Long-term simulations with the coupled WRF–CMAQ (Weather Research
and Forecasting–Community Multi-scale Air Quality) model have been conducted to
systematically investigate the changes in anthropogenic emissions of SO
Sulfate and nitrate are important secondary aerosols as they are key
contributors to the airborne PM
Section 2 gives a brief overview of each observation network together with
their measurements. The configurations of the coupled model together with
methodologies that are applied to each data set are also briefly discussed in
this section. The results from the analyses of these data sets are presented
in Sect. 3. In this section, the effects of the reduction in SO
The comprehensive observational data analysis presented in a previous study
by Gan et al. (2014a) is used in this study. This section provides a brief
overview of the observations. The reader is referred to Gan et al. (2014a)
for additional details of the observational data analysis. Data from several
observational networks including SURFRAD (Surface Radiation Budget Network),
Atmospheric Radiation Measurement (ARM), CASTNET (Clean Air Status and Trend
Network), and IMPROVE (Interagency Monitoring of Protection Visual
Environments) from 1995 to 2010 are used in this study for comparison with
model results across the US. The six sites from SURFRAD and one site from
ARM, listed in Table 1 and shown in Fig. 1, are the main focus in this
study. They are paired with the closest sites from CASTNET and IMPROVE with
the longest available measurements within the simulation period. Note that
some sites are farther away from the SURFRAD sites while some are closer
(see “Distance” in Table 1 for more information). For example, the
Bondville group has all three sites (SURFRAD, CASTNET, and IMPROVE) co-located
while the Goodwin Creek group has the IMPROVE site
Locations of various sites in the SURFRAD, ARM, CASTNET, and IMPROVE networks. This figure is adapted from Fig. 1 of Gan et al. (2014a).
Listing of site identification of each site for different networks and their measurement period which are used in this study. Distance means the approximate distance between SURFRAD/ARM sites and CASTNET or IMPROVE sites. This table is adapted from Gan et al. (2014a).
The coupled two-way WRF–CMAQ (Wong et al., 2012) model simulations were
performed with a configuration based on coupling WRFv3.4 and CMAQv5.0.2. For
this study, the output temporal resolution is 1 h while the modeling
domain covering the continental US (CONUS) (see Fig. 1) is discretized
with grid cells of 36 km by 36 km size in the horizontal and with 35 vertical layers of varying thickness (between the surface and 50 mb). Two
sets of simulations (with aerosol feedback, FB, and without aerosol
feedbacks, NFB) are performed from 1990 to 2010 but only results from 1995
to 2010 are analyzed in this study due to the lack of observations for
earlier time periods. Note that the aerosol feedback simulation involved
only the direct aerosol effects on radiation and photolysis. In the coupled
modeling system, CMAQ computes the concentration, composition, and size
distribution of particulate matter (aerosol) in the atmosphere. The presence
of aerosols in the atmosphere affects the radiation which in turn affects
the photolysis rates which dictate atmospheric photo-chemistry, surface
temperature that can affect thermally driven atmospheric chemical reactions,
planetary boundary layer height which dictates dilution and dispersion of
pollutants, and even cloud formation. The response of the meteorological/WRF model to aerosol loading can be significant under conditions of
significant pollution loading (Wang et al., 2014). In these feedback
simulations, aerosol effects are treated dynamically where the CMAQ
chemistry and radiation feedback modules are called every 5 and 20 WRF time
steps, respectively. While the time step of WRF is 60 s, the
meteorology fields are updated from the feedback module every 20 min.
The AOD calculation in the model is based on Mie and core-shell scattering
(Gan et al., 2014b) while the radiation calculation is based on a rapid
radiative transfer model (RRTM). Four-dimensional data assimilation (FDDA)
based on National Centers for Environmental Prediction (NCEP) Automated Data
Processing (ADP) Operation Global Surface Observation (
The WRF–CMAQ modeling system used in this study treats all relevant aerosol
species, including sulfate, nitrate, ammonium, dust and organic aerosols.
Likewise, the model also used a comprehensive emission data set (Xing et al.,
2015) which included aerosol precursors and primary particulate matter.
Additional details on the aerosol speciation represented in the CMAQ model
can be found in Carlton et al. (2010), Foley et al. (2010), and Appel et
al. (2013). Furthermore, Gan et al. (2014b) discuss how the optical
properties and AOD are estimated based on the predicted spatially and
temporally varying compositional characteristics which included the full
suite of inorganic and organic constituents. However, in the analysis in this
paper, we mainly focus on the change (i.e., reduction) of sulfate and nitrate
which are the aerosol species most affected by emission reductions under the
Clean Air Act and its amendments over the past 2 decades. The time varying
chemical lateral boundary conditions (BC) were obtained from a
108 km
List of model configuration.
First, the seven sites shown in Fig. 1 are separated into east and west regions. The results from each observation network are presented as time series of their network mean of the eastern US (i.e., averaging the annual mean of BON, GWN, PSU, and SGP to obtain the eastern network mean) and of the western US (i.e., averaging the annual mean of TBL, FPK, and DRA to obtain the western network mean). Note that they are shown as annual mean anomalies except AOD. Specific time series trends at each site for different observed variables can be found in Gan et al. (2014a). The same averaging technique is applied to the model output and emission data set. Model data is extracted from the grid cell where the site is located. After that, least-square fits (LSFs) are applied to both eastern and western network means for observations, model output, and emissions to determine the trends individually.
To ensure the estimated trends are statistically significant, a regression analysis is used to account for autocorrelation and variability in both observed and modeled data. This statistical methodology is constructed from Weatherhead et al. (1998), the general principle and its application can be found in Gan et al. (2014a). Note that the significance of the trend can be calculated using the ratio of the absolute trend relative to its uncertainty estimate. This ratio is assumed to be approximately normally distributed with a mean of zero and standard deviation of 1. Thus, if this ratio is 1.96 or greater, the trend is significant at the 95 % confidence level. In the same way, if this ratio is greater than 1.65, the trend is significant at the 90 % confidence level. The term “significant” in this study indicates that the estimated trend is statistically significantly different from zero at the given confidence level.
Annual mean anomalies of 1995–2010 SO
In addition to the time series and trends at specific modeling locations, our analysis also includes maps of trends in annual mean values calculated from the 1995–2010 WRF–CMAQ simulations over the CONUS domain overlaid with circles representing observed trends from the seven selected sites for each network. The size of the circle in Figs. 4, 5, 6, and 9 represents the level of the significance (e.g., the bigger the circle, the higher the significance). An analysis of the entire US for the whole 16-year period (except AOD is represented by the last 14 years) provides a better overall understanding of the spatial extent of the effects of the CAA implementation across the US than just the seven groups of sites.
Annual mean anomalies of 1995–2010 NO
Since this study attempts to determine the aerosol radiative effects, the
following discussion focusses only on the feedback simulations. First, the
observed and modeled surface aerosol and gas concentrations are assessed at
the CASTNET and IMPROVE monitors. Their time series trends are presented in
Figs. 2 and 3, respectively. As illustrated in Fig. 2a–f, the locations of
the CASTNET monitors in the western US show small, decreasing, or almost no
trends in observations, model, and emissions for all species (i.e., sulfur
dioxide, SO
Trends of 16 years for CASTNET observations, aerosol feedback (FB) simulation, and emissions. The table also shows the uncertainty estimates of the trends (standard error, SE), the ratio of the absolute trends relative to their uncertainty estimate, and the confidence level based on the method described in Weatherhead et al. (1998) and Gan et al. (2014a).
To assess the effects of reductions in anthropogenic emissions resulting from the implementation of the CAA during 1995–2010, the modeled trends in annual means across the entire CONUS domain for all species are presented in Fig. 4a–f along with observed trends at the seven sites (color-coded circles) from the CASTNET and IMPROVE networks. In general, at the location of the observations (circles), the modeled and observed trends are similar in direction and magnitude (i.e., similar color code). As shown in Fig. 4a–f, more substantial reductions are noted in the eastern US, in particular for sulfate. Again this result validates previous findings and indicates that there is a possibility of aerosol-direct effect-induced “brightening” in the US over the past 16 years (Gan et al., 2014a).
Before examining the total AOD, the PM
Map of annual trends based on 1995–2010 coupled WRF–CMAQ
simulations over the CONUS domain are depicted along with circles
representing observed trends for seven sites. Left column for
SO
As a result of the reduction in the tropospheric particulate matter burden,
the AOD was reduced in the eastern US over the 14-year period (1997–2010)
as illustrated in Fig. 6a–c. However, the AOD in the western US shows
very little change over this period. Even though the model-predicted AOD is
underestimated relative to the observations (see Fig. 6a, b and Table 5),
the model is still able to capture trends similar to observations,
especially in the eastern US (obs_west: 0.0009 year
Annual mean anomalies of 1995–2010 PM
Annual mean of 1997–2010 AOD in the
As discussed by Gan et al. (2014a), the “brightening” effects are evident
in the observed all-sky and clear-sky total SW radiation trends and this
finding was confirmed by the model results as illustrated in
Fig. 7a and b and Fig. 8a and b.
Stronger and better agreement is noted in the all-sky SW radiation trend
(see Fig. 9a and Tables 5 and 6) while there is a weaker model trend and
less agreement in the clear-sky SW radiation (see Fig. 9b and Tables 5
and 6). As shown in Table 5, the “brightening” occurs in the all-sky SW
radiation while the cloudiness of both model and observations exhibits
decreasing trends indicating the possibility that semi-indirect and/or
indirect effects of decreasing aerosols may be a contributing factor.
Aerosols can interact with clouds and precipitation in many ways, acting
either as cloud condensation nuclei or ice nuclei, or as absorbing
particles, redistributing solar energy as thermal energy inside cloud
layers. In other words, a decreasing troposphere burden of aerosols can
cause a decrease of averaged cloud cover, and then this effect leads to more
solar radiation reaching the surface. However, trends in cloud cover can be
influenced by many other factors which are very difficult to quantify based
solely on available observational information. A better representation of
clouds is needed for the model. We also note that trends in both all-sky
trends
with (FB) and without (NFB) aerosol-direct feedback for model prediction are
very similar but that the simulation with aerosol-direct effect predicts a
trend modestly closer to the observed trend in the eastern US
(obs_east: 0.6296 W m
Trends of 16 years for IMPROVE observations, aerosol feedback (FB) simulation, and emissions. The table also shows the uncertainty estimates of the trends (standard error, SE), the ratio of the absolute trends relative to their uncertainty estimate, and the confidence level based on the method described in Weatherhead et al. (1998) and Gan et al. (2014a).
Annual mean anomalies of 1995–2010 all-sky total SW radiation for SURFRAD observations (blue line) and model simulations (red line). Least-square fit trend lines are also shown for each time series. The left column represents the western US while the right column represents the eastern US.
Trends of 16 years for SURFRAD observations, aerosol feedback (FB), and no aerosol feedback (NFB) simulations. The table also shows the uncertainty estimates of the trends, the ratio of the absolute trends relative to their uncertainty estimate, and the confidence level based on the method described in Weatherhead et al. (1998) and Gan et al. (2014a).
Note: SW (all-sky shortwave radiation), SWC (clear-sky shortwave radiation), DIR (direct), DIF (diffuse), and AOD (aerosol optical depth).
In order to better examine the aerosol-direct effect, the following discussion focuses on clear-sky SW radiation. The trend of the clear-sky SW radiation from the model is underestimated compared to the observations and overestimated for the direct and diffuse components especially, for the east. One of the potential causes of this underestimation maybe related to the underestimation of particulate matter concentration and AOD (Gan et al., 2014b; Curci et al., 2015; Hogrefe et al., 2015). Another possible source of disagreement between modeled and observed trends in the clear-sky direct and diffuse components is not accounting for possible clear-sky “whitening”, proposed by Long et al. (2009) and mentioned by Gan et al. (2014a), which acts to repartition the downwelling SW radiation from the direct into the diffuse field.
Statistics information (observation mean, correlation coefficient,
Note: SW (all-sky shortwave radiation), SWC (clear-sky shortwave radiation), DIR (direct), DIF (diffuse), and AOD (aerosol optical depth).
Next, the clear-sky direct and diffuse SW radiation from observations and
model are examined; annual mean time series and trends over the CONUS domain
are plotted in Figs. 8c–f and 9c and d, respectively. If the
“brightening” effect is primarily caused by the anthropogenic aerosol-direct
effect, then in the absence of other forcing the clear-sky direct SW
radiation should show an increasing trend while the clear-sky diffuse SW
radiation would be expected to have a decreasing trend. However, in the
observation, the clear-sky direct SW radiation shows no trend (i.e., very
small increasing) while the clear-sky diffuse SW radiation has an increasing
trend. In the simulation, the aerosol-direct effects are clearly evident in
the clear-sky direct and diffuse SW radiation (i.e., the results are the
opposite of those in the observations, especially in the clear-sky diffuse
radiation). Overall, the clear-sky SW radiation may be related at least in
part to a decrease in aerosols, particularly in the eastern US where
extensive reductions in the anthropogenic emissions of SO
Nevertheless, as can be seen in Fig. 8b, the model trends in clear-sky
total SW radiation agree in the aggregate with the eastern SURFRAD sites over the last
11 years (i.e., clear-sky SW 2000–2010 trends for obs_east:
0.3055 W m
Figure 8b illustrates that the 1995–2010 eastern SURFRAD trend is
strongly influenced by two anomalous years (1998 and 1999). These anomalies
are likely associated with the very strong El Niño occurrence of
1998–1999,
which had significant impact on continental US weather patterns. For
example, El Niño affects (i.e., increases) the US rain, snowfall, water
vapor, and temperature in the atmosphere. As discussed in Long et al. (2009)
and Gan et al. (2014a), we allow for some amount of condensed water in the
atmospheric column under the “clear sky” classification. Dupont et al. (2008)
show that up to an optical depth of 0.15, primarily elevated ice
crystals are still typically classified as clear sky. Augustine and Dutton (2013)
show, using SURFRAD data, that there exists a moderate correlation
between ENSO (El Niño–Southern Oscillation), surface air temperature, and surface specific humidity at the
SURFRAD sites. Their Fig. 7 shows the 1998–1999 El Niño increasing the
yearly average specific humidity, with the Bondville and Goodwin Creek sites
exhibiting the greatest increase of almost 1 g kg
Annual mean anomalies of clear-sky total (top panels), direct (middle panels), and diffuse (bottom panels) SW radiation for the SURFRAD observations (blue line) and the modeled 16 years (red), together with their trends, respectively. The left column represents the western US, and the right column represents the eastern US.
In contrast with the observed trends, the simulation with aerosol-direct feedback effect shows a clear association between decreasing aerosol burden with increasing clear-sky SW radiation and also better agreement with trends in observed total SW radiation. However, the comparison of the clear-sky diffuse SW radiation in the feedback case with the observations shows that the radiative impacts of decreasing aerosol concentrations are confounded by other factors. As suggested by previous studies (Long et al., 2009; Augustine and Dutton, 2013; Gan et al., 2014a), some potential factors contributing to this discrepancy include increasing occurrences of contrail-generated ice haze that are caused by increasing air traffic producing an aggregate clear-sky “whitening” effect (a process missing in the current model), the traditional definition of “clear-sky” that allows for some small amount of condensed water in the column (Long et al., 2009, 2006; Dupont et al., 2008), and aerosol semi-direct and/or indirect effects (Ruckstuhl et al., 2008). For example, as a result of the increasing air traffic, ice haze layers associated with aircraft emission contrails (Hofmann et al., 1998) can potentially increase the diffuse radiation. More support for this theory was presented by Gan et al. (2014a); the pattern of US air carrier traffic (i.e., steady growth of air traffic from 1996 to 2007, followed by a decrease after 2008) agreed well with the pattern inferred in the observed clear-sky diffuse radiation especially during the last 3 years (i.e., both patterns decreased). Moreover, Haywood et al. (2009) and Gerritsen (2012) illustrated that increasing contrails do increase the diffuse radiation. This suggests that contrails or subvisual cirrus clouds and ice haze can play a role in the increasing trend noted in the observed clear-sky diffuse SW radiation. To capture this, a realistic characterization of air traffic emission and the optical properties of the contrails (e.g., crystal shapes, ice layers, and altitude) in the model is needed and will be pursued as part of a future study. Additionally, the water vapor concentration (Haywood et al., 2011) can possibly impact the surface radiation. Thus, further investigation is needed to quantify and attribute the causes of the increase of measured clear-sky diffuse SW radiation.
Map of annual trends based on 1995–2010 coupled WRF–CMAQ
simulations over the CONUS domain for
In general, the coupled WRF–CMAQ model is capable of replicating the observed trends in surface particulate matter concentration and AOD even though the magnitude of observed AOD is underestimated by the model. Possible causes of this underestimation could be underrepresentation of some particulate matter constituent species in the model such as sea salt, organic carbon and other hygroscopic properties in the aerosol optics calculations, and uncertainties in the representation of the mixing state (Gan et al., 2014b; Curci et al., 2015).
The analysis of modeled and observed clear-sky total SW trends shows they are more consistent with each other during 2000–2010 than those during 1995–2010, suggesting that the improved agreement for the more recent period may be due to better emission estimates. For example, wildfire emissions are provided by states after 2002 instead of national totals. As mentioned in Xing et al. (2013), for earlier years, information for some sectors was not as detailed as recent data, so scaling factors based on activities were used to estimate some of the earlier years' emission sources. This finding illustrates the importance of the accurate specification of the changes in emissions for capturing the changes in aerosol burden and their radiative effects. Shortwave “brightening” trends are apparent in both observations and model calculations for the past 16 years, though the magnitude is underestimated in the model. One reason for using the modeling is to fill in for the lack of spatial coverage of the observations, which in turn can help us to better understand the overall aerosol-direct effects in the US.
Our analysis suggests an association between the SW radiation
“brightening” (both all-sky and clear-sky) and troposphere aerosol burden
over the past 16 years especially in the eastern US, where large reductions
in airborne particulate matter have occurred. Even though the
“brightening” effect is underestimated in the clear-sky SW radiation in
the model, it is still able to capture the total SW trend derived from the
observations (i.e., both observations and model predictions illustrate
increasing trends but smaller magnitude in the model), especially for the
more recent years. As a consequence of the CAA controls, a dramatic
reduction in particulate matter concentrations, especially
SO
Radiation trends in the western US could be influenced by local terrain (Oliphant et al., 2003; Wen et al., 2009) influences as well as episodic long-range pollution transport, which may contribute to the lack of a clear relationship between trends in aerosol burden and surface radiation at these locations. As stated by Gan et al. (2014a), the long-range transport of aerosol/dust plumes can cause enhancements in both surface aerosol concentrations and AOD (Gan et al., 2008; Mathur, 2008; Miller et al., 2011; Uno et al., 2011) and possibly contribute to the noted trends of tropospheric aerosol burden both at the surface and aloft.
Trends of observed and simulated clear-sky diffuse SW radiation show opposite signs. Potential contributors to this discrepancy include increasing ice deposition in the upper atmosphere from growing air traffic that is not considered in the model, differences in the classification of “clear-sky” conditions between the radiation retrieval methodology and the model, differences in simulated cloudiness, and aerosol semi-direct and indirect effects not represented in the current model simulations. In general, the representation of the trends in clear-sky and all-sky SW radiation in the simulation with aerosol-direct effects relative to the observation are captured much better compared to the simulation without these effects. This indicates that at least some of the trends in the recent radiation brightening, especially in the eastern US, are likely influenced by decreasing aerosol levels in the region, which in turn have resulted from the control of emissions of anthropogenic particulate matter and precursor species.
This research was performed while Chuen-Meei Gan held a National Research Council Research Associateship Award at the US EPA. The research presented in this study was supported through an interagency agreement between the US Department of Energy (funding IA DE-SC0003782) and the US Environmental Protection Agency (funding IA RW-89-9233260). It has been subject to the US EPA's administrative review and approved for publication. The authors also would like thank John Augustine from NOAA-SURFRAD for his support and assistance in obtaining the SURFRAD data, as well as the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division (GMD) for their diligent efforts in operating and maintaining the SURFRAD sites. C. N. Long acknowledges the support of the Climate Change Research Division of the US Department of Energy as part of the Atmospheric Radiation Measurement (ARM) and Atmospheric System Research (ASR) programs, and the support of the Cooperative Institute for Research in Environmental Sciences (CIRES). The authors would like thank James Kelly from the US EPA for his comments. Edited by: J. Quaas