| Summary: | Temporally and spatially dense estimates of oceanic phytoplankton net primary productivity (NPP), which are commonly derived by mathematical models from satellite observations of ocean colour, are a cornerstone of current research efforts focused on the state and variability of ecosystems, biogeochemical cycles and climate. Using two exemplary NPP models, it was examined how uncertainties in model input terms might affect the accuracy of the output. In the first part of the dissertation, the response of NPP estimates to perturbing input values of mixed layer depth (MLD) was analyzed. Four series of NPP fields, two global and two covering the North Atlantic, were computed in monthly intervals during a period of several years. Each of the series resulted from identical remote sensing data but different MLD input. Due to the influence of MLD on the availability of light for photosynthesis, the NPP estimates were overall inversely related to MLD. However, the degree of this relationship varied considerably in space and time over most of the world ocean. During summer, NPP at middle and high latitudes was appreciably sensitive even to small MLD fluctuations, but had little or no response to large MLD perturbations in winter. On the other hand, subtropical regions were characterized by a largely opposite seasonal pattern. Tropical areas showed no seasonality and, apart from the equatorial Pacific, exhibited little sensitivity of NPP to MLD uncertainties. The observed variability in the NPP response was attributed not only to the model’s nonlinearity, but also to the presence of the photosynthetic saturation/limitation thresholds, as well as to the coincident sea surface irradiance and, in particular, the diffuse attenuation coefficient for downward irradiance (Kd). It was shown that Kd could be used as an indicator of the NPP sensitivity to uncertainties in MLD, the greatest sensitivity being associated with very large Kd values. Maximum differences between areally integrated annual NPP estimates, based on different ...
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