Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach

Yongli GAO; Mu MU; Kun ZHANG

doi:10.1007/s00343-020-0020-y

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Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach

Physics | Updated：2023-04-27

- Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach
- Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach
- 海洋湖沼学报（英文） 2020年38卷第5期页码：1382-1393
- Affiliations：
  
  1.Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
  2.University of Chinese Academy of Sciences, Beijing 100049, China
  3.College of Science, China University of Petroleum(East China), Qingdao 266580, China
  4.Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China
  5.Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
  6.Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
- Author bio：
  
  ZHANG Kun, kzhang@qdio.ac.cn
- Funds：
  
  the Qingdao National Laboratory for Marine Science and Technology(2016OPR0107);the National Natural Science Foundation of China(41806013);© Chinese Society for Oceanology and Limnology, Science Press, and Springer-Verlag Berlin Heidelberg 2020
- DOI：10.1007/s00343-020-0020-y
  中图分类号：
- 收稿：2020-01-13，
  
  录用：2020-2-21，
  
  网络首发：2020-04-03，
  
  纸质出版：2020-09
- Accepted：
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Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach[J]. 海洋湖沼学报（英文）, 2020,38(5):1382-1393.

Yongli GAO, Mu MU, Kun ZHANG. Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach[J]. Journal of Oceanology and Limnology, 2020, 38(5): 1382-1393.
Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach[J]. 海洋湖沼学报（英文）, 2020,38(5):1382-1393. DOI： 10.1007/s00343-020-0020-y.

Yongli GAO, Mu MU, Kun ZHANG. Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach[J]. Journal of Oceanology and Limnology, 2020, 38(5): 1382-1393. DOI： 10.1007/s00343-020-0020-y.

摘要

Abstract

Parameter 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.

关键词

Keywords

references

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