Interactive comment on “ Aerosol and dynamic effects on the formation and evolution of pyro-clouds ”

Dear Reviewer, We would like to thank you for the valuable and constructive comments/suggestions on our manuscript. We have revised the manuscript accordingly and please find our point-topoint responses below (line numbers refer to the new version of manuscript). In addition, the title of the manuscript is revised to be “Regime dependence of aerosol effects on the formation and evolution of pyro-convective clouds”.


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Abstract
A recent parcel model study (Reutter et al., 2009) showed three deterministic regimes of initial cloud droplet formation, characterized by different ratios of aerosol concentrations (N CN ) to updraft velocities.This analysis, however, did not reveal how these regimes evolve during the subsequent cloud development.To address this issue, we employed the Active Tracer High Resolution Atmospheric Model (ATHAM) with full microphysics and extended the model simulation from the cloud base to the entire column of a single pyro-convective mixed-phase cloud.A series of 2-D simulations (over 1000) were performed over a wide range of N CN and dynamic conditions.The integrated concentration of hydrometeors over the full spatial and temporal scales was used to evaluate the aerosol and dynamic effects.The results show that: (1) the three regimes for cloud condensation nuclei (CCN) activation in the parcel model (namely aerosollimited, updraft-limited, and transitional regimes) still exist within our simulations, but net production of raindrops and frozen particles occurs mostly within the updraft-limited regime.
(2) Generally, elevated aerosols enhance the formation of cloud droplets and frozen particles.The response of raindrops and precipitation to aerosols is more complex and can be either positive or negative as a function of aerosol concentrations.The most negative effect was found for values of N CN of ∼ 1000 to 3000 cm −3 .(3) The involvement of nonlinear (dynamic and microphysical) processes leads to a more complicated and unstable response of clouds to aerosol perturbation compared with the parcel model results.Therefore, conclusions drawn from limited case studies might require caveats regarding their representativeness, and high-resolution sensitivity studies over a wide range of aerosol concentrations and updraft velocities are strongly recommended.

Introduction
Clouds have a considerable effect on the radiation, climate, and water cycle of the Earth (IPCC, 2007).Aerosol-cloud interactions are one of the most uncertain factors influencing the formation, persistence, and ultimate dissipation of clouds (Stevens and Feingold, 2009).The interplay between atmospheric aerosols, cloud water, and precipitation has been studied intensively through cloud-resolving model simulations, analysis of satellite data, and long-term observational data.However, aerosol effects are still associated with significant uncertainty in light of the seemingly contradictory results from different studies.For instance, several studies have indicated that increasing aerosol concentrations could reduce cloud fraction and inhibit cloud formation (Albrecht, 1989;Ackerman et al., 2000;Kaufman et al., 2002;Koren et al., 2004), whereas positive effects of aerosols on the cloud fraction were suggested in other studies (Norris, 2001;Kaufman and Koren, 2006;Grandey et al., 2013).Some rainfall observations have also shown such non-monotonic effects (e.g., Qian et al., 2009).Increasing aerosol concentrations may either significantly suppress the frequency and amount of precipitation (Ackerman et al., 2003(Ackerman et al., , 2004;;Andreae et al., 2004;Altaratz et al., 2008;Rosenfeld et al., 2008;Qian et al., 2009), or enhance the accumulated precipitation (Williams et al., 2002;Lin et al., 2006;Bell et al., 2008).Changing aerosol concentrations have also been found to exert non-monotonic influences on a wide range of cloud properties, such as homogeneous freezing (Kay and Wood, 2008), frozen water particles (Saleeby et al., 2009;Seifert et al., 2012), and convection strength (Fan et al., 2009).These contrasting results indicate that the aerosol effect is a function of many factors, including relative humidity, surface temperature, and wind shear, together with aerosol properties such as chemical composition and size distribution (Levin and Cotton, 2007;Tao et al., 2007;Khain et al., 2008;Rosenfeld et al., 2008;Qian et al., 2009).The assessment of aerosol effects also depends on the observational or analysis scales, because different scales of study result in biases in the quantification of the results (McComiskey and Feingold, 2012).Stevens and Feingold (2009)  studies are of importance, and that further work is needed to characterize the dependence of aerosol-cloud-precipitation interactions on the state of the cloud system and to improve the representation of cloud regimes in models.While aerosol-cloud interactions appear puzzling at regional and global scales, the interplay at the microphysical scale, i.e., cloud condensation nuclei (CCN) activation, has been well characterized.CCN activation can be well predicted by the Köhler theory (Kohler, 1936) and by a series of extended equations (Shulman et al., 1996;Kulmala et al., 1997;Laaksonen et al., 1998).Simplified treatments that reduce the effects of aerosol chemistry on CCN activation to a single parameter have also proven effective; for example, the κ-Köhler equation has been demonstrated to be a practical method in the description of CCN activation and the prediction of CCN number concentrations (Petters and Kreidenweis, 2007;Su et al., 2010;Gunthe et al., 2011).When we upscale the activation of a single aerosol particle to aerosol populations at the cloud base, the impact of aerosols on the number of activated CCN still appears simple and can be well described (Conant et al., 2004;Fountoukis et al., 2007;Reutter et al., 2009;Tessendorf et al., 2013).In-situ aircraft measurements of clouds over marine and continental areas have demonstrated the significant relationship between anthropogenic aerosol concentration and cloud drop number concentration (Conant et al., 2004;Fountoukis et al., 2007).Reutter et al. (2009) implemented observationally-constrained CCN activation microphysics into parcel models, and they found three generic regimes of CCN activation at the cloud base (Fig. 1).The question remains, if CCN activation (microphysical scale) and initial warm cloud formation (air parcel scale) can be described accurately, why is it so difficult to describe the interaction at regional and global scales (Stevens and Feingold, 2009)?In particular, to what extent does complexity arise from the inclusion of other hydrometeor types, such as frozen particles and relevant microphysical processes during subsequent cloud evolution?At which scale do the aerosol-cloud interactions become complex?These questions are the first motivation for this study.Another motivation is to provide a complementary explanation to the ongoing debate over whether clouds are insensitive to aerosol particles, rendering aerosols even "ir-Introduction

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Full relevant" to the climate problem (Karydis et al., 2012;Carslaw et al., 2013;Stevens, 2013).Furthermore, we may be able to distinguish under which conditions cloud formation is aerosol-limited or updraft-limited as discussed in Reutter et al. (2009).If this could be accomplished, it would have the advantage that in future work one could for many purposes neglect aerosol effects on clouds in areas that are usually updraft limited.
Biomass burning generates significant amounts of smoke aerosols, and the fires loft soil particles that contain minerals (Pruppacher and Klett, 1997), both of which could serve as effective CCN and ice nuclei (IN) (Hobbs and Locatelli, 1969;Hobbs and Radke, 1969;Kaufman and Fraser, 1997;Sassen and Khvorostyanov, 2008), thereby affecting the formation of clouds and precipitation.Mostly because of human activities, the risk of wild fires has increased significantly, especially during the last two decades, and they are identified as an important source of atmospheric aerosols (Reid et al., 2005;Luderer et al., 2006;Trentmann et al., 2006;Rosenfeld et al., 2007;Fromm et al., 2008).As an extreme consequence of biomass burning, pyro-clouds feed directly from the smoke and heat released from fires (Andreae et al., 2004;Luderer, 2007) and provide a good example with which to study aerosol-cloud interactions (Reutter et al., 2009).Luderer (2007) systematically simulated the evolution of pyro-cumulonimbus clouds (pyroCb) caused by a forest fire and performed sensitivity studies on the response of convective dynamics to the release of sensible heat by the fire, meteorological conditions, and ambient aerosols.Their results were consistent with observations, and illustrated that fire heating and large-scale meteorological conditions played an important role in the formation and transport of pyroCb.Li et al. (2008) investigated the response of clouds and precipitation to different aerosol concentrations in a convective cloud event with a two-moment bulk microphysical scheme, and found that the aerosol effects on the cloud system varied under different meteorological and aerosol conditions, due to the complicated interactions between cloud microphysics and dynamics.In these previous studies, only a few sensitivity cases were studied.Within our work, we have developed a more complete understanding of these interactions by conducting Introduction

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Full over 1000 simulations, allowing us to study whether the responses of the hydrometeors to aerosol and dynamic forcing have continuity, and the reasons behind this behavior.
In this study, we used the Active Tracer High Resolution Atmospheric Model (ATHAM) to study the impact of aerosols on a single pyro-convective cloud under various dynamic conditions.The single convective clouds represent the up-scaled cases closest to the parcel model simulation.A process scale with resolution of ca. 1 km has been suggested as the appropriate scale at which to characterize processes related to aerosol-cloud interactions (McComiskey and Feingold, 2012).In addition to cloud droplets, precipitable hydrometeors (raindrops, ice, snow, graupel, and hail) were also included in the study and their responses to aerosols examined.For a better understanding of the mechanisms, we employed the process analysis (PA) method, which has been widely utilized to investigate the formation and evolution of gaseous pollutants and particulate matter (Tonse et al., 2008;Yu et al., 2009;Liu et al., 2010).The PA calculates the time-integrated rate of change in the mass or number concentration of each hydrometeor type caused by a particular process, thereby enabling the determination of the relative importance of the major microphysical processes under different dynamic forcing and aerosol conditions.

ATHAM: model and configuration
ATHAM is a non-hydrostatic model that we used to study both cloud formation and evolution in response to changes in updrafts and aerosol particle concentration.ATHAM was designed initially to investigate high-energy plumes in the atmosphere and applied to simulate volcanic eruptions and fire plumes (Herzog, 1998;Oberhuber et al., 1998).The model comprises eight modules: dynamics, turbulence, cloud microphysics, ash aggregation, gas scavenging, radiation, chemistry, and soil (Herzog et al., 1998(Herzog et al., , 2003;;Oberhuber et al., 1998;Graf et al., 1999).Cloud microphysical interactions are Introduction

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Full represented by an extended version of the two-moment scheme developed by Seifert and Beheng (2006), which includes the hail modifications by Blahak (2008), and is able to predict the numbers and mass mixing ratios of six classes of hydrometeors (cloud water, ice crystals, raindrops, snow, graupel, and hail; detailed in Table S1) and water vapor.It has been validated successfully against a comprehensive spectral bin microphysics cloud model (Seifert et al., 2006).The cloud nucleation (CCN activation) module is based on the lookup table derived from parcel model simulations for pyroconvective clouds (Reutter et al., 2009).
As our main purpose was to demonstrate a general pattern of sensitivity of clouds and precipitation to a wide range of aerosol concentrations (N CN ) and updrafts (represented by the intensity of fire forcing, which triggers updraft velocities), we performed two-dimensional simulations rather than the more expensive three-dimensional runs.The fire forcing and meteorological conditions were set up to simulate the Chisholm forest fire (Luderer, 2007;Rosenfeld et al., 2007), which is a well-documented case of pyro-convection.The 2-D simulations were performed at the cross section of the fire front.The simulation domain was set at 85 × 26 km with 110 × 100 grid boxes in the x and z directions.The horizontal grid box size at the center of the x direction was equal to 500 m, and it enlarged towards the lateral boundaries due to the stretched grid (Fig. S1).The vertical grid spacing at the surface and the tropopause was set to 50 and 150 m, respectively.The lowest vertical level in our simulation was placed 766 m above sea level, corresponding to the lowest elevation of the radiosonde data, which is close to the elevation of Chisholm at about 600 m (ASRD, 2001).
The simulations were initialized horizontally homogeneously with radiosonde data from about 200 km south of the fire on 29 May 2001, which is the same as in Luderer (2007) (Fig. S2).Open lateral boundaries were used for the model simulations.The means of wind speed and specific humidity were nudged towards the initial profile at the lateral boundaries.The fire forcing was introduced in the middle grid in the bottom layer of the domain, and its intensity remained constant throughout the simulation of Introduction

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Full each scenario.Each case was run for 3 simulated hours until the clouds were fully developed and had reached steady state.

Aerosol particles and fire forcing
Atmospheric aerosol particles affect cloud formation through two pathways by acting as CCN and IN.Following the previous study of Reutter et al. (2009), we limited the scope of aerosol-cloud interactions to CCN activation only.So, in this study, changes in N CN do not directly influence frozen hydrometeors by providing IN, but do so indirectly through their impact on CCN activation and subsequent processes.
In this study, 1302 cases (31N CN × 42 fire forcing values) were simulated to evaluate the interplay of aerosol concentration and updrafts on the formation of clouds and precipitation.The N CN varied from 200 to 100 000 cm −3 .In each case, N CN was prescribed (distributed uniformly across the modeling domain and kept identical throughout the simulation).A similar treatment and approach has been used in previous studies (Seifert et al., 2012;Reutter et al., 2013).As mentioned above, we used the lookup table of Reutter et al. (2009), which implies the use of their aerosol size distribution as well (log-normal distribution with a geometric mean diameter of 120 nm and a geometric standard deviation of 1.5).For the present study, the aerosol characteristics, such as size distribution, chemical composition, hygroscopicity and mixing state are in fact rather unimportant, compared with the order-of-magnitude changes in the aerosol number concentration (Reutter et al., 2009;Karydis et al., 2012).Therefore, the effects of variations in aerosol characteristics were not considered in our study.In all simulations, clouds were triggered by the fire forcing, which was assumed constant during the simulation.The fire forcing intensity varied from 1 × 10 3 to 3 × 10 5 W m −2 .The correlation between the initial fire forcing and corresponding updraft velocity at the cloud base was probed and is described in Sect.3.1.Introduction

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Process analysis
Cloud properties are subject to several tens of microphysical processes, e.g., cloud droplet nucleation, autoconversion, freezing, condensation, evaporation, etc. (Seifert and Beheng, 2006).Elevated concentrations of hydrometeors can be caused either by an increase in their sources or by a decrease in their sinks.To improve the understanding of the aerosol-cloud interactions, we employed the process analysis (PA) method to quantify the causation of changes in the concentrations of individual hydrometeor classes.
In addition to the standard model output (e.g., time and spatial series of mass and number concentrations of hydrometeors, and meteorological output), our PA method archives additional parameters, i.e., the time rate of change of hydrometeors due to individual microphysical processes.Table A1 summarizes all the acronyms and their corresponding microphysical processes.the input fire forcing (range of 1×10 3 to 3×10 5 W m −2 , N CN = 1×10 3 cm −3 ) in Fig. S3b.

Fire forcing and updraft velocity
The shaded area indicates the variability of estimation over each simulation period.
According to the figure,w at cloud base varies monotonically from 1.8 to 27 m s −1 as fire forcing increases from 1 × 10 3 to 3 × 10 5 W m −2 .The positive relationship suggests that fire forcing could be a good indicator of vertical velocity.

Sensitivity regimes for hydrometeors and precipitation
In this section, we show the modeled dependency of various hydrometeors on N CN and fire forcing (FF).Note here only the characteristics of dependency are presented, while the underlying mechanisms will be discussed and interpreted in more detail in Sect.3.3.For an individual hydrometeor type, the averaged concentrations (over the entire domain and simulation period) were used as metrics in our evaluation, and the condensed water reaching the surface was used as a metric for precipitation.

Cloud droplets
To investigate the sensitivity of an individual hydrometeor to changes in N CN and FF, we adopted the definition of relative sensitivity RS Y (X ) (of one variable Y against the variable X ) as In this study, X is the factor affecting cloud formation, i.e., N CN and FF, and Y is the mass or number concentration of each hydrometeor type (cloud droplets, raindrops, as well as frozen particles).By using a natural logarithmic calculation of the variables (i.e., X , Y ), the percentage change of an individual parameter relative to its magnitude could be reflected better.This logarithmic sensitivity evaluation has been applied commonly in the assessment of aerosol-cloud interactions (Feingold, 2003;McFiggans Introduction

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Figure 2a shows the dependence of cloud water droplets (N CD ) on N CN and FF.The shape of the isolines is generally consistent with the regime designations reported by Reutter et al. (2009).Following Reutter et al. (2009), a value of the RS(N CN ) to RS(FF) ratio of 4 or 1/4 was taken as the threshold value to distinguish different regimes (the same criteria were employed for rainwater and frozen water content).Red dashed lines in Fig. 2a indicate the borders between different regimes.This resulted in an aerosollimited regime in the upper left sector of the panel (N CD is sensitive mainly to N CN and is insensitive to fire forcing), an updraft-limited regime in the lower right sector of the panel (N CD displays a linear dependence on FF and a very weak dependence on N CN ), and the transitional regime along the ridge of the isopleth (FF and N CN play comparable roles in the change of N CD ).The regimes of Reutter et al. (2009) are derived from simulations of the cloud parcel model of CCN activation at the cloud base.Our results demonstrate that the general regimes for CCN activation still prevail, even when considering full microphysics and the larger temporal and spatial scales of a single pyro-convective cloud system.Figure 2c and 2d demonstrate the changes of normalized number concentrations (relative to the maximum value) as a function of aerosols and fire forcing, respectively.High sensitivities were found for low conditions of N CN and FF.As N CN or FF increases, their impact becomes weaker (Fig. 3a and b).The reduced sensitivity of cloud droplets to aerosols can be explained by the buffering effect of the cloud system, so that the response of the cloud system to aerosols is much smaller than would have been expected had internal interactions not been considered (Stevens and Feingold, 2009).
Compared with N CD , the cloud mass concentration (M CD ) is less sensitive to N CN , and there is hardly an aerosol-limited regime in the contour plot for M CD (Figs. average 10 times higher than that of M CD , independent of the intensity of the FF.As N CN increases, M CD becomes insensitive to the change of N CN .This strongly suggests that when we evaluate the cloud responses to the changes in the ambient aerosol particles for global models or satellite data, we should focus more on the aerosol effect on cloud droplet number concentration, rather than on the liquid water path.However, the responses of both the normalized N CD and M CD to changes in FF (Fig. 2d and f, respectively) appear similar.Averaged RS(FF) values over simulated FF ranges for N CD (0.60) and M CD (0.50) are commensurate (Fig. 3b and 3d, respectively), which implies that both the number and mass concentrations of cloud droplets are very sensitive to updrafts.

Raindrops
The response of the raindrop number concentration (N RD ) is more complex.When FF is weak (< 20 000 W m −2 ), the aerosol effect could be either positive with low N CN (< 2000 cm −3 ) or negative with high N CN (> 2000 cm −3 ).When FF is strong (> 20 000 W m −2 ), N RD decreases monotonically as N CN increases.The effect of FF on N RD is non-monotonic (Figs.4a and d and 5b).Under low aerosol conditions, FF plays the most positive role at a value of about 6000 W m −2 , above which the FF effect becomes negative.However, under high aerosol conditions, there are two regions (FF = 3000 and 15 000 W m −2 ) in which the FF effect is especially significant.As shown in Fig. 4, FF exhibits positive effects on raindrop formation (M RD ), whereas the aerosol shows a slightly positive effect with low N CN , but a negative effect with large N CN .The normalized mass concentrations (M RD ) relative to the maximum value as a function of aerosols and FF are also displayed in Fig. 4e and f.The influence of FF is much more significant than that of N CN in most cases.For example, the upper left corner (an aerosol-limited regime for N CD ) becomes a transitional regime for M RD with RS (FF) of 0.1 and RS (N CN ) of −0.06 (Fig. 5).High RS(N CN ) values of M RD were found at low N CN conditions, and this decreases as N CN increases (Fig. 5c).The N CN Introduction

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Full  −3 ).In contrast to cloud droplet number concentration, an aerosollimited regime for M RD hardly exists in our simulations (Fig. 4b).The response of the raindrops to aerosols is much weaker than the response of cloud droplets to aerosols.This finding is consistent with the idea of clouds acting as a buffered system formulated by Stevens and Feingold (2009).However, the feedbacks that were possibly introduced between the cloud macrophysics and microphysics, and the contribution of these two effects (microphysical and macrophysical buffers) still need further investigation.

Frozen water contents
Within our microphysical scheme, frozen water contents are grouped into four main classes: ice crystals, snow, graupel, and hail (Seifert and Beheng, 2006).Aerosols exert influence on the frozen water contents via the process of ice nucleation (in), but the processes that convert between the different hydrometeor classes and water vapor play a greater role in changing the concentrations of frozen particles, especially the processes of cloud freezing to form ice (cfi) and the vapor depositional growth of ice and snow (vdi and vds respectively).Figure S4 illustrates the percentage mass contributions of the individual frozen hydrometeor classes to the total frozen mass.The percentages of each hydrometeor are calculated based on average values over the entire simulation period.Generally, greater concentrations of aerosol result in more snow and less graupel.This is in agreement with previous studies on convective clouds (Seifert et al., 2012;Lee and Feingold, 2013), and can be explained by the suppression of the warm rain processes under high aerosol condition.High N CN delays the conversion of the cloud water to form raindrops, so that more cloud water content can ascend to altitudes with sub-zero temperatures, hence freeze into small frozen particles.The percentage of ice crystals does not change much, contributing approximately 20 % on average.The dependence of total frozen particles on FF and N CN is summarized in Fig. 6.The FF and N CN show positive effects for both the number and mass concentrations of the Introduction

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Full frozen water particles (N FP and M FP , respectively).High RS(N CN ) and RS(FF) values were found at low N CN and FF conditions (Fig. 7), respectively.As N CN or FF increases, their impact becomes weaker, as indicated by a decreasing RS.According to the ratio of RS(FF)/RS(N CN ), both N FP and M FP are within the updraft-limited regime.Again, smaller RS(N CN ) values for M FP compared with N CD illustrate the weaker impact of N CN on the production of frozen particles.

Precipitation rate
Surface precipitation rate is a key factor in climate and hydrological processes.Many field measurements, remote sensing studies, and modeling simulations have attempted to evaluate the magnitude of aerosol-induced effects on the surface rainfall rate (Rosenfeld, 1999(Rosenfeld, , 2000;;Tao et al., 2007;Li et al., 2008;Sorooshian et al., 2009).The response of averaged surface precipitation rate (over 3 h simulations) to FF and N CN is shown in Fig. 8.The FF has a positive effect on the precipitation, and RS(FF) shows a decreasing trend as FF increases (Fig. 9b).The effect of N CN is more complex.Both positive and negative RS (N CN ) were found in our study.There are generally two different regimes: a precipitation-enhanced regime and a precipitation-suppressed regime.In the precipitation-enhanced regime (N CN <∼ 1000 cm −3 ), N CN has a positive effect on the precipitation rate, and increasing N CN will reduce RS (N CN ) (Fig. 9a).In the precipitation-suppressed regime, aerosols start to reduce the precipitation corresponding to a negative RS(N CN ).Within the precipitation-suppressed regime, there is also an extreme RS(N CN ) at a value of N CN of a few thousand particles per cm 3 .In the literature, both positive (Tao et al., 2007) and negative effects (Altaratz et al., 2008) of aerosols have been reported in previous case studies.Our simulations suggest that this apparently contradictory phenomenon might be the expression of the same physical processes under different aerosol and dynamic conditions.Regarding the temporal evolution, low N CN results in earlier rainfall (Fig. S5), which is consistent with current understanding, observations (e.g., Rosenfeld, 1999Rosenfeld, , 2000)), and modeling evidence (e.g., the convective cumulus cloud study by Introduction

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Full Li et al., 2008).Note that the general relationship between precipitation and aerosols described in this study is based on simulations over a period of 3 h.Simulations for a longer period should be carried out in future studies to investigate the influence of aerosols on precipitation over longer time scales.

Clouds
Figure 10 shows the temporal evolution of horizontally-averaged M CD under these four pairs of FF and N CN conditions.It is clear that increasing N CN leads to enhanced formation of cloud droplets; stronger updrafts not only result in more M CD , but also tend to prolong the lifetime of cloud droplets.
Figure 11 summarizes the microphysical processes that act as the main sources (positive values) and sinks (negative values) for cloud droplets.For N CD , the dominant source term is the cloud nucleation (CCN activation) process, in which aerosols are activated under supersaturated water vapor and form cloud droplets.As cloud nucleation happens mostly at the cloud base and so is not strongly affected by cloud dynamical feedbacks, the response of N CD shows similar regimes to cloud parcel models (Reutter et al., 2009).To help explain the regime designation, we divide N CD into two factors: an ambient aerosol number concentration (N CN ) and an activated fraction (N CD /N CN ).Introduction

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Full Given the aerosol size distributions, the N CD /N CN ratio is determined approximately by the critical activation diameter (D c ) above which the aerosols can be activated into cloud droplets.The D c is a function of ambient supersaturation.Stronger updrafts result in higher supersaturation, smaller D c and hence, larger N CD /N CN ratios.Under high updraft conditions, N CD /N CN is already close to unity.A further increase in the updraft velocity will still change the supersaturation and D c , but it will not significantly influence the N CD /N CN ratios and N CD .In this case, N CD is approximately proportional to N CN .Under weak updrafts, the N CD /N CN ratio is sensitive to ambient supersaturations.In this case, a larger supersaturation induced by stronger updrafts can effectively change the N CD /N CN ratio and thus N CD is sensitive to the updraft velocity.On the other hand, the stronger dependence of N CD /N CN on the supersaturation also changes the role of aerosols.As more aerosols reduce supersaturation, increasing N CN tends to reduce the activated fraction, N CD /N CN .Taking N CN = 60 000 cm −3 (FF = 2000 W m −2 ), for example, a 10 % increase in N CN causes a 4 % decrease in N CD /N CN , whereas a 10 % decrease in N CN leads to an 8 % increase in N CD /N CN .The impact of changing N CN on the N CD /N CN ratio counteracts partly or mostly the positive effect of N CN on cloud droplet formation.
The changes of M CD are influenced mainly by the following sources: (1) the condensation of water vapor on the present cloud droplets (vdc) and (2) the cloud nucleation process (cn), and by the following sinks: (3) the autoconversion (au) and (4) the accretion of cloud droplets (ac), and (5) the freezing of cloud droplets to form cloud ice (cfi), which includes heterogeneous (Seifert and Beheng, 2006) and homogeneous freezing processes (Jeffery and Austin, 1997;Cotton and Field, 2002).Concerning their relative contributions, depositional growth of cloud droplets (vdc) is the major source at low aerosol concentrations.As N CN increases, cloud nucleation (cn) becomes more significant and can even outweigh vdc at high aerosol concentrations.The processes of autoconversion (au) and accretion (ac) are the major sinks at weak updrafts.As FF in-Introduction

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Full creases, the conversion to frozen particles, especially to ice (the cfi process), becomes increasingly important.Concerning the absolute contribution, increasing FF enhances the change rate of the conversion of water vapor to the condensed phase (R vdc and R cn ), whereas increasing N CN tends to reduce it.The FF effect is straightforward and the aerosol effect here is complicated.Aerosols can enhance both the condensation and the evaporation of water vapor from the cloud droplets due to the increase in the surface-to-volume ratio of cloud droplets; condensation increases M CD and evaporation reduces M CD .In our study, the net effects are negative (which is expressed in the sign of ∆R vdc /∆N CN ) and the positive effect of N CN on cn is insufficient to change the overall trend.A similar result has been reported by Khain et al. (2005) for deep convective clouds.They found that high CCN concentrations led to both greater heating and cooling, and that the net convective heating became smaller as CCN increased.

Rain
Figure 12 exhibits the temporal evolution of the horizontally-integrated M RD .It confirms the result of Fig. 4 that stronger updrafts tend to increase the raindrop concentration, whereas increased aerosol concentration reduces it.This dependence appears simple and similar under different conditions.However, the underlying mechanisms for different scenarios are quite complex.
Dynamic conditions strongly influence the pathways of rain formation and dissipation.For weak updraft cases, rain droplets (e.g., Fig. 13) are produced mainly from autoconversion (au) and accretion (ac), and partly from melted snows (smer) or graupel (gmer).Under this condition, raindrops may appear at altitudes as high as 5-7 km (e.g., Fig. 12a).For high updraft cases, strong updrafts deliver cloud droplets to higher freezing altitudes (Fig. 10).The cloud droplets then turn directly into frozen particles (cloud → ice crystals), without formation of raindrops as an intermediate stage (cloud → rain → larger frozen particles; Fig. 15).Most raindrops are formed from melted frozen droplets and consequently, they appear below ∼ 4 km (Fig. 12b).The weaker Introduction

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Full cloud → rain conversion with higher updrafts also influences the conversion of rain to frozen particles, and is the reason why the rrg process (riming of raindrops to form graupel) under HULA becomes relatively less important than it is under LULA.The aerosols also modify the pathways of rain formation.Taking weak updraft cases, for example, the accretion process (ac) dominates the cloud → rain conversion under low aerosol concentrations, but is replaced by autoconversion (au) under high aerosol concentrations (Fig. 13).The reason for this is that au is the process that initializes rain formation.Once rain embryos are produced, accretion of cloud droplets by raindrops is triggered and becomes the dominant process of rainwater production, as observed for shallow clouds (Stevens and Seifert, 2008) and stratiform clouds (Wood, 2005).
High aerosol loading reduces au, inhibiting the initialization of rain and the following accretion processes at the early stage (0-100 min).In this case, cloud droplets are prone to be converted to ice crystals rather than to raindrops through ac (Figs. 13 and 15).Melted frozen particles are also a major source of raindrops.Under low aerosol concentrations, most of them form from melted graupel particles, whereas under high aerosol concentrations, they are converted mainly from snowflakes.This is consistent with the aerosol impact on the relative abundance of frozen particles shown in Fig. S4.A higher aerosol concentration leads to a higher fraction of smaller frozen particles (ice crystals and snowflakes).The main difference between low and high updrafts is that cloud conversion is the main source in the former case, whereas in the latter case, melted graupel/snow particles become the main contributors.

Frozen water content
The time evolutions of frozen water content in Fig. 14 are the manifestations of the aerosol and updraft effects.They confirm the results in Fig. 6 that both N CN and FF exert positive effects on the generation of frozen particles, and that the influence of FF is the more important factor.
As shown in Fig. 15, the effect of FF is straightforward, boosting vapor deposition (vdi) and cloud droplet freezing on ice (cfi).The vdi is always the most important path-Introduction

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Full way for the formation of frozen particles in our simulations, whereas riming of raindrops to form graupel (rrg) and cfi show comparable contributions in the LULA and HULA cases, respectively.Over a wide range of aerosol concentrations and updraft velocities, our results have extended and generalized the results of Yin et al. (2005), in which vdi and cfi were suggested as the dominant processes controlling the formation of ice crystals in individual mixed-phase convective clouds.Although snow is the dominant constituent of frozen particle mass (Fig. S4), the deposition of vapor on ice (vdi) rather than on snow is the major pathway for frozen particles.The increase of snow mass is mostly caused by collecting of ice (ics) and ice self-collection (coagulation of ice particles, iscs), which are internal conversions not counted as either a source or a sink of frozen water content.Increasing FF enhances the upward transport of water vapor and liquid water to higher altitudes where frozen particles can be formed effectively through vdi and cfi.On the other hand, stronger FF reduces the residence time of cloud droplets in the warm environment (to form raindrops), which could explain the attenuation of rrg in the HULA case.
Positive effects of aerosols on the frozen water content have been demonstrated in Sect.3.2.4.As shown in Fig. 15, such positive effects are achieved through the enhancement of the vdi process.The depositional growth rate R vdi is a function of the number concentration (N ice ) and size (D ice ) of ice, together with the ambient supersaturation over ice (S ice ).In our simulations, the averaged S ice and D ice are not sensitive to the aerosol disturbance; it is the N ice that has been increased significantly because of elevated aerosol concentrations.Higher N ice provides a larger surface area for water vapor deposition on the existing ice crystals and increases R vdi .Lee and Penner (2010) have suggested similar mechanisms for cirrus clouds.Introduction

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Conclusions
In this study, the roles of fire forcing (FF, which triggers updraft velocities) and aerosol number concentration (N CN ) on the formation and evolution of pyro-convective clouds have been studied in detail and the results are summarized as follows: 1.Both increasing aerosols and FF enhanced the formation of cloud droplets.
There are three distinct regimes for the cloud number concentration: an updraftlimited regime (high RS(FF)/RS(N CN ) ratio), an aerosol-limited regime (low RS(FF)/RS(N CN ) ratio), and a transitional regime (intermediate RS(FF)/RS(N CN ) ratio), which agrees well with the regimes derived from a parcel model (Reutter et al., 2009).The cloud mass concentration is less sensitive to aerosols, and there are two regimes for mass concentration: an updraft-limited regime, and a transitional regime.
2. The production of rain water content (i.e., M RD ) was positively correlated with updrafts, and the aerosol effect could be either slightly positive with low N CN or negative with large N CN .The N CN had mostly negative effects on M CD under intermediate N CN conditions (N CN of several 1000 cm −3 ).M RD was generally within an updraft-limited regime, i.e., M RD was very sensitive to changes in updrafts, but insensitive to aerosol concentrations (RS(FF)/RS(N CN ) > 4).The aerosol and FF effects on raindrop number concentrations (N RD ) are quite complicated; both of them could have either positive or negative effects on the N RD .
3. Both updrafts and aerosols showed positive effects on the domain-averaged number and mass concentrations of frozen particles (N FP and M FP respectively).N FP and M FP were also within the updraft-limited regime, which is characterized by large RS(FF)/RS(N CN ) ratio.In this regime, N FP and M FP were directly proportional to fire forcing, and independent of aerosols.Introduction

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Full 5.In addition, when N CN and FF became too large, their impact became weaker, as indicated by a decreasing RS.
The PA provided further insight into the mechanisms of aerosol-cloud interactions.By evaluating the contribution of the relevant microphysical processes to the formation of an individual hydrometeor, the PA revealed the dominant factors responsible for the changes in hydrometeor number and mass.(1) Cloud nucleation (cn) initializes cloud droplet formation and is the major factor that controls the number concentration of cloud droplets.As expected, the increase in cloud droplet mass can be attributed mostly to the condensational growth (vdc).( 2) Under weak FF, autoconversion (au) and accretion (ac) are the main sources of rain droplets.Under strong FF, the major source is the melting of frozen particles.(3) For the frozen content, water vapor deposition on existing ice crystals (vdi) is the most important contributor.In addition to CCN activation, the PA also highlights the importance of other microphysical processes in regulating cloud evolution, which is worthy of further scrutiny.
While the general trend is clear, the inclusion of nonlinear (dynamic and microphysical) processes leads to a complex and unstable response of clouds to aerosol perturbations.This applies to the response of all hydrometeors and precipitation, as indicated by the large standard deviation of RS in Figs. 3,5,and 7.This should also hold when variations in other parameters (e.g., meteorological conditions) are introduced.Compared with our results, the RS derived from cloud parcel modeling is much smoother (Fig. 8 in Reutter et al., 2009).The difference is probably caused by complex interactions between cloud microphysics and dynamics (Khain et al., 2008;Fan et al., 2009).These highly nonlinear processes result in a more unstable and chaotic response of cloud evolution to aerosol and dynamic perturbations.Because of this non-linearity, sensitivities of clouds based on limited case studies may require caveats, because they may not be as representative as expected, and so cannot safely be extrapolated to conditions outside of the range explored.To understand better the role of aerosols in cloud formation, we recommend high-resolution ensemble sensitivity studies over a wide range of dynamic and aerosol conditions.7797 Introduction

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Full General current understanding and global modelling studies suggest that for cloud droplet number concentration, the updraft-limited regime may be more characteristic of continental clouds, while the aerosol-limited regime may be more characteristic of marine clouds (e.g., Karydis et al., 2012), suggesting that aerosol effects are generally more important for the marine environment.For this case study, then, we conclude that aerosol effects on cloud droplet number concentrations and thus cloud radiative properties (first indirect effect) are likely more important than effects on precipitation and thus cloud lifetime (second indirect effect), since precipitation is far less sensitive to aerosol number concentrations than to updraft velocity.This is in agreement with other studies (e.g., Seifert et al., 2012).However, it must still be determined whether this conclusion applies to other cloud types and over longer time scales.
In future work, we intend to extend the current studies to: (1) include other types of clouds with other meteorological or atmospheric conditions; (2) investigate the cloud response over longer timescales, as different observational scales could introduce biases in the quantification of aerosol effects on clouds (McComiskey and Feingold, 2012); and (3) evaluate the relative contribution of microphysical and dynamic effects to cloud buffering effects (Stevens and Feingold, 2009;Seifert et al., 2012).

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Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | also suggested that regime-centered Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Fire forcing does not affect the cloud activation of aerosols directly, but it can affect activation indirectly by triggering strong updraft velocities.Updrafts are of importance in the formation of clouds and precipitation for redistributing energy and moisture.In pyro-convective clouds, the updraft velocities range from ca. 0.25 to 20 m s −1 (Reutter et al., 2009) and are far greater than the typical magnitudes of updrafts of 1-10 cm s −1 (Tonttila et al., 2011).The probability distribution function of vertical velocities (w) at cloud base layer under different fire forcing conditions is shown in Fig. S3a.The velocity on top of the input fire forcing is usually the largest, and decreases towards the lateral sides.As the characteristic velocities, the maximum velocity at cloud base in Fig. S3a, are plotted against Introduction Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | et al.
2b and 3c).There are only two regimes indicated by the red dashed line in Fig. 2b: an updraftlimited regime in the lower right sector of the panel, and the transitional regime in the upper sector (an aerosol-and updraft-sensitive regime).The RS(N CN ) of N CD is on Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | exhibits the most negative effects on M RD under intermediate N CN conditions (N CN of several 1000 cm Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .Fig. 1 .
Fig. 1.Overview of the research approaches on multi-scale cloud initialization and development.

Fig. 15 .
Fig. 15.Same as Fig. 11 but for the frozen particles.The acronyms indicate rrg/h: riming of rain to form graupel/hail; g/h/smer: graupel/hail/snow multiplication to form ice crystals; c/rfi: freezing of cloud water/raindrops to form ice crystals; rfh/g: freezing of raindrops to form hail/graupel; gep: evaporation of graupel; vdi/g/s: depositional growth of ice crystals/graupel/snow.

Fig. 15 .
Fig. 15.Same as Fig. 11 but for the frozen particles.The acronyms indicate rrg/h: riming of rain to form graupel/hail; g/h/smer: graupel/hail/snow multiplication to form ice crystals; c/rfi: freezing of cloud water/raindrops to form ice crystals; rfh/g: freezing of raindrops to form hail/graupel; gep: evaporation of graupel; vdi/g/s: depositional growth of ice crystals/graupel/snow.