Accurately representing vertical turbulent fluxes in the planetary boundary layer is vital for moisture and energy transport. Nonetheless, the parameterization of the boundary layer remains a major source of inaccuracy in climate models. Recently, machine learning techniques have gained popularity for representing oceanic and atmospheric processes, yet their high dimensionality limits interpretability. This study introduces a new neural network architecture employing non-linear dimensionality reduction to predict vertical turbulent fluxes in a dry convective boundary layer. Our method utilizes turbulent kinetic energy and scalar profiles as input to extract a physically constrained two-dimensional latent space, providing the necessary yet minimal information for accurate flux prediction.
We obtained data by coarse-graining Large Eddy Simulations covering a broad spectrum of boundary layer conditions, from weakly to strongly unstable. These regimes are employed to constrain the latent space disentanglement, enhancing interpretability. By applying this constraint, we decompose the vertical turbulent flux of various scalars into two main modes of variability: wind shear and convective transport.
Our data-driven parameterization accurately predicts vertical turbulent fluxes (heat and passive scalars) across turbulent regimes, surpassing state-of-the-art schemes like the eddy-diffusivity mass flux scheme. By projecting each variability mode onto its associated scalar gradient, we estimate the diffusive flux and learn the eddy diffusivity. The diffusive flux is found to be significant only in the surface layer for both modes and becomes negligible in the mixed layer. The retrieved eddy diffusivity is considerably smaller than previous estimates used in conventional parameterizations, highlighting the predominant non-diffusive nature of transport.