Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework. (6th November 2019) - Stanford, McKenna W.; Morrison, Hugh; Varble, Adam; Berner, Judith; Wu, Wei; McFarquhar, Greg; Milbrandt, Jason
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 = a D 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 = c D 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 Summary: Representing 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. Key Points: An observationally based stochastic framework was implemented into the P3 microphysics scheme to allow for variable mass‐size relationship The scheme shows little precipitation structure variability relative to perturbed initial and boundary conditions in convection simulations Stochastic microphysics produces variability in cloud optical depth (and thus cloud radiative forcing) for a given ice water path