ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-8097-2018Insight into global trends in aerosol composition from 2005 to 2015 inferred
from the OMI Ultraviolet Aerosol IndexInsight into global trends in aerosol composition from 2005 to 2015HammerMelanie S.melanie.hammer@dal.caMartinRandall V.https://orcid.org/0000-0003-2632-8402LiChiTorresOmarManningMaxBoysBrian L.Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, CanadaHarvard-Smithsonian Center for Astrophysics, Cambridge, MA, USAAtmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USAMelanie S. Hammer (melanie.hammer@dal.ca)8June201818118097811214December201712February201824May201825May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/8097/2018/acp-18-8097-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/8097/2018/acp-18-8097-2018.pdf
Observations of aerosol scattering and absorption offer valuable information
about aerosol composition. We apply a simulation of the Ultraviolet Aerosol
Index (UVAI), a method of detecting aerosol absorption from satellite
observations, to interpret UVAI values observed by the Ozone Monitoring
Instrument (OMI) from 2005 to 2015 to understand global trends in aerosol
composition. We conduct our simulation using the vector radiative transfer
model VLIDORT with aerosol fields from the global chemical transport model
GEOS-Chem. We examine the 2005–2015 trends in individual aerosol species
from GEOS-Chem and apply these trends to the UVAI simulation to calculate
the change in simulated UVAI due to the trends in individual aerosol species.
We find that global trends in the UVAI are largely explained by trends in
absorption by mineral dust, absorption by brown carbon, and scattering by
secondary inorganic aerosol. Trends in absorption by mineral dust dominate
the simulated UVAI trends over North Africa, the Middle East, East Asia, and
Australia. The UVAI simulation resolves observed negative UVAI trends well
over Australia, but underestimates positive UVAI trends over North Africa and
Central Asia near the Aral Sea and underestimates negative UVAI trends over
East Asia. We find evidence of an increasing dust source from the desiccating
Aral Sea that may not be well represented by the current generation of
models. Trends in absorption by brown carbon dominate the simulated UVAI
trends over biomass burning regions. The UVAI simulation reproduces observed
negative trends over central South America and West Africa, but
underestimates observed UVAI trends over boreal forests. Trends in scattering
by secondary inorganic aerosol dominate the simulated UVAI trends over the
eastern United States and eastern India. The UVAI simulation slightly
overestimates the observed positive UVAI trends over the eastern United
States and underestimates the observed negative UVAI trends over India.
Quantitative simulation of the OMI UVAI offers new insight into global trends
in aerosol composition.
Introduction
Atmospheric aerosols have significant climate impacts due to their ability to
scatter and absorb solar radiation and to their indirect effect through
modification of cloud properties. The exact magnitude of the direct radiative
forcing remains highly uncertain (IPCC, 2014), although most studies agree it
is significant (Andreae and Gelencsér, 2006; Mann and Emanuel, 2006;
Mauritsen, 2016). Storelvmo et al. (2016) estimate that changes in global
aerosol loading over the past 45 years have caused cooling (direct and
indirect) that masks about one-third of the atmospheric warming due to
increasing greenhouse gas emissions. Aerosol absorption has been estimated to
be the second-largest source of atmospheric warming after carbon dioxide
(Ramanathan and Carmichael, 2008; Bond et al., 2013; IPCC, 2014), although
considerable uncertainty remains regarding the exact magnitude (Stier et al.,
2007). The large uncertainty regarding the direct radiative impacts of
aerosols on climate is driven by the large variability in aerosol physical
and chemical properties, as well as their various emission sources, making it
extremely difficult to fully understand their interactions with radiation
(Pöschl, 2005; Moosmüller et al., 2009; Curci et al., 2015;
Kristiansen et al., 2016). Global observations of trends in aerosol
scattering and absorption would offer valuable constraints on trends in
aerosol sources and composition.
The emissions of aerosols and their precursors have changed significantly
over the past decade. In North America and Europe, the anthropogenic
emissions of most aerosol species (e.g. black carbon, organic aerosols) and
aerosol precursors (e.g. sulfur dioxide and nitrogen oxides) have decreased
due to pollution controls (Leibensperger et al., 2012; Klimont et al., 2013;
Curier et al., 2014; Simon et al., 2015; Xing et al., 2015; C. Li et al.,
2017). By contrast, emissions of aerosols and aerosol precursors have
increased in developing countries due to increased industrial activity,
particularly in China and India. Chinese emissions of black carbon (BC),
organic carbon (OC), and nitrogen oxides (NOx) have been
increasing over the past decade (Zhao et al., 2013; Cui et al., 2015),
although in the most recent years NOx emissions have been declining,
driven by denitration devices at power plants (Liu et al., 2016). Due to the
wide implementation of flue-gas desulfurization equipment in most power
plants in China, emissions of sulfur dioxide (SO2) in some regions
have been decreasing since about 2006–2008 (Lu et al., 2011; Wang et al.,
2015; Fioletov et al., 2016). Indian emissions of anthropogenic aerosols and
their precursors have been increasing over the past decade (Lu et al., 2011;
Klimont et al., 2017). There have also been significant changes in global
dust and biomass burning emissions. Shao et al. (2013) use synoptic data to
estimate a global decrease in dust emissions between 1974 and 2012, driven
largely by reductions from North Africa with weaker contributions from
Northeast Asia, South America, and South Africa. By examining trends in
burned area, Giglio et al. (2013) estimate a decrease in global biomass
burning emissions between 2000 and 2012. Trends in aerosol composition
produced by these changing emissions may be detectable from satellite
observations of aerosol scattering and absorption.
Detection of aerosol composition from passive nadir satellite observations is
exceedingly difficult; few methods exist. The aerosol-type classification
provided by retrievals from the MISR instrument, enabled by multi-angle
viewing, is one such source of information about aerosol composition from
constraints on particle size, shape, and single scattering albedo (SSA) (Kahn
and Gaitley, 2015). MISR retrievals have been used to classify particles
relating to events such as biomass burning, desert dust, volcanic eruptions,
and pollution events (e.g. Liu et al., 2007; Kalashnikova and Kahn, 2008; Dey
and Di Girolamo, 2011; Scollo et al., 2012; Guo et al., 2013). The most
commonly used satellite product for aerosol information is aerosol optical
depth (AOD), the columnar extinction of radiation by atmospheric aerosols.
AOD can be retrieved from satellite measurements of top-of-atmosphere (TOA)
radiance in combination with prior knowledge of aerosol optical properties.
Several studies have examined trends in satellite AOD. Following trends in
emissions, over the past decade positive trends in satellite AOD have been
observed over Asia and Africa, corresponding to regions experiencing
industrial growth (de Meij et al., 2012; Chin et al., 2014; Mao et al., 2014;
Mehta et al., 2016), while negative trends in satellite AOD have been
observed over North America and Europe, largely due to pollution controls
(Hsu et al., 2012; de Meij et al., 2012; Chin et al., 2014; Mehta et al.,
2016). Studies such as these demonstrate the information about the evolution
of aerosol abundance offered by total column AOD retrievals, but
measurements of absorption would complement the scattering information in AOD
retrievals by providing independent information on aerosol composition.
The Ultraviolet Aerosol Index (UVAI) is a method of detecting aerosol
absorption from satellite-measured radiances (Herman et al., 1997; Torres et
al., 1998). Because the UVAI is calculated from measured radiances, a priori
assumptions about aerosol composition are not required for its calculation,
thus yielding independent information on aerosol scattering (Herman et al.,
1997; Torres et al., 1998, 2007; de Graaf et al., 2005; Penning de Vries et
al., 2009) and absorption. The UVAI has been widely applied to examine
mineral dust (Israelevich et al., 2002; Schepanski et al., 2007; Badarinath
et al., 2010; Huang et al., 2010) and biomass burning aerosols (Duncan et
al., 2003; Guan et al., 2010; Torres et al., 2010; Kaskaoutis et al., 2011;
Mielonen et al., 2012), including brown carbon (BrC) (Jethva and Torres, 2011;
Hammer et al., 2016). The UVAI is not typically used to examine scattering
aerosol, but aerosol scattering causes a net decrease in the overall
value of the UVAI, meaning that the UVAI could be used to detect changes due
to both aerosol absorption and scattering. Prior interpretation of the UVAI
has been complicated by its dependence on geophysical parameters, such as
aerosol layer height (Herman et al., 1997; Torres et al., 1998; de Graaf et
al., 2005). Examining trends in the UVAI would provide an exciting
opportunity to investigate the evolution of aerosol absorption and scattering
over time, if the multiple parameters affecting the UVAI could be accounted
for through simulation.
In this work, we apply a simulation of the UVAI, which was developed and
evaluated regionally and seasonally in Hammer et al. (2016), to interpret
trends in recently reprocessed Ozone Monitoring
Instrument (OMI) UVAI observations for 2005–2015 to
understand global changes in aerosol composition. We interpret observed UVAI
values by using a radiative transfer model (VLIDORT) to calculate UVAI values
as a function of simulated aerosol composition from the global 3-D chemical
transport model GEOS-Chem. By using scene-dependent OMI viewing geometry
together with scene-dependent modelled atmospheric composition we enable
quantitative comparison of model results with observations. Comparison of
trends in observed OMI UVAI values to the trends in simulated UVAI values,
which are calculated using known aerosol composition, enables qualification
of how changes in aerosol absorption and scattering could influence the
observed UVAI trends and identification of model development needs. We
conduct our analysis at the global scale to understand trends worldwide.
Section 2 describes the OMI UVAI observations and our UVAI simulation.
Section 3 examines the trends in emissions of GEOS-Chem aerosols and their
precursors for 2005–2015 to provide context for the trends in our simulated
UVAI. Section 4 compares the mean values during 2005–2015 of the OMI UVAI and
our simulated UVAI. Section 5 compares the 2005–2015 trends in OMI and
simulated UVAI values. In Sect. 6 we examine the sensitivity of the UVAI to
changes in the abundance of individual aerosol species. Trends in our UVAI
simulation are interpreted by applying the trends in the GEOS-Chem aerosol
species to calculate the associated change in UVAI. Section 7 reports the
conclusions.
MethodsOMI Ultraviolet Aerosol Index
The OMI UVAI is a method of detecting absorbing aerosols
from satellite measurements in the near-UV wavelength region and is a product
of the OMI near-UV algorithm (OMAERUV) (Herman et al., 1997; Torres et al.,
1998, 2007). The OMAERUV algorithm uses the 354 and 388 nm radiances
measured by OMI to calculate the UVAI as a measure of the deviation from a
purely Rayleigh scattering atmosphere bounded by a Lambertian reflecting
surface. Positive UVAI values indicate absorbing aerosol while negative
values indicate non-absorbing aerosol. Near-zero values occur when clouds and
Rayleigh scattering dominate. Negative UVAI values due to aerosol scattering
are often weak and have historically been affected by noise in previous
datasets (Torres et al., 2007; Penning de Vries et al., 2015). Because UVAI
values are calculated from TOA radiance which contains
total aerosol effects, the presence (or absence) of scattering aerosol along
with absorbing aerosol can either weaken (or strengthen) the absorption
signal. Therefore the UVAI could be used to detect changes over time due to
both aerosol absorption and scattering.
The main source of error affecting a trend analysis of the UVAI is the OMI
row anomaly, which has reduced the sensor viewing capability for specific scan
angles since 2008
(http://projects.knmi.nl/omi/research/product/rowanomaly-background.php, last access: 22 August 2017). The sudden suppression of observations for specific
viewing geometries (i.e. the row anomaly) could cause an additional spurious
trend in the UVAI trend calculation. We address this concern by considering
only scan positions 3–23, which remain unaffected by the row anomaly, and
also by using the recently reprocessed OMAERUV UVAI that is less sensitive to
scan-angle-dependent cloud artifacts due to the implementation of a
Mie-scattering-based water cloud model (Torres et al., 2018). We focus on
cloud-filtered observations by excluding scenes with OMI UVAI radiative cloud
fraction exceeding 5 % to further reduce uncertainty due to clouds.
Furthermore, we focus on 10 years of observations so that multiple
observations can reduce the random error of UVAI observations.
Because the OMI UVAI is calculated directly from OMI-measured radiances,
instrument degradation over time could be a significant source of uncertainty
(Povey and Grainger, 2015). Schenkeveld et al. (2017) found that the OMI
radiances in the channel used for the UVAI have changed by only
∼ 1–1.15 % over the entire OMI record. Applying this change to the
radiances results in a change in the absolute UVAI of ∼ 10-4,
which is negligible. Schenkeveld et al. (2017) also calculated the trend in
the ratio of the 354/380 nm radiances measured by OMI for pixels unaffected
by the OMI row anomaly and over the tropical Pacific where the presence of
aerosol is expected to be minimal, to assess the change in the spectral
dependence of OMI's overall radiance calibration over the course of the
mission. They found that the trend in the 354/380 nm radiance ratio over the
entire OMI record was < 0.5 % per decade. We estimate the effect
of instrument degradation on our trend analysis by calculating the change in
UVAI associated with the 0.5 % per decade trend in the 354/380 nm
radiance ratio. Applying the trend in 354/380 nm radiance ratio to the UVAI
calculation globally resulted in a negligible change in the UVAI of
∼ 2 × 10-4 yr-1. To avoid the influence of any
possible spurious trends due to instrument degradation on our trend analysis,
we subtract the trend in global mean UVAI from the cloud-filtered UVAI prior
to interpretation.
We perform trend analysis on monthly mean time series data for the years
2005–2015 using generalized least squares (GLS) regression, as described by
Boys et al. (2014). Prior to regression, the data are aggregated to monthly
mean values, and the monthly time series data are deseasonalized by
subtracting the monthly mean for the period 2005–2015 to focus on the
long-term trend. Deseasonalization is a recommended method to accurately
calculate a long-term trend in a seasonally varying time series (Weatherhead
et al., 1998, 2002; Wilks, 2011) and is widely employed for the trend
analysis of geophysical data including temperature, chemical species
concentrations, relative humidity, cloud cover, and aerosol parameters
(Reynolds and Reynolds, 1988; Prinn et al., 1992; Pelletier and Turcotte,
1997; Zhang et al., 1997; Dai, 2006; Norris and Wild, 2007; Hsu et al., 2012;
Boys et al., 2014; Li et al., 2014; Ma et al., 2016). Each pixel is required
to have data for at least 60 % of the time period before regression is
performed. In the following section, we discuss our UVAI simulation and the
implementation of the new UVAI algorithm in the simulation.
Simulated UVAI
We simulate the UVAI using the VLIDORT radiative transfer model (Spurr,
2006), following Buchard et al. (2015) and Hammer et al. (2016). We calculate
the TOA radiances at 354 and 388 nm needed for the UVAI
calculation by supplying VLIDORT with the OMI viewing geometry for each
scene, as well as the GEOS-Chem simulation of vertical profiles of aerosol
extinction, spectrally dependent single scattering albedo, and the
corresponding spectrally dependent scattering phase function. Thus these
parameters account for the sensitivity of the UVAI to aerosol layer height
and spectrally dependent aerosol optical properties.
We introduce to the UVAI simulation a Mie-scattering-based water cloud model
(Deirmendjian, 1964) for consistency with the reprocessed OMI UVAI dataset.
Following Torres et al. (2018), we compute the radiances used in the UVAI
calculation as a combination of clear and cloudy sky conditions. We use the
same cloud fractions and cloud optical depths used in the OMI UVAI algorithm
for coincident OMI pixels. We avoid cloudy scenes by considering only pixels
with OMI radiative cloud fraction of less than 5 %. For the UVAI
calculation we use the surface reflectance fields provided by OMI. We
calculated the 2005–2015 trends in these surface reflectance fields and
found that they were statistically insignificant globally and on the order of
10-5 yr-1. We calculated the change in UVAI due to a change in
surface reflectance of this order of magnitude and found that the change in
UVAI was negligible. We also calculated the change in UVAI due to changes in
simulated aerosol altitude, but found that the trends in aerosol altitude
were negligible (order 10-5 hPa yr-1). Therefore we focus our
analysis on trends in aerosol composition which have a larger effect on the
UVAI as demonstrated below.
We use the GEOS-Chem model v11-01 (http://geos-chem.org, last access: 22 August 2017) as input to the UVAI simulation and to calculate the
sensitivity of the UVAI simulation to aerosol composition. The simulation is
driven by assimilated meteorological data from MERRA-2 Reanalysis of the NASA
Global Modeling and Assimilation Office (GMAO). Our simulation is conducted
at a spatial resolution of 2∘× 2.5∘ with 47
vertical levels for the years 2005–2015. We supply VLIDORT with GEOS-Chem
aerosol fields coincident with OMI observations.
GEOS-Chem contains a detailed oxidant-aerosol chemical mechanism (Bey et al.,
2001; Park et al., 2004). The aerosol simulation includes the
sulfate–nitrate–ammonium system (Fountoukis and Nenes, 2007; Park et al.,
2004; Pye et al., 2009), primary carbonaceous aerosol (Park et al., 2003),
mineral dust (Fairlie et al., 2007), and sea salt (Jaeglé et al., 2011).
Semivolatile primary organic carbon and secondary organic aerosol (SOA) formation
is described in Pye et al. (2010). We update the original semi-volatile
partitioning of SOA formed from isoprene with the irreversible
uptake scheme in Marais et al. (2016). HNO3 concentrations are reduced
following Heald et al. (2012). Aerosol optical properties are based on the
Global Aerosol Data Set (GADS) (Koepke et al., 1997) as originally
implemented by Martin et al. (2003), with updates for organics and secondary
inorganics from aircraft observations (Drury et al., 2010), for mineral dust
(Lee et al., 2009; Ridley et al., 2012), and for absorbing BrC
(Hammer et al., 2016). Here we update the mineral dust optics at ultraviolet
wavelengths using a refractive index that minimizes the difference between
the mean simulated and OMI UVAI values to allow focus on trends. Aerosols are
treated as externally mixed.
Trend in emissions of (a) sulfur dioxide (SO2)
(kg SO2 km-2 yr-1), (b) nitrogen oxides
(NOx) (kg NO km-2 yr-1), ammonia (NH3)
(kg NH3 km-2 yr-1), black carbon (BC)
(kg C km-2 yr-1), primary organic carbon (POA)
(kg C km-2 yr-1), and dust (kg km-2 yr-1) used in
our GEOS-Chem simulation. The trends are calculated from the generalized
least squares regression of monthly time series values during 2005–2015.
Anthropogenic emissions are from the EDGARv4.3.1 global inventory (Crippa et
al., 2016) with emissions overwritten in areas with regional inventories for
the United States (NEI11; Travis et al., 2016), Canada (CAC), Mexico (BRAVO;
Kuhns et al., 2005), Europe (EMEP; http://www.emep.int/, last access: 22 August 2017), China (MEIC v1.2; C. Li et al., 2017), and elsewhere
in Asia (MIX; C. Li et al., 2017). Emissions from open fires for individual
years from the GFED4 inventory (Giglio et al., 2013) are included. The
long-term concentrations from this simulation have been extensively evaluated
versus ground-based PM2.5 composition measurements where available and
versus satellite-derived PM2.5 trends (M. Li et al., 2017).
The Supplement evaluates trends in simulated SO2, NO2,
and AOD versus satellite retrievals from multiple instruments and algorithms.
We find broad consistency between our simulated NO2 and SO2
column trends with those from OMI (Supplement Figs. S1 and S2). Our simulated
AOD trends are generally consistent with the trends in satellite AOD
retrievals, with the exception of positive trends in AOD over western North America and
near the Aral Sea in most retrieval products and a negative trend in AOD
over Mongolia/Inner Mongolia in all retrieval products (Fig. S3).
We filter our GEOS-Chem aerosol simulated fields based on the coincident OMI
pixels, which are regridded to the model resolution of
2∘× 2.5∘. This allows for the direct comparison
between our GEOS-Chem simulation and the OMI UVAI observations.
Trend in emissions of GEOS-Chem aerosols and their precursors
Figure 1 shows the trends in emissions of aerosols and their precursors from
our GEOS-Chem simulation calculated from the GLS regression of monthly time
series values for 2005–2015. Cool colours indicate negative trend values,
warm colours indicate positive trend values, and the opacity of the colours
indicates the statistical significance of the trends. The trends in emissions
of sulfur dioxide (SO2) and nitrogen oxides (NOx) follow
similar patterns (Fig. 1a and b, respectively). Negative trends (-1 to
-0.01 kg km-2 yr-1) are present over North America and Europe,
corresponding to pollution controls (Leibensperger et al., 2012; Klimont et
al., 2013; Curier et al., 2014; Simon et al., 2015; Xing et al., 2015; C. Li
et al., 2017). Positive trends (0.5 to 1 kg km-2 yr-1) in both
species are present over India and eastern China, but the positive trends
in emissions of SO2 over eastern China are interspersed with negative
trends (-1 to -0.5 kg km-2 yr-1) in SO2 emissions,
corresponding to the deployment of desulfurization equipment in power plants
in recent years (Lu et al., 2011; Klimont et al., 2013; Wang et al., 2015).
Ammonia (NH3) emissions (Fig. 1c) have positive trends (0.001 to
0.05 kg km-2 yr-1) over most of South America, North Africa, the
Middle East, and most of Asia with larger trends (0.1 to
0.5 kg km-2 yr-1) over India and eastern China. There are
positive trends (0.001 to 0.05 kg km-2 yr-1) in BC emissions (Fig. 1d) over North Africa, Europe, the Middle East, India,
and China and negative trends (-0.05 to
-0.001 kg km-2 yr-1) over North America, Europe, West Africa,
and central South America. The trends in primary organic aerosol (POA)
emissions (Fig. 1e) follow a similar pattern as the trends in BC emissions,
except there are negative trends (-0.1 to
-0.05 kg km-2 yr-1) over eastern China and the negative
trends (-1 to -0.1 kg km-2 yr-1) over West Africa and
central South America are larger in magnitude reflecting regional changes in
fire activity (Chen et al., 2013; Andela and van der Werf, 2014). There are
also positive trends (0.001 to 0.05 kg km-2 yr-1) over the
northern United States and Canada. The trends in dust emissions (Fig. 1f)
show the largest magnitude of all the various species, although many have low
statistical significance, with areas of positive and negative trends
(> 1 and < -1 kg km-2 yr-1) over North
Africa, positive trends (> 1 kg km-2 yr-1) parts of
the Middle East, and negative trends
(< -1 kg km-2 yr-1) over northern China and southern
Australia.
Seasonal mean UVAI values for the 2005–2015 period as observed by
OMI for MAM (May, April, March), JJA (June, July August), SON (September,
October, November), and DJF (December, January, February). Grey indicates
persistent cloud fraction greater than 5 %.
Seasonal mean UVAI values for the 2005–2015 period from our
simulation coincidently sampled from OMI for MAM (May, April, March),
JJA (June, July August), SON (September, October, November), and
DJF (December, January, February). Grey indicates persistent cloud fraction
greater than 5 %.
Mean UVAI values for 2005–2015
We examine the seasonal long-term mean UVAI values for insight into the
spatial distribution of the aerosol absorption signals. Figures 2 and 3 show
the seasonal mean UVAI values for 2005–2015 for OMI and the simulation,
respectively. Positive UVAI values between 0.2 and 1.5 indicating aerosol
absorption are present over major desert regions globally for both OMI and
the simulation, particularly over the Saharan, Iranian, and Thar deserts.
These positive signals are driven by the absorption by mineral dust (Herman
et al., 1997; Torres et al., 1998; Buchard et al., 2015). The simulation
underestimates some of the smaller dust features captured by OMI, such as
over western North America, South America, Australia, and parts of Asia,
perhaps reflecting an underestimation in the simulated mineral dust lifetime
(Ridley et al., 2012) and missing dust sources (Ginoux et al., 2012; Guan et
al., 2016; Huang et al., 2015; Philip et al., 2017). The seasonal variation
in the observed and simulated UVAI is similar albeit with larger simulated
values in spring (MAM) over North Africa. In all seasons, the UVAI values
driven by absorption by dust in the simulation are concentrated mostly over
North Africa, while for OMI the UVAI values are more homogeneous over the
Middle East and Asia as well. Positive UVAI values of ∼ 0.2–1 over
West and Central Africa appearing in both the OMI and simulated values
correspond to absorption by BrC from biomass burning activities in
these regions (Jethva and Torres, 2011; Hammer et al., 2016). Over ocean most
data are removed by our strict cloud filter.
Trends in OMI (a) and simulated (b) UVAI values
coincidently sampled from OMI calculated from the generalized least squares
regression of monthly time series values during 2005–2015. The opacity of the
colours indicates the statistical significance of the trend. Grey indicates
persistent cloud fraction greater than 5 %.
Seasonality of the trends in OMI UVAI values calculated from the
generalized least squares regression of monthly time series values during
2005–2015 for MAM (May, April, March), JJA (June, July August),
SON (September, October, November), and DJF (December, January, February).
The opacity of the colours indicates the statistical significance of the
trend. Grey indicates persistent cloud fraction greater than 5 %.
Seasonality of the trends in simulated UVAI values coincidently
sampled from OMI calculated from the generalized least squares regression of
monthly time series values during 2005–2015 for MAM (May, April, March),
JJA (June, July August), SON (September, October, November), and
DJF (December, January, February). The opacity of the colours indicates the
statistical significance of the trend. Grey indicates persistent cloud
fraction greater than 5 %.
Trend in GEOS-Chem aerosol concentrations for (a) secondary
inorganic aerosol (SIA), (b) dust, (c) total organic
aerosol (OA), (d) brown carbon (BrC), (e) black carbon
(BC), and (f) sea salt. The trends are calculated from the GLS
regression of monthly aerosol concentration time series values during
2005–2015. The opacity of the colours indicates the statistical significance
of the trend. Grey indicates persistent cloud fraction greater than 5 %.
Annual mean change in simulated UVAI values for 2008 due to the
doubling of concentrations of (a) secondary inorganic aerosol (SIA),
(b) dust, (c) total organic aerosol (OA),
(d) brown carbon (BrC), (e) black carbon (BC), and
(f) sea salt from the GEOS-Chem simulation. Grey indicates
persistent cloud fraction greater than 5 %.
Change in simulated UVAI values due to the 2005–2015 trends in
(a) secondary inorganic aerosols (SIA), (b) dust,
(c) total organic aerosol (OA), (d) brown carbon (BrC),
(e) black carbon (BC), and (f) sea salt from the GEOS-Chem
simulation. Grey indicates persistent cloud fraction greater than 5 %.
Trend in UVAI values between 2005 and 2015
Figure 4 shows the trend in OMI and simulated UVAI values (coincidently
sampled from OMI) calculated from the GLS regression of monthly UVAI time
series values during 2005–2015. Several regions exhibit consistency between
the OMI and simulated UVAI trends. There are statistically significant
positive trends in both OMI and simulated UVAI values over the eastern United
States (OMI: 1.0 × 10-5 to 2.5 × 10-4 yr-1;
simulated: 2.5 × 10-4 to
5.0 × 10-4 yr-1) and Canada and parts of Russia (OMI:
1.0 × 10-5 to 2.5 × 10-4 yr-1; simulated:
5.0 × 10-4 to 2.0 × 10-3 yr-1).
Positive UVAI trends (1.0 × 10-5 to
2.5 × 10-4 yr-1) in both OMI and simulated values are
present over Europe, although the simulated trends have low statistical
significance. Statistically significant positive UVAI trends
(5.0 × 10-4 to 2.0 × 10-3 yr-1) in OMI
values are apparent over North Africa, which generally are captured by the
simulation but with low statistical significance. Negative UVAI trends
(-1.5 × 10-3 to -1.0 × 10-5 yr-1) in
both OMI and simulated values are apparent over most of South America,
southern Africa, and Australia. Negative UVAI trends
(-2 × 10-3 to -5.0 × 10-4 yr-1) in both
OMI and simulated values are present over West Africa, with low statistical
significance that could be related to the filtering of persistent clouds. OMI
and simulated UVAI values show negative trends (-2 × 10-3 to
-5. × 10-4 yr-1) over India, although the simulated
trends have lower statistical significance.
Some regions have trends in OMI UVAI values which are not captured by the
simulation. Statistically significant positive UVAI trends
(2.5 × 10-4 to 1.5 × 10-3 yr-1) over the
western United States are apparent in the OMI values but not in the
simulation. Zhang et al. (2017) found positive trends in aerosol absorption
optical depth from OMI retrievals that they attributed to positive trends in
mineral dust over the region, which were not captured by their GEOS-Chem
simulation. Statistically significant positive UVAI trends
(5.0 × 10-4 to 2.0 × 10-3 yr-1) in OMI
values exist over the Middle East, while the simulation has negative trends
with low statistical significance. The OMI UVAI reveals a region of
statistically significant negative trends (-2 × 10-3 to
-5.0 × 10-4 yr-1) over Mongolia/Inner Mongolia which is
not captured by the simulation. There is also a small area of statistically
significant positive UVAI trends (1.5 × 10-3 to
2.0 × 10-3 yr-1) in OMI values of over Central Asia
between the Caspian Sea and the Aral Sea which is not captured by the
simulation. Trends in surface reflectance from the diminishing Aral Sea
cannot solely explain the UVAI trends since they extend over the Caspian Sea.
Trends in mineral dust are a more likely explanation as discussed further
below.
Figures 5 and 6 show the seasonality of the OMI and simulated UVAI trends
respectively. The positive UVAI trends over the eastern United States are
strongest in summer (JJA) for both OMI and the simulation. The positive UVAI
trends over North Africa and the Middle East are present for all seasons for
OMI and for most seasons in the simulation, except in JJA for North Africa
and spring (MAM) for the Middle East. The simulation underestimates the
observed UVAI trend over North Africa in SON, perhaps related to an
underestimation in trends in mineral dust emissions in the simulation during
this season. He et al. (2014) examined the 2000–2010 trends in global
surface albedo using the Global Land Surface Satellites (GLASS) dataset and
found no significant trends over this region during SON. The negative trend
in UVAI values over West Africa is most apparent in the fall (SON) and winter
(DJF) for both OMI and the simulation. The negative OMI UVAI trends over
Mongolia/Inner Mongolia and the positive OMI UVAI trends near the Aral Sea
are strongest in JJA and weakest in DJF, providing evidence of a mineral
dust source. The OMI UVAI trend over Mongolia/Inner Mongolia may be part of a
longer-term trend. Guan et al. (2017) examined dust storm data over northern
China (including Inner Mongolia) for the period 1960–2007 and found that
dust storm frequency has been declining over the region due to a gradual
decrease in wind speed. The current generation of chemical transport models
is unlikely to represent the source near the Aral Sea without an explicit
parameterization of the drying sea. The desiccation of the Aral Sea over
recent decades has resulted in a steady decline in water coverage over the
area (Shi et al., 2014; Shi and Wang, 2015) and has led to the dried up sea
bed becoming an increasing source of dust activity in the region (Spivak et
al., 2012). Indoitu et al. (2015) found that most dust events are directed
towards the west, consistent with the OMI observations. An increase in
surface reflectance due to the drying up of the sea bed could also positively
influence trends in UVAI. He et al. (2014) found a positive trend over
2000–2010 in surface albedo over the region in JJA and SON, corresponding to
when the OMI UVAI trends are strongest.
Contribution of individual aerosol species to the simulated UVAI
To further interpret the UVAI trends, we examine the trends in aerosol
concentrations from our GEOS-Chem simulation (Fig. 7). Figure 7a shows the
trends in secondary inorganic aerosol (SIA). There are statistically
significant negative trends over the eastern United States (-1 to
-0.05 µg m-2 yr-1) and statistically significant
positive trends over the Middle East (0.05 to
0.5 µg m-2 yr-1), India (0.05 to
1 µg m-2 yr-1), South America, and southern Africa
(0.05 to 0.25 µg m-2 yr-1). Figure 7b shows the trends
in dust. Similar to the trends in emissions, the trends in dust
concentrations are of the largest magnitude of the various species, but
often with low statistical significance. There are positive trends over the
Middle East (> 2 µg m-2 yr-1), India (0.05
to 2 µg m-2 yr-1), and northwest China (1 to
2 µg m-2 yr-1). There are also positive trends (0.05 to
0.25 µg m-2 yr-1) with low statistical significance
over the United States, northern South America, southern Africa, and northern
Australia. There is a combination of positive and negative trends
(> 2 and < -2 µg m-2 yr-1) over
North Africa, and negative trends over China and Mongolia
(< -2 µg m-2 yr-1) and Australia (-1 to
-0.5 µg m-2 yr-1). Figure 7c and d show the trends
in total organic aerosol (OA) and the absorbing BrC component
of OA, respectively. Positive trends over Canada and parts of Russia (0.05 to
0.5 µg m-2 yr-1) in total OA are mainly due to the
positive trend in BrC. Statistically significant negative trends in total OA
(-1 to -0.05 µg m-2 yr-1) over the eastern United
States are dominated by scattering organic aerosol. Statistically
significant negative trends (-2 to
-0.05 µg m-2 yr-1) over West Africa and South America
for total OA are dominated by the trend in absorbing BrC. Figure 5e and f
show the trends in BC and salt, respectively. There are
positive trends (0.05 to 0.25 µg m-2 yr-1) in BC with
low statistical significance over India and China. Sea salt trends are
negligible.
To gain further insight into how changes in aerosols effect the trends in
simulated UVAI, we examine the sensitivity of the UVAI to changes in
individual aerosol species. Figure 8 shows the change in annual mean UVAI due
to doubling the concentration of individual aerosol species. This information
facilitates interpretation of the observed UVAI trends by identifying the
chemical components that could explain the observed trends. Doubling
scattering SIA concentrations (Fig. 8a) decreases the UVAI between -0.25
and -0.1 over most of the globe, with the largest changes over the eastern
United States, Europe, parts of the Middle East, India, and southeast China.
Doubling dust concentrations (Fig. 8b) produces the largest changes in UVAI,
causing increases between 0.5 and 1 over North Africa and smaller increases
between 0.2 and 0.5 over the Middle East, Europe, and parts of Asia and
Australia. Figure 8c and d show the changes in UVAI due to doubling total
OA concentrations and the absorbing BrC component, respectively. The doubling
of BrC increases the UVAI between 0.1 and 0.5 over Canada, West and Central
Africa, India, parts of Russia, eastern China, and central South America.
Doubling total OA concentrations over central South America causes a net
decrease of ∼-0.1 as the scattering component of total OA cancels
out the absorption by BrC. Doubling BC concentrations (Fig. 8e) increases the
UVAI of 0.1 over Central Africa, India, and southeast China, while doubling
sea salt concentrations (Fig. 8f) has negligible effect on the UVAI.
Figure 9 shows the change in simulated UVAI due to the 2005–2015 trends in
individual aerosol species from our GEOS-Chem simulation. The change for each
species is calculated by applying the aerosol concentration trends for the
individual aerosol type while leaving the concentrations unchanged for the
other aerosol species, then taking the difference between this perturbed UVAI
simulation and an unperturbed simulation. Negative trends in scattering SIA
(Fig. 9a) increase the UVAI by 1.0 × 10-4 to
7.5 × 10-3 yr-1 over the eastern United States and by
1.0 × 10-4 to 2.5 × 10-3 yr-1 over Europe,
corresponding to regions of positive UVAI trends in both OMI and the
simulation (Fig. 4). Increasing SIA decreases the UVAI by
-2.5 × 10-3 to -1.0 × 10-4 yr-1 over
the Middle East, India, and east China. Trends in dust concentrations
(Fig. 9b) cause the largest change in UVAI with regional increases
> 1 × 10-2 yr-1 and regional decreases
< -1 × 10-2 yr-1. Simulated UVAI trends due to
mineral dust are mostly negative over North Africa, East Asia, and Australia,
while mostly positive over the Middle East. Noisy trends in regional
meteorology cause heterogeneous trends in dust and in the UVAI, with low
statistical significance. Figure 9c and d show the change in UVAI due to the
trends in total OA and the absorbing BrC component of total OA, respectively.
Most of the changes in UVAI due to the trends in total OA are caused by the
trends in the absorbing BrC component, with increases in the UVAI between
2.5 × 10-3 and 1 × 10-2 yr-1 over Canada
and parts of Russia, corresponding to regions of positive UVAI trends for
both OMI and the simulation (Fig. 4). There are decreases in the UVAI
< -1 × 10-2 yr-1 over central South America
and West Africa due to the negative trends in BrC, corresponding to regions
of negative UVAI trends for both OMI and the simulation (Fig. 4). Over the
eastern United States there is a mixture of increases and decreases in the
UVAI due to the trends in scattering organic aerosol. Positive trends in BC
increase the UVAI (Fig. 9e) by 1.0 × 10-4 to
2.5 × 10-3 yr-1 over India and China. There are no
obvious changes in the UVAI due to the trends in sea salt (Fig. 9f).
Conclusions
Observations of aerosol scattering and absorption offer valuable information
about aerosol composition. We simulated the UVAI,
a method of detecting aerosol absorption using satellite measurements, to
interpret trends in OMI observed UVAI during 2005–2015 to understand global
trends in aerosol composition. We conducted our simulation using the vector
radiative transfer model VLIDORT with aerosol fields from the global chemical
transport model GEOS-Chem.
We demonstrated that interpretation of the OMI UVAI with a quantitative
simulation of the UVAI offers information about trends in aerosol
composition. We found that global trends in the UVAI were largely explained
by trends in absorption by mineral dust, absorption by BrC, and
scattering by secondary inorganic aerosols. We also identified areas for
model development, such as dust emissions from the desiccating Aral Sea.
We examined the 2005–2015 trends in individual aerosol species from
GEOS-Chem and applied these trends to the UVAI simulation to calculate the
change in simulated UVAI due to the trends in individual aerosol species. The
two most prominent positive trends in the observed UVAI were over North
Africa and over Central Asia near the desiccating Aral Sea. The simulated
UVAI attributes the positive trends over North Africa to increasing mineral
dust despite an underestimated simulated trend in fall (SON) that deserves
further attention. The positive trends in the observed UVAI over Central Asia
near the shrinking Aral Sea are likely due to increased dust emissions, a
feature that is unlikely to be represented in most chemical transport models.
The most prominent negative trends in the observed UVAI were over East Asia,
South Asia, and Australia. The simulation attributed the negative trends over
East Asia and Australia to decreasing mineral dust, despite underestimating
the trend in East Asia. The simulation attributed the negative trend over
South Asia to increasing scattering secondary inorganic aerosols, a trend
that the observations imply could be even larger. We found the positive
trends in the UVAI over the eastern United States that were strongest in
summer (JJA) in both the observations and the simulation were driven by
negative trends in scattering secondary inorganic aerosol and organic
aerosol. Observed negative trends in winter (DJF) were less well simulated.
Over West Africa and South America, negative trends in UVAI were explained by
negative trends in absorbing BrC. Thus, trends in the observed UVAI
offer valuable information on the evolution of global aerosol composition
that can be understood through quantitative simulation of the UVAI.
Looking forward, the availability of the UVAI observations from 1979 to the
present offers a unique opportunity to understand long-term trends in aerosol
composition. The recent launch of the TROPOspheric Monitoring Instrument
(TROPOMI; Veefkind et al., 2012) and the forthcoming geostationary
constellation offer UVAI observations at finer spatial and temporal
resolution. The forthcoming Multi-Angle Imager for Aerosols (MAIA; Diner et
al., 2018) satellite instrument offers an exciting opportunity to derive even
more information about aerosol composition by combining measurements at
ultraviolet wavelengths with multi-angle observations and polarization
sensitivity.
The OMAERUV UVAI dataset (version 1.8.9.1) used in this
study is available from the NASA Goddard Earth Sciences Data and Information
Services Center
(https://disc.sci.gsfc.nasa.gov/datasets/OMAERUV_V003/summary; last
access: 22 August 2017). The GEOS-Chem chemical transport model used here is
available at www.geos-chem.org (last access: 20 August 2017). The
VLIDORT radiative transfer model (Spurr, 2006) is available at
http://www.rtslidort.com/mainprod_vlidort.html (last access:
7 April 2016).
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-8097-2018-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the Natural Science and Engineering Research
Council of Canada and the Killam Trusts. Computational facilities were
provided in part by the Atlantic Computational Excellence Network and the
Graham consortiums of Compute Canada.
Edited by: Kostas Tsigaridis
Reviewed by: two anonymous referees
ReferencesAndela, N. and van der Werf, G. R.: Recent trends in African fires driven by
cropland expansion and El Niño to La Niña transition, Nat. Clim.
Chang., 4, 791–795, 10.1038/nclimate2313, 2014.Andreae, M. O. and Gelencsér, A.: Black carbon or brown carbon? The
nature of light-absorbing carbonaceous aerosols, Atmos. Chem. Phys., 6,
3131–3148, 10.5194/acp-6-3131-2006, 2006.Badarinath, K. V. S., Kharol, S. K., Kaskaoutis, D. G., Sharma, A. R.,
Ramaswamy, V., and Kambezidis, H. D.: Long-range transport of dust aerosols
over the Arabian Sea and Indian region – A case study using satellite data
and ground-based measurements, Global Planet. Change, 72, 164–181,
10.1016/j.gloplacha.2010.02.003, 2010.Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A.
M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling
of tropospheric chemistry with assimilated meteorology: Model description and
evaluation, J. Geophys. Res., 106, 23073, 10.1029/2001JD000807, 2001.Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T.,
DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne,
S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M.,
Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K.,
Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U.,
Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.:
Bounding the role of black carbon in the climate system: A scientific
assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, 10.1002/jgrd.50171,
2013.Boys, B. L., Martin, R. V., van Donkelaar, A., MacDonell, R. J., Hsu, N. C.,
Cooper, M. J., Yantosca, R. M., Lu, Z., Streets, D. G., Zhang, Q., and Wang,
S. W.: Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate
Matter, Environ. Sci. Technol., 48, 11109–11118, 10.1021/es502113p,
2014.Buchard, V., da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A.,
Govindaraju, R., Torres, O., Campbell, J., and Spurr, R.: Using the OMI
aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA
Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743–5760,
10.5194/acp-15-5743-2015, 2015.Chen, Y., Morton, D. C., Jin, Y., Collatz, G. J., Kasibhatla, P. S., van der
Werf, G. R., DeFries, R. S., and Randerson, J. T.: Long-term trends and
interannual variability of forest, savanna and agricultural fires in South
America, Carbon Manag., 4, 617–638, 10.4155/cmt.13.61, 2013.Chin, M., Diehl, T., Tan, Q., Prospero, J. M., Kahn, R. A., Remer, L. A., Yu,
H., Sayer, A. M., Bian, H., Geogdzhayev, I. V., Holben, B. N., Howell, S. G.,
Huebert, B. J., Hsu, N. C., Kim, D., Kucsera, T. L., Levy, R. C., Mishchenko,
M. I., Pan, X., Quinn, P. K., Schuster, G. L., Streets, D. G., Strode, S. A.,
Torres, O., and Zhao, X.-P.: Multi-decadal aerosol variations from 1980 to
2009: a perspective from observations and a global model, Atmos. Chem. Phys.,
14, 3657–3690, 10.5194/acp-14-3657-2014, 2014.Crippa, M., Janssens-Maenhout, G., Dentener, F., Guizzardi, D., Sindelarova,
K., Muntean, M., Van Dingenen, R., and Granier, C.: Forty years of
improvements in European air quality: regional policy-industry interactions
with global impacts, Atmos. Chem. Phys., 16, 3825–3841,
10.5194/acp-16-3825-2016, 2016.Cui, H., Mao, P., Zhao, Y., Nielsen, C. P., and Zhang, J.: Patterns in
atmospheric carbonaceous aerosols in China: emission estimates and observed
concentrations, Atmos. Chem. Phys., 15, 8657–8678,
10.5194/acp-15-8657-2015, 2015.Curci, G., Hogrefe, C., Bianconi, R., Im, U., Balzarini, A., Baró, R.,
Brunner, D., Forkel, R., Giordano, L., Hirtl, M., Honzak, L.,
Jiménez-Guerrero, P., Knote, C., Langer, M., Makar, P. A., Pirovano, G.,
Pérez, J. L., San José, R., Syrakov, D., Tuccella, P., Werhahn, J.,
Wolke, R., Žabkar, R., Zhang, J., and Galmarini, S.: Uncertainties of
simulated aerosol optical properties induced by assumptions on aerosol
physical and chemical properties: An AQMEII-2 perspective, Atmos. Environ.,
115, 541–552, 10.1016/j.atmosenv.2014.09.009, 2015.Curier, L., Kranenburg, R., Timmermans, R., Segers, A., Eskes, H., and
Schaap, M.: Synergistic Use of LOTOS-EUROS and NO2 Tropospheric
Columns to Evaluate the NOx Emission Trends Over Europe,
239–245, 2014.Dai, A.: Recent Climatology, Variability, and Trends in Global Surface
Humidity, J. Climate, 19, 3589–3606, 10.1175/JCLI3816.1, 2006.de Graaf, M., Stammes, P., Torres, O., and Koelemeijer, R. B. A.: Absorbing
Aerosol Index: Sensitivity analysis, application to GOME and comparison with
TOMS, J. Geophys. Res., 110, D01201, 10.1029/2004JD005178, 2005.Deirmendjian, D.: Scattering and Polarization Properties of Water Clouds and
Hazes in the Visible and Infrared, Appl. Opt., 3, 187–196,
10.1364/AO.3.000187, 1964.de Meij, A., Pozzer, A., and Lelieveld, J.: Trend analysis in aerosol optical
depths and pollutant emission estimates between 2000 and 2009, Atmos.
Environ., 51, 75–85, 10.1016/j.atmosenv.2012.01.059, 2012.Dey, S. and Di Girolamo, L.: A decade of change in aerosol properties over
the Indian subcontinent, Geophys. Res. Lett., 38, L14811,
10.1029/2011GL048153, 2011.
Diner, D. J., Brauer, M., Bruegge, C., Burke, K. A., Chipman, R., Di
Girolamo, L., Garay, M. J., Hasheminassab, S., Hyer, E., Jerrett, M.,
Jovanovic, V., Kalashnikova, O. V., Liu, Y., Lyapustin, A. I., Martin., R.
V., Nastan, A., Ostro, B. D., Ritz, B., Schwartz, J., Wang, J., and Xua, F.:
Advances in multiangle satellite remote sensing of speciated airborne
particulate matter and association with adverse health effects: from MISR to
MAIA, J. Appl. Remote Sens., submitted, 2018.Drury, E., Jacob, D. J., Spurr, R. J. D., Wang, J., Shinozuka, Y., Anderson,
B. E., Clarke, A. D., Dibb, J., McNaughton, C., and Weber, R.: Synthesis of
satellite (MODIS), aircraft (ICARTT), and surface (IMPROVE, EPA-AQS, AERONET)
aerosol observations over eastern North America to improve MODIS aerosol
retrievals and constrain surface aerosol concentrations and sources, J.
Geophys. Res., 115, D14204, 10.1029/2009JD012629, 2010.Duncan, B. N., Martin, R. V., Staudt, A. C., Yevich, R., and Logan, J. A.:
Interannual and seasonal variability of biomass burning emissions constrained
by satellite observations, J. Geophys. Res., 108, 4100,
10.1029/2002JD002378, 2003.Fairlie, D. J., Jacob, D. J., and Park, R. J.: The impact of transpacific
transport of mineral dust in the United States, Atmos. Environ., 41,
1251–1266, 10.1016/j.atmosenv.2006.09.048, 2007.Fioletov, V. E., McLinden, C. A., Krotkov, N., Li, C., Joiner, J., Theys, N.,
Carn, S., and Moran, M. D.: A global catalogue of large SO2 sources
and emissions derived from the Ozone Monitoring Instrument, Atmos. Chem.
Phys., 16, 11497–11519, 10.5194/acp-16-11497-2016, 2016.Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient
thermodynamic equilibrium model for
K+–Ca+2–Mg+2–NH4+–Na+–SO42-–NO3–Cl–H2O
aerosols, Atmos. Chem. Phys., 7, 4639–4659,
10.5194/acp-7-4639-2007, 2007.Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth-generation global fire
emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328,
10.1002/jgrg.20042, 2013.Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.:
Global-scale attribution of anthropogenic and natural dust sources and their
emission rates based on MODIS Deep Blue aerosol products, Rev. Geophys., 50,
RG3005, 10.1029/2012RG000388, 2012.Guan, H., Esswein, R., Lopez, J., Bergstrom, R., Warnock, A., Follette-Cook,
M., Fromm, M., and Iraci, L. T.: A multi-decadal history of biomass burning
plume heights identified using aerosol index measurements, Atmos. Chem.
Phys., 10, 6461–6469, 10.5194/acp-10-6461-2010, 2010.Guan, Q., Sun, X., Yang, J., Pan, B., Zhao, S., Wang, L., Guan, Q., Sun, X.,
Yang, J., Pan, B., Zhao, S., and Wang, L.: Dust Storms in Northern China:
Long-Term Spatiotemporal Characteristics and Climate Controls, J. Climate,
30, 6683–6700, 10.1175/JCLI-D-16-0795.1, 2017.Guan, X., Huang, J., Zhang, Y., Xie, Y., and Liu, J.: The relationship
between anthropogenic dust and population over global semi-arid regions,
Atmos. Chem. Phys., 16, 5159–5169, 10.5194/acp-16-5159-2016,
2016.Guo, Y., Tian, B., Kahn, R. A., Kalashnikova, O., Wong, S., and Waliser, D.
E.: Tropical Atlantic dust and smoke aerosol variations related to the
Madden-Julian Oscillation in MODIS and MISR observations, J. Geophys.
Res.-Atmos., 118, 4947–4963, 10.1002/jgrd.50409, 2013.Hammer, M. S., Martin, R. V., van Donkelaar, A., Buchard, V., Torres, O.,
Ridley, D. A., and Spurr, R. J. D.: Interpreting the ultraviolet aerosol
index observed with the OMI satellite instrument to understand absorption by
organic aerosols: implications for atmospheric oxidation and direct radiative
effects, Atmos. Chem. Phys., 16, 2507–2523,
10.5194/acp-16-2507-2016, 2016.He, T., Liang, S., and Song, D.-X.: Analysis of global land surface albedo
climatology and spatial-temporal variation during 1981–2010 from multiple
satellite products, J. Geophys. Res. Atmos., 119, 10281–10298,
10.1002/2014JD021667, 2014.Heald, C. L., Collett Jr., J. L., Lee, T., Benedict, K. B., Schwandner, F.
M., Li, Y., Clarisse, L., Hurtmans, D. R., Van Damme, M., Clerbaux, C.,
Coheur, P.-F., Philip, S., Martin, R. V., and Pye, H. O. T.: Atmospheric
ammonia and particulate inorganic nitrogen over the United States, Atmos.
Chem. Phys., 12, 10295–10312, 10.5194/acp-12-10295-2012,
2012.Herman, J. R., Bhartia, P. K., Torres, O., Hsu, C., Seftor, C., and Celarier,
E.: Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data, J.
Geophys. Res., 102, 16911, 10.1029/96JD03680, 1997.Hsu, N. C., Gautam, R., Sayer, A. M., Bettenhausen, C., Li, C., Jeong, M. J.,
Tsay, S.-C., and Holben, B. N.: Global and regional trends of aerosol optical
depth over land and ocean using SeaWiFS measurements from 1997 to 2010,
Atmos. Chem. Phys., 12, 8037–8053, 10.5194/acp-12-8037-2012, 2012.Huang, J., Minnis, P., Yan, H., Yi, Y., Chen, B., Zhang, L., and Ayers, J.
K.: Dust aerosol effect on semi-arid climate over Northwest China detected
from A-Train satellite measurements, Atmos. Chem. Phys., 10, 6863–6872,
10.5194/acp-10-6863-2010, 2010.Huang, J. P., Liu, J. J., Chen, B., and Nasiri, S. L.: Detection of
anthropogenic dust using CALIPSO lidar measurements, Atmos. Chem. Phys., 15,
11653–11665, 10.5194/acp-15-11653-2015, 2015.Indoitu, R., Kozhoridze, G., Batyrbaeva, M., Vitkovskaya, I., Orlovsky, N.,
Blumberg, D., and Orlovsky, L.: Dust emission and environmental changes in
the dried bottom of the Aral Sea, Aeolian Res., 17, 101–115,
10.1016/j.aeolia.2015.02.004, 2015.
IPCC: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A:
Global and Sectoral Aspects, Contribution of Working Group II to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Field, C. B., Barros, V. R., and Dokken, D. J., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 2014.Israelevich, P. L., Levin, Z., Joseph, J. H., and Ganor, E.: Desert aerosol
transport in the Mediterranean region as inferred from the TOMS aerosol
index, J. Geophys. Res.-Atmos., 107, AAC 13-1–AAC 13-13,
10.1029/2001JD002011, 2002.Jaeglé, L., Quinn, P. K., Bates, T. S., Alexander, B., and Lin, J.-T.:
Global distribution of sea salt aerosols: new constraints from in situ and
remote sensing observations, Atmos. Chem. Phys., 11, 3137–3157,
10.5194/acp-11-3137-2011, 2011.Jethva, H. and Torres, O.: Satellite-based evidence of wavelength-dependent
aerosol absorption in biomass burning smoke inferred from Ozone Monitoring
Instrument, Atmos. Chem. Phys., 11, 10541–10551,
10.5194/acp-11-10541-2011, 2011.Kahn, R. A. and Gaitley, B. J.: An analysis of global aerosol type as
retrieved by MISR, J. Geophys. Res.-Atmos., 120, 4248–4281,
10.1002/2015JD023322, 2015.Kalashnikova, O. V. and Kahn, R. A.: Mineral dust plume evolution over the
Atlantic from MISR and MODIS aerosol retrievals, J. Geophys. Res., 113,
D24204, 10.1029/2008JD010083, 2008.Kaskaoutis, D. G., Kharol, S. K., Sifakis, N., Nastos, P. T., Sharma, A. R.,
Badarinath, K. V. S., and Kambezidis, H. D.: Satellite monitoring of the
biomass-burning aerosols during the wildfires of August 2007 in Greece:
Climate implications, Atmos. Environ., 45, 716–726,
10.1016/j.atmosenv.2010.09.043, 2011.Klimont, Z., Smith, S. J., and Cofala, J.: The last decade of global
anthropogenic sulfur dioxide: 2000–2011 emissions, Environ. Res. Lett.,
8, 14003, 10.1088/1748-9326/8/1/014003, 2013.Klimont, Z., Kupiainen, K., Heyes, C., Purohit, P., Cofala, J., Rafaj, P.,
Borken-Kleefeld, J., and Schöpp, W.: Global anthropogenic emissions of
particulate matter including black carbon, Atmos. Chem. Phys., 17,
8681–8723, 10.5194/acp-17-8681-2017, 2017.
Koepke, P., Hess, M., Schult, I., and Shettle, E. P.: Global Aerosol Dataset,
report, Max-Planck Inst. fur Meteorol., Hamburg, Germany, 1997.Kristiansen, N. I., Stohl, A., Olivié, D. J. L., Croft, B., Søvde, O.
A., Klein, H., Christoudias, T., Kunkel, D., Leadbetter, S. J., Lee, Y. H.,
Zhang, K., Tsigaridis, K., Bergman, T., Evangeliou, N., Wang, H., Ma, P.-L.,
Easter, R. C., Rasch, P. J., Liu, X., Pitari, G., Di Genova, G., Zhao, S. Y.,
Balkanski, Y., Bauer, S. E., Faluvegi, G. S., Kokkola, H., Martin, R. V.,
Pierce, J. R., Schulz, M., Shindell, D., Tost, H., and Zhang, H.: Evaluation
of observed and modelled aerosol lifetimes using radioactive tracers of
opportunity and an ensemble of 19 global models, Atmos. Chem. Phys., 16,
3525–3561, 10.5194/acp-16-3525-2016, 2016.Kuhns, H., Knipping, E. M., and Vukovich, J. M.: Development of a United
States–Mexico Emissions Inventory for the Big Bend Regional Aerosol and
Visibility Observational (BRAVO) Study, J. Air Waste Manage., 55, 677–692,
10.1080/10473289.2005.10464648, 2005.Lee, C., Martin, R. V., van Donkelaar, A., O'Byrne, G., Krotkov, N., Richter,
A., Huey, L. G., and Holloway, J. S.: Retrieval of vertical columns of sulfur
dioxide from SCIAMACHY and OMI: Air mass factor algorithm development,
validation, and error analysis, J. Geophys. Res., 114, D22303,
10.1029/2009JD012123, 2009.Leibensperger, E. M., Mickley, L. J., Jacob, D. J., Chen, W.-T., Seinfeld, J.
H., Nenes, A., Adams, P. J., Streets, D. G., Kumar, N., and Rind, D.:
Climatic effects of 1950–2050 changes in US anthropogenic aerosols – Part
2: Climate response, Atmos. Chem. Phys., 12, 3349–3362,
10.5194/acp-12-3349-2012, 2012.Li, C., Martin, R. V., van Donkelaar, A., Boys, B. L., Hammer, M. S., Xu,
J.-W., Marais, E. A., Reff, A., Strum, M., Ridley, D. A., Crippa, M., Brauer,
M., and Zhang, Q.: Trends in Chemical Composition of Global and Regional
Population-Weighted Fine Particulate Matter Estimated for 25 Years, Environ.
Sci. Technol., 51, 11185–11195, 10.1021/acs.est.7b02530, 2017.Li, J., Carlson, B. E., Dubovik, O., and Lacis, A. A.: Recent trends in
aerosol optical properties derived from AERONET measurements, Atmos. Chem.
Phys., 14, 12271–12289, 10.5194/acp-14-12271-2014, 2014.Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T.,
Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H.,
Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian
anthropogenic emission inventory under the international collaboration
framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963,
10.5194/acp-17-935-2017, 2017.Liu, F., Zhang, Q., van der A, R. J., Zheng, B., Tong, D., Yan, L., Zheng,
Y., and He, K.: Recent reduction in NOx emissions over China:
synthesis of satellite observations and emission inventories, Environ. Res.
Lett., 11, 114002, 10.1088/1748-9326/11/11/114002, 2016.Liu, Y., Koutrakis, P., and Kahn, R.: Estimating fine particulate matter
component concentrations and size distributions using satellite-retrieved
fractional aerosol optical depth: part 1 – method development., J. Air Waste
Manage., 57, 1351–1359, 10.3155/1047-3289.57.11.1351, 2007.Lu, Z., Zhang, Q., and Streets, D. G.: Sulfur dioxide and primary
carbonaceous aerosol emissions in China and India, 1996–2010, Atmos. Chem.
Phys., 11, 9839–9864, 10.5194/acp-11-9839-2011, 2011.Ma, Z., Hu, X., Sayer, A. M., Levy, R., Zhang, Q., Xue, Y., Tong, S., Bi, J.,
Huang, L., and Liu, Y.: Satellite-Based Spatiotemporal Trends in PM2.5
Concentrations: China, 2004–2013, Environ. Health Persp., 124, 184–92,
10.1289/ehp.1409481, 2016.Mann, M. E. and Emanuel, K. A.: Atlantic hurricane trends linked to climate
change, EOS T. Am. Geophys. Un., 87, 233–241, 10.1029/2006EO240001,
2006.Mao, K. B., Ma, Y., Xia, L., Chen, W. Y., Shen, X. Y., He, T. J., and Xu, T.
R.: Global aerosol change in the last decade: An analysis based on MODIS
data, Atmos. Environ., 94, 680–686, 10.1016/j.atmosenv.2014.04.053,
2014.Marais, E. A., Jacob, D. J., Jimenez, J. L., Campuzano-Jost, P., Day, D. A.,
Hu, W., Krechmer, J., Zhu, L., Kim, P. S., Miller, C. C., Fisher, J. A.,
Travis, K., Yu, K., Hanisco, T. F., Wolfe, G. M., Arkinson, H. L., Pye, H. O.
T., Froyd, K. D., Liao, J., and McNeill, V. F.: Aqueous-phase mechanism for
secondary organic aerosol formation from isoprene: application to the
southeast United States and co-benefit of SO2 emission controls,
Atmos. Chem. Phys., 16, 1603–1618, 10.5194/acp-16-1603-2016,
2016.Martin, R. V., Jacob, D. J., Yantosca, R. M., Chin, M., and Ginoux, P.:
Global and regional decreases in tropospheric oxidants from photochemical
effects of aerosols, J. Geophys. Res., 108, 4097, 10.1029/2002JD002622,
2003.Mauritsen, T.: Arctic climate change: Greenhouse warming unleashed, Nat.
Geosci., 9, 271–272, 10.1038/ngeo2677, 2016.Mehta, M., Singh, R., Singh, A., Singh, N., and Anshumali: Recent global
aerosol optical depth variations and trends – A comparative study using
MODIS and MISR level 3 datasets, Remote Sens. Environ., 181, 137–150,
10.1016/j.rse.2016.04.004, 2016.Mielonen, T., Portin, H., Komppula, M., Leskinen, A., Tamminen, J., Ialongo,
I., Hakkarainen, J., Lehtinen, K. E. J., and Arola, A.: Biomass burning
aerosols observed in Eastern Finland during the Russian wildfires in summer
2010 – Part 2: Remote sensing, Atmos. Environ., 47, 279–287,
10.1016/j.atmosenv.2011.07.016, 2012.Moosmüller, H., Chakrabarty, R. K., and Arnott, W. P.: Aerosol light
absorption and its measurement: A review, J. Quant. Spectrosc. Ra., 110,
844–878, 10.1016/j.jqsrt.2009.02.035, 2009.Norris, J. R. and Wild, M.: Trends in aerosol radiative effects over Europe
inferred from observed cloud cover, solar “dimming,” and solar
“brightening,” J. Geophys. Res., 112, D08214, 10.1029/2006JD007794,
2007.Park, R. J., Jacob, D. J., Chin, M., and Martin, R. V.: Sources of
carbonaceous aerosols over the United States and implications for natural
visibility, J. Geophys. Res., 108, 4355, 10.1029/2002JD003190, 2003.Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M., and Chin, M.:
Natural and transboundary pollution influences on sulfate-nitrate-ammonium
aerosols in the United States: Implications for policy, J. Geophys. Res.,
109, D15204, 10.1029/2003JD004473, 2004.Pelletier, J. D. and Turcotte, D. L.: Long-range persistence in
climatological and hydrological time series: analysis, modeling and
application to drought hazard assessment, J. Hydrol., 203, 198–208,
10.1016/S0022-1694(97)00102-9, 1997.Penning de Vries, M. J. M., Beirle, S., and Wagner, T.: UV Aerosol Indices
from SCIAMACHY: introducing the SCattering Index (SCI), Atmos. Chem. Phys.,
9, 9555–9567, 10.5194/acp-9-9555-2009, 2009.Penning de Vries, M. J. M., Beirle, S., Hörmann, C., Kaiser, J. W.,
Stammes, P., Tilstra, L. G., Tuinder, O. N. E., and Wagner, T.: A global
aerosol classification algorithm incorporating multiple satellite data sets
of aerosol and trace gas abundances, Atmos. Chem. Phys., 15, 10597–10618,
10.5194/acp-15-10597-2015, 2015.Philip, S., Martin, R. V, Snider, G., Weagle, C. L., van Donkelaar, A.,
Brauer, M., Henze, D. K., Klimont, Z., Venkataraman, C., Guttikunda, S. K.,
and Zhang, Q.: Anthropogenic fugitive, combustion and industrial dust is a
significant, underrepresented fine particulate matter source in global
atmospheric models, Environ. Res. Lett., 12, 44018,
10.1088/1748-9326/aa65a4, 2017.Pöschl, U.: Atmospheric Aerosols: Composition, Transformation, Climate
and Health Effects, Angew. Chem. Int. Edit., 44, 7520–7540,
10.1002/anie.200501122, 2005.Povey, A. C. and Grainger, R. G.: Known and unknown unknowns: uncertainty
estimation in satellite remote sensing, Atmos. Meas. Tech., 8, 4699–4718,
10.5194/amt-8-4699-2015, 2015.Prinn, R., Cunnold, D., Simmonds, P., Alyea, F., Boldi, R., Crawford, A.,
Fraser, P., Gutzler, D., Hartley, D., Rosen, R., and Rasmussen, R.: Global
average concentration and trend for hydroxyl radicals deduced from ALE/GAGE
trichloroethane (methyl chloroform) data for 1978–1990, J. Geophys. Res.,
97, 2445, 10.1029/91JD02755, 1992.Pye, H. O. T., Liao, H., Wu, S., Mickley, L. J., Jacob, D. J., Henze, D. K.,
and Seinfeld, J. H.: Effect of changes in climate and emissions on future
sulfate-nitrate-ammonium aerosol levels in the United States, J. Geophys.
Res., 114, D01205, 10.1029/2008JD010701, 2009.Pye, H. O. T., Chan, A. W. H., Barkley, M. P., and Seinfeld, J. H.: Global
modeling of organic aerosol: the importance of reactive nitrogen
(NOx and NO3), Atmos. Chem. Phys., 10, 11261–11276,
10.5194/acp-10-11261-2010, 2010.Ramanathan, V. and Carmichael, G.: Global and regional climate changes due to
black carbon, Nat. Geosci., 1, 221–227, 10.1038/ngeo156, 2008.Reynolds, R. W. and Reynolds, R. W.: A Real-Time Global Sea Surface
Temperature Analysis, J. Climate, 1, 75–87,
10.1175/1520-0442(1988)001<0075:ARTGSS>2.0.CO;2, 1988.Ridley, D. A., Heald, C. L., and Ford, B.: North African dust export and
deposition: A satellite and model perspective, J. Geophys. Res., 117, D02202,
10.1029/2011JD016794, 2012.Schenkeveld, V. M. E., Jaross, G., Marchenko, S., Haffner, D., Kleipool, Q.
L., Rozemeijer, N. C., Veefkind, J. P., and Levelt, P. F.: In-flight
performance of the Ozone Monitoring Instrument, Atmos. Meas. Tech., 10,
1957–1986, 10.5194/amt-10-1957-2017, 2017.Schepanski, K., Tegen, I., Laurent, B., Heinold, B., and Macke, A.: A new
Saharan dust source activation frequency map derived from MSG-SEVIRI
IR-channels, Geophys. Res. Lett., 34, L18803, 10.1029/2007GL030168, 2007.Scollo, S., Kahn, R. A., Nelson, D. L., Coltelli, M., Diner, D. J., Garay, M.
J., and Realmuto, V. J.: MISR observations of Etna volcanic plumes, J.
Geophys. Res.-Atmos., 117, D06210, 10.1029/2011JD016625, 2012.Shao, Y., Klose, M., and Wyrwoll, K.-H.: Recent global dust trend and
connections to climate forcing, J. Geophys. Res.-Atmos., 118, 11107–11118,
10.1002/jgrd.50836, 2013.Shi, W. and Wang, M.: Decadal changes of water properties in the Aral Sea
observed by MODIS-Aqua, J. Geophys. Res.-Oceans, 120, 4687–4708,
10.1002/2015JC010937, 2015.Shi, W., Wang, M., and Guo, W.: Long-term hydrological changes of the Aral
Sea observed by satellites, J. Geophys. Res.-Oceans, 119, 3313–3326,
10.1002/2014JC009988, 2014.Simon, H., Reff, A., Wells, B., Xing, J., and Frank, N.: Ozone Trends Across
the United States over a Period of Decreasing NOx and VOC
Emissions, Environ. Sci. Technol., 49, 186–195, 10.1021/es504514z,
2015.
Spivak, L., Terechov, A., Vitkovskaya, I., Batyrbayeva, M., and Orlovsky, L.:
Dynamics of Dust Transfer from the Desiccated Aral Sea Bottom Analysed by
Remote Sensing, Springer, Berlin, Heidelberg, 97–106, 2012.Spurr, R. J. D.: VLIDORT: A linearized pseudo-spherical vector discrete
ordinate radiative transfer code for forward model and retrieval studies in
multilayer multiple scattering media, J. Quant. Spectrosc. Ra., 102,
316–342, 10.1016/j.jqsrt.2006.05.005, 2006.Stier, P., Seinfeld, J. H., Kinne, S., and Boucher, O.: Aerosol absorption
and radiative forcing, Atmos. Chem. Phys., 7, 5237–5261,
10.5194/acp-7-5237-2007, 2007.Storelvmo, T., Leirvik, T., Lohmann, U., Phillips, P. C. B., and Wild, M.:
Disentangling greenhouse warming and aerosol cooling to reveal Earth's
climate sensitivity, Nat. Geosci., 9, 286–289, 10.1038/ngeo2670, 2016.Torres, O., Bhartia, P. K., Herman, J. R., Ahmad, Z., and Gleason, J.:
Derivation of aerosol properties from satellite measurements of backscattered
ultraviolet radiation: Theoretical basis, J. Geophys. Res., 103, 17099,
10.1029/98JD00900, 1998.Torres, O., Tanskanen, A., Veihelmann, B., Ahn, C., Braak, R., Bhartia, P.
K., Veefkind, P., and Levelt, P.: Aerosols and surface UV products from Ozone
Monitoring Instrument observations: An overview, J. Geophys. Res., 112,
D24S47, 10.1029/2007JD008809, 2007.Torres, O., Chen, Z., Jethva, H., Ahn, C., Freitas, S. R., and Bhartia, P.
K.: OMI and MODIS observations of the anomalous 2008–2009 Southern
Hemisphere biomass burning seasons, Atmos. Chem. Phys., 10, 3505–3513,
10.5194/acp-10-3505-2010, 2010.Torres, O., Bhartia, P. K., Jethva, H., and Ahn, C.: Impact of the ozone
monitoring instrument row anomaly on the long-term record of aerosol
products, Atmos. Meas. Tech., 11, 2701–2715,
10.5194/amt-11-2701-2018, 2018.Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu,
L., Yu, K., Miller, C. C., Yantosca, R. M., Sulprizio, M. P., Thompson, A.
M., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Cohen, R. C.,
Laughner, J. L., Dibb, J. E., Hall, S. R., Ullmann, K., Wolfe, G. M.,
Pollack, I. B., Peischl, J., Neuman, J. A., and Zhou, X.: Why do models
overestimate surface ozone in the Southeast United States?, Atmos. Chem.
Phys., 16, 13561–13577, 10.5194/acp-16-13561-2016, 2016.Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J.,
Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele,
M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann,
P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.:
TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global
observations of the atmospheric composition for climate, air quality and
ozone layer applications, Remote Sens. Environ., 120, 70–83,
10.1016/J.RSE.2011.09.027, 2012.Wang, S., Zhang, Q., Martin, R. V, Philip, S., Liu, F., Li, M., Jiang, X.,
and He, K.: Satellite measurements oversee China's sulfur dioxide emission
reductions from coal-fired power plants, Environ. Res. Lett., 10, 114015,
10.1088/1748-9326/10/11/114015, 2015.Weatherhead, E. C., Reinsel, G. C., Tiao, G. C., Meng, X.-L., Choi, D.,
Cheang, W.-K., Keller, T., DeLuisi, J., Wuebbles, D. J., Kerr, J. B., Miller,
A. J., Oltmans, S. J., and Frederick, J. E.: Factors affecting the detection
of trends: Statistical considerations and applications to environmental data,
J. Geophys. Res.-Atmos., 103, 17149–17161, 10.1029/98JD00995, 1998.Weatherhead, E. C., Stevermer, A. J., and Schwartz, B. E.: Detecting
environmental changes and trends, Phys. Chem. Earth, Parts A/B/C, 27,
399–403, 10.1016/S1474-7065(02)00019-0, 2002.
Wilks, D. S.: Statistical methods in the atmospheric sciences, Academic
Press, 2011.Xing, J., Mathur, R., Pleim, J., Hogrefe, C., Gan, C.-M., Wong, D. C., Wei,
C., Gilliam, R., and Pouliot, G.: Observations and modeling of air quality
trends over 1990–2010 across the Northern Hemisphere: China, the United
States and Europe, Atmos. Chem. Phys., 15, 2723–2747,
10.5194/acp-15-2723-2015, 2015.Zhang, L., Henze, D. K., Grell, G. A., Torres, O., Jethva, H., and Lamsal, L.
N.: What factors control the trend of increasing AAOD over the United States
in the last decade?, J. Geophys. Res.-Atmos., 122, 1797–1810,
10.1002/2016JD025472, 2017.Zhang, Y., Wallace, J. M., Battisti, D. S., Zhang, Y., Wallace, J. M., and
Battisti, D. S.: ENSO-like Interdecadal Variability: 1900–93, J. Climate,
10, 1004–1020, 10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2, 1997.Zhao, B., Wang, S. X., Liu, H., Xu, J. Y., Fu, K., Klimont, Z., Hao, J. M.,
He, K. B., Cofala, J., and Amann, M.: NOx emissions in China: historical
trends and future perspectives, Atmos. Chem. Phys., 13, 9869–9897,
10.5194/acp-13-9869-2013, 2013.