Global climate models represent small-scale processes such as convection using subgrid models known as parameterizations, and these parameterizations contribute substantially to uncertainty in climate projections. Machine learning of new parameterizations from highresolution model output is a promising approach, but such parameterizations have been prone to issues of instability and climate drift, and their performance for different grid spacings has not yet been investigated. Here we use a random forest to learn a parameterization from coarse-grained output of a three-dimensional high-resolution idealized atmospheric model. The parameterization leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation. Retraining for different coarse-graining factors shows the parameterization performs best at smaller horizontal grid spacings. Our results yield insights into parameterization performance across length scales, and they also demonstrate the potential for learning parameterizations from global high-resolution simulations that are now emerging.
Traditional parameterizations in general circulation models (GCMs) rely on simplified physical models and suffer from inaccuracies which lead to model biases and large uncertainties in climate projections (Bony &
This study relates the occurrence of the midwinter minimum in eddy activity over the North Pacific with the seasonality in jet characteristics. During winter, the Pacific jet core is typically around latitude 32 ∘ N and has features of a merged subtropical eddy-driven jet. On the other hand, during transition seasons, the jet is at higher latitudes (≈40 ∘ N) and resembles more an eddy-driven jet. We find that these differences in jet characteristics play a role in the occurrence of the midwinter minimum. It is found that a midwinter minimum-like behavior in eddy activity, as observed, is obtained in idealized simulations where zonally symmetric temperature profiles are adjusted to mimic the seasonality of longitudinally averaged temperature observed across the North Pacific. Furthermore, we find in both reanalysis data and the idealized simulations that a poleward shift of the January jet leads to an increase in eddy kinetic energy.
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