| Abstract: | A physical and a biological one-dimensional upper layer model for the stimualtion of the annual cycles of both the physical and the phytoplankton dynamics, are used to estimate the annual primary production in the central North Sea. The simulations are driven with actual 3-hourly meteorological standard observations and estimated radiation data for the 25 years 1962 to 1986. The high variability of the forcing generates a considerable variability in the physical and biological oceanic mixed layer dynamics.As an example, the model results from two years with contrasting meteorological conditions, 1963 and 1967, are discussed in detail. The mixing regimes generated are very different which result in different annual phytoplankton cycles. During 1963 when conditions were warm and windless, the early establishment of a calm upper layer water mass enabled a strong spring plankton bloom; whereas in 1967, which was stormy and cold, convective overturning continued until April, suppressing an early spring bloom and prolonging the blooming into summer.For the meteorological conditions observed in 1962 to 1986, the simulations yield an integrated annual water column gross production of 83.5–99.0 gC m−2a−1 and an integrated annual water column net production ranging between 43.0 and 64.2 gC m−2a−1 for the central North Sea. Grazing by the prescribed copepod population ranges from 24.5 to 40.0 gC m−2a−1. The production events are described irregularly over the different years, total gross production varies only about 17%, and total net production by about 21%. The nutrient taken up by the algae is 2.6 to 3.2 times the winter concentration of that layer which in summer is situated above the seasonal thermocline. The additional nutrient is provided by local regeneration and by turbulent entrainment from below the thermocline. Local regeneration in the upper layer provides about 2.4 and 0.3 times the entrained amount of phosphate during spring and summer, respectively. In the 25 years 16 late summer or early fall storm events entrained more than 1.2mmol P m−2d−1 into the depleted upper layer, potentially initiating new production events.The simulated annual cycles can be validated with the available data only in the sense that the variability, but not single events, can be compared to measurements. Such comparisons between simulated and field data show that the simulation reproduces the general features of annual phytoplankton cycles. This establishes confidence in those calculated estimates, for which field data are not directly comparable. It is concluded that weather-induced variability can explain most of the observed variability in phytoplankton in annual cycles.A typical annual cycle of phytoplankton biomass dynamics is presented. Ratios of daily process contributions show that the balances between the different processes change during the annual cycle. Diagrams of the mean and seasonal phosphorus flow are derived from the simulations. Two thirds of the primary production are channelled through the copepods, and one third is lost by other processes. Organic matter corresponding to more than the initial amount of nutrients in the mixed layer is sedimenting out of the upper layer, and about the same amount is regenerated at the bottom and mixed into the water column at the end of the year.The critical points in the model: grazing, recycling of nutrients and mixing in the bottom boundary layer, are discussed. The model still needs to be refined with respect to these processes in order to achieve the delicate balances required to generate fall blooms. A series problem is the appropriateness of primary production measurements for a comparison with simulated quantities. Attempts should be made to establish a one-to-one correspondence between model-derived production quantities and measurements.Single events are important, so both sampling strategies and the estimation of fluxes from data should take account of the possible occurrence of such events, which may have been missed in the observations, by presenting ranges covering the realistic variance rather than mean values. |
