Articles | Volume 19, issue 3
https://doi.org/10.5194/bg-19-845-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-845-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Feb 2022
Research article |
| 10 Feb 2022
| 10 Feb 2022
Reconstruction of global surface ocean pCO2 using region-specific predictors based on a stepwise FFNN regression algorithm
Guorong Zhong
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Beijing 101407, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Beijing 101407, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Jinming Song
CORRESPONDING AUTHOR
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Beijing 101407, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Baoxiao Qu
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Fan Wang
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Beijing 101407, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Yanjun Wang
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Bin Zhang
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Xiaoxia Sun
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Beijing 101407, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Wuchang Zhang
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Zhenyan Wang
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Jun Ma
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Huamao Yuan
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Beijing 101407, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Liqin Duan
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
University of Chinese Academy of Sciences, Beijing 101407, China
Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
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Cited
7 citations as recorded by crossref.
- Machine learning reveals regime shifts in future ocean carbon dioxide fluxes inter-annual variability D. Couespel et al. 10.1038/s43247-024-01257-2
- The Southern Ocean carbon sink has been overestimated in the past three decades G. Zhong et al. 10.1038/s43247-024-01566-6
- The carbon sink of the Coral Sea, the world's second largest marginal sea, weakened during 2006–2018 S. Zhang et al. 10.1016/j.scitotenv.2023.162219
- Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest H. Wu et al. 10.1016/j.scitotenv.2023.169209
- Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. 10.5194/bg-20-3919-2023
- Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. 10.1021/acsomega.2c06441
- The increasing big gap of carbon sink between the western and eastern Pacific in the last three decades G. Zhong et al. 10.3389/fmars.2022.1088181
7 citations as recorded by crossref.
- Machine learning reveals regime shifts in future ocean carbon dioxide fluxes inter-annual variability D. Couespel et al. 10.1038/s43247-024-01257-2
- The Southern Ocean carbon sink has been overestimated in the past three decades G. Zhong et al. 10.1038/s43247-024-01566-6
- The carbon sink of the Coral Sea, the world's second largest marginal sea, weakened during 2006–2018 S. Zhang et al. 10.1016/j.scitotenv.2023.162219
- Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest H. Wu et al. 10.1016/j.scitotenv.2023.169209
- Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. 10.5194/bg-20-3919-2023
- Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. 10.1021/acsomega.2c06441
- The increasing big gap of carbon sink between the western and eastern Pacific in the last three decades G. Zhong et al. 10.3389/fmars.2022.1088181
Latest update: 21 Nov 2024
Short summary
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.
A predictor selection algorithm was constructed to decrease the predicting error in the surface...
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