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Abstract The use of coarse resolution and strong grid‐scale dissipation has prevented global ocean models from simulating the correct kinetic energy level. Recently parameterizing energy backscatter has been proposed to energize the model simulations. Parameterizing backscatter reduces long‐standing North Atlantic sea surface temperature (SST) and associated surface current biases, but the underlying mechanism remains unclear. Here, we apply backscatter in different geographic regions to distinguish the different physical processes at play. We show that an improved Gulf Stream path is due to backscatter acting north of the Grand Banks to maintain a strong deep western boundary current. An improved North Atlantic Current path is due to backscatter acting around the Flemish Cap, with likely an improved nearby topography‐flow interactions. These results suggest that the SST improvement with backscatter is partly due to the resulted strengthening of resolved currents, whereas the role of improved eddy physics requires further research. 

Abstract Coupled climate simulations that span several hundred years cannot be run at a high‐enough spatial resolution to resolve mesoscale ocean dynamics. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ocean equations using data from ocean‐only simulations with idealized geometry. We present a stochastic Deep Learning parameterization that is trained on data generated by CM2.6, a high‐resolution state‐of‐the‐art coupled climate model. We train a Convolutional Neural Network for the subgrid momentum forcing using macroscale surface velocities from a few selected subdomains with different dynamical regimes. At each location of the coarse grid, rather than predicting a single number for the subgrid momentum forcing, we predict both the mean and standard deviation of a Gaussian probability distribution. This approach requires training our neural network to minimize a negative log‐likelihood loss function rather than the Mean Square Error, which has been the standard in applications of Deep Learning to the problem of parameterizations. Each estimate of the conditional mean subgrid forcing is thus associated with an uncertainty estimate–the standard deviation—which will form the basis for a stochastic subgrid parameterization. Offline tests show that our parameterization generalizes well to the global oceans and a climate with increasedlevels without further training. We then implement our learned stochastic parameterization in an eddy‐permitting idealized shallow water model. The implementation is stable and improves some statistics of the flow. Our work demonstrates the potential of combining Deep Learning tools with a probabilistic approach in parameterizing unresolved ocean dynamics. 

Abstract The resolution of climate models is limited by computational cost. Therefore, we must rely on parameterizations to represent processes occurring below the scale resolved by the models. Here, we focus on parameterizations of ocean mesoscale eddies and employ machine learning (ML), namely, relevance vector machines (RVMs) and convolutional neural networks (CNNs), to derive computationally efficient parameterizations from data, which are interpretable and/or encapsulate physics. In particular, we demonstrate the usefulness of the RVM algorithm to reveal closed‐form equations for eddy parameterizations with embedded conservation laws. When implemented in an idealized ocean model, all parameterizations improve the statistics of the coarse‐resolution simulation. The CNN is more stable than the RVM such that its skill in reproducing the high‐resolution simulation is higher than the other schemes; however, the RVM scheme is interpretable. This work shows the potential for new physics‐aware interpretable ML turbulence parameterizations for use in ocean climate models. 

Abstract Purpose of Review Assessment of the impact of ocean resolution in Earth System models on the mean state, variability, and future projections and discussion of prospects for improved parameterisations to represent the ocean mesoscale. Recent Findings The majority of centres participating in CMIP6 employ ocean components with resolutions of about 1 degree in their full Earth System models (eddy-parameterising models). In contrast, there are also models submitted to CMIP6 (both DECK and HighResMIP) that employ ocean components of approximately 1/4 degree and 1/10 degree (eddy-present and eddy-rich models). Evidence to date suggests that whether the ocean mesoscale is explicitly represented or parameterised affects not only the mean state of the ocean but also the climate variability and the future climate response, particularly in terms of the Atlantic meridional overturning circulation (AMOC) and the Southern Ocean. Recent developments in scale-aware parameterisations of the mesoscale are being developed and will be included in future Earth System models. Summary Although the choice of ocean resolution in Earth System models will always be limited by computational considerations, for the foreseeable future, this choice is likely to affect projections of climate variability and change as well as other aspects of the Earth System. Future Earth System models will be able to choose increased ocean resolution and/or improved parameterisation of processes to capture physical processes with greater fidelity.