Microphysical Piggybacking: Understanding the Coupling Between Cloud Dynamics and Microphysics

Grabowski, W., National Center for Atmospheric Research (NCAR)

Cloud-Aerosol-Precipitation Interactions

Cloud-Aerosol-Precipitation Interactions

Grabowski WW. 2014. "Extracting Microphysical Impacts in Large-Eddy Simulations of Shallow Convection." Journal of the Atmospheric Sciences, 71(12), 10.1175/jas-d-14-0231.1.

Grabowski WW. 2015. "Untangling Microphysical Impacts on Deep Convection Applying a Novel Modeling Methodology." Journal of the Atmospheric Sciences, 72(6), 10.1175/jas-d-14-0307.1.


Rain accumulations in 5-member ensembles of piggybacking simulations of daytime convective development contrasting pristine (PRI) and polluted (POL) CCN conditions. Left panels: evolutions of rain accumulations for drivers (D; solid lines)) and piggybackers (P, dashed lines), respectively. Right panel: difference between driver and piggybacker (D-P) accumulations. Note a larger D-P difference for POL driving compared to PRI driving. This suggests an impact on the cloud dynamics.


Rain accumulations in 5-member ensembles of piggybacking simulations of daytime convective development contrasting pristine (PRI) and polluted (POL) CCN conditions. Left panels: evolutions of rain accumulations for drivers (D; solid lines)) and piggybackers (P, dashed lines), respectively. Right panel: difference between driver and piggybacker (D-P) accumulations. Note a larger D-P difference for POL driving compared to PRI driving. This suggests an impact on the cloud dynamics.

Science

A novel modeling approach was developed to clearly document the impact of cloud microphysics (i.e., the model representation of growth and fallout of cloud and precipitation particles) on cloud simulations. A traditional approach is to perform parallel simulations with different microphysics schemes or scheme parameters. In such parallel simulations, clearly separating physical impacts from merely different flow realizations (i.e., due to Ed Lorenz’s famous “butterfly effect”) is cumbersome. In the new approach, a single simulation is performed with different microphysical schemes or scheme parameters, with one scheme driving the flow, and the other scheme(s) piggybacking on the flow -- that is, following the flow but not affecting it.

Impact

The novel methodology was used to study the impact of cloud condensation nuclei (CCN) on the dynamics of shallow and deep convection. This is a controversial subject because it is virtually impossible to clearly separate the impact of CCN from the impact of different environmental conditions (“the weather”) based on observations. Cloud modeling offers a clear way forward because exactly the same environmental conditions (“the weather”) can be prescribed in the model. The novel methodology makes the evaluation of the impact in cloud modeling straightforward and highly accurate. For instance, it shows a weak dynamical impact and a strong microphysical impact of CCN on deep convection in a cloud model with 2-moment microphysics (Grabowski and Morrison, J. Atmos. Sci., 2016, in review). This methodology can also be used in other areas of high-resolution modeling, such as cloud-seeding assessments or impact studies involving other model parameterizations.

Summary

A novel modeling methodology has been developed to provide a simple and accurate assessment of the impact of cloud microphysical processes on simulated cloud systems, and to separate purely microphysical impacts (e.g., increased surface rainfall due to change in the partitioning of condensed water in cloud and precipitation) from changes in the cloud dynamics (e.g., modified cloud buoyancy and updraft strength).