Limitations of Separate Cloud and Rain Categories in Parameterizing Collision‐Coalescence for Bulk Microphysics Schemes. (16th June 2022)

• Record Type: Journal Article
• Authors: Igel, A. L.; Morrison, H.; Santos, S. P.; van Lier‐Walqui, M.
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-16
• Subjects: autoconversion -- collision‐coalescence -- bulk microphysics schemes -- parameterizations; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2022MS003039
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Warm rain collision‐coalescence has been persistently difficult to parameterize in bulk microphysics schemes. We use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that an important limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution, the simulation of cloud‐to‐rain conversion is substantially improved. We also find large sensitivity to the threshold size to distinguish cloud and rain in traditional schemes; substantial improvement is found by decreasing the threshold from 40 to 25 μm. Neither the use of an assumed functional form for the size distribution nor the choice of predicted distribution moments has a large impact on the ability of AMP to simulate rain production. When predicting four total moments of the liquid drop size distribution, either with a traditional two‐category, two‐moment scheme with a reduced size threshold, or a four‐moment single‐category scheme, errors in the evolution of mass and the cloud size distribution are similar, but the single‐category scheme has a substantially better representation of the rain size distribution. Optimal moment combinations for the single‐category approach are investigated and appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision‐coalescence rates. Plain Language Summary: Weather and climate forecast models have always struggled to simulate the production of rain from warm, shallow clouds. Consequently, these models often cannot reproduce observed surface rain rates and cloud radiative forcing. Here, we investigate why this rain production is so difficult for the bulk microphysics schemes in these models. We address several possibilities: the drop size distribution assumption, the choice of predicted cloud and rain properties, the definition of rain, and the decision to treat cloud and rain drops as separate categories. We find the latter is most likely to be the source of difficulty. Most existing models choose to distinguish between cloud and rain drops, which necessitates methods to transfer mass (and other properties) from the cloud category to the rain category during rain production. We find that if we instead use a single liquid drop category that contains both cloud and rain drops, we can substantially improve the prediction of rain formation. This is true even when we use the same total number of predicted properties in each approach. These results imply that we could improve rain production in models without any additional computational cost by moving to a single liquid drop category in bulk microphysics schemes. Key Points: A single category, four moment scheme simulates autoconversion and accretion far better than a two category, two moment scheme The rain mode forms at diameters that are much smaller than are traditionally considered to be rain Using one versus two liquid categories is more important than assumptions about drop size distributions