ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-8809-2015The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling studyKuikF.friderike.kuik@iass-potsdam.deLauerA.https://orcid.org/0000-0002-9270-1044BeukesJ. P.Van ZylP. G.https://orcid.org/0000-0003-1470-3359JosipovicM.VakkariV.LaaksoL.FeigG. T.Institute for Advanced Sustainability Studies (IASS) Potsdam, GermanyUnit for Environmental Sciences and Management, North-West University, Potchefstroom, South AfricaFinnish Meteorological Institute, Helsinki, FinlandSouth African Weather Service, Pretoria, South Africanow at: Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyF. Kuik (friderike.kuik@iass-potsdam.de)12August20151515880988309February201510March201525June201515July2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/8809/2015/acp-15-8809-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/8809/2015/acp-15-8809-2015.pdf
South Africa has one of the largest industrialized economies in Africa.
Emissions of air pollutants are particularly high in the
Johannesburg-Pretoria metropolitan area, the Mpumalanga Highveld and the
Vaal Triangle, resulting in local air pollution. This study presents and
evaluates a setup for conducting modeling experiments over southern Africa
with the Weather Research and Forecasting model including chemistry and
aerosols (WRF-Chem), and analyzes the contribution of anthropogenic
emissions to the total black carbon (BC) concentrations from September to
December 2010.
The modeled BC concentrations are compared with measurements obtained at the
Welgegund station situated ca. 100 km southwest of Johannesburg. An
evaluation of WRF-Chem with observational data from ground-based measurement
stations, radiosondes, and satellites shows that the meteorology is modeled
mostly reasonably well, but precipitation amounts are widely overestimated
and the onset of the wet season is modeled approximately 1 month too early
in 2010. Modeled daily mean BC concentrations show a temporal correlation of
0.66 with measurements, but the total BC concentration is underestimated in
the model by up to 50 %.
Sensitivity studies with anthropogenic emissions of BC and co-emitted
species turned off show that anthropogenic sources can contribute up to
100 % to BC concentrations in the industrialized and urban areas, and
anthropogenic BC and co-emitted species together can contribute up to 60 %
to PM1 levels. Particularly the co-emitted species contribute
significantly to the aerosol optical depth (AOD). Furthermore, in areas of
large-scale biomass-burning atmospheric heating rates are increased through
absorption by BC up to an altitude of about 600 hPa.
Introduction
South Africa is one of Africa's largest economies and anthropogenic
emissions of air pollutants from South Africa are of increasing concern. Due
to South Africa's growing economy, fossil fuel consumption and energy demand
are rising, with most of the electricity produced by coal-fired power plants
(Lourens et al., 2011; Tiitta et al., 2014). Further contributions come from
its large mining and metallurgical industry and domestic combustion,
especially in informal settlements around towns (Venter et al., 2012). A
large portion of South African anthropogenic emissions originate from the
area around Johannesburg and Pretoria (Freiman and Piketh, 2003), a
metropolitan area with a combined population of more than 10 million
(Lourens et al., 2012), as well as the Mpumalanga Highveld and Vaal Triangle
industrial areas that have both been declared pollution hotspots by the
South African government (Lourens et al., 2011). Furthermore, large-scale
biomass burning emissions contribute particularly during the dry winter
season to air pollutant concentrations (e.g. Swap et al., 2003; Vakkari et
al., 2014). The issue of air pollution in South Africa has been recognized
and explored in several recent studies, mainly focusing on the measurement
and characterization of both gaseous species and particulate matter (e.g. Laakso et al., 2008, 2012; Vakkari et al., 2011, 2013; Venter et
al., 2012; Jaars et al., 2014; Tiitta et al., 2014;
Beukes et al., 2013).
So far, regional modeling studies for southern Africa have mainly focused on
the meteorology (e.g. Crétat et al., 2011). Solmon et al. (2006) modeled
aerosols, including BC, with a domain covering Europe and large parts of
Africa but did not include South Africa. They identify poorly developed
emission inventories – especially for Africa – as one of the main deficiencies
in modeling aerosol concentrations and note a lack of measurement data for
model evaluation in Africa. The African Multidisciplinary Monsoon Analysis
(AMMA) project was designed to address these gaps. Part of it was dedicated
to comparing the performance of chemical transport and chemistry climate
models, simulating the distribution of trace gases and aerosols over West
Africa. The findings provide recommendations for future air chemistry
modeling, emphasizing the need for improved anthropogenic emission
inventories (Ruti et al., 2011). Laakso et al. (2013) simulated particle
growth in South Africa with an offline global aerosol model. They found that
the model does not reproduce the observed particle formation
characteristics. This was attributed mainly to the emissions, with their
monthly resolution not capturing the emissions' variability. Tummon et al. (2010)
simulated the direct and semi-direct effects of biomass-burning
aerosols in southern Africa, reporting regional changes induced by aerosols
including surface cooling and heating of the atmosphere in higher altitudes,
leading to enhanced tropospheric stability and a decreased height of the
planetary boundary layer. The study does not explicitly assess
anthropogenically emitted aerosols. To the authors' knowledge, no
peer-reviewed study exists to date that is aimed at modeling anthropogenic
black carbon (BC) with a regional model over southern Africa.
Black carbon is an important component of air pollution. It is a
carbonaceous aerosol that is produced during the incomplete combustion of
carbon-based fuels and materials. It is an aggregate of rapidly coagulating
small carbon spheres, with a total size generally below 1 µm. Black
carbon is characterized by its strong absorption of visible and
near-infrared light and by its resistance to chemical transformation (Ogren
and Charlson, 1983; Goldberg, 1985; Petzold et al., 2013).
In addition to the burning of biomass and industrial processes, in
particular domestic cooking and heating, as well as the transport sector are
major sources of BC in Africa (Bond et al., 2013). Fine particulate matter,
and thus BC contained within, is associated with several adverse effects on
human health. These include respiratory and cardiovascular morbidity, such
as aggravation of asthma, respiratory symptoms and an increase in hospital
admissions, as well as mortality from cardiovascular and respiratory
diseases and from lung cancer (Janssen et al., 2012). Some empirical studies
suggest that long-term exposure to PM2.5 containing a high BC fraction
may have larger mortality effects than other PM2.5 mixtures (Smith et
al., 2009).
The efficient absorption of solar radiation by BC makes these aerosols the
most important absorber of visible light in the atmosphere. In addition to
absorbing light while being suspended in the atmosphere, BC can reduce the
amount of reflected sunlight when deposited on high albedo surfaces such as
snow and ice. After carbon dioxide, emissions of BC are thought to make the
second strongest contribution to current global warming (Ramanathan and
Carmichael, 2008; Hodnebrog et al., 2014), though the exact climate forcing
of BC is still under debate (e.g. Samset et al., 2014).
As BC has a short residence time in the atmosphere (few days) in comparison
to CO2 (several years up to more than 100 years), emission reduction
measures would rapidly lead to a decrease in concentrations, which would
have beneficial effects for both air quality and climate (Ramanathan and
Carmichael, 2008; Shindell et al., 2012). These properties lead to BC often
being classified as a Short-Lived Climate-forcing Pollutant (SLCP) (e.g. Schmale et al., 2014).
The metropolitan areas in South Africa are highly populated, and at the same
time the population is highly vulnerable to air pollution and climate change
because of their rather limited resources for adaptation. This is why an
assessment of the contribution of anthropogenic BC emissions to the observed
aerosol concentrations is needed as a first step for assessing potential
emission-reduction scenarios. This is the aim of this study, which is done
for the entire subcontinent of southern Africa, but with the main focus on
the country of South Africa.
This study presents (Sect. 2) and evaluates (Sect. 3) a model setup for
southern Africa, using the Weather Research and Forecasting (WRF) model
online coupled to air chemistry and aerosol processes (WRF-Chem, Grell et
al., 2005; Fast et al., 2006; Skamarock et al., 2008). The evaluation
includes a comparison of the model results to different observational and
reanalysis data. An important data set is the ground measurements conducted
at Welgegund, ca. 100 km southwest of Johannesburg, detecting both pollution
plumes coming from the industrialized and urban areas, as well as air masses
representing the regional southern African background. It is one of the only
regionally representative and comprehensive long-term inland atmospheric
measurement stations (Beukes et al., 2013). In addition, data from
observations of particulate matter (PM2.5 and PM10) and aerosol
optical depth (AOD) are compared with the model results. With two
sensitivity studies, the contribution of anthropogenic BC and co-emitted
species to aerosol concentrations, AOD, and the impact on atmospheric
heating rates is also analyzed (Sect. 4).
General features of the setup, physics and chemistry schemes used
in the configuration of the Weather Research and Forecasting model with
chemistry (WRF-Chem).
General featuresDomain size4–50∘ E, 5–39 ∘ S Modeling time period26 Aug–31 Dec 2010 Resolution15×15km, 31 vertical levels (top at 10 hPa) PhysicsSchemeRemarkscloud microphysicsLin et al.radiation (shortwave)Goddardcalled every 15 minradiation (longwave)Rapid Radiative Transfer Model (RRTMG)called every 15 minboundary layer physicsMellor-Yamada-Janjić (MYJ)called every time step (90 s)land surface processesNoah LSMcumulus convectionGrell 3-Dcalled every time step (90 s)chemistryRADM2 with CMAQ aqueous chemistrychem_opt = 41photolysisFast-Jcalled every 15 minaerosolsMADE/SORGAMdustGOCART (online)dust_opt = 3Model and model simulationsModel description and setup
We apply the Weather Research and Forecasting model (WRF) version 3.5.1
(Skamarock et al., 2008) with chemistry and aerosols (WRF-Chem, Grell et
al., 2005; Fast et al., 2006). We use the RADM2 chemistry scheme with the
MADE/SORGAM aerosol module and aqueous phase chemistry (CMAQ) (Table 1).
RADM2 in combination with the MADE aerosol module has already been widely
used in literature (e.g. Grell et al., 2011; Minsenis and Zhang, 2010;
Tuccella et al., 2012). Aqueous phase chemistry has been switched on as we
expect this to be of relevance particularly when simulating aerosols during
the wet season. The model has been set up with one domain covering large
parts of southern Africa (4–50∘ E, 5–39∘ S, Fig. 1) at a horizontal resolution of 15km×15km.
WRF-Chem is configured with 31 vertical σ-levels, of which 14 levels
are below 700 hPa. The model top is at 10 hPa.
Domain overview and elevation (left), enlarged for South Africa
(right), locations of all stations with data used for the model evaluation
(see text) and the location of Johannesburg included in the figure.
We use the Modern-Era Retrospective Analysis for Research and Applications
(MERRA) from the National Aeronautics and Space Administration (NASA) as
initial and lateral atmospheric boundary conditions (Rienecker et al.,
2011). The MERRA data with a horizontal resolution of 0.5∘×0.67∘ at 6-h time intervals and at 32 pressure levels between
1000 and 10 hPa are interpolated to the model grid using the standard WRF
Preprocessing System (WPS). European Centre for Medium-Range Weather
Forecasts (ECMWF) Interim reanalysis (ERA-Interim) data are used as initial
conditions for soil temperature and soil moisture (Dee et al., 2011). The
modeled temperature, horizontal wind, humidity, surface pressure, and
geopotential height are nudged to the lateral boundary conditions within a
buffer zone of 5 grid points normal to the lateral boundaries. This buffer
zone is excluded in the analyses of the model results shown below. We
prescribe sea surface temperatures (SSTs) using the National Oceanic and
Atmospheric Administration (NOAA) optimum interpolation (OI) daily analysis
(Reynolds et al., 2007). The SST data are based on daily mean satellite
observations from the Advanced Very High Resolution Radiometer (AVHRR) and
the Advanced Microwave Scanning Radiometer (AMSR) with a horizontal
resolution of 0.25∘×0.25∘. The diurnal SST variation
is included in our SST forcing and is calculated following the surface
energy budget method of Zeng and Beljaars (2005). Chemical boundary
conditions for trace gases and particulate matter are created from
simulations with the global chemistry transport Model for Ozone and Related
chemical Tracers (MOZART-4/GEOS-5, Emmons et al., 2010).
The physics and chemistry modules used in the WRF-Chem configuration are
summarized in Table 1.
Emissions
Anthropogenic emissions are taken from the EDGAR HTAP v2.2 inventory,
released in fall 2013 (EDGAR: Emission Database for Global Atmospheric
Research of the Joint Research Centre, JRC, of the European Commission, in
cooperation with the Task Force on Hemispheric Transport of Air Pollution,
TF HTAP, organized by the United Nations Economic Commission for Europe's
Convention on Long-range Transboundary Air Pollution, LRTAP). The data set
combines different available national or regional inventories. Gaps are
filled by the bottom-up global emission inventory EDGARv4.3, which is
calculated based on activity data and corresponding emission factors (see
Janssens-Maenhout et al. (2012) for details on v1.0 of the data set;
LRTAP-Wiki (2014) for updated information on v2). It should be noted that
emission data for southern Africa are entirely based on EDGARv4.3, since
currently no comprehensive regional inventories are available. EDGAR HTAP
v2.2 reports monthly data for the emissions from the energy, industry,
transport and residential sectors and annual data for emissions from
shipping and aviation (only takeoff and landing included here). Emission
data from small agricultural fires are not available in v2.2 and were
therefore taken from v1.0 (annual data for 2005). The authors of the data
set recommend using a satellite product for large-scale burning
(Janssens-Maenhout et al., 2012), as indicated below.
For biomass burning emissions, the Fire Inventory from the National Center
for Atmospheric Research (NCAR) version 1 (FINN, Wiedinmyer et al., 2011) is
used. The data are based on daily satellite observations of fires and land
cover, which are combined with emission factors and estimated fuel loadings.
Fires of approximately 1 km2 size are detected (Wiedinmyer et al.,
2011). Biogenic emissions are calculated online by the Model of Emissions of
Gases and Aerosols from Nature (MEGAN, Guenther et al., 2006).
Simulations
In this study, we performed a reference run (RR) with the model setup and
emissions described above and two sensitivity runs (S1 and S2) with modified
emissions but an otherwise identical setup. The model integration covers the
time period from 26 August through 31 December 2010. Although September is
the first spring month, it is still part of the dry season since rain
usually occurs after mid-October. Black carbon concentrations in the
interior of South Africa usually peak in September. In contrast, December
2010 is part of the wet season, which has significantly lower ambient BC
levels. The first five days of all experiments were discarded as a spin-up
period and excluded from the analysis presented.
Sensitivity studies
In the first sensitivity run (S1), all energy-related anthropogenic BC
emissions and emissions from small agricultural fires are set to zero.
Following Bond et al. (2013), energy-related emissions include all emissions
from industry, transport (including aviation and shipping), energy
production and residential heating. In addition, large scale biomass burning
emissions are reduced to 35 % of the initial values. It is assumed that
65 % of the large-scale biomass-burning emissions are caused by humans
directly or indirectly. This is based on estimates of the portion of
pre-industrial biomass burning emissions of 37 % globally (Bond et al.,
2013), as well as 33 % globally and 36 % for southern Africa (Dentener
et al., 2006).
When aiming at reducing BC concentrations it is usually not feasible to only
cut the BC emissions. If emissions from a certain source are reduced,
usually also the emissions of co-emitted species such as sulfur dioxide and
organic carbon are reduced. Those species can have a cooling impact on the
climate. Hence, when assessing the maximum effect of cutting anthropogenic
BC emissions on aerosol loadings, or the impact on meteorological variables,
it is not sufficient to only consider a case without anthropogenic BC
emissions. An integrated assessment of such emission cuts also needs to
consider the contribution of the co-emitted components such as organic
particles or sulfur dioxide (SO2).
The above-mentioned second sensitivity simulation (S2) looks at the impact
of both anthropogenic BC and co-emitted (often climate-cooling) aerosols. In
addition to the reductions of BC (S1), also the emissions of co-emitted
organic carbon (OC), primary sulfate aerosols (SO4) and SO2 are
reduced in S2. The emissions are reduced in the same way as BC, i.e. the
anthropogenic emissions are set to zero and the biomass burning emissions
are reduced to 35 % of the original values. This is a simplifying
assumption, as there might be sources among the anthropogenic source
categories that do not emit BC but that do emit OC or SO2 and vice
versa. However, there is not enough information in the anthropogenic
emission data used in this study (EDGAR HTAP v2.2) to make additional
assumptions on sources emitting BC, but no OC or SO2, or the other way
round. For instance, the ratio of BC to OC emissions is constant throughout
the whole model domain. Ideally, specific reduction factors should be
employed. As there is no such information available for southern Africa, the
above-described set up is used to estimate the overall effect of
anthropogenic BC sources on aerosol loadings and atmospheric heating rates.
The anthropogenic contribution to aerosol concentrations, aerosol optical
depth (AOD) and atmospheric heating rates is estimated as the differences
between the reference run and the respective sensitivity simulations (S1, S2).
Model evaluation
For the model evaluation and a consistency check of the emissions, various
observational and reanalysis data have been used (see Sect. 3).
A major data source for evaluating the model results from WRF-Chem is data
obtained at the Welgegund measurement station (Fig. 1), the only long-term
monitoring station measuring BC representative of the interior of South
Africa (e.g. Venter et al., 2012; Vakkari et al., 2013; Tiitta et al.,
2014). The station was set up at Welgegund in 2010 and is jointly operated by
the North-West University (South Africa), the University of Helsinki and the
Finnish Meteorological Institute. The station consists of an atmospheric
monitoring trailer (Petäjä et al., 2013). Measured quantities used in
this study include several meteorological parameters (temperature, relative
humidity, wind speed and direction, and pressure), trace gases
(SO2, NO/NOx, O3, CO) and equivalent BC
determined with a Multi-Angle Absorption Photometer (MAAP) corrected for the
filter change artefact (Hyvärinen et al., 2013). There are no major
anthropogenic pollution sources close to the station. Beukes et al. (2013)
identified five important source regions for air masses analyzed at
Welgegund. These include metallurgical industries in the Bushveld Igneous
Complex (western and eastern limb) in the north to northeast of Welgegund,
the Johannesburg-Pretoria megacity to the east, the Vaal Triangle
(petrochemical, metallurgical and other industries) and the Mpumalanga
Highveld (coal mining, coal-fired power plants, petrochemical operations and
metallurgical smelters) between the east and south east. Furthermore,
large-scale biomass-burning emissions originate mostly from the sector east
of the measurement station (from north to south), because the biome in the
western sector is drier and there is thus less plant material for combustion
available from this sector. Additionally, the sector between north and south
to the west of Welgegund is representative of the regional background of
southern Africa.
In addition, observations of AOD from two AERONET-stations (Holben et al.,
1998) and observations of PM2.5 and PM10 provided by the South
African Air Quality Information System (SAAQIS) of the South African Weather
Service (SAWS) from three stations in the vicinity of the
Pretoria-Johannesburg megacity are used (Fig. 1). The SAAQIS stations' main
purpose is the monitoring of air quality in areas with high air pollution.
The stations are classified as urban (Witbank station), residential (Zamdela
station) and located in an urban residential area (Secunda station). As
these are stations close to anthropogenic, non-biomass burning emission
sources, aerosol concentrations are expected to be mainly dominated by local
anthropogenic emissions. Hence, these comparisons are used as a consistency
check for the emission data used here.
Selected meteorological variables, monthly means for September and
December 2010, comparison of WRF-Chem model results with different data sets
(a – sea level pressure, comparison with ERA-Interim reanalysis data, b
–
precipitation amount, comparison with GPCP data, c – cloud fraction,
comparison with PATMOS-x satellite data, d – wind speed, comparison with
ERA-Interim reanalysis data).
Comparison with observations and reanalysis dataMeteorologyWRF-Chem model results for southern Africa
The comparison of the sea-level pressure modeled with WRF-Chem to
ERA-Interim reanalysis data (Dee et al., 2011) shows that WRF-Chem
represents the common features of the southern African pressure distribution
well (Fig. 2a). In September, over the south Atlantic and the south Indian
Ocean, the edges of two subtropical highs can be identified. Another high is
over the east coast of the continent, which is part of the high pressure
belt around 30∘ S that influences the daily weather patterns of
southern Africa (Tyson and Preston-Whyte, 2000). The spatial correlation
between the WRF-Chem monthly mean and the ERA-Interim reanalysis monthly
mean in September is high (r=0.95) and the domain-averaged mean bias with
respect to the ERA-Interim reanalysis is small (-0.6hPa). In December, both
WRF-Chem results and ERA-Interim reanalysis show that the low pressure area
over the northern part of the model domain associated with the Intertropical
Convergence Zone (ITCZ) is moving southwards compared to September,
resembling the easterly low situation, which is usually the dominant
synoptic situation in December (Tyson and Preston-Whyte, 2000). This
constellation, associated with the ITCZ moving southwards at the beginning
of the wet season, is responsible for strong precipitation over the
subcontinent. The spatial correlation between the two data sets is slightly
lower in December (r=0.79) than in September. The domain-averaged monthly
mean pressure of the WRF-Chem results is biased with respect to the
ERA-Interim reanalysis by -2.6hPa in December.
In September 2010, WRF-Chem simulated over the area within the northern low
pressure region some precipitation, possibly indicating too early of an onset of
the rainy season (Fig. 2b). Compared with the Global Precipitation
Climatology Project (GPCP) precipitation data (Huffman et al., 2001),
WRF-Chem overestimated the precipitation amounts in September 2010 as most
parts of the subcontinent do not receive any significant amount of
precipitation. The mean bias for the whole model domain is 1.11 mmday-1. In
December, the WRF-Chem results show large amounts of precipitation over the
whole eastern part of the subcontinent, including Madagascar and the
Mozambique Channel. WRF-Chem strongly overestimated the amounts of
precipitation during all months of the modeled period, with a maximum
monthly mean bias of +6.47mmday-1 (domain average) in December 2010
(+200 %). In addition to the northeastern part of the model domain and
the Mozambique Channel, precipitation in the model was also strongly
overestimated near the Drakensberg Mountains and over/on the edges of the
South African Highveld. The spatial correlation is rather low in both
September (r=0.38) and December (r=0.36). It should, however, be noted
that satellite-based precipitation data sets such as the GPCP data over
southern Africa also include uncertainties. It is known, for instance, that
satellite-based precipitation estimates tend to underestimate rainfall
amounts during the dry seasons (Huffman et al., 2001, 2007).
Furthermore, underestimation of precipitation is documented for the GPCP
data in areas of complex terrain (Huffman et al., 2001), which applies to
the region around the Drakensberg Mountains and the edge of the Highveld,
i.e. the escarpment.
Previous studies have shown that the extent and location of the model domain
of a regional model is important when running simulations over South Africa
(Crétat et al., 2011, and references therein). However, in a pre-study
different configurations of WRF have been tested and the precipitation bias
found in all simulations did not show any significant improvement when
adjusting the extent of the model domain to, for instance, include
Madagascar. Precipitation biases have been reported in the literature when
studying South African meteorology with different models. Results suggest
that this phenomenon might be related to too strong of an atmospheric water
cycle and too strong of an advection of moisture in the models. Another reason for
the precipitation bias over the continent might be the complex topography
(Crétat et al., 2011), as for example suggested by the overestimation
near the Drakensberg mountains and the edge of the Highveld. Crétat et
al. (2011) have shown that the precipitation bias in WRF depends partly on
the choice of the cumulus scheme in combination with the PBL and
microphysics schemes. However, their setup providing best results could not
be used in combination with WRF-Chem as certain chemistry options such as
aqueous phase chemistry are not available for all convection schemes.
In September 2010, most parts of the subcontinent including the northern
parts of South Africa, Botswana and Zimbabwe were almost entirely cloud free
(Fig. 2c). The modeled monthly mean cloud-free area is somewhat larger than
the one obtained from the PATMOS-x satellite data (Heidinger et al., 2014).
The cloud fraction in the model is particularly larger than observed in
regions where precipitation is simulated, but the overall bias is negative
with -13 % (-27 %) in September and -5 % (-7 %) in December. The
spatial correlation between the two data sets is high (r=0.85) in
September. As already seen for sea level pressure and precipitation, the
spatial correlation is smaller for December 2010 (r=0.57), which might be
related to more frequent and stronger convection in December, which is
difficult to capture with a model.
The monthly mean 2 m temperature is modeled well in September 2010 (not
shown), showing only a small domain averaged bias of 0.4 ∘C
compared with the ERA-Interim reanalysis data and a high spatial correlation
(r=0.93). In December, the modeled 2 m temperature is lower than the
ERA-Interim reanalysis particularly in the areas above the continent where a
positive precipitation bias is found. The overall mean bias of the model
results compared to the ERA-Interim reanalysis in December is small with
-0.03∘C, and the spatial correlation is still high with
(r=0.91).
Monthly mean of modeled and observed meteorology, BC and selected
trace gases, mean bias of the WRF-Chem daily means with respect to
observations and temporal correlation coefficient (Pearson) of daily means
over 1 month or the whole period September to December 2010 (all).
Observational data are station measurements at Welgegund for all variables
except for precipitation, which is obtained from the TRMM satellite data.
WRF-Chem model results of meteorological variables at Welgegund in
comparison with Welgegund station measurements and precipitation satellite
data (TRMM). Shown are daily means (daily sums in the case of
precipitation).
The outgoing longwave radiation (OLR, not shown) over some parts of the
continent is up to 25 to 50 Wm-2 lower than the PATMOS-x satellite data
for September 2010. Again, the bias is particularly high in areas with a
strong bias in precipitation, with a domain-averaged monthly mean bias of
-21.4Wm-2 (-7 %). The spatial correlation with the PATMOS-x data is
r=0.85. Likewise, in December, the modeled OLR over the Mozambique Channel
is up to 100 Wm-2 smaller than in the satellite data, with a
domain-average bias of the monthly mean of -17.3Wm-2 (-7 %) in
December. The spatial correlation of the OLR is r=0.72 in December 2010.
The underestimation by the model in areas with large amounts of
precipitation suggests that the cloud top heights are overestimated in
WRF-Chem, even though the cloud fraction is underestimated. This in turn
suggests that the cloud thickness is overestimated by the model, which could
explain the stronger than observed precipitation. The cloud and
precipitation biases can also explain the negative bias in the surface
temperature in regions with significant precipitation. The model biases of
the different variables are consistent and might be the result of the
difficulty in reproducing the observed convection, clouds and precipitation
with WRF.
Compared with the ERA-Interim reanalysis, WRF-Chem captures the monthly mean
10 m wind speed (Fig. 2d) fairly well in the dry season (September), with
some local positive, as well as negative deviations. The mean bias averaged
over the whole model domain is -0.4ms-1 (-8 %) in September and
1.3 ms-1
(30 %) in December. The spatial correlation is fairly high with r=0.87
in September and r=0.85 in December.
Comparison to measurements at Welgegund
For comparing the model results to measurements done at the Welgegund
station, the modeled daily means of all variables considered except
precipitation are averaged over the 3×3 nearest grid points surrounding the
measurement station. The WRF-Chem precipitation data are compared to TRMM
satellite data (Huffman et al., 2007) and averaged over the 25 (5×5) nearest
grid points to be comparable to the TRMM nine grid point average. All the
comparative data are presented in Fig. 3 and Table 2.
The TRMM data show that precipitation events become more frequent from
mid-October 2010 on, with almost no precipitation observed beforehand. From
this, we qualitatively derive the beginning of the rainy season around
mid-October 2010. In contrast, the beginning of the rainy season in the
model is about 1 month too early. In addition, the amplitudes of the
precipitation events are much higher, at times up to three times as high as
the TRMM values (e.g. in mid-December with ca. 20 mmday-1 as indicated by the
TRMM data and more than 60 mmday-1 in WRF-Chem). The modeled time series of
the precipitation in September is not correlated with the TRMM data
(r=-0.09). In December, there is a rather low correlation (r=0.20), and
a mean bias of 2.17 mmday-1 (44 %).
The monthly mean specific humidity measured at Welgegund increases from 5.39 gkg-1
in September to 11.86 gkg-1 in December 2010. This increase is captured
qualitatively by the model, but the WRF-Chem results are positively biased
in September (+0.96gkg-1, +18 %), October and November, with the bias
decreasing over time. In December 2010, the modeled monthly mean specific
humidity is in good agreement with the observations with a mean bias of
-1.5 % (-0.18gkg-1). The temporal correlation between modeled and measured
daily mean values is high in September (r=0.76). While the monthly mean is
modeled well in December 2010, the time series of the daily values are
uncorrelated (r=0.06), meaning that the model does not capture the
day-to-day pattern.
The monthly mean 2 m temperature at Welgegund does not vary much over the
whole modeling period, with 19.4 ∘C measured in September and
21.0 ∘C in December. It is simulated quite well in September, with
only a slight mean warm bias of +0.2∘C. From October on, the
modeled monthly means are biased slightly negative. In December, the mean
bias is -1.0∘ C. As is evident from Fig. 3, as well as a
comparison of the standard deviations (SDs, not shown), the model captures
the day-to-day variability well. From October on, the timing of the modeled
minima and maxima, as well as the minimum and maximum values agree less well
with the observations. This is also seen in the temporal correlation
coefficient of the daily means, which decreases from September (r=0.84) to
December (r=0.44). The temporal correlation over the whole period is
r=0.69.
The monthly mean 10 m wind speed measured at Welgegund varies between 5.0 ms-1
(September) and 6.1 ms-1 (December). The model results are biased
negatively in all 4 months, with the smallest bias in September (-0.2ms-1, -4 %)
and the largest bias in December (-2.2ms-1, -36 %). From the
beginning of October, the model has some difficulty in capturing the maximum
daily mean wind speeds well, and underestimates the minimum daily mean wind
speeds. The temporal correlation coefficient indicates that the daily mean
wind speed is captured better in September (r=0.77), while the model
results and the measurements are uncorrelated in December (r=-0.07).
Averaged over the whole modeling period, the modeled 10 m wind direction
deviates slightly from the measured wind direction at Welgegund (not shown).
While the frequency of northerly winds – the dominant wind direction – is
modeled quite well (around 27 %), the portion of wind coming from the
northeast is clearly underestimated (less than 10 % in the model compared
with around 20 % in the measurements). In contrast, the northwesterly wind
direction is slightly overestimated. Wind coming from the southwest to the
southeast does not play a major role at Welgegund, which is correctly
reproduced by the model. The overestimation of wind coming from the
northeast by the model is particularly prevalent in September (not shown).
In November the modeled main wind direction is shifted only slightly to the
east compared with the observations. The wind direction bias in October and
December is small.
Atmospheric profiles and inversion layer height
For an analysis of the simulation of the atmospheric vertical structure,
WRF-Chem temperature and humidity profiles were compared with available
radiosonde measurements at Pretoria, Bloemfontein, de Aar and Cape Town
(MetOffice, 2006, see Fig. 1 for the location of the stations). As data
availability for 2010 is sparse, average profiles of measurements obtained
between the years 1997 and 2012 are used for comparison. This is done as a
consistency check to see whether the model is able to reproduce the main
climatological features of the vertical profiles. The radiosonde
measurements are not directly comparable to the WRF-Chem model results. The
comparison shows that WRF-Chem is able to capture the basic (climatological)
features of the vertical profiles of temperature and humidity with the
modeled vertical profiles being within the variability given by two times
the standard deviation (2σ-range, not shown).
In addition to the average temperature and humidity profiles, the inversion
layer height has been calculated from each measured vertical profile and
from WRF-Chem at the times corresponding to the radiosonde ascents. The
inversion height is determined according to the following criteria,
following Cao et al. (2007):
An inversion is characterized by increasing temperature with height and decreasing relative humidity.
If present, the inversion is located between 825 and 350 hPa (inland stations)
and between 950 and 600 hPa in Cape Town (elevation 42 m). The lowest couple of hundred meters are excluded to exclude radiative inversions at the surface.
Only inversions with temperatures above 0 ∘C are searched for in order to avoid artifacts caused by falling ice particles.
If there are several inversions within one profile, the inversion with the largest decrease in relative humidity is chosen.
In Pretoria the monthly mean inversion height varies between 740 and 710 hPa
between September and December (Fig. 4). The inter-annual variability
given by the 25th and the 75th percentiles lies between 800 and 650 hPa.
The mean inversion height is slightly lower in September and October than in
November and December. Similar behavior is also found at the other three
radiosonde stations. The mean inversion height modeled with WRF-Chem at the
four stations is generally slightly lower by about 50 hPa than the mean
inversion height obtained from the radiosonde measurements (mostly between
ca. 800 and 750 hPa for the inland stations, with few exceptions). A
comparison of the frequencies of measured and modeled inversions suggests
that WRF-Chem might underestimate the number of days with an inversion
present. We discuss the role of the inversion layer height for near-surface
concentrations of BC in Sect. 3.2.3.
Inversion heights for each month (September–December) and four
different radiosonde stations, lines represent the median, shaded areas the
25th and 75th percentiles, dots the mean values.
Black CarbonModeled monthly mean concentrations
Figure 5 shows the modeled monthly mean near-surface BC concentrations for
September, October, November and December 2010, with “near-surface”
meaning the lowest model layer, centered around about 30 m above the ground.
The highest monthly mean BC concentrations in September are modeled in the
Johannesburg-Pretoria area with values up to 15 µgm-3. In
Zimbabwe, where the emission inventory also shows relatively high
anthropogenic emissions, the BC concentrations are comparable to the
Johannesburg-Pretoria levels (up to 2.5 µgm-3). In the north of
the model domain at the border between Zambia and Angola mean BC
concentrations are as high as 5 µgm-3 with biomass burning being
the main BC source in that area. Over land, the lowest modeled monthly mean
BC concentrations are found in the southeast of South Africa in the dryer
Karoo regions, with values of less than 0.1 µgm-3. This region
is relatively far from both anthropogenic sources and from large-scale
biomass burning areas.
Monthly mean near-surface BC concentrations (lowest model layer)
modeled with WRF-Chem, September–December 2010.
BC concentrations at Welgegund, measured and modeled with WRF-Chem:
probability density functions (PDFs) for September–December 2010. The PDFs
are calculated from the observed 15-min values and the 3-hourly values
(instantaneous values) from the model results.
Comparison of modeled daily means of BC and gaseous species with
station measurements at Welgegund.
It can also be seen from Fig. 5 that the mean modeled concentrations are
generally much higher in September 2010, which corresponds to the end of the
dry season in the model, than in the following months. Especially in
November and December, concentrations are lower, possibly due to a
combination of higher removal of BC from the atmosphere (wet scavenging),
the lack of large scale biomass burning as a major source and a less stable
atmosphere (i.e. a smaller number of days with an inversion).
For comparison, the measured annual mean in Berlin, Germany ranges between
around 2 µgm-3 at urban background stations and around 3.5 µgm-3 at measurement sites close to busy roads (2012 values,
Senatsverwaltung für Stadtentwicklung und Umwelt, 2013). BC
concentrations are especially high in some regions in Asia, e.g. in
Kathmandu, Nepal, with an annual mean measured as 8.4 µgm-3
(Sharma et al., 2012).
Comparison with Welgegund data
At Welgegund the measured monthly mean BC concentrations decrease steadily
from September (dry season) to December (wet season), with 1.47 µgm-3 in September, 0.88 µgm-3 in October, 0.31 µgm-3 in November and 0.19 µgm-3 in December (Table 2). The
maximum daily mean concentrations in September are about 3 µgm-3
and in October about 2 µgm-3. As is evident from Fig. 6 the
observed probability density function (PDF) for the mostly dry month of October
is similar to the one for September, while the PDFs for the wetter months of
November and December are much narrower and have distinct peaks at BC
concentrations below 0.5 µgm-3.
The monthly BC means modeled with WRF-Chem are generally smaller than those shown
by the measurements in September and October (0.73 µgm-3 and
0.43 µgm-3, corresponding to a bias of -50 and -51 %) and
slightly higher than observed in November (0.32 µgm-3, biased by
3 %) and in December (just above 0.25 µgm-3, biased by
32 %). Over the whole period, the mean bias is negative (-0.28µgm-3, -39 %). The modeled PDFs in September and October are too
narrow and the peaks around ca. 0.5 µgm-3 are at concentrations
too low compared with the measurements. The modeled PDF for October
resembles rather a wet season PDF than a dry season PDF, which is in line
with the results we described for the simulated precipitation, showing that
the beginning of the wet season is modeled ca. 1 month too early.
Even though the magnitude of the peak values and the average of the daily
mean time series are underestimated in September and October, the time
series of modeled and measured daily means are reasonably well correlated
(temporally) with correlation coefficients of r=0.62 (September) and
r=0.67 (October) (Fig. 7 and Table 2).
As the Welgegund station is not directly surrounded by sources of BC, apart
from smaller local grass fires, most of the BC measured at Welgegund is
transported to the station (Tiitta et al., 2014). Thus, the BC
concentrations at Welgegund are strongly impacted by how effectively
pollutants are transported from the industrialized areas, as well as from
the biomass-burning areas mainly located in the sector east of the station.
96 h back-trajectories of air masses at Welgegund (Beukes et al., 2013) show
that anthropogenic BC can be transported to Welgegund in different ways:
either directly, with wind at Welgegund coming from the northeast to east,
or by air masses re-circulated over the continent with wind at Welgegund
from the north.
The pollution roses shown in Fig. 8 give a first estimate for the direction
from which BC is transported to the station. The measurements show that very
high concentrations are most frequently observed during periods with wind
from the north or northeast corresponding to the above-mentioned
transportation pathways. These pathways are reproduced by the model, which
simulates the highest BC concentrations with wind coming from the northern
to eastern sectors. However, as previously mentioned and visible from Fig. 8,
the main wind directions in the model are shifted from the northeast to
the northwest.
Pollution rose at Welgegund, comparison of WRF-Chem model results
and station measurements. The plot shows the BC concentration
modeled/measured for wind coming from the indicated directions and is
created from the non-averaged data, e.g. 15-min values for the observations
and 3-h values for the model results.
Discussion
Several factors are likely to influence the modeled BC concentration,
including the bias in modeled meteorology (e.g. precipitation, wind
direction), a low quality of the emission inventories, the choice of
chemical boundary conditions or uncertainties and limitations in the
representations of important processes in the model (e.g. the particle size
distribution, the parametrization of convection or the boundary layer).
A too-early beginning of the rainy season and an overestimation of the
precipitation amounts are likely to result in a too-strong wet deposition of
aerosols including BC in the model and are likely two reasons contributing to
the underestimation of the modeled mean BC concentrations particularly during
the dry season at Welgegund. This is especially the case in October 2010,
being mostly dry in the observations, but showing significant precipitation
in the model. As BC has a typical atmospheric residence time of a few days, a
full quantitative analysis on the impact of the overestimation in
precipitation on modeled BC concentrations would require back-trajectories
for several days, which is beyond the scope of this study. We argue
qualitatively that the modeled overestimation of precipitation might
contribute to the modeled underestimation of BC, as we know that BC has to be
transported to the measurement site, because there are no significant sources
close by.
Highest BC concentrations are modeled with wind from the northern to eastern
wind sectors, which is consistent with the measurements at Welgegund.
However, the shift in the modeled main wind direction to the northwest
compared with the measurements likely also contributes to the
above-discussed model bias in the BC concentrations. This is especially the
case in September when the northeastern component of the wind is
underestimated in the model, which is the second most frequently observed
wind direction at Welgegund in this month. BC peak concentrations are
measured particularly during these wind episodes.
Furthermore, the negative bias in modeled wind speed at Welgegund might also
contribute to an underestimation of BC transported to Welgegund. However,
this bias is fairly small and is likely not a main reason for the
underestimation of modeled BC during the dry season.
The lack of BC transported from the industrialized and urban areas to
Welgegund in September being a reason for the underestimation of modeled BC
at the measurement station is further supported by the plots in Fig. 5
showing the geographical distribution of the modeled BC: higher BC
concentrations resulting from urban emissions are found downwind of Pretoria
and Johannesburg, while the Welgegund station is located just outside the
area of the urban pollution plume with typical concentrations between 1 and
2.5 µgm-3 inside the plume. When comparing the BC concentrations
measured at Welgegund to the model results at an equivalent location of
Welgegund situated downwind of the modeled main wind direction at the same
distance from the urban areas around Johannesburg and Pretoria as the
Welgegund site (not shown), model and measurements are in much better
agreement during the entire simulation period: in September, the modeled
mean BC concentration at the equivalent location is above 1 µgm-3, and around 0.5 µgm-3 in October, reducing the model
bias to values between -30 and -40 %. This further supports that the
modeled meteorology plays an important role in explaining the model bias of
the BC concentration at Welgegund.
In principle, the height and strength of inversion layers can also influence
the BC concentrations. A too-low number of inversion days in the model, i.e. an underestimation of days with stable atmospheric conditions, could result
in pollutants being too-well mixed and in concentrations being too small. A
too low inversion height in the model would increase the concentrations in
the boundary layer during the inversion events and might counteract some of
this. While the inversion height is captured quite well in the model, the
number of inversion days is probably underestimated. The scarcity of
radiosonde data in the fall of 2010 does not allow for a more detailed
analysis of the inversion height statistics and a comparison of the modeled
BC concentrations on days with and without inversion layers during the dry
season. However, inversion layers are not thought to play a dominant role
for the BC concentrations measured at Welgegund as the concentrations are
dominated by transport processes over at least 100 km to the station
allowing for ample mixing. This is supported by the finding that BC
concentrations at Welgegund do not show a distinct diurnal cycle in
September 2010 (not shown), which would be expected if the inversion played
a significant role.
In general, the modeled meteorology and the modeled BC time series agree
reasonably well with the observations at Welgegund during the dry season. A
major contribution to the lower correlation of modeled meteorology, as well
as the BC daily means with the Welgegund measurements during the wet season
is likely caused by the difficulty of the model in reproducing the observed
convection activity, which plays a major role particularly during the wet
season.
Emission inventories of energy-related emissions of BC for Africa have
rather large uncertainties (Bond et al., 2013). This certainly plays an
important role for the modeled BC concentrations, as the modeled
concentrations can only be as good as the emission data used as model input.
For example, day-to-day variations in BC concentrations due to the
variability of energy-related anthropogenic BC emissions cannot be
represented by the model as the emission inventory used (EDGAR HTAP) has a
time resolution of one month or less. However, the analysis in this study
suggests that the energy-related anthropogenic emissions are at least within
the correct order of magnitude, as further elaborated in Sect. 3.3.
Furthermore, the FINN biomass burning emission inventory with a time
resolution of one day seems to capture the biomass burning events relatively
well. This is suggested by the fairly good temporal correlation of the
modeled daily means of BC with the measurements during the dry season, as
biomass burning episodes play an important role for high levels of BC at
Welgegund during the dry season (Tiitta et al., 2014).
Black carbon at Welgegund is measured as equivalent BC. It is therefore
possible that BC is overestimated as additional non-BC absorbing material is
also classified as BC. Studies disagree on the exact amplitude of the
measurement uncertainty of the MAAP ranging from very little increase in
absorption due to non-absorbing coatings of BC particles (e.g. Lack et al.,
2012; Cappa et al., 2012, 2013) to a factor of two (e.g. Shiraiwa et al.,
2010; Wang et al., 2014). However, we do not believe that the measurement
uncertainty alone could explain a bias of 50 % in the dry season, but
rather a combination of the model deficiencies and uncertainties discussed
above.
Aerosol optical depth and particulate matter concentrationsAerosol optical depth
Compared to the MOderate Resolution Imaging Spectroradiometer (MODIS) (Remer
et al., 2005) satellite observations (MODIS Terra and Aqua monthly level-3
data, collection 5.1) of the aerosol optical depth (AOD), WRF-Chem captures
the main geographical pattern over southern Africa qualitatively correctly,
as exemplarily shown for September (Fig. 9),with high AOD values (larger
than 0.3) in the northwest of the model domain, where biomass burning is
strong, and a lower AOD in South Africa (mostly between 0.1 and 0.3).
Comparison of modeled AOD with MODIS satellite observations,
September 2010.
Especially in the northwest of the model domain over the ocean the model
results deviate strongly from the MODIS data (up to 90 %). The biases
could be caused by several reasons which make a quantitative comparison
difficult. In order to conduct a thorough quantitative evaluation of the
model results with the satellite data, the model would have to include
sampling of the data as seen from the satellite (e.g. taking into account
the cloud cover and the specific satellite overpass times). This could not
be done here. Furthermore, the uncertainty of the satellite data that can be
quite large particularly for large AOD values (Ruiz-Arias et al., 2013)
would have to be taken into account. This can also be seen in Fig. 9 showing
ground-based AOD measurements from the AERONET network for comparison.
We therefore also compare modeled monthly mean aerosol optical depths with
AERONET measurements at Skukuza, located in the Kruger National Park on the
eastern border of South Africa, and Elandsfontein, located in the
industrialized Highveld east of Johannesburg (Holben et al., 1998, see Fig. 1
for the location of the stations). Here, only measurements obtained under
cloud-free conditions are used. Daily mean AODs are not available for every
day from September 2010 to December 2010, with 10 missing days at
Elandsfontein in September, 14 missing days at Skukuza in each October and
November, and 19 missing days at Skukuza in December. The measured AODs at
500 and 675 nm are linearly interpolated to the AOD at 550 nm, which is
calculated by the model.
Monthly mean and mean bias of modeled and observed AOD, PM2.5
and PM10.
The mean AOD (Table 3) is higher at Elandsfontein, which is located closer
to anthropogenic aerosol sources, than at Skukuza. AOD is modeled reasonably
well at both stations and during most months. Measured (modeled) means at
Elandsfontein amount to ca. 0.31 (0.32) in September, 0.40 (0.54) in
October, 0.21 (0.35) in November and 0.15 (0.40) in December, and at Skukuza
to ca. 0.30 (0.25) in September, 0.32 (0.34) in October, 0.20 (0.21) in
November and 0.14 (0.14) in December. Overall, the comparisons of the model
results with the AERONET AOD show a reasonably good performance of WRF-Chem
in simulating the AOD at this location. The fairly good agreement of the
model results with measurements close to anthropogenic sources
(Elandsfontein) suggests that total energy-related anthropogenic aerosol
emissions are at least within the correct order of magnitude.
Particulate matter
The model results are further compared with the measurements conducted at
stations of the South African Weather Service (see Sect. 2) from September 2010 to December 2010, including Secunda (PM10 and PM2.5), Witbank
(PM10 and PM2.5) and Zamdela (PM2.5); as presented in Table 3
(see Fig. 1 for the location of the stations).
Averaged over the whole modeling period and all stations, WRF-Chem
underestimated PM10 by -26 % (observed: 58.42 µgm-3,
modeled: 43.50 µgm-3), and overestimated PM2.5 by 51 %
(observed: 27.02 µgm-3, modeled: 40.84 µgm-3). This
could indicate that the size-distribution of primary particles such as
mineral dust assumed in the model for the emissions of these particles might
be too small.
WRF-Chem underestimated the PM10 concentrations in September at Witbank
up to -66 %. It is biased positively in October (6 %), November (31 %)
and December (43 %). At Secunda, a slight negative bias is found during
all 4 months, from -32 % in September to only -1 % in November. The
PM2.5 concentrations are – given the large uncertainties and model
deficiencies as discussed for BC in Sect. 3.2.3, such as the low quality of
emission inventories – modeled reasonably well for September at all three
stations, with the modeled values biased for Witbank +28 % and Zamdela
+32 % and only biased by -3 % in Secunda. For October, November and
December the modeled concentrations at Secunda and Witbank are positively
biased, with both the modeled range of daily means and the median being
higher than the measurements. The bias is smaller at Zamdela, especially
during the wet season.
The biases might suggest that the different sources of PM might not be
represented correctly in the emission data, or that the assumed particle
size distributions are not representative for southern African conditions.
Gaseous species at Welgegund
To further assess the performance of WRF-Chem, results are compared with
measurements at Welgegund (Fig. 7) for ozone (O3), sulfur dioxide
(SO2), nitrogen oxides (NO+NO2= NOx) and carbon monoxide
(CO). The statistics including bias and temporal correlation coefficients
are summarized in Table 2.
WRF-Chem has a negative bias in CO (-31ppb/-15 % in September,
-14ppb/-13 % in December, -18 % overall) and ozone (-8ppb/-15 % in
September, -5ppb/-15 % in December, -19% overall). The modeled
SO2 is in good agreement with the measurements (bias -0.04ppb, -2 %)
in September, but overestimated in December (1.6 ppb, +160 %). Averaged
over the whole period the modeled bias in SO2 at Welgegund is
0.9 ppb
(65 %). Likewise, the modeled NOx is overestimated throughout the
entire simulation period with biases ranging from 1.7 ppb (56 %) in
September to 6.7 ppb (270 %) in December. The model bias of CO and
O3
is rather similar throughout all modeled months, while that of NOx and
SO2 is much higher in December (wet season) than in September (dry
season).
Particularly very high modeled NOx values in December are probably
related to very high emissions, which are higher than the maximum values
found in the EDGAR HTAP emission inventory over Europe. This supports that
emission inventories for Africa still have large uncertainties particularly
for individual species and source regions.
In addition, we have compared model results for NO2 (tropospheric
column) and CO (lowest model layer) with satellite data (not shown). These
qualitative comparisons show that the emission hotspots seem to be in the
right locations.
Summary and conclusions from the model evaluation
The evaluation of WRF-Chem with ground observations, satellite data and the
comparison to reanalysis and model data has highlighted some points that
need improvement but also showed that overall both meteorology, aerosols and
gaseous species are simulated reasonably well during the dry season, given
the large uncertainties in, for instance, the emission data or the lateral
boundary conditions as observations are generally very sparse in this
region. Concerning the meteorology, a bias in precipitation exists with
precipitation amounts being overestimated by the model particularly during
the wet season over the Indian Ocean between Madagascar and continental East
Africa as well as in the ITCZ. The comparison of the model with measurement
data obtained at the Welgegund measurement site confirms that precipitation
amounts are mostly overestimated and that the beginning of the rainy season
in the model is about 1 month too early (mid-September instead of
mid-October). Furthermore, the main modeled wind direction at Welgegund is
shifted towards the north which directly affects the modeled transport of
atmospheric pollutants from the Johannesburg-Pretoria area towards the
measurement station.
As for the modeled BC concentration at Welgegund, it is biased low in
comparison to the measurement data in the dry season. The main reasons for
this underestimation are likely the shift in main wind direction in the
model, as well as the modeled early beginning of the rainy season, likely
leading to enhanced wet deposition. Both of these shortcomings are expected
to result in less BC transported to the measurement station than shown by
the observations. This shows the importance of capturing the observed
meteorology with the model in addition to reasonable emission estimates. An
evaluation of a large-scale model with only a few available comprehensive
measurement stations is challenging and underlines the need for further
comprehensive monitoring sites in southern Africa. Especially the lack of
comprehensive measurement stations in the western part of South Africa makes
the model evaluation challenging. The effort of setting up further
monitoring sites is underway (see Sect. 5).
The reasonably good temporal correlation of the BC daily means time series
with measurements suggests that the biomass burning emissions, with a 1-day resolution, capture the biomass burning events reasonably well. The
comparison of measured AOD, PM10, PM2.5 with model results in
near-source regions further suggests that the total energy-related
anthropogenic aerosol emissions in these regions seem to be within the
correct order of magnitude. This might, however, not necessarily be true for
individual species such as NOx.
Overall, the qualitative reasonably good results as well as the
identification of plausible reasons for the low bias of modeled BC at
Welgegund suggest that the model setup is suitable for a first assessment of
the contribution of anthropogenic BC and co-emitted species to aerosol
concentrations on a regional scale and their impact on meteorology.
In addition to the above-discussed uncertainties in the model, model
parameterizations and model parameters such as assumed particle
size-distributions might not be well suited for application in this region.
We therefore consider the results of this study on the anthropogenic
contribution to BC concentrations in southern Africa as a very first and
rough estimate and as a potential basis for comparison with future studies
using improved models and better input data.
Contribution of anthropogenic BC sources to aerosol loadingsBlack CarbonNear-surface concentrations
The sensitivity run (S1) shows that anthropogenic emissions are the main
contributors to BC loadings in many parts of the southern Africa (Fig. 10a).
In September, anthropogenic BC contributes between 90 and 100 % to the
simulated BC loadings in the center of the industrialized and urban area
around Johannesburg and Pretoria, and the contribution is similarly high in
coastal areas (especially around Cape Town), which are generally more
populated than the south-western inland areas. In these coastal regions,
savannah fires do not contribute significantly to the BC concentrations
compared with the northern part of the subcontinent. At Welgegund, the share
of anthropogenic BC in September ranges between 80 and 90 % (i.e. up
to 5 µgm-3 in September and up to 2.5 µgm-3 in
December). Energy-related anthropogenic emissions do not seem to play a
large role in areas with strong biomass burning, where the share of BC
concentrations caused by total anthropogenic emissions (60–70 %) is
in the same range as the assumed fraction of anthropogenic biomass burning
emissions (65 %). In December the anthropogenic portion of BC is up to
100 % in an area covering most parts of eastern South Africa and Zimbabwe,
including Welgegund. In coastal areas the results for December are similar
to the September result.
(a) Contribution of anthropogenic BC sources to BC
concentrations, (b) contribution of anthropogenic BC sources to AOD (left:
contribution of anthropogenic BC only, right: contribution of anthropogenic
BC and co-emitted aerosols). For (b), the model results have been interpolated
to a lon-lat-grid of 0.2∘×0.2∘, and only grid cells
statistically significant at a confidence level of 95 % are shown.
Vertical BC distribution (a), anthropogenic contribution to BC
concentrations (b) and contribution of anthropogenic BC to atmospheric
heating rates (c). All figures show the monthly mean results for September 2010.
Vertical distribution
The mean BC differences in September are analyzed further at two latitudinal
cross sections displaying the vertical profile of BC (Fig. 11): a
northern cross section averaged over the latitudes 14.25 to
12.75∘ S, and a southern cross section averaged over
27.25 to 25.75∘ S. In order to reduce the noise, the
data have, in addition to the monthly averaging, also been binned into
45-km
bins consisting of three grid cells in the longitudinal direction. The vertical
coordinate (pressure) is divided into 12 bins, each 50 hPa, the averaging
time is 1 month. The northern cross section covers areas with strong
biomass-burning emissions, and the southern cross section includes the
Johannesburg-Pretoria megacity.
The cross sections show distinct differences between the two source regions
of BC (Fig. 11a): in the northern domain, high BC concentrations of up to
0.5 µgm-3 are found up to ca. 500 hPa. It can be seen further
that on average the plume does not rise much higher than 500 hPa, but is
then rather transported out onto the South Atlantic Ocean. This is
consistent with a persistent stably stratified 500-hPa layer described by
Tyson and Preston-Whyte (2000). The cross section over the industrialized
Highveld shows a different picture: the anthropogenic BC contribution
decreases more rapidly with height, and the highest concentrations are found
near the surface (the elevation of Pretoria is about 1300 m a.s.l.).
The anthropogenic contribution to BC concentrations ranges up to 90 % in
the urban core of the southern cross section (Fig. 11b). In particular in
this highly industrialized area the anthropogenic contribution to BC
loadings is large and dominates the modeled concentrations. This is
important when assessing for instance the health impact of (anthropogenic)
BC. The share of anthropogenic BC ranges between 60 and 70 % in the
biomass-burning area, both at the surface and at higher layers.
Estimated contribution of biomass burning emissions to BC
concentrations in the Johannesburg-Pretoria urban area and at Welgegund.
Biomass burning vs. energy-related emissions
The total share of BC from biomass-burning emissions (natural and
anthropogenic) is estimated from the sensitivity run S1 by scaling up the
modeled BC concentrations by a factor of 100 %/35 % = 2.86. The scaled
concentrations are then compared to the reference run (RR). In the urban and
industrialized areas (averaged over 1.5∘×1.5∘ around
the metropolitan area of Johannesburg and Pretoria), the estimated
contribution of biomass-burning emissions to the total near-surface BC
concentrations amounts up to 62 % (Fig. 12), but with much lower average
values (25 % in September, 16 % in October, 5 % in November, 4 % in
December). The model results further suggest that the contribution of
biomass burning to the total BC is much higher at Welgegund, with monthly
averages of 57 % in September, 44 % in October, 16 % in November and
10 % in December, confirming the findings of Tiitta et al. (2014) that
biomass burning plays an important role for the BC levels observed at
Welgegund during the dry season.
Particulate matter and aerosol optical depth
BC particles are usually in the sub-micron size range (e.g., Petzold et al.,
2005; Schwarz et al., 2008; Kondo et al., 2011) contributing only little to
PM2.5 and PM10 as these are often dominated by other particle
types. In the following, we therefore focus on the contribution of BC to
PM1.
In September, the PM1 concentration modeled with WRF-Chem (not shown)
reaches peak values of up to 55 µgm-3 around Johannesburg and
Pretoria, and up to 30 µgm-3 in areas of highest biomass-burning
emissions. In the northern part of the continent and in the surroundings of
Johannesburg and Pretoria, modeled concentrations range mostly in between 10
and 15 µgm-3. In December the modeled PM1 concentrations
are highest around Johannesburg and Pretoria with values up to 30 µgm-3.
In the northern areas dominated by biomass burning the contribution of
anthropogenic BC to the modeled (near-surface) PM1 concentration in
September ranges between 5 and 7.5 % with some spatial variations. The
contribution of the modeled anthropogenic BC to PM1 ranges up to 10
to 15 % in the surroundings of the Johannesburg-Pretoria area and between
7.5 and 10 % at Welgegund. Averaged over the whole urban area around
Johannesburg and Pretoria, the mean contribution of anthropogenic BC to
PM1 in September amounts to 6 %.
The measured contribution of (total) BC to PM1 at Welgegund is 13 %
(average over one year, Tiitta et al., 2014). This value is not directly
comparable to the model result for September but within a similar range as
that calculated by the model for anthropogenic BC (10 %). Despite the
underestimation of the absolute BC concentrations by the model both model
results and measurements suggest that anthropogenic BC is an important
contributor to PM1. This is especially important when assessing the
health effect of PM.
When also accounting for the co-emitted species (OC and SO2), the
modeled contribution of both BC and co-emitted species to PM1 in
September is highest around Johannesburg and Pretoria amounting up to
60 %, underlining the importance of co-emitted species when assessing the
contribution of anthropogenic BC sources to PM1 concentrations.
The difference in AOD between the reference run and the sensitivity
simulation S1 (Fig. 10b) shows a similar spatial variability in September as
PM1. Only those grid cells with the differences significantly different
from 0 at a 95 % confidence level are shown. The differences in AOD range
between 5 and 10 % in both areas of strong biomass burning and over
the industrialized Highveld. In December, the differences in AOD are not
statistically significant and longer model integrations would be needed to
improve the signal-to-noise ratio.
When also accounting for co-emitted species (S2) the contribution to AOD
ranges up to 0.3 (50 %) in the northwest of the model domain. The
contribution decreases towards the southeast, with an island of high
contribution to AOD near Pretoria and Johannesburg (up to 0.15). In
December, the total anthropogenic contribution of BC and co-emitted species
to AOD is highest over Johannesburg and Pretoria with a maximum of up to 0.3.
The increase in AOD results in a reduction of incoming solar radiation at
the surface or surface dimming (not shown), but the integration time was too
short for the results to be statistically significant. This result is,
however, consistent with what has been reported in the literature (e.g. Tummon et al., 2010).
Contribution of anthropogenic BC to atmospheric heating rates
The vertical heating rate difference (ΔHR, only the shortwave
contribution is considered here) is assessed for September along the same
cross sections discussed above (Fig. 11c). Atmospheric heating by
anthropogenic BC in the model reaches up to ca. 0.7 Kday-1 around 600 hPa in
the northern cross section and near the surface around Johannesburg and
Pretoria in the southern cross section. The strongest change in heating
rates are spread vertically up to 600 hPa in the northern cross section,
because emissions from biomass burning are efficiently transported to these
heights in the model. In contrast, the maximum changes in heating rates
caused by anthropogenic BC in the southern cross section are found close to
the surface over the industrialized Highveld.
These model results suggest that OC and SO2 co-emitted with
anthropogenic BC play only a minor role for the heating rates compared with
BC itself. The peak values in heating rates caused by anthropogenic BC and
co-emissions (S2) amount to about 0.7 Kday-1, which are in the same range as
modeled for anthropogenic BC alone (S1).
Conclusions
This study presents and evaluates a model setup for studying air chemistry
and aerosol processes and their impacts in southern Africa. In addition, a
consistency check on the emission input data is done by comparing PM
measurements with the model results in urban regions that are expected to be
dominated by anthropogenic emissions. It then assesses the contribution of
anthropogenic BC and co-emitted species to aerosol concentrations (BC and
PM1) and to aerosol optical depth, and assesses the impact of BC
sources on atmospheric heating rates.
The evaluation of the WRF-Chem model applied over southern Africa shows that
the main features of the meteorology such as temperature and sea-level
pressure are modeled reasonably well, but some parameters, such as
precipitation, are more problematic. Precipitation is very challenging to
model: for example, Crétat et al. (2011) show that WRF has difficulties
in reproducing observed precipitation amounts and patterns over southern
Africa for a variety of different physics options.
Black carbon monthly mean concentrations are underestimated at Welgegund in
September and October by ca. 50 %. Reasons contributing to the
underestimation during the dry season are likely the biases in the modeled
meteorological variables resulting in less than observed BC transported from
the industrial source regions to Welgegund. These are in particular the
shifted wind direction in September, the early beginning of the rainy season
in the model in October and the overestimation of precipitation amounts. The
importance of the wind direction bias for the BC bias is supported by the
fact that the amplitude of the modeled monthly mean BC concentration is
closer to the measured means when comparing to the results for an
equivalent location (same distance from the urban and industrialized
areas of Johannesburg and Pretoria as Welgegund but downwind of the modeled
main wind direction). In November and in December the monthly mean BC
concentrations at Welgegund are small and in reasonably good agreement with
the measurements.
Besides the modeled meteorology, the high uncertainties in the emission
inventories, the choice of chemical boundary conditions or uncertainties and
limitations in the representations of important processes in the model (e.g.
the particle size distribution, the parametrization of convection or the
boundary layer) are likely to contribute significantly to the model biases
in BC concentrations.
The modeled BC concentrations at Welgegund have a temporal correlation with
measurements of 0.62 and 0.67 in September and October, respectively. This
reasonable correlation can be attributed to the well-modeled day-to-day
variability of the meteorology. This also suggests that the temporal
resolution and pattern of the biomass burning emissions, which contribute
significantly to the total BC at Welgegund, are a reasonable estimate of the
real biomass burning emissions.
The comparison of the model results for AOD, PM2.5, and PM10 with
AERONET data and observations in the industrialized Highveld and Vaal
triangle region, as well as the model qualitatively capturing the
geographical pattern of the AOD retrieved from MODIS satellite data,
suggests that the magnitudes of the energy-related anthropogenic aerosol
emissions used here (EDGAR HTAP) are, despite the generally low quality of
emissions inventories for South Africa, a reasonable first estimate of the
emissions. This is, however, not necessarily true for individual species
such as NOx or other source regions.
Two large source regions for anthropogenic BC are the industrialized and
urban areas on the South African Highveld around Johannesburg and Pretoria
(including the Mpumalanga industrial Highveld and the Vaal Triangle), and
areas of large-scale biomass burning in the dry season. These are also the
areas where the modeled BC concentrations are the highest on the
subcontinent. These concentrations are strongly influenced by anthropogenic
BC sources contributing up to 100 % in the industrialized and urban areas
around Johannesburg and Pretoria both during the dry and the wet season.
Biomass burning BC contributes only little to the total BC modeled in the
area around Pretoria and Johannesburg but contributions increase
significantly towards the outskirts of the area, e.g. at Welgegund.
An analysis of the atmospheric heating rates shows a slight surface heating
over the industrialized South African Highveld (S1) that might be largely
canceled by cooling due to surface dimming caused mainly by co-emitted
species (S2). Black carbon from biomass burning at higher layers of the
atmosphere leads to increased atmospheric heating rates and local warming in
the lower and middle troposphere, possibly impacting the atmospheric
stability in this region.
The high computational cost of WRF-Chem at 15 km resolution over the entire
southern African subcontinent did not allow for long-term simulations.
Instead, it was chosen to simulate only 4 months from September 2010 to
December 2010, which include parts of the dry season, the wet season as well
as the transition period. Because of the strong convection during the wet
season, the signal-to-noise ratio of the model results is low particularly
in December. Integrating over longer time intervals, spanning several
seasons or years, would help increase the signal-to-noise ratio, thus
increasing the robustness of the obtained results. Possible refinements of
the model include a more detailed specification of the particle size
distributions used for the emissions, ideally based on long-term
measurements of the aerosol size-distribution in different source regions
(natural and anthropogenic aerosols). Furthermore, future studies could
assess whether a nudging to meteorological observational/reanalysis data
would improve the model results, or the usage of urban parametrizations for
improving the results for urban areas. The latter would, however, most
likely require changing the urban scheme's parameters, as these schemes have
not been developed for African cities.
For deepening the analysis of the different impacts of anthropogenic BC it
is crucial to have good observational data, e.g. for BC and particulate
matter, vertical profiles of temperature and humidity. Furthermore, long-term measurements of BC and other aerosols are not only crucial for
improving aerosol and chemistry modeling over southern Africa, but also
serve for monitoring air quality and assessing air quality management plans
and further identifying main sources within the different source categories.
So far, only few measurements existed especially of fine particulate matter
and BC. However, further monitoring of BC concentrations has recently been
initiated: in 2012, continuous BC measurements using the MAAP have commenced
at the Secunda, Witbank and Zamdela stations. The network expanded (August 2013)
to include additional monitoring stations in the Vaal Triangle
operated by the South African Weather Service (SAWS). In addition,
aethalometers have been installed in SAWS owned stations in the Karoo and
Bojanala areas (September 2014).
Reliable emission inventories with a high temporal and spatial resolution
are important in order to improve the modeling of aerosols and air
chemistry. More research efforts are needed to create such inventories. For
example, further model simulations could aim at analyzing contributions of
individual source categories to the modeled BC concentrations. For this,
tagging of the individual emitted species would be needed. There is also a
need for weekly and diurnal emission profiles as well as the vertical
distribution including, for instance, the stack height of important point
sources, or the contribution of the different sources within one source
category. This is particularly the case for the Pretoria and Johannesburg
area, and the industrialized areas in the Mpumalanga Highveld and the Vaal
Triangle, where further growth is expected and air pollution is already a
recognized problem.
Acknowledgements
We acknowledge the UK Met Office for the supply of its Global Radiosonde
Data through the British Atmospheric Data Centre. The MODIS AOD data used in
this study were produced with the Giovanni online data system, developed and
maintained by the NASA GES DISC. All model simulations with WRF-Chem were
performed at the high performance cluster computer of the Potsdam Institute
for Climate Impact Research (PIK). For creating figures, the open-source
software R and the openair-package (Carslaw, 2014; Carslaw and Ropkins,
2012) have been used.
Edited by: V.-M. Kerminen
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