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

Microphysics methods for climate models and numerical weather prediction 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‐fidelity 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 when using few basis functions and to a smoothed bin scheme with more degrees of freedom. Tested in an idealized box setting, the method improves spectral accuracy for droplet collision‐coalescence and may eliminate the need for precipitation autoconversion rates required by 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. Plain Language Summary: Clouds and aerosols affect global warming by reflecting and absorbing radiation and by storing and transporting water. Climate models need a way to efficiently track the size and number of cloud droplets, ice, and aerosols in order to accurately predict the impact that these "microphysical" particles have on climate. Existing methods of microphysics rely on many uncertain parameters and are either too complicated or too simple to take advantage of today's computational resources. We propose a new way to represent cloud droplets that can both reduce uncertainties and make use of increased computing power. Key Points: A new microphysics method using collocation of basis functions is presentedThe method improves spectral accuracy over bulk methods at a lower cost than bin methodsThe method applies to a flexible range of computational complexity, providing a way to unify microphysics models [ABSTRACT FROM AUTHOR]