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A Comparison of Three Different Modeling Strategies for Evaluating Cloud and Radiation Parameterizations

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  • 1 Atmospheric Sciences Group, Pacific Northwest National Laboratory, Richland, Washington
  • | 2 Department of Atmospheric Sciences, University of Washington, Seattle, Washington
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Abstract

Parallel simulations of clouds and radiation fields by a single-column model (SCM), a regional circulation model, and a global circulation model (GCM), each using the same treatment of all physical processes and approximately the same spatial resolution, are compared with observations at the Atmopheric Radiation Measurement Clouds and Radiation Testbed in the southern Great Plains. Significant differences between model simulations are evident for individual cloud systems, but these differences are not systematic, varying from cloud system to cloud system. Several systematic differences between model simulations and observations are identified. These biases are about the same for each model and are much larger than differences between model simulations, suggesting that for some purposes one model can serve as a testbed for parameterizations developed for another.

The role of nudging in the simulations is explored by driving the SCM with large-scale forcing from a GCM simulation. The authors find that nudging of SCM temperature and humidity toward the GCM simulation, using the inverse of the advective timescale for the nudging coefficient, reduces errors in the SCM simulation when artificial errors in the forcing are introduced. The authors also find that nudging of temperature and humidity hides physics errors introduced in the SCM, but only if the physics errors involve processes that directly influence temperature or humidity. Thus, errors in the treatment of nucleation, collision–coalescence, collection, and gravitational settling would not be hidden by nudging, but errors in the treatment of radiative heating, condensation/vapor deposition, evaporation/sublimation, melting, cumulus convection, and subgrid or resolved transport of heat and moisture would be hidden by nudging.

Corresponding author address: Dr. Steven J. Ghan, Pacific Northwest National Laboratory, Battelle Boulevard, P.O. Box 999, Richland, WA 99352.

Email: steve.ghan@pnl.gov

Abstract

Parallel simulations of clouds and radiation fields by a single-column model (SCM), a regional circulation model, and a global circulation model (GCM), each using the same treatment of all physical processes and approximately the same spatial resolution, are compared with observations at the Atmopheric Radiation Measurement Clouds and Radiation Testbed in the southern Great Plains. Significant differences between model simulations are evident for individual cloud systems, but these differences are not systematic, varying from cloud system to cloud system. Several systematic differences between model simulations and observations are identified. These biases are about the same for each model and are much larger than differences between model simulations, suggesting that for some purposes one model can serve as a testbed for parameterizations developed for another.

The role of nudging in the simulations is explored by driving the SCM with large-scale forcing from a GCM simulation. The authors find that nudging of SCM temperature and humidity toward the GCM simulation, using the inverse of the advective timescale for the nudging coefficient, reduces errors in the SCM simulation when artificial errors in the forcing are introduced. The authors also find that nudging of temperature and humidity hides physics errors introduced in the SCM, but only if the physics errors involve processes that directly influence temperature or humidity. Thus, errors in the treatment of nucleation, collision–coalescence, collection, and gravitational settling would not be hidden by nudging, but errors in the treatment of radiative heating, condensation/vapor deposition, evaporation/sublimation, melting, cumulus convection, and subgrid or resolved transport of heat and moisture would be hidden by nudging.

Corresponding author address: Dr. Steven J. Ghan, Pacific Northwest National Laboratory, Battelle Boulevard, P.O. Box 999, Richland, WA 99352.

Email: steve.ghan@pnl.gov

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