Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach
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Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach* GAO Yongli1, 2, 3, MU Mu1, 4, ZHANG Kun1, 5, 6, ** 1
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071,
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
China
Received Jan. 13, 2020; accepted in principle Feb. 21, 2020; accepted for publication Apr. 3, 2020 © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keyword: deep chlorophyll maximum (DCM) simulation; parameter uncertainty; conditional nonlinear optimal perturbation related to parameters (CNOP-P); sensitivity
1 INTRODUCTION The deep chlorophyll maximum (DCM) is a subsurface peak in chlorophyll a, and refers to the equilibrium of phytoplankton growth with opposing resource gradien
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