A zero-dimensional model for phytoplanktonicproduction in turbid, macro-tidal, well-mixed estuaries is proposed. It is based on the description of light-dependentalgal growth, phytoplankton respiration and mortality. The model is forced by simple time-functions for solar irradiance, water depth and light penetration. The extinction coefficientis directly related to the dynamics of suspended particulate matter. Model results show that the description of phyto-plankton growth must operate at a time resolution sufficientlyhigh to describe the interference between solarly and tidallydriven physical forcing functions. They also demonstrate that in shallow to moderately deep systems, simulations using averaged, instead of time-varying, forcing functions lead to significant errors in the estimation of phytoplankton productivity. The highest errors are observed when the temporalpattern of light penetration, linked to the tidal cycle of solidssettling and resuspension, is neglected. The model has alsobeen applied using realistic forcing functions typical of two locations in the Scheldt estuary. Model results are consistentwith the typical phytoplankton decay observed along the lon-gitudinal, seaward axis in the tidal river and oligohaline part of this estuary.
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To protect marine ecosystems threatened by climate change and anthropic stressors, it is essential to operationally monitor ocean health indicators. These are metrics synthetizing multiple marine processes relevant to the users of operational services. Here we assess if selected ocean indicators simulated by operational models can be controlled (here meaning constrained effectively) by biogeochemical observations, by using a newly proposed methodological framework. The method consists in firstly screening the sensitivities of the indicators with respect to the initial conditions of the observable variables. These initial conditions are perturbed stochastically in Monte Carlo simulations of one-dimensional configurations of a multi-model ensemble. Then, the models are applied in three-dimensional ensemble assimilation experiments, where the reduction of the ensemble variance corroborates the controllability of the indicators by the observations. The method is applied for ten relevant ecosystem indicators (ranging from inorganic chemicals to plankton production), seven observation types (representing data from satellite and underwater platforms), and an ensemble of five biogeochemical models of different complexity, employed operationally by the European Copernicus Marine Service. We demonstrate that all the indicators are controlled by one or more types of observations. In particular, the indicators of phytoplankton phenology are controlled and improved by the merged observations from the surface ocean colour and chlorophyll profiles. Similar observations also control and reduce the uncertainty of the plankton community structure and production. However, the uncertainty of the trophic efficiency and POC increases when assimilating chlorophyll-a data, though observations were not available to assess whether that was due to a worsen model skill. We recommend that the assessment of controllability proposed here becomes a standard practice in designing operational monitoring, reanalysis and forecast systems, to ultimately provide the users of operational services with more precise estimates of ocean ecosystem indicators.
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RBINS Staff Publications 2023
