Development, Analysis, and Implementation of Computationally Efficient Models for Resolving CEllular and COMmunity Composition of Plankton (CECOMP)

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Phytoplankton in oceans and marine systems play important roles in global biogeochemical cycles. Coupled earth system models, which are useful for estimating future and hypothetical states of ecosystems at various scales, use parameterizations of growth, export, and trophic transfer of carbon and other elements bound to plankton. Often, these parameterizations either completely ignore, or rely on oversimplified representations of underlying processes. As a result, the variability in the ratios of chlorophyll, carbon, nitrogen, phosphorus and other relevant elements bound to phytoplankton biomass are either misrepresented, or estimated based on heuristic formulations, behavior of which may become questionable outside the range of parameter fitting conditions. In this project, our objective is to develop model components that can improve the representation of algal growth and composition by accounting for acclimation processes in a computationally efficient manner. Those model components will be designed modularly, such that they can be easily coupled with existing plankton models. We will assess the utility of these model components for: i) understanding the mechanisms linking ecophysiology and abiotic elemental ratios, ii) reducing the need for site specific parameter tuning, and hence iii) enhancing predicitive capacity of the models. For reaching these objectives, we have organized the project into two groups of work packages: 1) Model development and analysis of their behaviors in idealized setups; 2) Implementation in realistic 3-D model setups for the North Pacific and North Sea coastal systems. The idealistic setups to be elaborated in the first group will capture the characteristic features observed in the specific study regions to be studied in the second group, such that the findings obtained within each group will be relevant for the other. Thus, this project will synergistically combine these two complementary methodologies, which are often employed in isolation.