Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 1: Implementation and model behaviour

Abstract

Abstract. Uncertainties in projections of marine biogeochemistry from Earth system models (ESMs) are associated to a large degree with the imperfect representation of the marine plankton ecosystem, in particular the physiology of primary and secondary producers. Here, we describe the implementation of an optimality-based plankton–ecosystem model (OPEM) version 1.1 with variable carbon : nitrogen : phosphorus (C:N:P) stoichiometry in the University of Victoria ESM (UVic; Eby et al., 2009; Weaver et al., 2001) and the behaviour of two calibrated reference configurations, which differ in the assumed temperature dependence of diazotrophs. Predicted tracer distributions of oxygen and dissolved inorganic nutrients are similar to those of an earlier fixed-stoichiometry formulation in UVic (Nickelsen et al., 2015). Compared to the classic fixed-stoichiometry UVic model, OPEM is closer to recent satellite-based estimates of net community production (NCP), despite overestimating net primary production (NPP), can better reproduce deep-ocean gradients in the NO3-:PO43- ratio and partially explains observed patterns of particulate C:N:P in the surface ocean. Allowing diazotrophs to grow (but not necessarily fix N2) at similar temperatures as other phytoplankton results in a better representation of surface Chl and NPP in the Arctic and Antarctic oceans. Deficiencies of our calibrated OPEM configurations may serve as a magnifying glass for shortcomings in global biogeochemical models and hence guide future model development. The overestimation of NPP at low latitudes indicates the need for improved representations of temperature effects on biotic processes, as well as phytoplankton community composition, which may be represented by locally varying parameters based on suitable trade-offs. The similarity in the overestimation of NPP and surface autotrophic particulate organic carbon (POC) could indicate deficiencies in the representation of top-down control or nutrient supply to the surface ocean. Discrepancies between observed and predicted vertical gradients in particulate C:N:P ratios suggest the need to include preferential P remineralisation, which could also benefit the representation of N2 fixation. While OPEM yields a much improved distribution of surface N* (NO3--16⋅PO43-+2.9 mmol m−3), it still fails to reproduce observed N* in the Arctic, possibly related to a misrepresentation of the phytoplankton community there and the lack of benthic denitrification in the model. Coexisting ordinary and diazotrophic phytoplankton can exert strong control on N* in our simulations, which questions the interpretation of N* as reflecting the balance of N2 fixation and denitrification.

MESMO 3: Flexible phytoplankton stoichiometry and refractory DOM

“…Temperature dependence was added by Arteaga et al (2014) following the simple logarithmic temperature dependence on maximum nutrient uptake rate following (Eppley, 1972). (Chien et al, 2020;Pahlow et al, 2020). However, as we are not describing any results in this paper, we will only mention here that there is an option to calculate C:N:P using this stoichiometry model in MESMO 3.…”

“…The full description of the optimality-based stoichiometry model and its parameter calibration are presented specifically for the UVic model elsewhere (Chien et al, 2020;Pahlow et al, 2020).…”

FABM-NflexPD 1.0: Assessing an Instantaneous Acclimation Approach for Modelling Phytoplankton Growth

“…Thus, it can be concluded that IA provides improved realism over the computationally equivalent FS approach. For simulating a few years of the dynamics of the single phytoplankton group in a 1D setup as we did here, differences in computational costs relative to the fully dynamic variant are nearly negligible, but for simulating decades/centuries or millennia in a 3D setup (e.g., as in Pahlow et al, 2020), and/or when multiple clones/types are considered (e.g., 350 in Dutkiewicz et al, 2020), differences in computational costs can indeed be substantial.…”

“…Furthermore, given its mechanistic basis, this approach can be expected to reproduce biological feedbacks more realistically (Flynn et al, 2015), and thereby improve the generality and portability of models (Anugerahanti et al, in prep.). Recent applications of these models in 3D setups with realistic forcings (Kerimoglu et al, 2017;Pahlow et al, 2020) have indicated that accounting for acclimation enhances the ability of models to reproduce field observations. Moreover, a consistent representation of phytoplankton composition allows identification of potential alterations in trophic transfer efficiencies as mediated by changes in food quality of prey in response to environmental change (Kerimoglu et al, 2018;Kwiatkowski et al, 2018).…”

Author response to two reviewers on gmd-2020-408

“…implemented in global ocean biogeochemical models, such as the Pelagic Interactions Scheme for Carbon and Ecosystem Studies (PISCES) (Kwiatkowski et al, 2018(Kwiatkowski et al, , 2019 and the University of Victoria Earth System Model (UVIC) (Chien et al, 2020;Pahlow et al, 2020). However, as we are not describing any results in this paper, we will only mention here that there is an option to calculate C:N:P using this stoichiometry model in MESMO 3.…”