1992-2020 Global Ocean Carbon Dioxide Partial Pressure Grid Data Products

Dataset Overview

By combining the stepwise regression method with the feedforward neural network,we have constructed a stepwise feedforward neural network fitting self algorithm to select prediction parameters closely related to the partial pressure of carbon dioxide in surface seawater in different regions of the global ocean. We divided the global ocean into 11 regions using self-organizing map neural network,and selected the combination of prediction parameters in each region to minimize the average error of carbon dioxide partial pressure prediction. Based on these prediction parameters,the monthly global ocean surface seawater CO2 partial pressure of 1 ° from January 1992 to December 2020 is constructed by using the feedforward neural network × 1 ° grid point data. The average error with the original dataset SOCAT is 12.44 μ ATM,standard error is 19.41 μ atm.

Dataset Details

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Spatial Resolution: 1度

Time Resolution: 月平均

Product Number: 2017YFA0603200_012

Create Institution: Marine Science Data Center of Chinese Academy of Sciences

Created By: Zhong Guorong

Creation Date: 2022-10-17T02:54:02.947Z

File Size: 2

Data Format: NetCDF

Type Of Data: 栅格

Data Label:

Partial pressure of carbon dioxide in surface seawater

Global oceans (topographic depth greater than 200m)

Stepwise feedforward neural network

Paper Citation

Zhong, G., Li, X., Song, J., Qu, B., Wang, F., Wang, Y., Zhang, B., Sun, X., Zhang, W., Wang, Z., Ma, J., Yuan, H., and Duan, L.: Reconstruction of global surface ocean pCO2 using region-specific predictors based on a stepwise FFNN regression algorithm, Biogeosciences, 19, 845–859, https://doi.org/10.5194/bg-19-845-2022, 2022.

Data Citation

Zhong Guorong. 1992-2020 Global surface ocean pCO2 product based on a stepwise FFNN algo-rithm. 2022

Data Licence Agreement

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Funded Projects

National Key Research and Development Program "Global Change and Response" Key Project "Development of Data Processing Methods and Products for Observing Key Parameters of Marine Environmental Change (2017YFA0603200)"；Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19060000).