This paper presents a modelling study on the fate of CHBr3 and its
product gases in the troposphere within the context of tropical deep
convection. A cloud-scale case study was conducted along the west coast of
Borneo, where several deep convective systems were triggered on the afternoon and
early evening of 19 November 2011. These systems were sampled by the Falcon
aircraft during the field campaign of the SHIVA project and analysed using a
simulation with the cloud-resolving meteorological model C-CATT-BRAMS at 2×2km resolution that represents the emissions, transport by
large-scale flow, convection, photochemistry, and washout of CHBr3
and its product gases (PGs). We find that simulated CHBr3 mixing
ratios and the observed values in the boundary layer and the outflow of the
convective systems agree. However, the model underestimates the background
CHBr3 mixing ratios in the upper troposphere, which suggests a
missing source at the regional scale. An analysis of the simulated chemical
speciation of bromine within and around each simulated convective system
during the mature convective stage reveals that >85% of the
bromine derived from CHBr3 and its PGs is transported vertically to
the point of convective detrainment in the form of CHBr3 and that
the remaining small fraction is in the form of organic PGs, principally
insoluble brominated carbonyls produced from the photo-oxidation of
CHBr3. The model simulates that within the boundary layer and free
troposphere, the inorganic PGs are only present in soluble forms, i.e. HBr,
HOBr, and BrONO2, and, consequently, within the convective clouds,
the inorganic PGs are almost entirely removed by wet scavenging. We find that
HBr is the most abundant PG in background lower-tropospheric air and that this
prevalence of HBr is a result of the relatively low background tropospheric
ozone levels at the regional scale. Contrary to a previous study in a
different environment, for the conditions in the simulation, the insoluble
Br2 species is hardly formed within the convective systems and
therefore plays no significant role in the vertical transport of bromine. This
likely results from the relatively small quantities of simulated inorganic
bromine involved, the presence of HBr in large excess compared to HOBr and
BrO, and the relatively efficient removal of soluble compounds within the
convective column.
Introduction
Organic brominated compounds cause stratospheric ozone loss (Engel and Rigby,
2018). A compilation of model and observational evidence shows that both
longer-lived (e.g. methyl bromide (CH3Br)) organic bromine
compounds and so-called very-short-lived species (VSLS) are required to
explain the ranges of total Bry within the stratosphere (strat-Bry)
of 5±2pptv (Engel and Rigby, 2018). Recent observation campaigns
(Andrews et al., 2016; Navarro et al., 2015; Wales et al., 2018) show some
minor variations but broadly agree with this compiled range.
Recent studies using global chemistry transport models (CTM) and chemistry
climate models (CCMs) estimate the VSLS contribution to strat-Bry to
range from 2–8 pptv (Liang et al., 2010; Hossaini et al., 2012;
Hossaini et al., 2016; Aschmann and Sinnhuber, 2013; Liang et al., 2014; Wales
et al., 2018; Tegtmeier et al., 2020), which is broadly consistent with the
ranges compiled in Carpenter et al. (2014) and Engel and Rigby (2018). The
model estimates differ due to the considered VSLS and which assumptions are
made for surface emissions, chemistry, and washout in the troposphere.
Brominated VSLS are primarily of biogenic and oceanic origin produced by
macroalgae (Leedham et al., 2013) and phytoplankton. Observations indicate
that VSLS emissions are larger towards coasts compared to the open ocean
(e.g. Quack and Wallace, 2003; Carpenter et al., 2009). Bromoform
(CHBr3), with three Br atoms per molecule, has the largest emissions
among the different brominated VSLS (Engel and Rigby, 2018). For these reasons
we focus on CHBr3 in the present study.
Global estimates of CHBr3 emissions range between 120 and
820 Ggyr-1 (Liang et al., 2010; Warwick et al., 2006; Butler
et al., 2007; Ordoñez et al., 2012; Pyle et al., 2011; Ziska et al., 2013;
Engel and Rigby, 2018). Most current inventories show that emissions are
predominantly distributed in the tropics, but there is considerable
uncertainty regarding the precise spatial and temporal distribution of
emissions at the regional (Ashfold et al., 2014 Fiehn et al., 2017, 2018) and
the global scale (Hossaini et al., 2013). Furthermore, recent work suggests
that the extratropical zones may also be important source regions (Keber
et al., 2020).
Tropical deep convection is the primary mechanism by which emissions of
short-lived tropospheric trace gases and aerosols are transported to the upper
troposphere. If convective outflow detrains above the level of zero radiative
heating (LZRH), it undergoes net radiative heating and eventual
buoyancy-driven slow ascent to the stratosphere. For clear-sky conditions, the
LZRH is approximately 15 km in the tropics and can be as low as
11 km for air masses within clouds resulting from convective outflow
(Corti et al., 2005, 2006). Tropical deep convection can also loft air masses
directly into the stratosphere through a process called convective
overshooting, but this process is less frequent (e.g. Liu and Zipser, 2005;
Luo et al., 2008).
After emission, CHBr3 undergoes oxidation in the troposphere during
transport either via reaction with the hydroxyl radical (OH) or via photolysis
(approximate lifetime of 16 d, Burkholder et al., 2018). The oxidation
products are organic and inorganic product gases (PGs) (Hossaini et al., 2010;
Krysztofiak et al., 2012). The most important organic PGs are the brominated
organic peroxides, CBr3O2H and CHBr2O2H, and the
brominated carbonyl species, CBr2O and CHBrO. The inorganic PGs
consist of the bromine radical (Br), molecular bromine (Br2),
bromine oxide (BrO), hypobromous acid (HOBr), hydrogen bromine (HBr), and
bromine nitrate (BrONO2). These PGs have a range of solubilities,
and thus washout within convective systems is expected to exert a strong
control on the vertical transport of bromine to the upper troposphere
(Hossaini et al., 2010).
Since CHBr3 transport by deep convection occurs at the local scale
and involves complex chemistry, the analysis of the detailed CHBr3
and PG processes occurring within deep convection and in its vicinity requires
fine-scale modelling at the kilometre resolution with detailed chemistry
(e.g. Barth et al., 2001; Marécal et al., 2006). These processes are the
convective-scale transport and mixing, the full bromoform degradation scheme
in the gaseous phase, the speciation of the resulting PGs into organic and
inorganic forms, the partitioning of PGs across the gas–aqueous phases due to
their solubilities and interactions with hydrometeors during formation, mature
and decaying convective stages, hydrolysis of BrONO2 within cloud
and rain droplets, and aqueous-phase chemistry of dissolved gases in cloud and
rain droplets. This knowledge, gained from studies at the convective scale,
may then improve the representation of the fate of chemical species in global
models. Because of their coarse resolution, current state-of-the-art global
three-dimensional models use sub-grid-scale parameterisations of deep convection
and are not able to resolve all convective events. These parameterisations are
a known source of uncertainty for tracer transport, including CHBr3,
from the boundary layer to the upper troposphere (e.g. Hoyle et al., 2011;
Liang et al., 2014; Hossaini et al., 2016; Butler et al., 2018). Also, because
global models need to compromise between complexity and computing resources,
they include simplifications of CHBr3 chemical processes and their
interactions with hydrometeors. For instance, in Hossaini et al. (2012),
CHBr3 degradation is assumed to release Bry immediately. The
Liang et al. (2014) study is based on a stratospheric model and uses a OH
climatology in the troposphere. Aschmann and Sinnhuber (2013) represent in
their stratospheric model the partitioning between inorganic species and HBr
uptake on ice, but no organic products and no explicit tropospheric chemistry
are included. Only two detailed model studies examining VSLS degradation
chemistry (both gas and aqueous phase), which were idealised cases, have been
carried out at the convective scale (Krysztofiak et al., 2012; Marécal
et al., 2012). Krysztofiak et al. (2012) focused on developing and optimising
a photochemical mechanism for CHBr3 degradation for use within
models, and they estimated the Henry law coefficients for some of the organic
bromine species included within the optimised mechanism. Marécal et
al. (2012) implemented the CHBr3 photochemical scheme of Hossaini
et al. (2010) in addition to aqueous-phase uptake and chemistry based on
Henry's law coefficients from Krysztofiak et al. (2012). Using idealised
simulations of a tropical convective cloud, Marécal et al. (2012) explored
the CHBr3 chemistry at the cloud scale and highlighted the
importance of aqueous-phase processes for understanding source gas and PG
chemistry and transport.
The previous studies of VSLS chemistry and transport at the convective scale
were only idealised cases that used a set of simplifying assumptions (e.g. no
emissions, constant vertical profiles for initial conditions, and no synoptic-scale meteorological forcing) and artificial perturbations to the modelled
atmosphere to induce their simulated convection. Thus, these cases were not
realistic, and it would not have been relevant to compare them to
observations. We wish to expand upon that previous work, e.g. Marécal et
al. (2012), by carrying out a real-world case study. In this paper we present
a study based on high-resolution cloud-resolving modelling of the transport,
chemistry, and washout of CHBr3 and its derivatives within
convective clouds along the west coast of Borneo. Specifically, we aim to look
at the bromine compound speciation in the areas of convective entrainment and
detrainment and both within and outside of several convective systems. The
modelling is supported using aircraft observations of CHBr3, which
we use to establish the credibility of the simulated chemical processes. The
case study corresponds to tropical deep convection reaching the upper
troposphere that is far more common than overshooting convection. Because part
of the air masses within clouds resulting from convective outflow clouds in
the tropical upper troposphere reach the stratosphere by radiative ascent,
this paper is relevant for global stratospheric studies, in particular with
global models that are only representing this type of pathway and not
overshooting convection.
Section 2 gives a description of the measurement campaign over the west coast
of Borneo, it provides an overview of the case study, and it includes a
description of the meteorological situation based on observations. Section 3
describes the model used, its new developments, and the simulation setup.
Section 4 presents our modelling results, and includes (i) an evaluation of
the simulated meteorology with respect to the situation described in Sect. 2;
(ii) a detailed analysis of the transport, chemistry, and washout of bromoform
and its PGs; and (iii) a discussion of the chemistry. Section 5 discusses the
limitations, and Sect. 6 presents a summary and our conclusions.
The SHIVA Campaign and case study overview
The EU-funded SHIVA (Stratospheric Ozone: Halogen Impacts in a Varying
Atmosphere) project
(http://shiva.iup.uni-heidelberg.de/, last access: 15 November 2021) was designed to address uncertainties in our understanding of VSLS,
their contribution to stratospheric bromine, and their impact on stratospheric
ozone. A measurement campaign within SHIVA was carried out in November 2011
that focused on the Southeast Asia Maritime Continent (SEA-MC) to better
understand the emissions and the transport of oceanic VSLS, including
CHBr3, to the upper troposphere and stratosphere. This region was
selected for two reasons. First, it represents globally the most important
region for deep convection (Liu and Zipser, 2005), and, second, the SEA-MC was
believed to be an important region for VSLS emissions due to its many
coastlines and its location in the tropics. The campaign primarily relied on
measurements of chemical species aboard the Deutsches Zentrum für Luft-
und Raumfahrt (DLR) Falcon aircraft based in Miri, Sarawak, Malaysia, which
were complemented by ship- and ground-based observations (Pfeilsticker et al.,
2013). By sampling convective outflows under the influence of high
CHBr3 coastal emission zones, several of the SHIVA flights were
particularly well designed to document the impact of deep convection on the
bromoform distribution in the upper troposphere. We selected the case study
from the SHIVA campaign on the afternoon of 19 November 2011. Figure 1 shows
hourly maps of brightness temperature contours measured by the 11 µm channel IR108 aboard the MTSAT-2 satellite (http://database.rish.kyoto-u.ac.jp/arch/ctop/index_e.html, last access: 15 November 2021; Nishi et al., 2017; Hamada et al., 2008; CEReS, 2015). Note that the brightness
temperature contours in Fig. 1 are chosen to highlight only cloud tops in the
upper troposphere. The brightness temperature imagery illustrates that several
deep convective systems initiated inland along the west coast of Borneo, where
CHBr3 emissions are expected to be strong (Ziska et al., 2013). The
Falcon aircraft sampled two of these convective systems, which we henceforth
refer to as Obs_Conv1 and Obs_Conv2 and are shown in the green box and pink
box in Fig. 1, respectively.
The contours show the brightness temperatures from MTSAT-2
at 05:00 UTC, 06:00 UTC, 07:00 UTC, 08:00 UTC, 09:00 UTC, 10:00 UTC, 11:00 UTC,
and 12:00 UTC on 19 November 2011 (plots are labelled with time in UTC). The system called Obs_Conv1 is shown by a green rectangle and Obs_Conv2 by a pink
rectangle. The black line shows the coast with the land to the east. The
vertical axis is latitude (degrees north), and the horizontal axis is
longitude (degrees east). In the 09:00 UTC panel, the Falcon aircraft
trajectory on the afternoon of 19 November 2011 is overplotted. The red
crosses indicate the location where the aircraft crossed convective outflows
as determined in Krysztofiak et al. (2018).
The temporal evolution of both convective systems develops over several hours
(05:00 UTC to 12:00 UTC) and
is shown in Fig. 1a through h, which indicates that the two follow a similar
development scenario. They were both initiated in the early afternoon inland close to
the west coast of Borneo from offshore low-level winds encountering the steep
topography of the island. These low-level winds come from the
north/north-westerly large-scale flow as indicated by ECMWF analysis (not
shown), possibly combined with local diurnal variations of the sea breeze
(Johnson and Priegnitz, 1981). The initial convective cells developed
vertically and then horizontally to form an anvil from its outflow (also named
stratiform part of the convective system) in the upper troposphere driven off
the coast by the easterly/south-easterly upper-tropospheric flow.
Obs_Conv1 was already well developed at 05:00 UTC (13 h local
time: 13:00 LT) and was located around 5.5∘ N and
116∘ E at this time. Obs_Conv1 produced a large anvil on its west
flank that started weakening after 10:00 UTC (18:00 LT). The
anvil of Obs_Conv1 was well sampled by the Falcon aircraft during its mature
stage at altitudes between 11 and 13 km from 08:05 to 09:35 UTC
(16:05 to 17:35 LT). The trajectory of the aircraft is plotted in Fig. 1 in
the 09:00 UTC panel and shows the intersection of the Falcon and the
anvil cloud. The other convective cell, Obs_Conv2, initiated at
06:00 UTC (14:00 LT) and was located at about 4.3∘ N
and 114.4∘ E. It later produced an anvil of convective outflow that
developed and moved north-westward similarly to Obs_Conv1. It lasted several
hours and started to decay from 10:00 UTC (18:00 LT).
The Falcon flight on the afternoon of 19 November 2011 was aimed at sampling
the outflow of the Obs_Conv1 system. Krysztofiak et al. (2018) identified
from humidity data and webcam images the times when the aircraft flew within
the convective outflow (i.e. in cloudy conditions) and when it was in
cloud-free conditions. This information has been used here to show in Fig. 1
(09:00 UTC panel) where the flight sampled cloud-free air or cloudy
air. The Falcon aircraft sampled the Obs_Conv1 system multiple times but also
flew within the Obs_Conv2 system at ∼12.5km altitude around
09:20 UTC (visible at 5.2∘ N and 114.8∘ E) and in
cloud-free conditions below Obs_Conv2 on its way back to Miri around
09:50 UTC at an altitude of ∼6km.
Since CHBr3 emissions and its marine boundary layer (BL) mixing
ratios are large close to Borneo's west coast (Ziska et al., 2013;
Fuhlbrügge et al., 2016), CHBr3 was transported from the BL
aloft by the Obs_Conv_4.35N and Obs_Conv_3.75N systems as confirmed by
observations of elevated CHBr3 mixing ratios relative to the
background conditions during the flight (Sala et al., 2014; Krysztofiak
et al., 2018).
Model simulationsModel description
We use the Chemistry-Coupled Aerosol and Tracer Transport model to the
Brazilian developments on the Regional Atmospheric Modeling System
(C-CATT-BRAMS) (Longo et al., 2013), which is a version of the CATT-BRAMS
model (Freitas et al., 2009) coupled online with a chemistry model. This
system is capable of resolving meteorological processes and the resultant
tracer transport and chemistry. C-CATT-BRAMS has its original heritage in the
Regional Atmospheric Modeling System version 6 (RAMS) (Walko et al.,
2000). RAMS is a fully compressible non-hydrostatic model consistent with
Tripoli and Cotton (1982). RAMS can run in a nested grid configuration and
includes various physical parameterisations to simulate sub-grid-scale
meteorological processes for turbulence, shallow cumulus convection, deep
convection, surface–air exchanges, cloud microphysics, and radiation. Note
that for kilometre-scale simulations, convection is resolved explicitly, and thus
sub-grid-scale convective parameterisations are not needed. BRAMS builds upon
RAMS with the inclusion of several modifications that serve to improve the
model performance within the tropics. For example, BRAMS includes an ensemble
implementation of the deep and shallow cumulus convection schemes, a soil
moisture initialisation using model prognostication combined with a remote
sensing rainfall product, and more realistic surface characteristics for
vegetation type derived from the MODIS (Moderate Resolution Imaging
Spectroradiometer) NDVI (normalised difference vegetation index) product
(Freitas et al., 2009).
The model represents microphysical processes using the single-moment bulk
parameterisation (Walko et al., 1995), whereby rain, cloud, pristine ice, snow,
aggregates, graupel, and hail are considered. The radiation scheme used in the
model calculates the effects of clouds, hydrometeors, and aerosols upon
radiation (Toon et al., 1989). The model considers turbulent mixing using the
turbulent kinetic energy (mean kinetic energy per unit mass for eddies in
turbulent flow) as a prognostic variable (Mellor and Yamada, 1982).
The chemistry scheme used in C-CATT-BRAMS simulates gas- and aqueous-phase
chemistry, photochemistry, uptake described by Henry's law, and hydrolysis.
Marécal et al. (2012) and Krysztofiak et al. (2012) provide a detailed
overview of the equations describing the chemistry solved by the model. To
summarise, the model calculates chemical loss and production rates, and it
computes chemical species concentrations in the gas phase, in cloud particles,
and in rain droplets. To this end, the chemistry scheme couples with the
microphysical scheme which explicitly resolves cloud and precipitation
processes (Marécal et al., 2012). In practice, the model considers, within
the bulk microphysical scheme, the effects on the chemical species of
condensation, evaporation, water vapour deposition, and sedimentation. In
addition, the reversible exchange of gases between the gas and aqueous phases
(cloud and liquid hydrometeors) is estimated using Henry's law and
accommodation constants. Once within the condensed phase, the model includes
the transfer of chemical species from within cloud particles to the different
types of hydrometeors during coalescence and riming and within the individual
types of hydrometeor. In brief, it assumes Henry's law with cloud water and
uses the production of precipitation to determine how much of the soluble
trace gas is removed by wet deposition. We use retention coefficients to
describe the proportion of a chemical compound that is retained in the
condensed phase upon the transition from one type of hydrometeor to
another. This approach represents liquid-to-ice processes like riming that are
the dominant process for the formation of ice hydrometeors in convective
clouds. We simplify the treatment of retention for the formation of ice
precipitates by assuming a retention coefficient of 1 (i.e. the entirety of
the compound) for all chemical species dissolved in liquid precipitate that
undergo freezing. This is a frequently used assumption within washout schemes
in global- and regional-scale chemical models. The uptake of bromine species
onto ice hydrometeors is not represented as it was found in Marécal
et al. (2012) to not have an important effect on bromine removal.
The photolysis rates are computed online in the model using the Fast-TUV
(Tropospheric Ultraviolet and Visible) radiative model (Tie et al., 2003).
This is done in such a way as to consider the effects of clouds on photolysis
rate in an interactive way.
The BrONO2 hydrolysis reaction within cloud particles and rain
droplets has been added to the chemical scheme. Its mathematical
implementation is described by Marécal et al. (2012). The reaction scheme
considers the reaction within cloud particles and rain droplets separately
using the mean mass radius and mean mixing ratios for cloud particles and rain
droplets from the bulk microphysical scheme, the thermal velocity, the gas-phase diffusivity, and the accommodation coefficient of BrONO2.
Note that bromine–chlorine reactions have not been included in the chemistry
scheme since Marécal et al. (2012) showed that it has only a small impact
on the production of Brx even under the most favourable conditions
possible.
New model developments
Several important changes have been applied to the model to simulate chemical
and physical processes associated with CHBr3 degradation chemistry
and transport. A photochemical mechanism for the degradation of
CHBr3 was developed, tested, and optimised for use in C-CATT-BRAMS
(Krysztofiak et al., 2012). The development of the new mechanism also included
the estimation of the most favoured branching ratios for the halogenated
peroxy reactions with the hydroperoxy radical (XRO2+HO2)
(see Table 1 of Krysztofiak et al., 2012, for more details) using ab initio
calculations of the standard reaction enthalpies. Reaction rates were either
estimated from the analogous chlorine compounds or via a generalised
expression. In addition, to properly simulate the uptake and washout of PGs
into cloud particles and rain droplets, Henry's law coefficients had to be
estimated using predictive methods: the bond contribution method (Meylan and
Howard, 1991) and the molecular connectivity index (Nirmalaklandan and Speece,
1988) for the brominated organic peroxides, CBr3O2H and
CHBr2O2H, and the brominated carbonyl species, CBr2O
and CHBrO. Krysztofiak et al. (2012) discusses the validity of these
estimates. The Henry law constants of bromine species are shown in
Table 1. Note that the information on BrONO2 is not included in
Table 1 since it undergoes rapid hydrolysis in water, and thus its removal is
uptake limited (see Marécal et al., 2012, for details).
Values used in the determination of the Henry law constants
HXa and effective Henry constants
HX′b of bromine species X used in
the model.
SpeciesH298 (moll-1atm-1)aH (K)KA298 (moll-1)HXa at cloud baseHX′b at cloud base(20 ∘C)(20 ∘C) and cloud/rainpH of 5HBr0.71e10 200e1×109d1.271.27×1014HOBr6.1×103c5900d08.55×103–Br20.76e4177e00.97–Br1.7c5200c02.29–CBr3OOH1.96×105f5200g02.63×105–CHBr2OOH2.25×104f5200g03.03×104–CHBrO74f5800h01.02×102–CBr2O21.5f5600h02.96×101–CHBr33.4×10-2c1800c03.77×10-2–
aHX=H298expaH1T-1298.bHX′=HX×1+KA298H+.c Sander (2015). d As for HOCl (Sander, 2015).
e Yang et al. (2005). f Krysztofiak et al. (2012).
g As for CH3OOH (Sander,
2015). h Mean temperature dependency of RCHO and RR′CO (Sander,
2015).
These new developments coupled with a simple tropospheric chemistry scheme
including carbon monoxide (CO), methane (CH4), ozone (O3),
oxidised nitrogen (NOy), and hydrogen oxide radicals (HOx) (Barth et
al., 2007) were successfully implemented by Marécal et al. (2012).
Building on the new mechanism implemented in Marécal et al. (2012),
non-methane hydrocarbon (NMHC) chemistry was added to the chemical mechanism
in order to provide a more realistic description of the chemistry for the
SHIVA real case study. The NMHC mechanism is a reduced version of the Regional
Atmospheric Chemistry Mechanism (Stockwell et al., 1997) called the Regional
Lumped Atmospheric Chemical Scheme (ReLACS, Crassier et al., 2000). We term
this modified version of the ReLACS scheme RELASH. RELASH includes 60 chemical
species, includes 166 chemical reactions, treats NMHCs with up to eight carbon atoms via
lumping scheme, and is designed to describe tropospheric chemistry only. The
full chemical mechanism is described in S1 of the Supplement.
Model configuration
The model simulation was run for 3 d from 12:00 UTC
on 17 November to 12:00 UTC on 20 November 2011. We used a nested grid
configuration with three grids. The coarsest and largest grid covers from 90
to 135∘ E and from 14∘ S to 23∘ N and uses a
spatial resolution of 50×50km; the next coarsest grid covers
from 106 to 123∘ E and from 2∘ S to 12∘ N at a
resolution of 10×10km; and the finest-scale grid covers from
112.7 to 117.4∘ E and from 3.3 to 7.6∘ N and has a spatial
resolution of 2×2km. This horizontal spatial configuration
allows the finest grid to completely include the two convective systems
(Obs_Conv1 and Obs_Conv2) and the region covered by Falcon flight on the
afternoon of 19 November. The finer domain and its associated model orography
are plotted in Fig. 2. This illustrates well the abrupt topography on the west
side of Borneo island, leading to the development of deep convection in
sea-breeze conditions. The model has 53 vertical levels with varying vertical
separation using finer resolution within the BL. The top of the model reaches
to 26.6 km. The model meteorology was initialised and forced along the
coarse-grid boundaries of C-CATT-BRAMS using 6-hourly European Centre for
Medium-Range Weather Forecasts (ECMWF) analysis fields for vector wind
components, temperature, geopotential height, and specific humidity. We used
the ECMWF operational analysis at 0.5∘×0.5∘
resolution.
Map of topography (in m) used in C-CATT-BRAMS for the finest-scale
model grid. The vertical axis is latitude (degrees north), and the horizontal
axis is longitude (degrees east).
Within the coarsest two model grids we enabled the parameterisations for
shallow cumulus convection and for deep convection. We used the deep
convection parameterisation from Grell and Dévényi (2002) as
implemented in CATT-BRAMS in Freitas et al. (2009). We allowed the model to
resolve clouds and convective processes directly for the finest-resolution
grid. The topography used in the model has a 10 km resolution within
the coarsest two grids and a 1 km resolution within the finest
grid. Sea surface temperatures (SSTs) were initialised using the satellite
observed weekly average SSTs (Reynolds et al., 2002).
To describe CHBr3 emissions, we have implemented the emission
inventory of Ziska et al. (2013). This is a bottom-up inventory based on the
atmospheric and oceanic measurements of the HalOcAt (Halocarbons in the Ocean
and Atmosphere) database project
(https://halocat.geomar.de/, last access: last access: 15 November 2021). Using SHIVA flight measurements and the TOMCAT CTM, Hossaini
et al. (2013) showed that this inventory performs best for bromoform in the
Maritime Continent region compared to the inventories of Liang et al. (2010),
Warwick et al. (2006) updated by Pyle et al. (2011), and Ordóñez
et al. (2012). The Ziska et al. (2013) emissions have a 1∘×1∘ resolution. A diurnal variability linked to solar zenith angle is
applied to these emissions such that they peak at solar noon. They are shown
in Fig. 3 for the largest domain used in the C-CATT-BRAMS simulation. Note
that the emissions are large on the west coast of Borneo island where
convection develops on the afternoon of 19 November 2011.
Map of the annual CHBr3 emission distribution in pmolm-2h-1 used in C-CATT-BRAMS in the largest model domain (Ziska et al., 2013). The vertical axis is latitude (degrees north), and the horizontal
axis is longitude (degrees east). The red rectangle corresponds to the
domain of the finest-grid domain displayed in Figs. 1 and 2.
Various chemical species were initialised and forced along the coarse-grid
boundaries using 6-hourly output from the TOMCAT CTM (Chipperfield, 2006).
The chemical species were CHBr3, O3, hydrogen peroxide,
nitrogen oxide, nitrogen dioxide (NO2), nitric acid, pernitric acid,
CO, methane, ethane, propane, isoprene, HCHO, ethaldehyde,
acetone, peroxy acetyl nitrate, peroxy propyl nitrate, methyl hydroperoxide,
ethyl hydroperoxide, Br2, BrO, HOBr, HBr, and BrONO2, the
bromo carbonyls Br2C(=O) and HBrC(=O), and bromo peroxides
(CHBr2OOH and CBr3COOH). The TOMCAT simulation was run
using the Ziska et al. (2013) emissions to ensure the consistency between the
C-CATT-BRAMS simulation and its chemical boundary conditions from TOMCAT. For
some of these species we had to perform lumping, splitting, and scaling by
reactivity in order to achieve consistency with the chemical mechanism used in
C-CATT-BRAMS.
Results
Consistent with the objectives of this paper, the results shown and discussed
in this section are only those of the finest-resolution grid (2×2km; see Fig. 2) since it gives a detailed description of the
meteorology and chemical composition within and in the vicinity of deep
convection developing over the west coast of Borneo.
Meteorology
Simulations with limited area models with horizontal resolutions of the order
of 1 km, as in the present study, are largely used to study in detail
the development of convective systems since they provide an explicit
representation of their dynamical and thermodynamic processes. At this
resolution, there is no need to use sub-grid-scale parameterisations for
convection. But even at this fine resolution, modelling tropical deep
convective systems remains a challenge when one wants to reproduce the exact
time, intensity, and structure (extent of convective cloud component versus
stratiform cloud component) compared to observations. This is particularly
true in maritime conditions because of the uncertainties in the representation
of hydrometeor properties and processes and because of their sensitivity to
large-scale meteorological conditions (e.g. Varble et al., 2014, and
references therein). We do not attempt to make a detailed comparison of the
model simulations with the particular convective systems sampled by the Falcon
(Obs_Conv1 and Obs_Conv2). Instead, we now evaluate if the observed, general
features of the development of the deep convective systems (described in
Sect. 2 and Fig. 1) are well captured by the model.
Same as in Fig. 1 but for the brightness temperatures calculated
using the simulation fields (see explanations in the text; plots are labelled with time in UTC): 05:00 UTC,
06:00 UTC, 07:00 UTC, 08:00 UTC, 09:00 UTC, 10:00 UTC, 11:00 UTC, and 12:00 UTC.
The system called Mod_Conv_4.35N is shown by a
red rectangle, Mod_Conv_3.75N by a blue
rectangle, and Mod_Conv_5.4N by a purple
rectangle. The vertical axis is latitude (degrees north), and the horizontal
axis is longitude (degrees east).
To show the evolution of the modelled convective systems, we plot in Fig. 4
the model-derived brightness temperatures at 11 mm (IR108 channel
wavelength) estimated from cloud top pressures from 05:00 UTC to
12:00 UTC on 19 November 2011 using RTTOV v12.3 (Radiative Transfer for
TOVS, https://nwpsaf.eu/site/software/rttov/, last access: 23 June 2021,
Saunders et al., 2018). Figure 4 shows that the model simulates three deep
convective systems that develop during the afternoon. These systems (called
hereafter Mod_Conv_4.35N, Mod_Conv_3.75N, and Mod_Conv_5.4N) can be
identified and followed in time by coloured rectangles. Consistent with the
observations, the analyses of the simulated meteorological fields show that
all three systems are triggered inland close to the coast in the early
afternoon from the large-scale low-level winds (north/north-westerlies)
enhanced by local sea breeze that encounters the fairly steep topography of
west Borneo (Fig. 2). This process is illustrated in Fig. 5a, which shows the
simulated low-level wind direction and intensity at 06:00 UTC
(14:00 LT, a time when convection is at an early stage) and the
associated temperature field that exhibits a sea–land positive gradient.
After 06:00 UTC the deep convective systems move offshore towards the
west/north-west driven by the upper-tropospheric winds, and they develop an
anvil from their outflow. Upper-tropospheric winds at 09:00 UTC
(17:00 LT) are shown in Fig. 5b. Compared to the evolution of the
observed brightness temperatures (Fig. 1), the vertical extension of the
convective part of the systems during the mature stage tends to decrease a bit
too rapidly in the model (Fig. 4), leading to a less extended anvil, likely due
to a too rapid removal of precipitation related to uncertainties in the
microphysical parameters. However, the values of brightness temperatures in
the convective systems, which are a proxy of the cloud top height, are similar
in the model and the observations, showing that the model provides a good
estimate of the height of the convective systems that developed on the west
coast of Borneo on the studied day.
Simulated (a) temperature and horizontal wind at the lowest model
level (24 m) at 06:00 UTC. (b) Horizontal wind at 11 700 m altitude at 09:00 UTC from
the C-CATT-BRAMS model. The vertical axis is latitude (degrees north), and
the horizontal axis is longitude (degrees east).
Characteristics of the observed and simulated deep convective
systems. The cloud top heights for the observed convective systems are based
on Hamada and Nishi (2010) and Iwasaki et al. (2010). The outflow refers to
the stratiform part of the convective system.
Location where the convective system first reaches ∼14.5km altitudeTime when the convective system first reaches ∼14.5km altitudeTime when the outflow starts to dissipateEstimated maximum top altitude of the outflowObs_Conv15.5∘ N–116.0∘ E∼05:00 UTC (∼13:00 LT)After 10:00 UTC (18:00 LT)14.5±0.5km at 09:00 UTC (17:00 LT) 15.5±0.5km at 10:00 UTC (18:00 LT)Obs_Conv24.3∘ N–114.4∘ E∼07:00 UTC (∼15:00 LT)After 11:00 UTC (19:00 LT)15.5±0.5km at 10:00 UTC (18:00 LT) 14.5±0.5km at 11:00 UTC (19:00 LT)Mod_Conv_4.35N4.3∘ N–114.2∘ E∼05:00 UTC (∼13:00 LT)At least 08:00 UTC (16:00 LT) (out of the domain) ∼15.5km at 07:00 UTC (15:00 LT) ∼15.5km at 08:00 UTC (16:00 LT)Mod_Conv_3.75N3.75∘ N–113.9∘ E∼08:00 UTC (∼16:00 LT)At least 11:00 UTC (19:00 LT) (out of the domain) ∼14.5km at 10:00 UTC (18:00 LT) ∼14.5km at 11:00 UTC (19:00 LT)Mod_Conv_5.4N5.4∘ N–115.8∘ E∼08:00 UTC (∼16:00 LT)After 11:00 UTC (19:00 LT)∼14.5km at 10:00 UTC (18:00 LT) ∼14km at 11:00 UTC (19:00 LT)
Other quantitative characteristics of the observed (Obs_Conv1, Obs_Conv2)
and modelled (Mod_Conv_4.35N, Mod_Conv_3.75N, Mod_Conv_5.4N) convective
systems are compared in Table 2. For Obs_Conv1 and Obs_Conv2, we use the
estimates of the observed cloud top heights derived from brightness
temperatures (Hamada and Nishi, 2010; Iwasaki et al., 2010) only where the
uncertainty is ∼0.5km or lower. For the model, we use a set of
cross sections from the 3D fields to estimate the model cloud tops. Table 2
shows a general agreement on altitudes between the observations and the
model. Regarding the timing, the two observed convective systems originating
on the west coast of Borneo do not reach the upper troposphere at the same
time (05:00 UTC for Obs_Conv1 and 07:00 UTC for Obs_Conv2),
and the duration before they start to dissipate also varies (5 h for
Obs_Conv1 and 4 h for Obs_Conv2). In the model, the three convective
systems also show variations of these two parameters which are close to those
observed. The time when convection first reaches the upper troposphere in the
model is only off by 1 h maximum (08:00 UTC) compared to
observations (07:00 UTC) and still occurs in the afternoon. Regarding
the dissipation time, there is an uncertainty because two of the systems leave
the model domain. However, the model simulates anvils that last at least
3 h before they start to dissipate.
Overall, from collating the information from Figs. 1 and 4 and Table 2, we
find that the simulation represents the general characteristics of the
observed convective systems well, in particular
the origin of their development from the interaction of the large-scale flow
and the steep orography of the west coast of Borneo combined to local
effects,
the location of the initial convective cell about 30 km inland on the west
coast of Borneo,
the development of an anvil (the stratiform part) from the convective
outflow during the afternoon moves offshore,
the cloud top height of the outflow,
the transport of the convective systems north-westwards and westwards, and
the duration of the system of several hours and decay during early evening.
The main discrepancy is that the condensed water in the simulated convective
part of the systems tends to precipitate a bit too efficiently compared to
observations. Nevertheless, in its early stages, the convective part of the
system, as evidenced by condensed water, reaches altitudes greater than
14.5 km. Achieving this altitude gives a strong indication that the
intensity of the main updraught transporting bromoform into the upper
troposphere is predicted well by the model.
In conclusion, the model is able to simulate the general meteorology of the
observed convective systems, at least within the constraints and uncertainties
of kilometre-scale modelling of convection (Varble et al., 2014).
The three convective systems we examine detrain into altitudes ranging between
11 and 15 km. According to Corti et al. (2005) and Corti
et al. (2006), the LZRH can be as low as 11 km for air masses within
ice clouds due to the effects of their radiative properties. Ice clouds are
present in the anvils of all three of the simulated systems, and they could
cause a shift in the radiative balance and sufficient heating to lower the
altitude of the LZRH. This would imply that the simulated air masses could
gain positive buoyancy sufficient to reach the stratosphere over long enough
time and large enough spatial scales. Thus, the study of the chemistry and
washout within these systems could have relevance for the transport of
CHBr3 and its PGs to the stratosphere.
Comparison of the measured and modelled bromoform statistics and
convective transport efficiency
Before discussing the results of the simulated chemistry in detail
(Sect. 4.3), we evaluate if the simulation gives reasonable results for
CHBr3 concentrations and for convective transport efficiency
compared to the aircraft observations.
We firstly use statistical characteristics for this comparison. We choose this
approach for two reasons. First, because of differences in location and timing
between the observed and simulated convection events and, second, because of
spatial uncertainties in the emission inventory used in the simulation. This
approach allows a clearer comparison of the observations and simulation by
removing effects arising from inherent temporal and spatial uncertainties.
In order to compare the convective transport efficiency between the observed
and simulated systems, we follow the approach proposed by Cohan et al. (1999)
and used by Bertram et al. (2007). To estimate the air fraction, f,
originating from the boundary layer (BL) and transported by convection, we use
the relationship from Cohan et al. (1999):
[X]UTconv=f⋅[X]BL+(1-f)⋅[X]UTnoconv,
where the mean mixing ratios in the boundary layer, the upper troposphere
within the convective systems, and the upper troposphere in the vicinity but
outside the convective systems are represented by [X]BL,
[X]UTconv, and [X]UTnoconv, respectively. f ranges
from 0 to 1, with large values corresponding to an efficient convective
transport of air masses from the boundary layer to the upper troposphere. This
formulation of f is chosen because it was recently applied to the SHIVA
aircraft data (Krysztofiak et al., 2018). It relies on the assumption of a low
variability of background concentrations with altitude, which is fulfilled for
CHBr3 in our case study (not shown). Previous studies based on
observations and reported in Krysztofiak et al. (2018) provide estimates of
f in the range 0.17 to 0.36.
Table 3 shows the modelled and observed mixing ratios of CHBr3 that
are used to calculate the f fraction. The mixing ratios are divided into
three subsets corresponding to the boundary layer ([X]BL), the
upper troposphere within the convective systems ([X]UTconv), and in
the upper troposphere in the vicinity but outside the convective systems
([X]UTnoconv). The details on how the estimates from the
observations and from the model were determined are given in Supplement S2.
Estimates from the model simulations of the CHBr3 mixing
ratios (all in pptv) in the boundary layer [X]BL, in the UT outside
convection [X]UTnoconv, and in the UT within convection [X]UTconv.
f is the air fraction originating from the boundary layer and transported by
convection. Details on the method used are given in Supplement S2. The
error listed for f is calculated by propagating the standard deviation errors
on each [X] term used to calculate it. The equations to explain the
propagation of error are given in Supplement S3.
[X]BL(mean ±1σ)[X]UTnoconv(mean ±1σ)[X]UTconv(mean ±1σ)Fraction fMod_Conv_4.35N2.11±0.240.29±0.070.62±0.180.18±0.11Mod_Conv_3.75N1.20±0.250.33±0.130.62±0.130.33±0.23Mod_Conv_5.4N1.58±0.370.34±0.110.56±0.120.18±0.14Obs_Conv1 and Obs_Conv2 from CHBr3 SHIVA observations on the afternoon of 19 November 20111.82±0.860.51±0.040.73±0.120.17±0.15Mean from observations of 4 SHIVA flights*0.29±0.25
Asterisk (*) corresponds to the SHIVA estimate from four
flights and boat data (for data access please see data availability section) from different days including the flight on the
afternoon of 19 November 2011, using measurements of different species
(CHBr3, CO, CH4, and CH3I) (Krysztofiak et al., 2018).
Mod_Conv_4.35N and Mod_Conv_5.4N give values of the fraction of air
transported by convection from the BL that are close to the estimates of f
based on CHBr3 observations gathered on 19 November 2011.
A higher f fraction is calculated for Mod_Conv_3.75N, meaning that this
system was more efficient for transport of CHBr3 from the BL to the
UT. However, this high fraction f is consistent with the average value
calculated from all SHIVA aircraft data (0.29±0.25) determined by
Krysztofiak et al. (2018) using carbon monoxide measurements from the SPIRIT
instrument (Catoire et al., 2017) and the GHOST CHBr3 measurements
aboard the Falcon (Sala et al., 2014). The GHOST instrument is a
gas-chromatograph mass spectrometer and had an error of ±17.7% that was primarily driven by uncertainties in the gas
standard (Sala et al., 2014). Table 3 shows an overall good consistency (both
in terms of magnitude and simulated variability) between the model results,
the findings of Krysztofiak et al. (2018), and the SHIVA measurements
concerning f. Furthermore, Fuhlbrügge et al. (2016) used a trajectory
model and found similar values of f at 10–13 km of between
30 %–40 % for 19 November for the west coast Borneo region. It is
also worth noting that Mod_Conv_3.75N has a higher uncertainty and agrees
with the fraction f of Obs_Conv1 and Obs_Conv2 within the combined
uncertainties.
We now examine the magnitude of the CHBr3 mixing ratios in the BL
and in the UT both inside and outside of the convection using Table 3 and
Fig. 6. The box-and-whisker plots in Fig. 6 giving the median (i.e. 50th
percentile) and the 5th, 25th, 75th, and 95th percentiles provide complementary
statistical information to Table 3 on the variability of the observed and
simulated CHBr3 mixing ratios. Differences between the median and
the mean are a measure of the skewness of the distribution of points. The 5th,
25th, 75th, and 95th percentiles give additional information characterising the
low and high values of the distribution of the bromoform mixing ratios.
Box-and-whiskers plots (5th percentile, 25th percentile, median, 75th
percentile, 95th percentile) for the three simulated convective system and from the
observations of CHBr3 concentrations in parts per trillion by volume. The green bars show the observed mixing ratios, the red those of Mod_Conv_4.35N, the blue those of Mod_Conv_3.75N, and the purple those of Mod_Conv_5.4N. From left to right the results as shown for the
boundary layer, non-convective upper troposphere, and convective troposphere.
In the BL, there is a large spread in the observations because of the very
large local variability of the emissions that the model cannot capture due the
resolution of the CHBr3 emission inventory we used. Nevertheless,
the median BL mixing ratios of all three lie within the 25th and 75th
percentile of the observed BL mixing ratios, and the 5th–95th percentile range
of simulated mixing ratios across all three simulated systems lies within the
5th–95th percentile range of observed mixing ratios in the BL. In the BL, the
Mod_Conv_4.35N mean and median mixing ratios are higher compared (see Fig. 6
and Table 3) to the observations. Meanwhile, the mean and median mixing ratios
in the BL below Mod_Conv_5.4N are a bit lower than that observed, and
Mod_Conv_3.75N shows the lowest mean and median there (see Table 3 and
Fig. 6). This is likely related to two combined factors: the emissions are
weaker in the southern part of Borneo's west coast where Mod_Conv_3.75N
takes place (see Fig. 3), and Mod_Conv_4.35N initiates in closer proximity
to the coast compared to the other two systems where CHBr3 emissions
and BL mixing ratios are higher.
The mean and median UT mixing ratios in the simulation, which largely depend
on the chemistry initial conditions from the TOMCAT simulation, are
underestimated compared to observations, which leads to lower mixing ratios
both within and out of the convection (differences of 0.17 to
0.22 pptv in UTnoconv, and of 0.11 to 0.17 pptv in
UTconv). Hossaini et al. (2013) previously showed a comparable
0.08 pptv average negative bias in TOMCAT relative to the SHIVA
aircraft measurements of CHBr3 throughout the entire duration of the
flight on the afternoon of 19 November. TOMCAT's negative bias is slightly
smaller than in our case because our sampling focuses on a smaller
spatio-temporal domain (between 08:20 UTC and 09:40 UTC) in
proximity to the convective system, whereas the TOMCAT negative biases were
larger than the 0.08 pptv average for the full flight. The negative
bias of the UT mixing ratios is probably linked to an underestimate in the
emissions somewhere to the east of Borneo where these background UT air masses
originate from in the TOMCAT simulation. This finding is consistent with
Fuhlbrügge et al. (2016), who showed that local sources alone cannot
account for the observed CHBr3 levels in the UT. Furthermore, Keber
et al. (2020) indicate that underestimates in the background tropical UT might
arise due to underestimates in extratropical CHBr3 sources.
In the background UT (UTnoconv), we see small differences in the
mean and median mixing ratios in the vicinity of the three simulated
convective systems, with slightly higher values and variability (see all
percentiles in Fig. 6 and 1σ in Table 3) for Mod_Conv_3.75N and
Mod_Conv_5.4N because they developed in locations previously affected by
convective transport of bromoform.
The mean and median mixing ratios for all three systems are close with
slightly higher values (see Fig. 6 and Table 3) for Mod_Conv_4.35N due to
higher BL concentrations and for Mod_Conv_3.75N due to its more efficient
transport (highest f in Table 3). Mod_Conv_4.35N shows the highest
variability (see all percentiles in Fig. 6 and 1σ in Table 3) within
convection (UTconv) and the lowest outside (UTnoconv),
indicating that there is less mixing during the detrainment in the UT in this
system.
Note that the results presented in this section have little sensitivity to the
threshold in ice concentration used to define the sampling of grid points
within the convection and outside in its vicinity (see Supplement S4).
As a complement to the statistical comparison, Fig. 7 shows a spatial
comparison. We choose Mod_Conv_5.4 for this comparison because it is the
closest in time and space to Obs_Conv1. For the flight observations, we only
select those gathered in the upper troposphere at ∼12–13 km
altitude range, which corresponds to the sampling of Obs_Conv1. We plot the
modelled bromoform mixing ratio at 12.5 km altitude. To account for
the shift in time between the model and the observations, we plot the model
fields at 10:00 UTC when the anvil is well developed over the ocean as
in the observations around 09:00 UTC. The observations are also
slightly shifted in space in order to match the Mod_Conv_5.4 location. In
Fig. 7a we show the model bromoform mixing ratio and in Fig. 7b the same field
but adding 0.17 pptv, which is the model bias in both UTconv and
UTnoconv for Mod_Conv_5.4 with respect to the measurements. Figure 7a
illustrates well the model bias linked to the initial conditions. By removing
this bias in Fig. 7b, we find a good consistency between the model and the
observations. This confirms the findings of the statistical analysis.
Map of the modelled CHBr3 mixing ratios in parts per trillion by volume for
Mod_Conv_5.4N at 10:00 UTC and 12.5 km altitude
(a). The squares represent the CHBr3 mixing ratios measured by the
GHOST instrument within and in the vicinity of Obs_Conv1. 10:00 UTC corresponds to the time when Mod_Conv_5.4N
is at the convective mature stage, i.e. the anvil is well developed.
Because of the difference in location between Obs_Conv1 and
Mod_Conv_5.4N, the observations are shifted in
space to fit with the centre of Mod_Conv_5.4N
anvil. Panel (b) is similar to (a) but with 0.17 pptv added to the modelled CHBr3 to account for the underestimation of Mod_Conv_5.4N in the UT (conv and noconv) from Table 3.
In summary, this evaluation shows that the simulation provides reasonable
results compared to observations for the transport efficiency and for
CHBr3 concentrations knowing that the UT background values are
underestimated in the initial conditions.
Cross-section analyses of the simulated chemical processes
We now briefly explain the underlying methodology used to interpret the model
results (in the following Sects. 4.3.1–4.3.3) and to conclude
whether the convective transport leads to an enhancement or deficit in the
mixing ratios for CHBr3 and its PGs within the UT. The enhancements
or deficits in the CHBr3 and PG mixing ratios in the convective
column and UT that we report are based on comparisons of the simulated mixing
ratios in each region of the atmosphere involved in the convective system,
i.e. from BL to vertical component of convective system and to outflow. We
use the simulated mass mixing ratios of condensed water as a metric to define
what is within and what is outside of the convective systems and outflow. Mass
mixing ratios of 0.5 and 0.01 gkg-1 are used to define the most
intense and outer limits of the convection systems, respectively.
Bromoform
In this section, we analyse the cross sections of the simulated chemical
fields of CHBr3 within the convective systems. As an illustration of
how CHBr3 evolves during the convection, Fig. 8 shows a vertical
cross section of the 5 h time evolution of CHBr3 mixing
ratios in the central part of the Mod_Conv_5.4N system. The cross section is
taken as close to the centreline of the convective system as possible. We
selected Mod_Conv_5.4N since it corresponds most closely in space to
Obs_Conv1. We can see the complete evolution of the convective system from a
situation with elevated CHBr3 concentrations in the boundary layer
close to the location of the convection (of up to 2.1 pptv) at the
very early stage of the system (07:00 UTC, Fig. 8a). Then the
convective column ascends in a relatively vertical fashion (08:00 UTC,
Fig. 8b) and afterwards develops an anvil on its west side (from
09:00 UTC, Fig. 8c–e). The concentrations in the anvil are naturally
at their highest at the time and location of convective detrainment and reach
up to 0.9 pptv at 09:00 UTC (17:00 LT) and begin to
decrease after 1 h (up to 0.75 pptv) as the anvil is advected
north-westward by the high-altitude winds.
Vertical cross section within the most active part of
Mod_Conv_5.4N convective system (located at
5.4∘ N) showing time evolution of CHBr3 mixing ratio in parts per trillion by volume
for 07:00, 08:00, 09:00, 10:00, and 11:00 UTC. The white and black lines
represent the 0.01 and 0.5 gkg-1 contour of the simulated
condensed water (cloud and precipitation in ice and liquid phase).
The analysis of the transport, chemical processes, and Br-atom speciation done
hereafter is based on vertical cross sections chosen in the central part of
each convective system at the time when the anvil is in its mature stage; the
precipitation and vertical transport within the convective column are also
near their maximum at this stage. This means that these cross sections are
representative of the most intense convective activity, which demonstrates the
combined effects of intense vertical transport, washout, and development of
the anvil. All of the numbers presented in the following sections correspond
to those of the cross sections presented in the figures. Also, to be able to
compare the contribution of the different bromine species to the total Br-atom
mixing ratios, all the figures hereafter are expressed as Br-atom mixing ratios
(henceforth known as pptv Br).
We show the concentrations of CHBr3 in the three convective systems
in Fig. 9a–c and their percentage contribution to the total Br mixing ratio
in Fig. 10a–c. Note that Fig. 9c is identical to Fig. 8c except scaled by the
number of bromine atoms, i.e. a factor of 3. Each of the three simulated
convective systems exhibits different CHBr3 mixing ratios within
its convective columns and within its outflow anvils. This variability is
because they each entrained different boundary layer mixing ratios of
CHBr3, and they also detrained into UT regions with slightly
differing CHBr3 backgrounds and have different transport
efficiencies (Table 3).
Vertical cross sections of mixing ratios (expressed in Br pptv) of
CHBr3(a–c), inorganic (d–f), and organic (g–i)
bromine compounds. The left, middle, and right columns correspond to cross
sections of Mod_Conv_4.35N at 4.35∘ N at 06:00 UTC, Mod_Conv_3.75N at 3.75∘ N at 09:00 UTC, and Mod_Conv_5.4N at 5.4∘ N at 09:00 UTC. The white and black lines represent the 0.01 and 0.5 gkg-1 contours of the simulated condensed water (cloud and
precipitation in ice and liquid phase), respectively. Note that panel (c) is
identical to Fig. 8c except scaled upwards by three for the number of bromine
atoms.
Vertical cross sections similar to Fig. 9 but for the percentage
contributions from CHBr3 and inorganic and organic bromine compounds to
the total Br mixing ratio. Note that for organic bromine the scale is from 0 %
to 10 %.
Despite these variations, we see a consistent result in the Br-atom speciation
and mixing ratios of each convective system in Figs. 9 and 10. CHBr3
is elevated above background levels in all of the atmospheric regions
dynamically linked to the boundary layer (surface to 600–800 m
height) on a timescale well below the lifetime of CHBr3 (i.e. the
boundary layer, convective columns, and convective outflow). We can see that
the CHBr3 in these air masses have only undergone limited
photochemical ageing because CHBr3 accounts for >85%
of the total Br mixing ratio (Fig. 10a–c) in these atmospheric regions.
However, we consistently see lower CHBr3 contributions to the total
Br mixing ratio in atmospheric regions above the boundary layer not affected
either directly or indirectly by convection. These regions include the low
(from the top of the boundary layer to ∼2km height), middle (from
∼2km height to ∼8km), and upper troposphere (from
∼8km height to ∼13–14 km), where the model
typically simulates 0.45–0.9 pptv Br of CHBr3, accounting
for 60 %–70 % of the total Br mixing ratio in the absence of
convection to any vertical level. We can see some evidence of elevated
CHBr3 in the upper troposphere related to the transport of outflow
from distant convection, for instance in Fig. 9c at around 116.5∘ E
longitude and at 11 to 12 km. Within these air masses the model
simulates intermediate mixing ratios and Br-atom contributions signifying air
masses of intermediate CHBr3 ageing and mixing. Note that the sharp
changes in bromine mixing ratios that we see above 14 km in Fig. 9 and
onwards are due to the vertical transition into the tropical tropopause layer,
which is influenced by the stratosphere, where we find 0–0.45 pptv Br
of CHBr3, accounting for 15 %–20 % of the total simulated Br
mixing ratio there.
Inorganic and organic PGs
Figure 9d–f show that there are relatively low levels of inorganic bromine
(Br, Br2, BrO, HOBr, HBr, BrONO2) concentrations in the
boundary layer even in the areas not directly affected by convective
precipitation, with values typically in the range of 0 to 0.4 pptv Br,
i.e. <5 % contribution to the total Br mixing ratio (Fig. 10d–f). The
highest simulated inorganic bromine mixing ratios (0.3–0.4 pptv Br)
in the boundary layer occur to the west of the Mod_Conv_5.4N system but still
only contribute <10 % to the total boundary layer pptv Br (see
Fig. 10f). This spatial variability in the boundary layer inorganic bromine
mixing ratios around each convective system arises due to differences in
precipitation location and timing over the course of the simulation prior to
19 November 2011. Precipitation events occurring in the 2 preceding days
deplete the boundary layer of inorganic bromine due to washout (analysis not
shown). In the boundary layer, organic PGs (CHBrO, CBr2O,
CHBr2OOH, and CBr3COOH) concentrations are up to
0.2 pptv Br (up to 10 % contribution to total bromine but
very locally) and are formed due to CHBr3 photochemical loss
(Figs. 9g–i and 10g–i).
Air masses in the convective column itself and convective outflow are almost
entirely depleted of inorganic bromine with mixing ratios of <0.1pptv Br and with contributions to the total Br mixing ratio well
below 5 %. There, organic compounds are being driven from the low
levels up to the upper troposphere in the main ascent and the outflow and show
enhanced mixing ratios within the convective column compared to the free
troposphere (Fig. 9g–i). Still, organic PGs have a contribution to the total
bromine only up to ∼4%.
In the free troposphere, inorganic and organic bromine concentrations are
enhanced between 1 and 4 km to the west of each convective system
(Figs. 9d–i and 10d–i). There, total inorganic (respectively organic) PGs peak up
to 1 pptv Br (respectively 0.2 pptv Br), which constitutes a portion
up to 45 % (respectively 5 %) of the total Br mixing
ratio. Among the three convective systems, Mod_Conv_5.4N exhibits the
highest concentrations of the organic PGs.
Above 4 km altitude in convection-free areas, inorganic bromine is
mainly in the 0.2–0.4 pptv Br range (15 %–35 % contribution
to total Br) that is higher than within convection (Figs. 9d–f and 10d–f).
There, organic PGs have low concentrations (0.02–0.03 pptv Br) and
contribute only to 1 %–3 % (Figs. 9g–i and 10g–i).
The impact of PG solubility
Given that washout is an important process within the convective systems, the
relative solubilities of each component we look at are relevant for explaining
the concentration levels of the inorganic and organic PGs. Table 1 shows the
Henry law constants for the PGs. In order of increasing solubility, we first
list the inorganic bromine PGs – BrO, Br, Br2, HOBr, HBr, and
BrONO2 – and then the organic PGs – CHBrO, CBr2O,
CHBr2OOH, and CBr3COOH. In discussions from this point on,
we will classify the inorganic PGs into two groups: soluble inorganic that
includes HOBr, HBr, and BrONO2; and insoluble inorganic, comprising
Br, Br2, and BrO. We also classify the bromo-carbonyls (CHBrO and
CBr2O) as insoluble organic and the bromo-methyl peroxides
(CHBr2OOH and CBr3COOH) as soluble organic PGs. Note that,
except for Br2, CHBr3 is less soluble than its PGs. The
soluble inorganic, insoluble inorganic, soluble organic, and insoluble organic
bromine compounds and their relative contributions are shown in Figs. 11 and
12.
Vertical cross sections of mixing ratios (expressed in Br pptv)
of soluble inorganic (HOBr, HBr, and BrONO2) (a–c), insoluble
inorganic (Br, Br2, and BrO) (d–f), soluble organic
(bromo-methyl peroxides) (g–i), and insoluble organic (bromo-carbonyls)
(j–l) bromine compounds. The left, middle, and right columns
correspond to cross sections of Mod_Conv_4.35N
at 4.35∘ N at 06:00 UTC, Mod_Conv_3.75N
at 3.75∘ N at 09:00 UTC, and Mod_Conv_5.4N at 5.4∘ N at 09:00 UTC.
Vertical cross sections similar to Fig. 11 but for the percentage
contributions from soluble inorganic (a–c), insoluble inorganic
(d–f), soluble organic (g–i), and insoluble organic (j–l)
bromine compounds to the total Br mixing ratio. Note that for insoluble
inorganic and organic bromine, the scale is from 0 % to 5 %.
The gas-phase mixing ratios of soluble inorganic bromine species are depleted
to near zero in the convective columns and in the immediate area of
detrainment for each system (Fig. 11a–c). Similarly, soluble inorganic
species make almost no contribution to the total gas-phase Br within each
convective system (Fig. 12a–c). This depletion of soluble inorganic bromine
species occurs even though their boundary layer mixing ratios range between
0.1 and 0.5 pptv Br and the soluble inorganic species form the bulk of
the inorganic bromine at all levels in the troposphere outside of convective
systems. This strongly implies that the model simulates the near-total removal
of the soluble inorganic bromine species within the convective columns. The
insoluble inorganic species (Figs. 11d–f and 12d–f) only make a negligible
contribution to the total bromine budget throughout the troposphere, only
reaching peak mixing ratios of 0.1 pptv Br and 4 % of the
total bromine in areas of the lower troposphere not affected by convection. We
also see no enhancement of the insoluble inorganic species (which include
Br2) within the convection column or in the fresh convective outflow
above the levels seen in the rest of the troposphere. There is also only a
negligible contribution of insoluble inorganic bromine in the UT affected by
previous convection as illustrated in Fig. 12e and f (to the east of both
Mod_Conv_3.75N and Mod_Conv_5.4N).
The contribution to the total Br mixing ratio from the soluble organic bromine
PGs (Figs. 11g–i and 12g–i) is also negligible (at a maximum of ∼1% in the low troposphere not affected by convection). The bulk
of organic PG species are instead in the form of insoluble species (Figs. 11
and 12j–l). The enhancements of the organic PGs we see in Fig. 9g–i within
the convective system are due to the insoluble bromo-carbonyls, i.e. up to
0.08 pptv compared to the background free troposphere
0.02 pptv (Figs. 11j–l and 12j–l). These compounds contribute a
maximum of between 75 %–95 % of the PG bromine total within each of
the three the convective columns, which is based on figures (not shown) of the
relative contribution of the insoluble bromo-carbonyls to the total PG mixing
ratio. We see across all three convective systems that the relative
contribution of the insoluble bromo-carbonyls is at a maximum at the point PG
mixing ratios are at a minimum. This indicates that these compounds survive
complete washout during vertical ascent but play a small role in the vertical
transport of bromine within the convection systems.
Vertical cross sections of mixing ratios (expressed in Br pptv)
of HBr (a–c) and HOBr (d–f) bromine compounds. The left, middle,
and right columns correspond to cross sections of Mod_Conv_4.35N at 4.35∘ N at 06:00 UTC, Mod_Conv_3.75N at 3.75∘ N at 09:00 UTC, and
Mod_Conv_5.4N at 5.4∘ N at 09:00 UTC.
Vertical cross sections similar to Fig. 13 but for the percentage
contributions of HBr (a–c) and HOBr (d–f) to the total inorganic
Br mixing ratio.
In order to understand further the behaviour of inorganic bromine we need to
examine its composition. Figures 13 and 14 show HBr and HOBr mixing ratios and
their percentage contribution to the total inorganic bromine. Inorganic
bromine is almost entirely composed of HBr, HOBr, and BrONO2 within
the background troposphere outside of the convective systems in our
simulations. HBr dominates the inorganic Br-atom contribution in the regions
of the UT outside of the convective systems (Fig. 13a–c). HBr provides
between 40 %–65 % of the total Br mixing ratio in the regions of the
low and mid-troposphere (1.5–4 km height) unaffected directly by
precipitation washout (Fig. 14a–c), while HOBr and BrONO2 represent
small fractions (0 %–35 % and 0 %–20 %, respectively) of the
remainder (Figs. 13d–f and 14d–f, BrONO2 not shown). The lower troposphere
to mid-troposphere (1.5–4 km) is the only tropospheric region where
HOBr and BrONO2 have significant mixing ratios of up to 0.3 and
0.25 pptv. Otherwise, HBr dominates the total inorganic Br mixing ratio
in the BL and UT. Note that the very high HOBr and HBr relative contributions
of up to 100 % within the most active part of the convective systems shown
in Fig. 14d–f are not meaningful since the total inorganic bromine mixing
ratios are negligible there.
Discussion
We now present a discussion of the most important processes that control the
overall speciation of the PGs in the background lower troposphere unaffected
by convection in our simulation. This is important because this speciation
determines the starting mix of chemical compounds present in the surrounding
environment prior to convection and then ultimately what is available for
entrainment into the convective system. The speciation of PGs in the lower
troposphere is relevant for determining their potential transport to the upper
troposphere since they individually have different solubilities.
A key finding is that inorganic bromine dominates the PG budget within the
background lower troposphere during this case study. However, during
convective transport the inorganic PGs present in the low-tropospheric
background air are almost entirely removed by washout since HBr is by far its
most prevalent component in the marine boundary layer, and the next two most
abundant inorganic PGs (HOBr and BrONO2) are also highly soluble.
The regional tropospheric composition present in our simulations is the
underlying cause of this prevalence of HBr, and this in turn causes the
efficient washout of inorganic PGs.
Table 4 shows an example built from mixing ratios at a point in the marine
boundary layer at 200 m altitude in proximity to Mod_Conv_5.4N and
gives an indication of the relative reaction rates for these
conditions. Mixing ratios at this location are selected because the marine
boundary layer air masses are those that are entrained within the convective
systems and are therefore most relevant. Note that the relative rates give an
indicator of the preferred reaction for a particular species, e.g. for
bromine atoms between Reactions (R1), (R2), and (R3) shown below. However, the calculated rates cannot be used to quantitatively compare the production and loss of a
compound, e.g. production and loss of BrO, because they only give an
instantaneous approximate estimate of the rates for single reactions within
the complex chemical system solved by the numerical solver.
Rate constants and reaction rates at a point in the marine boundary
layer (200 m, 988.7 mb, and 298.7 K) within an air mass being advected into
the convective updraught of Mod_Conv_5.4N (5.8∘ N
115.5∘ E). This was done based on chemical species' mixing ratios at this
location for all of the Reactions (R1)–(R9) discussed in Sect. 4.3.4. This was
in a region with no cloud or rain. For Reactions (R8) and (R10), the reactions taking
place in the aqueous phase, we select some representative rain and cloud
water mass mixing ratios (0.5 and 0.1 gkg-1, respectively) to
demonstrate the reaction rates in the presence of liquid water condensate.
In the case of Reaction (R10) the hypothetical reaction rate is calculated using the
aqueous concentration of HOBr at Henry's law equilibrium, and due to the
extremely high solubility of HBr it is assumed that all of its gas-phase
mixing ratio is dissolved in solution. For both Reactions (R8) and (R10) the reaction
rates in cloud and rain droplets are shown separately, but in the case of
Reaction (R10) they cannot be combined additively.
Within the photochemical scheme of C-CATT-BRAMS, bromine atoms can react in
the gas phase in one of three ways via Reactions (R1), (R2), or (R3) (shown in order of
decreasing reaction rate).
R1Br+O3→BrO+O2R2Br+HCHO→HBrR3Br+HO2→HBr
Bromine radicals can react with ozone via Reaction (R1) to form BrO, which is normally
the dominant reaction pathway for Br in the troposphere, and this is the case
here too. In addition, bromine radicals can react with HCHO or HO2
to form HBr, but combined the rate of HBr formation via Reactions (R2) and (R3) is less than a third of the production rate of BrO. However, under relatively low ozone
conditions (13.1 ppbv in this example) and in the presence of
relatively high HO2 (37.2 pptv) and only low NOx levels
(e.g. 4.6 pptv of NO2 and 0.6 pptv of NO), as we
find in our simulations, the reactions between BrO and (in order of decreasing
importance) HO2 (Reaction R4), NO2 (Reaction R5), and NO (Reaction R6) are enough to
suppress BrO mixing ratios to only negligible levels.
R4BrO+HO2→HOBrR5BrO+NO2→BrONO2R6BrO+NO→Br+NO2
Combined Reactions (R4)–(R6) suppress boundary layer BrO mixing ratios down to very
low levels (0.5 ppqv) within this example air mass during the daytime. During
the night BrO production will shut off, and residual NO as well as
NO2 react with BrO, resulting in the latter's complete removal.
Reaction (R4) leads directly to modest HOBr formation. Reaction (R5) leads to BrONO2
formation, but its mixing ratios are also kept low by photolysis in
combination with hydrolysis within cloud and rain droplets.
R7BrONO2+hν→Br+NO3R8BrONO2(aq)→HOBr(g)+HNO3(g)
The hydrolysis of aqueous-phase BrONO2 Reaction (R8) is another pathway leading
to HOBr formation, which is dependent on the presence of condensed moisture
(note that for numerical reasons, in the model, the HOBr and HNO3
from Reaction R8 are produced in the model in the gas phase as their partitioning into
the aqueous phase is determined by Henry's law and air–liquid diffusion
limited uptake). However, despite modest formation rates of HOBr via Reactions (R4) and (R8),
the mixing ratios of HOBr are suppressed to relatively low levels during
daytime by photolysis.
HOBr+hν→Br+OH
Meanwhile, HBr (formed by Reactions R2 and R3, albeit relatively slowly) only has a very
slow rate of photolysis, and it is thus the single inorganic bromine compound
with a lifetime long enough to allow accumulation in the gas phase under these
conditions. HOBr and BrONO2 are the next most abundant inorganic
PGs, but their lifetimes (∼10 and ∼20min, respectively)
are kept low during the daytime by photolysis. If background ozone levels were
higher it would allow greater formation rates of BrO and in turn HOBr and
BrONO2 since BrO is the start point in the reaction pathways for both
species.
The maritime continent has been noted previously for having low O3
levels throughout the depth of the troposphere during the winter monsoon
resulting from westward transport of moist tropical air across the equatorial
Pacific (Rex et al., 2014). The relatively low ozone levels are reproduced in
our simulations (daytime values of as low as 10–15 ppbv at
2 km) and arise from the same mechanism across the equatorial Pacific
represented in the TOMCAT global model forcing.
The low levels of BrO have another implication. Since BrO is directly involved
in the two chemical pathways leading to HOBr formation (Reactions R4 and R8), the low
levels of BrO can be implicated in the lack of significant quantities of HOBr
(Figs. 13 and 14). HBr and HOBr react with one another in the aqueous phase to
produce Br2 via Reaction (R10) extremely rapidly.
HOBr(aq)+Br(aq)-+H+→Br2(aq)+H2O
Since this reaction has an effective stoichiometry of 1:1 (Br- and
H+ are both derived from HBr) and our model simulates that HBr is in a
vast excess compared to HOBr in the gas phase in the troposphere, HOBr is the
limiting reactant for Br2 formation. In the example in Table 4, this
excess is more than a factor of 103 in the gas phase and, after
accounting for the difference in solubility, a factor of 109 in the
aqueous phase. This leads to a relatively low reaction rate despite the speed
of the reaction implied by the rate constant. At the levels shown in Table 4
this equates to a lifetime of HOBr in solution of approximately 2×10-2 s. This imbalance in the stoichiometry within the aqueous phase is
simulated throughout the lower troposphere.
The atmospheric implication of Reaction (R10) is that two soluble gases (HBr and HOBr)
could potentially react rapidly within cloud and rain droplets to form the
insoluble gas Br2. Reaction (R10) is known to lead to the production and
release of significant quantities of Br2 in other environments
combining both the aqueous and gas phases, e.g. in the polar ice regions
(McConnell et al., 1992) and within volcanic plumes (Oppenheimer et al.,
2006). In these examples the gas-phase and aqueous-phase chemistry combine to
form a feedback loop, leading to what is known as the “bromine explosion”
(Wennberg, 1999). Thus, Reaction (R10) could have the potential to significantly alter
the Br-atom speciation within convective clouds in a short time in such a way
as to reduce the PGs' overall solubility; this would therefore promote the
vertical transport of Br atoms within convective systems even in the presence
of abundant falling hydrometeors that would otherwise wash out soluble gases
like HBr very rapidly. Figure 15 shows Br2 mixing ratios in Br
pptv. In our simulation, only negligible levels of Br2 form during
the most active phase of each of the simulated convective systems
(Fig. 15a–c). At most we see up to 0.04 pptv of Br2 within
Mod_Conv_5.4N (Fig. 15c), and this is only within the lowermost sections of
the system, and these levels do not propagate vertically into the UT. These
peaks in Br2 formation coincide with spatially limited regions with
elevated HOBr resulting from slightly higher background levels of BrO at these
points.
Vertical cross sections of mixing ratios (expressed in Br pptv)
of Br2. The left, middle, and right columns correspond to cross sections
of Mod_Conv_4.35N at 4.35∘ N at 06:00 UTC, Mod_Conv_3.75N at 3.75∘ N at 09:00 UTC, and Mod_Conv_5.4N at 5.4∘ N at 09:00 UTC. Note that the lines representing the 0.01 and 0.5 gkg-1 contours of the simulated condensed water are plotted in this
figure as red and orange, respectively.
From this analysis, we should expect more production of Br2 in
convective systems in environments with higher levels of background ozone,
which would consequently have more HOBr. Indeed, there may be some
observational support for this in data collected during the CONTRAST field
campaign. Chen et al. (2016) showed that the observed levels of BrO and the
sum of HOBr+Br2 were both below the limit of detection (0.6–1.3
and 1.5–3.5 pptv, respectively) in background tropical tropospheric
air where ozone levels were relatively low (<50ppbv). They found
that BrO and HOBr+Br2 were only above the limit of detection in
biomass burning plumes where ozone levels were significantly elevated (>50ppbv). Their findings are at least consistent with our
expectations of a link between ozone and the bromine speciation between HBr,
BrO, HOBr, and Br2.
The residence time of cloud and rain droplets in the atmosphere can impact
aqueous-phase chemistry in cloud and rain droplets. For other aqueous
chemistry systems occurring in cloud and rain droplets, the rate of chemical
reactions could occur slower than it takes for cloud or rain droplets to fall,
leading to wet scavenging of the chemical species involved. Bela et al. (2018)
report an example of aqueous-phase chemistry in convective cloud and rain
where the wet scavenging removal of H2O2 was found to be much
faster than its production via aqueous-phase chemistry in cloud and rain. By
contrast, the rate constant for Reaction (R10) is approximately 107 times faster
than the rate constants involved in the formation of H2O2
described by Bela et al. (2018), and the lifetime of HOBr in solution is
approximately 2×10-2 s as a result. Thus, the residence time of
cloud and rain droplets is not a limiting factor for Reaction (R10).
Comparison with Marécal et al. (2012)
While other studies exist of convective-scale modelling of ozone and aerosols
(Crumeyrolle et al., 2008; Tulet et al., 2002), Marécal et al. (2012) is
the only other study to have simulated the transport and photochemistry of
bromoform at the convective scale, and so we therefore compare our findings
with this study. The C-CATT-BRAMS model configuration in Marécal
et al. (2012) differs from the setup used in this study in the following ways.
The model domain was set to be at the same latitude and longitude as Darwin
(Australia).
There were no emissions of CHBr3 or any other VSLS. The only source of
CHBr3 was from a 2 km homogenous layer above the surface set as an
initial mixing ratio of either 1.6 or 40 pptv in the two scenarios.
Marécal et al. (2012) used a chemical mechanism that did not include a
representation of non-methane hydrocarbon chemistry. This chemistry was
added in this study by including the ReLACS chemical mechanism.
Apart from CHBr3 set in the lowermost 2 km, the model initial and
boundary conditions for the chemical species were defined from a single
vertical profile from the MOCAGE CTM from over Darwin. The mixing ratios of
all PGs were initialised at 0 pptv in all model grids and layers.
The meteorology in Marécal et al. (2012) involved an initial setup that
applied a single vertical profile of meteorological conditions throughout
the entire horizontal domain. The vertical profile was defined from a
radiosonde profile obtained above Darwin corresponding to November just
prior to the main wet season. Convection was artificially forced in the
simulation by introducing a perturbation in the lower model layers of
increased temperature and humidity.
Marécal et al. (2012) showed in their analysis of the artificially induced
tropical deep convective system that most of the Br transported to the UT by
convection is in the form of CHBr3, which is consistent with the
results here. Nevertheless, in their case using an initial CHBr3 of
1.6 pptv in the BL (which is the most comparable to this study), we
can see notable differences in the composition of the convective outflow. Both
organic and inorganic PGs are in higher abundance within the upper reaches of
each convective system in this study compared to Marécal et al. (2012). In
particular, the mixing ratios of all the inorganic PGs are greatly increased
by over a factor of 10 here compared to Marécal et al. (2012). As a
result, Marécal et al. (2012) found a higher relative contribution to the
total bromine PG mixing ratio in the UT for organic with respect to inorganic
PGs, i.e. 86 % compared to 46 % in this present
study. The background tropospheric air contains elevated HBr levels in our
simulation (Figs. 13 and 14) resulting from photochemical ageing of
convectively lofted CHBr3 represented both within the TOMCAT CTM
initial and boundary conditions. It is likely that the major differences in
simulated inorganic and organic PG mixing ratios within the convective system
arise as a result of the idealised CHBr3 and PG background and
initialisation used within Marécal et al. (2012) compared to the more
realistic simulation of the PG background (specifically HBr) in this study.
The small difference in latitude and atmospheric conditions between Darwin and
Borneo is enough to cause longer CHBr3 lifetimes in our simulation
with respect to both photolysis (15.2 d compared to ∼19d here) and OH (22.6 d compared to 52.6 d
here). Overall, both processes contribute to an increase in CHBr3
lifetime from 9 to ∼14d here (note both are fully consistent
with the compiled ranges from Burkholder et al., 2018). The CHBr3
lifetimes with respect to photolysis and OH are important for defining the
relative partitioning of bromine between CHBr3 and its different PGs
prior to the convective activity on 19 November. The relative importance of
photolysis has therefore increased in this study due to these changes, which
could lead to an increase in the relative formation rates of inorganic bromine
PGs compared to the organic PGs. This occurs because the photolysis of bromine
cleaves off single bromine radicals that then contribute directly to the
inorganic PG budget. Furthermore, the remaining organic PGs contain one fewer
bromine atom as a result. Meanwhile, reaction with OH leads to the
abstraction of a hydrogen atom, which creates organic PGs containing either
two or three bromine atoms. Given the idealised initialisation and
experimental design within Marécal et al. (2012), it is not possible to
properly assess these conjectures here, but it would be of interest to
evaluate more formally differences in PG speciation arising in different
photochemical environments that display differences in the CHBr3
lifetime with respect to reaction with OH and to photolysis.
Marécal et al. (2012) showed that significant amounts of Br2
were only released into the gas phase from Reaction (R8) in cloud droplets when their
idealised simulation was initialised with very high CHBr3
(40 pptv) in the BL. In the more realistic case where boundary layer
CHBr3 was ∼1.6pptv, the formation and release of
Br2 via Reaction (R8) was very limited, which is consistent with our findings
here as shown in Fig. 15. Following the causal link we identify between low
ozone and resulting low HBr and HOBr, these similar results for Br2
could be explained by the low background O3 simulated over the two
regions (Borneo and Darwin) in both studies. In Marécal et al. (2012) the
background O3 was 14 ppbv at 2 km as compared to the
10–15 ppbv range simulated over the inner domain at 2 km
altitude in this study.
Limitations
There are a few limitations to this study. First, it is difficult to
generalise these results to other tropical areas because we studied a region
with a relatively low ozone background in the troposphere, which impacts the
tropospheric bromine chemistry. Furthermore, other tropical regions could have
vastly different CHBr3 emissions, and in the case where emissions
are much higher, as explored in Marécal et al. (2012), we could expect
more Br2 formation, resulting in an increased role for Br2
in the transport of bromine to the upper troposphere. Pertinent to this point,
Hossaini et al. (2016) highlight the Indian subcontinent and Southeast Asia
as another region that is potentially of importance for transport of bromoform
and its PGs to the upper troposphere within deep convection. They predict it
to make a more minor contribution to the vertical transport of bromoform
compared to the maritime continent, but the background tropospheric conditions
are very different from the present study due to its proximity to large
pollutant and ozone precursor sources, and its contrasting conditions could
make it an interesting case study.
Another limitation is that we neglect other VSLS in our simulations and only
focus on CHBr3, and as a result our findings are specific for the
case where CHBr3 is the only VSLS. Including other VSLS would alter
the relative contributions made by CHBr3 and its PGs to the vertical
transport of bromine to the UT.
Another limitation is that our analysis is performed on cross sections of the
most mature convective systems, and so our analyses offer a snapshot of the
most intense convective activity and its effects.
Lastly, the performance of the various top-down and bottom-up CHBr3
inventories varies significantly by region, and the divergent global emission
estimates represent a significant source of uncertainty in estimates of
strat-BryVSLS (Hossaini et al., 2013). Despite the difference
between observed and simulated CHBr3 mixing ratios that we identify
in the background UT, we argue that the choice in using the Ziska et al. (2013)
emission inventory was the correct one. Here, we cite Hossaini et al. (2013),
who determined that the Ziska et al. (2013) emissions gave the closest
agreement to observations when evaluated in the TOMCAT CTM over the same
region of Borneo. Indeed, our results show consistent CHBr3 mixing
ratios compared to the simulations of Hossaini et al. (2013) for the
19 November flight, and together these findings provide tentative model-based
evidence that the Ziska et al. (2013) emissions provide useful estimates of
CHBr3 emissions in this region. Furthermore, Ziska et al. (2013)
project a CHBr3 emission climatology ranging between
1200–1600 pmolm-2h-1 within the region of interest along
the north-west coast of Borneo, while the emissions measured locally (over
several days) by Fuhlbrügge et al. (2016) range from
300–4300 pmolm-2h-1. We conclude from this that Ziska
et al. (2013) provide a good estimate of CHBr3 emissions over this
specific area.
Summary and conclusions
We used a convective-scale model (2×2km resolution) in order
to gain a better understanding of the effects of tropical deep convection on
the transport of CHBr3 and the speciation of its PGs in the
troposphere. Until now, two modelling studies have been carried out at the
convective scale but only for idealised cases (Krysztofiak et al., 2012;
Marécal et al., 2012). Our objective was to go a step further by modelling
a real case study of deep convection that occurred along the west coast of
Borneo on the afternoon/early night of 19 November 2011, during the SHIVA
field campaign.
It was shown that the meteorological development of the convective systems in
the model has general characteristics similar to those observed. To further
evaluate the simulation, we compared our modelled CHBr3 mean
concentrations and convection transport efficiency to those derived by
Krysztofiak et al. (2018) from SHIVA measurements. The comparison showed an
underestimation of the CHBr3 background concentrations within the
upper troposphere related to underestimates in the Ziska et al. (2013)
emissions to the east of Borneo. These findings are consistent with those of
Keber et al. (2020), who showed similar underestimates in the background
tropical UT, and with Fuhlbrügge et al. (2016), who showed that local sources
alone cannot account for the observed CHBr3 levels in the
UT. Nevertheless, the fraction f of air from the BL driven in the UT by the
convective systems is consistent with Krysztofiak et al. (2018): 0.18±0.14 to 0.33±0.23 for the model and 0.17±0.15 to 0.29±0.25 for
the observations.
Despite variation in the timing, location, and availability of CHBr3
for entrainment in the BL below each convective system, the same general
behaviour is observed across all three simulated convective systems. Most of
the bromine (>85%) transported to the UT in each convective
system is in the form of CHBr3. Within the convective systems, the
remaining 1 %–2 % of the total bromine present is mostly in the form
of organic PGs, i.e. the insoluble brominated carbonyls CHBrO and
CBr2O (86 % as a contribution to the total PG). This is
despite the inorganic PGs making a larger contribution than the organic PGs to
the total Br mixing ratios in the free troposphere (i.e. 45 %
versus only 5 %). Falling hydrometeors within the convective column
efficiently remove the inorganic PGs, whose tropospheric budget is dominated
by the extremely soluble HBr gas. Overall, we conclude that organic PGs are
more important than inorganic PGs for the vertical transport of bromine within
the convective columns for the conditions that we study here.
The insoluble inorganic PGs, BrO and Br2, are only present at
negligible mixing ratios and play no significant role in the vertical
transport of bromine. Our interpretation is that the lower-tropospheric
inorganic PG budget is shifted heavily in favour of HBr formation due to the
low background O3 mixing ratios simulated in this region. This
limits the availability of lower-tropospheric HOBr, leading to only very
limited formation of Br2 within the cloud and rain droplets within
the lower regions of the convective system resulting from the reaction between
HBr and HOBr. More BrO and HOBr would form in cases with higher background
O3, which could potentially lead to enhanced Br2 formation
within other convective systems and a more important role of the inorganic PGs
for the vertical transport of bromine.
In the future, it would be of interest to evaluate findings from global CTM
studies of VSLS transport to the UT and stratosphere in light of our findings
based on modelling at the convection scale. This additional work, however, is
beyond the scope of this study since it would require considerable additional
technical work to reconcile the differences in spatial scale and conditions
since global CTMs derived grid-scale mixing ratios representing the mean both
within and outside of convective systems. We hope to reconcile these issues
and test the hypotheses raised in this paper as part of future work. This
future effort could provide insights into the processes represented within
CTMs.
Code availability
As a third-party user of the open-source model C-CATT-BRAMS, we cannot grant access to the model code directly ourselves. However, the C-CATT-BRAMS code package is available upon request by email at gmai@cptec.inpe.br (Longo et al., 2013). Modifications to the chemical mechanism specific to bromine chemistry are available upon request by email (paul.hamer@nilu.no).
Data availability
The model datasets produced by the C-CATT-BRAMS model are available on request by email from the corresponding author (paul.hamer@nilu.no). For access to all other datasets produced by the co-authors, i.e., TOMCAT model output, SPIRIT and GHOST-MS instrument data, and the bromoform emissions, please contact the corresponding author (paul.hamer@nilu.no), who can then forward requests to data owners. Other data used in the paper are available from external links, i.e., MTSAT-2 data (http://database.rish.kyoto-u.ac.jp/arch/ctop/index_e.html, last access: 15 November 2021; CEReS, 2015) and NDVI data (https://modis.gsfc.nasa.gov/data/dataprod/mod13.php, last access: 15 November 2021; NASA, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-16955-2021-supplement.
Author contributions
PDH co-designed the study, wrote the main text of the paper, ran
the model, analysed the model results, and created many of the figures; VM co-designed the study, helped develop this version of
C-CATT-BRAMS, wrote some of the text, and created some of the figures; RH
ran the TOMCAT simulations and helped edit the manuscript; MP co-designed the chemical mechanism and the study and helped
develop this version of C-CATT-BRAMS; GK co-designed the chemical mechanism, helped develop this version
of C-CATT-BRAMS, and edited the manuscript; FZ developed the emissions and
provided us with the emission data; AE headed the GHOST-MS team aboard the Falcon aircraft and helped
to edit the manuscript; SS, TK, and HB are members of the GHOST-MS team aboard the Falcon
aircraft; EA collected the CHBr3 observations on the Sonne boat cruise
during SHIVA; MC helped run the TOMCAT CTM; VC helped to run instrumentation aboard the Falcon, assisted in
the SHIVA campaign planning, and edited the manuscript; AAS helped in the
interpretation of the meteorological results; MD helped in the planning and implementation of the SHIVA aircraft
campaign; PSM helped enable the SHIVA campaign to take place in Malaysia; HS helped in the planning and implementation of the SHIVA aircraft
campaign; KP headed the planning and implementation of the SHIVA aircraft
campaign.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was supported by the EU Stratospheric Ozone: Halogen Impacts in a
Varying Atmosphere (SHIVA) project (SHIVA-226224-FP7-ENV-2008-1). We are
grateful to the support given by the National Oceanography Directorate (NOD)
and the Economic Planning Unit (EPU) of Malaysia. In particular, we are
grateful for the support given by Nor Aieni Binti Haji Mokhtar
(NOD) and Munirah Abd Manan (EPU), without which the SHIVA campaign in
the western Pacific would not have been possible. We would like to
acknowledge the use of computing hours on the FUXI high-performance computer
at the Laboratoire d'Aérologie, Toulouse, France. MTSAT-1R, MTSAT-2, and Himawari-8 data are provided by the Center for Environmental Remote Sensing (CEReS), Chiba University. We thank
Birgit Quack for
substantial comments and advice on the manuscript preparation. CATT-BRAMS is
a free software provided by CPTEC/INPE and distributed under the CC-GNU-GPL
license. For CJH.
Financial support
This work was supported by the European Commission via the Stratospheric
Ozone: Halogen Impacts in a Varying Atmosphere (SHIVA) project (project no. SHIVA-226224-FP7-ENV-2008-1 and grant no. 226224).
Ryan Hossaini is supported by a NERC independent research fellowship (grant no. NE/N014375/1).
Elliot Atlas was supported by funds from NASA
Upper Atmosphere Program (grant no. NNX17AE43G).
Review statement
This paper was edited by Rolf Müller and reviewed by two anonymous referees.
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