CITRATE 1.0 : Phytoplankton continuous trait-distribution model with one-dimensional physical transport applied to the North Pacific

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Chen, Bingzhang and Smith, Sherwood Lan (2018) CITRATE 1.0 : Phytoplankton continuous trait-distribution model with one-dimensional physical transport applied to the North Pacific. Geoscientific Model Development, 11. pp. 467-495. ISSN 1991-9603 (https://doi.org/10.5194/gmd-11-467-2018)

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Abstract

Diversity plays critical roles in ecosystem func- tioning, but it remains challenging to model phytoplankton diversity in order to better understand those roles and repro- duce consistently observed diversity patterns in the ocean. In contrast to the typical approach of resolving distinct species or functional groups, we present a ContInuous TRAiT-basEd phytoplankton model (CITRATE) that focuses on macro- scopic system properties such as total biomass, mean trait values, and trait variance. This phytoplankton component is embedded within a nitrogen–phytoplankton-zooplankton– detritus–iron model that itself is coupled with a simplified one-dimensional ocean model. Size is used as the master trait for phytoplankton. CITRATE also incorporates “trait diffusion” for sustaining diversity and simple representa- tions of physiological acclimation, i.e., flexible chlorophyll- to-carbon and nitrogen-to-carbon ratios. We have imple- mented CITRATE at two contrasting stations in the North Pacific where several years of observational data are avail- able. The model is driven by physical forcing including ver- tical eddy diffusivity imported from three-dimensional gen- eral ocean circulation models (GCMs). One common set of model parameters for the two stations is optimized using the Delayed-Rejection Adaptive Metropolis–Hasting Monte Carlo (DRAM) algorithm. The model faithfully reproduces most of the observed patterns and gives robust predictions on phytoplankton mean size and size diversity. CITRATE is suitable for applications in GCMs and constitutes a proto- type upon which more sophisticated continuous trait-based models can be developed.

ORCID iDs

Chen, Bingzhang ORCID logoORCID: https://orcid.org/0000-0002-1573-7473 and Smith, Sherwood Lan;
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