Spanning the Gap from Bulk to Bin: A Novel Spectral Microphysics Method

Abstract

Microphysics methods for climate models typically track one, two, or three moments of a droplet size distribution for various categories of liquid, ice, and aerosol. Such methods rely on conversion parameters between these categories, which introduces uncertainty into predictions. While higher-resolution options such as bin and Lagrangian schemes exist, they require too many degrees of freedom for climate modeling applications and introduce numerical challenges. Here we introduce a flexible spectral microphysics method based on collocation of basis functions. This method generalizes to a linear bulk scheme at low resolution and a smoothed bin scheme at high resolution. Tested in an idealized box setting, the method improves spectral accuracy for droplet collision-coalescence and improves precipitation predictions relative to bulk methods; furthermore, it generalizes well to multimodal distributions with less complexity than a bin method. The potential to extend this collocation representation to multiple hydrometeor classes suggests a path forward to unify liquid, ice, and aerosol microphysics in a single, flexible, computational framework for climate modeling.