Articles | Volume 19, issue 3
Biogeosciences, 19, 845–859, 2022
https://doi.org/10.5194/bg-19-845-2022
Biogeosciences, 19, 845–859, 2022
https://doi.org/10.5194/bg-19-845-2022
Research article
10 Feb 2022
Research article | 10 Feb 2022

Reconstruction of global surface ocean pCO2 using region-specific predictors based on a stepwise FFNN regression algorithm

Guorong Zhong et al.

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Cited articles

Bates, N. R.: Interannual variability of the oceanic CO2 sink in the subtropical gyre of the North Atlantic Ocean over the last 2 decades, J. Geophys. Res.-Oceans, 112, C09013, https://doi.org/10.1029/2006JC003759, 2007. 
Boyer, T. P., Garcia, H. E., Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Reagan, J. R., Weathers, K. A., Baranova, O. K., Seidov, D., and Smolyar, I. V.: World Ocean Atlas 2018 [Dissolved Inorganic Nutrients and Dissolved Oxygen], NOAA National Centers for Environmental Information [data set], https://accession.nodc.noaa.gov/NCEI-WOA18 (last access: 4 August 2020), 2018. 
Broullón, D., Pérez, F. F., Velo, A., Hoppema, M., Olsen, A., Takahashi, T., Key, R. M., Tanhua, T., González-Dávila, M., Jeansson, E., Kozyr, A., and van Heuven, S. M. A. C.: A global monthly climatology of total alkalinity: a neural network approach, Earth Syst. Sci. Data, 11, 1109–1127, https://doi.org/10.5194/essd-11-1109-2019, 2019. 
Broullón, D., Pérez, F. F., Velo, A., Hoppema, M., Olsen, A., Takahashi, T., Key, R. M., Tanhua, T., Santana-Casiano, J. M., and Kozyr, A.: A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach, Earth Syst. Sci. Data, 12, 1725–1743, https://doi.org/10.5194/essd-12-1725-2020, 2020. 
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A predictor selection algorithm was constructed to decrease the predicting error in the surface ocean partial pressure of CO2 (pCO2) mapping by finding better combinations of pCO2 predictors in different regions. Compared with previous research using the same combination of predictors in all regions, using different predictors selected by the algorithm in different regions can effectively decrease pCO2 predicting errors.
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