Air-sea flux variability has contributions from both ocean and atmosphere at different spatio-temporal scales. Atmospheric synoptic scales and the air-sea turbulent heat flux that they drive are well represented in climate models, but ocean mesoscales and their associated variability are often not well resolved due to non-eddy-resolving spatial resolutions of current climate models. We deploy a physics-based stochastic subgrid-scale parameterization for ocean density, that reinforces the lateral density variations due to oceanic eddies, and examine its effect on air-sea heat flux variability in a comprehensive coupled climate model. The stochastic parameterization substantially modifies sea surface temperature (SST) and latent heat flux (LHF) variability and their co-variability, primarily at scales near the resolution of the ocean model grid. Enhancement in the SST-LHF anomaly covariance, and correlations, indicate that the ocean-intrinsic component of the air-sea heat flux variability improves with respect to high-resolution satellite observations, especially in Gulf Stream region.

A document by Nora Loose. Click on the document to view its contents.

Imperfect models are often used for forecasting and state estimation of complex dynamical systems, typically by mapping a reference initial state into model phase space, making a forecast, and then mapping back to the reference space. In many cases these mappings are implicit, and forecast errors thus reflect a combination of model forecast errors and mapping errors. Techniques to infer parameterizations and parameters to reduce model bias have been the subject of intense scrutiny; however, we lack a general framework for discovering optimal mappings between system and model attractors. Here we propose a novel Machine Learning paradigm for inferring cross-attractor transformations (CATs) that minimize forecast error. CATs are pairs of transformations from the phase space of a reference system to the phase space of a model and vice versa that serve as a bridge between the attractors of a true system and an imperfect model. A computationally efficient analog approximation to tangent linear and adjoint models is developed to enable efficient stochastic gradient descent algorithms to train CAT parameters. Neural networks constructed with a custom analog-adjoint layer permit specification of affine transformations as well as more general nonlinear transformations.

Air-sea flux variability has contributions from both ocean and atmosphere at different spatio-temporal scales. Atmospheric synoptic scales and the air-sea turbulent heat flux that they drive are well represented in climate models, but ocean mesoscales and their associated variability are often not well resolved due to non-eddy-resolving spatial resolutions of current climate models. We deploy a physics-based stochastic subgrid-scale parameterization for ocean density, that reinforces the lateral density variations due to oceanic eddies, and examine its effect on air-sea heat flux variability in a comprehensive coupled climate model. The stochastic parameterization substantially modifies sea surface temperature (SST) and latent heat flux (LHF) variability and their correlations, primarily at scales near the resolution of the ocean model grid. Changes in the SST-LHF anomaly correlations indicate that the ocean-intrinsic component of the air-sea heat flux variability improves with respect to the satellite observational product, especially in western boundary current extensions.

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.

Energy exchanges between large-scale ocean currents and mesoscale eddies play an important role in setting the large-scale ocean circulation but are not fully captured in models. To better understand and quantify the ocean energy cycle, we apply along-isopycnal spatial filtering to output from an isopycnal 1/32$^\circ$ primitive equation model with idealized Atlantic and Southern Ocean geometry and topography. We diagnose the energy cycle in two frameworks: (1) a non-thickness-weighted framework, resulting in a Lorenz-like energy cycle, and (2) a thickness-weighted framework, resulting in the Bleck energy cycle. This paper shows that (2) is the more useful framework for studying energy pathways when an isopycnal average is used. Next, we investigate the Bleck cycle as a function of filter scale. Baroclinic conversion generates mesoscale eddy kinetic energy over a wide range of scales, and peaks near the deformation scale at high latitudes, but below the deformation scale at low latitudes. Away from topography, an inverse cascade transfers kinetic energy from the mesoscales to larger scales. The upscale energy transfer peaks near the energy-containing scale at high latitudes, but below the deformation scale at low latitudes. Regions downstream of topography are characterized by a downscale kinetic energy transfer, in which mesoscale eddies are generated through barotropic instability. The scale- and flow-dependent energy pathways diagnosed in this paper provide a basis for evaluating and developing scale- and flow-aware mesoscale eddy parameterizations.