Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Field Campaign Report
General circulation models and downscaled regional models exhibit persistent biases in deep convective initiation location and timing, cloud top height, stratiform area and precipitation fraction, and anvil coverage (e.g., Del Genio 2012, Del Genio et al. 2012, Hohenegger and Stevens 2013, Song et al. 2013). Despite important impacts on the distribution of atmospheric heating, moistening, momentum, and precipitation (e.g., Hartmann et al. 1984, Fritsch et al. 1986, Houze 1989, 2004, Donner et al. 2001, Del Genio and Kovari 2002, Schumacher et al. 2004, Nesbitt et al. 2006, Storelvmo 2012), nearly all climate models fail to represent mesoscale convective organization (Del Genio 2012), while system evolution is not represented at all (Ovchinnikov et al. 2006). Recent advances in cumulus parameterization coupled with increasing model resolution have improved predictions, but even relatively higher-resolution models without parameterized deep convection have some persistent kinematic and microphysical biases (e.g., Blossey et al. 2007, Matsui et al. 2009, Luo et al. 2010, Lang et al. 2011, Varble et al. 2011, Fridlind et al. 2012, Hagos et al. 2014, Varble et al. 2014a-b, Fan et al. 2017, Stanford et al. 2017, Han et al. 2019). To improve representation of convective systems in models requires adequate characterization of their predictability as a function of environmental conditions. Because of the significant sensitivities of deep convective initiation, intensity, lifetime, propagation, and mesoscale convective organization to many factors including multi-scale atmospheric circulations, ambient environmental stability, humidity, wind distributions, and cloud microphysical processes, this characterization relies on comprehensively observing many cases of convective initiation, non-initiation, organization, and non-organization.