A document by Andrea Jenney. Click on the document to view its contents.

In this study, we explore the relationship between anvil cloud fraction and horizontal model resolution in small domain radiative-convective equilibrium (RCE) simulations, building on the findings of \citeA{jeevanjee22}. Using the System of Atmosphere Modeling (SAM) model, we find that finer resolutions yield higher anvil cloud fractions due to larger convective updrafts mass flux and increased mass detrainment at anvil levels. Employing two different microphysics schemes, we illustrate that finer resolution can enhance mass flux through either stronger cloud evaporation or weaker upper-troposphere stability, as the consequence of enhanced horizontal mixing. Moreover, we refine an analytical zero-buoyancy plume model to investigate the effects of adjusting entrainment rate and evaporation rate on vertical atmosphere profiles in a simple theoretical framework. Our solutions of the zero-buoyancy plume model suggest that stronger horizontal mixing can lead to larger convective updraft mass flux, consistent with the analysis in numerical simulations. We also observe the likelihood of atmospheric profiles converging at a grid size of approximately 100m, potentially as a result of converging entrainment rate and mixing strength. These insights have implications for global storm-resolving simulations, implying a possible convergence of high cloud and deep convection properties as the horizontal resolution approaches around 100m.

Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, their development has been stymied by uncertainty surrounding whether or not improved offline performance translates to improved online performance (i.e., when coupled to a large-scale general circulation model (GCM)). A key barrier has been the limited sampling of the online effects of the ML design decisions and tuning due to the complexity of performing large ensembles of hybrid physics-ML climate simulations. Our work examines the coupled behavior of full-physics ML parameterizations using large ensembles of hybrid simulations, totalling 2,970 in our case. With extensive sampling, we statistically confirm that lowering offline error lowers online error (given certain constraints). However, we also reveal that decisions decreasing online error, like removing dropout, can trade off against hybrid model stability and vice versa. Nevertheless, we are able to identify design decisions that yield unambiguous improvements to offline and online performance, namely incorporating memory and training on multiple climates. We also find that converting moisture input from specific to relative humidity enhances online stability and that using a Mean Absolute Error (MAE) loss breaks the aforementioned offline/online error relationship. By enabling rapid online experimentation at scale, we empirically answer previously unresolved questions regarding subgrid ML parameterization design.