BNL-205751-2018-JAAM PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE The Challenge of Identifying Controls on Cloud Properties 10.1002/2017JD027457 and Precipitation Onset for Cumulus Congestus Key Points: Sampled During MC3E • The transition from shallow cumulus to congestus is governed by David B. Mechem1 and Scott E. Giangrande2 destabilization of the thermodynamic fi pro les 1Department of Geography and Atmospheric Science, University of Kansas, Lawrence, KS, USA, 2Environmental and Climate • The evolution of traditional stability measures by themselves is not able to Sciences Department, Brookhaven National Laboratory, Upton, NY, USA explain differences in the simulations • Conditionally sampled cloud properties (in particular, mean cloud Abstract Controls on precipitation onset and the transition from shallow cumulus to congestus are buoyancy) are able to show the explored using a suite of 16 large-eddy simulations based on the 25 May 2011 event from the Midlatitude differences among the simulations Continental Convective Clouds Experiment (MC3E). The thermodynamic variables in the model are relaxed at various timescales to observationally constrained temperature and moisture profiles in order to better reproduce the observed behavior of precipitation onset and total precipitation. Three of the simulations Correspondence to: stand out as best matching the precipitation observations and also perform well for independent D. B. Mechem, comparisons of cloud fraction, precipitation area fraction, and evolution of cloud top occurrence. All three [email protected] simulations exhibit a destabilization over time, which leads to a transition to deeper clouds, but the evolution of traditional stability metrics by themselves is not able to explain differences in the simulations. Citation: Conditionally sampled cloud properties (in particular, mean cloud buoyancy), however, do elicit differences Mechem, D. B., & Giangrande, S. E. (2018). The challenge of identifying among the simulations. The inability of environmental profiles alone to discern subtle differences among the controls on cloud properties and preci- simulations and the usefulness of conditionally sampled model quantities argue for hybrid pitation onset for cumulus congestus observational/modeling approaches. These combined approaches enable a more complete physical sampled during MC3E. Journal of Geophysical Research: Atmospheres, 123, understanding of cloud systems by combining observational sampling of time-varying three-dimensional 3126–3144. https://doi.org/10.1002/ meteorological quantities and cloud properties, along with detailed representation of cloud microphysical 2017JD027457 and dynamical processes from numerical models. Received 14 JUL 2017 Accepted 23 FEB 2018 Accepted article online 1 MAR 2018 1. Introduction Published online 23 MAR 2018 Global Climate Models (GCMs) continue to struggle representing boundary layer clouds (stratocumulus and shallow cumulus) (Bony & Dufresne, 2005; Medeiros et al., 2008; Medeiros & Stevens, 2011; Song et al., 2013; Stevens & Bony, 2013; Wyant et al., 2007) and the transitions to deeper cloud types (Dai, 2006; Del Genio & Wu, 2010; Kim et al., 2010; Yuan et al., 2013). Stratocumulus clouds scatter much of the incoming solar radia- tion back to space and therefore exert a cooling effect on the climate system. In the trade latitudes, vertical transports from shallow cumulus balance the large-scale subsidence and set the equilibrium depth of the boundary layer. Over the continent, the deepening of shallow cumulus clouds is an intrinsic part of the diurnal cycle of convection, with shallow cumulus, under the right conditions, deepening into congestus and eventually to cumulonimbus. In the midlatitudes, shallow stratocumulus and cumulus systems are a robust signal of baroclinic waves (Field & Wood, 2007) and, from the framework of a fixed geographic location, are a highly transient component of synoptic wave passage. Difficulty in representing cloud and cloud transitions also extends to high-resolution cloud process models that are used to construct data sets employed for formulating and evaluating GCM cloud dynamical and microphysical process parameterizations (e.g., Grabowski et al., 2006; Stevens et al., 2005; van Zanten et al., 2011). A number of these model intercomparison studies have been conducted over the years, focused mainly on highly idealized model configurations of weakly forced cloud systems (Ackerman et al., 2009; Brown et al., 2002; Siebesma et al., 2003; Stevens et al., 2001, 2005; van Zanten et al., 2011) where cloud evolution is not predominantly governed by synoptic-scale forcing. These large-eddy simulation (LES) models assume horizontally homogeneous initial conditions, and any external forcing is typically applied evenly across the model domain. In effect, this imposes a constraint of statistical homogeneity over the LES domain. Model intercomparisons of cumulus transitions are predominantly forced using the diurnal cycle in surface fl ©2018. American Geophysical Union. uxes while also assuming large-scale homogeneity and hence weak horizontal advective tendencies (e.g., All Rights Reserved. Brown et al., 2002; Grabowski et al., 2006). MECHEM AND GIANGRANDE 3126 Journal of Geophysical Research: Atmospheres 10.1002/2017JD027457 The combination of Lagrangian frameworks and/or weak forcing found in the previous studies negates the importance of advective forcing terms. This is a desirable simplification but one that is typically only justified in quiescent or near-barotropic conditions. Moreover, this assumption is not appropriate for situations having strong shear associated with baroclinic synoptic systems, and it precludes exploration of transient cloud sys- tems associated with substantial synoptic-scale or mesoscale variability. Examples of systems for which this idealized framework is not appropriate include deepening cumulus in the warm sector or postcold frontal stratocumulus and cumulus congestus (e.g., Kollias & Albrecht, 2000; Mechem et al., 2010, 2015). In contrast, an Eulerian modeling framework is ideal for comparisons with fixed sites such as the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site in northern Oklahoma (e.g., Mather & Voyles, 2013). An Eulerian modeling approach requires forcing terms in the form of horizontal advective tendencies of temperature and moisture, in addition to the large-scale vertical motion and surface fluxes required in the Lagrangian approach. These forcings can be supplied by the variational analysis method of Zhang and Lin (1997), which incorporates ARM observations together with numerical weather prediction (NWP) analysis to create observationally constrained forcing tendencies (Xie et al., 2014). This ARM variational analysis product has been used extensively in process-modeling studies (e.g., Khairoutdinov & Randall, 2003; Mechem et al., 2015; Xie et al., 2005; Xu et al., 2002, 2005). LES fields produced under this approach represent a semidiagnostic response to the large-scale forcing and follow an assumption of a separation of scales between the large-scale forcing fields and the cloud response. Temporally, this assumption corresponds to cloud microphysical and dynamical responses occurring on timescales shorter than the forcing. In cases of highly transient forcing (i.e., forcing timescale of the same order as the cloud response), this assumption can be violated and the framework may not be well posed. Recently, Mechem et al. (2015) explored the impact of details in forcing for reproducing the temporal evolution of highly transient cloud system properties using high-resolution LES with size-resolving (bin) microphysics. In particular, they assessed the importance of using time-varying versus steady state forcing configurations and the impact of the spatial scale of the LES forcing. Advanced observational metrics, derived from newer ARM instrumentation including scanning cloud radar, indicate that time-varying forcing leads to model behavior better matched to the observed cloud field evolution. Those observations included cloud fraction, precipitation area fraction, precipitation onset time, probability distribution functions (PDFs) of cloud top height, and total precipitation. Although time-varying forcing configurations in Mechem et al. (2015) produced the best match with observations, including realistically deepening cloud systems and coexisting shallow cumulus and con- gestus, all the simulations exhibited flaws. Specifically, the transition from cumulus to congestus occurred more rapidly than the observed transition, and simulations overestimated areal-averaged precipitation rate and precipitation areal fraction. The better performing runs trended closer to the observed precipitation onset behavior, but even the best simulations produced precipitation too quickly, suggesting an overestima- tion of the large-scale forcing imposed by the NWP model at the heart of the variational analysis procedure. The focus for this study is the further evaluation of the controls on the transition from shallow cumulus to congestus, the onset of precipitation, and subsequent evolution for the 25 May 2011 event. Mechem et al. (2015) indicated that this transition to congestus for the 25 May event appeared tied to the evolution of stability (combination of moisture and temperature) as a control on transition time (e.g., Xie et al., 2014; Zhang & Klein, 2013), which was improved through the use of time-varying