Investigation of aerosol indirect effects on simulated flash-flood heavy rainfall over Korea
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- Lim, K.S. & Hong, S. Meteorol Atmos Phys (2012) 118: 199. doi:10.1007/s00703-012-0216-6
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This study investigates aerosol indirect effects on the development of heavy rainfall near Seoul, South Korea, on 12 July 2006, focusing on precipitation amount. The impact of the aerosol concentration on simulated precipitation is evaluated by varying the initial cloud condensation nuclei (CCN) number concentration in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme. The simulations are performed under clean, semi-polluted, and polluted conditions. Detailed analysis of the physical processes that are responsible for surface precipitation, including moisture and cloud microphysical budgets shows enhanced ice-phase processes to be the primary driver of increased surface precipitation under the semi-polluted condition. Under the polluted condition, suppressed auto-conversion and the enhanced evaporation of rain cause surface precipitation to decrease. To investigate the role of environmental conditions on precipitation response under different aerosol number concentrations, a set of sensitivity experiments are conducted with a 5 % decrease in relative humidity at the initial time, relative to the base simulations. Results show ice-phase processes having small sensitivity to CCN number concentration, compared with the base simulations. Surface precipitation responds differently to CCN number concentration under the lower humidity initial condition, being greatest under the clean condition, followed by the semi-polluted and polluted conditions.
The aerosol effect on precipitation processes, also known as the cloud lifetime effect (Albrecht 1989), is complex especially for mixed-phase convective clouds. Khain (2009) reviewed previous modeling studies (Lynn et al. 2007; Tao et al. 2007; Lee et al. 2008; Xue et al. 2008; Fan et al. 2007) and observational studies (Givati and Rosenfeld 2004; Yum and Hudson 2002) concerning the cloud lifetime effect. He concluded that much of the disparity among the reported results concerning precipitation response to aerosols can be attributed to cloud type and atmospheric conditions, which varied among the evaluated studies. His principal conclusions are as follows. (1) Aerosols seem to decrease precipitation from isolated deep clouds developing in very dry, unstable atmospheres. (2) Condensate generation increases with increasing humidity. As a result, under high humidity typical of tropical convection, aerosols increase precipitation from deep convective clouds. (3) As aerosol loading increases, evaporation of the precipitation mass increases and leads to the acceleration of downdrafts, which foster the formation of secondary clouds. In short, the aerosol-induced dynamic effect increases condensate generation and, consequently, increases precipitation in the zone of convection.
More recently, several modeling studies have utilized bulk microphysics schemes to research aerosol–cloud precipitation interactions (Seifert and Beheng 2006b; Seifert et al. 2006; Lee et al. 2008; Khain and Lynn 2009). Khain and Lynn (2009) showed that the Thompson bulk scheme (Thompson et al. 2008), which only predicts number concentrations of cloud ice, is much less-sensitive to aerosols than the bin microphysics scheme (Khain et al. 2004; Khain and Lynn 2009). While the bulk scheme predicts a decrease in precipitation in clean air under both low and high humidity, the bin scheme predicts a decrease in precipitation in polluted air under low humidity and an increase in precipitation under high humidity. One weak point in the study of Khain and Lynn (2009) is the treatment of the aerosol effects in the bulk scheme. They modify the gamma shape parameter of the size distribution for cloud water to incorporate aerosol indirect effects. More specifically, for a clean maritime (dirty continental) cloud condensation nuclei (CCN) distribution, a very high (small) value of the gamma shape parameter is introduced. Meanwhile, bulk microphysics schemes have achieved greater flexibility in the description of the particle size distribution by predicting additional moments such as number concentration of cloud water or CCN (Seifert and Beheng 2006b; Phillips et al. 2007; Lim and Hong 2010). Seifert et al. (2006) addressed that both the bin (Khain et al. 2004) and bulk (Seifert and Beheng 2006a, b) modeling approaches show similar results regarding the vertical structure of clouds, updraft velocities and surface precipitation as well as the sensitivity of these parameters to changes in CCN characteristics. Lee et al. (2008) also showed that the bulk microphysics scheme (Phillips et al. 2007) is capable of simulating different precipitation responses in an imposed large-scale environment supporting cloud development. Previous studies showed the usefulness of a bulk microphysics scheme in modeling studies of aerosol indirect effects (Seifert et al. 2006, 2011; Lee et al. 2008; Noppel et al. 2010), but some of studies have been limited to simple configurations tested on idealized testbeds (Seifert et al. 2006; Lee et al. 2008). The two-dimensional cloud-system model affords substantial computational advantages over the three-dimensional (3D) model, but as Phillips and Donner (2007) note, some aspects of cloud system dynamics and microphysics differ between the two- and three-dimensional models.
The Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) microphysics scheme is a double‐moment bulk‐cloud microphysics scheme based on the WRF Single‐Moment Six‐Class (WSM6) microphysics scheme (Lim and Hong 2010). In addition to predicting the mixing ratios of the six water species (water vapor, cloud droplets, cloud ice, snow, rain, and graupel), which is afforded by WSM6, the WDM6 scheme also predicts the number concentrations of cloud droplets and raindrops together with the prognostic variable of CCN number concentration. Thus, in WDM6, the impact of aerosol concentration on simulated precipitation can be evaluated by varying the initial CCN number concentration. Evaluation of the WDM6 microphysics scheme on precipitating moist convection in 3D platforms has confirmed that the skill statistics of precipitation forecasts over Korea from June to August 2008 are generally better with the WDM6 scheme than with the WSM6 scheme (Hong et al. 2010).
The purpose of this study is to investigate aerosol indirect effects on the development of heavy rainfall near Seoul, South Korea, on 12 July 2006, using a bulk-type WDM6 microphysics scheme. This study will allow us to examine the ability of WDM6 scheme to simulate aerosol effects not only on single cloud systems (Lim and Hong 2010; Lim et al. 2011) but also on multi mesoscale convective systems. We also evaluate environmental effects on precipitation response under different aerosol number concentrations. This paper is organized as follows. Section 2 describes the selected 3D moist convection case and the numerical experimental setup. The results and summary are presented in Sects. 3 and 4, respectively.
2 Case description and numerical experimental setup
The numerical experiments were executed using the WRF version 3.2 (Skamarock et al. 2008), which is a fully compressible non-hydrostatic model with an Arakawa-C grid system. The model physics packages include the WDM6 microphysics scheme (Lim and Hong 2010), the simplified Arakawa–Schubert (SAS) deep cumulus scheme (Arakawa and Schubert 1974; Han and Pan 2011), the Yonsei University planetary boundary layer (YSUPBL) (Hong et al. 2006; Hong 2010), a simple cloud-interactive radiation scheme (Dudhia 1989), and rapid radiative transfer model (RRTM) longwave radiation (Mlawer et al. 1997) schemes. In contrast to the previous version, WDM6 microphysics scheme uses a semi-Langrangian advection scheme for falling hydrometeors (Juang and Hong 2010) replacing the Eulerian advection method with sub-time steps. Initial and boundary conditions were derived from National Centers for Environmental Prediction (NCEP) final analysis data (FNL) on 1° × 1° global grids every 6-h (available at http://dss.ucar.edu/datasets/ds083.2/data/), without specific assimilation of observational data. All experiments employ a prognostic sea surface skin temperature scheme (Zeng and Beljaars 2005), revised roughness length formulation (Donelan et al. 2004), and daily SST, forced by the observed SST. The SST data are based on the optimally interpolated sea surface temperature (OISST) on a 1° × 1° grid. The weekly OISST was linearly interpolated in time to derive daily values during the integration period. The experiments were performed for 48 h starting at 0000 UTC 11 July 2006.
Description of experiments conducted in this study
Initial CCN number concentration (cm−3)
The WDM6 microphysics scheme that includes a prognostic variable of CCN number concentration
Same as the CNTR_M
Same as the CNTR_M
Same as the CNTR_M run but with the 5 % reduced relative humidity at the initial time
Same as the DRY_M
Same as the DRY_M
3.1 Aerosol effects on the development of heavy rainfall over Korea
The CNTR_M experiment well captures the localized precipitation core, even though the modeled rainfall amount near Goyang is about 40 mm less than the observed total, and the maximum precipitation core is located south of the observed core (cf. Fig. 1b, a). The observed precipitation maximum near 36°N at the western coast is weakened in the model result, relative to the observed value. Figure 1d shows the time series of simulated rainfall near the central part of the Korean peninsula including Goyang. The model well captures the first rainfall peak over Goyang shown in Fig. 1b; however, the model shows surface precipitation initiating earlier than it was observed. The model accurately reproduces the amount and time of precipitation at the second peak, which was observed at 0600 UTC 12 July.
Figure 4 shows the effects of aerosol on convection developments. In general, the three CNTR experiments, which have different initial CCN number concentrations, show similar features of convection cell evolution. However, the CNTR_C and CNTR_EC runs develop stronger convective cells over the central eastern part of the Korean peninsula at 1900 UTC 11 July. At 0500 UTC 12 July, the second rainfall period, the CNTR_C and CNTR_EC runs simulate intense convective lines over the northwestern part of strong convective echoes extending southwestward from the northeastern part of South Korea to the Yellow Sea, where we marked with the dashed black circles in Fig. 4c, f, i. The CNTR_C run shows a broader area of intense convection than the CNTR_EC run. As shown above, the intensive precipitation events occurred primarily during the first and second periods and the model well captured the observed characteristics of the convective cells in terms of their intensity and location during those periods. Thus, the analysis of the aerosol effects on the development of the convective storm will focus on the period of the first and second peaks ending at 1100 UTC 12 July 2006 in the model.
Domain-averaged surface rainfall amount (mm/35 h) over the entire 3-km domain and the heavy precipitation region for all of the experiments conducted in this study
Over the entire 3-km domain
Over the heavy precipitation region
List of symbols used in cloud microphysical budgets analysis
Production rate for condensation/evaporation of cloud water
kg kg−1 s−1
Production rate for accretion of rain by graupel
kg kg−1 s−1
Production rate for accretion of cloud water by graupel (or snow)
kg kg−1 s−1
Production rate for deposition/sublimation rate of graupel
kg kg−1 s−1
Production rate for melting of graupel to form rain
kg kg−1 s−1
Production rate for accretion of rain by cloud ice
kg kg−1 s−1
Production rate for deposition/sublimation rate of ice
kg kg−1 s−1
Production rate for accretion of snow by rain
kg kg−1 s−1
Production rate for accretion of cloud water by rain
kg kg−1 s−1
Production rate for auto-conversion of cloud water to form rain
kg kg−1 s−1
Production rate for evaporation/condensation rate
kg kg−1 s−1
Production rate for accretion of cloud ice by snow
kg kg−1 s−1
Production rate for accretion of rain by snow
kg kg−1 s−1
Production rate for auto-conversion of cloud ice to form snow
kg kg−1 s−1
Production rate for deposition/sublimation rate of snow
kg kg−1 s−1
Production rate for melting of snow to form cloud water
kg kg−1 s−1
Figure 7 shows the vertical profiles of time-domain-averaged mixing ratios of hydrometeors for the CNTR_M, CNTR_C, and CNTR_EC experiments. The three experiments show significantly different results in terms of the distribution of cloud water and rain. The CNTR_EC experiment simulates the highest mixing ratio of cloud water, followed by the CNTR_C and CNTR_M experiments. In contrast, the highest mixing ratio of rain is shown in the CNTR_M experiment due to the effective auto-conversion process from cloud water to rain. Meanwhile, the CNTR_C and CNTR_EC experiments show similar distributions of ice phases and simulate more ice and snow amounts, compared to the CNTR_M experiment. This is because of the enhanced processes of deposition from water vapor and accretion of cloud water by graupel or snow (Paacw). Paacw is enhanced under the high CCN condition, whereas accretion processes of rain by graupel and ice (Pgacr and Piacr) are suppressed. These differences in mixed-phases cloud processes result in similar graupel amounts among the three experiments, as seen in Fig. 7e.
3.2 Sensitivity experiments under dry environment
As in the CNTR experiments, CCN number concentration affects the amount of surface precipitation even though it does not affect the mesoscale features of sea level and wind fields (see Fig. 9b, c). During the initial stage, the experiments do not significantly differ by CCN number concentration. However, during the mature stage from 0100 UTC to 0600 UTC 12 July, the DRY_M experiment produces the largest amount of surface precipitation, followed by the DRY_C and DRY_EC experiments (Fig. 9d). Note that under low humidity, maximum precipitation does not occur in the semi-polluted condition. More rain accumulated in the DRY_M experiment than in the DRY_C and DRY_EC experiments. Thus, we can see that precipitation responds differently to aerosols under different environmental conditions. This finding is in agreement with those of previous studies, which have mentioned that environmental conditions can affect precipitation response to aerosol concentrations for supercell storms (Khain and Lynn 2009), single convective clouds (Khain et al. 2005, 2008) and squall lines (Tao et al. 2007). Table 2 shows the domain-averaged surface rainfall amount for the DRY experiments. Here, we can notice that precipitation is more strongly affected by a global change in relative humidity than by global changes in aerosol concentrations.
4 Summary and concluding remarks
Aerosol effects on the development of heavy rainfall near Seoul, South Korea, on 12 July 2006 were investigated, focusing on precipitation amount. The impact of the initial aerosol concentration on the development of a convective storm was evaluated by varying the number and mass of aerosols, reflected in the CCN number concentration, within the WDM6 microphysics scheme. The observed 3D heavy rainfall structure, documented in previous observation studies, was well captured by the WDM6 microphysics scheme. Under the polluted condition, our model shows reduced surface precipitation during the initial stage due to the inefficient conversion of cloud water to rain. The experiment showed the greatest amount of surface precipitation during the mature stage of the simulations, under the semi-polluted condition, due to enhanced ice-phase processes. However, surface precipitation was lower during the same stage, under the polluted condition, due to suppressed auto-conversion and enhanced rain evaporation processes. Thus, the greatest amount of surface precipitation was seen in the experiment under semi-polluted air, followed by those under clean and polluted air. It is worth noting that the change in averaged precipitation amount under different aerosol concentrations is not significant under the testbed of 3D mesoscale convective system configured in this study. Aerosol concentration does not change the mesoscale environment, but it does affect cloud microphysical budgets. Recent studies also addressed that there are no significant differences in cumulative precipitation between simulations, which varies aerosol number concentrations in multi-cloud systems. This is known as a buffering mechanism among microphysical processes and various cloud types (Lee 2011; Stevens and Feingold 2009).
It has been shown that aerosol conditions over the Korean peninsula may be considerably different than that in other areas. Yum et al. (2005) observed CCN number concentrations using the the Desert Research Institute (DRI) instantaneous CCN spectrometer at the Korea Global Atmosphere Watch (GAW) Observatory (KGAWO) (30°32′N, 126°19′E) on the west coast of the Korean peninsula from May 1–22, 2004. They classified data into maritime or continental types according to air mass back trajectories. The average maritime and continental CCN concentrations at 1 % supersaturation were 2,406 and 5,292 cm−3, respectively. They reported that maritime CCN number concentration over the Korean peninsula exceeds even the continental values of the other countries observed by special observation projects. Thus, we feel it important to stress that our simulations under the semi-polluted and polluted air conditions show good agreement with the observed PDFs of rainfall intensity.
To evaluate effects of the environment on precipitation response under different aerosol number concentrations, we executed sensitivity experiments in which simulations were initiated with the 5 % reduced humidity. The decrease in humidity was shown to effect dramatic changes in the amount and distribution of precipitation. Notably, the experiment under clean air reproduced the greatest amount of surface precipitation, followed by the semi-polluted and polluted air experiments. The clean air experiment showed the most efficient rainfall production by auto-conversion from cloud water to rain. The amounts of ice were comparable across all experiments. Warm rain processes were responsible for the difference in surface precipitation amounts between the dry environment experiments. Our study indicates that humidity can change the precipitation response under the varying CCN number concentrations for the 3D heavy rainfall case and have greater influence on the characteristics of simulated convective system than aerosol number concentrations.
Meanwhile, Tao et al. (2012) reviewed past efforts and summarized current understanding of aerosol effects on convective precipitation processes and proposed future research directions to achieve a better understanding of aerosol–cloud-precipitation interactions. They pointed out that idealized CCN concentrations and uniform horizontal distribution are the weakness of current cloud-scale modeling in dealing with aerosol information and stressed that inclusion of aerosol chemical composition is the way to improve understating of aerosol–precipitation interaction. In general, a bulk microphysics approach suffers from an inaccuracy in representation of microphysical processes because of its simple treatment of hydrometeor size distribution, compared with a bin approach. Further improvement and evaluation of the WDM6 bulk microphysics scheme through a comparison of bin microphysics scheme and observed microphysics properties, which can be availed from a special observation, should be a next step for an accurate assessment of aerosol indirect effect using bulk microphysics schemes.
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012-0000158). The authors would like to express their gratitude to Jong-Jin Baik, Seong Soo Yum, Jhoon Kim, and Jimy Dudhia for their comments in the review of a thesis from which this work has been derived.