Seasonal nutrient co-limitation in a temperate shelf sea: A modelling approach

Nutrient limitation on phytoplankton growth plays a critical role in ocean productivity, the functioning of marine ecosystems, and the ocean carbon cycle. In the Celtic Sea, a temperate shelf sea, many studies have shown the importance of nitrate on phytoplankton growth focusing on the seasonal cycle of nitrate and feedbacks with the physical environment; but only recently has it been demonstrated, through discrete measurements, that dissolved iron also plays an important role in the ecosystem of the region. A well-established one-dimensional model has been developed to analyse the nutrient co-limitation between dissolved iron and dissolved inorganic nitrogen in the Celtic Sea. This model allows us to study the full seasonal cycle and inter-annual variability of these two nutrients. Simulations show that dissolved iron is an important nutrient for the development of the spring bloom, while nitrate plays a more important role during the summer season. Sensitivity analyses show that these results are robust when varying the nutrient-related parameters; the largest variability observed for primary production was observed when varying the nutrient sediment flux rates for dissolved iron and nitrate while less impact on phytoplankton production occurs when changing the half saturation constants. Here, we demonstrate that dissolved iron is an important nutrient for the development of the spring bloom and it should not be neglected as a state variable when modelling the Celtic Sea or other temperate shelf seas

Meteorological controls on primary production in shelf seas

Shelf seas are regions of high biological activity, contributing 15-30% of global oceanic primary production, with temperate shelf seas as an important global carbon sink. To understand how shelf seas will respond to environmental changes it is important to fully understand phytoplankton dynamics and inter-annual variability of phytoplankton production in these areas. Previous modelling works have shown that meteorology can affect phytoplankton seasonal dynamics but there is still debate in the literature about the direct mechanisms that affect long-term phytoplankton productivity. Challenges can include a lack of long-term observations and mismatch of these with numerical models due to a range of factors from the simplicity of the assumptions made in model structure to inappropriate parameterisations. This study explores the effects of wind speed, cloud coverage, air temperature, and relative humidity on primary production using a simple one-dimensional biological and physical coupled model to simulate the inter-annual variability of seasonal stratification and productivity over the last five decades (1965 - 2015) and shelf sea observations (2014 - 2015) in the Central Celtic Sea location. Results with the simplest model structure show that wind speed has the largest effect on the inter-annual variability of primary production associated to the onset of thermal stratification. However, a mismatch with observations and inconclusive results motivated further model development, introducing a nutrient-phytoplankton-zooplankton framework and modelling photo-acclimation. Extensive analysis of the new models was performed by calibration and sensitivity analyses, demonstrating that the addition of these processes produces substantial changes in the model dynamics, improving the model fit to observations. This allows reassessment of the questions analysed with the simplest model, providing new insights into the meteorological impacts on phytoplankton productivity, showing that wind speed has a direct influence on the timing of thermal stratification and, therefore, in the timing of the spring bloom, affecting annual production. In contrast to the simpler model, cloud coverage is shown to have the largest effect on the annual phytoplankton production, affecting the available light in the water column with large daily variations directly affecting daily primary production during the spring bloom and summer growth periods. Thus, in this work it is demonstrated that the structure and parameterisation of the model influences the fidelity of the simulations

Constraining the response of phytoplankton to zooplankton grazing and photo-acclimation in a temperate shelf sea with a 1-D model – towards S2P3 v8.0

An established one-dimensional Shelf Sea Physics and Primary Production (S2P3) model has been developed into three different new models: S2P3-NPZ which includes a nutrient–phytoplankton–zooplankton (NPZ) framework, where the grazing rate is no longer fixed but instead varies over time depending on different functions chosen to represent the predator–prey relationship between zooplankton and phytoplankton; S2P3-Photoacclim which includes a representation of the process of photo-acclimation and flexible stoichiometry in phytoplankton; and S2P3 v8.0 which combines the NPZ framework and the variable stoichiometry of phytoplankton at the same time. These model formulations are compared to buoy and conductivity–temperature–depth (CTD) observations, as well as zooplankton biomass and in situ phytoplankton physiological parameters obtained in the central Celtic Sea (CCS). Models were calibrated by comparison to observations of the timing and magnitude of the spring phytoplankton bloom, magnitude of the spring zooplankton bloom, and phytoplankton physiological parameters obtained throughout the water column. A sensitivity study was also performed for each model to understand the effects of individual parameters on model dynamics. Results demonstrate that better agreement with biological observations can be obtained through the addition of representations of photo-acclimation, flexible stoichiometry, and grazing provided these can be adequately constrained

When to add a new process to a model – and when not: A marine biogeochemical perspective

Models are critical tools for environmental science. They allow us to examine the limits of what we think we know and to project that knowledge into situations for which we have little or no data. They are by definition simplifications of reality. There are therefore inevitably times when it is necessary to consider adding a new process to a model that was previously omitted. Doing so may have consequences. It can increase model complexity, affect the time a model takes to run, impact the match between the model output and observations, and complicate comparison to previous studies using the model. How a decision is made on whether to add a process is no more objective than how a scientist might design a laboratory experiment. To illustrate this, we report on an event where a broad and diverse group of marine biogeochemists were invited to construct flowcharts to support making the decision of when to include a new process in a model. The flowcharts are used to illustrate both the complexity of factors that modellers must consider prior to making a decision on model development and the diversity of perspectives on how that decision should be reached. The purpose of this paper is not to provide a definitive protocol for making that decision. Instead, we argue that it is important to acknowledge that there is no objectively “best” approach and instead we discuss the flowcharts created as a means of encouraging modellers to think through why and how they are doing something. This may also hopefully guide observational scientists to understand why it may not always be appropriate to include a process they are studying in a model