We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO 3 -, PO 4 3 -, O 2 , and surface chlorophyll a concentrations.
5According to our metric the optimal model solutions comprise low rates of global N 2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the "best" model solutions we therefore also consider the model's ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO 3 inventory. Global O 2 varies by 10 a factor of two and NO 3 by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O 2 , which is also affected by the subsistence N quota of ordinary phytoplankton (Q N 0, phy ) and zooplankton maximum specific ingestion rate. Q N 0, phy is revealed as a major determinant of the oceanic NO 3 pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q N 0, phy , is a prerequisite for understanding the marine nitrogen inventory.The basic structure of most marine ecosystem models has been designed for resolving mass fluxes between nutrients, phy-25 toplankton, zooplankton and detritus, typically referred to as NPZD models. Mathematical formulations that describe growth and fate of marine phytoplankton and zooplankton biomass have been successfully applied over a range of scales, from local 0D-ecosystem models (e.g., Fasham et al., 1990;Edwards, 2001) to global 3D models (Sarmiento et al., 1993;Keller et al., 2012;Nickelsen et al., 2015). However, most of these NPZD models lack a sound mechanistic foundation, preventing them from explicitly accounting for the organisms' regulation of their internal physiological state. For example, N 2 fixation by algae 30 is often diagnosed from the availability of dissolved nutrients, so that it only occurs when the ratio of nitrate-to-phosphate concentrations falls below the Redfield ratio of 16:1 (Deutsch et al., 2007;Ilyina et al., 2013). As these assumptions neglect a number of environmental and ecological controls (e.g., grazing, often also temperature), they do not adequately describe the behaviour of plankton organisms and their sensitivity to changes in their environment. With the introduction of refined mechanistic (physiological) descriptions we here aim at alleviating this deficiency. In this study we introduce a new marin...