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

Name: 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
Creator: Jiye Zeng
Published: 2022-02-21T12:29:36.683Z
Keywords: Carbon Budget, Ocean

Zeng, Jiye

doi:10.17632/znyppsv6v7.1

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

Published: 21 February 2022| Version 1 | DOI: 10.17632/znyppsv6v7.1

Contributor:

Jiye Zeng

Description

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.

Files

Steps to reproduce

A data based machine learning product. Models include Random Forest, Gradient Boost Machine, and Feed-forward Neural Network in python. CO2 data came from SOCAT 2021. Predictors include sea surface temperature, sea surface salinity, chlorophyll-a, and mixed layer depth.

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

Description

Files

Steps to reproduce

Categories

Licence