Abstract
Abstract. A cloud system-resolving model (the Weather Research and Forecasting model) with 1 km horizontal grid spacing is used to investigate the response of an idealized supercell storm to increased cloud droplet concentrations associated with polluted conditions. The primary focus is on exploring robustness of simulated aerosol effects in the face of complex process interactions and feedbacks between the cloud microphysics and dynamics. Simulations are run using sixteen different model configurations with various microphysical or thermodynamic processes modified or turned off. Robustness of the storm response to polluted conditions is also explored for each configuration by performing additional simulations with small perturbations to the initial conditions. Differences in the domain-mean accumulated surface precipitation and convective mass flux between polluted and pristine conditions are small for almost all model configurations, with relative differences in each quantity generally less than 15%. Configurations that produce a decrease (increase) in cold pool strength in polluted conditions also tend to simulate a decrease (increase) in surface precipitation and convective mass flux. Combined with an analysis of the dynamical and thermodynamic fields, these results indicate the importance of interactions between microphysics, cold pool evolution, and dynamics along outflow boundaries in explaining the system response. Several model configurations, including the baseline, produce an overall similar storm response (weakening) in polluted conditions despite having different microphysical or thermodynamic processes turned off. With hail initiation turned off or the hail fallspeed-size relation set to that of snow, the model produces an invigoration instead of weakening of the storm in polluted conditions. These results highlight the difficulty of foreseeing impacts of changes to model parameterizations and isolating process interactions that drive the system response to aerosols. Overall, these findings are robust, in a qualitative sense, to small perturbations in the initial conditions. However, there is sensitivity in the magnitude, and in some cases sign, of the storm response to polluted conditions with small perturbations in the temperature of the thermal used to initiate convection (less than ±0.5 K) or the vertical shear of the environmental wind (±5%). It is concluded that reducing uncertainty in simulations of aerosol effects on individual deep convective storms will likely require ensemble methods in addition to continued improvement of model parameterizations.
Highlights
Storm weakening is illustrated by timeseries of domainaveraged accumulated surface precipitation and convective mass flux, MFc, at a height of 8.25 km for PRIS, moderately polluted (MOD), and POLL (Fig. 3)
MFc at a height of 8.25 km is representative of overall changes in convective mass flux between PRIS, MOD, and POLL; MFc is reduced by a similar magnitude in POLL across most of the mid- and upper-troposphere between about 4 and 11 km compared to MOD or PRIS (Fig. 4)
The primary focus was on exploring robustness of simulated aerosol effects in the face of complex process interactions and feedbacks between the cloud microphysics and dynamics
Summary
Numerous studies using cloud system-resolving (or cloudresolving) models have indicated that aerosols can affect the characteristics of moist deep convection through their impact on cloud microphysics (e.g., Wang, 2005; Khain et al, 2005; Lynn et al, 2005; Seifert and Beheng, 2006; van den Heever et al, 2006; Teller and Levin, 2006; Phillips et al, 2007; Tao et al, 2007; Fan et al, 2007; Lee et al, 2008; Khain 2009; Khain and Lynn, 2009; Fan et al, 2009; Noppel et al, 2010; Storer et al, 2010; Ekman et al, 2011; Lee 2011; Lebo and Seinfeld, 2011; hereafter LS11; see Levin and Cotton, 2009 and Tao et al, 2012 for reviews). System complexity leads to rapid, nonlinear growth of small perturbations and solution drift among different realizations of deep convective storms (Hack and Pedretti, 2000; Tan et al, 2004; Zhang et al, 2007; Wang et al, 2012). This can make it difficult to ascertain robustness of aerosol impacts based on single realizations (c.f., Morrison and Grabowski, 2011), which is the approach utilized by most studies
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