NIES-ML3 ensemble product of surface ocean CO2 concentrations and air-sea CO2 fluxes reconstructed by using three machine learning models with new CO2 trends

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Global oceans have absorbed a substantial portion of the anthropogenic carbon dioxide (CO2) emitted into the atmosphere. Data-based machine learning estimates for the oceanic CO2 sink have become an import part of the Global Carbon Budget in recent years. This product is the result of our new study on ocean CO2 trends using Random Forest, Gradient Boost Machine, and Feedforward Neural Network. Using the time-dependent trends for ocean CO2 reconstruction substantially reduced the error of using a constant trend and therefore improved the oceanic sink estimate.

Description

Creator
Release date
2022/03/11
Temporal coverage
1980 - 2020
Data provider
NIES
Email: cgerdb_admin(at)nies.go.jp
DOI
File format
Data volume
493 MB
Version
ver.2022.0 (Last updated: 2022/03/11)
Language
English

Data Set

Parameters
Surface ocean CO2 concentration and air-sea CO2 flux
Domain
Global
Time resolution
Monthly
Spatial resolution
1 x 1 degree
Calculation method
Random Forest, Gradient Boost Machine, Feedforward Neural Network
Keywords
[GCMD_Platform]
Models/Analyses > Models
[GCMD_Science]
Oceans > Ocean Chemistry > Carbon Dioxide
[Free keywords]
Carbon Dioxide, CO2, Ocean, Flux, Budget, Global, Machine Learning, Random Forest, Gradient Boost Machine, Neural Network
Update history
[2022/3/11]
Data from 1980 to 2020 are released. ver.2022.0.

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License
Citation format
When this data set is referred to in publications, it should be cited in the following format.
Zeng, J (2022), NIES-ML3 ensemble product of surface ocean CO2 concentrations and air-sea CO2 fluxes reconstructed by using three machine learning models with new CO2 trends, ver.xxxx.x *1, NIES, DOI:10.17595/20220311.001, (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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