Broadening of Modeled Cloud Droplet Spectra Using Bin Microphysics in an Eulerian Spatial Domain

Abstract

This study investigates droplet size distribution (DSD) characteristics from condensational growth and transport in Eulerian dynamical models with bin microphysics. A hierarchy of modeling frameworks is utilized, including parcel, one-dimensional (1D), and three-dimensional large-eddy simulation (LES). The bin DSDs from the 1D model, which includes only vertical advection and condensational growth, are nearly as broad as those from the LES and in line with observed DSD widths for stratocumulus clouds. These DSDs are much broader than those from Lagrangian microphysical calculations within a parcel framework that serve as a numerical benchmark for the 1D tests. In contrast, the bin-modeled DSDs are similar to the Lagrangian microphysical benchmark for a rising parcel in which Eulerian transport is not considered. These results indicate that numerical diffusion associated with vertical advection is a key contributor to broadening DSDs in the 1D model and LES. This DSD broadening from vertical numerical diffusion is unphysical, in contrast to the physical mixing processes that previous studies have indicated broaden DSDs in real clouds. It is proposed that artificial DSD broadening from vertical numerical diffusion compensates for underrepresented horizontal variability and mixing of different droplet populations in typical LES configurations with bin microphysics, or the neglect of other mechanisms that broaden DSDs such as growth of giant cloud condensation nuclei. These results call into question the ability of Eulerian dynamical models with bin microphysics to investigate the physical mechanisms for DSD broadening, even though they may reasonably simulate overall DSD characteristics.

University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern Large-Eddy Simulation tool for warm cloud modeling with Lagrangian microphysics

“…they are at a similar level of precision as the SDM. Bin microphysics are troubled by artificial broadening of the 5 size spectrum of droplets due to numerical diffusion associated with advection in the physical space (Morrison et al, 2018).…”

Using an Arbitrary Moment Predictor to Investigate the Optimal Choice of Prognostic Moments in Bulk Cloud Microphysics Schemes

“…Inherently, AMP assumes that the underlying bin scheme is perfect. This is the primary limitation of the study since problems with bin schemes are known to exist—for example, numerical diffusion across bins can lead to artificially wide distributions (see Morrison et al, , for a recent summary of these problems). Regardless, they are built on the fundamental physical principles and equations that underlie the three processes that are investigated in this study with a minimal number of simplifying assumptions.…”

Scaling of an Atmospheric Model to Simulate Turbulence and Cloud Microphysics in the Pi Chamber

“…Validation has typically been accomplished through model intercomparisons (e.g., Ovchinnikov et al, ; Siebesma et al, ) and carefully designed field projects (e.g., Khairoutdinov & Kogan, ; Neggers et al, ; Stevens et al, ). For detailed cloud studies, aerosols and clouds represented through “bin microphysics” (e.g., Khain et al, ) are often considered the gold standard, but recently, it has been recognized that numerical artifacts can become dominant (Morrison et al, ). This surprising result emphasizes yet again the critical importance of rigorous model evaluation against the best possible measurements (Wyngaard, ).…”