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Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model

文献类型：期刊论文

作者Zhao, Xianzhi9,10; Gong, Xiang9,10; Gong, Xun1,7,8; Liu, Jiyao9,10; Wang, Guoju9,10; Wang, Lixin6,9,10; Guo, Xinyu5; Gao, Huiwang2,3,4
刊名FRONTIERS IN MARINE SCIENCE
出版日期2024-05-21
卷号11页码:17
关键词deep neural network Gaussian activation 3-D chlorophyll structure subsurface chlorophyll maximum long-term trend
DOI10.3389/fmars.2024.1378488
通讯作者Gong, Xiang(gongxiang@qust.edu.cn)
英文摘要Insufficient studies in characterizing vertical structure of Chlorophyll-a (Chl-a) in the ocean critically limit better understanding about marine ecosystem based on global climate change. In this study, we developed a Gaussian-activation deep neural network (Gaussian-DNN) model to assess vertical Chl-a structure in the upper ocean at high spatial resolution. Our Gaussian-DNN model used the input variables including satellite data of sea surface Chl-a and in-situ vertical physics profiles (temperature and salinity) in the northwestern Pacific Ocean (NWPO). After validation test based on two independent datasets of BGC-Argo and ship measurement, we applied the Gaussian-DNN model to reconstruct temporal evolution of 3-D Chl-a structure in the NWPO. Our modelling results successfully explain over 80% of the Chl-a vertical profiles in the NWPO at a horizontal resolution of 1 degrees x 1 degrees and 1 m vertical resolution within upper 300 meters during 2004 to 2022. Moreover, according to our modelling results, the Subsurface Chlorophyll Maxima (SCMs) and total Chl-a within 0-300 m depths were extracted and presented seasonal variability overlapping longer-time trends of spatial discrepancies all over the NWPO. In addition, our sensitivity testing suggested that sea-water temperatures predominantly control 3-D structures of the Chl-a in the tropical NWPO, while salinity played a key role in the temperate gyre of the NWPO. Here, our development of the Gaussian-DNN model may also be applied to craft long term, 3-D Chl-a products in the global ocean.
WOS关键词STEADY-STATE SOLUTIONS ; CENTRAL NORTH PACIFIC ; SUBSURFACE CHLOROPHYLL ; VERTICAL PROFILES ; EDDY VARIABILITY ; MAXIMUM LAYERS ; GLOBAL OCEAN ; ARGO DATA ; PHYTOPLANKTON ; SURFACE
资助项目Ministry of Science and Technology of the People's Republic of China[2019YFE0125000] ; National Nature Science Foundation of China-Shandong Joint Fund[U1906215] ; National Natural Science Foundation of China[41406010] ; Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences Opening Fund[2020KFJJ04]
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology
语种英语
WOS记录号WOS:001237851500001
出版者FRONTIERS MEDIA SA
源URL[http://ir.qdio.ac.cn/handle/337002/186044]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Gong, Xiang
作者单位1.China Univ Geosci, Inst Adv Marine Res, Guangzhou, Peoples R China
2.Qingdao Marine Sci & Technol Ctr, Lab Marine Ecol & Environm Sci, Qingdao, Peoples R China
3.Ocean Univ China, Key Lab Marine Environm & Ecol, Minist Educ, Qingdao, Peoples R China
4.Ocean Univ China, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Minist Educ, Qingdao, Peoples R China
5.Ehime Univ, Ctr Marine Environm Study, Matsuyama, Japan
6.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
7.Qilu Univ Technol, Key Lab Comp Power Network & Informat Secur, Minist Educ, Shandong Comp Sci Ctr,Natl Supercomp Ctr Jinan,Sha, Jinan, Peoples R China
8.China Univ Geosci, State Key Lab Biogeol & Environm Geol, Hubei Key Lab Marine Geol Resources, Wuhan, Peoples R China
9.Qingdao Univ Sci & Technol, Qingdao Innovat Ctr Artificial Intelligence Ocean, Qingdao, Peoples R China
10.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Xianzhi,Gong, Xiang,Gong, Xun,et al. Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model[J]. FRONTIERS IN MARINE SCIENCE,2024,11:17.
APA Zhao, Xianzhi.,Gong, Xiang.,Gong, Xun.,Liu, Jiyao.,Wang, Guoju.,...&Gao, Huiwang.(2024).Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model.FRONTIERS IN MARINE SCIENCE,11,17.
MLA Zhao, Xianzhi,et al."Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model".FRONTIERS IN MARINE SCIENCE 11(2024):17.

入库方式： OAI收割

来源：海洋研究所

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