The representation of unresolved processes is a key source of uncertainty in weather and climate models. Climate science and weather forecasting indeed heavily rely on numerical simulations of the Earth's atmosphere and oceans (Bauer et al., 2015;Neumann et al., 2019). But even the most advanced applications are currently far from resolving explicitly the wide variety of space-time scales and physical processes involved. This will likely remain the case for the foreseeable future because of the nonlinearity of fluid dynamics and thermodynamics, and because of the finite nature of computational resources (Fox-Kemper et al., 2014;. Weather and climate models will therefore keep relying on approximated representations of the effect of unresolved processes in the form of subgrid parametrization schemes (Fox-Kemper et al., 2019;Schneider, Lan, et al., 2017). Parametrization schemes accounting for the impact of turbulence in the atmosphere and oceans at various scales will in particular remain essential components of these models.