U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Comparing Model Simulations of Arctic Mixed-Phase Clouds: Importance of Ice Size Distribution
Published: March 14, 2014
Posted: August 11, 2014

To improve understanding and model representation of processes in mixed-phase Arctic clouds, a team of researchers, led by U.S. Department of Energy scientists at Pacific Northwest National Laboratory, analyzed simulations of these clouds in 11 different high-resolution, large-eddy simulation (LES) models. Using simulations guided by observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), they explored the processes that controlled cloud structure and evolution in the numerical simulations. In contrast to previous intercomparison studies, all 11 numerical models used the same ice particle properties and a common radiation parameterization. This constrained setup exposed the importance of ice particle size distributions (PSDs) in influencing cloud evolution in the simulations.

Numerical models use two different approaches (bin or bulk) to represent ice PSDs. In the more accurate, but computationally more expensive bin approach, the models predict how the number of particles within a given size range (or bin) changes as the cloud evolves. This approach results in an explicit size distribution that can be used to calculate variables such as ice water path, particle fall speeds, and cloud mass. In the computationally cheaper bulk approach, which is the method used in large-scale climate models, a fixed shape is assumed for PSD and the models predict higher-order moments of the distribution to calculate the necessary cloud variables.

In this study, researchers found a clear separation in liquid water path (LWP) and ice water path (IWP) predicted by models with bin and bulk microphysical treatments. This difference was attributed primarily to the assumed shape of the ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict PSD, bulk schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an under-prediction of IWP by a factor of two in the considered case. Sensitivity tests indicated LWP and IWP are much closer to the bin model simulations when a modified shape factor, which is similar to that predicted by the bin model simulation, is used in the bulk scheme. These results demonstrate the importance of ice PSD representation in determining liquid and ice partitioning and the longevity of mixed-phase clouds. The authors suggest that future work to improve modeling of mixed-phase clouds in climate models should focus on methods for predicting the shape and width of ice PSD for use in bulk schemes.

Reference: Ovchinnikov, M., A. S. Ackerman, A. Avramov, A. Cheng, J. Fan, A. M. Fridlind, S. Ghan, J. Harrington, C. Hoose, A. Korolev, G. M. McFarquhar, H. Morrison, M. Paukert, J. Savre, B. J. Shipway, M. D. Shupe, A. Solomon, and K. Sulia. 2014. “Intercomparison of Large-Eddy Simulations of Arctic Mixed-Phase Clouds: Importance of Ice Size Distribution Assumptions,” Journal of Advances in Modeling Earth Systems 6(1), 223-48. DOI:10.1002/2013MS000282. (Reference link)

Contact: Sally McFarlane, SC-23.1, (301) 903-0943, Rickey Petty, SC-23.1, (301) 903-5548
Topic Areas:

  • Research Area: Atmospheric System Research
  • Facility: DOE ARM User Facility

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)