Comparison of convective clouds observed by spaceborne w-band radar and simulated by cloud-resolving atmospheric models
November 15, 2013
Hosted by Dave Randall (advisor), Eric Maloney, Thomas Birner, Chandra Venkatachalam (Electrical and Computer Engineering)
Deep convective clouds (DCCs) play an important role in regulating global climate through vertical mass flux, vertical water transport, and radiation. For general circulation models (GCMs) to simulate the global climate realistically, they must simulate DCCs realistically. GCMs have traditionally used cumulus parameterizations (CPs). Much recent research has shown that multiple persistent unrealistic behaviors in GCMs are related to limitations of CPs. Two alternatives to CPs exist: the global cloud-resolving model (GCRM), and the multiscale modeling framework (MMF). Both can directly simulate the coarser features of DCCs because of their multi-kilometer horizontal resolutions, and can simulate large-scale meteorological processes more realistically than GCMs. However, the question of realistic behavior of simulated DCCs remains. How closely do simulated DCCs resemble observed DCCs?
In this study I examine the behavior of DCCs in the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and Superparameterized Community Atmospheric Model (SP-CAM), the latter with both single-moment and double-moment microphysics. I place particular emphasis on the relationship between cloud vertical structure and convective environment. I also emphasize the transition between shallow clouds and mature DCCs. The spatial domains used are the tropical oceans and the contiguous United States (CONUS), the latter of which produces frequent vigorous convection during the summer. CloudSat is used to observe DCCs, and A-Train and reanalysis data are used to represent the large-scale environment in which the clouds form.
The CloudSat cloud mask and radar reflectivity profiles for CONUS cumuliform clouds (defined as clouds with a base within the planetary boundary layer) during boreal summer are first averaged and compared. Both NICAM and SP-CAM greatly underestimate the vertical growth of cumuliform clouds. Then they are sorted by three large-scale environmental variables: total preciptable water (TPW), surface air temperature (SAT), and 500hPa vertical velocity (W500), representing the dynamical and thermodynamical environment in which the clouds form. The sorted CloudSat profiles are then compared with NICAM and SP-CAM profiles simulated with the Quickbeam CloudSat simulator. Both models have considerable difficulty representing the relationship of SAT and clouds over CONUS. For TPW and W500, shallow clouds transition to DCCs at higher values than observed. This may be an indication of the models’ inability to represent the formation of DCCs in marginal convective environments. NICAM develops tall DCCs in highly favorable environments, but SP-CAM appears to be incapable of developing tall DCCs in almost any environment. The use of double moment microphysics in SP-CAM improves the frequency of deep clouds and their relationship with TPW, but not SAT. Both models underpredict radar reflectivity in the upper cloud of mature DCCs. SP-CAM with single moment microphysics has a particularly unrealistic DCC reflectivity profile, but with double moment microphysics it improves substantially. SP-CAM with double-moment microphysics unexpectedly appears to weaken DCC updraft strength as TPW increases (indicated by decreasing upper cloud reflectivity), but otherwise both NICAM and SP-CAM represent the environment-versus-DCC relationships fairly realistically.