EcoGEnIE 0.1: Plankton Ecology in the cGENIE Earth system model

Abstract

Abstract. We present an extension to the cGENIE Earth System model that explicitly accounts for the growth and interaction of an arbitrary number of plankton species. The new package ('ECOGEM') replaces the implicit, flux-based, parameterisation of the plankton community currently employed, with explicitly resolved plankton populations and ecological dynamics. In ECOGEM, any number of plankton species, with ecophysiological traits (e.g. growth and grazing rates) assigned according 5 to organism size and functional group (e.g. phytoplankton and zooplankton) can be incorporated at run-time. We illustrate the capability of the marine ecology enabled Earth system model ('EcoGE-NIE') by comparing results from one configuration of ECOGEM (with eight generic phytoplankton and zooplankton size classes) to climatological and seasonal observations. We find that the new ecological components of the model show reasonable agreement with both global-scale climatological 10 and local-scale seasonal data. We also compare EcoGENIE results to a the existing biogeochemical incarnation of cGENIE. We find that the resulting global-scale distributions of phosphate, iron, dissolved inorganic carbon, alkalinity and oxygen are similar for both iterations of the model. A slight deterioration in some fields in EcoGENIE (relative to the data) is observed, although we make no attempt to re-tune the overall marine cycling of carbon and nutrients here. The increased capa-15 bilities of EcoGENIE in this regard will enable future exploration of the ecological community on much longer timescales than have previously been examined in global ocean ecosystem models and particularly for past climates and global biogeochemical cycles.

Continental configuration controls ocean oxygenation during the Phanerozoic

“…We base our analysis on a series of past ocean circulation scenarios generated using the cGENIE 20 Earth System Model (see Methods). In these, we consider both the potential role of changing ecological structure in the ocean (using the size-structured plankton model of ref 21 ) as well as the influence of temperature on metabolic rates (following ref 22 ) in creating 3D realizations of the distribution of [O2] in the ocean. Based on the continental reconstructions of Scotese and Wright 23 , we conducted one simulation every 20 Myrs through the Phanerozoic (540 Ma to 0 Ma), for a total of 28 simulated time slices.…”

“…We configured the model on a 36×36 equal-area grid with 16 unevenly spaced vertical levels to a maximum 5500 m depth in the ocean. The cycling of carbon and associated tracers (including O2 and SO4) in the ocean is based on a single (phosphate) nutrient limitation of biological productivity accounting for plankton ecology based on Ward et al 21 and Wilson et al 43 , but adopts the Arrhenius-type temperaturedependent scheme for the remineralization of organic matter exported to the ocean interior of Crichton et al 22 .…”

From cell size and first principles to structure

“…Cells are divided into size group with each group i representing the biomass B i within a range of cells with the geometric mean mass m i . For simplicity we have assumed that cells have constant C:N mass ratio ρ C:N = 5.68, but the model can be extended to dynamic stoichiometry (Ho et al, 2020;Ward et al, 2018). The rate of change (the growth rate) of biomass in a size group is:…”

“…Size-based concepts are now increasingly used in biogeochemical models to increase the diversity within functional groups according to size (Terseleer et al, 2014;Dutkiewicz et al, 2020;Stock et al, 2014). Most size-based models retain the distinction of functional trophic groups by operating with separate phyto-and zooplankton size distributions (Poulin and Franks, 2010;Ward et al, 2018). A recent strand is purely size-based models where the only difference between cells are their size and no a priori distinction between trophic strategy is imposed (Ward and Follows, 2016;Ho et al, 2020;Chakraborty et al, 2020).…”

A critical trade-off between nitrogen quota and growth allows Coccolithus braarudii life cycle phases’ to exploit varying environment

“…These traits have been shown to influence phytoplankton distribution and can be defined in deterministic modelling frameworks (Hansen, 1994;Hansen et al, 1997;Ward et al, 2017;Litchman et al, 2007;Follows et al, 2007;Dutkiewicz et al, 2015). For instance, cell size influences nutrient uptake rates (Ward et al, 2017;Litchman et al, 2007), while coccosphere size influences grazing dynamics by increasing the coccolithophore diameter (Hansen, 1994;Hansen et al, 1997). Nutrient quota influences nutrient requirement (Litchman et al, 2007) and storage (Falkowski and Oliver, 2007 ;Grover, 1991).…”