Remote and free-tropospheric aerosols represent a large fraction of the
climatic influence of aerosols; however, aerosol in these regions is less
characterized than those polluted boundary layers. We evaluate aerosol size
distributions predicted by the GEOS-Chem-TOMAS global chemical transport
model with online aerosol microphysics using measurements from the peak of
Whistler Mountain, British Columbia, Canada
(2182 m a.s.l., hereafter
referred to as Whistler Peak). We evaluate the
model for predictions of aerosol number, size, and composition during periods
of free-tropospheric (FT) and boundary-layer (BL) influence at “coarse”
4
Atmospheric aerosol particles impact human health, climate, and visibility. The magnitude of these impacts has a strong dependence on the size, concentration, and composition of the particles (Rosenfeld et al., 2008; Clement et al., 2009). These particles can impact climate by acting as seed particles for cloud formation, altering the brightness and/or the lifetime of clouds, or by scattering incoming solar radiation (e.g., Boucher et al., 2013). These impacts of aerosols on clouds and climate are driven by the number concentration of cloud condensation nuclei (CCN), the particles large enough to serve as seeds for condensation of water to form cloud droplets (typically diameters larger than 30–100 nm). Aerosol–cloud interactions are among the most uncertain properties in climate forcing estimations (Boucher et al., 2013). Aerosol size distributions, which are fundamental to aerosol–cloud interactions, evolve in the atmosphere as a direct result of microphysical processes such as condensation, coagulation, nucleation, primary emissions, and deposition. Quantitatively estimating the climatic effect of aerosols involves understanding the evolution of aerosol size distributions.
Atmospheric aerosols emitted from or formed near the Earth's surface may remain in the planetary boundary layer (BL) or may be transported to the free troposphere (FT). Aerosols in the FT tend to have longer lifetimes than aerosols in the BL as deposition rates are lower in the FT (Croft et al., 2014). Therefore, aerosols in the FT can be transported over great distances and can affect remote regions where local emissions may be minimal.
One important characteristic about measurements at high-elevation mountain surface sites is that there are periods where they can be used to investigate and understand FT aerosols. However, these measurements are frequently influenced by a variety of aerosol sources including advection of BL air with upslope flow. The complexity of air-mass influences at high-elevation sites often makes measurements at these sites difficult to compare to simulations of regional and global models that do not resolve the sub-grid topography. While global models have been used to understand the processes shaping aerosols at mountaintop sites (e.g., Yu and Hallar, 2014), these models have resolution too coarse to explicitly resolve topographic meteorology effects of many mountain peaks. Synoptic meteorology, including advection and subsidence, influences the particles observed at mountain sites (Collaud Coen et al., 2011; Gallagher et al., 2011); however, one may expect chemical transport models to resolve these processes if synoptic meteorology is well represented. A major issue in comparing model simulations to mountaintop measurements is determining the appropriate model layer that accurately represents the high-elevation measurements under the various mountaintop conditions. Therefore, although measurements from these unique sites may be used to evaluate global models, we must first understand how to properly sample the model for comparison to the measurements.
The west coast of North America is routinely impacted by trans-Pacific
transported aerosol. Long-term measurements have been taken by Environment
Canada at Whistler Mountain, Whistler, British Columbia, at a site situated
approximately 100 km from the west coast of Canada at the peak elevation of
2182 m a.s.l. (50.06
In this study, we use a global chemical transport model with online aerosol microphysics to investigate contributions to Whistler Peak aerosol from BL upslope flow, long-range transport of Asian anthropogenic aerosol, local biomass-burning emissions, and other sources. We compare model simulations to measurements taken from Whistler Peak with the goals of (1) determining how to sample the model for comparison to mountaintop measurements and (2) understanding how various sources influence the aerosol size distributions at Whistler. In the following section, the measurements and model simulations used in this study are described. Section 3 describes the results, highlighting the data-filtering techniques, the comparison of measured and simulated particle number concentrations, and the influence of Asian anthropogenic emissions and biomass-burning aerosol on particle size distributions.
Continuous high-elevation surface-based aerosol size distribution
measurements are taken by Environment Canada at Whistler Peak, located in the
Coast Mountains in southwestern British Columbia (Fig. 1). The Whistler
Peak site began continuous measurements of particle size distributions, trace
gases (e.g., O
Location and elevation of Whistler Mountain, British Columbia
(50.06
Time series of N14 (the total number of particles with particle
diameter,
In this study, the Goddard Earth Observing System chemical transport model,
GEOS-Chem (
The surface layer in the simulated 4
We test the sensitivity of aerosol size distributions in GEOS-Chem-TOMAS to
(a) the removal of Asian anthropogenic emissions and (b) the removal of
biomass-burning emissions. Simulations are summarized in Table 1. In all
simulation names, the C indicates coarse simulations
(4
Summary of the GEOS-Chem-TOMAS simulations used in this study. Note
that C indicates coarse simulations (4
Summary of the slope of the linear regression (
Figure 3a and b show 1 : 1 plots comparing measured and simulated N14 and
N80 (the total number of particles with particle diameter,
One-to-one plots of measured and simulated N14 and N80 (the total
number of particles with particle diameter,
A characteristic of Whistler Peak is that the measurement site frequently
experiences in-cloud conditions (Macdonald et al., 2011). Previous work
showed that measurements with relative humidity (RH)
Histogram of the frequency of data points as a function of
measured N80 (the total number of particles with particle diameter,
Whistler Peak may be encompassed by an air mass originating from lower
altitudes if the BL is very deep (over 1.5 km) or if there is
upslope flow. To separate conditions of upslope flow or deep BL
from FT conditions, we also define a threshold temperature.
When the measured temperature exceeds the threshold temperature, upslope flow
is assumed and the model surface layer is used. When the measured temperature
is less than the threshold temperature, then FT air is assumed at the peak
and the 1580 m (890 m) model layer is used. Various temperature thresholds
were imposed for determining which model level to use. The
temperature-filtered simulated particle size distribution that most
accurately represents the measured particle size distribution based on
correlation statistics summarized in Table 3 is when a threshold of about
2
Summary of the slope of the linear regression (
Previous studies have used other methods to represent BL influence at Whistler Peak and other high mountaintop sites; however, these methods were used to identify days of BL influence, whereas we seek to sort simulated hourly time points into either BL or FT influence. We therefore synthesized the following methods to test an alternate filter based on N14, rather than attempting to apply each method. Obrist et al. (2008) and Weiss-Penzias et al. (2006) noted diurnal water vapor cycles as indicators of BL influence at Colorado and Oregon mountain peaks; in New Hampshire, Grant et al. (2005) identified days with BL influence using early morning minimum and afternoon maximum temperatures. Moreover, daily total particle number (condensation nuclei, CN, in our case, N14) increases indicated BL uplift in Hawaii (Bodhaine, 1996) and Switzerland (e.g., Baltensperger et al., 1997). At Whistler Mountain, Macdonald et al. (2011) used temperature data from multiple vertical levels on the mountain to define a stability index as an indicator of BL influence; however, many of the temperature-measurement sites used in Macdonald et al. (2011) were not operational during our time period. Gallagher et al. (2011) described the frequency of BL influence at Whistler by evaluating how well the change in CN concentration throughout each day adhered to a typical sinusoidal pattern, noting that confidence in the influence of vertical transport is higher on days when CN correlates strongly with water vapor.
We tested a filter based on CN changes throughout the day on simulated
Whistler measurements informed by these studies. Though some of the studies
discussed above used water vapor, we used CN because Gallagher et al. (2010)
found that CN was a more robust indicator of BL influence at Whistler. We
identified BL influence days using increasing CN concentration from morning
to midday (09:00–11:00 average
Observed and temperature-dependent simulated mean particle
number size distributions for the measurement period for the
The low performance of this CN-cycle method could be due to overly strict criteria; the BL could influence peak aerosol on days when CN does not increase from morning to early afternoon. In particular, during periods of sustained high-pressure systems, for example when the site was influenced by the BL throughout both day and night, this CN filter would not result in identifying the BL influence. Also, Whistler frequently observes new-particle formation events. Gallagher et al. (2011) estimated that the new-particle formation at Whistler was generally correlated with upslope flow and BL air. However, it is likely that not all new-particle formation events are associated with BL air and thus would contribute error to using CN as a classification of BL air. Thus, the methods described above to identify days of BL influence from observed water vapor, timing of temperature extrema, and CN increases may not be robust for sorting simulated hourly time points into either BL or FT influence as we do here.
The RH filter combined with the temperature-dependent model level assumption
improves comparisons with measurements. Figure 3e and f include the RH filter
and the temperature filter in the coarse simulated and measured comparison of
N14 and N80. With these two filters included in the analysis, the slope of
the regression for N14 and N80 both significantly improve (0.44 and 0.54,
respectively) as well as the
In this section, we address the seasonal cycle of data availability and completeness once the filters have been applied. Figure 2 shows a time series of N14 for all measurements (black points), and the temperature and RH filtered points are shown in green when the 890 m simulated layer is selected and red when the surface simulated layer is selected from the nested simulations. Times where black points exist but no red or green points exists show that the model data have been filtered using RH for in-cloud conditions. Periods where there are no points are time periods where the scanning mobility particle sizer was not operating at Whistler. The bracketed number in the legend corresponds to the total number of data points for each condition. There are clear seasonal trends in N14 at Whistler Peak, with high particle number concentrations during the summer months, and relatively low particle number concentrations during the winter months. The summer maximum is due in part to the advection of BL air due to upslope flow to the peak as well as influence from biomass burning during the Northern Hemisphere boreal forest fire season, as we will show. For the period of July through September, 77 % of the points are identified as BL in 2010 and 65 % in 2011. For the period December 2010 through February 2011, 100 % of the points are identified as FT.
Whistler Peak experiences conditions where trans-Pacific FT air transports
anthropogenic aerosol from Asia and influences aerosol size distributions.
Figure 6a and b show 1 : 1 plots for measured and simulated N14 and N80,
respectively, from the BASE and noAsia (Asian anthropogenic emissions turned
off) simulations, where the gray crosses represent all points (implementing
the temperature and RH filter as discussed earlier) and the green crosses
represent all points where
N14
One-to-one plots for measured and simulated (nested resolution)
The overall impact that transport from Asia has on the number size
distribution at Whistler is shown in Fig. 6c, which shows the simulated
contribution to particle number concentration due to Asian anthropogenic
aerosol (BASE–noAsia) as a function of particle diameter,
Whistler Peak also experiences periods of increased concentrations of
biomass-burning aerosol. Similar to Fig. 6a and b, Fig. 7a and b show 1 : 1
plots for measured and simulated N14 and N80, respectively, from the BASE and
noBioB (biomass-burning emissions turned off) simulations. The gray crosses
represent all points (implementing the temperature and RH filter as discussed
earlier) and the red crosses represent all points where
N14
The impact of biomass-burning aerosol on the particle size distribution is
quantified in Fig. 7c, which shows the simulated contribution to particle
number concentration due to biomass-burning aerosol (BASE–noBioB) as a
function of particle diameter,
One-to-one plots for measured and simulated (nested resolution)
In addition to the periods with biomass-burning influence, Whistler Peak has
periods with high particle number concentrations without influence from
biomass-burning emissions. Figure 8 shows 2-day back trajectories (HYSPLIT
version 4.9; Draxler and Hess, 1997, 1998; Draxler, 1999) for July 2010
including only times with low biomass-burning or Asian anthropogenic
influence (N80
Two-day back trajectories for July 2010 including only times with low
biomass-burning or Asian anthropogenic influence
(N80
Continuous high-elevation surface-based aerosol size distribution
measurements have been taken by Environment Canada at Whistler Peak
(50.06
To compare simulations to measurements at Whistler Peak, it was necessary to
develop filtering techniques to determine whether there was BL or FT
influence at Whistler Peak. We found that, using the measured temperature at
Whistler Peak as a proxy for upslope flow, we could improve our agreement
with measurements, and that temperature was a better proxy than a CN proxy
that had been previously used as a proxy for BL air at Whistler (Gallagher et
al., 2011), although it is possible that better proxies exist. The best
threshold temperature we found was 2
Due to the high elevation of Whistler Peak, the measurement site is often
influenced by long-range transport of Asian anthropogenic aerosol. To
investigate this, a base simulation (BASE) was compared to a simulation with
Asian anthropogenic emissions turned off (noAsia) at the nested resolution.
High Asian influence periods were determined when the difference in particle
number concentrations between the BASE simulation and the noAsia simulation
exceeded 50 cm
Whistler Peak experiences BL air influence particularly during summer months,
and during fire seasons upslope flow or deep boundary layers can transport
biomass-burning aerosol to the peak. To investigate this, the BASE simulation
was compared to a simulation with biomass-burning emissions turned off
(noBioB) at the nested resolution. Similar to the noAsia comparison, high
influence periods were determined when the difference between the BASE
simulation and the noBioB simulation exceeded 100 cm
Occasionally, Whistler Peak measured N80 in excess of 1000 cm
Mountain measurement sites are difficult to simulate in global chemical transport models. By using simple filters on simulated data, we were able to improve model–measurement comparisons. We were also able to test the sensitivity of the simulations to Asian anthropogenic emissions and local biomass burning to determine source apportionment at a high-elevation mountain site. These low-cost techniques could be used in other global models to more accurately represent mountain measurement sites, leading to a better understanding of mountain meteorology and chemistry; however, the details of the filtering likely need to be tuned for different models and mountains.
Jessica Ng was supported by the Center for Multiscale Modeling of Atmospheric Processes summer internship program at Colorado State University. Edited by: B. Ervens