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

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.