IOCAS-IR  > 海洋环流与波动重点实验室
Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach
Gao Yongli1,2,3; Mu Mu4; Zhang Kun1,5,6
2020-06-07
Source PublicationJOURNAL OF OCEANOLOGY AND LIMNOLOGY
ISSN2096-5508
Pages12
Corresponding AuthorZhang Kun(kzhang@qdio.ac.cn)
AbstractParameter uncertainty is a primary source of uncertainty in ocean ecosystem simulations. The deep chlorophyll maximum (DCM) is a ubiquitous ecological phenomenon in the ocean. Using a theoretical nutrients-phytoplankton model and the conditional nonlinear optimal perturbation approach related to parameters, we investigated the effects of parameter uncertainties on DCM simulations. First, the sensitivity of single parameter was analyzed. The sensitivity ranking of 10 parameters was obtained by analyzing the top four specifically. The most sensitive parameter (background turbidity) affects the light supply for DCM formation, whereas the other three parameters (nutrient content of phytoplankton, nutrient recycling coefficient, and vertical turbulent diffusivity) control nutrient supply. To explore the interactions among different parameters, the sensitivity of multiple parameters was further studied by examining combinations of four parameters. The results show that background turbidity is replaced by the phytoplankton loss rate in the optimal parameter combination. In addition, we found that interactions among these parameters are responsible for such differences. Finally, we found that reducing the uncertainties of sensitive parameters could improve DCM simulations remarkably. Compared with the sensitive parameters identified in the single parameter analysis, reducing parameter uncertainties in the optimal combination produced better model performance. This study shows the importance of nonlinear interactions among various parameters in identifying sensitive parameters. In the future, the conditional nonlinear optimal perturbation approach related to parameters, especially optimal parameter combinations, is expected to greatly improve DCM simulations in complex ecosystem models.
Keyworddeep chlorophyll maximum (DCM) simulation parameter uncertainty conditional nonlinear optimal perturbation related to parameters (CNOP-P) sensitivity
DOI10.1007/s00343-020-0020-y
Indexed BySCI
Language英语
Funding ProjectQingdao National Laboratory for Marine Science and Technology[2016OPR0107] ; National Natural Science Foundation of China[41806013]
WOS Research AreaMarine & Freshwater Biology ; Oceanography
WOS SubjectLimnology ; Oceanography
WOS IDWOS:000538711200001
PublisherSCIENCE PRESS
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/167757
Collection海洋环流与波动重点实验室
Corresponding AuthorZhang Kun
Affiliation1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.China Univ Petr East China, Coll Sci, Qingdao 266580, Peoples R China
4.Fudan Univ, Inst Atmospher Sci, Shanghai 200433, Peoples R China
5.Qingdao Natl Lab Marine Sci & Technol, Qingdao 266237, Peoples R China
6.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China
First Author AffilicationKey Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
Corresponding Author AffilicationKey Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences;  Center for Ocean Mega-Science, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gao Yongli,Mu Mu,Zhang Kun. Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach[J]. JOURNAL OF OCEANOLOGY AND LIMNOLOGY,2020:12.
APA Gao Yongli,Mu Mu,&Zhang Kun.(2020).Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach.JOURNAL OF OCEANOLOGY AND LIMNOLOGY,12.
MLA Gao Yongli,et al."Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach".JOURNAL OF OCEANOLOGY AND LIMNOLOGY (2020):12.
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