Introduction
In the Amazon Basin, trace gases from biomass-burning, urban,
and biogenic emissions are important sources of ozone precursors, which are
efficiently transported by intense convective activity to the upper
troposphere, where they can be dispersed over long distances by regional and
global circulations. Additionally, convective overshooting may inject heat,
moisture, and trace gases into the tropical tropopause layer, impacting
stratospheric ozone and other aspects of global climate (Fueglistaler et al.,
2009). In the dry-to-wet transition season, regional smoke and haze plumes
from biomass burning are observed (Longo et al., 2009). On the other hand, in
the wet-to-dry transition season, biogenic emission of volatile organic
compounds (VOCs), particularly from the Amazon rainforest, may maintain the
atmospheric oxidative capacity for generating ozone and other photochemical
pollutants (Lelieveld et al., 2008).
The Amazon Basin continues to rapidly urbanize, and anthropogenic emissions
of O3 precursors are also expected to grow. Emissions from cities in the
tropics may have a larger impact on the upper troposphere due to high solar
radiation levels and intense convective transport (Gallardo et al., 2010). In
the upper troposphere, O3 acts as a greenhouse gas, increasing surface
radiative forcing (Smithson, 2002). Inhalation of elevated levels of ozone
can irritate the lungs, aggravate asthma, and cause emphysema, bronchitis, and
premature death (Schwela, 2000). High ozone concentrations can also decrease
photosynthesis in plants and damage leaf tissue, harming wild ecosystems and
reducing crop productivity (Reich and Amundson, 1985). Thus, an improved
understanding/quantification of O3 temporal and spatial variability in
the tropical rainforest environment is important for projecting future
impacts of land use and climate change in the Amazon Basin and other tropical
rainforest regions worldwide on their expanding human populations and
significant biodiversity.
Analyses of satellite, aircraft, and ground-based observations of O3
over Amazonia since the 1980s have demonstrated the influence of long-range
transport of African biomass burning and Northern Hemisphere inputs, local
fire sources, NO soil and biogenic VOC emissions, and convective transport on
spatial and seasonal variability in O3. In particular, data from the
ABLE-2B aircraft and ground campaign during the 1987 wet-to-dry transition
season and the BARCA observations offer the opportunity to compare the
regional O3 distribution across decades.
Previous analyses of satellite ozone data have noted early year O3
maximums in the tropical Southern Hemisphere primarily associated with
cross-Atlantic transport of biomass burning emissions from Africa (Fishman
and Larson, 1987; Thompson et al., 1996), Northern Hemisphere fires, and
lightning NOx (Edwards et al., 2003). In the Amazon region, ground-based and
aircraft campaigns (e.g., Crutzen et al., 1985; Kirchhoff et al., 1990;
Browell et al., 1996; Kaufman et al., 1998; Longo et al., 1999; Andreae et
al., 2001, 2002, 2004; Zhou et al., 2002; Cordova et al., 2003; Rummel et
al., 2007; Kuhn et al., 2010; Martin et al., 2010; Toon et al., 2010) have
observed daytime background O3 levels of 10–20 ppb, decreasing to very
low values (circa 5 ppb)
at night due to O3 deposition to the forest. However, nighttime values
can be increased up to 30 ppb by convective downdrafts (Betts et al., 2002;
Cordova et al., 2003). Elevated levels of 60–80 ppb are found due to
production from regional fire emissions and recirculated urban pollution from
SE Brazil, as well as deep convective transport of boundary layer air to the
middle and upper troposphere.
Thus, satellite observations enable the attribution of tropical O3
maxima to biomass burning and lightning NOx sources, while
ground-based measurements allow the identification of key surface processes
in the Amazon Basin affecting O3 amounts. These processes include
O3 production from soil NOx emissions and removal via dry
deposition to the forest canopy. Aircraft campaigns complete the suite of
observations, allowing the examination of convective lofting of surface
emissions, with biomass burning emissions being of particular importance on
the regional scale. In situ data on cloud properties and chemical species, as
well as observations of land use changes, boundary layer dynamics, and
larger-scale cloud–aerosol interactions, are scant in this region.
Therefore, models are essential tools for understanding the interactions of
physical and chemical processes that contribute to O3 formation, as well
as monitoring and predicting atmospheric chemistry composition, weather, and
climate at local, regional, and global scales. In turn, the observations help
constrain uncertainties in the model representations of parameterized
convection, turbulence, land surface, and other subgrid scale processes that
affect the simulated transport and chemical transformation of the atmospheric
composition (Beck et al., 2013).
Motivated by the impact of O3 in the Amazon Basin on human and ecosystem
health and global climate, we collected aircraft observations of O3
during BARCA and conducted regional chemistry simulations in order to answer
the following scientific questions: how does O3 vary spatially,
seasonally, and across decades over the Amazon Basin? What are the sources
and sinks of O3 in this region? How well can state-of-the-art regional
chemistry models reproduce O3 distributions over the Amazon Basin?
The structure of this paper is as follows. In Sect. 2, the measurements taken
during the BARCA aircraft campaign are presented, followed by the
meteorological conditions and emission regimes during the two phases of the
campaign. The ABLE-2 campaigns from the 1980s are also described in this
section. Sections 3.1–3.3 detail the aircraft observations, the setups of
the CCATT-BRAMS and WRF-Chem simulations, and the ground-based and remote
sensing observations used in the analysis. In Sect. 4.1, the O3 aircraft
observations are presented, followed by the analysis of observed and modeled
transition season meteorology in Sect. 4.2, and the findings from the O3
simulations and process studies in Sect. 4.3. Final discussions and
conclusions are found in Sect. 5.
BARCA aircraft campaigns
The Regional Carbon Balance in Amazonia (BARCA) Large-Scale
Biosphere-Atmosphere (LBA) experiment was an aircraft campaign was based in
Manaus and conducted during the dry-to-wet (November and December 2008) and
wet-to-dry (May 2009) transition seasons. BARCA was the first flight campaign
to sample O3 and other trace gases on a regional scale in both
transition seasons. It offers a unique opportunity, alongside satellite
observations and modeling studies, to understand the regional ozone
distribution in the Amazon under different meteorological and emission
regimes.
The BARCA flights were conducted with the EMB 110 Bandeirante aircraft of
the Brazilian National Institute for Space Research (INPE). In situ
measurements were made of carbon dioxide (CO2), carbon monoxide (CO),
methane (CH4), ozone (O3), and aerosol number concentration and
optical properties. Flask samples were collected to determine CO2,
CH4, sulfur hexafluoride (SF6), CO, nitrous oxide (N2O),
hydrogen, and the oxygen : nitrogen ratio (O2 / N2). The flights
consisted of quasi-Lagrangian measurements, which attempt to sample an air
parcel at multiple locations along its path in order to constrain regional
and basin-wide fluxes of these species. The aircraft had a ceiling of 4500
m, and flights usually consisted of ascending and descending vertical
profiles separated by short (5–30 min) horizontal legs. A detailed
description of the aircraft measurements can be found in Andreae et al. (2012). Fig. 1 shows a map of the flight tracks from BARCA A and B. Both
experiment periods included flights to the north, south, and east of Manaus,
as well as local flights near Manaus. Only BARCA A included flights to the
west of Manaus, because intense convective activity in that region during
BARCA B precluded flying. During BARCA B, fire activity was low throughout
the Amazon region due to heavy precipitation, while during BARCA A, intense
fire activity occurred on the northern coast of Brazil and scattered fires
were present throughout the southeastern Amazon.
Andreae et al. (2012) summarized the BARCA campaign, meteorological
background, carbon monoxide and aerosol observations, and CO results from
several regional transport and chemistry models. These included versions of
the CCATT-BRAMS and WRF-Chem simulations analyzed in greater detail in this
paper. Meteorological analysis showed that during BARCA A, when the
Inter-Tropical Convergence Zone (ITCZ) was to the north of the Amazon Basin,
inflow to the Amazon was primarily from the Southern Hemisphere. During
BARCA B, the ITCZ extended to 20∘ S and air at low levels was of
Northern Hemisphere origin, including some smoke from West African fires. On
the other hand, the mid-tropospheric air was of mixed origin.
Flight tracks during BARCA.
The highest CO levels were observed on the flights on 25–27 November in the
southeastern Amazon, influenced by regional biomass burning, since maximum
values were observed from 1–3 km. These are typical of injection heights of
smoke plumes from savanna fires (Freitas et al., 2007). The excess CO from
biomass burning was between about 30 and 200 ppb, increasing from north to
south across the Basin. According to analysis of tracer simulations, during
BARCA A, biomass burning contributed on average about 56 ppb (31 %) to
the total CO of around 180 ppb, while the background was 110 ppb
(61 %). Biomass burning influence was indicated by CO mixing ratios of up
to 300 ppb, condensation nuclei (CN) approaching 10 000 cm-3, and a
low CN to CO ratio (ΔCN / ΔCO) signifying aged smoke.
This influence was highest in the southern Amazon from 1 to 3 km. Manaus
back trajectories at 500 and 4000 m came from eastern Amazon fires rather
than the intense African fires occurring at the same time. During BARCA B,
little biomass burning influence was observed. CN counts were
300–500 cm-3 and a CO enhancement of circa 10 ppb above the mixing
ratios in air entering the basin from the Atlantic was seen. Small boundary
layer enhancements were attributed to a source from the oxidation of biogenic
VOCs (Andreae et al., 2012).
Andreae et al. (2012) also showed simulated vertical CO profiles from
CCATT-BRAMS and WRF-Chem simulations, as well as the Stochastic Time Inverted
Lagrangian Transport (STILT) model with two different meteorological field
inputs and the WRF greenhouse gas module (WRF-GHG). The simulated CO profiles
matched mean observed values, but were overly vertical (too low near the
surface and too high above 3 km). This suggested that the models had too
much convective transport or vertical mixing from the planetary boundary layer (PBL) schemes. However, the probability densities were consistent
with observations in the boundary layer, indicating that horizontal
dispersion was reasonable. Beck et al. (2013) evaluated different CH4
wetland emissions schemes and maps using WRF-GHG. They found the best
agreement with BARCA CH4 data for days when convective transport, as
evaluated by comparison of upstream Tropical Rainfall Monitoring Mission (TRMM)
and WRF precipitation amounts, was well represented in the model. This
indicates that proper representation of convective transport in models is
essential for prediction of vertical distributions of pollutants in the
Amazon Basin.
It is interesting to compare BARCA data to observations from the NASA Amazon
Boundary Layer Experiments ABLE campaigns (ABLE-2A and -2B), which took place
during the dry season of 1985 and wet-to-dry transition of 1987. During the
dry season (July–August 1985), the Amazon Boundary Layer Experiment
(ABLE-2A) integrated aircraft, ground-based, and satellite observations to
study the processes affecting the chemical composition in mixed layers over
Amazonia (Harriss et al., 1988). Jacob and Wofsy (1988) used a photochemical
model of the Amazonian boundary layer to study the diurnal cycle of isoprene,
NOy and O3 during ABLE-2A. They found that photochemical
production spurred by NO emissions from soils increased daytime O3 to
about 20 ppb. However, at night, dry deposition to the forest caused O3
to drop below 5 ppb. Model results were consistent with the NO values of
25–60 ppt observed in the lower boundary layer over central Amazonia
(Torres and Buchan, 1988). Isoprene emissions were found to have little
effect on O3 levels, as the oxidation of CO would produce sufficient
hydrogen oxides (HOx) to generate 20 ppb of O3. However,
O3 production in the model was highly sensitive to NOx
emissions, and downward transport from the free troposphere became the
dominant source of O3 in the PBL when NO emissions were decreased below
the average value of 44 ± 14 µ g N m-2 h-1 NO
measured by Kaplan et al. (1988). Lidar observations during ABLE-2A showed
highly variable O3 levels, with some small regions with up to
30–40 ppb, attributed to variable NO flux from the canopy (Browell et al.,
1988). ABLE-2B was conducted during the wet-to-dry transition season
(April–May 1987) (Harriss et al., 1990). Periodic inputs from the Northern
Hemisphere were found to be a pollution source over Amazonia, and dry
deposition in the region provided a significant sink in the global O3
budget. As part of ABLE-2, near-continuous O3 surface measurements
(1.5 m above the soil surface) showed daytime maximums of 3.7 ppb inside a
forest and 5.7 ppb in a clearing (typical standard deviations of 0.3 ppb).
Additionally, tower measurements at the clearing site showed higher O3
values of 6.7 ppb at 7 m above the soil surface and 6.9 ppb at 15 m
above the soil surface (Kirchhoff et al., 1990). Furthermore, 20 ozonesondes
launched in the clearing showed typical mixing ratios of 40 ppb from 500 to
300 hPa, with values about 10 ppb lower in the wet than dry season. Andreae
et al. (2012) showed that CO mixing ratios were about 10 ppb higher during
ABLE-2B than in BARCA B everywhere except the southern region, reflecting the
global trend towards decreasing CO emissions since the 1980s, particularly in
the Northern Hemisphere. The CO comparison also showed a similar enhancement
of 10–20 ppb in the lowest 1 km above the surface, attributed to diffuse
biogenic sources, and also indicated that the much higher enhancements during
the dry season in BARCA A must be due to anthropogenic or biomass burning
inputs. The O3 comparison is expected to yield information about
long-term trends in O3 production in the Amazon Basin, as well as the
relative importance of biogenic, urban, and fire sources.
Data and Methods
BARCA aircraft measurements
During the BARCA campaign, in situ measurements of O3 were conducted
aboard the EMB 110 Bandeirante INPE aircraft using a dual-cell, UV
Photometric analyzer (Ozone Analyzer, Model 49i, Thermo Fisher Scientific,
United States). During BARCA A, 1 min averages of the original 1 s data
were taken, while during BARCA B 1 s data were stored. The detection
limit for both campaigns was 1 ppb. The intake for O3 was
forward facing, located 185 mm from the fuselage on the lower fuselage in
front of the propellers to minimize effects of turbulence. The inlet lines
consisted of stainless steel tubes with a bend radius of 100 mm and an inner
diameter of 11.5 mm. The sample air was not heated or dried before
measurement, so reported values are molar mixing rations, nmol mol-1,
abbreviated “ppb”, with respect to ambient humid air (Andreae et al., 2012).
Model description and simulation setup
Simulations of BARCA A and B were conducted with the Chemistry Coupled
Aerosol-Tracer Transport model to the Brazilian developments on the Regional
Atmospheric Modeling System (CCATT-BRAMS; Freitas et al., 2009; Longo et
al., 2013) and Weather Research and Forecasting with Chemistry (WRF-Chem;
Grell et al., 2005) Version 3.4.1 coupled chemistry and meteorology models.
The model physics and chemistry options that were used are listed in Table 1. Both models used a two-way nested grid configuration, with a 140 km grid
covering Africa and South America (southwest corner: 60∘ S,
100∘ W, northeast corner: 20∘ N, 50∘ W), to
encompass the cross-Atlantic transport of biomass burning emissions from
Africa, and a 35 km resolution grid covering most of South America
(southwest corner: 35∘ S 85∘ W, northeast corner:
15∘ N, 30∘ W), as depicted in Fig. 3.
The simulations were initialized on 1 October 2008 00:00 UTC and 1 April
2009 00:00 UTC for BARCA A and B, respectively. Boundary conditions and
analysis nudging on the outer domain were given by the NCEP GFS analysis
(http://rda.ucar.edu/datasets/ds083.2/) with a 6-hourly time resolution
and 1∘×1∘ spatial resolution. Chemistry initial and
boundary conditions were provided by 6-hourly analyses from the Model of
Atmospheric Chemistry at Large Scale (Modélisation de la Chimie
Atmosphérique Grande Echelle, MOCAGE) global model (Josse et al., 2004;
Teyssèdre et al., 2007) with a T42 (circa 2.8∘) spatial resolution. Sea
surface temperature was provided by the NOAA Optimum Interpolation (OI) Sea
Surface Temperature (SST) V2 (available at
http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html) with
1∘×1∘ spatial resolution. Soil moisture was
initialized with the TRMM-based soil moisture operational product (GPNR)
developed by Gevaerd and Freitas (2006).
O3 observations during (a) BARCA A, clean conditions (west,
north, and around Manaus regions), (b) BARCA A, polluted conditions (east and
south regions), and (c) BARCA B. The central mark is the median, the edges of
the box are the 25th and 75th percentiles, the whiskers extend to the most
extreme data points not considered outliers, and outliers are plotted
individually as red plus signs. Values are drawn as outliers if their values
exceed q3+w(q3-q1) or are less than q1-w(q3-q1), where q1 and
q3 are the 25th and 75th percentiles, respectively, and w is the maximum
whisker length with the default value of 1.5. For normally distributed data,
the whiskers encompass approximately the 2.7 to 99.3 percentiles.
CCATT-BRAMS and WRF-Chem physics and chemistry options for the BARCA
simulations.
CCATT-BRAMS
WRF-Chem
Short/longwave radiation
Based on CARMA
RRTMG
Cloud microphysics
Single moment bulk
WSM-5
Deep/shallow convection
Grell and Dévényi (GD)
Grell 3-D
Trace gas chemistry
RACM
RACM
Photolysis
F-TUV
F-TUV
Aerosol scheme
Smoke aerosol
GOCART
Wet deposition
Convective and grid scales
Convective scale only
Land surface albedo (fraction) and locations of the coarse (140 km
resolution) and nested (35 km resolution) domains for WRF-Chem simulations.
The PBL parameterization in CCATT-BRAMS is based on Mellor and Yamada (1982), while in WRF-Chem
the Mellor-Yamada Nakanishi and Niino 2.5 level PBL scheme (MYNN; Nakanishi and Niino, 2004) was used.
In CCATT-BRAMS, shallow and deep convection are
parameterized based on the mass-flux approach of Grell and Dévényi (2002). CCATT-BRAMS also uses the turbulent kinetic energy (TKE) from the
planetary boundary layer (PBL) scheme to determine if convection will be
triggered within a grid cell. In WRF-Chem the Grell 3-D (G3) scheme was used,
which includes shallow convection and subsidence spreading of convective
outflow into neighboring grid cells. The Noah land surface model (Koren et
al., 1999) was used in WRF-Chem and the Land Ecosystem–Atmosphere Feedback
model v.2 (LEAF-2; Walko et al., 2000) was utilized in CCATT-BRAMS. Land use
was provided by the United States Geological Survey (USGS) global 1 km
vegetation data set, updated with a land cover map for the Brazilian legal
Amazon region for use in meteorological models (PROVEG) (Sestini et al.,
2003). PROVEG is based on the Landsat Thematic Mapper (TM) images with
spatial resolution of 90 m×90 m from the year 2000 and
deforestation data from the Amazon Deforestation Monitoring Program (PRODES)
for the year 1997. For WRF-Chem, albedo and greenness fraction were
calculated offline using the updated vegetation data set, Moderate Resolution
Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index
(NDVI) data from the years 2001–2002, and vegetation parameters from the
LEAF-2 land surface model as implemented in CCATT-BRAMS.
Emissions were generated with PREP-CHEM-SRC (Freitas et al., 2011)
Version 1.2. Fire emissions were estimated from GOES, AVHRR, and MODIS fire
count data, using the Brazilian Biomass Burning Emission Model (3BEM; Longo
et al., 2009). Anthropogenic emissions were estimated from the RETRO, GOCART,
and EDGAR v4.0 global databases updated with South American inventories
(Alonso et al., 2010). Emissions are obtained from RETRO if available for
that species (CO, NOx, chlorinated hydrocarbons, acids, esters,
alcohols, ethers, benzene, ketones, methanal, other alkanals, other
aromatics, C2H2, C2H4, C2H6, C3H6,
C3H8, C4H10, C5H12, C6H14 plus higher
alkanes, other VOCs, toluene, trimethylbenzenes, xylene), then from EDGAR
v4.0 (NMVOC, SO4, CO2, SF6, N2O), otherwise from GOCART
(BC, OC, SO2, DMS) in order to use the most consistent emissions
inventory possible. Biogenic emissions were provided by a monthly climatology
for the year 2000 produced with the Model of Emissions of Gases and Aerosols
from Nature (MEGAN; Guenther et al., 2006). The MEGAN 2000 climatology
includes numerous biogenic species (acetaldehyde, formaldehyde, other
ketones, acetone, isoprene, propane, methane, propene, ethane, methanol,
sesquiterpenes, ethene, monoterpenes, and toluene), but not soil NO
emissions. In WRF-Chem, the same Gaussian diurnal cycle with peak at
15:00 UTC (11:00 LT) is applied to both anthropogenic and biogenic
emissions, while in CCATT-BRAMS the diurnal cycle of biogenic emissions
follows the solar radiation cycle. In both models, the biomass burning daily
cycle peaks at 18:00 UTC (15:00 LT).
In both CCATT-BRAMS and WRF-Chem, the Regional
Atmospheric Chemistry Mechanism (RACM) was used (Stockwell et al., 1997). In
WRF-Chem, the Goddard Chemistry Aerosol Radiation and Transport (GOCART; Chin
et al., 2002) aerosol scheme was used with aerosol direct radiative effects.
CCATT-BRAMS has a smoke aerosol scheme with intensive optical properties
(extinction efficiency, single scattering albedo, and asymmetry parameter)
calculated in an offline Mie code based on observations of climatological
size distribution and complex refractive index from AERONET sites in the
southern Amazon (Rosário, 2011; Rosário et al., 2013).
CCATT-BRAMS includes scavenging of soluble species in the convective scheme
following Berge (1993), as described in Freitas et al. (2005), where the wet
removal rates are a function of the precipitation rate, liquid water content,
and precipitable water. In the cloud microphysics scheme the wet deposition
follows Barth et al. (2001), whereby low solubility species partition into
the liquid phase according to Henry's Law and high solubility species by
diffusion-limited mass transfer. In WRF-Chem, at the
convective-parameterizing scale, a constant fraction of gas and aerosol
species in convective updrafts are removed (complete removal for sulfur
dioxide (SO2), sulfate (H2SO4), ammonium (NH3), nitric
acid (HNO3), and sea salt; no removal for hydrophobic organic (OC) and
black carbon (BC) and dimethyl sulfide (DMS); and 50 % removal for all
other aerosol species). On the other hand, no wet scavenging is included for
cloud water and precipitation resolved by the microphysics scheme, because
this option is not currently available in WRF-Chem for the RACM chemical
mechanism. O3 production in the upper troposphere is affected by the net
convective transport of soluble HOx precursors (including
hydrogen peroxide (H2O2), methyl hydroperoxide (CH3OOH), and
formaldehyde (CH2O)). However, uncertainties remain about the scavenging
efficiencies of these and other soluble species by deep convective storms.
Simulations of an idealized thunderstorm by several cloud-resolving models
yielded varying results for CH2O, H2O2, and HNO3 in
convective outflow due to differing microphysics and assumptions about
retention of chemical species during cloud drop freezing (Barth et al.,
2007b).
Monthly mean precipitation (mm h-1) on the 35 km resolution
domain (dark gray line) for November 2008 from (a) TRMM 3B43,
(b) CCATT-BRAMS,
and (c) WRF-Chem and for May 2009 from (c) TRMM 3B43, (d) CCATT-BRAMS and
(f) WRF-Chem. The subregions for the precipitation comparison are indicated by
black lines.
Mean daily cycle of surface incident shortwave radiation from the
Manaus AERONET site (solid line, dotted line denotes 1 standard
deviation), WRF-Chem (crosses), and CCATT-BRAMS (circles) for the BARCA A
period (October–November 2008).
The CCATT-BRAMS simulations employ a lightning NOx parameterization
based on convective cloud top height (Stockwell et al., 1999). In WRF-Chem,
lightning production of NOx was not included, because these
parameterizations have not yet been evaluated for the Amazon region. In the
tropics and over continents, lightning production is comparable to other
sources of NOx, including biomass burning and soil release, and it is
the primary source over oceans (Bond et al., 2002). Since lightning NOx
production peaks in the upper troposphere, it could be an important
contributor to ozone production. The impact of wet deposition and lightning
NOx production on O3 distributions will be more closely examined
in future modeling studies of tropospheric chemistry in the Amazon.
For model results evaluation, the mean vertical O3 profiles for
observations, CCATT-BRAMS, and WRF-Chem were calculated for the regions to
the west, north, south, east, and around Manaus. Horizontal flight legs were
excluded from the analysis to eliminate the influence of plumes in the
boundary layer. As the model output has a much coarser spatial and temporal
resolution than the aircraft measurements, the model value is interpolated to
the observation time and location. To calculate the mean simulated profiles,
the four grid points closest in latitude and longitude to each observation
were determined at the 2 model hours that bracket the observations. At each
of these grid points and hours, vertical profiles were extracted from the
model output and then linearly interpolated to the observed GPS height. The
four points from each time were averaged, weighting by the inverse distance
to the observed longitude and latitude. Finally, the prior and posterior hour
values were averaged with appropriate weights. Thus, 16 model points were
used with spatial and temporal weights to obtain each model value for
comparison to observations. The observed and model time series were then
separated into five regions to the west, north, east, and south of Manaus,
and in the region of Manaus itself. The mean value and standard deviation
were calculated for each region and 500 m vertical bin.
Monthly mean precipitation (mm h-1) for TRMM 3B43, CCATT-BRAMS
and WRF-Chem models for three regions: the Amazon (15∘ S–10∘ N, 50–80∘ W), northeast Brazil
(15∘ S–0∘ N, 35–50∘ W), and
southeast South America (15–35∘ S, 35–65∘ W).
Nov 2008
May 2009
Region
TRMM 3B43
CCATT-BRAMS
WRF-Chem
TRMM 3B43
CCATT-BRAMS
WRF-Chem
Amazon
0.24
0.22
0.51
0.20
0.15
0.40
Northeast
0.12
0.07
0.08
0.37
0.23
0.49
Southeast
0.19
0.11
0.24
0.10
0.06
0.07
Values of RMSE and bias for CCATT-BRAMS and WRF-Chem simulations
for 26 METAR and 52 SYNOP stations in the Amazon Basin for BARCA A
(October–November 2008) and BARCA B (April–May 2009).
Oct–Nov 2008
Apr–May 2009
CCATT-
WRF-Chem
CCATT-
WRF-Chem
BRAMS
BRAMS
T (K)
Mean Obs.
295.97
293.89
RMSE
2.30
2.81
1.70
2.44
Bias
1.04
-2.42
-0.06
-2.28
Td(K)
Mean Obs.
289.26
288.49
RMSE
2.68
1.72
1.76
1.67
Bias
-1.92
-0.81
-0.99
-0.83
Wind Spd.
Mean Obs.
3.00
2.59
(m s-1)
RMSE
1.41
1.33
1.15
1.00
Bias
-0.60
0.16
-0.51
0.07
Sfc. Press.
Mean Obs.
1013.17
1016.09
(hPa)
RMSE
2.16
1.43
1.09
1.34
Bias
-2.01
-1.02
-0.79
-1.17
Precip. TRMM
Mean Obs.
0.49
0.62
(mm h-1)
RMSE
2.42
4.50
3.03
7.12
Bias
0.28
3.47
0.25
5.84
PBL height at 21:00 UTC (17:00 LT) estimated from CCATT-BRAMS and
WRF-Chem using methods based on turbulent kinetic energy (TKE) and theta
(θ) and the diagnostic from the WRF MYNN PBL scheme.
Forest
Pasture
PBL Height (km)
Method
TKE
Theta
WRF
TKE
Theta
WRF
MYNN
MYNN
BARCA A
CCATT-BRAMS
Mean
1.103
1.610
–
1.143
1.636
–
(Nov 2008)
SD
0.621
0.646
–
0.581
0.640
–
WRF-Chem
Mean
1.211
1.131
0.983
1.258
1.087
0.991
SD
0.655
0.390
0.423
0.665
0.470
0.455
BARCA B
CCATT-BRAMS
Mean
0.628
1.067
–
0.669
1.049
–
(May 2009)
SD
0.515
0.554
–
0.527
0.564
–
WRF-Chem
Mean
0.828
0.922
0.815
0.845
0.933
0.766
SD
0.443
0.336
0.288
0.432
0.282
0.272
To facilitate comparison of other models with the data presented in Fig. 2,
mean profiles from the large regions corresponding to clean (west, north, and
around Manaus regions) and polluted (east and south regions) regions during
BARCA A and all regions during BARCA B are presented in Fig. 16. From the
models, all horizontal grid points falling within the corresponding region's
longitude and latitude bounds for each flight day (Table 6) and the closest
model output times (12:00–18:00 UTC / 08:00–14:00 LT) were averaged into 500 m vertical
bins.
Satellite and ground-based O3 and meteorological data
In addition to the in situ O3 data, the model results were compared
with OMI/MLS monthly mean tropospheric ozone mixing ratios and total column
ozone (http://acd-ext.gsfc.nasa.gov/Data_services/cloud_slice/#pub) (Ziemke et al., 2006). In this
product, the tropospheric values are estimated by subtracting the
stratospheric contribution from total column measurements. A cloud-slicing
method is used to detect O3 inside optically thick clouds. This method
was able to detect elevated O3 levels of around 50 ppb in the upper
parts of convective clouds over South America and Africa, comparable to
background cloud-free levels in the tropics (Ziemke et al., 2009). In this
study, the model total tropospheric O3 column and mean tropospheric
O3 mixing ratio were calculated by summing O3 mixing ratios,
weighted by the model level air density, from the first model level to the
level below the tropopause. The tropopause level was determined by the World
Meteorological Organization (WMO) definition of a temperature lapse rate
less than 2 K km-1 (Logan, 1999).
Mean daily cycles of surface (a) latent (LE) and sensible (H) heat
and (c) incident shortwave (Sin) and incoming (Lin) and outgoing
(Lout) longwave radiation fluxes for a forest site and (b) heat and
(d) radiation fluxes for a pasture site, comparing observations (solid lines)
from von Randow et al. (2004) for the dry-to-wet transition season
(July–September 1999–2000) and from WRF-Chem (crosses) and CCATT-BRAMS (circles) for the
BARCA A period (October–November 2008).
Mean daily cycles of surface (a) latent (LE) and sensible (H) heat
and (c) incident shortwave (Sin) and incoming (Lin) and outgoing
(Lout) longwave radiation fluxes for a forest site and (b) heat and
(d) radiation fluxes for a pasture site, comparing observations (solid lines)
from von Randow et al. (2004) for the wet-to-dry transition season
(February–March 1999, January–March 2000) and from WRF-Chem (crosses) and CCATT-BRAMS
(circles) for the BARCA B period (April–May 2009).
The models were also compared with soundings measuring O3, temperature,
and relative humidity conducted at sites in Paramaribo, Suriname
(5.8∘ N, 55.2∘ W) and Natal, Brazil (5.4∘ S,
5.4∘ W) during the BARCA periods as part of the Southern
Hemisphere ADditional OZonesondes (SHADOZ) network
(http://croc.gsfc.nasa.gov/shadoz/) (Thompson et al., 2003a, b, 2007).
Monthly mean precipitation over the Amazon region was obtained from the 3B43
TRMM and Other Data Precipitation
Product at a spatial resolution of 0.25∘×0.25∘ (obtained from http://trmm.gsfc.nasa.gov/)
(Kummerow et al., 1998; Kawanishi et al., 2000). TRMM 3B43 is derived from
retrievals of 3-hourly precipitation amount from the precipitation radar
(PR), TRMM microwave imager (TMI), and visible and infrared scanner (VIRS)
aboard the TRMM satellite, merged with rain gauge data from the Climate Anomaly
Monitoring System (CAMS) and the Global Precipitation Climatology Project
(GPCP). Satellite estimates of precipitation are used for model evaluation
due to their more complete spatial and temporal coverage compared to rain
gauge data. Buarque et al. (2011) found that mean annual rainfall from
Brazilian rain gauge and TRMM 3B42 3-hourly data at 488 sites in the Amazon
Basin for the years 2003–2005 agreed within 5 %. Other characteristics of
the rainfall distribution, such as the number of days with rainfall,
differed more substantially. Mean precipitation during the dry-to-wet
(November 2008) and wet-to-dry (May 2009) transition seasons was calculated for the
TRMM 3B43 data and the CCATT-BRAMS and WRF-Chem models for three regions:
the Amazon (15∘ S–10∘ N, 50–80∘ W), northeast Brazil (15∘ S–0∘ N,
35–50∘ W), and southeast South America
(15–35∘ S, 35–65∘ W).
The values are listed in Table 2. The mean precipitation on the 35 km
resolution domain for the 2 months is shown in Fig. 4, as well as the
delineations of the subregion boxes.
Surface downward shortwave radiation (Level 1.5) data obtained with a Kipp and
Zonen CM-21 pyranometer (305–2800 nm) were obtained from the Solar Radiation
Network (SolRad-Net) site at Manaus (2.56∘ S, 60.04∘ W,
93 m a.s.l.) (http://aeronet.gsfc.nasa.gov/cgi-bin/bamgomas_interactive).
Mean daily cycles of fluxes of sensible and latent heat and radiation were
obtained from flux tower measurements for the wet (February–March 1999, January–March
2000) and dry (July–September 1999–2000) seasons at forest (Rebio Jarú,
10.08∘ S, 61.93∘ W, 145 m a.s.l.) and pasture (Fazenda
Nossa Senhora, 10.75∘ S, 62.37∘ W, 293 m a.s.l) tower
sites (von Randow et al., 2004).
Surface meteorological station data was obtained for the BARCA region for
October–November 2008 and April–May 2009 from 52 SYNOP (INMET) and 26 METAR
(airport) stations, the locations of which are depicted in Fig. 10. The
models were also evaluated against TRMM 3B42 3-hourly precipitation rates at
the 78 surface station locations in the Amazon. Values of root mean squared
error (RMSE) and bias for the CCATT-BRAMS and WRF-Chem simulations are shown
in Table 3.
Mean vertical profiles at Manaus from radiosoundings (black, gray
line denotes 1 standard deviation), CCATT-BRAMS (blue) and WRF-Chem
(green) for October–November 2008 at (a) 00:00, (b) 12:00, and (c) 18:00 UTC.
Meteorological soundings from the Manaus airport (3.15∘ S,
59.98∘ W) were conducted at 00:00 UTC (12 in October–November 2008, 60 in
April–May 2009) and 12:00 UTC (49 in October–November 2008, 60 in April–May 2009).
During BARCA A, 13 additional soundings were conducted at 18:00 UTC from 18 November–1 December 2008.
Fisch et al. (2004) found that in the dry season (14–25 August 1994),
higher sensible heat fluxes over pasture increase the maximum height at 21:00 UTC (17:00 LT) of the convective boundary layer (CBL) from around 1100 m for
forest (Rebio Jarú) to 1650 m over pasture (FNS). On the other hand,
during the wet season (January–February 1999) the height of the CBL was similar for
both land types, around 1000 m. The simulated height of the PBL at 21:00 UTC
above the forest and pasture sites (Table 4) was analyzed from model output
using two different methods: TKE, the first level above the surface where the
turbulent kinetic energy (TKE) from the PBL schemes dropped below 0.01 m2 s-1 and theta, the first level above the surface where theta exceeded
theta of the level below by 0.6 K. In addition, WRF MYNN is the diagnostic from the
WRF PBL scheme which takes into account TKE as well as stability.
Mean vertical profiles at Manaus from radiosoundings (black, gray
line denotes 1 standard deviation), CCATT-BRAMS (blue) and WRF-Chem
(green) for April–May 2009 at (a) 0 and (b) 12:00 UTC.
Locations of surface meteorological stations for model
evaluation.
Results and discussion
BARCA O3 observations
The vertical distributions of O3 measured by the aircraft during BARCA
A and B are depicted in Fig. 2. Observations during the dry-to-wet
transition (BARCA A) are plotted separately for clean (west, north, and
around Manaus regions) and fire-influenced polluted (east and south regions)
conditions. The longitude and latitude bounds and flight dates included in
each geographic region from BARCA A and BARCA B are listed in Table 6. The
O3 distributions are similar during BARCA A in the clean regions and
BARCA B, with median values ranging from 10 to 25 ppb. However, there is more
variability, as measured by the difference between the 25th and
75th percentiles, in the BARCA A data. This may be due to downward
mixing of O3 transported long-range from fires in Africa or
recirculated from the polluted southeast Brazil region. In the
fire-influenced regions during BARCA A, medians range from 25 to 45 ppb,
peaking at a typical plume injection height for savanna fires of 2–3 km. The
highest variability is seen in polluted conditions during BARCA A,
particularly at 2–3 km, indicating the influence of small-scale fire plumes.
This variability of O3 in the PBL presents a challenge to the regional
models, since the effects of small-scale processes such as plume rise and
convection are parameterized and averaged across the grid cell.
Observed and simulated meteorology
Tropospheric O3 distributions are driven by both chemical processes,
including chemistry and emissions of O3 precursors, and meteorological
ones, such as solar radiation, tracer transport, and removal. During the
dry-to-wet transition season, increased actinic fluxes stimulate the
production of hydroxyl (OH) radicals from O3 photolysis that can lead to
net O3 production (Seinfeld and Pandis, 2006). In November 2008, a band
of elevated precipitation extended in TRMM 3B43 observations from the
northwest Amazon to southeast Brazil but the northern Amazon between Manaus
and Belém was relatively dry (Fig. 4a).
O3 as observed (black circles) and simulated with
CCATT-BRAMS (blue stars) and WRF-Chem (base case: green diamonds, 2DEPVEL: cyan circles, and 0.5ENOx: yellow squares) from BARCA
flights from (a) Manaus to Belém on 18 November 2008, (b) Belém to Manaus on 19 November
2008, (c) Manaus to Belém on 21 May 2009, and (d) Belém to Manaus on
23 November 2009.
Mean vertical O3 profiles for BARCA A flights for
observations (black, gray line denotes 1 standard deviation), CCATT-BRAMS
(blue), and WRF-Chem (base case: green, 2DEPVEL: cyan, and 0.5ENOx: yellow) simulations by region:
(a) north, (b) east, (c) west, (d) south, and
(e) around Manaus. ABLE-2A observations (gray) from the same regions are
included for comparison.
On the other hand, lower levels of O3 in the wet-to-dry transition
season are largely associated with increased presence of convective clouds
and precipitation. Decreased surface temperatures and incident solar
radiation due to cloudiness suppress emissions of biogenic VOCs such as
isoprene (Fall and Wildermuth, 1998). In addition, higher surface humidity
and precipitation decrease the occurrence of fires (Morton et al., 2013;
Chen et al., 2013) that emit NOx and VOCs (Freitas et al., 2007).
O3 precursors are further decreased by wet removal within the storms
(Barth et al., 2007a). In May 2009, intense precipitation as observed by
TRMM 3B43 stretched from the northwest Amazon to the northeast coast of
Brazil (Fig. 4b). In radiosoundings at Manaus, a more pronounced decrease in
dew point temperature is observed in May 2009 (Fig. 9) than in November 2008 (Fig. 8) in upper levels (300–400 hPa) from 00:00 to 12:00 UTC, likely due to
precipitation.
Land cover also impacts surface heat and moisture exchange and can thus
affect convective triggering. In both transition seasons, surface sensible
heat fluxes are higher and latent heat fluxes are lower at the pasture
compared to forest sites (Figs. 6a–b and 7a–b). However, incident solar
radiation and thereby peak sensible heat flux (Fig. 7) are lower in the
wet-to-dry than dry-to-wet transitions (Fig. 6) for both forest and pasture
sites.
Now we summarize the key findings of the model-data meteorological
comparison and their implications for the chemistry simulations. The models
capture the seasonal spatial distribution of precipitation over northern
South America (Fig. 4), and the signs of NE-SE differences are correctly
modeled by both models during both seasons, i.e., the NE is drier than the
SE during November and vice-versa during May. For the Amazon, CCATT-BRAMS
slightly underestimates the precipitation rates in both seasons, but the
rate in WRF-Chem is about twice that of TRMM 3B43 (Table 2). This may lead
to errors in the strength and vertical distribution of convective transport
and the amount of convective wet removal.
Peak shortwave radiation during the dry-to-wet transition at Manaus
is within the error bars of the observations for both models (Fig. 5).
However, for the southern Amazon forest and pasture sites, peak shortwave radiation may
be overestimated (underestimated) by 50–100 W m-2 by CCATT-BRAMS
(WRF-Chem) (Figs. 6–7), suggesting that there is insufficient (excessive)
cloudiness in the models. This will increase (decrease) surface temperature
and evaporation, and therefore increase (decrease) O3 production from
photolysis.
In the dry-to-wet transition season (Fig. 6), the peak observed Bowen ratio
(sensible/latent heat flux) is lower at the forest site than the pasture
site (0.23–0.38 vs. 0.8). However, in WRF-Chem, the Bowen ratio at 13:00 LT
shows a smaller contrast between the forest and pasture sites (0.40 vs.
0.51), due to underestimated sensible heat flux at the pasture site. In the
wet-to-dry transition season (Fig. 7), the peak Bowen ratio is lower at both
forest and pasture sites for this season (0.18–0.39 vs. 0.33–0.59). On the
other hand, in WRF-Chem, the peak latent and sensible heat flux and thus the Bowen
ratio are nearly constant at the forest and pasture sites (0.39 vs. 0.38).
This indicates that the Noah land surface model is not properly representing
the impact of conversion of forest to pasture and the resulting increase in
sensible heat flux.
At the surface stations (Table 3), both models overestimate precipitation on
average. Dew point temperature is underestimated by 1–2 K and temperature is
underestimated in all cases by 0.1–2.4 K except by CCATT-BRAMS during BARCA
A, which overestimated temperature by about 1 K. All of these biases will
decrease photochemical O3 production at the surface. The models
generally show good agreement with soundings at Manaus (Figs. 8–9), but
excess moisture (positive dewpoint bias of 10 K) in CCATT-BRAMS above 500 hPa may lead to increased photochemical production of O3 at
mid-levels.
Next we compare the CBL heights for wet and dry seasons reported by Fisch et al. (2004) with the simulated PBL heights in the dry-to-wet and wet-to-dry
transitions (Table 4). The models represent the pattern of lower PBL heights
in the wet-to-dry than dry-to-wet transitions, and similar PBL heights at
the forest and pasture sites. However, for the dry-to-wet transition, the
PBL heights are indistinguishable between forest and pasture sites for both
models, and generally closer to the observed forest (1.1 km) than pasture
(1.65 km) values. Additionally, for the wet-to-dry transition, the mean PBL
height for all models and diagnostics except theta for CCATT-BRAMS are lower than
observed (1 km). Overall, the models underestimate the PBL depth, which may
contribute to an overestimate of O3 near the ground. Despite these
limitations, the models are able to capture the meteorological contrast
between the dry-to-wet and wet-to-dry transition seasons.
Observed and simulated chemistry
Mean O3 profiles
An example of observed and simulated O3 during the flight legs between
Manaus and Belém in BARCA A and B is shown in Fig. 11. While the models
capture the pattern of increasing O3 values with height, the models
underestimate elevated O3 values from 2.5 to 4.5 km and overestimate
low values near the surface (1–1.5 km). The models also do not reproduce the
variability in the high values, likely due to the aircraft intersection of
biomass burning plumes. This is expected given the coarse horizontal grid
resolution. Thus, mean profiles are analyzed in order to study differences
among the regions and seasons and to assess the models' abilities to capture
the impacts of such small-scale processes on regional O3 distributions.
The mean vertical O3 profiles for observations, CCATT-BRAMS and WRF-Chem
for the regions to the west, north, south, east, and around Manaus are shown
for BARCA A and B in Figs. 12 and 14, respectively, and NO profiles
corresponding to the aircraft tracks are depicted in Figs. 13 and 15,
respectively. Mean profiles from longitudinal surveys over Amazonia of
O3 during ABLE-2A (Browell et al., 1988) and ABLE-2B (Harriss et al.,
1990) and NO during ABLE-2A (Torres and Buchan, 1988) are included for
comparison. In BARCA B, O3 values were at or near background values in
all regions, ranging from 8–15 ppb at the surface to 2–15 ppb at
4–4.5 km, and the models are generally within 5–10 ppb of the
observations. During BARCA A, while the W region still had low O3 values
(5 ppb at the surface to 20 ppb at 4–4.5 km), the N, S, and M regions
ranged from 15–20 ppb at the surface to 30–35 ppb at 4–4.5 km, and the
E region presented the most elevated values, from 25–55 ppb. ABLE-2A
O3 profiles are similar in all regions, ranging from 15–20 ppb near
the surface to 30–40 ppb from 4–6 km, so that the BARCA values are higher
in the fire-influenced east and south regions, lower in the north and west
regions, and very similar around Manaus. The profiles from ABLE-2B are within
1 standard deviation of the BARCA B measurements, except for the north
region, where they are lower (5–15 ppb). These results suggest an
increasing influence of fire emissions to the east and south of Manaus, but
that O3 in clean regions has not changed much.
A similar model behavior is seen in the broad regional mean profiles (Fig. 16). All simulations over-estimate O3 throughout the PBL and lower
troposphere during clean conditions in BARCA A, but under-estimate O3
in polluted conditions. This is especially true from 2 to 4 km where biomass
burning plumes detrain O3 precursors. During BARCA B all simulations
show good agreement.
In order to understand the possible sources of model error, we now
individually examine the contributions of different chemical sources and
sinks, including surface emissions and deposition, boundary inflow, and
chemistry within the PBL.
Mean vertical NO profiles corresponding to BARCA A flights for
CCATT-BRAMS (blue) and WRF-Chem (base case: green, 2DEPVEL: cyan, and
0.5ENOx: yellow) simulations by region: (a) north, (b) east, (c) west, (d)
south, and (e) around Manaus. ABLE-2A observations (gray) from the same
regions are included for comparison.
Emissions
The sensitivities of O3 production to NOx or BVOC emissions
depend upon the relative amounts of VOCs and NOx present. Under
clean conditions with a high VOC : NOx ratio, O3
production is NOx sensitive; increases in NOx will lead to more
O3 while increased VOCs will have little impact. On the other hand, in
polluted areas with a high NOx : VOC ratio, the system is VOC
sensitive; that is, increased VOCs contribute to O3 production but
higher NOx actually depletes O3. Emissions of BVOCs can
increase O3 production by the following mechanism. Oxidation of BVOCs
can lead to formation of hydroperoxyl (HO2) and organic peroxy
(RO2/ radicals, which react with NO to form NO2. NO2 in turn
photolyzes to form O(3P), which reacts with O2 to form O3
(National Research Council, 1991). We expect the polluted east/south regions
during BARCA A to be VOC sensitive and the clean west, north, and around
Manaus regions during BARCA A and all regions in BARCA B to be
NOx sensitive. Kuhn et al. (2010) determined via aircraft
transects in the Manaus urban plume that most of the VOC reactivity was
provided by isoprene emissions from the surrounding rainforest, and
NOx emissions suppressed O3 production close to urban
sources, but stimulated it downwind.
For BARCA, the simulated mean monthly emission rates for two O3
precursors, NOx (anthropogenic and biomass burning) and isoprene
(biogenic) are shown in Fig. 17. In November 2008, elevated NOx emission
rates of up to 5×10-5 kg m-2 d-1 are seen from an area of
intense biomass burning in the northeast Amazon, as well as from more
scattered fires in the southeast Amazon. These are both regions that were
overflown by the aircraft (Fig. 1). In May 2009, the Amazon region is
largely free of fire. Because biogenic NO emissions (e.g., from soil) were
not included in the MEGAN climatology used in this study, background NO
emissions (in absence of fire) are likely too low. Typical model
anthropogenic NOx emissions values over the Amazon, primarily from
biofuel sources, were 0.008–13 µ g N m-2 hr-1 N. These
NOx emissions included in the models were less than one-third of the
mean values of 44 ± 14 µ g N m-2 h-1 NO measured by
Kaplan et al. (1988) during ABLE-2A. This is considered a threshold value
for NOx-driven O3 production to be the dominant O3 source in
the PBL. The model emissions were also much lower than the mean emissions
from forest of 35.8 µ g N m-2 h-1 NO measured in the 1998
dry season (Garcia-Montiel et al., 2003). Wetting the forest soil resulted
in small pulses of NO and therefore the mean emissions are expected to be
higher in the wet season than dry season.
Isoprene emissions are highest in the western and southern Amazon, reaching
15×10-5 kg m-2 d-1 in November 2008 and 5–10×10-5 kg m-2 d-1 in the aircraft sampling region. Due to decreased surface
temperature and incident solar radiation in the rainy season, isoprene
emissions in the Amazon Basin are much lower during BARCA B, 3–5×10-5 kg m-2 d-1. The MEGAN emissions are consistent with isoprene
emission measurements above the Amazonian canopy: a normalized flux of 5.76×10-5 kg m-2 d-1 in July 2000 at the end of the rainy
season (Rinne et al., 2002) and an average noontime flux of 18.7 ± 5.5×10-5 kg m-2 d-1 in September 2004 during the dry season
(Karl et al., 2007).
Observed average O3 dry deposition flux (nmol m-2 s-1) and
daytime (11:00–21:00 UTC) median deposition velocity (cm s-1) in the dry and
wet seasons (Rummel et al., 2007), and from WRF-Chem and CCATT-BRAMS simulations
from November 2008 (dry-to-wet transition) and May 2009 (wet-to-dry transition)
for Reserva Biológica Jarú (RBJ), Fazenda Nossa Senhora (RNS), and
Reserva Ducke (RD).
Dry Season
Wet Season
Site
Observed
CCATT-
WRF-Chem
Observed
CCATT-
WRF-Chem
BRAMS
BRAMS
RBJ (forest)
Flux
-5.69
-2.43
-3.25
-2.93
-1.59
-2.39
vd
0.6
0.3
0.5
1.2
0.4
0.8
FNS (pasture)
Flux
-4.68
-3.06
-2.49
-2.04
-2.07
-2.04
vd
0.6
0.4
0.4
0.7
0.4
0.7
RD (forest)
Flux
-1.82
-1.63
-2.68
vd
1.6
0.4
0.6
Longitude and latitude bounds and dates for each region of the
BARCA A and B campaigns.
BARCA A (Nov 2008)
BARCA B (May 2009)
Region
Longitude
Latitude
Days
Longitude
Latitude
Days
West
-60.06
-54.24
-12.00
-3.03
29, 30
-61.16
-59.46
-3.71
-2.39
28
North
-62.00
-59.11
-3.04
2.89
23
-61.81
-60.00
-3.04
3.71
19
Around Manaus
-61.52
-58.50
-4.39
1.00
16, 22
-62.14
-60.00
-4.07
-2.16
15, 17
East
-108.73
-48.45
-3.04
-1.33
18, 19
-60.34
-44.82
-4.39
0.14
21, 22, 23, 26
South
-67.69
-60.01
-3.40
0.12
25, 26
-63.93
-60.01
-8.77
-3.04
27
Mean vertical O3 profiles for BARCA B flights for observations
(black, gray line denotes 1 standard deviation), CCATT-BRAMS (blue), and
WRF-Chem (base case: green, 2DEPVEL: cyan, and 0.5ENOx: yellow)
simulations by region: (a) north, (b) east,
(c) west, (d) south, and (e) around
Manaus. ABLE-2A observations (gray) from the same
regions are included for comparison.
Mean vertical NO profiles corresponding to BARCA B flights for
CCATT-BRAMS (blue) and WRF-Chem (base case: green, 2DEPVEL: cyan, and
0.5ENOx: yellow) simulations by region: (a) north, (b) east, (c) west, (d)
south, and (e) around Manaus.
O3 as observed (black circles) and simulated with
CCATT-BRAMS (blue stars) and WRF-Chem (base case: green diamonds, 2DEPVEL: cyan circles, and
0.5ENOx: yellow squares) during (a) BARCA A, clean
conditions (west, north, and around Manaus regions), (b) BARCA A, polluted
conditions (east and south regions), and (c) BARCA B.
Mean emission rates (10-5 kg m-2 d-1) from
PREP-CHEM-SRC for the 35 km domain (dark gray outline) for NOx for (a) BARCA A
(November 2008) and (b) BARCA B (May 2009) and isoprene for (c) BARCA A
and (d) BARCA B periods.
Average O3 dry deposition flux (nmol m-2 s-1) as
simulated on the 35 km resolution domain (dark gray outline) by the
CCATT-BRAMS model for (a) November 2008 and (b) May 2009 and by the WRF-Chem
model for (c) November 2008 and (d) May 2009.
Deposition
Figures 18 and 19 show the average O3 dry deposition fluxes and median daytime deposition velocities, respectively, as simulated on the 35 km
resolution domain by the CCATT-BRAMS and WRF-Chem models for November 2008 and
May 2009. In the Amazon Basin, O3 deposition fluxes are higher in the
dry-to-wet transition season, with values reaching 3.5 nmol m-2 s-1 for CCATT-BRAMS and 6 nmol m-2 s-1 for WRF-Chem in the
northeast Amazon, near the region of concentrated biomass burning. These
values are also seen along the northern Andes and southeast Brazil, due to
recirculation of O3-rich air. In the wet-to-dry transition season,
O3 deposition is at a minimum in the western Amazon, with values of
0.5–1 nmol m-2 s-1 for CCATT-BRAMS and 2 nmol m-2 s-1 for WRF-Chem. For both models, deposition velocities are higher over the
rainforest than in the savanna to the east or south of the Amazon Basin, and
higher in the wet-to-dry transition than in the dry-to-wet transition. These
patterns are also seen in the tower observations in Table 5.
Same as Fig. 12, but daytime (11:00–21:00 UTC) median deposition
velocity (cm s-1).
Mean tropospheric O3 (ppb) on the 35 km domain from (a) OMI/MLS, (b) CCATT-BRAMS
and (c) WRF-Chem and total tropospheric column
O3 (Dobson units) from (d) OMI/MLS, (e) CCATT-BRAMS and (f) WRF-Chem
for November 2008.
Same as Fig. 14, but for May 2009.
Vertical profiles of potential temperature, relative humidity, and
O3 from SHADOZ soundings (black), CCATT-BRAMS (blue), and WRF-Chem
(green) and HYSPLIT back trajectories at 13:00 UTC at 1500 m
(circa 850 hPa, red), 6000 m (circa 470 hPa, blue), and 9000 m
(circa 310 hPa, green) for: Paramaribo on (a) 6 November and
(b) 25 November 2008, Natal on (c) 7 November and
(d) 19 November 2008, and Paramaribo on (e) 4 May and
(f) 11 May 2009.
O3 surface fluxes and dry deposition velocities predicted by the models
were compared with observations from several field campaigns (Table 5).
These include during the dry (May 1999) and wet (September–October 1999) seasons at
Reserva Biológica Jarú (RBJ, forest) and Fazenda Nossa Senhora (FNS,
pasture) from LBA-EUSTACH (Rummel et al., 2009; Kirkman et al., 2002) and
during the wet season at Reserva Ducke (RD, forest tower near Manaus,
2.95∘ S, 59.95∘ W) from ABLE 2B (April–May
1987) (Fan et al., 1990) and at FNS from LBA-TRMM (January–February 1999) (Sigler
et al., 2002). For the observations, the means of the hourly (WRF-Chem) and
3-hourly (CCATT-BRAMS) O3 dry deposition fluxes (nmol m-2 s-1) and the medians of the daytime (11:00–21:00 UTC) hourly mean deposition
velocities (cm s-1) are shown. The values were extracted from the grid
points closest to the tower locations. In the observations, O3 fluxes
are larger in the dry season, due to higher O3 mixing ratios. However,
deposition velocities are higher in the wet season, and O3 deposition
to the Amazon forest constitutes a globally significant O3 sink (Rummel
et al., 2009). Both models capture these patterns, but the models
underestimate the deposition velocities by 15–75 %, which may be partially
responsible for the low O3 fluxes at the Jarú forest site in both
seasons and the pasture site in the dry season.
Boundary conditions
The mean tropospheric and total tropospheric column O3 from OMI/MLS,
CCATT-BRAMS, and WRF-Chem for November 2008 and May 2009 are shown in
Figs. 20 and 21, respectively. The models significantly underestimate the
total columns from satellite and middle altitudes from BARCA. For both BARCA
A and B, the models represent the pattern of lower O3 over the Amazon
and higher values over northeast Brazil (BARCA A only) and at 30∘ S,
although the values are strongly underestimated. In November 2008 (Fig. 14),
OMI/MLS mean tropospheric O3 concentrations show an inflow of elevated
O3 (mean ca. 55 ppb, total 40–45 DU) on the northeast Brazilian coast
due to cross-Atlantic transport from biomass burning in southern and
sub-Saharan Africa. Additionally, a band of elevated O3 (mean
55–60 ppb, total 35–40 DU) passes over the South American continent at
around 30∘ S, also from cross-Atlantic transport. During this month,
Northern Hemisphere O3 levels to the north of South America are
relatively low (mean 35–40 ppb, total 25–30 DU). On the other hand, the
tropospheric ozone distribution in May 2009 (Fig. 15) is characterized by a
band of low ozone extending over the Amazon Basin and northeast Brazil
between 10∘ S and 10∘ N (mean 25–35 ppb, total
20–25 DU). In addition, slightly elevated values are found at around
30∘ S, primarily over the oceans (40–55 ppb, 30–35 DU), and
higher ozone is seen in the Northern Hemisphere (mean 50–55 ppb, total
35–40 DU to the north of 10∘ N). Both models capture the overall
distribution (inflow in NE Brazil in November 2008, lower values over the
Amazon Basin, elevated at 30∘ S) but values are underestimated
relative to OMI/MLS. In general the models overestimate O3 in the PBL
compared to aircraft measurements, but underestimate the total column values
relative to the OMI/MLS satellite product. This suggests that the total
column values in Amazonia are dominated by global pollution from Africa,
rather than local O3 production from biomass burning. A typical OMI
averaging kernel (cloud-free ocean conditions) shows maximum sensitivity from
594–416 hPa (Zhang et al., 2010). Therefore, OMI may not be detecting
O3 in the PBL from local sources, but rather primarily seeing global
pollution from Africa.
Above the boundary layer, from 3 to 4 km a.g.l., chemical inflow at the
eastern boundary of South America may contribute to O3 elevated above
background. In order to evaluate the model representation of this inflow,
vertical profiles from SHADOZ soundings on the northeast coast of South
America during the BARCA A and B periods were compared with CCATT-BRAMS and
WRF-Chem (Fig. 22). In addition, 120 h back trajectories from the sounding
locations at heights of 1500, 6000, and 9000 m above ground level (gal) were
calculated with the HYSPLIT model
(http://ready.arl.noaa.gov/hypub-bin/trajtype.pl?runtype=archive) using
meteorological data from the NCEP/NCAR global reanalysis. Inflow at
Paramaribo originated either in the Caribbean or the tropical Atlantic, while
at Natal, air parcels came from anti-cyclonic recirculation from southeastern
Brazil or the tropical Atlantic. Both models generally represent the SHADOZ
O3 profiles up to 600 hPa, but do not capture layers of elevated
O3 above 500 hPa. These are likely to be either from pollution
recirculated from southeast Brazil or possibly from African biomass burning.
The models also do not reproduce thinner layers of high O3 below
600 hPa. For example, at Natal on 7 November 2008 (Fig. 22c, air of African
origin at circa 850 and circa 470 hPa) and 19 November 2008 (Fig. 22d, air
from the central African coast at circa 850 hPa and recirculation from
southeastern Brazil at circa 470 and circa 310 hPa) and at Paramaribo on 11
May 2009 (Fig. 22f, air of tropical Atlantic origin at all three levels),
both models underestimate O3 above 500 hPa by 40–60 ppb (model values
of 30–50 ppb vs. observations maximum values of 80–100 ppb). A previous
analysis of ozone soundings and aircraft measurements at Natal suggested that
increases in tropospheric ozone in the southern Hemisphere springtime may be
due to stratospheric intrusion (Logan, 1985). This is consistent with the
November 2008 profiles at Natal; the models may not be capturing the
intrusion of stratospheric air masses seen in the observations, indicated by
upper tropospheric (> 500 hPa) layers with elevated O3 and
very low relative humidity (< 20 %). On the other hand, at
Paramaribo on 6 and 25 November 2008 and at Paramaribo on 4 May 2009, when
air masses at all levels were of Northern Hemisphere origin, the models
reproduced the nearly constant with altitude O3 values of 30–40 ppb.
Chemistry
The excess O3 in the PBL in the models could be due to either a low
deposition sink, as O3 dry deposition velocities in the models are
about half of observed values, or excessive model sensitivity to NOx
emissions, or both. Two additional simulations were conducted with WRF-Chem
to evaluate the model sensitivity to these processes: (1) doubling the
calculated deposition velocity for O3 only (2DEPVEL) and (2) halving
the NOx surface emission rates (0.5ENOx). The O3 profiles
corresponding to BARCA flights for these two simulations are also included
in Figs. 12 and 14. The corresponding NO profiles from all model simulations
as well as a mean profile over Amazonia from ABLE-2A are depicted in Figs. 13 and 14. The 0.5ENOx simulation reduces O3 more than 2DEPVEL
throughout the entire profile. In the dry-to-wet transition, 2DEPVEL reduces
O3 in the lower PBL by about 25 %, while 0.5ENOx decreases O3 by
around 40 %, and in the wet-to-dry-transition the reductions are about
10 and 30 %, respectively. In general the 0.5ENOx O3 profiles are
lower than observed in the first 500 m above the surface, but they provide
the best representation of the data for the north and west regions in the
dry-to-wet transition. They also provide a similarly good fit as 2DEPVEL for
the east, Manaus, and south regions, while in the wet-to-dry transition
0.5ENOx is closer to the observed value from 0–500 m in all regions except
the north. During BARCA A, NO in all WRF-Chem simulations in the north,
west, and Manaus regions is 10–15 ppt from 0–500 m above the surface,
increasing to a maximum of 20–50 ppt at 2 km a.g.l., and is generally lower
than the ABLE-2A observations in the PBL. In the east and south, where
biomass burning influence was seen, NO in 0–500 m a.g.l. increased from
20–50 ppt in the base simulation to 35–60 ppt in 2DEPVEL due to decreased
O3 and conversion of NO to NO2, and was generally within 1
standard deviation of the ABLE-2A measurements in the PBL. In BARCA B, NO
simulated by WRF-Chem is very low, 5–10 ppt in the entire profile, except
for the west region, where a mean NO of 30 ppt is seen from 0–500 m a.g.l.
This is again due to very low O3, and for the Manaus region, where
anthropogenic NOx sources may have contributed to NO values of 20 ppt.
These results suggest that adjustment of dry deposition parameterizations
are needed to increase O3 deposition velocities by about a factor of
two in agreement with ground observations. Future research will compare
simulated NOx fields with observations from more recent field
campaigns, as the results of these simulations also suggest that O3 in
WRF-Chem is very sensitive to NOx emissions.
In summary, chemistry simulations of the BARCA periods with CCATT-BRAMS and
WRF-Chem overestimated O3 in the PBL by 5–10 ppb in the wet-to-dry
transition (BARCA B), with background levels observed (10–20 ppb) in all
regions. In the dry-to-wet transition (BARCA A), the models generally
reproduced elevated O3 levels in the northeast and southeast Amazon,
where biomass burning emissions of precursors led to significant
enhancements of ambient O3. However, the models overestimate O3 in
the PBL by 5–10 ppb, whereas from 2 to 4 km the modeled values are generally
lower than observations. These discrepancies of models with observations may
result from an overly mixed (constant with altitude) profile due to overly
active vertical mixing from the PBL scheme from 1 to 2 km or too much downward
convective transport of O3 from 2 km to the surface, as observed by
Betts et al. (2002). In addition, the models may be missing sources of
O3 and/or precursors at 3–4.5 km in the model inflow boundary
conditions. In the lower boundary layer, the surface sink of O3 (dry
deposition) may be too low, or overestimation of NOx sources may
produce too much O3. Additional simulations with WRF-Chem showed that
O3 in the lower boundary layer was about twice as sensitive to
increases in O3 deposition velocity as reductions in NOx
emissions, but both simulations achieved better agreement with observations
than the base-case simulation. Although NO emissions over the forest were
less than half of observed values, likely due to the lack of inclusion of
soil emissions, sufficient O3 production occurred to match or exceed
aircraft observations, suggesting that the model chemistry is overly
NOx-sensitive.
Conclusions
The BARCA campaign offered the first regional aircraft survey of O3 in
the Amazon Basin in both the dry-to-wet and wet-to-dry transition seasons.
In both seasons, extremely low background O3 values (< 20 ppb)
were observed to the west and north of Manaus, and in the wet-to-dry
transition low O3 was also measured to the east and south and in the
region around Manaus. These background values are the lowest observed on
Earth, due to a combination of isolation from anthropogenic and biomass
burning NOx sources and O3 deposition to the forest canopy, and
the ecosystem and atmospheric chemistry is adjusted to these very low
values. According to models, the chemistry in the Amazon is very sensitive
to NOx emissions from soils, so that even a small overestimate of
NOx emissions generates too much O3 in the PBL. However, it is
likely that the model chemistry is incorrect in the PBL, because the models
have about the right amount of NOx but far too much O3 in the PBL.
Further simulations with WRF-Chem showed that the model O3 production
is very sensitive to both the O3 deposition velocities and the NOx
emissions, which were both about one-half of observed values. In polluted,
VOC-sensitive conditions, approximately the correct net amount of O3 is
generated in the PBL. This suggests there is insufficient VOC reactivity in
the models, since the correct amounts of O3 deposition velocities and
NOx emissions would both decrease O3 production. Additionally, in
clean, NOx-sensitive conditions, proportionally more O3 is
produced per unit NOx emissions and the O3 deposition velocities
are still too low, resulting in an overestimate. Therefore, we conclude that
the current model chemistry produces much more O3 per unit NOx
than the atmosphere at very low NOx, but may be about right in polluted
conditions. In addition, simulated O3 was lower than both the OMI/MLS
total tropospheric O3 and the BARCA observations in mid-levels,
indicating that the models are missing sources at mid-levels from long-range
and convective transport.
As the regional population grows in the Amazon basin, leading to increases in
both urban and fire NOx sources, this is indeed a big concern
because PBL O3 is lower in clean areas than the models predict, so that
the change to polluted conditions is larger, and that the chemistry to define
the path to higher NOx conditions is poorly represented. Future
modeling studies can include more complete organic chemistry and biogenic
emissions, including NO emissions from soil, as well as improved
representation of lightning NOx production, dry deposition,
convective transport, and wet scavenging processes, to address this
NOx sensitivity. Additionally, future field campaigns in the
Amazon that include aircraft observations of nitrogen oxides and hydrocarbons
and ground-based measurements of NO flux from the forest canopy may allow
better constraints on the Amazonian O3 budget.