ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-5131-2017The Climatology of Australian AerosolMitchellRoss M.ross.mitchell@csiro.auForganBruce W.CampbellSusan K.CSIRO Oceans and Atmosphere, Yarralumla, GPO Box 1700, Canberra, ACT, AustraliaAustralian Bureau of Meteorology, P.O. Box 1289K, Melbourne, AustraliaRoss M. Mitchell (ross.mitchell@csiro.au)20April20171785131515416November201616January201717March201720March2017This 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/17/5131/2017/acp-17-5131-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/5131/2017/acp-17-5131-2017.pdf
Airborne particles or aerosols have long been recognised for their major
contribution to uncertainty in climate change. In addition, aerosol amounts
must be known for accurate atmospheric correction of remotely sensed images,
and are required to accurately gauge the available solar resource. However,
despite great advances in surface networks and satellite retrievals over
recent years, long-term continental-scale aerosol data sets are lacking. Here
we present an aerosol assessment over Australia based on combined sun
photometer measurements from the Bureau of Meteorology Radiation Network and
CSIRO/AeroSpan. The measurements are continental in coverage, comprising 22
stations, and generally decadal in timescale, totalling 207 station-years.
Monthly climatologies are given at all stations. Spectral decomposition shows
that the time series can be represented as a weighted sum of sinusoids with
periods of 12, 6 and 4 months, corresponding to the annual cycle and its
second and third harmonics. Their relative amplitudes and phase relationships
lead to sawtooth-like waveforms sharply rising to an austral spring peak,
with a slower decline often including a secondary peak during the summer. The
amplitude and phase of these periodic components show significant regional
change across the continent. Fits based on this harmonic analysis are used to
separate the periodic and episodic components of the aerosol time series. An
exploratory classification of the aerosol types is undertaken based on
(a) the relative periodic amplitudes of the Ångström exponent and
aerosol optical depth, (b) the relative amplitudes of the 6- and 4-month
harmonic components of the aerosol optical depth, and (c) the ratio of
episodic to periodic variation in aerosol optical depth. It is shown that
Australian aerosol can be broadly grouped into three classes: tropical, arid
and temperate. Statistically significant decadal trends are found at 4 of the
22 stations. Despite the apparently small associated declining trends in
mid-visible aerosol optical depth of between 0.001 and 0.002 per year, these
trends are much larger than those projected to occur due to declining
emissions of anthropogenic aerosols from the Northern Hemisphere. There is
remarkable long-range coherence in the aerosol cycle across the continent,
suggesting broadly similar source characteristics, including a possible role
for intercontinental transport of biomass burning aerosol.
Introduction
Suspended atmospheric particles or aerosols are extensively studied because
of their multiple environmental roles, and because – unlike well-mixed gases
– their concentration and properties vary widely over space and time.
Aerosols adversely affect human health (particularly in urban areas) and
exert a further range of impacts due to their modulation of electromagnetic
radiation within the atmosphere. Scattering and absorption by aerosol remains
a major source of uncertainty in climate projections
, while satellite images of the Earth's
surface require correction for aerosol effects. In addition, aerosol data are
also required for the accurate assessment and forecasting of available solar
energy e.g..
Major enhancements in surface networks and satellite retrievals over the last
15 years have radically boosted knowledge of global aerosol. In particular,
surface networks including AERONET , SKYNET
and
GAW/PFR have provided regional aerosol
climatologies and supported process studies
e.g., while several major satellite programmes
have championed the characterisation of aerosols from orbit. Of note in this
respect are NASA's flagship EOS A-train instruments MODIS
, MISR
and CALIPSO
. In addition, the ATSR series of
instruments operated by ESA has made a significant contribution to satellite
aerosol measurement from space, particularly the AATSR instrument launched on
the ENVISAT platform in March 2002
. However,
retrieval of aerosol from satellites remains challenging
and requires ongoing
availability of validation data from surface networks. In addition to these
more recent sensors, the 30-year record available from the NOAA/AVHRR series
is proving valuable, as shown by the recent examination of the aerosol
indirect effect of maritime aerosol on cloud properties
.
Since aerosol emission is highly correlated with human population and land
mass, there has been a striking Northern Hemisphere bias in aerosol
characterisation efforts to date. However, there is a growing need for
improved characterisation of Southern Hemisphere aerosol. In particular, with
lower levels of anthropogenic aerosol (AA), the Southern Hemisphere is on
average less subject to “masking” of the greenhouse warming than the
Northern Hemisphere . Consequently, low-AA
regions including Australia experience the greenhouse gas component of
warming with less moderation from aerosol radiative forcing, to a degree
dependent on improved knowledge of Australian natural aerosol.
sought to quantify the unmasking implied
by projected future decreases in AA, and their findings will be further
considered below. Furthermore, recently
showed that the contribution of each hemisphere to the planetary albedo
averaged over the last 13 years has been in near-perfect balance, with the
increased reflection from Northern Hemisphere land exactly balanced by
increased atmospheric scattering in the Southern Hemisphere. While the full
ramifications of this finding are yet to be fully explored, there is a
pressing need to better characterise Southern Hemisphere aerosol emissions.
Australia is the largest dust source in the Southern Hemisphere
and is a significant source of
smoke aerosol from savanna burning .
Hence, it is clear that Australia's aerosol emissions make up a significant
fraction of the Southern Hemisphere total. Since aerosol contributes to the
planetary albedo both directly and indirectly through the modulation of
cloud properties, the need for improved continental-scale characterisation of
Australian aerosol is clear.
Studies of Australian aerosol to date have often been restricted in time and
space. While coastal maritime aerosol has been extensively studied
, studies
of the continental aerosol are more restricted. Thus,
characterised the aerosol cycle caused by
seasonal biomass burning at Broome in the tropical north-west of Australia in
the late 1980s, with more extended analysis of regional tropical aerosol
carried out by ; see also references
cited therein, including . Studies of the
smoke from eucalypt fires in southern temperate forests are few in number,
but include and
.
Australian dust emission has been well studied from a geomorphological
perspective
e.g. but
less so via sun photometry, where the measurand – column aerosol optical
depth – is the required input for climate and remote sensing studies.
However, analysed sun photometer and
nephelometer measurements from Tinga Tingana in the Australian arid zone
during the Millennium Drought (2002–2010) and found an approximate doubling
in both column and near-surface aerosol during the austral summer over the
duration of the drought. Feedbacks related to dust aerosol were modelled by
, who found that inclusion of
interactive dust in the model amplifies the impact of the El Niño–Southern Oscillation cycle on
Australian climate.
While satellite retrieval of aerosol over Australia is promising in terms of
the extensive areal and temporal coverage available over a sparsely populated
continent, the dense dark vegetation (DDV) assumption inherent in the MODIS
aerosol retrieval algorithm does not apply well over most of Australia
.
Problems in other sensors and retrieval methods are discussed by
, who show that the dual-view scan geometry
of the AATSR instrument offers particular advantages under Australian
conditions. However, the ongoing need for validation data from surface
networks is universal.
Details of the 22 stations considered in this study.
Trends in aerosol loading over the period 1995–2009 were recently compiled
by at 15 AERONET stations. They showed
that fine-mode anthropogenic aerosol is decreasing over those countries with
strict environmental regulations, and vice versa. Coarse-mode dominant
aerosol depends strongly on meteorological conditions subject to climate
change, with stations close to regions of increasing desertification showing
a significant increase in coarse-mode dominant aerosol.
In this paper we present a continent-wide climatology of Australian aerosol
obtained at 22 stations, spanning all mainland states, and comprising a total
of 207 station-years. The stations are operated by two agencies, the
Australian Bureau of Meteorology and the Commonwealth Scientific and
Industrial Research Organisation (CSIRO). The CSIRO stations are affiliated
with NASA's worldwide AERONET sun photometer system, as further described
below. Section 2 describes the two measurement systems and the data
processing applied to each. The result of this step is a time series of
multi-spectral monthly mean aerosol optical depth. Section 3 describes the
common analysis applied to these time series. Section 4 presents a discussion
of the results, with conclusions given in Sect. 5. The Appendix presents the
monthly aerosol climatology at each of the 22 stations in tabular form.
Observations
This study is based on aerosol measurements obtained at 22 stations across
the Australian continent between July 1997 and June 2015. The details of the
22 stations are given in Table . The stations are listed in latitudinal order, starting in the tropics. The columns headed
“First” and “Last” denote the extremities of the data record for a given
station, but not its continuity. The right-hand column labelled “Months”
gives the number of months for which sufficient observations were made to
generate a valid monthly mean. A given month is deemed to have a valid
monthly mean based on (a) the number of days in the month on which there were
sufficient (≥ 8) point measurements of aerosol optical depth to report
a daily mean (NumDayOK), and the total number of point aerosol optical depth
measurements in the month (NumObsMonth). The criterion applied in this study
was either (NumDayOK ≥ 8 & NumObsOK > 300) or
(NumDayOK ≥ 6 & NumObsOK > 600). The extent of spatial and
temporal coverage is displayed in Fig. , where the radius
of the filled circles indicates the number of months reported by each station.
Location of sun photometer stations in Australia. Bureau of
Meteorology stations are shown in blue, and CSIRO/AeroSpan stations in grey.
The radius of the filled circles scales with the duration of the data record at
a given station. The filled circles shown in the legend are for a duration of
5 years.
The measurements were obtained from sun photometers operated by the Bureau of
Meteorology (abbreviated as “Bureau” in Table and later
in the text) and CSIRO. The Bureau of Meteorology uses Middleton Solar SPO2
four-channel radiometers, with initially one and currently two radiometers at
each site including nominal wavelengths of 368, 412, 500, 610, 778, 812 and
868 nm. The 500 nm channel is common to all radiometers and aids in cross
calibration. The 610 nm channel is used with the 500 and 778 nm channels to provide
an initial estimate of total column ozone, and the 812 nm channel is used to
determine column water vapour. The full width at half maximum (FWHM) for the filters is typically 10 nm,
and an additional infrared blocking filter is used for the 368 nm channel to
eliminate any infrared transmission given the low spectral irradiance at
368 nm.
The sun photometers operated by CSIRO are Cimel CE-318. The network is
collectively known as AeroSpan (Aerosol characterisation via Sun Photometry:
Australian Network), which forms the Australian component of AERONET, a
worldwide sun photometer network operated by NASA
, with contributing sub-networks from several
countries outside the USA. The spectral channels used on the Cimel instrument
have become standardised and now include 340, 380, 440, 500, 670, 870, 1020
and 1640 nm, with a further channel at 936 nm yielding information on water
vapour absorption. The channels between 340 and 1020 nm employ a silicon
detector, while the 1640 nm channel uses an indium gallium arsenide (InGaAs)
detector. FWHM was likewise 10 nm for all channels.
Calibration of sun photometers refers to methods used to determine the
exoatmospheric response of the instrument to the solar irradiance in a given
spectral channel. This is a crucial procedure since, once complete, the ratio
between the actual instrument response and the exoatmospheric response gives
the total atmospheric extinction, from which the aerosol extinction can be
derived given knowledge of the gaseous absorption and scattering.
The use of different radiometers, wavelengths, and sampling strategies by the
two networks introduces traceability issues when amalgamating the data
series. In principle, the same calibration methodologies are used for in situ
calibration as described in a detailed inter-calibration study of the two
systems by at the Alice Springs
station in 2000. The central result of the study was
that the aerosol optical depths agreed to within 0.007 at the
95 % confidence level for the two common wavelengths 500
and 868 nm, well within the requirement. In addition Bureau participation in the four WMO Filter Radiometer Comparisons held at the World Radiation Center in Davos, Switzerland, every 5 years beginning in 2000, confirms that the Bureau of Meteorology methodologies produce aerosol optical depth within the WMO uncertainty requirement.
Both networks utilise a combination of Langley methods to find a reference
wavelength calibration and then apply the general method developed by
to calibrate the other wavelength channels
(excluding the channels affected by water vapour). The general method assumes
the stability of the relative aerosol size distribution – but not the
magnitude of the optical depth – throughout a morning or afternoon and uses
the reference wavelength aerosol optical thickness as the predictor for the
least squares solution of the wavelength calibration value at the top of the
atmosphere. As a Langley-type method involves an interpolation to the top of
the atmosphere in real atmospheric conditions, the selection of points not
impacted by water or ice particles is key to the uncertainty of the
top-of-the-atmosphere value. The two networks use different sampling strategies and
methods to select points for the calibration process. The operational
measurements are used to provide the in situ calibrations.
The Bureau of Meteorology radiometers are co-located at solar and terrestrial
irradiance stations that utilise the WMO Global Climate Observing System (or
Baseline Surface Radiation Network) solar monitoring methodologies
. The solar trackers employ active tracking
sensors that mimic a 868 nm radiometer but with a wide field of view, and
sample at 1 Hz. The standard deviation of the active tracking sensor is
recorded every minute with the 0.12 s samples from the SPO2 radiometers. If
the active sensor was not available the standard deviation of the direct
irradiance from a pyrheliometer is used as an equivalent indicator of
variability in the minute prior to the SPO2 measurement. The first pass of
the selection process of data to use for SPO2 calibration is to eliminate the
near-zero SPO2 signals that are less than 5 times the measurement resolution
(typically 0.005 V), as well as those periods when the standard deviation of the
active sensor signal is more than 0.005 V. As the 868 nm active sensor
signals in clear sun in typical Australian conditions is within 25 % of
the solar noon value for aerosol air masses less than 6, use of a fixed
standard deviation is sufficient. The remaining data for each wavelength are
then subject to the algorithm.
If sufficient data remain, namely at least 30 points for aerosol air masses
less than 6 and greater than 2, the calibration process is applied and the
result recorded.
The AeroSpan measurements are based on a “triplet” of the routine
measurements obtained 30 s apart, spanning a minute. The standard deviation
among the three measurements comprising the triplet is used for analogous
filtering, as described in more detail by
.
For both networks, determinations of the natural logarithm of the top of
atmosphere signal (lnV0) at 1 AU Earth–Sun distance are compiled during
the deployment period, which may range from months to over a year. The time
series of the lnV0 is then used to derive the time dependence of the
lnV0 through linear interpolation using a single slope where possible,
or a series of linear functions if the calibration is changing in a
non-linear manner. For some wavelengths of some radiometers the non-linear
changes were rapid and difficult to model; these periods have been eliminated
from the data analysis. The calibration functions for each wavelength
providing a top of atmosphere value for each day are used in the derivation
of optical depth for the entire measurement data set, not just the points
selected for the calibration process.
Determination of aerosol optical depth requires subtraction of all relevant
molecular extinction. For the present “aerosol” channels (i.e. not
including the 812/936 nm water vapour channels), these are nitrogen dioxide
and ozone. The correction for NO2 is small (typically <0.002 at
380–440 nm and <0.001 at 500 nm;
) and was excluded from the
present analysis; however, ozone absorption is more significant. The total
column ozone determined from the Total Ozone Monitoring Satellite (TOMS)
measurements was used in this analysis. In particular, TOMS-EP (Earth Probe)
was used from 1996 to September 2004, while TOMS-OMI (Ozone Monitoring
Instrument) was used from October 2004 to present. Currently, OMI satellite
level 3e daily averaged ozone data gridded at
0.25∘× 0.25∘ are obtained from the NASA Goddard
Earth Sciences Data and Information Services Center Website. The ozone value
used was based on the satellite data point nearest to the station
coordinates.
For the elimination of cloud-affected aerosol optical depth measurements the
two networks use different methods. In AeroSpan the filtering is similar to
that described by used by
AERONET. This first evaluates the short-term temporal variation based on
triplets, then applies a further test based on the daily standard deviation
of the aerosol optical depth. For the Bureau data the method of
is used after the elimination of
signals less than 5 times the measurement resolution. This applies a moving
temporal window of 15 min in span across a time series examining the
variance in the initial estimate of the aerosol component of the optical
depth.
Calculation of Ångström exponent
The Ångström exponent α captures the spectral slope of the aerosol optical depth, and is defined as
α=-ln(τ2/τ1)ln(λ2/λ1),
where τ is the spectral aerosol optical depth and α is defined
across the wavelength range (λ1-λ2). For the AeroSpan
stations, the Ångström exponent was defined using the wavelength pair
(440–870 nm), in part because these channels have always been present on
the CE-318 instruments. For the Bureau stations, the Ångström
exponents were calculated using the 500 nm wavelength and the 868 or 870 nm
channel when available. For the Bureau data sets prior to 2009, 868 nm was
only available from the active sensor, leading to an underestimate of the
true optical depth due to the large opening angle. A comparison of the Bureau
Ångström exponents using 500–778 and 500–868 was possible when the
868 nm channel was added in the last decade of measurements. The differences
in the Ångström exponents from the two different wavelength pairs
were more than a factor of 4 less than the estimated standard uncertainties
from the combined type A and type B uncertainties in the
monthly mean Ångström exponent. As a result, in the period before
2009, when 868 nm data were only available from the active sensor at Bureau
sites, the 500–778 nm pair was used to calculate the Ångström
exponent statistics reported below. A similar rationale supports the
comparability of the AeroSpan and Bureau Ångström exponents, even
though the basis wavelength pairs differ.
For the majority of the 22 stations, the annual mean aerosol optical depth at
the 500 nm wavelength is less than 0.06, and at 868 nm less than 0.03.
Hence, even with the low uncertainties (∼ 0.007) for the individual
aerosol optical depth measurements, the uncertainties in the individual
Ångström exponent measurements can be relatively large (> 0.2).
The monthly statistics of Ångström slopes based on individual values
was compared to the monthly mean wavelength pair gradients derived from the
general method calibrations. The differences in monthly means were typically
less than 0.1 except for those months where the mean aerosol optical depth at
500 nm was less than 0.020. The comparison of the Ångström exponent
methods of measurement and subsequent statistics is the subject of future
work.
Analysis
In this section we introduce the data sets, then define the model fitting
procedure used as a basis for the analysis. This allows separation of the
observed aerosol times series into a periodic component, with the residual
interpreted as an episodic component. This is used as a basis for
classification of the aerosol across the continent in terms of (a) the
periodic amplitudes found for the Ångström exponent (a measure of
aerosol size) and aerosol optical depth, (b) the harmonic content of the
periodic component of the aerosol optical depth, and (c) the balance between
the episodic and periodic components of the aerosol optical depth.
(a) Time series of monthly mean aerosol optical depth at
500 nm at Lake Argyle. The raw data are shown in black, the (repeated)
climatology in red, the interpolated data in blue (see text), and the model
fit in green. (b) The power spectrum of the interpolated
time series computed using a fast Fourier transform (FFT).
Monthly time series
Since the object of this work is to define the monthly climatology of
Australian aerosol, aerosol time series were preprocessed into monthly means
for subsequent analysis. The analysis involved (a) model fitting (as
described below) and (b) spectral decomposition using a fast Fourier
transform (FFT). Due to instrument malfunction and occasional removal for
calibration, these time series contain infrequent gaps. The model fitting was
carried out using all available data, simply ignoring periods when no data
were available. However, the FFT required a complete time series, so
interpolation for missing data was required. This was carried out using a
combination of spline fitting and fits based on Kalman filtering, as
provided in the R package. Testing showed that the interpolation had no
significant effect on the Fourier analysis, which was employed only to assess
the spectral content of the aerosol time series.
(a) Time series of monthly mean aerosol optical depth at
500 nm at Alice Springs. The raw data are shown in black, the (repeated)
climatology in red, the interpolated data in blue (see text), and the model
fit in green. (b) The power spectrum of the interpolated
time series computed using a fast Fourier transform (FFT).
(a) Time series of monthly mean aerosol optical depth at
500 nm at Wagga Wagga. The raw data are shown in black, the (repeated)
climatology in red, the interpolated data in blue (see text), and the model
fit in green. (b) The power spectrum of the interpolated
time series computed using a fast Fourier transform (FFT).
Examples of the time series are shown in Figs. ,
and . The left-hand panels show
the monthly time series (black), together with the (annually repeated)
climatology (red), and the fit (green; see below). Interpolated periods are
shown in blue. The right-hand panels show the power spectrum obtained from
the FFT. From the latter, the dominance of the annual cycle is clear, with
significant contributions at the 6- and 4-month periods, varying between
sites. This general pattern was evident at all sites. The increasing
complexity of the power spectrum at Wagga Wagga (Fig. )
arises due to the high incidence of episodic smoke plumes from forest fires
in the south-eastern eucalypt forests. Other stations – not shown here –
exhibit similar characteristics, supporting the analysis outlined here.
Model fitting
The equation describing the model fit is as follows:
f(t)=a0+a1t+∑j=1Jbjcos[2πωj(t-tj*)],
where a0 is the time-invariant offset of the fit to the (dimensionless)
aerosol optical depth time series, and a1 is the linear coefficient with
units of inverse time. The following term represents the sum of J
sinusoidal components, with amplitude bj and frequency ωj. The
phases of the sinusoidal components are encapsulated by tj*, which is the
time in months corresponding to the first maximum in the cosine function
following the zero-time reference for the jth harmonic component, referred
to below as the phase time. The zero-time reference was chosen as the first
December in the time series at each station. This aids the pictorial representation of the phase angles of the harmonic components, with the months of the phase time corresponding to the angle from due north. Hence phase times of December, March, June and September are depicted by angles of 0, 90, 180 and 270∘ respectively (see Fig. in Sect. ). Guided by the
identification of the annual cycle and its second and third harmonics in the
FFT analysis discussed above, three sinusoidal terms were employed (J=3),
with periods of 12, 6 and 4 months corresponding to frequencies ω of
1/12, 1/6 and 1/4 cycles per month, respectively.
An example of the Fourier decomposition of one of the aerosol time series –
in this case from Tinga Tingana – is shown in Fig. . The
sinusoidal components are shown in the lower panel, with their respective
phase times marked by arrows. Their sum (together with the offset and linear
term further discussed below) results in the fit depicted by the orange line,
representing the optimal fit to the data (shown here by the climatology in
black) derived using a least-squares optimisation provided within the R
language. Clearly, the fitting using the three-component sinusoid is capable
of representing the dual peaks in aerosol optical depth observed at Tinga
Tingana and in other in arid zone stations, the peaks occurring in the spring
and summer. Although the three-term sinusoidal fit has insufficient
high-frequency content to represent the narrow peaks observed at this site,
the higher-frequency components required are not found to be continent-wide,
and the use of this three-component harmonic analysis is sufficient for
classification of the various aerosol regimes as further discussed below.
Illustration of the Fourier decomposition of the periodic component
of the aerosol optical depth at Tinga Tingana. The fitted curve (orange)
results from the addition of the offset plus linear trend (dashed line) to
the three sinusoids corresponding to periods of 12 months (blue), 6 months
(red) and 4 months (green). The arrowed phase times of the three sinusoids
were 11.4, 2.9 and 1.1 months respectively. The phase times are referenced to
the first December in each time series, denoted as month zero in this plot.
The observed climatology is shown as the black solid line. The linear trend
apparent in the dashed line is not significant at the 5 % level (see
Table ).
A list of the fitted parameters for all sites is given in
Table . As well as the parameters defined in
Eq. (), the table lists the square of Pearson's correlation
coefficient (R2). The linear trend parameter a1 and its significance
are
further discussed below.
Parameters of the fits to the aerosol optical depth time series
derived from the 22 stations. R2 is the square of Pearson's correlation
coefficient of the fit between the model given in Eq. () and
the aerosol optical depth (AOD) time series at 500 nm. The parameters a0
and a1 give the AOD offset and linear coefficient respectively, the latter
in units of AOD per year. The following column tabulates the significance of
the trend, expressed as the probability that the trend arises from a random
distribution. Trends were considered significant for P(a1) of
< 6 %, indicated in bold face. Remaining columns list the coefficients
b and phase for the three sinusoidal components with periods of 12, 6
and 4 months, respectively. The phase is expressed as the time in
months at which the basis cosine function reaches its first maximum. The
station labelled Canberrab in the last row of the table demonstrates the
improvement in the fit when the effects of the firestorm of January 2003 are
excised. Exclusion of the months January, February, March and April 2003
leads to an increase in the value of R2 from 0.181 to 0.413.
12 months 6 months 4 months StationMonthsR2a0a1P(a1)b12t12b6t6b4t4Darwin1940.6700.1500.0000.6690.07910.30.0283.80.0131.8Jabiru1510.6850.165-0.0010.1980.08911.00.0264.20.0161.8Lake Argyle1770.6010.147-0.0020.0560.08611.00.0374.00.0231.5Broome1810.5270.136-0.0020.0050.06711.00.0294.00.0131.7Townsville370.6040.094-0.0060.1680.03111.30.0053.10.0111.3Tennant Creek820.6880.090-0.0040.0380.05410.90.0223.90.0111.7Learmonth870.7360.0550.0010.0000.03311.70.0093.40.0061.6Rockhampton1830.6920.087-0.0010.0150.04311.40.0123.80.0051.4Longreach310.6970.072-0.0110.0460.03611.40.0094.60.0031.6Alice Springs1800.5860.064-0.0010.0210.03311.20.0113.80.0061.7Birdsville1150.6880.056-0.0010.1000.02410.90.0103.60.0071.6Geraldton1150.3970.064-0.0000.3650.03111.90.0052.10.0061.3Tinga Tingana1210.3820.0480.0010.1570.02311.40.0112.90.0111.1Kalgoorlie790.7370.0460.0000.6860.01611.00.0073.10.0061.3Fowlers Gap340.7310.0420.0040.0100.01110.90.0063.00.0061.6Woomera420.7520.0400.0020.0440.01410.70.0073.60.0061.6Lake Lefroy460.7560.0490.0020.0260.01611.10.0052.80.0071.3Cobar380.7500.045-0.0010.3150.01311.40.0052.90.0091.5Mildura1020.4760.056-0.0000.0530.01111.00.0092.90.0081.3Adelaide1540.5100.0580.0000.8800.01410.40.0063.30.0061.2Wagga Wagga1900.3100.061-0.0000.9440.0170.10.0072.90.0081.2Canberra1480.1810.077-0.0020.1310.0250.60.0092.30.0121.1Canberrab1440.4130.0610.0000.3080.0180.40.0093.10.0051.2ResultsAmplitude and phase of the aerosol optical depth of harmonic components
A geographic depiction of the fitted amplitudes and phases at all sites is
shown in Fig. . This clearly shows the increase in amplitude
of the periodic components from south to north, and the general coherence of
their phase relationship across the continent. However, at Darwin the
phase time of the annual cycle is noticeably earlier (10.3 months) than at
all other tropical sites (11.0 months), suggesting an earlier onset of the
burning season at Darwin than at other sites. Comparison of the fitted functions with
those at other tropical stations (Broome, Lake Argyle and Jabiru) indeed
shows slightly earlier onset of the rise toward the October peak at Darwin
than at the other tropical stations. In addition, the other stations show a
broader peak declining later than at Darwin. This earlier decline from the
October peak contributes to earlier phase time at Darwin than at the other
sites. Both effects can be understood as the high population density around
Darwin relative to the other stations, leading to many fires early in the dry
season. By contrast, the burning in the more remote regions is more driven by
natural ignition, and hence peaks later when the fuel is drier. The reduced
influence of human activity – with fewer late season fires being lit closer
to Darwin – supports the persistence of the smoke aerosol later in the dry
season at the more remote stations.
Amplitude and phase of the 12-month (blue), 6-month (red) and
4-month (green) components of the sinusoidal functions fitted to the time
series of aerosol optical depth at 500 nm. The length of the bars is
proportional to their amplitude, while the angles measured clockwise from due
north indicate their phase. In (a), all three components are shown.
Panel (b) presents an expanded version of the 6-month (red) and
4-month (green) components, where the lengths of these two components are
scaled up by a factor of 3 relative to (a).
A regional phase change is also evident in south-eastern Australia, in the
west–east transect Adelaide, Mildura, Wagga Wagga and Canberra. The
corresponding phase times are 10.4, 11.0, 0.1 and 0.6 months respectively,
showing a trend toward a later spring–summer aerosol peak in moving from west
to east. This is caused by the increasing strength of the summer component
relative to the spring component in the compound peak, a consequence of the
increasingly forested and (hence) bushfire-prone character of the more
easterly sites. This is particularly true of Wagga Wagga and Canberra, where
major incursions of smoke aerosol are found in the austral summer months. In
contrast, the arid zone aerosol has a greater magnitude during the austral
spring.
Aerosol sources contributing to the noted rise during spring are
heterogeneous, with suggested contributions from fine aeolian dust
, a combination of biomass burning smoke, fine
dust, and maritime aerosol from long-range transport
, and exogenous biomass burning aerosol
originating in southern Africa or even South America
.
Both Geraldton (t12*=11.9 months) and Learmonth (11.7 months) on the
Western Australian coast show an aerosol peak near the end of the calendar
year and are thus distinct from both the tropical stations further north
and the temperate stations further south. These stations lie in the
significant but poorly studied north-west pathway for dust transport out of
the central arid zone , and their aerosol
dynamics warrants closer study.
Further results from the spectral analysis allow an exploratory
classification into different aerosol regimes, as described below in
Sects. , and .
Comparison of harmonic amplitudes of Ångström exponent and aerosol optical depth
The fitting procedure described above was applied to the time series of both
aerosol optical depth and Ångström exponent. For the Ångström
exponent, this leads to a set of fitted parameters analogous to those shown
in Table , although, for brevity, these are not shown.
However, once the optimised fit for each station is obtained, its
peak-to-peak amplitude can be determined for both the aerosol optical depth
and Ångström exponent. A scatterplot of the amplitudes found for the
Ångström exponent against that for the aerosol optical depth is shown
in Fig. , for all stations where the data record is 5 years
or more.
Plot of the periodic amplitudes of the Ångström exponent
against aerosol optical depth at 500 nm. The amplitudes were obtained from
the peak-to-peak excursion of the fitted functions representing the periodic
component of the respective aerosol quantities.
Some separation of different aerosol regimes is possible from
Fig. . In particular, the tropical stations are easily
identified by their large aerosol optical depth amplitude (Aτ>0.2)
and clustering around Ångström exponent amplitude (AAE∼1.0). A large number of stations lie within 0.03<Aτ<0.1 but with a
large range of AAE between 0.4 and 0.9. This rectangular region
was divided into “temperate” and “arid”, with the suggested division at
AAE∼0.6. Perhaps surprisingly given its coastal location,
this classification places Adelaide in the arid regime, possibly a result of
the transport of dust over Adelaide during the summer months. By contrast,
the more easterly stations at Wagga Wagga and Canberra have lower
AAE, as does Kalgoorlie, in temperate inland Western Australia.
The three stations Tennant Creek, Learmonth and Rockhampton fall outside the
suggested classification rectangles. Tennant Creek is clearly a tropical
station with somewhat reduced Aτ, consistent with its location between
Darwin and Alice Springs. Learmonth combines the AAE of a
tropical station with the Aτ of an arid zone station, while Rockhampton
shows a puzzlingly small Aτ given its location in coastal tropical
Queensland. The aerosol regimes at Learmonth and Rockhampton warrant further
study.
The 6- and 4-month components of the aerosol optical depth
For the tropical stations, the seasonal aerosol cycle is largely controlled
by the 12-month component, with the second and third harmonics of lesser
importance. However, for the arid zone and temperate stations, the higher-frequency components are crucial in defining the dual-peaked nature of the
compound spring–summer aerosol maximum, particularly the balance between the
spring and summer components. For example, the seasonal cycle of aerosol at
the arid zone station Tinga Tingana shown in Fig. has dual
peaks in September and January, which can be understood by the interaction of
the 6- and 4-month cycles with the annual cycle.
This is further explored in Fig. , which plots the amplitude
ratio of the 4- to 12-month component (A4:12), against that of the
6- to 12-month component (A6:12). This plot exhibits an reversed
“L-shaped” distribution, with a horizontal arm at A4:12∼0.2,
and vertical arm at A6:12∼0.4. The horizontal arm contains all
the tropical stations excepting Lake Argyle, confirming the weakness of the
4-month component at these stations. At the far left of this arm lie the two
west coastal stations of Geraldton and Learmonth, suggesting that their
seasonal cycle is largely determined by the 12-month component, with weak
contributions at both 6 and 4 months.
Plot of the amplitudes of the sinusoidal components with a period of
4 months (vertical axis) against the amplitude of the 6-monthly component
(horizontal axis), both relative to the 12-month amplitude. The relative
4-monthly amplitude controls the extent of double-peaking in the
spring–summer period.
The vertical arm contains a mixture of arid and temperate stations,
increasing toward Wagga Wagga and Canberra, the only stations where
A4:12 exceeds A6:12. This is a consequence of the large
summer smoke aerosol signal associated with these stations, as already
discussed. The “outlier” of Mildura arises because of the relatively small
amplitude of the annual component, rather than excessively high amplitudes at
6 and 4 months.
The balance between episodic and periodic aerosol
The residual or difference between the optimised model fit to the observed
time series (Eq. ) and the data itself is considered to be the
“episodic” component of the aerosol signal, in the sense that it is
determined by unpredictable episodes such as fires or dust storms which
depart from the “periodic” component that governs the parameters of the
fitted model. Figure shows the standard deviation of the
residual (expressed as a fraction of the peak-to-peak amplitude of its
periodic variation) against the peak-to-peak amplitude of the periodic
function fitted to the aerosol optical depth at 500 nm. The standard
deviation was chosen as it includes extreme values, unlike other measures of
variation such as the interquartile range.
The standard deviation of the episodic component of the aerosol
optical depth expressed as a fraction of the amplitude of the periodic
aerosol optical depth at 500 nm, plotted against the amplitude of the
periodic aerosol optical depth at 500 nm. This allows clear separation
between (a) the tropical stations, and (b) stations
effected by increasingly large episodic activity. Stations where the episodic
standard deviation is more than 30 % of the periodic component include
the smoke-affected temperate stations of Wagga Wagga and Canberra, and the
dust-dominated station at Tinga Tingana. The high episodic aerosol at Wagga
Wagga and Canberra is consistent with the increasing incidence of smoke
aerosol from forested areas from west to east.
Figure allows separation of (a) the four tropical stations,
(b) a cluster of 5 stations with episodic variation at ∼20 %, and
(c) an extended series of stations with episodic variation between 24 and
50 %. The stations with episodic variation above 30 % include the
smoke-affected Wagga Wagga (46 %) and Canberra (49 %) and the
dust-dominated Tinga Tingana (36 %). Geraldton (39 %) and Adelaide
(32 %) are also in this group, although the aerosol sources responsible
are less clear. This result suggests that proximity to forested areas prone
to irregular bushfires (unlike the savanna burning in the tropical north)
engenders the greatest episodic variation found across the sites studied.
Trends in aerosol optical depth
Trends in aerosol optical depth are difficult to discern because of the
inherent stochastic variability imposed on the periodic signal. Hence, long
time series are necessary to discern a small signal amidst much noise. For
the present data set, we limit the trend analysis to those time series longer
than 10 years. This avoids the pitfall that can appear when a multi-year (but
sub-decadal) regional change causes a change in aerosol loading. Such a
change caused an increase in dust aerosol over the Lake Eyre Basin during the
Millennium Drought (2003–2010) .
The model used in the present study does return a linear trend, in the form of the
coefficient a1 listed in Table , together with the
significance of the trend, expressed as the probability that the trend arose
from a random distribution. It is standard practice to declare the trend
significant if the probability of it arising randomly is 5 % or less. In
the present case, three of the stations fulfil this criteria, but we relax
it slightly to 6 % to enable inclusion of a fourth station. The four
stations satisfying both duration of record and significance criteria are
listed in Table .
Analysis of linear trends a1 expressed as the change in aerosol
optical depth per year. Only stations with at least a 10-year data record and
trend significance of P(a1)≤ 6 % are listed. σ(a1) is
the standard uncertainty in a1.
The monthly climatology of aerosol optical depth at 500 nm, for
(a) tropical, (b) unclassified, (c) arid zone, and
(d) temperate regions. The plots depict the monthly median, with the
vertical bars showing the interquartile range, i.e. the range in aerosol
optical depth between the 25th and 75th percentiles. The vertical bars are
offset between sites for clarity.
The table shows that the trends are negative but small. For Lake Argyle and
Broome in Australia's north-west (Kimberley) region, the trend expressed as a
change in aerosol optical depth at 500 nm is -0.002 per year, with a
standard uncertainty of ±0.0008. Assuming the uncertainty is normally
distributed, there is a 67 % probability that the trend lies between
-0.0028 and -0.0012 per year. For Rockhampton and Alice Springs, the best
estimate of the trend is -0.0009 per year, with a corresponding range of
-0.0012 to -0.0005. In summary, it can be stated that there is evidence
for a small decrease in aerosol loading at these four sites, with a stronger
(but still small) negative trend over the Kimberley stations.
The small negative trend at Alice Springs could well be due to the large
aerosol peak in the austral summer 2002–2003, at the beginning of the
Millennium Drought, followed by fairly regular annual cycling thereafter
(Fig. ). The more significant trend at Broome and Lake
Argyle is considered given an expectation of increasing intensity of the
monsoon in the north-west tropics
. Increasing monsoon rainfall
can suppress subsequent smoke emission due to lingering moisture in the
vegetation, although associated increased vegetation growth leads to
increased smoke emission in subsequent dry seasons. Evidence for this is seen
from Fig. in the suppressed aerosol immediately
following the 2010–2011 monsoon (the largest seasonal rainfall on record),
but with very large smoke emission during the following burning season
(2012). The balance between these competing effects will determine the
ultimate direction of any trend. The present analysis suggests that the trend
may be negative.
Further context for these trends emerges from a consideration of the
projected decrease in global aerosol following from scenarios under which
future anthropogenic aerosol (AA) emissions decline.
modelled the impact of one such projected
decline, following Representative Concentration Pathway 4.5
RCP4.5;.
found the expected “unmasking” of greenhouse warming accompanying reduced
aerosol emissions, with a less-expected increase in global rainfall caused by
changed atmospheric dynamics. RCP4.5 is based on highly spatially
inhomogeneous reduction in AA, with reductions over the centres of industrial
activity in the Northern Hemisphere reflected as a large reduction in
mid-visible aerosol optical depth over the Northern Hemisphere of 0.2 per
century over the period 2006–2100. The projected decrease over the Southern
Hemisphere is generally smaller, with a decline of < 0.05 per century over
Australia. Hence, the observed trends of between 0.001 and 0.002 per year
(0.1–0.2 per century) are larger than the modelled decrease (RCP4.5) by a
factor between 2 and 4. This suggests that, over Australia, changes in
natural aerosol are likely to dominate over inter-hemispheric transport of
AA, hence adding weight to the climatological aerosol characterisation
presented here.
Aerosol climatologies
Aerosol climatologies are presented in tabular and graphical formats. For
each of the 22 stations included in this study, statistical summaries of
aerosol optical depth at 500 nm and the Ångström exponent were
prepared in tabular form, based on all individual determinations of the
measurands. This allowed generation of quartiles and medians in addition to
means. The resulting tables list the monthly mean, lower quartile, median,
and upper quartile for both aerosol optical depth and Ångström
exponent, for all 22 stations listed in order of decreasing latitude, and
shown in Table .
For sites where the data record is over 5 years in length, the monthly
climatology of aerosol optical depth at 500 nm is displayed in
Fig. . The time axis runs from July to June (rather than
January to December) so as not to bisect the aerosol peak, which occurs in
the austral spring–summer period (September to February). The grouping of
stations into regions was based on the classification suggested by
Fig. , with the three sites lying outside the rectangles
labelled “Unclassified”, and Adelaide included with the “Temperate”
sites, both in view of its transitional characteristics, and to reduce
clutter in the “Arid zone” panel. The scale length corresponding to unit
optical depth is the same in all four panels to facilitate intercomparison.
Correlation among aerosol time series
The groupings adopted in Fig. are used to examine correlation
of the station time series within groups. The resulting values of Pearson's
correlation coefficient (R) between each station pair in a group are shown in
Tables –. Unsurprisingly, given the tight
clustering found in much of the foregoing analysis, the tropical group shows
high correlation between stations, with even the distant pair Broome–Jabiru
yielding R=0.8 (Table ). Although the correlation between
arid zone station pairs is weaker across the board, the pair Alice
Springs–Birdsville showed the highest coefficient in the present study, at
R=0.92 (Table ). The large episodic aerosol component in
the bushfire-prone part of the temperate group reduces expectations of high
correlation coefficients, so it is surprising to note the coefficient of
R=0.795 between Wagga Wagga and Canberra (Table ),
suggesting regional-scale linkage via correlated smoke episodes. The results
seen in Table for the “Unclassified” stations are yet more
surprising, particularly the coefficient of R=0.73 between the pair
Learmonth–Rockhampton, located on opposite sides of the continent at a
distance of ∼ 3730 km. The implication is that the mechanisms
responsible for aerosol generation and transport have much in common at both
regional and trans-continental scales.
Pearson's correlation coefficient between all tropical station
pairs, for stations with time series of 60 months or greater.
A further mechanism underlying this correlation is the intercontinental
transport of aerosol plumes from southern Africa and possibly South America
considered by . Although they measured the
plumes from only one site, such material is likely to be well dispersed
following long-range transport, and was shown to contribute ∼ 0.02 to
the mid-visible aerosol optical depth over Mildura. This is significant given
the aerosol “offset” of 0.05–0.06 typical of the arid zone stations
(Table ), so the widespread presence of this aerosol source
could well boost the correlation among widely separated stations. Further
analysis of the prevalence and uniformity of these layers is required.
Pearson's correlation coefficient between all unclassified station
pairs, for stations with time series of 60 months or greater.
UnclassifiedStationTennant CkLearmonthRockhamptonTennant Ck1.0000.7610.829Learmonth0.7611.0000.726Rockhampton0.8290.7261.000Summary and conclusions
Analysis of the aerosol records at 22 stations across the Australian
continent has been carried out with dual aims in mind: (a) generation of
benchmark aerosol climatologies across the continent, and (b) classification
of Australian continental aerosol types based on their measured time series.
The latter goal was achieved by Fourier analysis which suggested that most of
the seasonal variation could be explained by just three periodic terms,
corresponding to the annual cycle and its second and third harmonics. Fitted
coefficients arising from a model constructed on this basis support the
following principal conclusions.
The proposed model explains 50 % or more of the variance at 17 of the 22 sites
(77 %), and hence provides a basis on which to infer aerosol
classification and to examine the time series for possible trends.
Analysis of the fitted parameters suggests that Australian aerosol can
be classified into three groups – tropical, arid and temperate – although there
is overlap between the latter two groups on some measures.
Tropical aerosol is distinguished by a large and dominant annual mode,
resulting from savanna burning in Australia's tropical zone. Consideration of
the phase of this annual mode allows the separation of different burning
regimes, as seen in the earlier onset of burning near Darwin relative to the
more remote tropical stations. Individual time series show significant
interannual variability due to variation in the strength of the monsoon and
its effect on the fuel source.
Arid zone aerosol is seasonally bimodal, often showing dual peaks in the
austral spring and summer (typically September–January). This is associated
with an increase in amplitude of the 4-monthly harmonic relative to the
annual (12-month) signal. Arid zone sites are characterised by an increasing
episodic component toward active dust-source regions.
Temperate zone aerosol is characterised by a small-amplitude periodic
component, with an increasing episodic component caused by smoke aerosol
arising from fire-prone eucalypt forests. This episodic component increases
along a west–east transect with proximity to the forested areas of the
south-east.
Significant decadal trends in aerosol optical depth at the 6 % level
or below were found at 4 of the 22 stations. The magnitude of the trends was
small, with a decline in aerosol optical depth at 500 nm of ∼ 0.002
per year in the Kimberley region of the tropical north-west and ∼ 0.001
per year at Alice Springs and Rockhampton. However, these trends are much
larger than those projected to occur over Australia through future reductions
in Northern Hemisphere anthropogenic aerosol emissions.
Strong correlation was found both among stations in the same group and,
remarkably, between stations separated by the full width of the continent. It
follows that, on a monthly timescale, aerosol sources over Australia are
driven by mechanisms that do not vary greatly either on regional or
continental scales. There is evidence that intercontinental transport of
biomass burning aerosol may play a significant role in this.
Despite the strong coherence in Australian aerosol just noted, we caution
that this applies to monthly mean data. The extent to which such coherence
applies on shorter timescales is unclear, although its persistence among
tropical stations was explored by .
Although a monthly climatology is useful for low-frequency applications,
including solar resource prediction and arguably climate change, it is by no
means suitable for applications requiring instantaneous aerosol fields such
as the atmospheric correction of remote sensing imagery from satellites.
Hence, future work will consider the relation between the instantaneous and
monthly mean aerosol, as well as the predictive capacity of the latter. Future work
will also be directed to filling in the geographical gaps evident in the
present study, particularly the lack of stations over Victoria and Tasmania.
All Bureau of Meteorology aerosol data used in this paper
will be available from the Global Atmospheric Watch Aerosol Data portal by the
end of 2017. Aerosol data from the CSIRO/AeroSpan system are available from the
AERONET website at http://aeronet.gsfc.nasa.gov/.
Climatological tables
Monthly climatology of 500 nm aerosol
optical depth and Ångström exponent at all 22 stations, listed in
order of decreasing latitude. The column labelled “Nobs” lists the number of instantaneous determinations of the measurand underlying the statistics. The columns labelled 25Q and 75Q list the break
points at the first quartile (25 %) and third quartile (75 %)
respectively. For the Bureau stations (denoted “BoM” in the header), the
Ångström exponent before 2009 was calculated using the wavelength
pair (500, 778 nm). For 2009 and beyond, the wavelength pair (500, 868 nm)
was used (see text). For the CSIRO AeroSpan stations, denoted “CSIRO”, the
wavelength pair (440, 870 nm) was used throughout.
The authors declare that they have no conflict of
interest.
Acknowledgements
The CSIRO network was established with the support of the CSIRO Earth
Observation Centre, with valued contributions from Dean Graetz and
Denis O'Brien. We also thank Michael Milner, who restored and added SPO2
instruments to the Bureau of Meteorology network. In addition, thanks are due
to Bureau observing office staff, and the station managers at CSIRO sites, in
particular Greg Smith at Lake Argyle, Neale McShane and Stephan Pursell at Birdsville,
Robert Thorn at Jabiru, and Keith Leggett and Garry Dowling at Fowlers Gap.
The CSIRO component of this work is supported by the AERONET staff at NASA
Goddard Space Flight Center and the Earth Observation Informatics Future
Science Platform (EOI-FSP) led by Arnold Dekker. Edited by: S. Kazadzis Reviewed by: three
anonymous referees
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