Journal of Geophysical Research - Atmospheres
Supporting Information for
Organic aerosol processing in tropical deep convective clouds: Development of a new model
(CRM-ORG) and implications for sources of particle number
B. N. Murphy1,2,3,4, J. Julin2,3, I. Riipinen2,3, and A. M. L. Ekman1,3,*
1Department of Meteorology, Stockholm University, Stockholm, Sweden
of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, Sweden
3Bolin Centre for Climate Research, Stockholm, Sweden
4
Currently in the Atmospheric Modeling and Analysis Division, United States Environmental Protection Agency,
Research Triangle Park, USA
2Department
*Phone:
+46 8 162397; Fax : +46 8 159295 ; email: annica@misu.su.se
Contents of this file
Supplementary Discussion S1 to S2
Figures S1 to S10
Tables S1 to S2
Introduction
This document contains supporting discussion, figures and tables generated during the analysis of
the Cloud Resolving Model with Organics (CRM-ORG). First, the discussion documents basic
model details necessary to be aware of for reproducibility. Second, a discussion of the predicted
cloud’s microphysical properties is necessary to verify the model is behaving realistically. The
figures principally document specific details about the parameterizations and initial conditions
used in the model as well as several diagnostic outputs that characterize the model’s performance.
These diagnostics include metrics for the precipitation intensity and updraft velocity, calculation
of the sensitivity of cloud drop number concentration to initial aerosol number, comparison of
radar reflectivity against satellite observations, transport of trace gases to the upper troposphere
during the cloud event, and an estimate of condensation sink within the cloud versus altitude.
The data was collected and processed similarly to the data used in the manuscript and is available
upon request. The tables that follow provide important information about the organic aerosol
naming convention used in the manuscript.
1
S1. Description of chemical and microphysical processes in CRM-ORG
Hydrometeors in this model interact with each other and water vapor through
several processes including the following: condensation/evaporation, deposition,
sublimation, collision/coalescence, collection, autoconversion, freezing, melting and
riming. The hydrometeor distributions are described with a dual-parameter approach; the
model explicitly tracks the bulk number and mass of hydrometeor type and assumes each
population follows a modified gamma distribution with type-specific parameters [Wang
and Chang, 1993]. The rates of each microphysical interaction in addition to the fall
speed of the large hydrometeors are parameterized based on these assumptions.
CRM-ORG simulates the transport and reaction of several gas and particle phase
compounds in parallel to the cloud physics model (Fig. 1). The inorganic species
numerical description, documented exhaustively by Wang and Prinn [2000], includes
gas- and aqueous-phase reactions of O3, CO, NOx, HNO3, SO2, H2SO4, NH3 among
others (see Wang and Prinn [2000]; Tables 1 through 5). In total, 18 gas, 9 aqueous, and
9 solid (i.e. ice-bound) phase inorganic species are transported throughout the domain.
Transport and reactions involving the oxidation of organic compounds are discussed in
detail in section 2.2. In total, 35 gas and 32 aqueous-phase reactions are used to diagnose
the concentrations of intermediates and predict the production and loss of the tracer
species. Reaction and transport of chemical tracer compounds is performed by the model
at 30 s intervals (or the next meteorological time step if falling in the middle of a step),
and concentration information is passed back to the cloud physics submodel. Previous
studies have shown that this chemical description performs well against observations of
O3 [Wang and Prinn, 2000; Barth et al., 2007]. Aqueous-phase species in cloud and rain
2
water are advected in the model and are susceptible to mass transfer to the gas phase; 21
equilibrium reactions dynamically simulate this process. Adsorption of gases to ice
crystals and graupel is included, as well as regeneration from the solid hydrometeor if it
evaporates.
The aerosol module, based on the Wilson et al., [2001] and modified as described
by Ekman et al., [2004], treats relevant microphysical processes between the particles
themselves as well as interactions (i.e. scavenging and evaporative regeneration) with the
hydrometeor species (Fig. 2). For this work, the module was modified to include a more
detailed organic aerosol description. Three log-normal modes, nucleation, Aitken, and
accumulation (with initial geometric mean diameters equal to 7, 50, and 170 nm and
standard deviations equal to 1.5, 1.55, and 1.5, respectively) are employed to describe the
size distribution. These parameters were chosen to reflect the observed size distribution
during LBA-CLAIRE for air masses that were not recently influenced by deep convective
events. Aerosol prognostic variables include, for each mode, the number of particles, the
mass of sulfate, and the mass of three organic species of varying volatility (see section
2.2). Aerosol water concentrations are diagnosed from the concentration of sulfate in the
particles and it is assumed that all sulfate ions are accompanied by two ammonium ions
(important for the calculation of cloud droplet activation). Sulfuric acid produced in the
gas phase is assumed to be nonvolatile for the calculation of condensation to the particle
phase. Further, it is assumed that all sulfate aerosols are fully neutralized with ammonium
Particle coagulation is calculated using the full size of the particles (sulfate, organics,
ammonium, and water) with the assertion that when two particles of the same mode
coagulate, their combined mass stays in the same mode. If significant growth due to
3
condensation occurs in the nucleation or Aitken modes, a mode distribution scheme
moves particle number and mass concentration from the present mode to the next largest
mode. Aerosols are scavenged by cloud droplet activation, heterogeneous ice nucleation
parameterized as in Cotton et al. [1986], and impaction processes as described and
characterized in Ekman et al. [2004; 2007]. Scavenged aerosol mass is added to the
aqueous- or solid-phase counterpart species and transported appropriately in the model.
When hydrometeors evaporate and release aerosol mass, a representative size for the
regenerated particles is calculated using the amount of mass regenerated and the number
of hydrometeors that had evaporated. The resulting mass and number concentrations are
then added to the corresponding aerosol mode.
S2. Characterization of cloud microphysical properties
Three snapshots of the cloud radar reflectivity spatial profile appear in figure S5.
After 1.5 hours, the cloud top has already reached the tropopause. After 4.5 hours, the
anvil is fully developed and hydrometeor concentrations stretch towards the model
domain boundaries. Because hydrometeor concentration measurements at high altitudes
are sparse for Amazon deep convective events, we have evaluated the cloud properties
with average satellite-retrieved data from 2007-2009 isolated for springtime Amazon
deep convective events. This type of comparison will show if the modelled cloud
properties fall within the range of typical cloud observations over the Amazon region.
The modeled magnitude and vertical distribution of hydrometeors can be used to
calculate the altitude-dependent radar reflectivity predicted by the model. This altitudedependent radar reflectivity parameter is calculated from CloudSat observations and
4
reported as the Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWCRVOD [Stephens et al., 2008; Fig. S6]. The model overpredicts by a factor of two (19
versus 9 dbZ) from the surface through the first 13 km. At 14 km, the modelled radar
reflectivity approaches zero and the retrieval product is underestimated until about 16 km
when the observations then go the zero. Figure S6a shows the distribution of cloud
droplet number concentration values throughout the model domain as a function of initial
aerosol number concentration. The average change in median cloud droplet number
concentration with change in initial boundary layer CCN concentration (d[Nc]/d[CCN])
is 1 cm-3 / 575 cm-3. Little effect is seen by CRM-ORG on median cloud drop mass
concentrations with increasing particle loading (Fig. S6b). The cases chosen here are
generally clean; the highest initial particle concentrations are still quite low. As a result
the characteristic delay in the onset of precipitation with increasing particle number
usually found in model simulations of deep convective clouds (see for example, Ekman et
al., [2007]) is not observed in the model predictions.
References
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Figure S1. Nucleation rates predicted by the Metzger et al. (2010) parameterization used
in CRM-ORG (blue), rates predicted by the Riccobono et al., (2014) parameterization
derived from observations in the CLOUD campaign (red).
6
Figure S2. Homogeneous nucleation rate of 1 nm particles comprising organic molecules
with molecular weight 150 g mol-1, density 1.4 g cm-3, and mixing ratio 1 PPTV at a)
high temperature conditions relevant for the boundary layer and b) low temperature
conditions relevant for the outflow region of tropical deep convective clouds. Trends are
plotted for a range of surface tensions (σ).
7
Figure S3. Initial meteorological sounding for 28th March, 1998 obtained from the
European Centre for Medium-Range Weather Forecast. The max vertical velocity w is
estimated from the Convective Available Potential Energy. The temperature (red), dewpoint temperature (blue) and theoretical parcel curve (black) are pictured.
8
Max Vertical Advection [m s-1]
Figure S4. Maximum vertical velocity during the cloud simulation for 5 different model
runs. The base case model has a boundary layer number concentration equal to
approximately 500 cm-3 (green line).
9
a)
b)
c)
10
Figure S5. Development of the radar reflectivity of the simulated convective cloud at (a)
1.5 hours, (b) 3 hours and (c) 4.5 hours into the simulation. The units of radar reflectivity
pictured are dBZ. The values calculated are the median of the grid cells stretching
perpendicular to the horizontal axis shown.
Figure S6. Comparison of simulated radar reflectivity (blue, averaged from the initial
development of the cloud to the final simulation time step) with retrieval data from
CLOUDSAT observation system (black). Data are selected only for wet season retrievals
localized over the Amazon rainforest during convective events. The dark grey and blue
shading illustrate the variability in the observations and model predictions respectively,
with the bounds of the shaded regions indicating 3σ from the mean lines.
11
a. March 28, 1998
Boundary Layer (< 4 km)
c. March 28, 1998
Convective Layer (5-7 km)
b. March 29, 1998
Boundary Layer (< 4 km)
d. March 29, 1998
Convective Layer (5-7 km)
Figure S7. In-cloud aerosol size distributions predicted by CRM-ORG (solid lines) and
observed outside of the cloud by aircraft measurements during LBA-CLAIRE (triangles,
error bars). Model data are averaged for the final 30 minutes of the 5 hour simulation.
12
Figure S8. Isoprene mixing ratio (a) and OH radical concentration (b) as a function of
altitude for the initial conditions (dashed line), and after five hours in the cloud (blue,
solid) and out of the cloud (black, solid).
13
Figure S9. Condensation sink contributions [s-1] from the modeled hydrometeor types in
CRM-ORG. Also shown is the condensation sink for interstitial aerosols (pink, dotted
line). The cloud category here refers to liquid cloud droplets produced from activation of
aerosols and which have not been converted to rain drops.
14
Figure S10. Cloud drop number (a) and mass (b) frequency distributions for cloudy grid
cells throughout the model domain at 5 different initial particle loadings. Note that the
number concentration at altitudes corresponding to typical cloud base is smaller than that
in the boundary layer initially (see Fig. 3), but the relative change between cases (on the
x-axis above) is applied at all altitudes. Error bars and whiskers denote variability among
model grid cells (25/75 and 5/95 percentiles, respectively). Cloud drop parameters are
averaged from the initial development of the cloud (about 30 minutes) to the final
simulation time step.
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Table S1. Subscripts used to identify the phase of organic aerosol components in CRMORG.
Subscript
Examples
Description
The acronym specifies only the condensed-phase
material (not in hydrometeors)
[no subscript]
OA
particle (p)
OAparticle, OAp
Condensed-phase material. This is completely
interchangeable and synonymous with omitting the
phase subscript.
vapor (v)
OAvapor, OAv
Material currently in the vapor phase.
cloud water (cw)
OAcloud water, OAcw
Material scavenged or absorbed into cloud droplets.
rain water (rw)
OArain water, OArw
Material scavenged or absorbed into rain drops.
ice (i)
OAice, OAi
Material scavenged or absorbed into ice crystals or
graupel.
total (t)
OAtotal, OAt
Total material in all phases.
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Table S2. An illustrative, not exhaustive, list of nomenclature examples used to identify
specific organic aerosol components in CRM-ORG.
Acronym
Description
LV-OA
Low volatility organic material (at 298 K) that is condensed in
particle not activated or scavenged in cloud or rain water.
IV-OAv
Vapor-phase intermediate volatility material (at 298 K).
SV-OAcw
Semivolatile (at 298 K) material that was either present
aerosols that were activated into cloud drops or was scavenged
by hydrometeors from the vapor or particulate phase.
IV-OAp
Condensed phase organic aerosol material that has
intermediate volatility at 298 K.
SV-OAt
The sum of semivolatile (at 298 K) organic material in all
phases.
OAi
LV-OArw
The sum of all organic material of any volatility residing in
solid phase hydrometeors.
Low volatility (at 298 K) organic material in rain drops.
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