ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-2155-2016On the vertical distribution of smoke in the Amazonian atmosphere during the dry seasonMarencoFrancofranco.marenco@metoffice.gov.ukhttps://orcid.org/0000-0002-1833-1102JohnsonBenhttps://orcid.org/0000-0003-3334-9295LangridgeJustin M.MulcahyJanehttps://orcid.org/0000-0002-0870-7380BenedettiAngelahttps://orcid.org/0000-0002-9971-9976RemySamuelJonesLukeSzpekKatehttps://orcid.org/0000-0002-2073-586XHaywoodJimLongoKarlaArtaxoPaulohttps://orcid.org/0000-0001-7754-3036Satellite Applications, Met Office, Exeter, UKEarth System and Mitigation Science, Met Office Hadley Centre, Exeter, UKObservational Based Research, Met Office, Exeter, UKEarth System Core Development Group, Met Office Hadley Centre, Exeter, UKEuropean Centre for Medium-range Weather Forecasts, Reading, UKLaboratoire de Météorologie Dynamique, UPMC/CNRS, Paris, FranceCollege of Engineering, Maths and Physical Sciences, University of Exeter, Exeter, UKInstituto Nacional de Pesquisas Espaciais, São José dos Campos, BrazilInstitute of Physics, University of São Paulo, São Paulo, BrazilFranco Marenco (franco.marenco@metoffice.gov.uk)25February20161642155217423October201512November201528January20167February2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/2155/2016/acp-16-2155-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/2155/2016/acp-16-2155-2016.pdf
Lidar observations of smoke aerosols have been analysed from six
flights of the Facility for Airborne Atmospheric Measurements
BAe-146 research aircraft over Brazil during the biomass burning
season (September 2012).
A large aerosol optical depth (AOD) was observed, typically ranging
0.4–0.9, along with a typical aerosol extinction coefficient of
100–400 Mm-1.
The data highlight the persistent and widespread nature of the
Amazonian haze, which had a consistent vertical structure, observed
over a large distance (∼2200 km) during a period of 14 days.
Aerosols were found near the surface; but the larger aerosol load was
typically found in elevated layers that extended from 1–1.5 to 4–6 km.
The measurements have been compared to model predictions with the
Met Office Unified Model (MetUM) and the ECMWF-MACC model.
The MetUM generally reproduced the vertical structure of the Amazonian
haze observed with the lidar.
The ECMWF-MACC model was also able to reproduce the general features
of smoke plumes albeit with a small overestimation of the AOD.
The models did not always capture localised features such as
(i) smoke plumes originating from individual fires, and (ii) aerosols
in the vicinity of clouds.
In both these circumstances, peak extinction coefficients of the order of
1000–1500 Mm-1 and AODs as large as 1–1.8 were encountered, but
these features were either underestimated or not captured in the model
predictions.
Smoke injection heights derived from the Global Fire Assimilation
System (GFAS) for the region are compatible with the general height
of the aerosol layers.
Introduction
Biomass burning is the second largest source of anthropogenic
aerosols globally , and South America
features as one of the major source regions.
In Southern Amazonia, fire is often used for deforestation and for the
preparation of agricultural fields and pasture .
The dry season spans from July to October every year, and controls
the timing of the intensive burning of the vegetation.
Intense precipitation can still occur in this season, due to the
increase of convective
available potential energy (CAPE) and moisture, associated with the
Monsoon circulation .
The rate of biomass burning in the Brazilian rainforest varies
from year to year and is affected by meteorological conditions
as well as social factors .
The high loadings of biomass burning aerosols, with different degrees
of ageing, can affect the regional
weather and climate .
Episodes of poor air quality and low visibility are frequent, and the
aerosol loadings affect the radiation budget and the cloud
microphysics .
Moreover, the radiative balance
of the region is also affected by changes in the surface albedo caused
by burning of the vegetation.
The latter has an impact well beyond the burning
season, as it affects the regional surface energy budget
all year round, and has
an impact on convection, cloud formation and precipitation
.
The modified ratio of direct to diffuse radiation, and the
changes in meteorology, in turn will affect the photosynthetically
active radiation flux and the carbon cycle .
Given that Southern Amazonia is the Earth's largest hydrological
basin, the largest carbon sink, and the largest tropical
rainforest, the changes in the regional
atmosphere and biosphere introduced by biomass burning can have
a relevant impact at the global scale.
A detailed review of the literature on biomass burning emissions
can be found in and .
The large amount of heat released by forest fires can generate
strong updrafts and deep convection in their vicinity,
rapidly transporting aerosols to upper layers , followed by long-range transport
.
Aerosols can be transported for thousands of kilometres, and as they
travel they are modified through ageing processes
.
The composition of biomass burning aerosols is dominated by
fine carbonaceous particles (organics and black carbon; see
), and in the first 2 hours after emission
aerosol scattering can increase up to a factor of six due to
photochemistry and secondary particle formation; this is
particularly the case for smouldering fires .
Particle hygroscopicity and the concentration of CCN are also
enhanced during ageing .
Further downwind, these aerosols continue to exert an impact on
cloud formation, convection, and precipitation patterns
.
indicates two opposite mechanisms by which
biomass burning aerosols affect clouds and precipitation:
(i) in a stable atmosphere, for a given liquid water content the
formation of a larger number of smaller cloud droplet induces
warm rain suppression; and
(ii) in an unstable atmosphere, the aerosols enhance precipitation
and favour the formation of larger and long-lived cells (convective
cloud invigoration).
Moreover, have observed an increased ice
formation efficiency for clouds in the dry season, and a
coincidence with the seasonal aerosol cycle.
Knowledge of the vertical structure of the Southern Amazonia
smoke layer is key to understanding and assessing the aerosol–cloud
interactions .
showed that there are significant
uncertainties in the vertical distribution in global
models, whereas this information is critical in assessing the
magnitude and even the sign of the direct radiative forcing.
Of particular interest are the distribution of lofted layers
and the identification
of complex scenes involving both aerosols and clouds .
The South AMerican Biomass Burning Analysis (SAMBBA) campaign
was an intensive field project (September–October 2012), aimed
at collecting information on the atmosphere of the Amazon basin
during the dry season and the transition into the wet season
.
One important focus has been the impact of biomass burning aerosol
on the radiation budget, and its feedback on the dynamics and
hydrological cycle, including the influence on numerical weather
predictions, climate, and air quality.
The partnership involved mainly scientists from Brazil (National
Institute for Space Research, INPE, and University of Sao Paulo)
and from the United Kingdom (the Met Office and the Natural
Environment Research Council).
Research flights
During SAMBBA, the Facility for Airborne Atmospheric Measurements (FAAM)
research aircraft was based in Porto Velho, Brazil (8∘44′ S
63∘54′ W), and 20 research
flights were carried out between 14 September and 3 October 2012,
totalling 65 h of atmospheric research flying.
Porto Velho lies in the state of Rhondonia, where
biomass burning for deforestation and agriculture is prevalent,
and a large deforested area is evident.
The flights sampled a wide range of conditions, from
very low concentrations of gas phase and aerosol species over
the pristine Amazonia rainforest, through to major fire plumes
emitting very large amounts of pollutants.
Some of the flights were coordinated with satellite overpasses,
which allowed combining aircraft measurements with spaceborne
remote sensing (see, e.g., ).
The aircraft was equipped with several probes, able to sample the
atmosphere using both in situ and remote-sensing techniques.
Each research flight was planned around one of the following goals:
(a) in situ characterisation of fresh plumes (FP), achieved by flying
at low level in the immediate vicinity of a fire and sampling the
aerosols, trace gases and thermodynamic structure;
(b) radiative closure (RC) studies, achieved with a series of stacked
aircraft runs and profiles above a limited area, in order to tie
together the information derived by remote sensing and the in situ
probes; and
(c) survey flights (SF) at high altitude, where the properties of the
atmosphere are mainly sampled with remote-sensing techniques.
Besides Porto Velho (PV), the airports in Rio Branco (450 km WSW
of PV), Manaus (760 km NE of PV) and Palmas (1700 km E of PV)
were also used.
The circulation in this season is typically dominated by moderate to
strong Easterlies (trade winds), which build up large aerosol burdens
over Western Amazonia, where the low-mid tropospheric circulation
is halted by the Andes.
In this season, the north-western part of the basin is characterised by
the development of deep convective events accompanied by brief but
intense precipitation, whereas the Southern and Eastern parts are
typically dry.
The season in 2012, however, differed somewhat from the climatology.
A Northwesterly circulation on the Southwestern part of the basin
dispersed the aerosols, and as a result only a moderate aerosol
optical depth (AOD) was observed.
Moreover, convective precipitation spread further East than usual
during the second half of September.
Nevertheless, burning activity continued through the majority of the
campaign period, and
significant aerosol loading was found during most of the flights.
In the majority of cases, a variety of measurements confirmed that
the aerosols can be ascribed to smoke originated from forest fires.
A general feature throughout the campaign was the persistence of
aerosols above the boundary layer, with plumes up to altitudes
of 4–6 km, presumably caused by deep convection and lifting.
In situ observations with wing-mounted optical particle counters (PCASP
and CDP; see, e.g., )
showed a predominance of fine mode particles at all levels
(elevated and near-surface).
Moreover, measurements with the on-board AL 5002 VUV Fast Fluorescence
CO Analyser showed high carbon
monoxide concentrations.
The present study focuses on the results from the
airborne lidar during the high-altitude portions of six selected
flights, between 16 and 29 September (see Table
and Fig. ).
The criterion for selecting the flights has been the availability
of sufficiently extended high-altitude and cloud-free sections,
so that the aerosol extinction vertical profile could be estimated
from the lidar.
The six flights span the region between 8.5–12∘ N and 46–68∘ W, covering
a distance of ∼2200 km extending along an East–West
axis across the Brazilian Amazon basin, at an approximate mean latitude
of 10∘ S.
Measurements
Observations of atmospheric aerosols have been acquired with the
ALS-450 elastic backscattering lidar mounted on the FAAM research
aircraft.
This is an instrument manufactured by Leosphere; it is operated
at a wavelength of 355 nm; and it is mounted in a nadir-looking
geometry .
The system specifications are summarised in
and a more detailed description of the instrument can be found in
and .
Ground tracks for the six research flights listed in
Table . The location of the valid lidar profiles used for
the computation of aerosol extinction is highlighted as follows: grey,
profiles that pass our quality control test; blue, remaining profiles. The
following locations are also shown: Porto Velho (PV), Rio Branco (RB), Alta
Floresta (AF), Palmas (Pal), Manaus (Man), Cuiabà (Cui), and Brasilia
(Bra).
Lidar signals were acquired with an integration time of 2 s
and a vertical resolution of 1.5 m.
The analogue and photon counting signals are merged at
pre-processing by normalisation in an overlap area chosen
based on photon-counting thresholds.
Cloud signal in the vertical profiles was detected as a
“large spike”, and the thresholds given in
were applied to determine their top height at 2 s resolution.
In order to determine the aerosol properties, further integration
and vertical smoothing have been applied during data processing, to
reduce shot noise: the aerosol data presented here therefore have
a vertical resolution of 45 m and an integration time of 1 min.
This integration time corresponds to a 9±2 km footprint,
at typical aircraft speeds.
Research flights considered in this article. Time is UTC.
∗ Flight type: FP: fresh plume sampling, mainly in situ;
RC: radiative closure, combining in situ and remote sensing;
SF: high-altitude survey flight.
A first data selection was done as follows: all lidar
signals acquired when the aircraft was flying at an altitude lower
than 4 km have been omitted, and data have been discarded
if the lidar was pointing at more than 10∘ off the vertical
(due to the aircraft turning).
Lidar signals within 300 m of the aircraft have been discarded, due
to incomplete overlap between the emitter and the receiver field-of-view,
and at the far end profiles have been truncated to remove the surface spike
and any data beyond it.
As a general rule, a vertical profile where a cloud was detected has
either been omitted completely, or has been omitted in the portion between
the surface and the cloud top.
However, in a small number of cases where the cloud optical depth has
been considered sufficiently small, so as to not affect the derivation of
aerosol properties, data below a cloud have been kept but the cloud
layer itself has been rejected.
All assumptions have been reviewed manually, on a profile-by-profile
basis, with the possibility to override the cloud exclusion criteria
and to set the reference height interval necessary for the derivation of
aerosol extinction.
After the data selection discussed above, 334 vertical profiles have been retained.
Processing of the data followed a double iteration, first to
determine the lidar ratio (extinction-to-backscatter ratio),
and subsequently to process the data set to determine the extinction
coefficient.
The method detailed in is at the basis of
this processing, and it is based on setting the reference within
the aerosol layer, rather than on a Rayleigh-scattering portion
of the atmosphere.
The slope method is used for a first estimate of the extinction
coefficient at the reference, based on the lidar measurements
themselves.
As illustrated in that paper, in this geometry and at this
wavelength the forward solution to the lidar equation is
unstable and cannot be used when the aerosol layers are deep
and their extinction is large; hence the need for using this
method.
illustrates the application of this method
in comparison with CALIPSO retrievals and to constrained retrievals
using AERONET.
Lidar ratio
An initial subset of the lidar profiles has been selected, where
the signature of Rayleigh scattering has been clearly identified
above the aerosol layers.
This circumstance permits iteration using the method described in
by varying the lidar ratio (assumed constant
with height), until a good match to the overlying Rayleigh
scattering layer is reached: in this way, the lidar ratio
itself can be estimated.
Out of these lidar profiles, 270 indicate at least a moderate aerosol
load (AOD >0.25), and they have been kept to compute a distribution:
results are displayed in Fig. b.
The data set follows a Gaussian distribution, and is characterised
by a mean and standard deviation of 73.1 and 6.3 sr, respectively.
Moreover, Fig. a shows that the
distribution is not significantly affected by how we choose the
acceptance threshold (AOD >0.25).
The lidar ratio determined in this way is not substantially
affected by the choice of the lower reference extinction, and
is instead mainly affected by the higher layers, where the
transition between a large extinction coefficient and a molecular
layer is encountered.
This estimate of the lidar ratio for biomass burning aerosols is
in agreement with the findings reported in
, , , and .
(a) Lidar ratio and AOD, determined for each lidar profile
(see text). The data points are colour-coded with the flight number. The
horizontal blue solid line indicates the mean, the blue dotted lines indicate
one standard deviation from the mean, and the dashed red line indicates the
median. The vertical dashed line indicates the threshold (AOD >0.25) that
has been applied to the data set. (b) Histogram of lidar ratio
determinations, for 270 profiles with AOD >0.25. Mean: 73.1 sr, standard
deviation: 6.3 sr, median: 72.5 sr. A gaussian curve with the same mean and
standard deviation is overplotted (dashed line).
(a) SAMBBA campaign mean particle size-distribution,
determined with the wing-mounted PCASP optical particle counter (black dots).
The fit with a bimodal lognormal is also shown; the parameters of the two
lognormals are as follows: accumulation mode: Dp=0.184µm, σ=1.47, and Nt=868.5. Coarse mode: Dp=0.387µm, σ=2.13,
and Nt=2.16. (b) Contours of the lidar ratio computed for the
campaign mean particle size-distribution, by varying the refractive index.
The black solid and dotted lines indicate the mean and standard deviation of
the lidar ratio determined by lidar (73±6 sr). The red dots show
estimates of the Amazonian biomass burning aerosol refractive index taken
from the literature: (a) 1.5-0.02i; (b) 1.42-0.006i; (c) (1.47±0.07)-(0.008±0.005)i; (d) (1.47±0.03)-(0.0093±0.003)i.
The lidar ratio so determined, 73±6 sr, has been compared to
Mie scattering computations.
Figure a displays the campaign mean particle size-distribution
(PSD) determined with the PCASP, and its fit using two lognormals,
each of which is in the form
n(D)=Nt2πlnσe-12lnD/Dplnσ2D,
where D is diameter, and Dp, σ, and Nt are three fitted
parameters . We have computed the lidar ratio for this
size-distribution and for a range of refractive indices; see
Fig. b. The resulting lidar ratio is highly dependent on the
real and imaginary parts; refractive index estimates from the literature are
also shown in the figure. The lidar derivation of 73±6 sr is
compatible, for instance, with refractive indices from and
. Note that the estimates computed with refractive indices
from and also do not fall too far off.
Estimate of the aerosol extinction coefficient
Following the result of the first iteration on the lidar data,
a lidar ratio of 73 sr has been adopted for the full
data set, and a second iteration with the method introduced in
has been applied to determine the aerosol
extinction coefficient for all the 334 profiles.
This method (slope-Fernald method) is a variant of the Fernald–Klett
method , where the reference is
taken within an aerosol layer: this permits using the stable
(inward) solution to the lidar equation in the unfavourable
geometry represented by a nadir-looking lidar.
Note that this choice is necessary if, as found during this
campaign, no aerosol-free region below the aerosol layers is
available.
Figure shows typical resulting estimates
of the aerosol extinction coefficient, for a subset of the
vertical profiles (this selection is purely illustrative in
purpose).
For each profile, an estimate of the uncertainty that results
from the retrieval assumptions has been computed, by repeating
the derivation after having varied the lidar ratio by ±6 sr
(this being the uncertainty adopted above), and after having
varied the extinction value at the reference by ±50 %
(1σ statistical errors).
The latter value reflects the large uncertainty that arises from
the method, since reference is taken within
an aerosol layer instead of in Rayleigh scattering conditions.
Note however, how quickly the uncertainty decreases when moving
upwards from the reference height; the opposite is unfortunately
also true, i.e. where the reference height is taken at an altitude,
then uncertainty increases up to ±100 % near the surface.
In summary, very large uncertainties exist in the bottom part of
the vertical profiles, but they are quickly damped when moving
towards the higher layers.
At the top of the profiles, uncertainty is instead driven
by the lidar ratio, and is generally small.
A sample of the lidar vertical profiles of aerosol extinction
coefficient, discussed in this paper. The green lines indicate the estimate
of uncertainty. The red lines indicate the reference height interval used
(different for each profile). The time, date, flight number and coordinates
are indicated in the title to each plot. Each profile corresponds to an
integration time of 1 min.
Observed aerosol distribution
Figures and display the cross-sections
of aerosol extinction coefficient and of its estimated uncertainty,
as a function of along-track distance and height.
Generally, all six flights show a similar structure, with
a moderate magnitude of aerosol extinction, of the order of
150–200 Mm-1, between the surface and an upper altitude
of 4–6 km, with some localised patches showing higher magnitudes.
This general vertical structure was broadly coherent over distances
of thousands of kilometres and persisted over the 2-week period studied
here.
At smaller spatial scales, some noticeable features were observed,
and are described as follows.
Flight B742 shows four features where a large extinction coefficient
(approaching 1000 Mm-1) is detected at an altitude of 1.25 km,
at along-track distances of 115, 135, 310 and 360 km.
These correspond to plumes from single fires that were seen from
the cockpit.
Since the aircraft was
flying back and forth over the same area, these smoke plumes
were all located within a maximum distance of ∼25 km from each
other, and in fact the ones observed at 310 and 360 km along-track
distance were at the same location.
Cross-sections of the aerosol extinction coefficient determined from
the lidar for the six research flights with a 1-min integration time. The
black lines indicate the aircraft altitude and the surface elevation from a
digital elevation model, respectively. The green dots indicate cloud tops
detected with the lidar at 2 s resolution. The red numbered arrows indicate
the selected sections for the characterisation of the aerosol layer (see
Table ).
Cross-sections of the aerosol extinction coefficient
uncertainty.
Flight B743 also shows a plume from a single fire, centred
at an along-track distance of 1260 km; it extends from the surface
to 2 km altitude and has a size of ∼50 km in the along-track
horizontal direction; in this plume a peak extinction of
1270±40 Mm-1 was encountered.
Moreover, a higher altitude feature is observed, well above
the aerosol layer, and co-located with this intense plume but
apparently disconnected from it: its altitude is 3.7–4 km,
with a depth varying between 200 and 400 m (FWHM).
Its horizontal extent is of 270 km along-track, its aerosol optical
depth (AOD) peaks 0.09, and its extinction coefficient peaks 300 Mm-1.
The origin of this higher altitude feature is uncertain: it could
have been released by the same fire at an earlier time, i.e. if
the fire radiative power had been at anytime stronger; it may
also have originated from some other nearby fire; and finally
it may have been transported over a longer distance.
Flight sections considered for the characterisation of the aerosol
layer, displayed with red arrows in Fig. . For each section, the
layer height, layer depth, layer extinction and aerosol optical depth are
listed (see text). For each quantity, the average and standard deviation are
shown; for the layer extinction and aerosol optical depth, maximum values are
shown as well (in parentheses). The results for the whole data set are listed
as well.
Moreover, in flights B741 (first part) and B746 the presence
of clouds with tops at 2–4 km obscures the bottom part of
the aerosol layer; above these clouds, large extinction coefficients
are detected, peaking 1000–1500 Mm-1.
These large values are likely to be either directly caused by
nearby fires (hidden by the clouds themselves), or as a result of
convective lifting and detraining of smoke into a
layer around the cloud-top.
From the aerosol extinction coefficient described above, a
few quantities have been computed.
The layer extinction is computed as the vertically averaged
extinction, and the aerosol optical depth (AOD) as the vertically
integrated extinction.
The layer height has been defined, for each vertical profile,
as the weighted average of the aerosol vertical distribution,
and the layer depth as 2× (AOD) / (peak extinction).
Note that the definition of layer depth can be quite arbitrary;
however, the above definitions are consistent with .
The layer height, layer depth, layer extinction and
aerosol optical depth have been computed for each vertical
profile in the data set.
Note, however, that these derived quantities can be affected by
the vertical extent of the available data, which in
turn is affected by aircraft altitude, terrain height, and the
presence of low clouds.
As a quality control test, profiles for which the relative
error on AOD was larger than 50 %, and profiles that were
truncated (due to cloud) at a lower boundary which was 2.5 km
or higher above mean sea level have not been included in the
discussion of the derived quantities described above.
In order to characterise the aerosol layer in terms of representative
properties, the data set has been divided in the sections listed in
Table , numbered 1–10, and also displayed with red
arrows in Fig. .
For each of the shorter flights, a single section has been considered,
whereas when the distance travelled exceeded 1000 km two flight
sections have been considered.
For flight B742, since the aircraft travelled back and forth over the same
ground track several times, only the first part of the lidar cross-section
has been considered.
Due to the above quality criteria and to the fact that some flight
portions have not been included (e.g. the second part of flight B742),
the number of retained profiles is reduced from 334 to 276.
Table summarizes the flight sections averages and standard
deviations for the considered quantities; note that in this context,
standard deviation is a measure of variability for each given quantity.
The maximum of the layer extinction and of the aerosol optical depth
is also listed for each section; the maximum of
the layer extinction is in general different and lower than the
maximum value of extinction that is encountered in each section
(layer extinction being a vertically averaged quantity).
The geometrical properties of the aerosol vertical
distribution, i.e. the layer height and layer depth, show a
limited variability within each section, with standard deviation
around 10–15 % for layer height and 15–25 % for layer depth.
Flight B746 represents an exception and shows larger variability
in its second part (Sect. 10); however, for this flight a
large proportion of profiles are truncated due to low cloud, and
therefore the remaining data may possibly not provide a representative
sample.
Averaged over all six flights, the layer height is 2.0±0.4 km,
and the layer depth is 2.3±0.6 (average and standard deviation).
This indicates that the vertical distribution of the aerosols
does not vary much, despite the large distance travelled by the
aircraft (more than 2200 km between the eastmost and westmost
lidar profiles) and the relatively long time between the first
and the last flight (14 days).
The quantitative properties, i.e. mean extinction and AOD,
display a larger variability, as expected; however, this variability
is not huge.
The per-section average of layer extinction varies between 75 and
200 Mm-1 and the per-section average of AOD is between 0.5 and 0.9,
each of these quantities showing a standard deviation of 10–50 %
in each flight section.
When computed over all six flights, the average and standard deviation
of these quantities is 112±49 Mm-1 and 0.65±0.24,
respectively,
and the maximum values encountered over the data set were about three
times larger than the average.
The distribution of the layer properties, derived by airborne
lidar for the six flights considered in this paper, is shown in
Fig. .
Distributions of aircraft lidar observations of AOD, layer
extinction, layer height and layer depth, for the whole data set considered in
this paper (276 vertical profiles). A Gaussian curve with the mean and
standard deviation of the data set is superimposed (dashed
line).
The mean vertical distributions of aerosol extinction for each
of the ten sections are shown in Fig. .
The average over the ten sections is displayed in Fig. ,
and shows a general structure that can be summarised as follows.
Near the surface, and up to an altitude of ∼1 km, a surface
layer of extinction coefficient ∼200 Mm-1 is observed.
Above this layer, an elevated layer is found which has a slightly larger
extinction coefficient (peaking ∼250 Mm-1) and a significant
depth, extending from ∼1 to ∼5 km altitude.
When looking at the individual sections (Fig. ), variations
around this general structure can be observed: the lower layer
in some of the flights extends a bit higher (up to ∼1.5 km)
and can show a magnitude of the aerosol extinction coefficient
of 150–300 Mm-1; and the aerosols above can extend, depending
on the flight section, up to an altitude between 4 and 6 km.
The elevated aerosols show as a single well-defined elevated layer
in Sects. 2, 5 and 7 and as a more structured, multi-layer
atmosphere in the other sections.
The signature of the individual fire plumes described above
can be found in these average profiles; see e.g. the maximum
at an altitude of ∼1.25 km in Sect. 6, and at an altitude
of ∼1.6 km in Sect. 8.
These layers also show a larger
standard deviation, reflecting the variability between in-plume
and out-of-plume conditions.
Note also that Sects. 4, 9 and 10 are affected by low clouds
with large smoke concentrations above; this is reflected
in the large values of the mean + 1 standard deviation (up to
600–800 Mm-1).
Model simulations
The lidar data have been used to evaluate aerosol simulations
from two prediction models:
(i) a limited area model (LAM) configuration of
the Met Office Unified Model (MetUM), and
(ii) aerosol forecasts issued by the European Centre for
Medium-range Weather Forecasts (ECMWF-MACC).
The MetUM limited area model was set up for the SAMBBA
campaign over the Amazonia domain (latitude 25∘ S–18∘ N,
longitude 85–32∘ W), and has a resolution of 12 km, with
70 levels in the vertical .
Lateral boundary conditions for the meteorological fields
were driven provided by the operational global configuration
of the MetUM (Global Atmosphere 3.1, ).
The ECMWF-MACC simulations were global, although analysed
here over the Amazonian region only.
Both models were initialised using near-real time emissions from
the Global Fire Assimilation System (GFAS) emission data set
, valid for the forecast base time.
The GFAS data are a daily product based on all the
Moderate-Resolution Imaging Spectroradiometer (MODIS) overpasses,
over the course of any given day.
Assimilation using this inventory is known to lead to an
underestimation of AOD, due to the lack of detection of
small smouldering fires, and fires below canopies and clouds.
Studies show that for a better agreement it is therefore
necessary to scale up the emissions .
Whilst the ECMWF-MACC model used a scale factor of 3.4 ,
this was reduced to a factor of 1.7 for the MetUM based on an
initial assessment of AODs with AERONET and MODIS data earlier
in the season.
Panels (a–j): summary vertical profiles for each of the
10 flight sections listed in Table . Each plot displays the
mean vertical profile (black) and the ±1 standard deviation curves
(green) for the lidar data. The MetUM (blue) and ECMWF-MACC (red) mean
vertical profiles and their standard deviation, for each of the sections, are
also displayed. Panel (k): the 10 mean lidar vertical profiles
shown in panels (a–j), each representative of a
section.
In the MetUM simulations,
biomass burning aerosol was simulated on-line using the CLASSIC
aerosol scheme , while all
other aerosol species were represented by climatological
averages.
Direct aerosol effects were included in the simulations, but
indirect effects were not.
There are a number of uncertainties on some of the assumptions
used in the simulations; in particular associated with the potential
transport of aerosols from outside the domain boundaries, the rainout
of biomass burning aerosols and their ageing, and the source emissions.
Aerosol injection was prescribed between 0.1 and 3 km in height;
this is likely to affect the representation of the vertical extent
of the aerosol plumes, in particular from larger fires.
Moreover, emissions were not updated during the model simulation,
and therefore an assumption is made on persistence of the emission
field.
The CLASSIC aerosol scheme uses a prescribed aerosol size distribution
and refractive index, based on .
A climatological hygroscopic growth curve based on
is included in the model, and this information enables the calculation
of aerosol optical properties, including extinction coefficient.
Lidar summary vertical profile resulting from all the 276 lidar
profiles (black), together with the curves representing ±1 standard
deviation (green). The MetUM (blue) and ECMWF-MACC (red) mean vertical
profiles and their standard deviation are also shown for the same collection
of flight sections.
The ECMWF-MACC model issued by ECMWF is
provided as part of the EU-funded projects
Monitoring Atmospheric Composition and Climate, MACC, MACC-II and MACC-III
.
The initial package of ECMWF physical parameterisations dedicated to
aerosol processes mainly follows the treatment of the
Laboratoire de Météorologie Dynamique general circulation
model, LMD-Z .
Five types of tropospheric aerosols are considered in the model,
and are fully coupled with the meteorology: sea salt, dust, organic
carbon, black carbon, and sulphate.
Prognostic aerosols of natural origin, mineral dust and sea-salt, are
described using three size bins, and their emissions depend on model
parameters (surface winds among others).
Anthropogenic emissions are specified using current emission inventories,
and biomass burning emissions are taken from the GFAS inventory.
The simulations presented here were carried out using an experimental
version of the ECMWF-MACC model, which emits biomass burning aerosols
at an injection height provided by a Plume Rise Model
(PRM) that has been embedded into GFAS .
The PRM derives injection heights from MODIS observations of Fire
Radiative Power (FRP) and atmospheric profiles from ECMWF; these
are then gridded and assimilated in GFAS, and provided on a daily
basis, together with emissions.
Moreover, MODIS AOD data are routinely assimilated into the model,
in a 4D-Var framework.
All data are available online at http://www.copernicus-atmosphere.eu/.
The resolution of the ECMWF model is ∼80 km (T255), coarser than
that of the MetUM limited area model, and there are 60 model levels.
Figures and show the modelled aerosol
extinction coefficient along the tracks of the six flights.
Model clouds are also shown for the MetUM (green dashed contours);
they are defined as the gridboxes where the cloud fraction is larger
than 0.1 or the relative humidity is larger than 90 %.
The MetUM represents many realistic features of the aerosol layers,
although plumes from individual fires are in some cases not captured.
The ECMWF-MACC aerosol field is also realistic, but more smoothed
out, as is expected due to its lower resolution.
The comparison between the airborne measurements and the MetUM is quite
good for flight B733, where the model predicts elevated aerosol plumes,
in good agreement with the observations.
Differences appear, for instance, where
the model predicts a slightly deeper aerosol layer and at the same time
it underestimates the extinction coefficient for the elevated layers.
Similar intensities are found at 1.5–2 km, although the
features are not in exactly the same position as observed.
The ECMWF-MACC model simulates the aerosols as being mainly concentrated
in an elevated layer at ∼2.5 km, and as having a marked gradient,
increasing with along-track distance (eastward).
Overall, ECMWF-MACC overestimates the aerosol extinction in this
case.
Cross-sections of the aerosol extinction coefficient estimated from
the Met Office Unified Model (MetUM) along the tracks of the six research
flights. Also shown is the position of the model clouds (green dotted
line).
Cross-sections of the aerosol extinction coefficient estimated from
the ECMWF-MACC model, along the tracks of the six research
flights.
For the first part of flight B734 the lidar observes a relatively
homogeneous layer from the surface up to 3–4 km, but with variations
in its top altitude and some elevated thin plumes above.
A similar distribution is highlighted in the MetUM and ECMWF-MACC models,
although once again the exact position of the features is different.
In the second part of this flight, however, the lidar highlights
a deep elevated plume at 2.5–4.5 km, with an extinction coefficient
of the order of 200–250 Mm-1.
As a comparison, we notice that the MetUM predicts some aerosol
in the same place, although optically and geometrically thinner
and with an irregular structure within a cloudy field.
The ECMWF-MACC, on the other hand, predicts a plume with a similar
extinction but a higher altitude (4–6 km).
For flight B741, the difference between the models and the observations
is remarkably more pronounced.
In fact, for this case both models overestimate
aerosols near the surface, and show a rapidly decreasing concentration
above 3–4 km with a highly variable top of the aerosol layer
reaching in some places up to ∼7 km.
In the first part of this flight very little observational data were
available, due to the presence of deep clouds; a few lidar profiles
are however available, and they indicate an intense aerosol layer
(400–700 Mm-1)
at 2–4 km, hence with much larger altitude than the main layer in
both model outputs.
In the second part of this flight, the top of the aerosol layer at
3–4 km is much sharper than in the model predictions, with most of
the aerosols being found between ∼1 and ∼3 km.
For flight B742, the MetUM shows a slightly smaller aerosol extinction
coefficient than the lidar observations, and a slightly shallower
aerosol layer, but overall the vertical distribution is well
represented, with the exception of the individual fire plumes, that
appear much fainter.
For the same flight ECMWF-MACC shows larger extinction values.
For B743, the MetUM displays a large gradient of the extinction
coefficient along the track, with very large values on the first
part of the graph (eastern end) and small values towards the
right-hand side (Western end of the flight).
The predicted haze layer, moreover, is shallower
than the observations.
The ECMWF-MACC model displays a larger extinction than the
MetUM and a slightly deeper layer, in average closer to the
observations.
The sloping-down of the layers with along-track distance (East to West)
is well-captured by both models.
Again, however, the individual plume at an along-track distance of
1260 km is not captured in either model, and neither is
the co-located elevated plume.
This is not surprising, as the fire was not captured in the
GFAS inventory.
Finally, for B746 the overall structure and magnitude of the smoke
layer observed by the lidar is surprisingly well represented in both
models, with the exception of the very large values of extinction found
just above the low-level clouds.
Some of the largest discrepancies between the models and the observations
occur in regions affected by clouds; for instance during B741 (first
part, Sect. 4) and B746 (large extinction values above and near the
clouds).
This may be due to differences in location between modelled and
observed convection (and associated transport and/or wet deposition
of aerosol), or to errors in the water uptake of aerosols near to,
or within clouds.
This should not be considered surprising, as these processes are
difficult to model accurately, and still not well understood.
The blue and red lines in Figs. and
show the MetUM and
ECMWF-MACC mean and standard deviation, for each flight section and
for the campaign average profile.
These vertical profiles confirm the above conclusions; it is interesting,
in any case, to observe the similarity of the campaign average profile
derived with the lidar and the MetUM (Fig. ).
Although the MetUM average does not seem to capture the transition between
the first shallow layer (up to ∼1 km) and the elevated layer
between ∼1 and ∼5 km, such an elevated layer is shown
clearly in most of the profiles in Fig. (flight sections 1, 3, 5, and 7–10),
but by averaging over multiple profiles with opposite structures
(e.g. section 4) this is not apparent.
Note also the structure of the campaign mean ECMWF-MACC profile, with
a nearly constant extinction coefficient from the surface to 3 km,
followed by a decrease until the top of the layer at ∼6 km.
Again, this results from averaging profiles with opposite structures,
i.e. sections 2, 3 and 4, showing very large concentrations near the
surface, and sections 1, and 5–10 that show larger extinction in
the elevated layer.
It is also clear from the averaged profiles that the ECMWF-MACC
model shows larger aerosol extinction than the lidar and the MetUM,
and that the simulated layers extend slightly further in the vertical.
Finally, Fig. shows the hotspots reported in GFAS
during the campaign, coloured according to the injection height computed
in the PRM.
We can see that several fires with injection height between 3 and
5 km are observed, particularly in the eastern (upwind) part of the
basin, and this is where the smoke can have been generated.
Moreover, sporadic fires with very large injection heights (5–7 km)
are observed between 50 and 65∘ W.
The smoke layer depths observed by lidar and predicted by the models
are therefore generally compatible with the PRM injection heights.
Hotspots during 16–22 September (top) and 23–29 September
(bottom), as reported in the GFAS inventory. Each hotspot is coloured
according to the corresponding injection height computed by the plume rise
model embedded in GFAS.
Summary and conclusions
Research flights in Brazil, during SAMBBA (dry season of 2012),
offered an opportunity to map the vertical structure of the
Amazonian haze using airborne lidar.
The sampling region extended ∼2200 km along an
east–west direction, centred around a mean latitude of 10∘ S,
and the sampling period was 14-days long.
Lidar profiles underwent cloud screening and a series of quality
tests, including a manual profile-by-profile review of the
reference height interval and cloud screening.
High loadings of biomass burning aerosol were present, with an
average AOD of 0.65±0.24 and a layer extinction (vertically
averaged aerosol extinction) of 112±49 Mm-1.
Within the main aerosol layers, the extinction was often
much larger than this, and ranged 100–400 Mm-1 typically,
and reaching values as high as 1000–1500 Mm-1 locally.
The lidar generally showed a vertical structure of the
atmosphere consisting of an aerosol layer from the surface
to an altitude of 1–1.5 km; and elevated aerosols above and
up to 4–6 km, usually representing the major portion of the
airborne smoke.
This structure may be indicative of a divide between fresher
smoke near the surface and more aged aerosol higher up.
The elevated aerosols were sometimes found in the form of a
single well-defined layer, whereas at other times multiple
layers were observed.
On average, across the data set considered here
the layer height was 2.0±0.4 km
and the layer depth was 2.3±0.6 km (mean and standard deviation).
This general structure is likely to be a consequence of dynamical
processes, such as initial plume-rise, vertical transport by dry
and moist convection, and large-scale motion.
Lifting of the aerosols from the surface can be explained with fire
radiative power; see e.g. the plumes in B743, at an along-track
distance of 1260 km, where lifting up to 2–3 km is evident,
with an additional plume at ∼4 km.
In this respect, the injection heights computed in the PRM
(Fig. ), display a general consistency with the
plume depths reported in the present study.
Considering the large number of convective clouds encountered
during SAMBBA (mainly in the western half of the area sampled),
updrafts in cumulus and cumulonimbus can also be ascribed as
a mechanism for lifting smoke above the boundary layer.
The mean vertical distribution of the aerosols that we observed
is not too dissimilar to the results of other studies, such as
Figs. 5 and 14, Fig. 6a and b,
and Fig. 6e; note, however, than in
the latter paper the aerosols were found to be mainly
in the boundary layer, below 2–2.5 km.
The general vertical structure that we have found was fairly
consistent across the region sampled (which extended
∼2200 km in an East-West direction) and across the time
period considered (14 days).
As an exception to this, very large aerosol loads were found
(extinction 1000–1500 Mm-1 and AOD 1–1.8) in two circumstances:
(i) in individual fire plumes, and (ii) in the vicinity
of clouds.
The latter circumstance suggests either the uptake of water by
aerosol close to clouds , or that smoke has been
transported vertically within convective clouds and detrained to
form elevated layers with locally high aerosol extinction coefficient.
An evaluation of the biomass burning lidar ratio has also been
completed, using the lidar profiles themselves.
Consistency of the observed profiles with Rayleigh scattering
above the aerosol layers permitted the lidar ratio to be
estimated as 73±6 sr.
This estimate has been compared with Mie scattering calculations
using the campaign mean size-distribution obtained from wing-mounted
optical particle counters.
It has been found that the computed lidar ratio
is very dependent upon the refractive index, and indeed the
observed value is compatible with values of the real and
imaginary parts published in the literature.
The present research effort has been a good opportunity for a
general test of the inversion method, and
it represents its first application to a large number of lidar
profiles.
This method is a variant of the traditional Fernald-Klett approach,
where a far-range reference is taken within an aerosol layer
instead of in a Rayleigh scattering portion of the atmosphere
(the latter being only available at near-range, leading to
retrieval instabilities).
The method is suitable for the observation of deep and optically
thick layers, when observed in a nadir-viewing geometry.
A profile-by-profile evaluation of the uncertainties introduced by
the inversion assumptions has been included.
These uncertainties are shown in Fig. and can approach
values as large as 50–100 % near the surface, but they are much reduced
at altitudes larger than 1–2 km.
The observed structure of the aerosol layer has been compared
to predictions with a limited-area configuration of the MetUM
and with the ECMWF-MACC global model.
In most cases, the models represented the general vertical structure
of the aerosol layers and showed realistic features, such as
layer depth and magnitude of the extinction coefficient.
For instance, in many cases the models showed a similar
aerosol layer depth, and a similar magnitude of the
extinction coefficient, although some differences exist,
and the exact position of features was not always exactly
reproduced.
Certain features, such as individual fire plumes and high
extinction values in the vicinity of clouds, were however
not well captured.
We believe that it is important to highlight the strengths
and weaknesses of the models in predicting the vertical
structure, because the latter is usually considered a weak point.
It is to be noted that the MetUM SAMBBA LAM was set up
specifically to support the field campaign, and its
primary purpose was to facilitate flight planning, whereas
the purpose of the ECMWF-MACC simulations is somewhat
different.
The latter is an operational global composition model, with
forecast charts made available publicly on the web on a
daily basis, and for which specially zoomed charts can
be requested for campaign support.
In both cases, the simulations are judged to be useful
if they provide some skill in predicting the typical
vertical distribution of the aerosol, the regional
distribution, and the day-to-day variability of aerosol
loadings.
Our results show some skill in simulating these aspects,
even if the fine detail is not always captured, and we
conclude that the simulations have served their purpose well.
The MetUM LAM simulations were a first attempt at generating
forecasts of biomass burning aerosols with the CLASSIC prognostic
aerosol scheme, and provided an opportunity to test this potential
advance in the Met Office's operational atmospheric composition
modelling capability.
The present paper therefore addresses the benefits of the prognostic
treatment of biomass burning aerosols offer over an aerosol
climatology, and the fact that regional and vertical variations
can be predicted with some skill is very satisfying.
Aerosol schemes can be sensitive to the host atmospheric model
and its configuration (grid-resolution, dynamics, processes)
and to the scheme's assumptions.
An evaluation of the CLASSIC scheme with detailed observations
is important, as it highlights whether the simulated spatial
patterns can be considered realistic when run at high resolution.
Some aspects of the LAM aerosol simulations during SAMBBA were
also evaluated by , and showed that the
regional distribution and magnitude of AOD agrees well with
observations.
The current study adds the evaluation of the vertical profile
to this assessment, and moreover gives an indication that the
emission scaling factors used (1.7 for the MetUM and 3.4 for
ECMWF-MACC) is reasonable.
have also investigated the impact of the
prognostic biomass burning aerosols on the meteorology simulated
with the MetUM.
They have found an impact on the radiation balance, improvements
in forecasts of temperature and humidity, and they have highlighted
important changes in the representation of the regional hydrological
cycle.
In this respect, we believe that the vertical profile of the
aerosols is a key variable to take into account, and that our
data set can prove precious for such studies.
In the ECMWF-MACC model, injection heights are simulated interactively
from the PRM, and this led to some improvements in the vertical profile
of aerosol for flights B741, B742 and B746.
A separate paper on this topic is in preparation ,
and therein it will be shown that, e.g., for flight B742 the
simulation using the PRM is able to predict the two distinct smoke
layers that were observed, whereas only one broader layer is
predicted if the PRM is not used.
In conclusion, the airborne lidar has once again proven to be a powerful
tool for mapping aerosols along the vertical and horizontal axes.
The ability to vertically profile the atmosphere yields an
advantage over passive remote sensing, in that the atmospheric
structure can be resolved, and moreover the observed signal
is not sensitive to parameters such as layer temperature and
ground reflection or emission.
Lidar permits sampling of the whole atmospheric column, and
thus to retrieve a complete picture of the atmospheric structure,
and is thus complementary to in situ techniques that can yield
more detailed microphysical information but on a smaller spatial
scale.
We believe that our study also illustrates well the application
of lidar observations to model verification and assessment, and
it opens the door to a series of further studies: besides the
above-mentioned evaluation of the GFAS inventory ,
we are also working on an evaluation of the UKCA-MODE aerosol
scheme in the Met Office climate model .
Acknowledgements
Airborne data were obtained using the BAe-146-301 Atmospheric
Research Aircraft (ARA) flown by Directflight Ltd and managed by the
Facility for Airborne Atmospheric Measurements (FAAM), which is
a joint entity of the Natural Environment Research Council (NERC)
and the Met Office.
SAMBBA was funded by the Met Office and NERC (grant NE/J009822/1).
Patrick Chazette and the Commissariat à l'Energie Atomique et
aux Energies Alternatives (CEA) are kindly thanked for help fixing
our lidar prior to SAMBBA.
Edited by: S. Martin
ReferencesAbel, S. J., Haywood, J. M., Highwood, E. J., Li, J., and Buseck, P. R.:
Evolution of biomass burning aerosol properties from an agricultural fire in
southern Africa, Geophys. Res. Lett., 30, 1783, 10.1029/2003GL017342,
2003.Allen, G., Illingworth, S. M., O'Shea, S. J., Newman, S., Vance, A.,
Bauguitte, S. J.-B., Marenco, F., Kent, J., Bower, K., Gallagher, M. W.,
Muller, J., Percival, C. J., Harlow, C., Lee, J., and Taylor, J. P.:
Atmospheric composition and thermodynamic retrievals from the ARIES airborne
TIR-FTS system – Part 2: Validation and results from aircraft campaigns,
Atmos. Meas. Tech., 7, 4401–4416, 10.5194/amt-7-4401-2014, 2014.
Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo,
K. M., and Silva-Dias, M. A. F.: Smoking Rain Clouds over the Amazon,
Science, 303, 1337–1342, 2004.Angelo, C.: Amazon fire analysis hits new heights, Nature News,
10.1038/nature.2012.11467, 2012.Baars, H., Ansmann, A., Althausen, D., Engelmann, R., Heese, B., Müller,
D., Artaxo, P., Paixao, M., Pauliquevis, T., and Souza, R.: Aerosol profiling
with lidar in the Amazon Basin during the wet and dry season, J. Geophys.
Res., 117, D21201, 10.1029/2012JD018338, 2012.Bellouin, N., Rae, J., Jones, A., Johnson, C., Haywood, J., and Boucher, O.:
Aerosol forcing in the Climate Model Intercomparison Project
(CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate, J.
Geophys. Res., 116, D20206, 10.1029/2011JD016074, 2011.Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J.,
Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S.,
Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: , Aerosol analysis
and forecast in the European Centre for Medium-Range Weather
Forecasts Integrated Forecast System: 2. Data assimilation, J. Geophys.
Res., 114, D13205, 10.1029/2008JD011115, 2009.
Boucher, O., Pham, M., and Venkataraman, C.: Simulation of the atmospheric
sulfur cycle in the Laboratoire de Meteorologie Dynamique general
circulation model: Model description, model evaluation, and global and
European budgets, Institut Pierre Simon Laplace (Note Sci. IPSL 23), Paris, France,
2002.
Bourgeois, Q., Ekman, A. M. L., and Krejci, R.: Aerosol transport over the
Andes from the Amazon Basin to the remote Pacific Ocean: A multiyear CALIOP
assessment, J. Geophys. Res., 120, 8411–8425, 2015.Brito, J., Rizzo, L. V., Morgan, W. T., Coe, H., Johnson, B., Haywood, J.,
Longo, K., Freitas, S., Andreae, M. O., and Artaxo, P.: Ground-based aerosol
characterization during the South American Biomass Burning Analysis (SAMBBA)
field experiment, Atmos. Chem. Phys., 14, 12069–12083,
10.5194/acp-14-12069-2014, 2014.Chand, D., Anderson, T. L., Wood, R., Charlson, R. J., Hu, Y., Liu, Z., and
Vaughan, M.: Quantifying above-cloud aerosol using spaceborne lidar for
improved understanding of cloudy-sky direct climate forcing, J. Geophys.
Res., 113, D13206, 10.1029/2007JD009433, 2008.Chazette, P., Dabas, A., Sanak, J., Lardier, M., and Royer, P.: French
airborne lidar measurements for Eyjafjallajökull ash plume survey, Atmos.
Chem. Phys., 12, 7059–7072, 10.5194/acp-12-7059-2012, 2012.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D.,
Tanré, D., and Slutsker, I.: Variability of absorption and optical
properties of key aerosol types observed in worldwide locations, J. Atmos.
Sci., 59, 590–608, 2002.
Fernald, F. G.: Analysis of atmospheric lidar observations: some comments,
Appl. Opt., 23, 652–653, 1984.Fiebig, M., Stohl, A., Wendisch, M., Eckhardt, S., and Petzold, A.:
Dependence of solar radiative forcing of forest fire aerosol on ageing and
state of mixture, Atmos. Chem. Phys., 3, 881–891,
10.5194/acp-3-881-2003, 2003.Freitas, S. R., Longo, K. M., Chatfield, R., Latham, D., Silva Dias, M. A.
F., Andreae, M. O., Prins, E., Santos, J. C., Gielow, R., and Carvalho Jr.,
J. A.: Including the sub-grid scale plume rise of vegetation fires in low
resolution atmospheric transport models, Atmos. Chem. Phys., 7, 3385–3398,
10.5194/acp-7-3385-2007, 2007.
Gerbig, C., Kley, D., Volz-Thomas, A., Kent, J., Dewey, K., and McKenna,
D. S.:
Fast response resonance fluorescence CO measurements aboard the C-130:
Instrument characterization and measurements made during North Atlantic
Regional Experiment 1993, J. Geophys. Res., 101, 29229–29238, 1996.
Gerbig, C., Schmitgen, S., Kley, D., Volz-Thomas, A., Dewey, K., and Haaks, D.:
An improved fast-response vacuum-UV resonance fluorescence CO instrument,
J. Geophys. Res., 104, 1699–1704, 1999.Gonçalves, W. A., Machado, L. A. T., and Kirstetter, P.-E.: Influence of
biomass aerosol on precipitation over the Central Amazon: an observational
study, Atmos. Chem. Phys., 15, 6789–6800, 10.5194/acp-15-6789-2015,
2015.
Groß, S., Freudenthaler, V., Wiegner, M., Gasteiger, J., Geiß, A., and
Schnell, F.: Dual-wavelength linear depolarization ratio of volcanic
aerosols: Lidar measurements of the Eyjafjallajökull plume over
Maisach, Germany, Atmos. Environ., 48, 85–96, 2012.Guyon, P., Graham, B., Beck, J., Boucher, O., Gerasopoulos, E.,
Mayol-Bracero, O. L., Roberts, G. C., Artaxo, P., and Andreae, M. O.:
Physical properties and concentration of aerosol particles over the Amazon
tropical forest during background and biomass burning conditions, Atmos.
Chem. Phys., 3, 951–967, 10.5194/acp-3-951-2003, 2003.Haywood, J. M., Osborne, S. R., Francis, P. N., Keil, A., Formenti, P.,
Andreae, M. O., and Kaye, P. H.: The mean physical and optical properties of
regional haze dominated by biomass burning aerosol measured from the C-130
aircraft during SAFARI 2000, J. Geophys. Res., 108, 8473,
10.1029/2002JD002226, 2003.
Hobbs, P. V., Reid, J. S., Kotchenruther, R. A., Ferek, R. J., and Weiss, R.:
Direct radiative forcing by smoke from biomass burning, Science, 275,
1777–1778, 1997.
Huang, J., Guo, J., Wang, F., Liu, Z., Jeong, M.-J., Yu, H., and Zhang, Z.:
CALIPSO inferred most probable heights of global dust and smoke layers, J.
Geophys. Res., 120, 5085–5100, 2015.Johnson, B., Haywood, J., Langridge, J., Darbyshire, E., Morgan, W., Szpeck,
K., Brooke, J., Marenco, F., Coe, H., Artaxo, P., Longo, K., Mulcahy, J.,
Mann, G., Dalvi, M., and Bellouin, N.: Evaluation of biomass burning aerosols
in the HadGEM3 climate model with observations from SAMBBA, Atmos. Chem.
Phys. Discuss., in preparation, 2016.
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones,
L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der
Werf, G. R.: Biomass burning emissions estimated with a global fire
assimilation system based on observed fire radiative power, Biogeosciences,
9, 527–554, 10.5194/bg-9-527-2012, 2012.
Kaufman, Y. J., Hobbs, P. V., Kirchhoff, V. W. J. H., Artaxo, P., Remer, L. A.,
Holben, B. N., King, M. D., Ward, D. E., Prins, E. M., Longo, K. M., Mattos,
L. F., Nobre, C. A., Spinhirne, J. D., Ji, Q., Thompson, A. M., Gleason,
J. F., Christopher, S. A., and Tsay, S.-C.: Smoke, Clouds, and
Radiation-Brazil (SCAR-B) experiment, J. Geophys. Res., 103,
31783–31808, 1998.
Klett, J. D.: Lidar inversion with variable backscatter/extinction ratios,
Appl. Opt., 24, 1638–1643, 1985.Kolusu, S. R., Marsham, J. H., Mulcahy, J., Johnson, B., Dunning, C., Bush,
M., and Spracklen, D. V.: Impacts of Amazonia biomass burning aerosols
assessed from short-range weather forecasts, Atmos. Chem. Phys., 15,
12251–12266, 10.5194/acp-15-12251-2015, 2015.Koppmann, R., von Czapiewski, K., and Reid, J. S.: A review of biomass
burning emissions, part I: gaseous emissions of carbon monoxide, methane,
volatile organic compounds, and nitrogen containing compounds, Atmos. Chem.
Phys. Discuss., 5, 10455–10516, 10.5194/acpd-5-10455-2005, 2005.Koren, I., Remer, L. A., Kaufman, Y. J., Rudich, Y., and Martins, J. V.: On the
twilight zone between clouds and aerosols, Geophys. Res. Lett., 34, L08805,
10.1029/2007GL029253, 2007.
Koren, I., Martins, J. V., Remer, L. A., and Afargan, H.: Smoke Invigoration
Versus Inhibition of Clouds over the Amazon, Science, 321, 946–949, 2008.Labonne, M., Breon, F.-M., and Chevallier, F.: Injection height of biomass
burning aerosols as seen from a spaceborne lidar, Geophys. Res. Lett., 34,
L11806, 10.1029/2007GL029311, 2007.Lance, S., Brock, C. A., Rogers, D., and Gordon, J. A.: Water droplet
calibration of the Cloud Droplet Probe (CDP) and in-flight performance in
liquid, ice and mixed-phase clouds during ARCPAC, Atmos. Meas. Tech., 3,
1683–1706, 10.5194/amt-3-1683-2010, 2010.
Liu, P. S. K., Leaitch, W. R., Strapp, J. W., and Wasey, M. A.: Response of
particle measuring systems airborne ASASP and PCASP to NaCl and latex
particles, Aerosol Sci. Tech., 16, 83–95, 1992.
Lolli, S., Sauvage, L., Loaec, S., and Lardier, M.: EZ Lidar(TM): A new
compact autonomous eye-safe scanning aerosol lidar for extinction
measurements and PBL height detection. Validation of the performances
against other instruments and intercomparison campaigns, Óptica Pura y
Aplicada, 44, 33–41, 2011.Lopes, F. J. S., Landulfo, E., and Vaughan, M. A.: Evaluating CALIPSO's
532 nm lidar ratio selection algorithm using AERONET sun photometers in
Brazil, Atmos. Meas. Tech., 6, 3281–3299, 10.5194/amt-6-3281-2013, 2013.Magi, B. and Hobbs, P. V.: Effects of humidity on aerosols and southern
Africa during the biomass burning season, J. Geophys. Res., 108, 8495,
10.1029/2002JD002144, 2003.Marenco, F.: Nadir airborne lidar observations of deep aerosol layers, Atmos.
Meas. Tech., 6, 2055–2064, 10.5194/amt-6-2055-2013, 2013.
Marenco, F., Johnson, B., Turnbull, K., Newman, S., Haywood, J., Webster, H.,
and Ricketts, H.: Airborne Lidar Observations of the
2010 Eyjafjallajökull Volcanic Ash Plume, J. Geophys. Res., 116, D00U05,
10.1029/2011JD016396, 2011.Marenco, F., Amiridis, V., Marinou, E., Tsekeri, A., and Pelon, J.: Airborne
verification of CALIPSO products over the Amazon: a case study of daytime
observations in a complex atmospheric scene, Atmos. Chem. Phys., 14,
11871–11881, 10.5194/acp-14-11871-2014, 2014.Mattis, I., Ansmann, A., Wandinger, U., and Müller, D.: Unexpectedly high
aerosol load in the free troposphere over central Europe in spring/summer
2003, Geophys. Res. Lett., 30, 2178, 10.1029/2003GL018442, 2003.
Mercado, L. M., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild,
M., and Cox, P. M.: Impact of changes in diffuse radiation on the global land
carbon sink, Nature, 458, 1014–1017, 2009.Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars,
A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz, M.,
Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and
Untch, A.: Aerosol analysis and forecast in the European Centre for
Medium-Range Weather Forecasts Integrated Forecast System: Forward
modeling, J. Geophys. Res., 114, D06206, 10.1029/2008JD011235, 2009.Müller, D., Mattis, I., Wandinger, U., Ansmann, A., Althausen, D., and
Stohl, A.: Raman lidar observations of aged Siberian and Canadian forest
fire smoke in the free troposphere over Germany in 2003: Microphysical
particle characterization, J. Geophys. Res., 110, D17201,
10.1029/2004JD005756, 2005.
Omar, A. H., Winker, D. M., Kittaka, C., Vaughan, M. A., Liu, Z., Hu, Y.,
Trepte, C. R., Rogers, R. R., Ferrare, R. A., Lee, K.-P., Kuehn, R. E., and
Hostetler, C. A.: The CALIPSO Automated Aerosol Classification and Lidar
Ratio Selection Algorithm, J. Atmos. Ocean. Tech., 26, 1994–2014, 2009.Palmer, P. I., Parrington, M., Lee, J. D., Lewis, A. C., Rickard, A. R.,
Bernath, P. F., Duck, T. J., Waugh, D. L., Tarasick, D. W., Andrews, S.,
Aruffo, E., Bailey, L. J., Barrett, E., Bauguitte, S. J.-B., Curry, K. R., Di
Carlo, P., Chisholm, L., Dan, L., Forster, G., Franklin, J. E., Gibson, M.
D., Griffin, D., Helmig, D., Hopkins, J. R., Hopper, J. T., Jenkin, M. E.,
Kindred, D., Kliever, J., Le Breton, M., Matthiesen, S., Maurice, M., Moller,
S., Moore, D. P., Oram, D. E., O'Shea, S. J., Owen, R. C., Pagniello, C. M.
L. S., Pawson, S., Percival, C. J., Pierce, J. R., Punjabi, S., Purvis, R.
M., Remedios, J. J., Rotermund, K. M., Sakamoto, K. M., da Silva, A. M.,
Strawbridge, K. B., Strong, K., Taylor, J., Trigwell, R., Tereszchuk, K. A.,
Walker, K. A., Weaver, D., Whaley, C., and Young, J. C.: Quantifying the
impact of BOReal forest fires on Tropospheric oxidants over the Atlantic
using Aircraft and Satellites (BORTAS) experiment: design, execution and
science overview, Atmos. Chem. Phys., 13, 6239–6261,
10.5194/acp-13-6239-2013, 2013.Paugam, R., Wooster, M., Atherton, J., Freitas, S. R., Schultz, M. G., and
Kaiser, J. W.: Development and optimization of a wildfire plume rise model
based on remote sensing data inputs – Part 2, Atmos. Chem. Phys. Discuss.,
15, 9815–9895, 10.5194/acpd-15-9815-2015, 2015.Reddy, M. S., Boucher, O., Bellouin, N., Schulz, M., Balkanski, Y., Dufresne,
J.-L., and Pham, M.: Estimates of global multicomponent aerosol optical depth
and direct radiative perturbation in the Laboratoire de Meteorologie
Dynamique general circulation model, J. Geophys. Res., 110, D10S16,
10.1029/2004JD004757, 2005.
Reid, J. S. and Hobbs, P. V.: Physical and optical properties of young smoke
from individual biomass fires in Brazil, J. Geophys. Res., 103,
32013–32030, 1998.Reid, J. S., Eck, T. F., Christopher, S. A., Koppmann, R., Dubovik, O.,
Eleuterio, D. P., Holben, B. N., Reid, E. A., and Zhang, J.: A review of
biomass burning emissions part III: intensive optical properties of biomass
burning particles, Atmos. Chem. Phys., 5, 827–849,
10.5194/acp-5-827-2005, 2005a.Reid, J. S., Koppmann, R., Eck, T. F., and Eleuterio, D. P.: A review of
biomass burning emissions part II: intensive physical properties of biomass
burning particles, Atmos. Chem. Phys., 5, 799–825,
10.5194/acp-5-799-2005, 2005b.
Rémy, S., Veira, A., Paugam, R., Kaiser, J., Marenco, F., Burton, S.,
Benedetti, A., Engelen, R., Ferrare, R., and Hair, J.: Two global
climatologies of daily fire emission injection heights since 2003, Atmos.
Chem. Phys., submitted, 2015.Rizzo, L. V., Artaxo, P., Müller, T., Wiedensohler, A., Paixão, M.,
Cirino, G. G., Arana, A., Swietlicki, E., Roldin, P., Fors, E. O., Wiedemann,
K. T., Leal, L. S. M., and Kulmala, M.: Long term measurements of aerosol
optical properties at a primary forest site in Amazonia, Atmos. Chem. Phys.,
13, 2391–2413, 10.5194/acp-13-2391-2013, 2013.Rosenberg, P. D., Dean, A. R., Williams, P. I., Dorsey, J. R., Minikin, A.,
Pickering, M. A., and Petzold, A.: Particle sizing calibration with
refractive index correction for light scattering optical particle counters
and impacts upon PCASP and CDP data collected during the Fennec campaign,
Atmos. Meas. Tech., 5, 1147–1163, 10.5194/amt-5-1147-2012, 2012.Ryder, C. L., Highwood, E. J., Rosenberg, P. D., Trembath, J., Brooke, J. K.,
Bart, M., Dean, A., Crosier, J., Dorsey, J., Brindley, H., Banks, J.,
Marsham, J. H., McQuaid, J. B., Sodemann, H., and Washington, R.: Optical
properties of Saharan dust aerosol and contribution from the coarse mode as
measured during the Fennec 2011 aircraft campaign, Atmos. Chem. Phys., 13,
303–325, 10.5194/acp-13-303-2013, 2013.
Seifert, P., Kunz, C., Baars, H., Ansmann, A., Bühl, J., Senf, F.,
Engelmann, R., Althausen, D., and Artaxo, P.: Seasonal variability of
heterogeneous ice formation in stratiform clouds over the Amazon Basin,
Geophys. Res. Lett., 42, 5587–5593, 2015.Sena, E. T., Artaxo, P., and Correia, A. L.: Spatial variability of the
direct radiative forcing of biomass burning aerosols and the effects of land
use change in Amazonia, Atmos. Chem. Phys., 13, 1261–1275,
10.5194/acp-13-1261-2013, 2013.Sofiev, M., Ermakova, T., and Vankevich, R.: Evaluation of the
smoke-injection height from wild-land fires using remote-sensing data, Atmos.
Chem. Phys., 12, 1995–2006, 10.5194/acp-12-1995-2012, 2012.Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M. M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.: Climate
change 2013: The physical science basis, unedited online version, available
at: www.ipcc.ch/report/ar5/wg1/ (last access: 19 February 2016), 2013.Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer, S.,
Berntsen, T., Berglen, T., Boucher, O., Chin, M., Dentener, F., Diehl, T.,
Easter, R., Feichter, H., Fillmore, D., Ghan, S., Ginoux, P., Gong, S.,
Grini, A., Hendricks, J., Horowitz, L., Huang, P., Isaksen, I., Iversen, I.,
Kloster, S., Koch, D., Kirkevåg, A., Kristjansson, J. E., Krol, M.,
Lauer, A., Lamarque, J. F., Liu, X., Montanaro, V., Myhre, G., Penner, J.,
Pitari, G., Reddy, S., Seland, Ø., Stier, P., Takemura, T., and Tie, X.:
Analysis and quantification of the diversities of aerosol life cycles within
AeroCom, Atmos. Chem. Phys., 6, 1777–1813, 10.5194/acp-6-1777-2006,
2006.Vakkari, V., Kerminen, V.-M., Beukes, J. P., Tiitta, P., van Zyl, P. G.,
Josipovic, M., Venter, A. D., Jaars, K., Worsnop, D. R., Kulmala, M., and
Laakso, L.: Rapid changes in biomass burning aerosols by atmospheric
oxidation, Geophys. Res. Lett., 41, 2644–2651, 2014.
Walters, D. N., Best, M. J., Bushell, A. C., Copsey, D., Edwards, J. M.,
Falloon, P. D., Harris, C. M., Lock, A. P., Manners, J. C., Morcrette, C. J.,
Roberts, M. J., Stratton, R. A., Webster, S., Wilkinson, J. M., Willett, M.
R., Boutle, I. A., Earnshaw, P. D., Hill, P. G., MacLachlan, C., Martin, G.
M., Moufouma-Okia, W., Palmer, M. D., Petch, J. C., Rooney, G. G., Scaife, A.
A., and Williams, K. D.: The Met Office Unified Model Global
Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations, Geosci.
Model Dev., 4, 919–941, 10.5194/gmd-4-919-2011, 2011.