μatm yr−1. The rate was used to normalize multiple years’ fCO2 observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global fCO2 climatology. The model simulates monthly fCO2 distributions that agree well with observations and yields an anthropogenic CO2 update of −1.9 to −2.3 PgC yr−1. The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of Lamont–Doherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO2 uptake. The method’s benefits include but are not limited to the following: (i) a fixed structure is not required to model fCO2 as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global fCO2 in all seasons, and (iii) the model can be extended to produce global fCO2 maps at a higher resolution in time and space as long as the required data for input variables are available.
DOI 10.1175/JTECH-D-13-00137.1
Additional Notes Manuscript received 19 June 2013, In final form 21 February 2014
Document Type Article
Publication Status Published
Language English
Assigned Organisational Unit(s) 03731
Organisational Unit(s)
NEBIS System Number 000026010
Source Database ID FORM-1420555452
Description File Name MIME Type Size
No details could be found
There are no links available for this record.
This record has not been viewed during this period

@article{Zng2014,
  author = "Zeng, J. and Nojiri, Y. and Landsch{\"{u}}tzer, P. and Telszewski, M. and Nakaoka, S.",
  title = "{A} global surface ocean f{C}{O}2 climatology based on a feed-forward neural network",
  journal = "Journal of atmospheric and oceanic technology",
  year = 2014,
  volume = "31",
  number = "8",
  pages = "1838--1849",
  month = aug,
}


E-Citations record created: Tue, 06 Jan 2015, 14:44:16 CET