Search for: All records

Abstract Ocean general circulation models (OGCMs) are often used at horizontal resolutions that preclude the appearance of mesoscale eddies. The ocean mesoscale constitutes a significant component of ocean variability, and OGCMs whose resolutions are too coarse to represent the mesoscale are necessarily lacking this variability. In addition to being variable, the ocean mesoscale also induces variability on larger scales that could be resolved on a coarse grid, but coarse OGCMs often lack this variability too. This paper develops a stochastic parameterization that adds small increments to an OGCM's lateral velocity field, which excites natural modes of variability in the model. The rate at which these velocity increments add energy to the flow is tied to the rate at which the Gent‐McWilliams parameterization—a popular parameterization of the effect of mesoscale eddies on tracer transport—removes potential energy from the resolved scales. The stochastic parameterization is implemented in a non‐eddying OGCM, where it is shown to increase the variability significantly. 

Abstract Biased, incomplete numerical models are often used for forecasting states of complex dynamical systems by mapping an estimate of a “true” initial state into model phase space, making a forecast, and then mapping back to the “true” space. While advances have been made to reduce errors associated with model initialization and model forecasts, we lack a general framework for discovering optimal mappings between “true” dynamical systems and model phase spaces. Here, we propose using a data‐driven approach to infer these maps. Our approach consistently reduces errors in the Lorenz‐96 system with an imperfect model constructed to produce significant model errors compared to a reference configuration. Optimal pre‐ and post‐processing transforms leverage “shocks” and “drifts” in the imperfect model to make more skillful forecasts of the reference system. The implemented machine learning architecture using neural networks constructed with a custom analog‐adjoint layer makes the approach generalizable across applications. 

Abstract The Gent–McWilliams parameterization is commonly used in global ocean models to model the advective component of tracer transport effected by unresolved mesoscale eddies. The vertical structure of the transfer coefficient in this parameterization is studied using data from a 0.1° resolution global ocean‐ice simulation. The vertical structure is found to be well approximated by a baroclinic mode structure with no flow at the bottom, though horizontal anisotropy is crucial for obtaining a good fit. This vertical structure is motivated by reference to the vertical structure of mesoscale eddy velocity and density anomalies, which are also diagnosed from the data. 

Abstract Unresolved temperature and salinity fluctuations interact with a nonlinear seawater equation of state to produce significant errors in the ocean model evaluation of the large‐scale density field. It is shown that the impact of temperature fluctuations dominates the impact of salinity fluctuations and that the error in density is, to leading order, proportional to the product of a subgrid‐scale temperature variance and a second derivative of the equation of state. Two parameterizations are proposed to correct the large‐scale density field: one deterministic and one stochastic. Free parameters in both parameterizations are fit using fine‐resolution model data. Both parameterizations are computationally efficient as they require only one additional evaluation of a nonlinear equation at each grid cell. A companion paper will discuss the climate impacts of the parameterizations proposed here. 

Abstract We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low‐pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian operator appropriate to the data. The new method can approximate arbitrary filter shapes, including Gaussian filters, and can be extended to spatially varying and anisotropic filters. The new diffusion‐based smoother's properties are illustrated with examples from ocean model data and ocean observational products. An open‐source Python package implementing this algorithm, called gcm‐filters, is currently under development.