Improved Representation of Underwater Light Field and Its Impact on Ecosystem Dynamics: A Study in the North Sea

Abstract

Understanding ecosystem state on the North‐West European (NWE) Shelf is of major importance for both economy and climate research. The purpose of this work is to advance our modeling of in‐water optics on the NWE Shelf, with important implications for how we model primary productivity, as well as for assimilation of water‐leaving radiances. We implement a stand‐alone bio‐optical module into the existing coupled physical‐biogeochemical model configuration. The advantage of the bio‐optical module, when compared to the preexisting light scheme is that it resolves the underwater light spectrally and distinguishes between direct and diffuse downwelling streams. The changed underwater light compares better with both satellite and in situ observations. The module lowered the underwater photosynthetically active radiation, decreasing the simulated primary productivity, but overall, the improved underwater light had relatively limited impact on the phytoplankton seasonal dynamics. We showed that the model skill in representing phytoplankton seasonal cycle (e.g., phytoplankton bloom) can be substantially improved either by assimilation of satellite phytoplankton functional type (PFT) chlorophyll, or by assimilating a novel PFT absorption product. Assimilation of the two PFT products yields similar results, with an important difference in the PFT community structure. Both assimilative runs lead to lower plankton biomass and increase the nutrient concentrations. We discuss some future directions on how to improve our model skill in biogeochemistry without using assimilation, for example, by improving nutrient forcing, retuning the model parameters, and using the bio‐optical module to provide a two‐way physical‐biogeochemical coupling, improving the consistency between model physical and biogeochemical components.

Evaluation of Ocean Color Remote Sensing Algorithms for Diffuse Attenuation Coefficients and Optical Depths with Data Collected on BGC-Argo Floats

“…For example, for photosynthesis, a euphotic or isolume depth is defined based on a relative light level (e.g., 1% [3]) or an absolute intensity (e.g., 0.415 mol photons m −2 d −1 [4]) below which photosynthesis is assumed as zero. Moreover, accurately quantifying the attenuation of sunlight within the upper ocean is essential for physical and biogeochemical models, affecting the modeled upper-ocean temperature (e.g., [5,6]) and ecosystem dynamics (e.g., [7]). Sea-surface sunlight is globally available from space agencies ( [8]), and a variety of models to describe its attenuation through the water have been devised (e.g., [9,10]).…”

Towards a multi-platform assimilative system for North Sea biogeochemistry

“…The model free simulation was run from 01/09/2017 until the end of the year 2018 and was initialized from a 2016-2018 run of a very similar model configuration presented in Skákala et al [2020]. The free run outputs have been analysed for the period of the glider data availability (08/05-15/08, 2018).…”

“…[2018], and almost identical to Skákala et al [2020]: we use the CO6 NEMO version, based on NEMOv3.6, a development of the CO5 configuration explained in detail by O'Dea et al [2017]. The model has 7 km spatial resolution on the Atlantic Margin Model (AMM7) domain using a terrain-following z * − σ coordinate system with 51 vertical levels (Siddorn and Furner [2013]).…”

“…Reanalyses provide our best estimate of the ocean state by optimally combining the state-of-the-art knowledge from models with the most up-to-date observations. In marine biogeochemistry the prevailing approach is to assimilate satellite products into models, either for Ocean Color (OC) derived total chlorophyll (e.g Ishizaka [1990]; Carmillet et al [2001]; Natvik and Evensen [2003]; Hoteit et al [2005]; Triantafyllou et al [2007]; Gregg [2007, 2008]; Gregg [2008]; Fontana et al [2010]; Ford et al [2012]; Ciavatta et al [2011Ciavatta et al [ , 2016; Kalaroni et al [2016]; ; Pradhan et al [2019]), Phytoplankton Functional Type (PFT)-specific chlorophyll (Ciavatta et al [2018(Ciavatta et al [ , 2019; Skákala et al [2018Skákala et al [ , 2020), or surface radiances (Shulman et al [2013]; Ciavatta et al [2014]; Jones et al [2016]; Gregg and Rousseaux [2017]; Skákala et al [2020]). Additionally a number of studies have assimilated biogeochemical data from in situ measurements, either using -2-Confidential manuscript submitted to JGR -Oceans single-location profiles (e.g.…”

Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

“…Together, these form the ocean and sea ice components of the GC3.1 configuration (Williams et al, 2017) of the Hadley Centre Global Environment Model version 3 (HadGEM3), which is used for physical climate simulations submitted to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al, 2016). When combined with the physics version of the data assimilation scheme described below, the ocean and sea ice models are also used in version 14 of the Forecasting Ocean Assimilation Model (FOAM), earlier versions of which are described by Blockley et al (2014) and Storkey et al (2010). FOAM is run operationally at the Met Office to produce short-range forecasts of the physical ocean and sea ice state.…”