A statistical-equilibrium, geostrophic-turbulence regime of the stochastically forced, one-layer, reduced-gravity, quasi-geostrophic model is identified in which the numerical solutions are representative of global mean, mid-latitude, open-ocean mesoscale variability. A nominal best fit to observed SSH variance, autocorrelation, eddy, and spectral statistics is obtained for dimensional SSH stochastic-forcing variance production rate 1/4 cm^2/d, an SSH damping rate 1/62 1/wk, and a stochastic forcing autocorrelation timescale near or greater than 1 wk. This ocean mesoscale regime is nonlinear and appears to fall near the stochastic limit, at which wave-mean interaction is just strong enough to begin to reduce the local mesoscale variance production, but is still weak relative to the overall nonlinearity. Comparison of linearly-inverted wavenumber-frequency spectra shows that a strong effect of nonlinearity, the removal of energy from the resonant linear wave field, is resolved by the gridded altimeter SSH data. These inversions further suggest a possible signature in the merged altimeter SSH dataset of signal propagation characteristics from the objective analysis procedure.

As already presented at previous conferences, we have developed an innovative technique for ocean wave observations from space: A dedicated reprocessing of spotlight-mode synthetic aperture radar (SAR) data enables us to produce a series of images covering a total time interval of a few seconds, in which moving wave patterns become visible. In most cases, the duration and quality of the image series is sufficient for computing a wavenumber-frequency spectrum and applying a dispersion relation filter to separate signatures of actual ocean waves from contributions of other physical phenomena and noise. Then the extracted wave signatures can be inverted into surface slope and displacement fields (in the space-time domain) and / or spectra (in the wavenumber-frequency domain) by applying relatively simple linear modulation transfer functions. Altogether this process is much more straightforward than traditional wave retrieval techniques for SAR images, and we think it produces more accurate results, although a full quantitative validation is still to be completed. This presentation at Ocean Sciences 2020 will not focus on the waves themselves, but on secondary information that can be retrieved from the moving wave patterns: Since the dispersion relation of ocean waves depends on water depth and surface current, these two parameters need to be tuned to fit the shape of the “dispersion shell” to the observed spectral energy distribution in each subwindow of a SAR image for which the wave field is analyzed. Accordingly, depth and surface current are obtained as additional outputs of the dispersion relation filter. This methodology is well established in the marine radar community, but has not been applied to satellite data before, where it presents a range of new opportunities as well as specific challenges. We will discuss the potential of the proposed method for practical applications and show first examples of retrieved nearshore wave, depth, and surface current fields.

Operational ocean and wave models are used to produce forecasts for navigation but also required for a wide range of services, most of them relying on particle tracking methods. Improving the forecast capabilities and accuracy of a model is then a constant necessity in order to deliver more reliable services. The Marine Institute in Ireland is running several coastal operational models, one of them focuses on the area of Galway Bay in the west coast. At the moment it consists of a stand-alone ocean application, it is the purpose of the work presented here to set-up a coupled application with a wave model. Coupled models are a recent development in ocean modelling, developer teams have included ocean and wave coupling by combining existing models each dedicated to a specific physics. Two theoretical formulations are mostly used for the implementation, both giving the same equations of evolution and interaction terms known as the vortex-force formalism. One approach is using a Lagrangian framework defining an exact averaged operator following the fluid particles, the other approach is Eulerian making use of a multi-scale expansion. In both cases the larger current components are found to be forced by gravity and infra-gravity waves. The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) modelling system is a widely used code, the vortex-force formalism has been implemented in 2012 coupling the Regional Ocean Modeling System (ROMS) with the Simulation Wave Nearshore wave model (SWAN). The implementation has been validated with academic cases and used in several real case studies in the last decade. The work presented here is making use of COAWST, a coupled model is set-up for Galway Bay running a 1-year hind-cast application for 2017 and preliminary results are shown here. The performance of the coupled model is compared with each stand-alone model, using in-situ data as a reference. In the last releases of COAWST the wave model WAVEWATCHIII has been added and can be used in the coupled system. This new feature is tested and the results are compared against SWAN, both wave codes are solving the same equations but different technical choices have been made resulting in different capabilities.

Environmental change in coastal and shelf seas provides a challenge to fisheries and aquaculture worldwide. Coupled physical-biogeochemical models can be used to create projections of environmental conditions under different climate scenarios, but models of this type produce a wealth of information covering multiple environmental variables and a range of timescales. It is challenging to synthesize that information into a form which can be quickly and effectively conveyed to stakeholders. I describe how a range of approaches has been used to analyse projections of environmental conditions in European seas, created using a regional model with a resolution of 0.1 degrees. The projections run from the present-day to 2099 and include temperature, primary production, plankton biomass, pH and nutrient and oxygen concentrations for moderate and high carbon concentration scenarios. A range of formats that convey the scale and extent of projected change in the North East Atlantic, the North Sea and the Mediterranean, are included, aiming for simple, visual communication that goes beyond maps of changing sea surface temperature. Colour-coded tables show at a glance which variables are projected to show significant change at a range of locations. Time series and maps are used together to give complementary information. Contours show geographical shifts in conditions. In each case the content can be adjusted to fit the interests of different stakeholder groups: examples from fisheries and aquaculture are included here.

The diamondback terrapin (Malaclemys terrapin), the official state reptile of Maryland, is an estuarine turtle found along the eastern and Gulf coasts of the United States. The diamondback terrapin may experience risks due to increasing global temperatures because they display temperature-dependent sex determination (TSD). Terrapins, through TSD, produce more female offspring than male offspring if their nest temperatures are relatively warm, leading to ecological issues should global temperatures get too high. This study used temperatures from several individual field, monitored nests, along with air temperatures at the nesting site, recorded in the summer of 2018, to select a predictive model with the R software package rstanarm. The model was then used to predict the average nest temperature in the same nesting site using air temperatures that were recorded during the summer of 2019. This prediction was then compared to temperatures recorded from five nests within the nesting site in 2019 to evaluate its predictive capabilities. A regression spline mixed model (RSMM) was selected for having the lowest leave-one-out information criterion at 50905.95 LOOIC, with a standard error of 0.64 LOOIC. The prediction of average nest temperature developed by the RSMM was given a predictive interval of 95%. We found that the actual temperature of the average nest in 2019 had a prediction coverage of 92.97% from the prediction model, with a root squared mean error of 1.89oC and a predictive mean absolute error of 2.10oC. Benefits of the study involve the ability to predict the future diamondback terrapin nest temperatures within a specific nesting site so long as future air temperatures within that site are known.

Internal waves and vortical mode are ubiquitous in the ocean, providing a conduit for energy transfer from tides and wind-driven circulation to fine- and micro-scale turbulence associated with internal-wave breaking and dissipation. Numerical simulations of a Garrett-Munk internal-wave field are used to study the interactions between internal waves and vortical mode, and the generation of turbulent mixing events and semi-permanent finestructure (vortical mode). Regions of reduced stratification are found to occupy between ~5% and 25% of the model domain, depending on the strength of the anomalies. Decomposition of 3-dimensional model fields into linear internal-wave and vortical-mode components shows that both influence stratification, with ~88% of the anomalies due to linear internal waves and ~40% due to vortical mode, the overlap in percentiles representing regions that are a combination of the two. The time evolution of anomalies is examined, from the initial generation to eventual dissipation. Results further describe the preconditions of these regions, including their relation to a subcritical Richardson number and overturning / mixing events as quantified via Thorpe scales and turbulent dissipation rate.