MEPS 152:51-66 (1997)  -  doi:10.3354/meps152051

A realistic description of acclimation of the growth rate and carbon-to-chlorophyll a ratio (C:chl a) of phytoplankton to irradiance, nitrate concentration and temperature was incorporated into a 1-dimensional model of phytoplankton production dynamics. The properties of the model's steady-states are described as a guide to its behaviour and to parameter sensitivity. Seasonal cycles of chlorophyll a and C:chl a within the upper 200 m of the ocean were predicted at latitudes ranging from 0° to 60°N. Although limited to a consideration of physiological acclimation of a single taxon, our results compared well with observations.Predicted values of C:chl a ranged from 20 to >160 g C g^-1 chl a. Lowest values of C:chl a were predicted for the top of the nutricline within the seasonal thermocline in mid-summer. Highest values of C:chl a were predicted for the nutrient-depleted surface mixed layer in mid-summer. The seasonal range of C:chl a was greatest in tropical and subtropical waters and least at 60°N. Predictions of the vertical distribution of C:chl a at 20°N were consistent with published observations for the subtropical North Pacific Ocean. Predictions of the relationship between C:chl a and chlorophyll a in surface waters showed qualitative agreement with published observations for the North Atlantic Ocean. The value of C:chl a within the surface mixed layer depends on the mean irradiance within the surface mixed layer and the extent of draw-down of the limiting nutrient (nitrate) from winter/spring maxima. A balance between phytoplankton growth and loss to grazing led to quasi-steady-state conditions in the mixed layer in summer. Parameterisation of grazing was a critical determinant of summer C:chl a. Another critical parameter was the physiologically determined minimum value of C:chl a. Implications of physiological acclimation of C:chl a to our understanding of the role of phytoplankton in the ocean carbon cycle are discussed.

Phytoplankton · Chlorophyll · Modelling · Carbon · Seasonal · Latitudinal