Using ARM Cloud Observations to Confront Model Cloud Transitions
ARM radar observations and high-resolution simulations provide insight into transitions from shallow clouds to storm clouds and how to better capture these transitions in numerical models.
Due to their complexity and range of scales, all the equations controlling cloud processes cannot be explicitly represented in global Earth System Models. Therefore, clouds are represented in these models using simplified equations that are often based on benchmark simulations from high-resolution process models. But how reliable are the cloud properties and processes produced by these benchmark models? A new paper explores model transitions of cloud fields from shallow cumulus to deep, precipitating cloud systems in a highly variable meteorological environment observed during the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Midlatitude Continental Convective Clouds Experiment (MC3E) field campaign. The effort uses innovative ARM radar observations from the MC3E field campaign to evaluate a series of high-resolution simulations, which results in an improved understanding of cloud transitions and how to diagnose these transitions in models.
This research provides insights into the difficulties of constraining models from observations, since matching ARM profiling and scanning radar precipitation characteristics alone does not guarantee good simulations. The subtle changes governing cloud and precipitation transitions are not apparent in traditional meteorological observations, and the greatest insight into cloud transitions is found using conditionally sampled cloud properties from the simulations. This finding strongly argues for hybrid observational/modeling approaches. These combined approaches enable a more complete physical understanding of cloud systems by combining observational sampling of time-varying three-dimensional meteorological quantities and cloud properties from the ARM instrument suite, along with detailed representation of cloud microphysical and dynamical processes from numerical models.
Both Earth System Models and high-resolution process models continue to struggle representing boundary-layer clouds and the transitions to deeper cloud types. Furthermore, it is difficult to compare these models with observations in cases of substantial spatial and temporal variability. This difficulty results from a combination of imperfect models run with uncertain estimates of environmental forcing and comparison against incomplete and uncertain observations of cloud properties. A suite of 16 simulations based on the 25 May 2011 event from the Midlatitude Continental Convective Clouds Experiment (MC3E) is employed to better understand how variability or uncertainty in forcing controls precipitation onset and the transition from shallow cumulus to congestus.
Three of the 16 simulations best matching the observed total precipitation and onset time are chosen for deeper analysis. All three simulations exhibit a destabilization over time, which leads to a transition to deeper clouds. However, the evolution of traditional parcel-theory stability metrics are not by themselves able to explain differences among the simulations. Conditionally sampled cloud properties (in particular, mean cloud buoyancy), however, do elicit differences across the simulations, and provides insight to reject one of the simulations on physical grounds. The inability of environmental profiles alone to discern subtle differences among the simulations and the usefulness of conditionally sampled model quantities argue for hybrid observational/modeling approaches.
David B. Mechem
University of Kansas
This research was supported by the Department of Energy Office of Science grants DE-SC0006736, DE-SC0016522, and DE-SC0012704.Â Observational data were obtained from the Atmospheric Radiation Measurement (ARM) User Facility.
Mechem, D.B. and S.E. Giangrande. “The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E.” Journal of Geophysical Research: Atmospheres 123: 3126-3144 (2018). [DOI: