Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework

Abstract

Ice microphysics parameterizations in models must make major simplifications relative to observations, typically employing empirical relationships to represent average functional properties of particles. However, previous studies have established that ice particle properties vary even in similar cloud types and thermodynamic environments, and it remains unclear how this so-called "natural variability" impacts simulated deep convection. This uncertainty is addressed by implementing a stochastic framework into the Predicted Particle Properties microphysics scheme in the Weather Research and Forecasting model. The approach stochastically varies the coefficients of the mass-size (m-D) relationship (m=aD b ) for unrimed and partially rimed ice. Using guidance from aircraft in situ measurements obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), the scheme samples from distributions of the prefactor (a) and the exponent (b) of the m-D relationship. Simulations of two MC3E deep convective cases indicate that the stochastic m-D scheme produces considerable variability of anvil cirrus cloud optical depth (τ) distributions, even for the same ice water path (IWP). Thus, the stochastic scheme produces variable cloud radiative forcing that is independent of IWP. This τ-IWP relationship variability is nonexistent using the deterministic m-D ensemble. Additional sensitivity tests are performed in which the fallspeed-size relationship (V=cD d ) is stochastically varied, resulting in variable precipitation amounts and rain rate distributions. Results are presented in the context of satellite and precipitation observations and include comparison with other ensemble configurations using perturbed initial and lateral boundary conditions and small-amplitude noise added to the potential temperature field. Plain Language SummaryRepresenting snowflakes, hail, and other ice crystal types in weather and climate models is a challenging task, but properly doing so is often important in order to produce accurate forecasts. In reality, ice crystals in clouds take on many different shapes and sizes, but current models do not fully account for this variability. Thus, we implement a new method in a weather model that accounts for some of this variability in ice crystal shape and size, guided by observations obtained from aircraft flying through clouds. Our results show that accounting for variable ice crystal size and shape can alter how much sunlight reaches the surface during storms that produce expansive cloud cover. In addition, we find that altering the speed at which ice crystals fall to the ground changes the amount of precipitation accumulated during a precipitation event. These results thus provide guidance on how representing the wide range of different ice crystal shapes and sizes in weather models can impact forecasts of weather and predictions of climate.

Effects of Under-Resolved Convective Dynamics on the Evolution of a Squall Line

“…We evaluate the representation of precipitation structure in cloud system resolving simulations of the 20 May 2011 squallline event during the Mid-Latitude Continental Convective Clouds Experiment (MC3E; Jensen et al 2016) in Oklahoma using radar observations. This case has been previously simulated in several previous studies (Tao et al 2013;Fan et al 2015;Marinescu et al 2016;Saleeby et al 2016;Tao et al 2016;Fan et al 2017;Fridlind et al 2017;Xue et al 2017;Cheng and Zhang 2019;Han et al 2019;Stanford et al 2019). Biases in simulated precipitation structure are then connected to differences in observed and simulated mesoscale circulations.…”

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

“…Bin schemes certainly provide more sophistication in representing microphysical process rates, and they have many more degrees of freedom to evolve cloud and precipitation properties; however, evidence that they actually give consistently better results when compared to available observations is lacking. Given that predictability is inherently limited at cloud and convective scales and there is large case-to-case variability in simulation quality, a large number of individual real cases and/or ensembles may be needed to evaluate microphysics schemes rigorously through comparison with observations (Flack et al, 2019;Stanford et al, 2019). In situ observations, commonly viewed as the "gold standard" for evaluation of bin 10.1029/2019MS001689 Journal of Advances in Modeling Earth Systems microphysics scheme SDs, are also lacking in terms of the number of cases, sufficient coverage spatiotemporally for any individual case, and adequate characterization of sample volumes (e.g., for drizzle-sized drops).…”

Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples

“…Ryzhkov et al (2011) and Putnam et al (2017) compare simulated polarimetric radar signals with radar observations to evaluate microphysics schemes but focus on one or two convective cases. Given the large variability between convective cases, a large number of individual cases is necessary to test whether one scheme consistently outperforms others in reproducing observations (Flack et al, 2019;Stanford et al, 2019). Few studies have evaluated microphysics schemes on such a statistical basis.…”