The Community Atmosphere Model (CAM5), equipped with a technique to tag black carbon (BC) emissions by source regions and types, has been employed to establish source–receptor relationships for atmospheric BC and its deposition to snow over western North America. The CAM5 simulation was conducted with meteorological fields constrained by reanalysis for year 2013 when measurements of BC in both near-surface air and snow are available for model evaluation. We find that CAM5 has a significant low bias in predicted mixing ratios of BC in snow but only a small low bias in predicted atmospheric concentrations over northwestern USA and western Canada. Even with a strong low bias in snow mixing ratios, radiative transfer calculations show that the BC-in-snow darkening effect is substantially larger than the BC dimming effect at the surface by atmospheric BC. Local sources contribute more to near-surface atmospheric BC and to deposition than distant sources, while the latter are more important in the middle and upper troposphere where wet removal is relatively weak. Fossil fuel (FF) is the dominant source type for total column BC burden over the two regions. FF is also the dominant local source type for BC column burden, deposition, and near-surface BC, while for all distant source regions combined the contribution of biomass/biofuel (BB) is larger than FF. An observationally based positive matrix factorization (PMF) analysis of the snow-impurity chemistry is conducted to quantitatively evaluate the CAM5 BC source-type attribution. While CAM5 is qualitatively consistent with the PMF analysis with respect to partitioning of BC originating from BB and FF emissions, it significantly underestimates the relative contribution of BB. In addition to a possible low bias in BB emissions used in the simulation, the model is likely missing a significant source of snow darkening from local soil found in the observations.
Black carbon (BC) is the most light-absorbing component of anthropogenic aerosols, and it has been assessed to be responsible for a significant fraction of the climate warming in the Northern Hemisphere (Bond et al., 2013). BC-containing particles impact the radiative balance of the Earth-atmosphere system in several ways, including their “dimming effect” of reducing the amount of radiation reaching the surface, heating the atmosphere by absorbing radiation, and a darkening effect when incorporated in snow/ice at the surface, thereby increasing absorbed solar radiation (Flanner et al., 2007, 2009). The latter effect is of special interest due to the strong positive feedbacks it can trigger (e.g., Hansen and Nazarenko, 2004; Flanner et al., 2007; Bond et al., 2013). Largely because of this latter effect, BC may play a key role in causing climate change in the snow- and ice-covered regions of the globe, which have undergone accelerated change in recent decades (Lubin and Vogelmann, 2006; Levis et al., 2007; IPCC, 2013). There have been numerous studies, both observational and modeling, attempting to highlight and understand the role of BC in accelerating changes in the cryosphere (e.g., Warren and Wiscombe, 1980; Clarke and Noone, 1985; Hansen and Nazarenko, 2004; Jacobson, 2004; Flanner et al., 2007, 2009; Ming et al., 2008; Xu et al., 2009; Koch et al., 2009; Doherty et al., 2010, 2013; Qian et al., 2011, 2015; Huang et al., 2011; Ye et al., 2012; Wang et al., 2015). However, with a few notable exceptions, the focus of these studies has been either in the polar regions or sharply circumscribed mid-latitude mountainous regions. Some recent studies (e.g., Flanner et al., 2009; Shindell and Faluvegi, 2009; Bond et al., 2013) have pointed out that the climatic effect of BC might be greater at mid-latitudes, a relatively understudied region, from the standpoint of global mean forcing.
An important aspect of the BC–climate connection is the source attribution of BC in the Earth system. Such attribution is important for the formulation of mitigation strategies, a particularly acute issue for BC since its relatively short lifetime holds promise for mitigation of near-term climate warming. In addition, the global BC forcing estimate is very uncertain mostly because of large uncertainties in BC emissions (e.g., Bond et al., 2013). Observational and modeling source-attribution studies focusing on specific receptor regions are useful for identifying biases in emissions. Previous source attribution studies have primarily focused on sources of BC to the Arctic (e.g., Law and Stohl, 2007; Shindell et al., 2008; Hirdman et al., 2010a, b; Huang et al., 2010; Jacobson, 2010; Hegg et al., 2009, 2010; Stohl, 2006; Sharma et al., 2006, 2013; Sand et al., 2013; Wang et al., 2014), the Antarctic (e.g., Graf et al., 2010), or various mountain regions (Fagerli et al., 2007; Kopacz et al., 2011; Lu et al., 2012; Zhang et al., 2015; Wang et al., 2015). A number of studies have also suggested the importance of long-range transport of aerosols to North America (e.g., Jaffe et al., 1999; VanCuren, 2003; Park et al., 2005; Heald et al., 2006; Chin et al., 2007; Hadley et al., 2007; Eguchi et al., 2009; Clarke and Kapustin, 2010; Fischer et al., 2010; Yu et al., 2012, 2013). A few of these studies assessed transport of BC to North America from various remote source regions using numerical models. For example, Hadley et al. (2007) found that long-range transport from Asia was a major source of BC in the upper atmosphere over North America.
Recently, Wang et al. (2014) introduced an explicit aerosol tagging technique to a global aerosol-climate model to produce a detailed characterization of the fate of BC in receptor regions of interest emitted from various geographical source regions. Compared to other widely used approaches (e.g., the emissions perturbation approach) that have been previously employed to establish global aerosol source–receptor relationships, the tagging approach neither assumes a linear response to perturbations to get fractional contribution of different sources nor requires additional simulations for each source perturbation. Thus we believe the tagging technique is more computationally efficient and gives more accurate results. Zhang et al. (2015) extended the Wang et al. (2014) modeling tool so it tags source types/sectors in addition to source regions, and they conducted a BC source attribution analysis over the Himalayas and Tibetan Plateau. This modeling framework provides a powerful tool for looking at source attribution of BC in North America, an understudied mid-latitude region for BC in snow.
A key facet of employing any model such as that of Zhang et al. (2015) is an assessment of how well it actually reproduces observed values. Atmospheric observational data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) long-term surface monitoring network permit an assessment of model predictions of near-surface atmospheric concentrations of BC. Observations of BC in snow in the Arctic and northern China have been used to evaluate models in several previous studies (e.g., Flanner et al., 2007; Skeie et al., 2011; Wang et al., 2011; Lee et al., 2013; Jiao et al., 2014; Qian et al., 2014; Zhao et al., 2014). A recent study by Doherty et al. (2014) presented a large-area survey of observed BC concentrations in snow in western North America (Fig. S1 in the Supplement), affording an opportunity to make such an assessment for model predictions of BC in snow. For the first time, we use their measurements of BC in snow over North America to evaluate our global aerosol-climate model in terms of the amount and sources of BC in snow. The Doherty et al. (2014) study included a positive matrix factorization (PMF) source attribution analysis of BC in snow, making feasible an additional assessment of the source attribution of BC in snow in the enhanced Community Atmosphere Model version 5 (CAM5) model. Here we assess the CAM5 results against these observations and analyses for two receptor areas defined by the western North American region for which the Doherty et al. (2014) data are available.
Additionally, we present radiative transfer calculations in the atmosphere and snow with the evaluated model to assess the impact of the modeled BC as well as dust on the radiative balance for the studied region. This facilitates a comparison of the radiative forcing between this region and other mid-latitude or high-latitude regions.
Monthly-mean near-surface atmospheric BC concentrations for January,
February, and March of 2013 used in this study are from IMPROVE non-urban
background sites within the United States (Malm et al., 1994). Fine
particles (PM
While previous observation/model comparisons of BC in snow have typically compared BC mixing ratios in the surface snow, here we compare the average snow column BC mixing ratio (calculated as the sum of all BC in the snow column divided by the column equivalent water mass, hereafter BCC) over a specified period of time. This is likely a better metric for model comparison than the BC concentration in the top snow layer only, since surface snow mixing ratios at a given point in time can be strongly affected by, e.g., how recently new snow fell, accurate representation of BC mixing ratios in the most recent snowfall, and other processes that can vary on the timescale of days. In particular, melting of surface snow can strongly enhance surface snow mixing ratios but melting followed by percolation and refreezing redistributes BC particles within the snow column, resulting in no change to the total BC mass in the snow column. Indeed, Doherty et al. (2014) found that BCC is more regionally consistent than BC concentrations in top snow layer. Further, they showed that while there were vertical variations in the mixing ratio of BC in snow at their study sites there is no consistent vertical gradient. This is also the case in the model (Table S1 in the Supplement) consistent with the fact that BC emissions during the cold season do not have strong temporal gradient. Hence, in this study, we use the BCC data from Table 6 of Doherty et al. (2014) to evaluate our model.
The BCC estimates by Doherty et al. (2014) are based on samples of seasonal snow collected January through March 2013 at 67 sites in northwestern and north-central USA and Canada. Snow BC mixing ratios are estimated based on an optical measurement of spectrally resolved light absorption by all particles in the snow, using an ISSW (integrating sphere/integrating sandwich) spectrophotometer (Grenfell et al., 2011). Absorption is apportioned to BC and non-BC particulate components using the measured absorption Ångström exponent 450–600 nm along with assumed absorption Ångström exponents of the BC and non-BC components. Note that the absorption Ångström exponent is the slope of the logarithm of absorption versus the logarithm of wavelength. Absorption attributed to BC is then converted to a BC mass mixing ratio using a set of calibration standards with weighed amounts of synthetic BC. Full details of the analysis are given by Grenfell et al. (2011) and Doherty et al. (2014). Of relevance here is that this is not a direct measure of BC but an estimate of mass based on measured absorption and the assumed optical properties of these absorbing components.
An explicit BC source tagging capability was developed in the CAM5 by Wang et al. (2014), and they applied it
to establish source–receptor relationships for BC in the Arctic and quantify
source contributions from a few major geographical regions. Zhang et al. (2015)
extended this tool to quantifying sources of BC in the Himalayas and
Tibetan Plateau originating from biomass and biofuel (BB) and fossil-fuel (FF)
sectors in various regions. In this study, we use CAM5 with this
explicit BC tagging technique, including a recently improved representation
of convective transport and wet scavenging of aerosols (H. Wang et al.,
2013). We conduct a CAM5 simulation at a horizontal resolution of
1.9
Accurate BC emissions are critical to accurate modeled distributions of BC
in the atmosphere and snow, but BC emissions are highly uncertain (e.g.,
Bond et al., 2013). Instead of using the Intergovernmental Panel on Climate
Change (IPCC) AR5 present-day (year 2000) BC inventory (e.g., Lamarque et
al., 2010), we compile a new BC emission data set of year 2010 for our
simulation. The 2010 BC emission data set consists of three parts: (1) the
annually constant total BC emissions over land surfaces, obtained from the
ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived
Pollutants) V4a data set (Stohl et al., 2015), which was developed within the
framework of the ECLIPSE European project (
To prepare BC emissions for the source-type tagging in the CAM5 simulation, we first divide the total ECLIPSE BC emissions over land surface into two types, fossil fuel, and biofuel, using the ratio of biofuel to the total (biofuel plus fossil fuel) in each model grid provided by Dentener et al. (2006). In order to make the model source categories directly comparable to those given by the PMF analysis using the observational data, we then combine the GFED biomass burning emissions and ECLIPSE surface biofuel emissions to form the BB emission sector (biofuel and biomass). This is because, as discussed below, the PMF is unable to distinguish open burning (fires) from biofuel burning. The IPCC RCP6 shipping emissions and ECLIPSE surface fossil-fuel emissions are also combined to form the FF emission sector (fossil fuel). Figure S2 shows the geographical distributions of JFM (January, February, and March) mean BB and FF BC emission rate for year 2010 data set we compiled.
Following the division of source/receptor regions in Work Plan (WP 2.1) of
the Task Force on Hemispheric Transport of Air Pollution (
Figure 1b summarizes the fractional contributions to global total BC
emissions by different source regions and sectors. The JFM mean global total
BC emission rate is 7.69 Tg yr
Here we define two metrics, following Lee et al. (2013), to quantify the deviation of the simulated values from the observations.
Log-mean normalized bias (LMNB) is defined as
We also define metrics to quantify fractional contribution (
In addition to BC concentrations in snow, Doherty et al. (2014) also provide
a PMF analysis of the sources of light absorption by all particulates in the
snow. In brief, the PMF analysis determined the set of orthogonal factors,
each with an associated chemical “fingerprint”, that are associated with
variations in light absorption by all particulates in snow. Each of the
factors are then associated with specific source types (e.g., biomass
burning, fossil-fuel burning, soil, mineral dust) based on their
chemical fingerprints. The chemical markers from open biomass burning (e.g.,
forest fires) and biofuel burning (e.g., woodsmoke from fireplaces and wood
stoves) are quite similar, so biomass and biofuel sources cannot be
distinguished in the PMF; both sources would be included in the factor
identified as “biomass burning”. In order to do a comparison to CAM5,
which tracks the sources of BC only, rather than all light-absorbing species
to snow, we re-ran the PMF analysis so it determined the sources that
contribute to variations in snow BC only (i.e.,
The estimated snow BC concentration used in the PMF analysis and the
fraction of absorption due to the biomass burning, pollution/fossil-fuel and
soil sources (
We next calculate average fractional contributions by the BB and FF sources from the PMF analysis for each of the snow samples sites falling within a given model grid box, using Eq. (A1) in the Appendix. It is important to note that the sum of BB and FF contributions does not necessarily equal to 100 %. This is, of course, because of the soil source in the PMF model, a source of BC not present in CAM5. This renders the comparison between the model (i.e., CAM5) and observed (i.e., PMF) sources of BC imperfect, an issue that will be discussed further below. The CAM5 JFM mean fractional contributions for the BB and FF sectors in each model grid box, where observational/PMF data are available, are calculated using Eq. (A2). Note that the sum of BB and FF contributions equals to 100 %.
Based on the above procedures, we calculate the regional average of
fractional contributions from the BB and FF sectors from the PMF analysis
and from the CAM5 simulation using Eqs. (A3) and (A4), respectively. In
principle, another fraction corresponding to the soil contribution should
also be present in Eq. (A3) for the PMF analysis. By excluding this
fraction, we are essentially renormalizing our fractional contributions such
that
There are 42 non-urban IMPROVE observation sites available in the northwest of the USA (Fig. S4). For comparison with model results, measurements at sites located in the same model grid box are averaged first. As a result, we obtain 30 model/observation comparison pairs. The following analysis is based on the JFM mean modeled and observed values for these 30 comparison pairs.
Figure 2a shows the scatter plot of simulated versus observed JFM mean
near-surface BC concentrations. About 57 % of the ratios fall within a
factor of 2. The linear correlation coefficient (
In addition to evaluation of BC in the atmosphere, we also evaluate the model performance with respect to BC in snow. Figure 3 shows a comparison between CAM5 predictions of BCC and the corresponding observations of BCC from the 49 sampling sites given in Table 6 of Doherty et al. (2014). We obtain 36 observation/model comparison pairs by averaging measurements made at all sites located in the same model grid box. This results in 20 comparison pairs in northwestern USA and 16 in western Canada (Fig. 3d; BCC concentrations for individual pairs are summarized in Table S3). Modeled BCC does not differ appreciably between January, February, and March for the grid boxes where we made comparisons, so we use the mean BCC across all 3 months (JFM) in the comparison with the observation.
Figure 3a shows the scatter plot of the simulated JFM mean values compared to observed BCC over the 36 observation/model pairs. BCC is substantially lower in the modeled snowpack than in the observations. This model low bias in BCC is substantially larger than in near-surface atmospheric concentrations of BC (hereafter, referred to as BCS) discussed in the previous section. Indeed, the contrast in the model–observational bias of BCC as compared to the bias for BCS is quite interesting and suggestive of the sources of the bias in the BCC model–observational comparison. However, it is important to note here that we are not comparing BCS values with BCC values but rather comparing the model–observational biases of the two variables.
The linear correlation coefficient (
Figure 3b compares the simulated and observed BCC as a function of latitude.
The modeled JFM zonal mean of BCC over the longitude range of
93.75–123.75
Turning next to the regionally stratified LMNB and LMNE values, for the
northwestern USA region, the LMNB and LMNE are
The smaller error (LMNE) in BCC for western Canada than for BCS in the northwestern USA indicates the model might also be doing a better job of predicting BCS in western Canada than in the northwestern USA, but it is not possible to know this since all the BCS observations we have are from sites in the USA. For the northwestern USA sites the substantially larger low bias in BCC versus in BCS is quite interesting. A commonly invoked explanation for a low bias in model predictions of atmospheric BC has been flawed emissions inventories. For example, Mao et al. (2011) indicated that there is a large uncertainty in the emissions of BC from biomass burning in western North America. However, the larger low bias in BCC compared to BCS suggests that deficiencies in emissions inventories are not likely the primary explanation for the model underprediction of BCC in this instance, since a source-based bias should show up in both BCS and BCC (similar source attribution of BCS and BC deposition shown in Fig. 4), assuming the model representation of deposition/scavenging processes is not flawed. In fact, the small bias in model-predicted BCC in western Canada indicates that the model representation of BC deposition is less likely to be the primary cause of the large low bias in BCC in northwestern USA.
Fractional contributions to JFM mean BC total column burden,
deposition and near-surface concentrations over
In addition to emissions or model processes errors, another possibility for
the difference in modeled and observed BCC is a bias For
simplicity and consistency we use “model bias” below to describe the
difference between model results and observations, although the measurements
might have a significant bias or error.
Another possible cause of lower BCC in the model versus the observations is
a missing source of BC to snow in the model. The sources of BC in CAM5 are
biofuel burning, biomass burning, and fossil-fuel combustion. In the model,
emissions of BC from these sources are incorporated in surface snow either
in snowfall (wet deposition) or by settling directly to the surface snow
(dry deposition). In contrast to this, the PMF analysis suggests that a
significant source of BC in snow is soil. At first glance this seems
counter-intuitive, since soil itself does not produce BC. However, in
mid-latitude regions the snow is often patchy and intermixed with large
areas of exposed soil. This soil can mix with the snow mechanically (e.g., by
livestock; X. Wang et al., 2013) or by winds, which loft the soil and
deposit it to snow on scales of tens to hundreds of meters (Doherty et al.,
2014). These exposed soil areas are subject to BC deposition throughout the
year and likely accumulate a substantial reservoir of BC from a multitude of
sources (e.g., Schmidt and Noack, 2000; Hegarty et al., 2011). This
deposited BC is then subject to re-suspension via saltation and deposition
on the surrounding snow, along with the soil. As mentioned above, the
contribution of the soil/dust source to light absorption by snow impurities
for the Canadian sites is 17
The direct source tagging method in CAM5 provides a straightforward means of quantifying source–receptor relationships for BC reaching the receptor regions in North America originating from the various source regions and types. Figure 4a and b show relative contributions (as defined in Sect. 2.3, Eq. 3) to the JFM mean BC atmospheric column burden, deposition flux, and near-surface atmospheric concentrations for two receptor regions, the northwestern USA and western Canada (as outlined by white boxes in Fig. 3d). The contributions are shown explicitly for all major source regions and both source types (solid bar for BB and stippled bar for FF). The contributions of BB and FF from minor source regions are lumped together (black bar in Fig. 4a and b). Clearly, FF sources play a primary role in determining atmospheric concentrations and deposition fluxes of BC. Contributions of BB and FF from the North American sources (hereafter, for brevity, we use USA to denote four source regions NWU, NEU, SWU, and SEU; see Fig. 1a for region definitions) increase in importance moving from total column atmospheric burden to deposition fluxes and then to near-surface atmospheric concentrations of BC. North American sources, especially FF sources, are definitely the major sources of BC in the near-surface atmosphere and of BC deposited to the surface – i.e., to snow – as they are within or close to the receptor regions. Long-range transport of BC from distant sources in Asia and Africa (e.g., EAS, SAS, SEA, and AFME) to North America takes place mainly in the middle and upper troposphere (shown in Fig. S8); BC in this part of the atmosphere is less prone to wet removal and thus contributes more to column burden than to near-surface BC or deposition. The spatial distributions of JFM mean BC column burden and deposition along with BC transport pathways from various distant and domestic source regions and sectors to North America are shown in Figs. S6–S11.
Contributions to BC atmospheric column burden from all source regions are 38 % BB and 62 % FF for the northwestern USA receptor region and 37 % BB and 63 % FF for the western Canada receptor region. Contributions to BC column burden from the overseas combination of EAS, SAS, SEA, and AFME to the northwestern USA and western Canada receptor regions are 57 % (32 % BB and 25 % FF) and 63 % (32 % BB and 31 % FF), respectively, among which BB from SAS and FF from EAS are the two main overseas sources. Contributions to BC column burden in the receptor regions from the North American source regions (USA and WCA) are 41 % (5 % BB and 36 % FF) for the northwestern USA and 34 % (5 % BB and 29 % FF) for western Canada.
Relative to that for total column burden, the contribution from FF increases for deposition and is even greater for near-surface atmospheric BC. Contributions from the combined source regions of USA and WCA to BC deposition over two receptor regions, northwestern USA and western Canada, are 77 % (10 % BB and 67 % FF) and 81 % (11 % BB and 70 % FF), respectively. For near-surface atmospheric BC, the total FF contributions from the USA and WCA (western Canada and Alaska) increase to 82 % (76 % from USA) and 83 % (75 % from WCA) over northwestern USA and western Canada, respectively.
Figure 4c and d show emission source efficiency (as defined in Sect. 2.3, Eq. 4) in affecting the three JFM mean BC properties in both receptor regions. We use this efficiency (assuming a global mean efficiency of 1) as an index to quantify the sensitivity of BC in a receptor region to a fixed mass perturbation in emissions in different source regions and sectors. It is not surprising that BC in a given receptor region is most sensitive to local emissions (i.e., NWU for the northwestern USA receptor and WCA for the western Canada receptor). As was the case for source attributions in Fig. 4a and b, the emission source efficiency (Fig. 4c and d) of more local sources is lowest for total atmospheric column burden, then increases for deposition and near-surface atmospheric BC. The distant emission sources have quite low efficiencies, with significant non-local contributions only for the total column burden.
Differences in the vertical distribution of contributions to atmospheric BC are shown in more detail in Fig. 5a and b. Modeled vertical profiles of area-averaged BC mixing ratio and liquid cloud fraction over both receptor regions are also shown, in Fig. 5c and d, to indicate the altitude where wet scavenging of aerosols in clouds is most likely to occur. Clearly, the contribution of local sources significantly decreases above 800 hPa, while distant sources become progressively more important at higher altitudes (Fig. 5a and b). BC from distant sources contribute less to wet scavenging of BC mass than they do to column burden in the two receptor regions. Liquid clouds are at a maximum in the 600–800 hPa layer. Here, the BC profiles also show a minimum, possibly associated with cloud scavenging of BC in the model. This layer (600–800 hPa) has an intermediate local source contribution between those in the higher layers and the bottom layer (800–1000 hPa). Above 400 hPa, liquid clouds and thus wet removal are minimal. Below 800 hPa, below-cloud scavenging by precipitation removes BC from the air and in this altitude range BC sources are mostly local. This would increase the local source contribution to the total deposition flux.
Using the procedures described in Sect. 2.4, our PMF source attribution results are compared with the corresponding CAM5 source attributions (Table 1). Comparisons are done for each model grid box where we have a model/observation comparison pair. We reiterate that for both data sets BB includes emissions from both open biomass burning and biofuel burning.
As discussed in Sect. 2.4, the BB and FF fractions for the PMF analysis are
not precisely comparable to those from CAM5 since the PMF analysis has
identified an additional BC source, soil, which is not included in the CAM5
simulation. This is reflected in the fact that, while the sum of CAM5 BB and
FF contributions equals 1, the sum of BB and FF contributions from the PMF
analysis are commonly less than 1. Due to the lack of soil source in CAM5
and uncertainties in both measurements and emissions (e.g., spatial
distribution of sources and the partitioning between BB and FF sectors), it
is not surprising that there are quite large discrepancies between the CAM5
and PMF values for some individual comparison pairs. When compared to the
PMF values (which included contributions from FF, BB, and soil), CAM5
underestimates the BB contribution for 80 % of the comparison pairs
(modeled mean and standard deviation of 18 %
BB and FF fractional contributions based on the PMF and CAM5 source
attribution results for BC in snow for each model/observation comparison
pair (
For a better quantitative PMF/CAM5 comparison, relative contributions to BC were also calculated for a PMF analysis allowing for BC only from direct combustion sources, i.e., the BB and FF sources of BC considered in the CAM5 simulation. Average contributions of BC from combustion sources only are compared for our two receptor regions in Fig. 6. The two regions differ little in the partitioning of the BC between BB and FF sources, but in both regions the PMF indicates a larger role by BB than the model does. The PMF model attributes 32 % of the BC to BB for the northwestern USA region, while for western Canada the fraction is 28 %. CAM5 attributes 16 % of BC in the northwestern USA to BB and 15 % to BB in western Canada. Averaging over both regions, the PMF model attributes 30 % of the BC to BB while CAM5 allocates 16 % to this source. Compared to the PMF results, CAM5 overpredicts the ratio of FF to BB for the North American receptor region.
While certainly significant, the difference in source attribution between CAM5 and the factor analysis is not surprising. The factors that possibly cause the substantial model low bias in BCC could potentially generate biases in the source-type attribution. In addition, uncertainties in BC emission data and model treatment of BC aging/deposition processes can also be a source of bias in the attribution, including but not limited to (1) the partitioning of BC emissions into fossil fuel and biofuel based on the ratio provided by Dentener et al. (2006); (2) initial injection heights (up to 6 km) of biomass burning emissions that directly affect BC interaction with clouds and its wet deposition in CAM5; (3) treatment of the mixing of hydrophobic BC particles with hygroscopic components (e.g., sulfate and organics) that is important for BC aging and wet removal but does not differentiate BB or FF origin in the model. These factors, among many others, along with the possible measurement bias for samples with large soil dust concentrations, could explain the difference in source-type attribution between CAM5 and the PMF analysis. The data we have are not sufficient to distinguish between these possible sources of bias.
Regional average contributions from BB (red color) and FF (blue color) sector to combustion-sourced BC in snow in northwestern USA and western Canada based on the PMF analysis (solid bar) and CAM5 simulation (stippled bar). The contributions are calculated as in Eq. (A3) (observed values) and Eq. (A4) (modeled values).
Figure 7 shows the CAM5 modeled JFM mean atmospheric BC all-sky shortwave
direct radiative forcing (DRF) at the surface (dimming effect), at the top
of the atmosphere (TOA), and in the atmosphere (heating effect), and it also
shows the radiative forcing due to BC and mineral dust in snow (darkening
effect) as a function of latitude (zonally averaged over the longitude band
93.75–123.75
Modeled JFM and zonal mean radiative forcing (RF) values (in W m
The DRF by BC in the atmosphere (in-atmosphere heating) decreases with
latitude, as does DRF at the surface (cooling). The DRF of BC at the TOA
maximizes around 50
The color-coded numbers in Fig. 7 correspond to the various JFM mean radiative forcings averaged over the entire receptor regions, northwestern USA, and western Canada. The BC darkening effect on snow is significant and comparable to its DRF in the atmosphere, especially in western Canada where snow covers almost the entire area (Fig. S5). It is interesting to note that the BC darkening effect outweighs the BC dimming effect (i.e., cooling at the surface) and warming effect on the Earth–atmosphere system (i.e., DRF at the TOA) over both of the two regions. The modeled surface radiative forcing due to dust in snow is very small in these regions. However, Doherty et al. (2014) found that local soil dust, which is not considered in the CAM5 simulation, is a significant contributor to light absorption in snow over the U.S. Northern Plains, as well as at some sites in Canada. Intra-regionally transported desert dust has also been shown to have a significant impact on snow in the San Juan Mountains of Colorado (e.g., Painter et al., 2010, 2012) and in northwest China (X. Wang et al., 2013; Zhang et al., 2013). This suggests that CAM5 and other climate models that ignore the surface radiative forcing induced by soil and/or desert dust in snow may significantly underestimate the impact of light-absorbing impurities on snowmelt and climate.
In this study, the CAM5 global model, implemented with an explicit BC source tagging technique, has been employed to establish source–receptor relationships for atmospheric BC and its deposition to snow over a large receptor area encompassing a substantial portion of the Great Plains of North America. The model meteorological fields are constrained to agree with the MERRA reanalysis data sets for the year 2013. Model-predicted near-surface atmospheric BC concentrations and BC-in-snow concentrations in January, February, and March (JFM) were evaluated against atmospheric observations from the IMPROVE network and field measurements from a recent large-area survey of BC (and other light-absorbing particles) in snow over land (Doherty et al., 2014), respectively. We found that CAM5 had a small low bias (11 %) but a substantial random error (about a factor of 2) in the estimates of monthly-mean near-surface atmospheric BC concentrations. However, the model had a substantial error (a factor of 2) and a larger negative bias (37 %) in the prediction of BC-in-snow concentrations at all the snow sampling sites. A common explanation for a low bias in model predictions of atmospheric BC has been an underestimate of BC emissions and/or an overestimate of removal during the transport. However, systematic biases in emissions and/or model processes should show up consistently in both atmospheric BC and BC in snow and/or in adjacent geographic regions of sampling sites. Analysis of the geographic variation in the bias and error in modeled BC in snow versus that observed, along with the model–observational comparison of the atmospheric near-surface BC at the US sites, suggests that the negative model bias is more likely due to the lack of a soil source for BC in patchy snow rather than an underestimation of direct combustion emissions in the model simulation. Patchy snow at the US sites is prone to contamination of soil dust originating from the exposed soil areas. The soil dust may contain BC deposited from the atmosphere, which was not included in the emission inventory for the CAM5 simulation. Although our analysis supports this plausible explanation for the larger BC-in-snow model bias at the US sites, an underestimation of regional BC emissions still cannot be excluded as a cause of the model–observational difference. It is also possible that some of the difference between model and observation is due to a high bias in the measurements when BC is mixed with significant amounts of light-absorbing soil dust. However, the relatively good model–observational agreement for the Canadian sites makes it unlikely that BC-in-snow measurement bias is the sole source of the discrepancy between the CAM5 predications and the field observations.
The explicit direct source tagging technique in CAM5 permits a quantitative attribution of BC in receptor regions (northwestern USA and western Canada) to source regions (North American or more distant emissions) and source types (fossil fuel, FF, versus biomass/biofuel, BB). In the model, local sources generally contribute more to near-surface BC and deposition than distant sources. However, distant sources contribute significantly to the column BC burden, especially to BC in the middle and upper troposphere. At these altitudes wet removal is relatively weak, so little of this BC likely reaches the surface snowpack. In the model, FF is the dominant source type for total column BC over the two receptor regions. FF is also the dominant local source type for BC column burden, deposition, and near-surface BC. However, for all distant source regions combined the contribution of BB is larger than FF.
An observationally based PMF analysis of the sources of BC to snow, based on snow chemistry, is compared to the CAM5 source attribution based on source tagging. While the CAM5 source attribution was biased high for the FF sector and low for the BB sector compared to PMF, they both show that the contribution of the FF sector is much larger than that of the BB sector. For the two receptor regions examined in this study (northwestern USA and northwestern Canada), the relative contribution of the BB sector was underestimated by about a factor of 2 in CAM5 relative to that given by the PMF analysis. The quantitative difference in the source-type attribution between CAM5 and PMF analysis could be due to an underestimation of North American BB emissions, the lack of a soil source of BC with a high BB/FF ratio in the model, model treatment of aerosol aging/deposition processes such that the wet removal rate of BC from the BB sector is overestimated, and/or biases in the measurements.
Based on the CAM5 predictions of BC concentrations in both the air and snow, and of dust in snow, radiative forcing calculations were carried out for our two North American receptor regions (Fig. 3d). The darkening effect of BC in surface snow (i.e., snow albedo reduction due to the presence of BC) is substantially larger than the BC dimming effect (i.e., reduction in surface radiative flux due to BC in the atmosphere) but is comparable to BC heating in the atmosphere. The modeled surface radiative forcing due to dust in snow is small in the two regions. However, Doherty et al. (2014) found that local soil, which is not considered in the CAM5 simulation, is a significant contributor to light absorption in snow, suggesting that CAM5 and other climate models that ignore the local soil contributions to snow may significantly underestimate the impact of light-absorbing impurities on snowmelt and climate.
The average fractional contributions by the BB and FF sources from the PMF
analysis for each of the snow samples sites (
The CAM5 JFM mean fractional contributions for the BB and FF sectors in each
model grid box, where observational/PMF data are available, are calculated
using Eq. (A2).
The regional average of fractional contributions from the BB and FF sectors
from the PMF analysis and from the CAM5 simulation is calculated using
Eqs. (A3) and (A4), respectively.
This research is based on work supported by the US Department of Energy (DOE),
Office of Science, Biological and Environmental Research as part of
the Earth System Modeling Program. The Pacific Northwest National Laboratory (PNNL)
is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RLO1830.
The CESM project is supported by the National Science
Foundation and the DOE Office of Science. D. A. Hegg, S. J. Doherty,
C. Dang, and Q. Fu acknowledge support from the EPA STAR grant RD-82503801.
R. Zhang acknowledges support from the China Scholarship Fund. We gratefully
thank Stephen G. Warren for helpful advice and discussion on using the snow
impurity data. ECLIPSE emission data sets are available from