Abstract
<jats:p>The advent of quantum computing has opened unprecedented promises to tackle humanity' s most pressing challenges: computationally intractable problems might come within reach through quantum advantages. It is imperative for the field of climate modeling to explore possible uses of this emerging technology. Global climate models have successfully projected different future scenarios, but the spread in projections remains large, with subgrid-scale parameterizations being the main origin of these uncertainties. While machine learning models improve these parameterizations, quantum computing could bring decisive further improvements. Atmospheric turbulence affects the climate because it determines the rates of exchange of heat, moisture, and momentum between the Earth surface and the atmosphere, but has been especially hard to predict and model. Here, we develop a quantum machine learning-based subgrid-scale parameterization for the turbulent vertical heat flux. The training data are coarse-grained data from large-eddy-simulations of a dry convective boundary layer in an idealized setup without topography. Training various quantum and classical neural network models, we find that quantum models based on parametrized circuits with just 2 or 3 qubits achieve accuracies similar to classical models with the same number of trainable parameters. In contrast, the Smagorinsky closure applied to the coarse target resolution deviates strongly from the highly resolved flux. Both our quantum and classical models generalize well to unseen atmospheric conditions. Finally, the feature importances in our quantum models are on average more stable with respect to the random initial parameters, highlighting the possibility to exploit quantum advantages in climate and Earth system models.</jats:p>