The formation of ice particles in the Earth's atmosphere strongly affects the properties of clouds and their impact on climate. Despite the importance of ice formation in determining the properties of clouds, the Intergovernmental Panel on Climate Change (IPCC) was unable to assess the impact of atmospheric ice formation in their most recent report because our basic knowledge is insufficient (IPCC, 2007). Part of the problem is the paucity of quantitative information on the ability of various atmospheric aerosol species to initiate ice formation. Ice formation in clouds strongly impacts the properties of clouds and plays a major role in precipitation formation (Yin et al., 2002; Guo et al., 2014).

Saleeby and Cotton (2008) examined the relative impacts of ice forming nuclei (IFN) number concentrations on total snowfall near the Park Range of Colorado. The results showed that the enhancement of IFN displays a modest increasing trend in total precipitation. Rosenfeld and Nirel (1996) suggested that desert dust serving as giant cloud condensation nuclei (GCCN) and IFN can enhance precipitation and thereby influence the effectiveness of cloud seeding experiments. However, Li et al. (2006) studied the impacts of IFN number concentrations on precipitation of sand and dust weather, and the results showed that greater number concentrations of IFN result in the production of many smaller ice crystals, reducing the seeder-feeder mechanism, all of which act to suppress the precipitation. Therefore, the impacts of IFN number concentrations on precipitation are uncertain for different weather conditions.

Van den Heever (2006) found that variations in heterogeneous IFN, cloud condensation nuclei (CCN) and GCCN concentrations can have significant effects on both dynamical and microphysical processes of convective storms that form over Florida. Specifically, cloud-resolving model simulations showed that increasing aerosol concentrations at levels up to 4 km lead to stronger and more numerous updrafts, while enhancing ice nuclei (IN) concentrations produces ice particles at warmer temperatures, generates deeper anvils, and accelerates precipitation processes. Model studies also suggest that higher concentrations of IN cause precipitation to occur earlier in convective clouds and greatly affect their anvil development and structure. Higher concentrations of GCCN and IFN result in greater accumulated surface precipitation initially. By the end of the simulation, however, the accumulated precipitation is the greater for the scheme in which the aerosol concentrations are lower.

Cloud model simulations by Fridlind et al. (2004) and Phillips et al. (2005) emphasized the role of secondary activation by aerosols entrained into cumuli from the middle troposphere on impacting a predominant homogeneous freezing process and thereby determining the ice crystal distributions in some strong convective clouds over the Florida area. These results were supported by detailed analyses of cloud microphysical data (Heymsfield et al., 2005), but this latter study also pointed out that the more dominant homogeneous freezing process in initial cloud turrets impacts ice formation processes at later times or in subsequent cloud turrets.

Anthony et al. (2007) examined the potential effects of Saharan dust intrusions on the microphysical structure and optical properties of anvil-cirrus clouds. Enhancing IFN concentrations had an important impact on the mass fraction of pristine ice but a negligible effect on aggregates.

The effects of IFN on cloud characteristics and precipitation processes have received relatively little attention compared with CCN. This study examines the relative impacts of IFN number concentrations on cloud microstructure and precipitation of a severe storm in northern China.

The Regional Atmospheric Modeling System (RAMS) developed at Colorado State University (Saleeby and Cotton, 2004, 2005) was used in this study. Three two- way interactive nested model grids with horizontal grid spacings of 9, 3, and 1 km were employed (Fig. 1). A total of 42 vertical levels with variable grid spacing were employed, and the model top extended to ~20.5 km above the ground. The minimum grid spacing at the lowest level was 75 m, and the model used vertical grid stretching with a stretch ratio of 1.1 and maximum vertical grid spacing of 750 m. The complicated microphysical processes (condensation, sublimation, nucleation, ice multiplication, melting, collision and coalescence, auto-conversion, and evaporation) are presented. Detailed descriptions of the microphysical processes of the RAMS model can be found in the literature (Walko et al., 1995; Meyers et al., 1997; Cotton et al., 2003; Saleeby and Cotton, 2004; Yang et al., 2011, 2012). The model configuration used for the simulations is summarized in Table 1.

Figure 1

	Figure Option ViewDownloadNew Window
	Figure 1 Locations of the three nested domains. D1, D2, and D3 represent domain 1, domain 2, and domain 3, respectively. The mark “ +” stands for Beijing Haidian Station.

Table 1

Table 1

Table 1 The Regional Atmospheric Modeling System (RAMS) model configuration and options. Model aspect Setting Grid scheme Arakawa C grid (Mesinger and Arakawa, 1976); three domains Horizontal grid spacing domain 1: Δ x = Δ y = 9 km; 178 × 224 grid points domain 2: Δ x = Δ y = 3 km; 208 × 201 grid points domain 3: Δ x = Δ y = 1 km; 250 × 242 grid points Microphysical scheme Two-moment bulk microphysics (Meyers et al., 1997) Convection initiation Kain-Fritsch cumulus parameterization scheme (Kain and Fritsch, 1993) in domain 1; explicit convection in domains 2 and 3.

Table 1 The Regional Atmospheric Modeling System (RAMS) model configuration and options.

Explicit activation of CCN and GCCN were simulated based on Saleeby and Cotton (2004). The CCN and GCCN number concentrations (NCCN and NGCCN, respectively) were initialized horizontally homogeneous with a vertical profile that decreased linearly with heightup to 4 km AGL (above the ground level). Such a distribution of CCN approaches some in situ observations (Van den Heever et al., 2006; Yang et al., 2011, 2012). Initial surface NCCN were specified at 1000 cm^-3 and NGCCN of 10 m^-3. The details are described in the literature (Yang et al., 2011, 2012).

In the simulations, the IFN were represented as a density-weighted decaying concentration profile of N_IFN × ρ ^5.4 with a maximum number concentration of 100 L^-1; the maximum number of IFN to nucleate is dependent upon supersaturation with respect to ice and is given as:

N_IN(Meyers)=N_IFN× ρ ^5.4× exp(12.96× (S_i-0.40)), (1)

N_IN(DeMott)=N_IFN× ρ ^5.4× exp(12.96× (S_i-0.40)) × 10^{(-4.077× Si+0.0975)} (2)

The two formulas above provide the maximum number of ice crystals to nucleate via deposition condensation freezing upon IFN, where N_IN represents the number of pristine ice crystals predicted due to deposition-condensation freezing, N_IFN is IFN number concentrations, S_i is the ice supersaturation with respect to ice predicted by the model, and ρ represents air density. The DeMott formula, Eq. (2), indicates less ice forming activity than the Meyers formula, Eq. (1).

An ensemble of 18-h simulations was conducted for a squall line event that occurred in Beijing on 23 June 2011 (Yang et al., 2015). The model was initialized with real-time National Centers for Environmental Prediction reanalysis data from 0000 UTC 23 June 2011 (1° × 1° spatial resolution, 6-h time interval).

The results demonstrate that the variations in ice parameterization schemes have some different impacts on both microphysical and dynamical characteristics of convective storms in northern China, as well as on the surface precipitation rate.

It is apparent from the results that the updraft structure of the storm is influenced by varying ice parameterization schemes. The updrafts are stronger and more numerous in the Meyers scheme compared with the DeMott scheme, being driven by the latent heat release due to freezing of larger amounts of supercooled water associated with higher IFN concentrations. The microphysical characteristics are affected by the variations of ice parameterization schemes. The cloud is organized better and contains a larger peak of ice condensate with the Meyers scheme. Compared to the DeMott scheme, the mixing ratios of cloud water and hail augment, while graupel mixing ratios reduce, in the Meyers scheme. The surface accumulated precipitation is initially larger in the Meyers scheme. However, at the end of the simulation, the precipitation becomes greater for the simulations with the DeMott scheme. In short, the differences between the two schemes are not obvious, but the DeMott scheme has a relatively more reasonable result.