Institutional Repository of Key Laboratory of Marine Ecology & Environmental Sciences, CAS
Reconstruction of global surface ocean pCO(2) using region-specific predictors based on a stepwise FFNN regression algorithm | |
Zhong, Guorong1,2,3,4; Li, Xuegang1,2,3,4; Song, Jinming1,2,3,4; Qu, Baoxiao1,3,4; Wang, Fan1,2,3,4; Wang, Yanjun1,4; Zhang, Bin1,4; Sun, Xiaoxia1,2,3,4; Zhang, Wuchang1,3,4; Wang, Zhenyan1,3,4; Ma, Jun1,3,4; Yuan, Huamao1,2,3,4; Duan, Liqin1,2,3,4 | |
2022-02-10 | |
发表期刊 | BIOGEOSCIENCES |
ISSN | 1726-4170 |
卷号 | 19期号:3页码:845-859 |
通讯作者 | Li, Xuegang(lixuegang@qdio.ac.cn) ; Song, Jinming(jmsong@qdio.ac.cn) |
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. |
DOI | 10.5194/bg-19-845-2022 |
收录类别 | SCI |
语种 | 英语 |
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 |
WOS类目 | Ecology ; Geosciences, Multidisciplinary |
WOS记录号 | WOS:000758150300001 |
出版者 | COPERNICUS GESELLSCHAFT MBH |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.qdio.ac.cn/handle/337002/178075 |
专题 | 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.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm Sci, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101407, Peoples R China 3.Pilot Natl Lab Marine Sci & Technol, Marine Ecol & Environm Sci Lab, Qingdao 266237, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China |
第一作者单位 | 中国科学院海洋研究所; 中国科学院海洋大科学研究中心 |
通讯作者单位 | 中国科学院海洋研究所; 中国科学院海洋大科学研究中心 |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
bg-19-845-2022.pdf(8235KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 浏览 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论