Challenging Vertical Turbulence Mixing Schemes in a Tidally Energetic Environment: 1. 3‐D Shelf‐Sea Model Assessment

Abstract

Mixing in the ocean and shelf seas is critical for the vertical distribution of dynamically active properties, such as density and biogeochemical tracers. Eight different decadal simulations are used to assess the skill of vertical turbulent mixing schemes (TMS) in a 3‐D regional model of tidally active shelf seas. The TMS differ in the type of stability functions used and in the Ozmidov/Deardorff/Galperin limiter of the turbulence length scales. We review the dependence of the critical Richardson and Prandtl numbers to define the “diffusiveness” of the TMS. The skill in representing bias and variability of stratification profiles is assessed with five different metrics: surface and bottom temperatures and pycnocline depth, thickness, and strength. The assessment is made against hydrography from three data sets (28,000 profiles in total). Bottom and surface temperatures are found to be as sensitive to TMS choice as to horizontal resolution or heat flux formulation, as reported in other studies. All TMS underrepresent the pycnocline depth and benthic temperatures. This suggests physical processes are missing from the model, and these are discussed. Different TMSs show the best results for different metrics, and there is no outright winner. Simulations coupled with an ecosystem model show the choice of TMS strongly affects the ecosystem behavior: shifting the timing of peak chlorophyll by 1 month, showing regional chlorophyll differences of order 100%, and redistributing the production of chorophyll between the pycnocline and mixed layer.

Shelf Seas Baroclinic Energy Loss: Pycnocline Mixing and Bottom Boundary Layer Dissipation

“…There are of course differences between model and observed stratification, particularly severe at site D340BH, which is close to the poorly resolved topography of Barra Head. An investigation into the sensitivities of AMM60's ability to reproduce observed stratification is presented elsewhere (Luneva et al., 2019), and is not the focus here.…”

“…Partly in order to resolve this issue, and also to improve model stability, an ad hoc and high level of background diffusion is often applied throughout model domains (Jochum, 2009), justified on the dual grounds of tuning to observations and of numerical stability. This approach can lead to an improvement in reproducing observed levels of mixing in some areas, but often fails to do so within stratified regions (Luneva et al., 2019). Uniformly applied diffusive mixing also fails, by definition, to represent the significant temporal and spatial variability known to exist within ocean turbulence (Moum & Rippeth, 2009).…”

“…Uniformly applied diffusive mixing also fails, by definition, to represent the significant temporal and spatial variability known to exist within ocean turbulence (Moum & Rippeth, 2009). Indeed, a recent study (Luneva et al., 2019), which examined a number of commonly used numerical mixing schemes adapted to overcome missing sub‐grid scale dynamics in a 7 km resolution hydrodynamic model (the NEMO AMM7 model), demonstrated that many commonly used enhanced second order mixing schemes lead to an overly diffusive water column when implemented in the latest generation of shelf seas hydrodynamic models.…”

Model-Observations Synergy in the Coastal Ocean

“…In the North Sea, biases in both surface and bottom temperatures ( Figures 1A,B) suggest that stratification errors are linked either to errors in surface forcing or to vertical processes. Experiments with vertical mixing schemes (Luneva et al, 2019) and light attenuation schemes ( Figures 1C,D) suggest that changing these would reduce biases in bottom temperatures. However, there are no flux moorings in the North Sea to evaluate the surface forcing and very few (and infrequent) subsurface observations ( Figure 1E) to evaluate the full depth seasonal cycle.…”

Anthropogenic Mixing of Seasonally Stratified Shelf Seas by Offshore Wind Farm Infrastructure

“…The deficit in the predicted mid-water ε points to either an incorrect parameterisation of the small scale physics away from the boundaries or to the absence of key physical processes in the model. A recent study (Luneva et al, 2019) evaluated a range of alternative one dimensional turbulence closure schemes for the Northwest European shelf seas, packaged within the Generic Length Scale two-equation formulation (Umlauf and Burchard, 2005). Evaluating the schemes against profile data (28000 profile in total) confirmed that there was no outright winner, with all schemes under representing the thermocline properties and suggested that physical processes are still missing.…”