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The dynamics of the planktonic ecosystem in the coastal upwelling zone within the California Current System is studied using a three-dimensional (3-D), eddy-resolving circulation model coupled to an ecosystem/biogeochemistry model. Results reveal that the model is capable of replicating many of the large-scale, time-averaged features of the coastal upwelling system.

Abstract: Ocean color remote sensing has provided the scientific community with unprecedented global coverage of chlorophyll a, an indicator of phytoplankton biomass. Together, satellite-derived chlorophyll a and knowledge of Phytoplankton Functional Types (PFTs) will improve our limited understanding of marine ecosystem responses to physiochemical climate drivers involved in carbon cycle dynamics and linkages. Using cruise data from the Gulf of Maine and the Middle Atlantic Bight (N=269 pairs of HPLC and phytoplankton absorption samples), two modeling approaches were utilized to predict phytoplankton absorption and pigments. Algorithm I predicts the chlorophyll-specific absorption coefficient ( (m2 mgchla−1)) using inputs of temperature, light, and chlorophyll a. Modeled r 2 values (400–700nm) ranged from 0.79 to 0.99 when compared to in situ observations with ∼25% lower r 2 values in the UV region. Algorithm II-a utilizes matrix inversion analysis to predict (m−1, 400–700nm) and r 2 values ranged from 0.89 to 0.99. The prediction of phytoplankton pigments with Algorithm II-b produced r 2 values that ranged from 0.40 to 0.93. When used in combination, Algorithm I, and Algorithm II-a are able to use satellite products of SST, PAR, and chlorophyll a (Algorithm I) to predict pigment concentrations and ratios to describe the phytoplankton community. The results of this study demonstrate that the spatial variation in modeled pigment ratios differ significantly from the 10-year SeaWiFS average chlorophyll a data set. Contiguous observations of chlorophyll a and phytoplankton biodiversity will elucidate ecosystem responses with unprecedented complexity. [Copyright &y& Elsevier]

Functional relationships which parameterize growth based on the Eppley temperature relationship for phytoplankton maximal growth rates are increasingly being used in marine and freshwater ecosystem models. In this paper, we demonstrate the effect of using such generalized relationships in modelling studies. Two suites of numerical experiments are carried out to investigate the sensitivity of models to generalized growth relationships. In each experiment, 100 individual species or groups of phytoplankton are allowed to compete under a variety of growth versus temperature relationships. One suite of experiments is carried out within a simple ‘chemostat’ type model that is forced with seasonally varying temperature and photosynthetically available radiation (PAR) fields. A second suite of experiments is carried out using a biogeochemical mixed-layer model to demonstrate the sensitivity of these models to various temperature versus growth relationships. The key difference in the biogeochemical mixed-layer simulations is in the timing of the ecosystem response to seasonal variability of the mixed-layer depth and temperature. The Eppley growth versus temperature relationship overestimates phytoplankton growth by as much as 80% during the spring when growth rates are crucial to the timing of the spring blooms. This decrease in growth rates causes a delay in the spring phytoplankton bloom which in turn results in significant changes in all other model constituents. The results from both suites of experiments show that it is important to resolve the intrinsic growth dynamics of a population in order to properly resolve the maximum growth rates of phytoplankton populations. The results also present a possible explanation for why phytoplankton are commonly found growing within water colder than their optimal temperature for growth. A dynamic growth versus temperature model is introduced that is capable of resolving the growth dynamics of a population of phytoplankton under a variety of temperature forcing scenarios. This new growth versus temperature model/relationship will be useful in global biogeochemical models and demonstrates the importance of underlying population dynamics in controlling bulk community growth estimates. [Copyright &y& Elsevier]