Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

Abstract

In climate simulations, the impacts of the subgrid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the subgrid variability in a computationally inexpensive manner. This study shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a highresolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a nonzero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the highresolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations.

The Impact of Horizontal Resolution on Energy Transfers in Global Ocean Models

“…Using a coupled climate model, Ref. [62] found that the parameterisation by [26] reduced some of the biases in low-resolution climate models as well as increased the interannual variability of the overturning circulation. Horizontal pressure gradients can also be strengthened by strengthening horizontal gradients of sea surface height, which would require increasing the divergence of the barotropic velocities, e.g., using a stochastic parameterisation to represent unresolved divergent motions.…”

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

“…Williams et al (2016) report the reduction of biases in a non‐eddy‐permitting ocean model (FAMOUS) due to the introduction of stochastic perturbations to the temperature in the ocean and improved variability of the strength of the thermohaline circulation. The noise for the perturbations was informed by diagnostics of eddy statistics in an eddy‐permitting ocean model (HiGEM).…”

“…As in the uncoupled case, the stochastic perturbations increase the ensemble spread (see also Andrejczuk et al, 2016), especially in the eddy-active regions of the Southern Ocean and the western boundary currents. Williams et al (2016) report the reduction of biases in a noneddy-permitting ocean model (FAMOUS) due to the introduction of stochastic perturbations to the temperature in the ocean and improved variability of the strength of the thermohaline circulation. The noise for the perturbations was informed by diagnostics of eddy statistics in an eddy-permitting ocean model (HiGEM).…”

On the Chaotic Variability of Deep Convection in the Mediterranean Sea

“…Finally, both the intrinsic variability and the control of eddy fluxes by bathymetry are relevant for climate. Their parametrization in low-resolution models is hence highly needed: the former shows promising results (Brankart et al, 2015;Williams et al, 2016) whereas the latter has proven to be challenging (Isachsen, 2015).…”