Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 17, 9599-9621, 2017
https://doi.org/10.5194/acp-17-9599-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
09 Aug 2017
A ubiquitous ice size bias in simulations of tropical deep convection
McKenna W. Stanford1, Adam Varble1, Ed Zipser1, J. Walter Strapp2, Delphine Leroy3, Alfons Schwarzenboeck3, Rodney Potts4, and Alain Protat4 1Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah, 84112, USA
2Met Analytics, Inc., Aurora, Ontario, Canada
3Université Clermont Auvergne/CNRS, Laboratoire de Météorologie Physique, Clermont-Ferrand, France
4Australian Bureau of Meteorology, Research and Development Branch, Melbourne, Victoria, Australia
Abstract. The High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) joint field campaign produced aircraft retrievals of total condensed water content (TWC), hydrometeor particle size distributions (PSDs), and vertical velocity (w) in high ice water content regions of mature and decaying tropical mesoscale convective systems (MCSs). The resulting dataset is used here to explore causes of the commonly documented high bias in radar reflectivity within cloud-resolving simulations of deep convection. This bias has been linked to overly strong simulated convective updrafts lofting excessive condensate mass but is also modulated by parameterizations of hydrometeor size distributions, single particle properties, species separation, and microphysical processes. Observations are compared with three Weather Research and Forecasting model simulations of an observed MCS using different microphysics parameterizations while controlling for w, TWC, and temperature. Two popular bulk microphysics schemes (Thompson and Morrison) and one bin microphysics scheme (fast spectral bin microphysics) are compared. For temperatures between −10 and −40 °C and TWC  >  1 g m−3, all microphysics schemes produce median mass diameters (MMDs) that are generally larger than observed, and the precipitating ice species that controls this size bias varies by scheme, temperature, and w. Despite a much greater number of samples, all simulations fail to reproduce observed high-TWC conditions ( >  2 g m−3) between −20 and −40 °C in which only a small fraction of condensate mass is found in relatively large particle sizes greater than 1 mm in diameter. Although more mass is distributed to large particle sizes relative to those observed across all schemes when controlling for temperature, w, and TWC, differences with observations are significantly variable between the schemes tested. As a result, this bias is hypothesized to partly result from errors in parameterized hydrometeor PSD and single particle properties, but because it is present in all schemes, it may also partly result from errors in parameterized microphysical processes present in all schemes. Because of these ubiquitous ice size biases, the frequently used microphysical parameterizations evaluated in this study inherently produce a high bias in convective reflectivity for a wide range of temperatures, vertical velocities, and TWCs.

Citation: Stanford, M. W., Varble, A., Zipser, E., Strapp, J. W., Leroy, D., Schwarzenboeck, A., Potts, R., and Protat, A.: A ubiquitous ice size bias in simulations of tropical deep convection, Atmos. Chem. Phys., 17, 9599-9621, https://doi.org/10.5194/acp-17-9599-2017, 2017.
Publications Copernicus
Download
Short summary
Radar reflectivity is a valuable observational tool used to guide numerical weather model improvement. Biases in simulated reflectivity help identify potential errors in physical process and property representation in models. This study uniquely compares simulated and observed tropical convective systems to establish that a commonly documented high bias in radar reflectivity values at least partially results from the production of simulated ice particle sizes that are larger than observed.
Radar reflectivity is a valuable observational tool used to guide numerical weather model...
Share