Comparing Eddy‐Permitting Ocean Model Parameterizations via Lagrangian Particle Statistics in a Quasigeostrophic Setting

Abstract

This paper uses Lagrangian statistics—absolute dispersion, pair dispersion, and particle forecasting—to compare four eddy‐permitting models of idealized mesoscale ocean dynamics. The baseline model uses scale‐selective biharmonic damping of momentum to keep the velocity field smooth at the grid scale without overly smearing the partially resolved eddies. The second model uses a nonlinear space‐ and time‐varying Leith viscosity to absorb enstrophy near the grid scale without dissipating too much energy. The last two models use a strong biharmonic damping of momentum to keep the velocity field smooth at the grid scale; this dissipates an unrealistically large amount of kinetic energy, and the models add either deterministic or stochastic forcing to reinject this energy at larger scales. These models are called Jansen and Held models after Jansen and Held (2014, https://doi.org/10.1016/j.ocemod.2014.06.002). All four models correctly model the diffusive transport of particles over time scales more than a few weeks, as measured by the Lagrangian absolute dispersion, showing that all models correctly reproduce the large scales of coherent eddies. The Lagrangian relative dispersion (or pair dispersion) is more sensitive to structure at smaller scales in the velocity field. The Leith model has the least‐accurate relative dispersion, while the two Jansen and Held models have the most accurate. In initialized forecast experiments, there is very little difference in performance between the models.

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

“…After being applied in the context of the Deepwater Horizon oil spill (Liu and Weisberg, 2011;Mooers et al, 2012;Halliwell et al, 2014), this metric has been one of the most widely used statistics for trajectory evaluation. The SS has been used to evaluate different parameterizations in operational oil spill trajectory models (Ivichev et al, 2012;Röhrs et al, 2012;De Dominicis et al, 2014;Berta et al, 2015;Wang et al, 2016;French-McCay et al, 2017;Janeiro et al, 2017;Chen et al, 2018;Zhang et al, 2018;Tamtare et al, 2019), to assess the impact of data assimilation in the model's Lagrangian predictability (Sperrevik et al, 2015;Phillipson and Toumi, 2017), to estimate the accuracy of the gap-filled method for HFR data (Fredj et al, 2017), to test the ability of ocean models in simulating surface transport (Sotillo et al, 2016), and to evaluate the relative performance of ocean models and HFR surface currents in predicting trajectories for SAR operations (Roarty et al, 2016(Roarty et al, , 2018.…”

On energy exchanges between eddies and the mean flow in quasigeostrophic turbulence

“…Backscatter in idealized ocean models accounts in part for largescale variability (Kitsios, Frederiksen & Zidikheri 2013), and the development of parameterizations of kinetic energy backscatter for ocean models is a topic of current research (e.g. Kitsios et al 2013;Jansen & Held 2014;Chen, Barham & Grooms 2018). The backscatter associated with linear inviscid decay at small scales as seen here is in the form of potential energy while the aforementioned studies address kinetic energy backscatter.…”

Widespread global disparities between modelled and observed mid-depth ocean currents

“…As for the tolerance threshold (n), we selected n = 1.8, considering the expectations and requirements of the simulated model. SS has been used in several other studies to assess numerical ocean circulation model accuracy [77][78][79] . In general, SS varies between 0 and 1, whereby higher SS values indicate improved prediction skills.…”