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Reconstruction of global surface ocean pCO(2) using region-specific predictors based on a stepwise FFNN regression algorithm

文献类型：期刊论文

作者Zhong, Guorong1,2,3,4; Li, Xuegang1,2,3,4; Song, Jinming1,2,3,4; Qu, Baoxiao1,2,3; Wang, Fan1,2,3,4; Wang, Yanjun2,3; Zhang, Bin2,3; Sun, Xiaoxia1,2,3,4; Zhang, Wuchang1,2,3; Wang, Zhenyan1,2,3
刊名BIOGEOSCIENCES
出版日期2022-02-10
卷号19期号:3页码:845-859
ISSN号1726-4170
DOI10.5194/bg-19-845-2022
通讯作者Li, Xuegang(lixuegang@qdio.ac.cn) ; Song, Jinming(jmsong@qdio.ac.cn)
英文摘要Various machine learning methods were attempted in the global mapping of surface ocean partial pressure of CO2 (pCO(2)) to reduce the uncertainty of the global ocean CO2 sink estimate due to undersampling of pCO(2). In previous research, the predictors of pCO(2) were usually selected empirically based on theoretic drivers of surface ocean pCO(2), and the same combination of predictors was applied in all areas except where there was a lack of coverage. However, the differences between the drivers of surface ocean pCO(2) in different regions were not considered. In this work, we combined the stepwise regression algorithm and a feed-forward neural network (FFNN) to select predictors of pCO(2) based on the mean absolute error in each of the 11 biogeochemical provinces defined by the self-organizing map (SOM) method. Based on the predictors selected, a monthly global 1 circle x 1 circle surface ocean pCO(2) product from January 1992 to August 2019 was constructed. Validation of different combinations of predictors based on the Surface Ocean CO2 Atlas (SOCAT) dataset version 2020 and independent observations from time series stations was carried out. The prediction of pCO(2) based on region-specific predictors selected by the stepwise FFNN algorithm was more precise than that based on predictors from previous research. Applying the FFNN size-improving algorithm in each province decreased the mean absolute error (MAE) of the global estimate to 11.32 mu atm and the root mean square error (RMSE) to 17.99 mu atm. The script file of the stepwise FFNN algorithm and pCO(2) product are distributed through the Institute of Oceanology of the Chinese Academy of Sciences Marine Science Data Center (IOCAS, , Zhong, 2021.
资助项目National Key Research and Development Program of China[2017YFA0603204] ; Major Program of the Pilot National Laboratory for Marine Science and Technology (Qingdao) in the 14th 5year Plan ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060401] ; National Natural Science Foundation of China[91958103] ; National Natural Science Foundation of China[42176200] ; National Natural Science Foundation of China[41806133] ; Natural Science Foundation of Shandong Province[ZR2020YQ28]
WOS研究方向Environmental Sciences & Ecology ; Geology
语种英语
WOS记录号WOS:000758150300001
出版者COPERNICUS GESELLSCHAFT MBH
源URL[http://ir.qdio.ac.cn/handle/337002/178074]  
专题海洋研究所_海洋生态与环境科学重点实验室
通讯作者Li, Xuegang; Song, Jinming
作者单位1.Pilot Natl Lab Marine Sci & Technol, Marine Ecol & Environm Sci Lab, Qingdao 266237, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm Sci, Qingdao 266071, Peoples R China
4.Univ Chinese Acad Sci, Beijing 101407, Peoples R China
推荐引用方式
GB/T 7714		 Zhong, Guorong,Li, Xuegang,Song, Jinming,et al. Reconstruction of global surface ocean pCO(2) using region-specific predictors based on a stepwise FFNN regression algorithm[J]. BIOGEOSCIENCES,2022,19(3):845-859.
APA Zhong, Guorong.,Li, Xuegang.,Song, Jinming.,Qu, Baoxiao.,Wang, Fan.,...&Duan, Liqin.(2022).Reconstruction of global surface ocean pCO(2) using region-specific predictors based on a stepwise FFNN regression algorithm.BIOGEOSCIENCES,19(3),845-859.
MLA Zhong, Guorong,et al."Reconstruction of global surface ocean pCO(2) using region-specific predictors based on a stepwise FFNN regression algorithm".BIOGEOSCIENCES 19.3(2022):845-859.

入库方式： OAI收割

来源：海洋研究所

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