Using a modeling framework, this study investigates how a pyrocumulonimbus (pyroCb) event influences water vapor concentrations and cirrus-cloud properties near the tropopause, specifically focusing on how fire-produced aerosols affect this role. Results from a case study show that when observed fire intensity is high, there is an insignificant impact of fire-produced aerosols on the development of the pyroCb and associated changes in water vapor and cirrus clouds near the tropopause. However, as fire intensity weakens, effects of those aerosols on microphysical variables and processes such as droplet size and autoconversion increase. Due to this, aerosol-induced invigoration of convection is significant for pyroCb with weak-intensity fires and associated weak surface heat fluxes. This leads to a situation where there is a greater aerosol effect on the transport of water vapor to the upper troposphere and the production of cirrus clouds with weak-intensity fires, whereas this effect is muted with strong-intensity fires.
Recent studies (e.g., Pumphrey et al., 2011; Kablick et al., 2018) have shown that pyrocumulonimbus (pyroCb) may transport significant amounts of water vapor to the upper troposphere and the lower stratosphere (UTLS) and can possibly have an impact on seasonal UTLS water vapor budgets. Any change in water vapor in the UTLS has an exceptionally strong influence on the global radiation budget and thus Earth's climate (Solomon et al., 2010). PyroCbs develop cirrus clouds associated with overshooting convective tops that reach the UTLS. Changes in cirrus clouds in the UTLS are known to have a strong influence on the global radiation budget (Solomon et al., 2010). The level of our understanding of impacts of pyroCbs on water vapor and cirrus clouds in the UTLS on a global scale is very low, and studies have been conducted to improve this understanding (Fromm et al., 2010). However, this paper does not focus on these pyroCb impacts at the global scale. Instead, this paper aims to gain a process-level understanding of mechanisms that control local impacts of individual pyroCbs on water vapor and cirrus clouds in the UTLS. The examination of these mechanisms can provide useful information to parameterize interactions among pyroCbs, water vapor and cirrus clouds in climate models.
PyroCbs initiate over a fire, and the large surface energy release mainly through fire-induced latent- and sensible-heat fluxes at and near the surface affects the dynamic, thermodynamic and microphysical development of pyroCbs (Fromm et al., 2010; Peterson et al., 2017). However, questions remain about what role the large concentration of cloud condensation nuclei (CCN) contained in smoke has on the vertical development and microphysical properties of pyroCbs. Studies (e.g., Koren et al., 2005, 2008; Rosenfeld et al., 2008; Storer et al., 2010; Tao et al., 2012) showed that aerosols affected cumulonimbus clouds, thus raising the possibility that fire-generated aerosols may affect pyroCb development. As an example of aerosol impacts on cumulonimbus clouds, these studies have demonstrated that increases in aerosol loading can decrease the size of droplets (i.e., cloud-liquid particles). Individual aerosol particles act as seeds for the formation of droplets, so increasing aerosol loading leads to more droplets formed. More droplets mean more competition among them for available water vapor needed for their condensational growth, decreasing the size of individual droplets (Twomey, 1977; Albrecht, 1989). Aerosol-induced smaller droplet sizes reduce the efficiency of the growth of cloud-liquid particles to raindrops via autoconversion, a collection process among cloud-liquid particles in which they become raindrops (Pruppacher and Klett, 1978; Rogers and Yau, 1991). This reduced efficiency leads to less cloud liquid converted to rain and thus more cloud liquid available for transport by updrafts to altitudes above the freezing level. This eventually induces more freezing of cloud liquid, enhanced parcel buoyancy and the invigoration of updrafts and associated convection (Koren et al., 2005, 2008; Rosenfeld et al., 2008).
The role of fire-generated aerosols in the development of pyroCbs and their effects on water vapor and cirrus clouds in the UTLS lacks firm scientific understanding, and hence this paper focuses on this role of these aerosols. To examine the role, this study extends the previous modeling work by Kablick et al. (2018). The modeling work therein showed that the effects of fire-generated aerosols on the development of a specific pyroCb and its impacts on the UTLS water vapor and cirrus clouds were negligible compared to the effects of fire-generated heat fluxes. However, aerosol effects on cloud development vary with cloud properties such as typical updraft speeds (e.g., Khain et al., 2008; Lee et al., 2008; Tao et al., 2012). For simplicity herein, an “updraft” refers to the general upward motion of convective air, or to the actual updraft speed representing the updraft or convective intensity, depending on the context. Typical updrafts are determined by environmental instability as represented by convective available potential energy (CAPE). Lee et al. (2008) demonstrated that different clouds with different typical updrafts showed a different sensitivity of cloud microphysical and thermodynamic development to aerosol concentration. Hence, it is hypothesized that aerosol effects on pyroCb development and its impacts on the UTLS water vapor and cirrus clouds vary depending on the typical pyroCb updraft.
To examine the potential variation of aerosol effects on pyroCb development and its impacts on the UTLS water vapor and cirrus clouds with typical updrafts of pyroCbs, numerical simulations are performed. Simulated is the pyroCb case examined by Kablick et al. (2018) using a cloud-system resolving model (CSRM). The CSRM is capable of resolving cloud-scale dynamic and thermodynamic processes. The basic modeling methodology in this study is similar to that used by Kablick et al. (2018). However, this study uses a more sophisticated microphysical scheme, i.e., a bin scheme, rather than the two-moment bulk scheme used by Kablick et al. (2018).
Note that Kablick et al. (2018) examined aerosol effects on the convective development of a specific pyroCb case with a typical updraft framework. The present study expands upon that work by performing sensitivity simulations in which typical updrafts in the pyroCb vary, enabling us to ascertain the dependence of aerosol effects on typical updrafts. Note that CAPE, which determines typical updrafts in convective clouds, is strongly dependent on surface latent and sensible heat fluxes (e.g., Houze, 1993), and in the case of pyroCbs, these fluxes are strongly controlled by fire intensity. Therefore, the present sensitivity simulations enable us to study the dependence of those aerosol effects on fire intensity. Since fire intensity is the dominant driver of the pyroCb typical updrafts, these are henceforth referred to as fire-driven updrafts.
The effects of fire-induced increases in aerosol concentration on pyroCbs are likely dependent on how much aerosol concentration increases (aerosol perturbation; e.g., Koren et al., 2005, 2008, 2012; Rosenfeld et al., 2008). This study examines this dependence, not studied by Kablick et al. (2018).
We use the Advanced Research Weather Research and Forecasting (ARW) model, a nonhydrostatic compressible model, as the CSRM. Prognostic microphysical variables are transported with a fifth-order monotonic advection scheme (Wang et al., 2009). Shortwave and longwave radiation is parameterized by the Rapid Radiation Transfer Model (RRTM; Mlawer et al., 1997; Fouquart and Bonnel, 1980).
To represent microphysical processes, the CSRM adopts a bin scheme based on
the Hebrew University Cloud Model described by Khain (2009). The bin
scheme solves a system of kinetic equations for the size distribution
functions of water drops, ice crystals (plate, columnar and branch types),
snow aggregates, graupel and hail, as well as cloud condensation nuclei
(CCN) and ice nuclei (IN). Each size distribution is represented by 33 mass
doubling bins, i.e., the mass of a particle
A cloud-droplet nucleation parameterization based on Köhler theory represents cloud-droplet nucleation. Arbitrary aerosol mixing states and aerosol size distributions can be fed to this parameterization. To represent heterogeneous ice-crystal nucleation, the parameterizations by Lohmann and Diehl (2006) and Möhler et al. (2006) are used. In these parameterizations, contact, immersion, condensation–freezing, and deposition nucleation paths are all considered by taking into account the size distribution of IN, temperature and supersaturation. Homogeneous aerosol (or haze particle) and droplet freezing are also considered following the theory developed by Koop et al. (2000).
The control run for an observed pyroCb case involved a forested site in the
Canadian Northwest Territories (60.03
Figure 1 shows a satellite image of the observed pyroCb and the fire spot
(spatial length is
Visible image of the fire, smoke and cirrus cloud in association with the selected pyroCb. This image was taken by the visible infrared imaging radiometer suite onboard the Suomi spacecraft. Bright white represents cirrus (anvil) at the top of the pyroCb, and the red circle marks the fire spot. Dark white represents smoke produced by the fire. Adapted from Kablick et al. (2018).
For the selected pyroCb case, aerosol chemical composition, size
distribution and concentration are unknown. Hence, inside (outside) the fire
spot at the first time step, the concentration of aerosols acting as CCN is
prescribed to be 15 000 (150) cm
Reid et al. (2005) showed that aerosol mass produced by forest fires was
generally composed of (by mass)
The control run adopts the unimodal lognormal distribution as an initial
aerosol size distribution, a reasonable assumption for fire sites (Reid et
al., 2005; Knobelspiessel et al., 2011; Lee et al., 2014). Those studies
reported that in general, median and standard deviation aerosol diameters
range from
Airflow in clouds diffuses and advects aerosols. After activation or capture by precipitating hydrometeors, aerosols are transported within hydrometeors and removed from the atmosphere once these hydrometeors reach the surface. Once clouds disappear completely at any grid point, aerosol size distribution and number concentration recover to the background values at the first time step. This assumption simulates overall aerosol properties and their impacts on clouds and precipitation reasonably well (Morrison and Grabowski, 2011; Lebo and Morrison, 2014; Lee et al., 2016). This assumption means that fire continuously produces aerosols to maintain the initial background aerosol concentration.
Located to the northeast of the fire spot is the observed cirrus cloud at
the top of the pyroCb, since winds advect the cloud northeastward (Fig. 1). The extent of the observed cirrus cloud is
The simulated fire spot (red circle) and the field of cloud-ice mass density (cirrus cloud) at the top of the simulated pyroCb when it is about to enter its mature stage.
The average liquid-water path (LWP) over areas with non-zero LWP in the
control run is 960 g m
Compared are the control-run and observed reflectivity fields. Kablick et al. (2018) provide details about the reflectivity field observed by
CloudSat. Observations and the control run both show increasing reflectivity
up to
The vertical distribution of the radar reflectivity averaged along the Cloudsat path.
To see the role played by fire-generated aerosols in the development of the
pyroCb and its effects on the UTLS water vapor and cirrus clouds, we repeat
the control run by reducing aerosol concentration inside the fire spot from
15 000 cm
For the examination of the potential variation of effects of fire-generated aerosols on pyroCb development and its impacts on the UTLS water vapor and cirrus clouds with fire intensity and associated fire-driven updrafts, we repeat the control run by varying fire intensity. Remember that fire intensity controls surface latent and sensible heat fluxes on which fire-driven updrafts are strongly dependent. Therefore, variations in fire-induced surface latent and sensible heat fluxes can represent variations in fire intensity. As a first step, the control run is repeated by reducing fire-induced surface latent and sensible heat fluxes by factors of 2 and 4, respectively. The first repeated run represents a case with medium fire intensity, referred to as “the medium run”. The second repeated run represents a case with weak fire intensity, referred to as “the weak run”. Relative to these repeated runs, the control run represents a case with strong fire intensity. Then, to see effects of fire-generated aerosols on pyroCb development for each of those different fire intensities, the medium and weak runs are repeated with the identical initial aerosol concentration to that in the low-aerosol run. The repeated medium and weak runs are referred to as “the medium-low run” and “the weak-low run”, respectively. The control, medium and weak runs are the polluted-scenario runs, and the low-aerosol, medium-low and weak-low runs are the clean-scenario runs.
The effects of fire-generated aerosols on pyroCb development and its impacts on
the UTLS water vapor and cirrus clouds can also depend on the magnitude of
fire-induced increases in aerosol concentration in a fire spot. To test this
dependence, for each fire intensity, we repeat the polluted-scenario run by
increasing and decreasing the magnitude by a factor of 2 inside the fire spot
but not outside of the fire spot. The simulations with the increased
magnitude have an aerosol concentration of 30 000 cm
Summary of simulations.
The aerosol concentration of 30 000 cm
The updraft mass flux was averaged over the simulation period between 17:00 GMT on 5 August, approximately when the pyroCb started to form, and 12:00 GMT on 6 August (Fig. 4). The updraft mass flux is one of the most indicative variables of the upward air motion and magnitude of convective invigoration. Since the updraft mass flux is updraft speed multiplied by air density, and air density at each altitude varies negligibly, differences in updraft mass fluxes are mostly explained by those in updraft speeds among the simulations. Hence, with good confidence, differences in updraft mass fluxes mean those in updraft speeds.
Vertical distributions of the average updraft mass fluxes at all altitudes in cloudy areas (i.e., where the sum of LWC and IWC is non-zero) over the simulation period between 17:00 GMT on 5 August and 12:00 GMT on 6 August.
The upper troposphere is defined here to be between
Updraft mass fluxes in the control run are only
Vertical distributions of the average water-vapor mass density at altitudes above 13 km and over the simulation period between 17:00 GMT on 5 August and 12:00 GMT on 6 August. Colored lines represent the average values over cloudy grid columns (non-zero sum of LWP and IWP). The black line represents those values over non-cloudy columns (zero sum of LWP and IWP) in the control run.
Shown here are the average updraft mass fluxes at all altitudes in cloudy areas, the
average water-vapor mass density over altitudes between 13 and 16 km and
over cloudy columns except for the average background water-vapor mass
density which is also over altitudes between 13 and 16 km but over
non-cloudy columns and the average cirrus-cloud mass density between 9 and
13 km in cloudy areas. The non-zero
water-vapor mass density over cloudy columns extends to 16 km altitude (Fig. 5). These
average values are obtained over the simulation period between 17:00 GMT on
5 August and 12:00 GMT on 6 August. “Difference” is
the percentage difference between the polluted-scenario and clean-scenario
runs for each fire intensity
For the simulation period between 17:00 GMT on 5 August and 12:00 GMT
on 6 August, the average water-vapor mass fluxes at the
tropopause over cloudy and non-cloudy grid columns are
Similar to the situation with updraft mass fluxes, there is only a small
(
The altitude of homogeneous freezing is 9 km, so cirrus clouds composed of
ice crystals (or cloud ice) are between 9 and 13 km in the control run
(Fig. 6). The amount of cirrus clouds in the control run, represented by
the average cloud-ice mass density, ranges from 0.028 to 0.037 g m
Vertical distributions of the average cloud-ice mass density at all altitudes in cloudy areas (non-zero sum of LWC and IWC) over the simulation period between 17:00 GMT on 5 August and 12:00 GMT on 6 August.
Updrafts produce supersaturation, which leads to the primary source of
cloud-ice mass and associated cirrus clouds via deposition. Due to the
negligible variation of updraft mass fluxes, there are negligible variations
of supersaturation and deposition (Fig. 7). So, there is only a negligible
increase (
Same as Fig. 6 but for deposition rate.
In summary, the pyroCb and associated updrafts cause a substantial enhancement of the transport of water vapor to the UTLS at and above the tropopause. Wang (2019) also reported this enhancement. Using modeling work and satellite observation, Wang (2019) indicated that the upward transport of water vapor in deep convective storms was possibly a major pathway through which water substance entered the stratosphere. Wang (2019) showed that the upward transport of water vapor was aided by a gravity wave and its breaking in overshooting convective parcels. The pyroCb and its updrafts also produce cirrus clouds. The effects of fire-generated aerosols on the pyroCb updrafts, cirrus-cloud mass and the enhancement of the water vapor transport are insignificant when fire intensity is strong.
When fire-generated surface heat fluxes and fire intensity increase, in-cloud latent heat is also likely to increase because a major source of in-cloud latent heating is surface heat flux. Therefore, aerosol-induced perturbations of latent heating may be relatively small compared with large in-cloud latent heat contributed by surface fluxes with very intense burning. Thus, aerosol-induced increases in parcel buoyancy, updrafts and their impacts on water vapor and the amount of cirrus clouds are relatively small compared with the large buoyancy, strong fire-driven updrafts produced by strong fire intensity and their associated impacts on water vapor and the amount of cirrus clouds.
When fire intensity and fire-generated surface heat fluxes decrease, in-cloud latent heat is also likely to be smaller. Here, we are interested in how the magnitude of an aerosol-induced perturbation of latent heating for a pyroCb with weak fire intensity compares to that with strong fire intensity. This is to evaluate the possibility that with background in-cloud latent heat varying with fire intensity, the relative magnitudes of aerosol-induced perturbations of latent heat and surface flux-dominated latent heat may vary.
The average updraft mass fluxes in the low-aerosol, medium-low and weak-low
runs represent fire-driven updrafts for strong, medium and weak fire
intensities, respectively (Fig. 4). Due to different fire intensity and
associated CAPE, fire-driven updrafts vary between these runs. All weak,
medium and strong fire intensity cases show aerosol-induced increases in
updraft mass fluxes (Fig. 4 and Table 2). Of interest is that the greatest
percentage increase in updraft mass flux is in the case of weak fire
(weak-low to weak runs), the smallest increase is in the case of strong fire (low-aerosol to
control runs), and intermediate increase is in the case of medium fire (medium-low to
medium runs; Fig. 4 and Table 2). Here, the percentage difference,
including both the percentage increase and decrease, is the relative
difference in the value of variables between the polluted-scenario and
clean-scenario runs for each fire intensity. The following equation
determines this percentage difference for the strong fire intensity case:
In this section, we see that although fire-produced aerosols invigorate updrafts in all three types of fire intensity, the invigoration-induced increases in the UTLS water-vapor and cloud-ice mass increase as fire intensity weakens.
The simulation period is divided into four sub-periods for this next
analysis: period 1 is between 17:00 and 19:00 GMT on 5 August (initial
formation of the pyroCb), period 2 is between 19:00 and 21:00 GMT on 5 August, period 3 is between 21:00 and 23:00 GMT on 5 August (initial stages of cloud development) and period 4 between 23:00 GMT on
5 August and 12:00 GMT on 6 August (mature and decaying
stages). The average
Average
Using the average LWC and
In summary, the simulated LWC,
During period 1, as fire intensity weakens and updraft speed decreases, parcel equilibrium supersaturation decreases and thus, the minimum size of activated aerosol particles increases not only among the clean-scenario runs but also among the polluted-scenario runs. When the production of parcel supersaturation by updrafts and the consumption of supersaturation by droplets balance out, parcel supersaturation reaches parcel equilibrium supersaturation (Rogers and Yau, 1991). Mostly due to greater aerosol concentrations, associated average equilibrium supersaturation and minimum size of activated aerosol particles over areas with positive updraft speeds and period 1 are lower and larger, respectively, in the polluted-scenario run than in the clean-scenario run for each fire intensity (Rogers and Yau, 1991).
The average equilibrium supersaturation decreases from 0.21 % in the
control run to 0.10 % in the weak run. Associated with this, the average
minimum diameter increases from 0.09
The increase in the minimum activation size with weakening fire intensity
occurs closer to the right tail of the assumed unimodal aerosol size
distribution among the polluted-scenario runs than among the clean-scenario
runs. A smaller portion of the total aerosol concentration is in the size
range closer to the right tail of the distribution as long as the range is
on the right-hand side of the distribution peak where most aerosol
activation occurs. So, a similar increase in the average minimum activation
size for a weakened fire results in a smaller percentage reduction in the
total activated aerosol concentration and thus
In association with larger aerosol concentration and the assumed aerosol
size distribution, a smaller percentage variation of the number of activated
aerosols and
Autoconversion is proportional to the size of cloud droplets (Pruppacher and
Klett, 1978; Rogers and Yau, 1991; Khairoutdinov and Kogan, 2000; Liu and
Daum, 2004; Lee and Baik, 2017). Due to the larger
Average rates of condensation, deposition and cloud-liquid
freezing at all altitudes in cloudy areas and over periods
The increasing differences in autoconversion rates between the
polluted-scenario and clean-scenario runs increase those differences in the
amount of cloud liquid available for freezing with weakening fire intensity
(Fig. 9a). Thus, differences in the average rate of cloud-liquid freezing
and freezing-related latent heat over the period 2 between the runs increase
with weakening fire intensity (Fig. 9a). Enhanced freezing-related latent
heat strengthens updrafts in places where freezing occurs and this, in turn,
enhances deposition and deposition-related latent heat (Lee et al., 2017).
Although the average deposition over period 2 is slightly lower, those
strengthened updrafts enable the average deposition and deposition-related
latent heat to be greater in the polluted-scenario run than in the
clean-scenario run for each fire intensity during period 3 (Fig. 9a and b). Differences in the average freezing rate (and thus the average
freezing-related latent heating) between the runs do not change much up to
Time series of differences in average values of variables related
to aerosol-induced invigoration of convection at all altitudes in cloudy
areas between the
To satisfy mass conservation, the freezing- and deposition-enhanced updrafts above the freezing level induce more updraft mass fluxes below the freezing level in the polluted-scenario run than in the clean-scenario run for each fire intensity. This leads to more convergence around and below the cloud base in the polluted-scenario run. The higher mass fluxes and convergence below the freezing level, in turn, increase condensation starting around 22:30 GMT in the polluted-scenario run (Fig. 10). This induces greater average condensation and condensation-related latent heat in the polluted-scenario run during period 4 (Fig. 9c). Enhanced condensation in turn enhances updrafts, establishing a positive feedback between freezing, deposition, condensation and updrafts, thus further enhancing freezing, deposition, condensation and updrafts. This enhancement, due to the feedback, eventually determines the overall differences in the pyroCb properties and their impacts on the UTLS water vapor and cloud ice between the runs.
Due to the increasing differences in freezing-related latent heat between the polluted-scenario and clean-scenario runs with weakening fire intensity during period 2, those differences in the average freezing-affected updrafts and subsequently in deposition-related latent heat over period 3 increase with weakening fire intensity (Figs. 9a, b and 10). Those differences, calculated using Eq. (1), in deposition-related latent heat are 16 %, 181 % and 417 % for strong, medium and weak fire intensities, respectively (Figs. 9b and 10). Since percentage increases in deposition-related latent heat increase, the subsequent percentage increases in updrafts in the polluted-scenario run increase with weakening fire intensity, particularly during period 3 (Fig. 10). During period 4, due to these greater increases in updrafts in the polluted-scenario run with weaker fire intensity, the percentage increases in condensation in the polluted-scenario run increase with weakening fire intensity (Figs. 9c and 10). The greater increases in condensation cause the greater further enhancement of the increases in updrafts in the polluted-scenario run with weaker fire intensity. This leads to the overall greater effects of fire-produced aerosols on the UTLS water vapor and ice with weaker fire intensity.
This section shows that the smaller
Table 3 shows that for each of the strong-, medium- and weak-fire cases,
there are increases in the UTLS water-vapor and cirrus-cloud mass in the run
with fire-induced aerosol perturbations of 30 000 or 7500 cm
Average water-vapor mass density between 13 and 16 km over cloudy
columns and the average cirrus-cloud mass density between 9 and 13 km in
cloudy areas over the simulation period between 17:00 GMT on 5 August
and 12:00 GMT on 6 August. The numbers in parentheses are
the percentage differences:
Until now, we have taken interest in the sensitivity to fire intensity of an aerosol perturbation on pyroCb development, the UTLS water vapor and cirrus clouds. To isolate the sensitivity, we have shown comparisons among sensitivity simulations by varying only fire intensity while maintaining a constant aerosol perturbation. While working well for the isolation aspect, this strategy does not reflect reality well. It may be that weaker fire intensity produces a lower aerosol concentration. This possibility is not that unrealistic, since stronger fires likely involve more material burnt and higher aerosol emissions.
With this in mind, we make comparisons among three pairs of simulations: the
low-aerosol and control-30000 runs for strong fire vs. the medium-low and
medium runs for medium fire vs. the weak-low and weak-7500 runs for weak
fire. Among these three pairs, the magnitude of the fire-induced aerosol
perturbation decreases with weakening fire, emulating the possibility that
weaker fire intensity involves a lower amount of aerosols. The
perturbation-related aerosol concentration is 30 000 cm
Results in this section show that the increasing impacts of the fire-induced aerosol perturbations on the UTLS water vapor and cirrus clouds with weakening fire intensity are robust whether or not aerosol perturbations vary with fire intensity unless their variation is extremely high.
This study investigates an observed case of a pyroCb using a modeling framework. In particular, this study focuses on effects of fire-produced aerosols on pyroCb development and its impacts on the UTLS water vapor and cirrus clouds. Results show that the pyroCb efficiently transports water vapor to the tropopause and above. This leads to a much greater amount of water vapor around and above the tropopause (i.e., the UTLS) over the pyroCb compared to that outside the pyroCb. The pyroCb also generates a deck of cirrus clouds around the tropopause. The role of fire-produced aerosols or the fire-induced aerosol perturbation in the water-vapor transport to the UTLS and the production of cirrus clouds becomes significant as fire intensity weakens.
During the initial stage, there is a similar LWC between the
polluted-scenario and clean-scenario runs for each fire intensity. The
reduction in LWC with weakening fire intensity among the polluted-scenario
runs is similar to that among the clean-scenario runs. Much greater
The level of understanding of the role played by fire-produced aerosols in the development of pyroCbs and their impacts on the UTLS water vapor and cirrus clouds has been low. This study shows that fire-produced aerosols can invigorate updrafts and convection and thus enhance the transport of water vapor to the UTLS and the formation of cirrus clouds. We find that the mechanism that controls the invigoration of convection by aerosols in the pyroCb is consistent with the traditional invigoration mechanism proposed and described by Koren et al. (2005, 2008) and Rosenfeld et al. (2008). However, this study shows that for pyroCbs produced by strong fires, the aerosol-induced invigoration and its effects on the UTLS water vapor and cirrus clouds are insignificant. Note that traditional understanding generally focuses on the effects of fire-produced heat and water vapor and their associated surface fluxes on the pyroCb and does not consider the effects of fire-produced aerosols on the pyroCb. This understanding adequately explains the mechanics for pyroCbs in association with strong fires. This study suggests that when pyroCbs form over weak-intensity fires, those effects of fire-produced aerosols require consideration.
Note that when fire-induced aerosol perturbations are strongly reduced for cases of weaker-intensity fires compared with strong-intensity fires, the significance of the role of the fire-produced aerosol perturbation no longer increases and starts to decrease with weakening fire. This suggests that there may be a critical level of aerosol perturbation below which the increase in this significance with weakening fire intensity ceases.
The data used are currently private and stored in our private computer system. Opening the data to the public requires approval from funding sources. Since funding projects associated with this work are still going on, these sources do not allow the data to be open to the public; 2–3 years after these project ends, the data can be open to the public. However, if there is any inquiry about the data, contact the corresponding author: Seoung Soo Lee (slee1247@umd.edu).
SSL generated the research ideas and goals, performed the simulations and wrote the manuscript. GK and ZL selected the case, analyzed observations and provided data to set up the simulations while reviewing and providing comments on the manuscript. CHJ, YSC, JU and WJC revised the manuscript based on the reviewers' comments and performed associated analyses of simulation and observation data.
The authors declare that they have no conflict of interest.
The authors would like to express their gratitude for the valuable comments made by the two anonymous referees.
This research has been supported by the National Aeronautics and Space Administration (grant nos. 80NSSC20K0131 and NNX16AN61G), the National Science Foundation (grant no. AGS 1837811), the National Strategic Project-Fine particle of the National Research Foundation of Korea, funded by the Ministry of Science and ICT, the Ministry of Environment, the Ministry of Health and Welfare (grant no. NRF-2017M3D8A1092022), and the Ministry of Education (grant no. NRF-2018R1D1A1A09083227), and the National Institute of Environmental Research funded by the Ministry of Environment (grant no. NIER-2019-01-02-085). This research has also been supported by the National Research Foundation of Korea (NRF) grant funded by the South Korean government (MSIT) (grant no. NRF2020R1A2C1003215) and the “Construction of Ocean Research Stations and their Application Studies” project, funded by the Ministry of Oceans and Fisheries, South Korea.
This paper was edited by Graham Feingold and reviewed by two anonymous referees.