Data based estimate of global ocean CO2 flux

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Data based estimates of global air-sea CO2 flux are an important part of the Global Carbon Project (e.g., This product extends the flux estimate of doi:10.17595/20201020.001 using the grided data of CO2 observations of SOCAT 2021. We made two small changes in the input variables for training a neural network: (1) the log scaled mixed layer depth was used and (2) observed CO2 values over 1000 uatm were excluded.


Release date
Temporal coverage
1990 - 2021
Data provider
Email: cgerdb_admin(at)
File format
Data volume
285 MB
ver.2022.1 (Last updated: 2022/07/12)

Data Set

Surface ocean CO2 concentration and air-sea CO2 flux
Time resolution
Spatial resolution
1 x 1 degree
Calculation method
Feed forward Neural Network
Models/Analyses > Models
Oceans > Ocean Chemistry > Carbon Dioxide
[Free keywords]
Carbon Dioxide, CO2, Ocean, Flux, Budget, Global, Neural Network, Machine Learning
Update history
A new ocean CO2 trend extracted from observations in 1990-2020 was used in the model. ver.2022.1.
Data from 1980 to 2020 are released. ver.2021.1.

Reference Information

Zeng, J., Y. Nojiri, P. Landschützer, M. Telszewski, and S. Nakaoka, 2014: A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network. J. Atmos. Oceanic Technol., 31, 1838–1849, doi:10.1175/JTECH-D-13-00137.1.

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Citation format
When this data set is referred to in publications, it should be cited in the following format.
Zeng, J (2021), Data based estimate of global ocean CO2 flux, ver.xxxx.x *1, NIES, DOI:10.17595/20210806.001, (Reference date*2: YYYY/MM/DD)
*1 The version number is indicated in the name of each data file.
*2 As the reference date, please indicate the date you downloaded the files.

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