Introduction
Land management practices in South America designed to increase available
land for agriculture and pasture and increasing urbanization have resulted in
deforestation altering an estimated 18 % of the original forest area
. The biomass burning aerosol (BBA) emissions
from fires tend to be largest where there is rapid deforestation (e.g.
south-east Amazonia) and lowest in the tropical forests in central Amazonia
where the density of the forest canopy and larger amounts of moisture
generally prevent fires, and areas where the fires occur in already cleared
agricultural and pastoral land tend to have lower fuel loads and result in
reduced fire emission per unit area compared to areas of rapid deforestation
.
show that there is a clear positive relationship between deforestation, BBA
emissions and aerosol optical depth (AOD).
The biomass burning aerosols absorb and scatter radiation, and affect the
surface fluxes and atmospheric stability. BBA also increase the concentration
of cloud condensation nuclei (CCN) affecting the formation and lifetime of
clouds . They
consist largely of carbonaceous material, with a relatively low single
scattering albedo (SSA) indicating they are more absorbing than, for example, sulfate
aerosols. There is a direct effect on the radiative budget from both
scattering and absorption , as well as the absorption
results in atmospheric heating which, together with the reduced solar
radiation reaching the surface, changes the stability of the atmosphere.
Heating of the atmosphere where aerosols and clouds are co-located (in
altitude) is predicted to result in cloud cover changes as cloud “burns
off” or is prevented from forming due to the stabilization of the
atmospheric profile. This process has been termed the semi-direct effect
, although other studies have
pointed out that the semi-direct effect could have the opposite tendency, for
instance if increased atmospheric stability favours the persistence of
stratocumulus . The overall aerosol–cloud
interaction is complex, however, with the semi-direct effect depending on the
relative heights of the aerosol and the clouds, the type of cloud and the
regional dynamics, e.g. convergent or divergent flow
. The particles of smoke are also predicted to act
as cloud condensation nuclei , changing the size
and number of cloud droplets and resulting in changes to the reflectivity and
lifetime of the clouds, referred to as the indirect effect
. Effects of BBA on convection and cloud
formation characteristics can result in changes in precipitation
, which in turn will change hydrological
processes . Furthermore, changes in the proportions
of direct and diffuse radiation at the surface will affect photosynthesis and
net primary productivity .
investigated the effect of BBA on cloud formation and lifetime and suggested
that the stabilization of the lower atmosphere, and the reduction in fluxes
from the surface, can inhibit the formation of high and deep convective
clouds, despite the possible destabilization of the higher atmosphere due to
the increased heating of the atmosphere at lower levels. This effect is
dependent on the initial cloud cover fraction, however, as a lower initial
cloud cover permits more solar absorption and increases the aerosol heating
effects. The BB AOD is also a factor, with the suggestion that at low (high)
BB AOD, the indirect effect is more (less) important than the semi-direct
effect . used large
eddy simulation modelling for biomass burning (BB) in Amazonia to show that
where the BBA was at the cloud formation layer, this would act to reduce
cloud fraction, but BBA at lower levels may tend to either increase or
decrease cloudiness. However, they also found that surface sensible and
latent heat fluxes are sufficient in themselves to reduce cloudiness.
Assessing the relative importance of these various effects is crucial to
understanding the overall impact of BBA on the regional climate, but there is
some uncertainty in how to treat and describe aerosol properties in climate
models that can affect the results of such studies. Our approach uses new
observations of aerosol optical properties, and compares two different
scenarios, a high-BBA-emission experiment with a low-BBA-emission experiment,
so that we are considering changes due to decadal timescale variability in
emissions (rather than a comparison of the regional climate with BBA to one
without BBA). This provides some insight into how changes in deforestation
practices, which are positively related to BBA emissions and BBA AOD
, and the resulting changes in the
characteristics of the BBA over time might affect the regional climate.
The climate of South America shows considerable variability, due to its large
latitudinal extent (12∘ N to 53∘ S) covering tropical,
subtropical and extratropical climate zones. The Andes also produce large
east–west variation, compounded by the change in the east–west width of the
continent and the differences between the temperatures of the oceans, with
the south-western Atlantic being warm and the south-eastern Pacific being
cool . In the tropics the intertropical convergence
zone corresponds to an east–west belt of low pressure and low-level
convergence of the trade winds, resulting in an area of high annual mean
precipitation which is largely produced by deep convective clouds. The annual
precipitation shows a seasonal cycle, with austral winter (JJA) maximum
rainfall in the north of the continent. Towards the end of October the
convection shifts southwards, so that the austral summer is characterized by
heavy precipitation from the southern Amazon basin to northern Argentina.
During the austral autumn (MAM) the maximum precipitation moves back to the
north. This seasonal pattern is considered by some to be monsoonal
, although it does not exhibit the
reversal in low-level winds seen in other monsoon systems. The South American
monsoon generally has an onset in October, but the exact timing is variable,
and geographically dependent, with an extension of the rainy area in the
north-west of Amazonia down to southern Amazonia .
Generally there is an increase in precipitation from the north-west to the
south-east from central America to the SE Amazon area, with the largest
changes in the central Amazon area. Changes in the surface fluxes, with
increases in surface radiation and sensible and latent heat flux, are
considered to be initiators of the monsoon . As the
monsoon progresses, surface (sea level) pressure is expected to reduce over
the South Atlantic, while the subtropical high in the South Atlantic is
displaced eastwards and weakens . Increased
convection moves down from the
north-west to central Amazonia and Brazil, while the circulation pattern at
850 hPa changes from northerlies to north-westerlies
in the south-west part of Amazonia, and in
eastern Brazil from easterlies
to north-easterlies following the displacement of the South Atlantic high. Close to the mouth of the Amazon the large-scale northerly
anomalies and the reduction of the
zonal component of the trade winds are expected as part of the transition to
the monsoon .
The South American Biomass Burning Analysis (SAMBBA) project was designed to
use ground-based and aircraft observations of South American biomass burning
aerosols to investigate their impact on climate. It involved a consortium of
international institutions, led by the UK Met Office and the National
Institute for Space Research (INPE) Brazil, in partnership with the
University of São Paulo and seven universities in the UK (Exeter University,
Leeds University, Manchester University, University of Reading, University of
East Anglia, University of York, King's College London). The observational
flights were conducted in September 2012 over the Amazonian region and were
coordinated with ground measurements . The flights were designed to measure aerosol properties,
the atmospheric chemistry, and the clouds, meteorology and radiation budget
over Amazonia. The modelling presented here is part of the complementary
effort to use the flight campaign observation results to refine BBA
properties in global models and to ascertain the effects of these aerosols on
regional climate.
In this article we test the sensitivity of the (regional) South American
climate to realistic high- and low-BBA-emission scenarios using aerosol
properties constrained by aircraft campaign measurements. The main biomass
burning months are August and September, so the results discussed in this
paper focus on the changes in climate in September where the effects of the
BBA on the regional climate are likely to be greatest, with a particular
emphasis on cloud changes, the semi-direct effect and changes in atmospheric
stability which impact cloud changes. Finally, we also briefly examine the
influence of changing emissions of BBA on the South American monsoon onset.
Section 2 describes the methodology and model set-up, Sect. 3 describes the
results for the September biomass burning season, Sect. 4 focuses on the
impact on the monsoon, and Sect. 5 discusses the results and their
significance.
Map of South America with features referred to in the text marked.
Methods
Model set-up
Global climate model simulations were performed with the Met Office Unified
Model HadGEM3 GA3 at the resolution of N96 (1.25∘ by 1.875∘) with 85 vertical levels. Simulations were run for
30 years, using annually repeating prescribed sea surface temperatures in
order to minimize short-timescale variability and improve the statistical
analysis. A spin-up period of 1 year was used. Sea surface temperatures and
sea ice were prescribed using data from HadISST
using monthly means over 1997–2011. Greenhouse and other trace gases are
fixed at levels representative of 2000, identical to
. Ozone is a seasonally varying two-dimensional
latitude–height field from . The cloud scheme used was
the PC2 scheme . Monthly emission
periodic climatologies for non-BBA are used, including the 2-D sulfur cycle,
black carbon and organic carbon from fossil fuel burning, from the CMIP5
dataset with monthly means representing 2000–2010.
Biogenic secondary organic aerosols are represented by an AOD climatology.
The atmosphere was free-running.
Aerosols were simulated by the Coupled Large-scale Aerosol Scheme for
Simulations in Climate Models (CLASSIC), a mass-based (“bulk”) aerosol
scheme representing sulfate, fossil-fuel soot (black carbon),
fossil-fuel organic carbon, biomass burning aerosol, sea salt and
mineral dust aerosol species, where the physical and optical
properties of each are specified and are externally mixed. A full
description is given in the appendix of , and
provide a detailed description of the BBA
scheme.
The BBA scheme was originally introduced for HadGEM1
and soon revised for HadGEM2
to use updated BBA properties
based on the SAFARI-2000 aircraft field observations from southern Africa
. In order to take advantage of
these observations of ambient BBA available at the time, BBA was represented
as a separate aerosol species, rather than as separate BC (black carbon) and
OC (organic carbon) components. Mass is
emitted into a fresh mode, and subsequently converted into an aged mode with
an e-folding timescale of 6 h represented by an increase in mass by a
factor of 1.62 . The fresh and aged BBA modes are
represented separately in CLASSIC, with different optical, hygroscopic and
CCN properties for each (see Sect. ). The fresh and aged
modes correspond to different OC : BC ratios, which is justified by the
fact that BC and OC are internally mixed in BBA particles. The increase in
BBA mass with ageing represents an increase in the mass of OC in BBA as the
aerosol ages chemically and physically from condensation of VOCs within a
plume. Although many GCMs now represent BBA as separate components comprising
BC and OC, it is still advantageous to represent BBA as a single species with
fresh and aged modes, since both aircraft and remote sensing observations
characterize the ambient BBA rather than the BC and OC components. Therefore
although climate models which separate BC and OC may appear more
sophisticated, we lack the observational constraint to support and validate
their complexity, particularly in BBA source regions. In this capacity, the
BBA aerosol model properties and results can still be adjusted and/or
validated using the more recent SAMBBA field campaign results, which for the
most part also represent the ambient BBA rather than their BC and OC components.
Since the 6 h e-folding timescale is relatively short on a climate
simulation timescale, most BBA in our simulations resides in the aged mode.
Fresh BBA is not considered hydrophilic in CLASSIC. However, aged BBA exerts
an indirect effect on clouds, acting as a cloud condensation nuclei (CCN),
and is converted to smoke in cloud water by nucleation scavenging (analogous
to sulfate aerosol in the model). Cloud droplet number concentration (CDNC)
is calculated from the number concentration of CCN in the accumulation mode
of BBA according to
using a relationship based on multiple aircraft
observations and assuming externally mixed aerosols. CDNC is used to
calculate cloud droplet effective radius for the radiation scheme and for the
autoconversion rate of cloud water to rainwater in the large-scale
precipitation scheme . BBA is removed by wet and
dry deposition. In the simulations here, we adjust the optical and
hygroscopic growth properties based on observations (see
Sect. ) as we find BBA in the CLASSIC scheme demonstrates
too much hygroscopic growth and not enough absorption (see
, for more details).
BBA emission experiments
This paper compares the results of two 30-year climate simulations with high-
and low-BBA emissions. Fresh BBA emissions are injected into the atmosphere
as surface emissions (in the lowest model level), as well as at high levels
(equally in mass across model level 3 to 20 – roughly equivalent to
altitudes up to 3 km) in order to represent burning plumes reaching higher
altitudes. No plume rise routines are incorporated and burning plumes are not
explicitly represented in the model. Recent work found that simple plume
height parametrizations are sufficient in representing BBA emission heights
for global climate modelling , and the vertical
parameterization of emissions is also justified by the fact that CLASSIC is
able to adequately represent the vertical profile of BBA in terms of shape
and total column AOD compared to observations .
Monthly emission datasets are taken from Global Fire Emission Dataset (GFED)
version 3.1 for BC and OC. The emissions are
summed to provide total BBA emissions in terms of carbon mass,
allowing CLASSIC to incorporate oxygen mass and therefore calculate
BBA mass. Emissions from GFED3.1 are provided in terms of vegetation
sector: forest and deforestation fires provide high-level emissions,
while savannah, woodland and peat provide surface emissions. This
method does not allow for any spatial variation in BBA properties
(such as optical properties) due to spatial variations in vegetation
and/or burning type. However, additional simulations were run with
varied BBA absorption properties, and results indicate that these
changes were small, so we consider this impact to be minimal.
In all experiments the BBA emissions are scaled up by a factor of 2, in order
to produce agreement between modelled and observed AODs, a measure that has
been necessary in previous modelling studies using GFED3.1 and
references therein. Applying a
global biomass burning emission scaling factor is an important assumption,
but is not new to this study. (their Table 2)
show that multiple modelling studies have used scaling factors of up to
a value of 6 in the past; attempting good agreement between modelled and
observed BBA AODs and particulate matter concentrations is an ongoing
problem. also discuss the issue, noting that many
studies have had to apply emission scaling factors greater than 1 in order
to gain agreement between modelled and observed AODs and/or particulate
matter measurements for BBA regions, such as
,
,
,
,
,
and
.
use a scaling factor of 1.6 for CLASSIC
specifically, and here we increase this to 2, which is required to be higher
as a consequence of decreasing the f(RH) curve to be consistent with the
SAMBBA aircraft observations (see Sect. ).
find that of many model aerosol properties,
hygroscopic growth factors were the most important in determining the required
emission scaling factor. Thus we might expect to need to increase modelled
hygroscopic growth in order to match models and observations of BBA; yet we
find the reverse: the SAMBBA observations suggest that CLASSIC is already too
hygroscopic ( and this work,
Sect. ). It should also be noted that there are
significant differences between different emission inventories, as well as
between subsequent versions of the same emission dataset
, which may contribute to
additional adjustments required in GCM emission schemes. We encourage further
work in this area – both by future field work and modelling studies as well
as for emission inventories – in order to reduce these important
uncertainties.
BBA September fire
emissions for the low (a, c, 2000) and high (b, d, 2010)
emission experiments as applied in CLASSIC, from the GFED3.1 dataset. Panels
(a) and (b) show high-level BBA emissions, (c) and
(d) show surface emissions. Note the different scales between upper
and lower panels.
In order to test the sensitivity of climate to the BBA loading within the
South American region (60∘ S to 15∘ N and -85 to
-30∘ W) we define two experiments to correspond to high and low
emission cases, based on realistic variations of emissions observed for the
South American region. During 1997 to 2011, the time period covered by
GFED3.1 data, the highest emission year for the South American region was
2010, while the lowest emission year was 2000. Therefore high- and low-BBA-emission experiments are defined using South American BBA emissions from 2010
and 2000 respectively. Total annual emissions for the high and low emissions
experiments for the South American regions are 0.51 and 2.32 Tg respectively
(including both high-level and surface emissions). The geographical
distribution of emissions for September 2000 and September 2010, the month of
largest emissions, are shown in Fig. . Outside of South
America, BBA emissions are set to the 1997–2011 GFED3.1 mean, with monthly
variations, and do not vary between experiments, in order to place the focus
of the experiment solely on the impact of changing South American emissions.
The f(RH) scattering curves used in the CLASSIC aerosol scheme for
BBA. Red and black lines show the standard hygroscopic scattering behaviour
for fresh and aged BBA, and the green dashed line shows values applied in
this work, taken from for Porto Velho, Brazil.
Optical properties used in the standard CLASSIC configuration for
BBA at visible wavelengths (black: fresh BBA; red: aged BBA) and the new
representations applied in this work for fresh and aged BBA (green dashed
line). (a) Mass scattering coefficient; (b) mass absorption
coefficient; (c) mass extinction coefficient; (d) single
scattering albedo. AERONET SSA at 550 nm is also shown (purple) with
horizontal lines indicating long-term maximum, mean and minimum (see text for
details).
Hygroscopic growth and optical properties in the modified CLASSIC scheme
This section describes how the hygroscopic growth and optical properties for
BBA are altered from the standard CLASSIC values in order to be more in line
with observations, including those from the recent SAMBBA intensive aircraft
observations made during September 2012 .
Figure shows the hygroscopic scattering growth curve (f(RH))
of BBA in CLASSIC, and Fig. shows the dependence of
scattering, absorption, extinction and SSA on relative humidity. Optical
properties in CLASSIC are derived from aircraft measurements made during
SAFARI-2000 of southern African BBA and scattering
hygroscopic growth is taken from (MH2003), also for
southern African BBA. CLASSIC allows for the separate representation of
optical properties and hygroscopic growth of the fresh and aged BBA modes.
The f(RH) values at 80 % RH are 1.5 and 2.2 for fresh and aged BBA
respectively (see Fig. ), demonstrating a very strong
scattering increase at high humidities. Absorption in the model is not
sensitive to RH (Fig. b), which is borne out by more recent
measurements (e.g. ). Since extinction is the sum of
scattering and absorption, extinction in CLASSIC is also strongly sensitive
to RH, as is the single scattering albedo (SSA) (Fig. c and
d).
However, CLASSIC is not consistent with other measurements. For example,
(KH98) performed aircraft measurements in
the Amazon region around Brazilia, Cuiabá, Porto Velho and Marabá, and found
much lower f(RH) values for BBA in the range of 1.05 to 1.29 at 80 % RH for
regional haze from four different regions of Amazonia.
find that CLASSIC overestimates BBA hygroscopic growth, and therefore aerosol
scattering, AOD and SSA in moist conditions. In this work, we apply the f(RH)
curve of KH98 from Porto Velho, which was closest to the location of the
SAMBBA observations, and represents the lower bound of the KH98 f(RH) curves
(1.05 at 80 % RH), as a weakly hygroscopic case that gives a more reasonable
representation (as indicated by the green dashed line in Fig. )
than the existing CLASSIC properties. Since this f(RH) selection represents
the lower bounds of f(RH) from KH98, the AODs from the model should be viewed
as a lower limit, and could still be reasonably increased by selecting f(RH)
values of up to 1.29 at 80 % RH.
It is not clear why the f(RH) curves of MH2003 for southern Africa and KH98
for Amazonia are so different, since both were observed with an airborne
humidified nephelometer, and MH2003 do not comment on the differences.
However, hypothesize that the “regional air”
classified in MH2003 may have contained a substantial amount of hygroscopic
industrial sulfate aerosol, which could have behaved differently.
show some evidence that if combustion of peat swamps is
involved, gas-to-particle conversion can produce high sulfate contributions
in the BBA. We anticipate new state-of-the-art observations from the recent
CLARIFY project observations within the southern African BBA plume to expand
on this issue.
Figure shows the new optical properties applied in this
work. Scattering values are defined as identical to the original
CLASSIC aged values at 0 % RH, but increase as a function of RH
according to KH98 for the more realistic Porto Velho BBA
observations. The RH dependence and absolute values of absorption are
kept identical to the original CLASSIC data. Since extinction is the
sum of absorption and scattering, extinction at 0 % RH is identical to
the original CLASSIC data, but is much lower at high levels of moisture
due to the lower f(RH) scattering dependency.
Additionally, although Fig. shows a clear difference in
optical properties in original CLASSIC between the fresh and aged BBA modes,
the recent aircraft observations from SAMBBA did not reveal any differences
between fresh and aged BBA, except possibly for very fresh BBA close to the
source, under 1 h from emission (William Morgan, personal communication,
2015). Therefore in these experiments, since fresh BBA represents aerosol
within 6 h of emission, we set the optical properties of fresh and aged BBA
to be identical based on the new curves in Fig. .
As a result, the new SSA (Fig. d, green dashed line) is
close to the standard CLASSIC SSA values for aged BBA at low RH, and close to
the standard CLASSIC SSA values for fresh BBA at high RH. The new values are
also in agreement with long-term inversion data from the AERosol Robotic
NETwork (AERONET) stations (purple box) in the BB region, for six sites (L1.5
data from Ji_Parana SE, Rio_Branco, Alta_Floresta, Abracos_Hill, Balbina,
Manaus_EMBRAPA) which have more than 1 year of data in the BB season
(August–October). Horizontal lines shown in Fig. represent
maximum, mean and minimum SSA at 550 nm (linearly interpolated between 440
and 675 nm) across the 6 sites. The range of RH covered by the AERONET box
represents typical values encountered during the SAMBBA aircraft research
flights.
A wide range of SSA values for BBA is indicated from different observations,
as discussed by . For example, SAMBBA aircraft
observations show that cerrado burning in the eastern regions produces more
BC and less organic aerosol, and therefore a more absorbing BBA at 550 nm
(SSA = 0.79), while forest burning in the west produces less absorbing
BBA (SSA = 0.88 ± 0.05) . The range of AERONET retrievals shown in
Fig. is 0.89–0.94 (mean 0.92). Satellite-based retrievals
of BBA indicate even higher SSA values (William Morgan, personal
communication, 2015). Therefore since the various observations of SSA vary
greatly, an intermediate value of SSA of 0.92 at 60 % RH seems reasonable
for these experiments, as shown by the dashed line in
Fig. d. Further experiments were run with varied absorption
but are not presented here. Optical properties in all of the six spectral
bands covering the visible wavelengths were adjusted using the same
procedure.
Impact of biomass burning emissions in September
Introduction
September has the highest biomass burning emissions which can directly
influence surface fluxes whilst in situ, so we look first at September
monthly-mean fields. For quantitative results we define a “biomass burning
box” (BB box) (5–15∘ S, 40–70∘ W), selected on the basis
that this is the main area where AOD is affected by biomass burning aerosol.
The statistical significance for all plots is determined by a Student's
t-test using the 30-year time series to identify where differences are due to
the changes in the emissions, and not just inter-annual variability. We will
examine the effects of the aerosol emissions on the AOD, and the consequences of the AOD changes on the clouds, the longwave
(LW) and shortwave (SW) radiation and the surface fluxes, as well as the surface
temperature, pressure, circulation and precipitation. The results are shown
in Table , which gives the mean effect within the BB box on
several variables for the high emissions case, low emissions case, and the
difference (see Fig. a for the extent of the BB box).
Table of September mean values for the high and low
experiments and the differences between them. The percentage changes in the table are calculated as (high - low) / high. The means are
calculated over the biomass burning box (latitude 5–25∘ S,
longitude 40–70∘ W), which is outlined in Fig. a.
Field
High
Low
Difference
% change
((H -L ) / H)
AOD
0.67
0.19
0.48 ± 0.01
71.6
Cloud fraction
0.53
0.55
-0.02 ± 0.01
-3.8
SW down surface (clear sky) (W m-2)
284.42
298.19
-13.77 ± 0.39
-4.8
SW Down Surface (all sky) (W m-2)
241.24
248.61
-7.37 ± 2.29
-3.1
SW net surface (clear sky) (W m-2)
237.15
248.63
-11.48 ± 0.32
-4.8
SW net surface (all sky) (W m-2)
201.10
206.56
-5.46 ± 1.93
-2.7
SW up TOA (clear sky) (W m-2)
75.32
72.00
3.32 ± 0.09
4.4
SW up TOA (all sky) (W m-2)
110.80
112.16
-1.36 ± 1.67
-1.2
SW net TOA (clear sky) (W m-2)
327.55
330.87
-3.33 ± 0.89
-1.0
SW net TOA (all sky) (W m-2)
292.07
290.72
1.35 ± 1.8
0.5
LW up TOA (clear sky) (W m-2)
287.07
286.54
0.53 ± 0.93
0.2
LW up TOA (all sky) (W m-2)
264.31
261.24
3.07 ± 1.55
1.1
Sensible heat flux (W m-2)
58.30
62.59
-4.29 ± 0.99
-7.4
Latent heat flux (W m-2)
58.86
60.64
-1.78 ± 1.12
-3.0
Surface temperature (∘)
297.59
297.73
-0.14 ± 0.24
0.0
Precipitation (mm day-1)
1.79
2.05
-0.26 ± 0.1
-14.5
Surface pressure (hPa)
944.55
944.69
-0.14 ± 0.2
0.0
(a) The September mean biomass burning AOD at 0.44 µm for the high
emissions experiment. The outlined box contains the area used to
calculate mean values in Table . (b) The September mean biomass burning AOD at 0.44 µm for the low
emissions experiment. (c) Plot of the difference in the September AOD between
the high and low
emissions experiment. Stippling
represents 95 % confidence limit.
AOD and clouds
Figure a and b show the biomass burning AOD
at 0.44 µm over the 30-year
HadGEM3-GA3 run for the high and low emissions case respectively. The high
emissions case has a maximum BB AOD of approximately 1.6 across the central
biomass burning area, and values of up to 0.3 extend to the north and south.
The mean value for the outlined box is 0.68 ± 0.01. In the low
emissions case the highest values are 0.6, with lower values of 0.1 over most
of the biomass burning area; the mean in the box is 0.19 ± 0.005 (see
Table ). In both cases African biomass burning results in a
small transported BB AOD across the South Atlantic, which extends to the
coast of South America. In the two model runs, the biomass emissions from
Africa (and the rest of the world) are identical and are based on
climatological means. The differences (high - low) in BB AOD between the two runs
(Fig. c) are greatest over the BB region, as expected; the BB
AOD transported from Africa shows no difference between the two runs,
confirming that this is not the result of South American BBA. The mean BB AOD
difference in the BB box area is 0.48, a reduction of 71.6 % from the
high case to the low case. As discussed in the previous section, the
difference in emissions is 78 %, suggesting that the majority of the
emissions change is translated to a BB AOD change. The direct relationship
between particulate emission from fire and observed AOD in South America is
noted by .
(a) The September mean cloud fraction for the high
emissions experiment. (b) The September mean cloud fraction for the low
emissions experiment. (c) Plot of the September difference in the cloud fraction between
the high and low
emissions experiment. Stippling
represents 95 % confidence limit.
September mean vertical profile of cloud fraction for high
emissions, low emissions, H - L compared with the vertical profile of
the aerosol burden mass mixing ratio (MMR) in kilograms per kilogram (kg kg-1)
(both quantities averaged over BB box area outlined in area plots).
In Fig. a and b the total September average cloud fraction
for the high and low emissions experiments are plotted, showing the
distribution of cloud cover. In both cases the highest cloud fractions are
over the far north-west part of the continent, down into the Amazon basin. Along
the east coast and into the Brazilian Highlands (centred on 15∘ S,
45∘ W) there is less cloud cover, although it increases again towards
the Plate estuary (35∘ S, 58∘ W). There is also little cloud
over the Andes, but a substantial fraction over the eastern edge of the Andes
mountain range, and high cloud over the Caribbean area (12∘ N,
47∘ W). In Fig. c the change in cloud fraction
between the high and low emissions case is plotted – the stippled areas
denote a confidence level of 95 %. The cloud fraction is reduced by 0.05 in
much of the biomass burning area (3.8 % averaged over the BB box), although
there is a substantial effect to the north-east of the main AOD difference
(Fig. ). The reduction in cloud would be consistent with
semi-direct effects found in other modelling studies, whereby increased
atmospheric heating can reduce convective activity and burn off the clouds
within the aerosol layer . Outside of the area with
high AOD differences higher
emissions appear to result in a slight increase in cloud fraction
in the area to the east of the Andes and just north of the River Plate valley
(34∘ S, 55∘ W); however, the changes in these areas are not
statistically significant at the 95 % confidence level.
The profile of cloud fraction with height (averaged over the area outlined in
Fig. a) is shown in Fig. for the high
and low emissions cases. The biomass burning burden profiles are shown for
comparison. Cloud layers are evident at about 1 and 4.2 km, and the presumed
outflow from deep convective clouds at 9–14 km. Despite September being the
dry season these clouds were frequently observed from the aircraft during the
SAMBBA aircraft campaign .
In general the low emissions case has more cloud at all levels, with the most
marked differences in the high cloud amount at 9–14 km, and the mid-level
cloud at 3–6 km. Cloud changes below 2 km are small, in part because cloud
cover at these heights is minimal. suggest a
reduction in boundary-level clouds can occur where aerosols stabilize the
boundary layer and cool the surface, since the supply of water from the
forest canopy is reduced. Where the aerosol and cloud are at the same height,
i.e. below around 4 km, we would expect the reduction via the semi-direct
effect to be strongest as the heating of the atmosphere due to the presence
of absorbing aerosol promotes cloud evaporation .
However, we see a similar magnitude of cloud reduction for the medium-level
(3–5 km) and the high clouds at 9–14 km, where the aerosol burden is much
lower. A likely mechanism for this reduction in medium and high cloud cover
would be the stabilizing of the atmosphere, due to the aerosols in the lower
levels heating the atmosphere but cooling the ground, stabilizing the
boundary layer, reducing its height and thus reducing the amount of deep
convection occurring in the high emissions case . In
Fig. a the stable boundary layer diagnostic (which is defined to
be set to 1 where the surface buoyancy flux is < 0.0, at each time
step and each grid point; the average indicates the fraction of time in this
state) shows that the high emissions case has a more stable boundary layer,
especially in the areas where we see the most cloud reduction. This tends to
support the explanation that cloud reduction is related to the boundary layer
changes. The boundary layer height varies between 1 and 1.8 km in the BB
box, and in Fig. b the boundary layer height differences show a
reduction for the higher BBA case, due to the reduction in SW radiation
reaching the surface, and the reduction in sensible heat flux
.
The deep convection model diagnostic (defined to be set to 1.0 if deep
convection occurs during a model timestep, 0 if not, similar to the boundary
layer stability diagnostic mentioned above), shown in Fig. c, also
shows the statistically significant reduction in deep convection for the
higher emissions case, across most of the main area of BBA, and also the area
to the west side of the Amazon Basin. There is a dipole change in the
Caribbean, where the area along the north-east coast of South America shows a
statistically significant reduction, and just to the north there is a
(statistically significant) increase for the high emissions case. Although
this is not entirely congruent with the area of highest AOD difference, it is
clear that there is a significant influence of BBA on the deep convection as
represented by this diagnostic, suggesting that the reduction in cloud
fraction may be due predominantly to this mechanism. This change in deep
convection between simulations is likely to be contributing to the high- and
mid-level cloud changes, as in the model some of the mid-level cloud is
likely due to detrainment of deep convective cloud around the freezing level.
Where evaporation is reduced and moisture availability for cloud formation
is curtailed, this would also act to inhibit cloud formation
; the relative humidity (not shown) in the high
emissions case is higher at the surface (< 1 km) by around
10 %, but lower throughout the rest of the profile, with the largest
difference (-7.5 %) occurring at around 5 km. With a more stable
boundary layer in the high emissions case there is less turbulent transport
of moisture from the surface layer. This would suggest a drier atmosphere at
height is also contributing to reduced cloud formation in the high emissions
case.
(a) Plot of the September difference in the boundary layer
stability diagnostic between
the high and low
emissions experiment. (b) Plot of the September difference in
the boundary layer height between the high and low
emissions experiment (m). (c) Plot of the September difference in
the deep convection diagnostic between
the high and low
emissions experiment. Stippling
represents 95 % confidence limit.
Vertical profile of September
mean differences of droplet
effective radius (microns) (averaged over the BB box
5–25∘ S, 40–70∘ W)
Considering the indirect effect, in Fig. we see a
reduction in the effective radius of the liquid water drops where increasing
the aerosol amount reduces the effective radius, as suggested by, for
example,
. The
vertically integrated droplet concentration also increases for the higher
emissions case, which is consistent with predictions that the increase in
nucleation centres (aerosol particles) will increase the number of droplets.
and suggest that there is a
competition between the microphysical effects and radiative effects, where
high AOD results in reduced cloud where the radiative effects are dominant,
which is consistent with our results.
Effects on radiation
The September mean difference between high and low emissions cases for (a) the
clear-sky downwelling SW radiation at the surface, (b) the
all-sky downwelling SW radiation at the surface, (c) the clear-sky upwelling SW radiation at
TOA and (d) the all-sky upwelling SW radiation at TOA. Stippling
represents 95 % confidence limit.
In Fig. a and b we see the difference between the high and
low emissions case in the downward SW radiation at the surface, which largely
follows the areal extent of the difference in the AOD. The results for the
clear-sky (i.e. excluding all clouds) SW reaching the surface (in the BB box)
from our models show the mean reduction in the September mean downwelling
flux is -13.77 ± 0.39 W m-2 (a reduction of 4.8 %) for the
high emissions case compared to the low emissions case. The all-sky
differences include the effect of clouds and indeed changes in the cloud
fraction reduce the area mean change to -7.44 ± 2.29 W m-2,
indicating that scattering by the clouds above the aerosol reduces the
difference we see due to the aerosol changes alone. The competing effects of
the reduction in SW radiation at the surface due to the BBA and the increase
due to reducing cloud cover control the resulting impact on the SW radiation
at the surface, and in most of the BB box area the BBA has the stronger
effect. There is also a
statistically significant (at the 95 % confidence level) surface
reduction in SW to the north of the Plate Estuary (34∘ S,
55∘ W),
which we interpret as the effect of the increase in cloud in this
area, as the BB AOD difference is very small here, while the
cloud fraction increases (see Fig. a), resulting in
the reduction of SW radiation at the surface here.
The top-of-the-atmosphere (TOA) upwelling SW radiation differences are shown in
Fig. c and d, where the clear-sky differences show an
increase for the high emissions case in the same area as the BB AOD
differences between the two experiments. This illustrates the direct
radiative effect, which is stronger with higher emissions. The all-sky case
is much less clear-cut, as the influence of the clouds results in a
predominantly negative difference. This suggests that the effect of the
reduced cloud cover, and thus reduced scattering by clouds in the high
emissions case, dominates over the increased scattering from the increased
BBA (as seen in the clear-sky case). This changes the sign of the SW
radiative effect at the TOA, causing a net reduction in outgoing SW in the
region. These changes are not statistically significant, however.
September mean differences: (a) The difference between high and low emissions case for the
clear-sky outgoing LW radiation at TOA. (b) The difference between high and low emissions case for the
all-sky outgoing LW radiation at TOA (note the colour scales for
clear sky and all sky are different). (c) The difference between
high and low emissions case for the column integrated water
vapour.
The difference in outgoing longwave radiation at the TOA is shown in Fig. , where the clear-sky
difference shows a generally positive change, such that the increase in
aerosol results in an overall increase in the outgoing LW radiation over much
of the biomass burning area. However, since the aerosol properties prescribed
in the model relate to relatively small aerosol size the BBA has little
effect on LW radiation directly. As the only significant changes are seen in
areas outside the main biomass burning areas, it is much more likely that
these LW changes are related to secondary effects, for example water vapour
changes. Shown in Fig. c, the LW radiation changes are
consistent with decreased column water vapour in the high emissions
experiment, which leads to increased outgoing LW radiation at the TOA. The
aerosol properties prescribed in the model relate to relatively small aerosol
size, and therefore the effect of BBA on LW radiation is negligible. In the
all-sky case (Fig. b), the differences are significant in the
main biomass burning area, but can be directly related to the changes in
cloud fraction between the high and low emissions case; where the clouds are
reduced, we see a greater LW upwelling at the TOA, as the effective emitting
temperature is now lower in the atmosphere and warmer.
September mean SW heating rates (averaged over the BB box
5–25∘ S, 40–70∘ W) for high and low emissions: (a) clear
sky,
(b) all-sky and (c) all-sky–clear-sky differences, showing heating
rate changes due to cloud only.
The clear-sky SW heating rate is shown in Fig. a for the high
and low emissions results. The largest difference between the high and low
emissions case is below 5 km, coincident with the majority of the absorbing
aerosol, resulting in an increased heating rate of the atmosphere for the
high emissions compared to the low emissions case. As the BBA is absorbing
(with an SSA < 1.0) it absorbs some fraction of the SW, and an
increase in BBA results in an increase in the heating rate. The maximum
heating rate also appears to be at a slightly lower altitude for the high
emissions case. Above 5 km there is a much smaller difference, with a very
slight negative difference at 9 to 15 km (i.e. the high emissions have a
slightly lower heating rate at this altitude).
The all-sky heating (Fig. b) rate shows broadly similar
characteristics, but we see a dip in the heating rate in both cases at 500 m
height, which is more marked in the high emissions case, and subsequently
there is a less linear profile in both high and low emissions cases. The
presence of clouds provides absorption and scattering of SW radiation above
the main BBA layer resulting in an increase in the heating rate at levels
containing clouds, relative to the clear-sky case. The SW heating rate is
reduced below the cloud, as less SW radiation reaches these altitudes and
thus the heating rates here are reduced. The effect of the clouds is stronger
in the low emissions case, as there is more cloud here, but the differences
due to cloud cover compared to the clear sky are not large. The differences
between the all-sky and clear-sky heating rates (Fig. c)
illustrate the heating rate changes due to cloud only; beneath 4 km, clouds
cause a cooling but differences between the two experiments are minimal.
Above 4 km the clouds warm the atmosphere, with the extra cloud cover in the
low emissions case producing a larger cloud heating rate than for the high
emissions (reduced cloud) case.
September mean LW heating rates (averaged over the BB box
5–25∘ S, 40–70∘ W) for high and low emissions for (a) clear
sky,
(b) all sky and (c) all-sky–clear-sky differences.
In Fig. a the clear-sky LW heating rates show cooling up to
15 km (tropopause), with the largest cooling of -2.5 K day-1 at
4–5 km (note the difference in scales from the SW plots). The low emission
experiments show less cooling than the high emission experiment below 6 km,
which then reverses from 6 to 14 km. In the all-sky plot
(Fig. b) we see below 4 km that a reduction in cloud leads to
more LW cooling of the lower atmosphere. Above 4 km, the reduced cloud leads
to a slight warming of the atmosphere due to the upward longwave emissions
from below. Higher BBA emissions reduce the cloud cover resulting in a
reduction in the absorption of radiation, and thus less LW re-emission from
the clouds. There may also be an increase in LW emission due to the increased
temperatures in the BBA layer (0–4 km). The all-sky–clear-sky difference
plot (Fig. c) shows the impact of the cloud changes alone on
the heating rates, demonstrating
the effect of higher emissions reducing the cloud cover, reduced heating
between the cloud layers (e.g. near the surface and at 3 km) and increased
cooling within the cloud layers (1 and 4 km), but there is very little
change at higher altitudes. Within the BBA layer
we can see the semi-direct effect of the cloud burn-off, but the higher cloud
(12 km and above) appears to be responding to stability changes.
(a) September mean differences (high - low) for (a) sensible
heat flux (b) latent heat flux. Stippling represents the 95 % confidence interval.
The effects of the BBA on the sensible heat flux are shown in
Fig. a, where the higher emissions result in a reduced
sensible heat flux, due to the reduction in SW radiation cooling the surface
(cf. Fig. b). There are also significant differences around
the Plate Estuary, which may be due to an increase in cloud cover in this
area for the high emissions case which reduces the SW radiation reaching the
surface, and thus also reduces the sensible heat flux. The spatial agreement
with the main differences in BB AOD (see Fig. ) is
generally good, suggesting this is largely an effect of the increased BB AOD
in this case. The latent heat flux differences show statistically significant
reductions for the high emissions case in the area to the north of, and in
the centre of, the main BB area. However, these changes are not strongly
co-located with the BB AOD changes and are possibly related to reductions in
available moisture due to reduced precipitation and circulation changes
affecting the latent heat flux, which are investigated in the next section.
These results are consistent with those of .
Meteorology
September mean differences (high - low) for (a) surface
temperature,
(b)
total precipitation (stippling represents the
95 % confidence interval) and (c) moisture flux differences at
850 mb;
coloured contours are the magnitude of the moisture flux.
There is a mean change (in the BB box) in the surface temperature between the
high and low emissions runs of 0.14 ± 0.23 ∘C, with a
statistically significant maximum decrease of approximately 0.8 ∘C in
the north part of the BB box. There is a maximum increase of approximately
0.5 ∘C around the southern part of the Brazilian highlands; however,
this increase is not statistically significant (25∘ S, 50∘ W), as shown in Fig. a. The spatial pattern here reflects
the BB AOD differences in part, where the absorption and scattering of SW by
the aerosol reduces the surface temperature. There are increases in surface
temperature where the clouds are reduced near the southern Brazilian
Highlands; here the cloud reduction allows more of the SW radiation through,
and the extinction due to the BBA is somewhat lower than in the north of the
box. The competing effects of the direct effect reducing the SW radiation
reaching the surface and the reduction in cloud cover increasing the SW
radiation at the surface are clear, controlling the mixed geographical
response of the surface temperature overall.
The differences in total precipitation are shown in Fig. b, where the overall effect in much of the northern
part of the continent is a reduction in the total precipitation, particularly
marked in the western Amazon basin and the Caribbean. Further south we see an
increase in the area just north of the River Plate (30∘ S, 55∘ W), which corresponds to the area of increase in cloud fraction
seen in Fig. ; however, this increase is not statistically
significant at the 95 % confidence level. The decreased aerosol in the low
emissions case leads to a 14.5 % increase in precipitation in the BB box
with mean precipitation increasing from 1.78 mm day-1 (high emissions) to 2.05 mm day-1 (low emissions).
The decrease in precipitation seen in the high emissions experiment is
consistent with the reduced cloud and latent heat flux, and the more stable
boundary layer seen in this experiment. The resulting reduction in
precipitation could result in a reduction in soil moisture content, partly
explaining the reduction in latent heat flux shown in Fig. b. analysed rainfall
data in the Amazon and suggest that the influence of biomass burning on
precipitation is dependent in part on the degree of atmospheric instability.
In a more stable atmosphere, BBA tends to decrease the precipitation; they
also note that increasing cloud droplet number, and decreasing droplet size,
would act to reduce precipitation in the absence of strong convection. In
Fig. c the moisture flux at 850 mb shows the increase in
moisture transported by the low-level jet east of the Andes, which together
with the increased flux from the South Atlantic combines to produce the increase
in precipitation seen at 30∘ S, 50∘ W.
September mean wind circulation at 850 hPa for (a) high
emissions case (coloured contours are mean September pressure in
hPa), (b) low emissions case, and (c) differences in pressure and wind circulation
for high - low runs. Coloured contours in (a) and (b) are surface pressure
and in (c) surface pressure differences in hPa.
The surface pressure and 850 hPa circulation for high and low emissions are
shown in Fig. a and b. The ERA-Interim mean September
surface pressure and circulation (averaged over a similar timescale) are
shown in the Supplement (Fig. S1 and Fig. S2 in the Supplement) for
comparison and show that in general both surface pressure and the circulation
are well represented by the model, although the wind flow seems to be
somewhat more zonal between 0 and 10∘ S in the ERA-Interim plot. The
model results for surface pressure are broadly similar for both the high and
low emissions experiments, with a high-pressure area in the central Amazon
basin, and low pressure to the north and west. We also see high pressure
along the eastern side of the Andes, down towards the River Plate estuary,
and a high-pressure system in the south-east Atlantic, which shows some
difference in position between the high and low emissions case. The surface
pressure differences (Fig. c) show a slight increase in
the Amazon basin area, but a larger decrease down in the southern part of
Brazil and around the Plate Estuary. We also see a difference in the South
Atlantic, which is related to the position of the South Atlantic high
shifting between the high and low emissions case. The pressure differences
are of the order of 1 hPa, with significant changes at
the 95 % confidence level in the Caribbean area and in southern
Argentina.
The change in pressure patterns corresponds to the change in winds at
850 hPa. (Fig. a and b). The general circulation is
easterly across the Amazon basin, south-easterly in the Caribbean, and
tending to north-easterly to the south of the Amazon mouth. In the southern
part of Brazil we see a northerly direction, turning westerly at the southern
tip of the continent. Although the overall circulation patterns are similar
for the two experiments, we do see differences between the high and low
emissions case in Fig. c, the largest effect being a
strengthening of the low-level jet that runs along the eastern side of the
Andes, from around 10 to 30∘ S, down to the Plate Estuary. Analysing
the zonal and meridional components suggests that the most significant change
is in the zonal component, possibly suggesting a change in direction as well
as in strength. There is also a change in the circulation due to the shift in
the South Atlantic high-pressure system, where the easterlies are weaker in the
low emissions case, and have a more southward component in the high emissions
case. In the Caribbean area the prevailing easterlies and south-easterlies
are weaker in the high emissions case.
Impacts on the monsoon
suggest that local thermodynamical processes may be
important for onset of the monsoon, whilst the strength of the low-level jet
to the east of the Andes is an important source of moisture for the
subtropical convection that brings rainfall to the south of the region in the
wet season (e.g. ). As these features appear
sensitive to the aerosol emissions during September (see
Sect. ) we now consider whether there is any discernible
difference in the transition to monsoon regime between high and low aerosol
simulations. We do this in only a broad sense, since the temporal resolution
of our output is not sufficient to identify a specific monsoon onset date,
and in any case, the definition of onset is a matter of some debate in the
literature . The transition to the wet season
between September and November is broadly similar in the model as shown in
previous studies and is comprised of the following
a shift to northerly and north-easterly wind across the Amazon basin,
strengthening of the northerly low-level jet to the east of the Andes,
eastward movement and weakening of the high-pressure system,
a shift to cross-equatorial rather than zonal flow to the north of the region,
increased rainfall over the Amazonian basin that extends southwards
and eastwards moving through October and November.
These changes are shown in Fig. S5, where the high
emissions case is used to show the mean November meteorology,
exemplifying the changes described above.
Plot showing the differences in meteorological variables for
October and November: (a) differences in mean circulation for
October;
(b) differences in mean surface pressure for October; (c) differences in
mean precipitation for October; (d) differences in mean circulation
for November; (e) differences in mean surface pressure for November; (f) differences in mean precipitation for November.
(Note the difference in projection and area from previous plots.)
Significant differences are seen in October surface pressure between high and
low emissions simulations across the South Atlantic and South Pacific,
reflecting slightly different positions of the high pressure in each case,
and on the east of the Andes where pressure is lower in the higher emissions
case, but this does not extend into November (Fig. b
and e). We do, however, find a significant decrease in November precipitation
(Fig. f) across the Amazon basin area in the higher
emissions simulations, which is consistent with enhanced transport of moisture
from the Amazonian basin by the strengthened jet (Fig. c)
on the east of the Andes in the high emissions case.
The examination of biomass burning in regional climate
models suggested that aerosols might oppose the transition to monsoon state,
with strong absorption stabilizing the troposphere in the southern Amazonian
region. Whilst the results in our model suggest that the strengthening of the
jet east of the Andes may also have an effect, further simulations with
higher temporal resolution of output would be needed to establish the
mechanism by which aerosol emissions appear to affect the monsoon rainfall.
Discussion and conclusions
The aim of the work described here
was to investigate the impact of biomass burning emissions on the regional
climate in South America using the Met Office Unified Model HadGEM3 GA3 model.
We examine this through two 30-year climate runs with BBA emissions taken
from the GFED v3.1 dataset, representing low and high emission years. We
adjust ambient BBA optical and hygroscopic properties based on the literature
and on recent airborne in situ measurements from the SAMBBA project. We
employ a global BBA emission scaling factor of 2 in order to generate AODs
comparable to observations. Reconciling surface particulate matter
concentrations and AODs for BBA between models and observations is a
continuing problem for climate models and the scientific community, largely
impacted by the hygroscopic activity of BBA and choice of emission dataset
and version, and we urge further research in this area in order to reduce
modelling and observational uncertainties.
We have found clear semi-direct effects of the biomass burning aerosol in
September, with the results indicating a significant burning off and an
additional effect on cloud cover from reduced deep convection, as the
aerosols stabilize the boundary layer and suppress surface fluxes. Changes in
the cloud microphysical properties (i.e. effective radius of the droplets)
are evidence for the first indirect effect occurring as a result of increased
BBA. The changes in the SW radiation for higher BB emissions are as expected
from the direct effect, with a reduction in downwelling SW radiation at the
surface and an increase in outgoing SW radiation at the TOA in the clear-sky
case. The all-sky (cloud effects included) case shows less of a reduction at
the surface, due to the decrease in cloud cover, which indicates that the BBA
dominates the surface radiation SW flux while simultaneously decreasing cloud
cover. The effect on the outgoing SW radiation at the TOA is more mixed. The
LW radiation changes are controlled mainly by cloud changes, although WV
changes induced by the BBA also contribute. Atmospheric heating is increased
in the presence of more aerosol, and surface fluxes respond to the reduction
in the surface SW radiation with both the sensible and latent heat fluxes
being reduced. The reduced SW radiation also lowers the surface temperature,
where a combination of the aerosol and the aerosol–cloud interactions causes
reductions in surface temperature in areas of higher BB AOD, and an increase
in areas where the cloud cover is sufficiently reduced to counterbalance the
cooling effects of the BB AOD. There is a potential feedback from the
reduction in SW radiation at the surface and heating by the aerosol at higher
altitudes causing cloud burn-off and increased boundary layer stability; the increased
stability reduces cloud generation and leads to a further reduction in the
cloud cover. This process will break down if the increase in SW radiation
reaching the surface due to loss of cloud cover dominates over the BBA
effects, allowing the boundary layer to once again destabilize .
The mean September precipitation in parts of the BB area is significantly
reduced (up to 15 %) in the BB box, with some reduction also occurring in
parts of the Amazon basin, most markedly towards the western edge. There is
also an effect on the surface pressure and changes to the low-level (850 mb)
circulation, in particular the low-level jet east of the Andes and the South
Atlantic high-pressure system.
The impact on the monsoon is less clear-cut; however, we see distinct
differences in November between the high and low
emissions experiments. The changes in the surface pressure and circulation,
in particular the low-level jet which brings moisture down from the Amazon,
and the shift in position of the South Atlantic high-pressure system affect
monsoon development . There is significant
reduction in precipitation along the eastern side of the Andes and around the
Plate Estuary area. These changes in the precipitation due to the BBA suggest
that there is a continuation of the effect of the BBA on precipitation
through to November, and thus on the monsoon. We note that in order to see
any possible effects on the timing of monsoon onset a finer temporal
resolution in the model output would be required. A further caveat is that
the model is not a fully coupled atmosphere–ocean model, so the atmospheric
changes do not influence the sea surface temperature so that effects of
sea surface temperature changes, in particular on the monsoon development
, are not being modelled in
these experiments.
The experiments described use emission inputs for two different years in
order to gauge how the climate effects differ between years with high and low
emissions instead of comparing a BBA-free atmosphere with a high-BB-aerosol
case. Our approach does tend to lessen the signal to noise in the results
compared to a biomass burning vs. no biomass burning comparison, but allows
us to demonstrate that significant climate differences can result from the
realistic annual variations seen in the BBA emissions in South America, which
can be reasonably related to changes in deforestation, due to the strong
positive relationship demonstrated between deforestation rates and BBA
emissions .