ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-11507-2018Black carbon-induced snow albedo reduction over the Tibetan Plateau:
uncertainties from snow grain shape and aerosol–snow mixing state based on
an updated SNICAR modelBlack carbon-induced snow albedo reduction over the Tibetan PlateauHeCenlincenlinhe@ucar.eduhttps://orcid.org/0000-0002-7367-2815FlannerMark G.ChenFeiBarlageMichaelLiouKuo-NanKangShichangMingJinghttps://orcid.org/0000-0001-5527-3768QianYunAdvanced Study Program, National Center for Atmospheric Research,
Boulder, CO, USAResearch Applications Laboratory, National Center for Atmospheric
Research, Boulder, CO, USADepartment of Climate and Space Sciences and Engineering, University
of Michigan, Ann Arbor, MI, USAState Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing, ChinaJoint Institute for Regional Earth System Science and Engineering, and
Department of Atmospheric and Oceanic Sciences, University of California,
Los Angeles, CA, USAState key laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaCAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing,
ChinaMultiphase Chemistry Department, Max Planck Institute for Chemistry,
Mainz, GermanyAtmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USACenlin He (cenlinhe@ucar.edu)15August20181815115071152712May201822May201831July20184August2018This 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/18/11507/2018/acp-18-11507-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/11507/2018/acp-18-11507-2018.pdf
We implement a set of new parameterizations into the widely used Snow, Ice,
and Aerosol Radiative (SNICAR) model to account for effects of snow grain
shape (spherical vs. nonspherical) and black carbon (BC)–snow mixing state
(external vs. internal). We find that nonspherical snow grains lead to
higher pure albedo but weaker BC-induced albedo reductions relative to
spherical snow grains, while BC–snow internal mixing significantly enhances
albedo reductions relative to external mixing. The combination of snow
nonsphericity and internal mixing suggests an important interactive effect on
BC-induced albedo reduction. Comparisons with observations of clean and
BC-contaminated snow albedo show that model simulations accounting for both
snow nonsphericity and BC–snow internal mixing perform better than those
using the common assumption of spherical snow grains and external mixing. We
further apply the updated SNICAR model with comprehensive in situ
measurements of BC concentrations in the Tibetan Plateau snowpack to
quantify the present-day (2000–2015) BC-induced snow albedo effects from a
regional and seasonal perspective. The BC concentrations show distinct and
substantial sub-regional and seasonal variations, with higher values in the
non-monsoon season and low altitudes. As a result, the BC-induced regional
mean snow albedo reductions and surface radiative effects vary by up to an
order of magnitude across different sub-regions and seasons, with values of
0.7–30.7 and 1.4–58.4 W m-2 for BC externally mixed with fresh and aged
snow spheres, respectively. The BC radiative effects are further complicated by uncertainty
in snow grain shape and BC–snow mixing state. BC–snow internal mixing
enhances the mean albedo effects over the plateau by 30–60 % relative to
external mixing, while nonspherical snow grains decrease the mean albedo
effects by up to 31 % relative to spherical grains. Based on this study,
extensive measurements and improved model characterization of snow grain
shape and aerosol–snow mixing state are urgently needed in order to precisely
evaluate BC–snow albedo effects.
Introduction
Snow albedo, a critical element in the Earth and climate system, can be
significantly affected by light-absorbing impurities in snow (Warren and
Wiscombe, 1980; Hansen and Nazarenko, 2004; Jacobson, 2004; Flanner et al.,
2009; Liou et al., 2014), which further influences surface energy flux and
regional hydrological cycles (Menon et al., 2010; Qian et al., 2011, 2015)
through a positive snow albedo feedback (Qu and Hall, 2006). With the
strongest light-absorbing ability, black carbon (BC) has been identified as
one of the most important contributors to snow albedo reduction and snow
melting after its deposition onto global snowpack (Ramanathan and Carmichael,
2008; Bond et al., 2013), including over the Arctic (McConnell et al., 2007;
Meinander et al., 2013), North American mountains (Qian et al., 2009; Sterle
et al., 2013; Skiles and Painter, 2016; Wu et al., 2018), European glaciers
(Painter et al., 2013; Di Mauro et al., 2017), Asian seasonal snowpack (Wang
et al., 2013, 2017; Zhao et al., 2014), and the Tibetan Plateau (Xu et al.,
2009; Qian et al., 2011; Wang et al., 2015; Lee et al., 2017; Li et al.,
2017, 2018; Zhang et al., 2017a, b, 2018). In addition, snow albedo can be
affected by snow grain size, grain shape, and snowpack structures (Wiscombe
and Warren, 1980; Flanner et al., 2007; Kokhanovsky, 2013; Liou et al., 2014;
Qian et al., 2014; He et al., 2017a; Räisänen et al., 2017), which
complicates the BC–snow–radiation interactions. Thus, it is critically
important to account for the effects of snow grain properties and BC
particles in order to accurately estimate snow albedo and subsequent
hydro-climatic impacts.
The Tibetan Plateau (TP), also known as the Third Pole, is covered by the
largest mass of snow and ice outside the Arctic and Antarctic (Kang et al.,
2010; Yao et al., 2012). It is the source region of major Asian rivers,
providing fresh water for billions of people (Qin et al., 2006; Immerzeel et
al., 2010). Meanwhile, because of its thermal heating, the TP has profound
dynamical influences on the atmospheric circulation in the Northern
Hemisphere and long been identified to be critical in regulating the Indian
and East Asian monsoons (Manabe and Terpstra, 1974; Yeh et al., 1979; Yao et
al., 2012). The TP is very sensitive to the changes in snow albedo and cover,
which alter surface heat and water balances and further disturb the Asian
hydrological cycle and monsoon climate (Kang et al., 2010). Observations have
shown substantial BC concentrations in snow over the TP and suggested that BC
deposition is an important driver of strong albedo reductions and accelerated
glacier retreat in the region (Ming et al., 2008, 2013; Xu et al., 2009; Qu
et al., 2014; Ji et al., 2015; Niu et al., 2017; Li et al., 2017; Zhang et
al., 2018). Recent studies found that BC particles over the TP are primarily
from South and East Asia, while long-range transport from northern
mid-latitudinal source regions outside Asia also has non-trivial contributions
(Kopacz et al. 2011; Lu et al., 2012; He et al., 2014a, b; Zhang et al.,
2015; Li et al., 2016; Yang et al., 2018).
To estimate BC-induced snow albedo effects over the TP, previous studies
often used observed BC concentrations in snow/ice as inputs to snow albedo
models by assuming spherical snow grains and BC–snow external mixing (e.g.,
Ming et al., 2013; Jacobi et al., 2015; Schmale et al., 2017; Zhang et al.,
2018). This simplified treatment of BC–snow interactions has been widely used
in snow albedo modeling over various snow-covered regions (e.g., Warren and
Wiscombe, 1980; Flanner et al., 2007; Aoki et al., 2011). However, snow
grains are usually nonspherical (Dominé et al., 2003) and internally
mixed with BC particles (Flanner et al., 2012) in real snowpack, which could
significantly affect BC–snow albedo effects. For example, Kokhanovsky and
Zege (2004) pointed out that substantial errors could occur if assuming
spherical snow grains in albedo modeling. Dang et al. (2016) found that,
compared with spherical snow grains, the nonspherical counterparts lead to
higher pure snow albedo but smaller BC-induced albedo reduction for BC–snow
external mixing. In addition, Flanner et al. (2012) showed that there could
be up to 73 % of BC in global snowpack internally mixed with snow grains,
which increases BC-induced albedo effects by up to 86 % relative to purely
external mixing for spherical snow grains. Moreover, recent studies (He et
al., 2014b, 2018a; Liou et al., 2014), combining both effects of snow
nonsphericity and BC–snow internal mixing, revealed that the enhancement in
snow albedo reduction caused by internal mixing can be weakened by snow
nonsphericity effect. Therefore, ignoring these two critical factors in
previous studies could lead to biased estimates of BC-induced snow albedo
effects over the TP and elsewhere, which highlights the necessity of
accounting for the two features together in snow albedo modeling and
assessing the associated uncertainty.
Observed BC concentrations in snow over the Tibetan Plateau (TP)
during (a, d) monsoon, (b, e) non-monsoon, and (c, f) annual periods in 2000–2015 (see Table S1 for details).
(a–c) Spatial distributions of seasonal mean BC concentrations at
altitudes > 5200 (circles) and < 5200 m (triangles)
in six sub-regions, including the northwestern TP (NWTP), north of the TP (NOTP),
the northeastern TP (NETP), the southeastern TP (SETP), the central TP (CTP),
and the
Himalayas (HIMA). (d–f) Box plots of observed BC concentrations in
snow (shown in a–c) within each sub-region, with medians (middle
bars), interquartile ranges (between 25th and 75th percentiles; boxes), and
maxima/minima (whiskers) within ±1.5 × interquartile ranges.
Some box plots are shrunk due to limited samples. Results for altitudes
> 5200 and < 5200 m are shown as left and right
box plots within each sub-region, respectively, with circles and triangles
indicating mean values. Note that some sub-regions only have observations at
altitudes > 5200 or < 5200 m.
In this study, we implement a set of new BC–snow parameterizations (He et
al., 2017b) into the widely used Snow, Ice, and Aerosol Radiative (SNICAR)
model (Flanner et al., 2007) to consider the effects of snow nonsphericity
and BC–snow internal mixing. We further apply the updated SNICAR model with a
comprehensive set of in situ measurements of BC concentrations in the TP
snowpack to estimate the present-day (2000–2015) BC-induced snow albedo
effects and associated uncertainties from snow grain shape (spherical vs.
nonspherical) and BC–snow mixing state (external vs. internal) from a
regional and seasonal perspective. To the best of our knowledge, this is the
first attempt to quantify BC–snow albedo effects over the TP by taking into
account the aforementioned two factors concurrently with observational
constraints. We describe BC observations in the TP snowpack in Sect. 2. We
implement the BC–snow parameterizations and evaluate model results in Sect. 3.
We further quantify and discuss the BC–snow albedo effects and associated
uncertainties in Sect. 4. Finally, we present conclusions, implications,
and future work in Sect. 5.
BC observations in the Tibetan snowpack
We collect available in situ observations of BC concentrations in snow/ice
over the TP and surrounding areas during 2000–2015 from historical
measurements (see Table S1 for summary). Although the features of BC
concentrations at each site have been described in detail by previous
observational studies, the present analysis seeks to summarize all these
measurements in order to understand the regional and seasonal characteristics
of BC pollution in the TP snowpack and more importantly to estimate the
corresponding BC–snow albedo effects and associated uncertainties due to snow
grain shape and BC–snow mixing state using an updated snow model (see Section
3).
For detailed analyses, we divide the entire TP and surrounding areas into six
sub-regions (Fig. 1), including the northwestern TP (NWTP; 34–40∘ N,
70–78∘ E), north of the TP (NOTP; 40–45∘ N,
70–95∘ E), the northeastern TP (NETP; 34–40∘ N,
95–105∘ E), the southeastern TP (SETP; 27–34∘ N,
95–105∘ E), the central TP (CTP; 30–36∘ N,
78–95∘ E), and the Himalayas (HIMA). We note that NOTP represents
the Tianshan region. Due to its proximity to the TP, we analyze it together
with the TP snowpack in this study. Moreover, BC concentrations in the TP
snowpack show distinct altitudinal and seasonal variations within each
sub-region (Fig. 1a–f), with much larger values at relatively lower
altitudes (< 5200 m a.s.l.) and in the non-monsoon season
(October–May; Xu et al., 2009), compared with higher altitudes
(> 5200 m a.s.l.) and the monsoon season (June–September; Xu et al., 2009),
respectively. Thus, we conduct analyses according to different altitudes
(above or below 5200 m a.s.l.) and seasons (monsoon or non-monsoon). In
addition, for any observational site with multiple measurements during the
same season, we average the measurements to represent the mean BC pollution
condition for this site during the season. As a rather limited number of
sites provide vertically resolved BC measurements throughout snowpack, we
average BC concentrations throughout snow layers at these sites, which may
introduce some uncertainties.
Figure 1a–f show that BC concentrations in snow are generally much higher
during the non-monsoon period than during the monsoon period by up to one
order of magnitude, except for NWTP and NOTP. This is because the four
sub-regions (NETP, SETP, CTP, and HIMA) are dominated by the strong BC
emissions in the non-monsoon season (particularly winter and spring) over
South and East Asia (Lu et al., 2012; Zhang et al., 2015; Yang et al., 2018)
and the efficient wet removal of BC in Asia in the monsoon season (Xu et al.,
2009; He et al., 2014a). In contrast, the high concentrations during the
monsoon period over NWTP and NOTP are primarily caused by the enrichment of
BC via sublimation and/or melting of snow (Ming et al., 2009; Yang et al.,
2015) and emissions from Central Asia and Middle East (Kopacz et al., 2011;
Schmale et al., 2017).
Furthermore, BC concentrations are consistently larger at low altitudes
(< 5200 m) than at high altitudes (> 5200 m) by a
factor of 2–10 in each sub-region (Fig. 1a–f), which is consistent with
previous studies (Ming et al., 2009, 2013) that suggested that BC
concentrations decrease with increasing elevations. Such altitudinal contrast
in BC concentrations are maximal (with differences larger than one order of
magnitude) over HIMA and SETP. This elevational dependence can be attributed
to the stronger local emissions at lower elevations, the reduced efficiency
of BC transport to higher elevations, and the higher temperature at lower
elevations leading to stronger snow melting and hence BC enrichment in snow
(e.g., Ming et al., 2013; Niu et al., 2017; Zhang et al., 2018).
Among the six sub-regions, the high-altitude areas in HIMA and SETP show the
lowest BC concentrations (5–30 ppb) throughout the year (Fig. 1d–f), while
NETP (with only low-altitude sites) during the non-monsoon season is most
severely polluted by BC (∼ 4300 ppb). The results further indicate
that BC concentrations in low-altitude areas across different sub-regions are
comparable (190–450 ppb) during the monsoon season but are much more
variable during the non-monsoon season (Fig. 1d–f). The variation of BC
concentrations across the sub-regions is a result of combined effects of the
aforementioned factors (e.g., regionally and seasonally dependent impacts
from BC source, transport, removal, and snow aging). We note that the current
observations over the TP are still rather limited spatially and temporally,
leading to questions of representativeness and introducing uncertainty in the
analysis. Thus, the large sub-regional, altitudinal, and seasonal
heterogeneity of BC concentrations in the TP snowpack highlights an urgent
need for extensive measurements.
Model description, implementation, and evaluationSNICAR model
Flanner et al. (2007) developed a multi-layer Snow, Ice, and Aerosol
Radiative (SNICAR) model, which has been widely used for snowpack simulations
globally. It is also coupled to global climate models (e.g., Community Earth
System Model, CESM) to investigate effects of impurity contamination, snow
grain properties, and snow aging on snowpack albedo. A detailed model
description has been presented by Flanner et al. (2007) and implementation in
CESM is described by Oleson et al. (2013). Here we briefly summarize the key
model elements related to the present study. SNICAR simulates snowpack
radiative transfer based on the theory from Wiscombe and Warren (1980) and
the multi-layer two-stream radiative transfer solution from Toon et
al. (1989). It resolves vertical distributions of snow properties, impurity
distributions, and heating throughout the snowpack column, as well as impact
of underlying ground surfaces. The number of snow layers can be specified by
users according to research objectives. The default SNICAR model assumes
spherical snow grains and external mixing between impurities and snow grains.
As inputs to radiative transfer calculations, the optical properties
(extinction cross section (Qext), single-scattering albedo
(ω), and asymmetry factor (g)) of snow grains and impurities,
archived as lookup tables, are offline computed by the Mie theory based on
particle size distributions and refractive indices. SNICAR utilizes clear-
and cloudy-sky surface incident solar flux typical of mid-latitude winter.
The input parameters for SNICAR include incident radiation type
(direct or diffuse), solar zenith angle, number of snow layers with thickness,
density, and grain effective radius in each layer, underlying ground albedo,
and aerosol concentrations in snow. In this study, we use the stand-alone
version of SNICAR (available at
http://snow.engin.umich.edu/snicarcode/) and implement new
parameterizations of snow nonsphericity and BC–snow internal mixing into it
(see Sects. 3.2 and 3.3). The updated SNICAR model is available at
https://github.com/EarthSciCode/SNICARv2.
Implementation of nonspherical snow grains
Previous studies commonly used an effective spherical snow grain with an
equal volume to area ratio (i.e., equal surface area-weighted mean radius;
hereinafter effective radius, Re) to represent its nonspherical
counterpart (e.g., Fu et al., 1999; Grenfell et al., 2005). The
equal-effective-radius representation works well in computing extinction
efficiency and single-scattering albedo but is inaccurate for asymmetry
factor (Dang et al., 2016). To explicitly resolve snow grain shapes, Liou et
al. (2014) have developed a stochastic snow albedo model based on a
geometric-optics surface-wave (GOS) approach (Liou et al., 2011; He et al.,
2015, 2016; Liou and Yang, 2016). Further, He et al. (2017b) developed a
parameterization to account for snow nonsphericity effects on asymmetry
factors for three typical grain shapes, including spheroid, hexagonal plate,
and Koch snowflake (see Fig. 1 of He et al. 2017b). They parameterized the
asymmetry factor (gns) of nonspherical snow grains as follows:
gns=ghex×Cg,Cg=a0fs,xfs,hexa12Rsa2,
where ai (i=0–2) is the wavelength-dependent coefficient available
in He et al. (2017b). Rs (unit: µm) is equal to the snow
effective radius (Re) for spheroid or hexagonal plate, and
Re/0.544 for Koch snowflake due to its complex concave shape.
fs,x and fs,hex are the shape factors (i.e., ratio of
Rs of a nonspherical grain to that of an equal-volume sphere) of
a nonspherical grain (x: spheroid, hexagonal plate, or Koch snowflake) and
hexagonal plate, respectively. Cg is the correction factor, and
ghex is the asymmetry factor for hexagonal shapes computed as
follows (Fu, 2007):
ghex=1-g′2ω+g′,g′=b0+b1×AR+b2×AR2,for0.1≤AR≤1.0g′=c0+c1×lnAR+c2×ln2AR,for1.0<AR≤20,
where ω is the snow single-scattering albedo, and g′ is the
asymmetry factor related to geometric reflection and refraction. bi and
ci (i=0–2) are the wavelength-dependent coefficients available in
Fu (2007). AR is the snow aspect ratio (i.e., ratio of grain width to
length).
(a–c) Spectral (0.3–5 µm) asymmetry factors of
pure snow with effective radii (Re) of (a) 100,
(b) 500, and (c) 1000 µm for sphere (blue),
spheroid (red), hexagonal plate (green), and Koch snowflake (orange) derived
from the updated SNICAR model. (d–f) Spectral single-scattering
co-albedo of snow grains internally mixed with different BC concentrations
(indicated by different colors) for snow effective radii (Re) of
(d) 100, (e) 500, and (f) 1000 µm
derived from the updated SNICAR model. Note that the y axes in
(d–f) are in logarithmic scales.
(a–c) Spectral (0.3–5 µm) direct-beam albedo of
pure semi-infinite snow layers with effective radii (Re) of
(a) 100, (b) 500, and (c) 1000 µm for
sphere (blue), spheroid (red), hexagonal plate (green), and Koch snowflake
(orange) based on the updated SNICAR model. (d–f) Same as
(a–c), but for broadband albedo as a function of snow effective
radius (Re) at (d) visible (VIS, 0.3–0.7 µm),
(e) near-infrared (NIR, 0.7–5 µm), and (f) all
(0.3–5 µm) wavelengths. The results for diffuse snow albedo are
shown in Fig. S1 in the Supplement.
Here we implement the He et al. (2017b) parameterization (Eqs. 1–4) for snow
asymmetry factor into SNICAR to account for nonspherical shapes. Due to the
coarse spectral resolution (6 bands) of the parameterization, we further use
a piece-wise shape-preserved polynomial interpolation method (Fritsch and
Carlson, 1980) to interpolate the parameterized results into 470 bands
(0.3–5 µm with a 10 nm resolution) used in SNICAR. The same
interpolation method is also applied to implementing the single-scattering
co-albedo parameterization for BC-contaminated snow (see Sect. 3.3). We use
the extinction efficiency and single-scattering albedo of
equal-effective-radii spheres for those of the nonspherical grains.
(a–c) Spectral (0.3–1.5 µm) direct-beam albedo
of semi-infinite snow layers with effective radii (Re) of
(a) 100, (b) 500, and (c) 1000 µm for
pure snow (dotted lines), snow externally mixed with 100 ppb BC (dashed
lines), and snow internally mixed with 100 ppb BC (solid lines) with shapes
of sphere (blue), spheroid (red), hexagonal plate (green), and Koch snowflake
(orange) based on the updated SNICAR model. The results for 1000 ppb BC and
diffuse snow albedo are shown in Fig. S2 in the Supplement.
(d–f) Same as (a–c), but for broadband albedo reduction
as a function of BC concentration in snow with Re of
100 µm at (d) visible (VIS, 0.3–0.7 µm),
(e) near-infrared (NIR, 0.7–5 µm), and (f) all
(0.3–5 µm) wavelengths. The results for snow with Re
of 500 and 1000 µm and diffuse albedo reductions are shown in
Figs. S3 and S4 in the Supplement, respectively.
Figure 2a–c show the spectral snow asymmetry factors for different grain
shapes based on the updated SNICAR model. Compared with spherical snow
grains, nonspherical grains (particularly Koch snowflakes) result in up to
∼ 17 % smaller asymmetry factors at
wavelengths < ∼ 3.0 µm, consistent with previous
studies (Liou et al., 2014; Dang et al., 2016). We note that the results
slightly (< 3 %) overestimate the asymmetry factors at two
spectral peaks within 1.5–2.5 µm for spheroids with large sizes
(Re≥500µm), due to parameterization uncertainties
(He et al., 2017b).
As a result of the smaller asymmetry factors, nonspherical snow grains lead
to weaker forward scattering and hence higher albedo relative to their
spherical counterparts (Figs. 3 and S1 in the Supplement). We find up to
about 2 % and 27 % higher albedo for Koch snowflakes in the visible
(0.3–0.7 µm) and near-infrared (NIR, 0.7–5 µm) bands,
respectively, compared to equal-Re spheres (Fig. 3d and e). These
results show good agreement with the conclusions from previous studies (Wang
et al., 2017; He et al., 2018a). The results also have important implications
for snow grain size retrievals via the use of albedo models to match observed
spectral reflectance. For example, Dang et al. (2016) and He et al. (2018a)
suggested that if a nonspherical grain is simulated as a sphere, its
effective size has to be scaled down by a factor of 1.2–2.5 to obtain the
correct snow albedo.
Comparisons of SNICAR simulated direct-beam albedo of semi-infinite
snow layers between using the Flanner et al. (2012) lookup table (solid
lines) and the He et al. (2017b) parameterization (dashed lines) for BC
internally mixed with snow grains with an effective radius of
100 µm for sphere (blue), spheroid (red), hexagonal plate (green),
and Koch snowflake (orange). (a–c) Spectral (0.3–1.5 µm)
snow albedo for BC concentrations of (a) 100, (b) 500, and
(c) 1000 ppb. (d–f) Broadband snow albedo reduction as a
function of BC concentration in snow at (d) visible (VIS,
0.3–0.7 µm), (e) near-infrared (NIR,
0.7–5 µm), and (f) all (0.3–5 µm) wavelengths.
The results for snow effective radii of 500 and 1000 µm are shown
in Figs. S5 and S6 in the Supplement, respectively.
Comparisons of spectral pure snow albedo from observations (black)
and SNICAR simulations using observed snowpack properties (see Table 1 and
text for details) and assuming sphere (blue), spheroid (red), hexagonal plate
(green), and Koch snowflake (orange). (a) Observations are obtained
from laboratory measurements (Hadley and Kirchstetter, 2012).
(b) Observations are obtained from open-field experiments in New
York (Brandt et al., 2011). The effective radii (Re) for each
snow shape are obtained to best match observations at wavelengths of
1–1.3 µm. (c) Observations are obtained from field
measurements over Rocky Mountains (Painter et al., 2007).
(d) Observations are obtained from field measurements at the South
Pole (Grenfell et al., 1994).
Implementation of BC–snow internal mixing
Flanner et al. (2012) showed that the effect of BC–snow internal mixing can
be equivalent to applying an enhancement ratio to BC absorption cross
sections with the external mixing assumption and developed a lookup table for
the enhancement ratio. Recently, He et al. (2017b) explicitly resolved the
structures of BC–snow internal mixtures for different snow shapes and found
that inclusions of BC increase snow single-scattering co-albedo (1-ω)
and hence absorption but have negligible effects on snow asymmetry factor and
extinction efficiency. They further parameterized the effect of internal
mixing on 1-ω as follows:
E1-ω=d0×CBC+d2d1,
where E1-ω is the co-albedo enhancement defined as the ratio of
single-scattering co-albedo for BC-contaminated snow to that for pure snow,
which is a function of BC mass concentration in snow (CBC,
unit: ppb). di (i=0–2) is the wavelength-dependent
parameterization coefficient available in He et al. (2017b).
Here we implement the He et al. (2017b) parameterization (Eq. 5) for snow
single-scattering co-albedo to account for BC–snow internal mixing. We note
that the BC mass absorption cross section (MAC) at 550 nm used in He et
al. (2017b) is 6.8 m2 g-1 with a BC density of 1.7 g cm-3
and an effective radius of 0.1 µm. Thus, to obtain a BC MAC of
7.5 m2 g-1 at 550 nm recommended by Bond and Bergstrom (2006),
we adjust the BC size and density in this study. We assume a lognormal BC
size distribution with a geometric mean diameter of 0.06 µm
following Dentener et al. (2006) and Yu and Luo (2009) and a geometric
standard deviation of 1.5 following Flanner et al. (2007) and Aoki et
al. (2011). Then, we tune the BC density to 1.49 g cm-3 to match the
MAC (7.5 m2 g-1). The resulting BC size effect on E1-ω
is quantified using a parameterization developed by He et al. (2018b) as
follows:
E1-ω,RBC=kλ,RBC×E1-ω,RBC=0.05fλ,RBC,
with
dλ,RBC=RBC0.05mλ,fλ,RBC=RBC0.05nλ,
where E1-ω,RBC and E1-ω,RBC=0.05 are
the E1-ω for a certain BC effective radius (RBC, unit: µm) and a
RBC of 0.05 µm (reference case), respectively.
kλ,RBC and fλ,RBC are empirical
parameters relying on wavelength and BC size. mλ and nλ
are wavelength-dependent coefficients available in He et al. (2018b). We
should note that BC MAC could vary significantly in reality (e.g., from 2 to
15 m2 g-1 at 550 nm) due to uncertainties from particle density,
size, structure, and refractive index (Bond and Bergstrom, 2006). Thus, we
use the recommended value (7.5 m2 g-1) derived from a
comprehensive review of measurements to reduce the potential uncertainty from
BC MAC in this study. Compared with the current estimates, using a smaller BC
MAC (e.g., 6.8 m2 g-1 at 550 nm as used in He et al., 2017b)
would lead to weaker BC-induced snow albedo reductions, the quantification of
which is beyond the scope of this study and will be investigated in
future work. In addition, the adjusted BC density and size used in the
present study are still within the observed ranges, with
1.2–1.9 g cm-3 for densities (Bond and Bergstrom, 2006; Long et al.,
2013) as well as 0.01–0.15 µm and 1.2–2.0 for geometric mean
diameters and standard deviations (Bond et al., 2006), respectively.
Figure 2d–f show the spectral single-scattering co-albedo of snow internally
mixed with BC based on the updated SNICAR model. The strongest co-albedo
enhancement (up to about 4 orders of magnitude for 1000 ppb BC) is in the
visible band, with negligible effects at wavelengths
> 1 µm. As a result of the enhanced snow absorption,
snow albedo reduces about two-fold more due to internal mixing than external
mixing (Figs. 4 and S2–S4 in the Supplement). In contrast, BC decreases snow
albedo much less for nonspherical snow grains than spherical grains (Figs. 4
and S3–S4 in the Supplement), suggesting an important interactive effects of
snow grain shape and BC–snow mixing state on snow albedo reductions. For
example, BC-sphere external mixing leads to similar visible albedo reductions
with BC-hexagonal plate internal mixing. This is consistent with our previous
findings (He et al., 2018a). Although the internal mixing effect dominates at
the NIR wavelengths (Fig. 4e), the NIR albedo reduction is a factor of 3–5
lower than the visible reduction. Thus, both snow nonsphericity and BC–snow
internal mixing play comparably important roles in determining all-wavelength
albedo reductions (Fig. 4f). This highlights the significance of
simultaneously accounting for these two factors in accurate estimates of
BC–snow albedo effects.
Parameter values used in SNICAR simulations when comparing with
observed snow albedo (see Figs. 6 and 7). The observed snowpack properties
are used in each case when they are available. Four types of snow shapes
(sphere, spheroid, hexagonal plate, and Koch snowflake) and/or two types of
BC–snow mixing (internal and external) are assumed in the simulations.
Observational cases Model parameters SolarUnderlyingSnowSnowSnowSnowBC contentzenithgroundlayerthicknesseffectivedensity(ppb)ReferencesTypeRadiationanglealbedo(cm)radius (µm)(kg m-3)(ppb)Pure snow Hadley andLaboratoryDirect0∘01Semi-55/65/1105500Kirchstetter (2012)measurementinfiniteBrandt etOpen-fieldDiffuse0∗21580/95/140/160150al. (2011)experiment40500∗300Painter etFielddiffuse0∗1100750350al. (2007)measurementGrenfell etFieldDiffuse0.6Multiple layers with layer-specific al. (1994)measurementproperties (see reference for details) BC-contaminated snow Pedersen etFieldDiffuse0.21Multiple cases with case-specific 150∗Case-specific (seeal. (2015)measurementproperties (see reference for details) reference for details)Svensson etOpen-fieldDirect61.3∘0.1Multiple layers with layer-specific snow 232/489/554/al. (2016)experimentproperties and vertically averaged BC 1030/6420concentrations (see reference for details) Meinander etFieldDirect55∘0∗20.5100035087.1al. (2013)measurement9.55000350Brandt etOpen-fieldDiffuse0∗21580/95/140/1601502250al. (2011)experiment40500∗30020Hadley andLaboratoryDirect0∘01Semi-55/65/110550110/450/860/Kirchstetter (2012)measurementinfinite1680
∗ The parameters are assumed in simulations due to the lack of
measurements. Note that the assumed underlying ground albedos have rather
small effects on albedo simulations due to thick snow optical depths.
Moreover, to cross-validate model results, we compare the simulated snow
albedo and its reduction for BC–snow internal mixing using the He et
al. (2017b) parameterization with those using the Flanner et al. (2012)
lookup table. We find very good agreement (mean differences
< 3 %) between the two schemes for different snow sizes and
shapes (Figs. 5 and S5–S6), although the He et al. (2017b) parameterization
leads to slightly stronger and weaker albedo reductions for higher
(> 1000 ppb) and lower (< 1000 ppb) BC concentrations,
respectively. Compared with the lookup table method, the newly implemented
parameterization in this study can be applied to a wider range of snow grain
size, shape, and BC concentration scenarios without sacrificing computational
efficiency.
Comparisons with observationsPure snow albedo
We evaluated spectral pure snow albedo from SNICAR simulations by comparing
with observations (Fig. 6) from laboratory measurements (Hadley and
Kirchstetter, 2012), open-field experiments (Brandt et al., 2011), and field
measurements in the Rocky Mountains (Painter et al., 2007) and at the South
Pole (Grenfell et al., 1994). To conduct reasonable comparisons, we used the
observed snow density, grain size, thickness, snowpack layer, direct or diffuse
radiation, solar zenith angle, and underlying ground albedo in model
simulations for each case (see Table 1 and Fig. 6 for details), except for
underlying ground albedos in the Brandt et al. (2011) and Painter et
al. (2007) cases and the grain size of the second snow layer in the Brandt et
al. (2011) case because of unavailable measurements. Thus, we assumed black
underlying grounds (albedo = 0) in the two cases, which has negligible
effects on albedo estimates due to thick snow optical depths. In the Brandt
et al. (2011) case, we further assumed an effective radius of
500 µm (typical for aged snow) in the second snow layer to make it
optically semi-infinite, which is consistent with the observed condition. We
also assumed four types of snow shapes (sphere, spheroid, hexagonal plate,
and Koch snowflake) in the simulations to investigate shape effects, due to
the lack of measurements.
We find that model simulations generally capture the observed patterns of
spectral snow albedo in all cases (Fig. 6). However, assuming spherical
grains tends to underestimate snow albedo in the NIR band, while using
nonspherical grains improves model results. For example, compared with the
observations (Painter et al., 2007), simulations assuming snow spheres show a
systematic underestimation of up to ∼ 0.1 at wavelengths
> 0.7 µm, particularly at 1.0–1.2 µm
(Fig. 6c), while simulations assuming hexagonal plates well match the
observations. Similarly, in the observational case of Grenfell et al. (1994),
assuming hexagonal plates and Koch snowflakes substantially reduces model
underestimates at 1.5–2.5 µm relative to assuming spheres, though
leading to a slight overestimate at 0.9–1.3 µm (Fig. 6d). In
contrast, in comparison with the laboratory measurements from Hadley and
Kirchstetter (2012), the spherical assumption works reasonably well,
particularly for large sizes, with only slight (< 0.05)
underestimates. This is because the snow grains created in those experiments
tend to be spherical. Nevertheless, using spheroids and hexagonal plates in
this case still leads to slightly better model results for large
(Re= 65 and 110 µm) and small
(Re= 55 µm) grain sizes, respectively (Fig. 6a).
In the observational case of Brandt et al. (2011), they determined snow
effective sizes by matching model results with the measured NIR
(1.0–1.3 µm) albedo. We find that assuming different snow shapes
results in drastically different grain sizes retrieved by matching their
measured NIR albedo (Figs. 6b and 7d), with effective radii of 80 and
160 µm for spheres and Koch snowflakes, respectively. This implies
the necessity of accounting for realistic grain shapes in snow grain size
retrievals.
We note that model results in all cases show slight but consistent albedo
overestimates around 400 nm compared with observations (Fig. 6), probably
due to the uncertainty of ice refractive indices. In this work, we used ice
refractive indices from the most widely used database (Warren and Brandt,
2008) obtained from measurements in the Antarctic, which shows a very low ice
absorption coefficient around 400 nm. However, based on more recent
measurements in Antarctic snow, Picard et al. (2016) found a much higher ice
absorption coefficient around 400 nm than that from Warren and
Brandt (2008), which suggested that the uncertainty in ice visible absorption
is probably larger than generally appreciated. Therefore, the weak snow
absorption caused by refractive indices used in this study could lead to the
overestimates in modeled albedo around 400 nm.
BC-contaminated snow albedo
We further compared BC-contaminated snow albedo from SNICAR simulations with
observations (Fig. 7) from laboratory measurements (Hadley and Kirchstetter,
2012), open-field experiments (Brandt et al., 2011; Svensson et al., 2016),
and field measurements in the Arctic (Meinander et al., 2013; Pedersen et
al., 2015). Similar to Sect. 3.4.1, we used the observed BC concentration in
snow, snow density, grain size, thickness, snowpack layer, direct or diffuse
radiation, solar zenith angle, and underlying ground albedo in model
simulations for each case (see Table 1 and Fig. 7 for details), except for
the snow density in the Pedersen et al. (2015) case and the underlying ground
albedo in the Meinander et al. (2013) case because of unavailable
measurements. Thus, we assumed a typical fresh snow density of
150 kg m-3 in the former case and a black underlying ground
(albedo = 0) in the latter case. Compared with assuming a black
underlying ground, we find that using a non-black underlying ground albedo
typically observed over the Tibetan Plateau (Qu et al., 2014) only leads to very small
(< 5 %) relative differences in albedo calculations in the
Meinander et al. (2013) case. Due to the lack of measurements, we further
assumed BC internally or externally mixed with different snow shapes in the
simulations to quantify the combined effects of BC–snow mixing state and snow
grain shape.
Compared with the widely used assumption of BC externally mixed with
spherical snow grains, we find that accounting for both internal mixing and
snow nonsphericity improves model simulations (Fig. 7). For example, assuming
BC-sphere external mixing leads to a systematical underestimate of polluted
snow albedo for < 2000 ppb BC compared with the observations from
Svensson et al. (2016), while assuming BC-Koch snowflake internal mixing
reduces the model underestimate (Fig. 7b), with the normalized mean
bias (NMB) and root-mean-square error (RMSE) decreasing from -0.04 to 0.01
and from 0.033 to 0.019, respectively. Similarly, in the observational case
of Pedersen et al. (2015), simulations assuming BC-spheroid external mixing
perform better than those assuming BC-sphere external mixing (Fig. 7a),
reducing the NMB from -0.012 to -0.003 and RMSE from 0.028 to 0.025.
Compared with the observations of Meinander et al. (2013), model results
using spherical snow grains underestimate the spectral snow albedo
contaminated by BC (Fig. 7c), regardless of model assumptions of BC–snow
mixing state. Using nonspherical grains instead increases the simulated
albedo and reduces model biases in this case, although it is still unable to
fully capture the observed pattern (Fig. 7c). Considering that snow grains
tend to be spherical in the observations from Hadley and Kirchstetter (2012),
we assumed BC-sphere external/internal mixing in the comparisons. The model
results with external mixing are systematically biased high, particularly for
large BC concentrations (>110 ppb), while using internal mixing
effectively reduces the albedo overestimates (Fig. 7e). As such, the
observations fall between the results of external and internal mixing,
suggesting a combination of partial external and internal mixing would best
match the observations. Compared with the way of increasing BC MAC for
BC–snow external mixing to reduce model overestimates in polluted snow
albedo, which was used in Hadley and Kirchstetter (2012), the present study
provides a physically based alternative (i.e., internal mixing) for model
improvements. In fact, it is very likely that a large portion of BC is
internally mixed with snow grains in the experiments of Hadley and
Kirchstetter (2012), as they produced the BC-contaminated snow via
freezing of aqueous hydrophilic BC suspensions.
Comparisons of BC-polluted snow albedo from observations and SNICAR
simulations using observed snowpack properties (see Table 1 and text for
details). (a) Observations (x axis) are obtained from field
measurements in the Arctic (Pedersen et al., 2015). Model results (y axis)
for spheres (circles) and Koch snowflake (triangles) are shown as lower and
upper limits for shape effects, along with BC–snow external (blue) and
internal (orange) mixing. Also shown is the best case (red asterisks;
BC-spheroid external mixing) that matches observations.
(b) Observations (red asterisks; broadband albedo for
0.285–2.8 µm) are obtained from open-field experiments in Finland
(Svensson et al., 2016). Model results for spheres (circles) and Koch
snowflake (triangles) are shown as lower and upper limits for shape effects,
along with BC–snow external (blue) and internal (orange) mixing.
(c) Observations (black lines) are obtained from field measurements
in the European Arctic (Meinander et al., 2013). Model results assuming
sphere (blue), spheroid (red), hexagonal plate (green), and Koch snowflake
(orange) along with BC–snow external (dashed lines) and internal (solid
lines) are shown. (d) Observations (black) are obtained from
open-field experiments in New York (Brandt et al., 2011). BC is assumed to be
externally mixed with snow spheres (blue), spheroids (red), hexagonal plates
(green), and Koch snowflakes (orange). The effective radii (Re)
for each snow shape are obtained to best match observations at wavelengths of
1–1.3 µm. (e) Observations (circles) are obtained from
laboratory measurements (Hadley and Kirchstetter, 2012). BC is assumed to be
externally (solid lines) and internally (dashed lines) mixed with snow
spheres.
Regional and seasonal mean BC-induced all-sky snow albedo reductions
and surface radiative effects during (a, d) monsoon, (b, e) non-monsoon, and (c, f) annual periods in 2000–2015 over six
Tibetan Plateau (TP) sub-regions (see Fig. 1), including the northwestern
TP (NWTP), north of the TP (NOTP), the northeastern TP (NETP), the southeastern
TP (SETP), the central TP (CTP), and the Himalayas (HIMA). (a–c) Box plots
of mean snow albedo reductions within each sub-region based on SNICAR
simulations using the observed BC concentrations in snow (Fig. 1), snow
thicknesses, and snow densities (see text for details). Results for altitudes
> 5200 and < 5200 m are shown as left and right
box plots within each sub-region, respectively, with circles and triangles
indicating mean values. Model results assume BC externally and internally
mixed with spheres, spheroids, hexagonal plates, and Koch snowflakes for
fresh (blue, Re=100µm) and aged (red, Re=1000µm) snow. Each data point used for the box plot is the
sub-regional average assuming a type of snow shape and BC–snow mixing, and
hence the box plot indicates the variation caused by effects of snow shape and
BC–snow mixing state. Note that some sub-regions only have BC observations at
altitudes > 5200 or < 5200 m (see Fig. 1).
(d–f) Same as (a–c), but for BC-induced all-sky surface
radiative effects caused by the snow albedo reductions shown in
(a–c). Calculations use the surface downward solar radiation and
cloud cover fraction from the MERRA-2 reanalysis fields (see text and
Table S2 in the Supplement for details).
We note that the snow grain sizes reported by the aforementioned field
studies are retrieved by different methods, including matching snow model
results with measured albedo spectra (Painter et al., 2007; Hadley and
Kirchstetter, 2012; Pedersen et al., 2015) and visual estimates with tools
(Grenfell et al., 1994; Meinander et al., 2013; Svensson et al., 2016) that
are not equivalent to the snow effective size (i.e., surface area-weighted
mean radius) defined in SNICAR. This could introduce uncertainties to snow
albedo calculations and model–observation comparisons.
BC–snow albedo effects and uncertainties over the Tibetan Plateau
Based on the observed BC concentrations in snow (see Sect. 2), we applied the
updated SNICAR model (see Sect. 3) to quantify the present-day (2000–2015)
BC–snow albedo reduction and associated surface radiative effects over the
TP. We conducted albedo simulations at each observational site using the
measured snowpack thickness and density (see Table S1 in the Supplement)
concurrently with BC measurements. If the snow property measurements are
missing at certain site, the data from nearby sites are used instead. We then
computed the regional mean values by averaging across all sites within each
sub-region and season. We used typical effective radii of 100 and
1000 µm for fresh and aged snow, respectively, to demonstrate snow
aging and size effects. Due to the lack of measurements for snow grain shape and
BC–snow mixing state, we considered eight simulation scenarios with the
combination of four snow shapes (sphere, spheroid, hexagonal plate, and Koch
snowflake) and two mixing states (internal and external). In the simulations,
the underlying ground albedo over the TP is 0.1 at the visible band
(0.3–0.7 µm) and 0.2 at the NIR band (0.7–5 µm),
following observations (Qu et al., 2014). We adopted a solar zenith cosine of
0.65 (i.e., an angle of 49.5∘), which is equivalent to the
insolation-weighted solar zenith cosine in the sunlit hemisphere. The effect
of solar zenith angle on snow albedo can be approximated via changing snow
effective size (Marshall, 1989). Previous studies (e.g., Aoki et al., 2003;
Dang et al., 2016) indicated that the impact of snow shape and BC
contamination decreases with an increasing solar zenith angle. Following Dang
et al. (2017), we compute all-sky snow albedo via averages of clear- and
cloudy-sky albedo weighted by cloud cover fraction. The mean cloud cover
fraction and all-sky surface downward solar radiation in different
sub-regions and seasons (see Table S2 in the Supplement) are derived from the
multi-year (2000–2015) monthly mean Modern-Era Retrospective Analysis for
Research and Applications version 2 (MERRA-2) reanalysis meteorological
fields (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/) with a spatial
resolution of 0.5∘× 0.625∘.
Regional and seasonal mean BC-induced all-sky snow albedo reductions
for fresh snow over the Tibetan Plateau during 2000–2015. See Table S3 for
results of aged snow.
* Six sub-regions: the Himalayas (HIMA), the central Tibetan Plateau (CTP),
the northwestern Tibetan Plateau (NWTP), the northeastern Tibetan Plateau (NETP),
the southeastern Tibetan Plateau (SETP), and north of the Tibetan Plateau (NOTP).
Each sub-region is further divided into high (> 5200 m) and low
(< 5200 m) altitudes.
Figure 8a–c show the regional mean BC-induced snow albedo reductions in
different sub-regions and seasons. The spatiotemporal distribution of albedo
reductions generally follows that of BC concentrations in snow (Fig. 1d–f),
with stronger albedo reductions in low-altitude areas and the non-monsoon
period. We find that snow albedo decreases by a factor of 2–3 more for aged
snow (Table S3 in the Supplement) than for fresh snow (Table 2), due to
larger grain sizes for aged snow. This aging and size effect dominates the albedo
reductions in most of TP sub-regions, particularly during the monsoon season
(Fig. 8a–c). However, in severely polluted sub-regions including the
low-altitude areas of NETP, SETP, CTP, and HIMA during the non-monsoon
season, the effects of snow grain shape and BC–snow mixing state are
comparable to those of snow size/aging (Tables 2 and S3 in the Supplement).
For example, BC-sphere internal mixing leads to an albedo reduction of 0.114
for fresh snow in low-altitude CTP during the non-monsoon season, while
BC-Koch snowflake external mixing leads to a reduction of 0.119 for aged
snow.
Regional and seasonal mean BC-induced all-sky surface radiative
effects (W m-2) for fresh snow over the Tibetan Plateau during
2000–2015. See Table S4 in the Supplement for results of aged snow.
* Six sub-regions: the Himalayas (HIMA), the central Tibetan
Plateau (CTP), the northwestern Tibetan Plateau (NWTP), the northeastern Tibetan
Plateau (NETP), the southeastern Tibetan Plateau (SETP), and north of the Tibetan
Plateau (NOTP). Each sub-region is further divided into high
(> 5200 m) and low (< 5200 m) altitudes.
Moreover, BC–snow internal mixing enhances the mean albedo reductions by
30–60 % (relative difference) across all the sub-regions and seasons,
with similar enhancements for different snow shapes and sizes (Tables 2 and
S3 in the Supplement). For example, assuming BC-sphere external mixing leads
to an annual albedo reduction of 0.066 (0.164) for fresh (aged) snow in NETP,
while the internal mixing counterpart results in a reduction of 0.095
(0.225). Our results are partially different from those in He et al. (2018a),
which showed a stronger enhancement (relative difference) in albedo reduction
caused by internal mixing for nonspherical grains than spherical grains, due
to different environmental conditions and snow albedo models used in the two
studies. We further find that nonspherical snow grains weaken the mean albedo
reductions by up to 31 % relative to spherical grains in different
sub-regions and seasons, with the strongest weakening for Koch snowflakes
(Fig. 8a–c). The nonsphericity effect is smaller for aged snow compared with
fresh snow (Tables 2 and S3 in the Supplement), consistent with our previous
findings (He et al., 2018a).
Although the BC concentrations in the TP snowpack tend to dominate the
regional and seasonal pattern of snow albedo reductions for fresh/aged snow
(Figs. 1d–f and 8a–c), the combined effects of snow grain shape and BC–snow
mixing state can complicate the picture. For example, with the widely used
assumption of BC externally mixed with snow spheres, the non-monsoon albedo
reductions are 0.034 and 0.067 for high-altitude CTP and low-altitude SETP
with BC concentrations of 332 and 1111 ppb in fresh snow, respectively.
However, if BC particles were internally mixed with snow spheres in CTP and
externally mixed with Koch snowflakes in SETP, the albedo reductions in the
two areas would become the same (0.047), regardless of the substantially
different BC concentrations. This points toward an imperative need for both
extensive measurements and improved model characterization of snow grain
shape and aerosol–snow mixing state for accurate quantification of BC-induced
snow albedo reductions over the TP and elsewhere with strong heterogeneity of
snowpack properties and contamination.
Figure 8d–f show the regional mean surface radiative effects caused by
BC-induced snow albedo reductions, which vary from 0.7 to 11.2 W m-2
across different sub-regions during the monsoon season and from 1.2 to
30.7 W m-2 during the non-monsoon season for BC externally mixed with
fresh snow spheres. The sub-regional variation increases to 1.4–37.7 and
3.5–58.4 W m-2 for aged snow during the monsoon and non-monsoon
periods, respectively (Tables 3 and S4 in the Supplement). In general, the
spatiotemporal distribution of surface radiative effects follows that of snow
albedo reductions (Fig. 8a–f). The impacts of snow nonsphericity and
BC–snow internal mixing on the surface radiative effects are similar to those
on the albedo reductions discussed above. The maximum surface radiative
effect over the TP can reach up to 45.4 (79.9) W m-2 in NETP during
the non-monsoon season for BC internally mixed with fresh (aged) snow spheres
(Tables 3 and S4 in the Supplement). The mean BC-induced snow albedo effects
in the relatively clean TP areas (e.g., high-altitude HIMA and SETP) are
comparable to those over the Arctic and North American snowpack (Dang et al.,
2017; He et al., 2018a), while the effects in the contaminated TP areas
(e.g., low-altitude HIMA, CTP, SETP, and NETP) are generally similar to those
in the low-elevation Alps (Painter et al., 2013) and northern China snowpack
(Wang et al., 2017).
Previous studies have shown accelerated snowmelt caused by BC–snow albedo
effects in the TP. For example, Yasunari et al. (2010) estimated that
BC-induced albedo reductions over Himalayan glaciers could result in an extra
snowmelt of 1–7 mm day-1 during the melting/summer season. Qian et
al. (2011) found a BC-induced snowmelt of up to 1.3 mm day-1 in late
spring and early summer averaged over the entire TP. Our results further
suggest that the uncertainty associated with snow shape and BC–snow mixing
state could lead to a substantial variation in BC-induced albedo reduction
and hence snowmelt, which has significant implications for runoff and water
management in Asia. Accurate quantifications of the impact of snow grain
shape and BC–snow mixing state on snowmelt and subsequent hydrological
processes require interactive land surface and/or climate modeling, which
will be investigated in future work.
We note that the present estimates of BC-induced snow albedo effects have
uncertainties and limitations. For example, different techniques have been
used to measure BC concentration in snow and ice, which may lead to discrepancies
and inconsistency among observations and in model–observation comparisons
(Qian et al., 2015 and references therein). Additionally, BC measurements across
the TP are from various sample types, such as surfaces of snowpack (with
fresh and aged snow) and glacier (with both snow and firn and granular ice), which
may introduce uncertainty to the understanding of BC contamination patterns
(Zhang et al., 2017a; Li et al., 2018). In addition, in the model, we do not
account for the vertical variability of BC and snow grain properties in the
TP snowpack as well as some complex snowpack processes, including dynamic
snow aging and melting, post-depositional enrichment, and melting water
scavenging, which may exert non-trivial effects on BC–snow albedo effects
(e.g., Flanner et al., 2007; Qian et al., 2014; Dang et al., 2017). These
uncertainties associated with modeling and measurements may decrease the
signal-to-noise ratio for the detection of BC effects on snow albedo,
particularly in relatively clean regions with small BC-induced albedo
reductions (e.g., < 0.01). Thus, improved and robust estimates
require both accurate snow albedo modeling and snowpack measurements.
Conclusions, implications, and future work
We implemented a set of new BC–snow parameterizations into SNICAR, a widely
used snow albedo model, to account for the effects of snow nonsphericity and
BC–snow internal mixing. We evaluated model simulations by comparing with
observations. We further applied the updated SNICAR model with a
comprehensive set of in situ measurements of BC concentrations in the Tibetan
Plateau (TP) snowpack (glacier) to quantify the present-day BC-induced snow
albedo effects and associated uncertainties from snow grain shape and BC–snow
mixing state.
Based on the SNICAR model updated with new BC–snow parameterizations, we
found that nonspherical snow grains tend to have higher pure albedos but
lower BC-induced albedo reductions compared with spherical snow grains, while
BC–snow internal mixing substantially enhances albedo reductions relative to
external mixing. Compared with observations, model simulations assuming
nonspherical snow grains and BC–snow internal mixing perform better than
those with the common assumption of snow spheres and external mixing. The
results suggest an important interactive effect from snow nonsphericity and
internal mixing, and highlight the necessity of concurrently accounting for
the two factors in snow albedo and climate modeling.
We further applied the updated SNICAR model with comprehensive in situ
observations of BC concentrations in snow and snowpack properties over the TP
to quantify the present-day (2000–2015) BC-induced snow albedo effects. We
found that BC concentrations show distinct sub-regional and seasonal
variations. The concentrations are generally higher in the non-monsoon season
and low-altitudes (< 5200 m) than in the monsoon season and
high-altitudes (> 5200 m), respectively. The spatiotemporal
distributions of snow albedo reductions and surface radiative effects
generally follow that of BC concentrations. As a result, the BC-induced mean
albedo effects vary by up to an order of magnitude across different
sub-regions and seasons, with values of 0.7–30.7 (1.4–58.4) W m-2
for BC externally mixed with fresh (aged) snow spheres.
Moreover, the BC–snow albedo effects over the TP are significantly affected
by the uncertainty in snow grain shape and BC–snow mixing state. We found
that BC–snow internal mixing enhances the mean albedo effects by 30–60 %
relative to external mixing across different sub-regions and seasons, while
nonspherical snow grains reduce the albedo effects by up to 31 % relative
to spherical grains. These effects become comparably important with the snow
aging and size effect over polluted areas. Therefore, the combined effects of
snow grain shape and BC–snow mixing state can complicate the spatiotemporal
features of BC–snow albedo effects over the TP, with significant implications
for regional hydrological processes and water management.
In summary, this study points toward an imperative need for improved
measurements and model characterization of snow grain shape and aerosol–snow
mixing state in order to accurately estimate BC–snow albedo effects. In
future work, we will incorporate the new features of the updated SNICAR model
into land surface and climate models, including CESM-Community Land Model
(CLM) for global modeling and WRF-Noah-MP for regional modeling, to account
for the effects of snow grain shape and aerosol–snow mixing state and to
assess the associated uncertainties and hydrological feedbacks in
global/regional climate system.
Users can access the data used and produced by this study
via the supplementary materials and the corresponding author without any
restrictions. The updated SNICAR model can be downloaded at
https://github.com/EarthSciCode/SNICARv2 (last access: 12 August 2018).
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-11507-2018-supplement.
CH designed and performed the parameterization
implementation and model simulations. MF offered data and help in developing
model codes. FC and MB helped refine model experiments. SK and JM provided
black carbon observations. KNL and YQ gave valuable comments. CH prepared the
manuscript and all co-authors helped improve the manuscript.
The authors declare that they have no conflict of
interest.
Study of ozone, aerosols, and radiation
over the Tibetan Plateau (SOAR-TP) (ACP/AMT inter-journal SI) SI statement:
“this article is part of the special issue: Study of ozone, aerosols, and
radiation over the Tibetan Plateau (SOAR-TP) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The authors thank the three reviewers for their constructive comments. Cenlin He
thanks Wenfu Tang and Roy Rasmussen for helpful discussions. Cenlin He was
supported by the NCAR Advanced Study Program (ASP) Fellowship. The National
Center for Atmospheric Research (NCAR) is sponsored by the National Science
Foundation (NSF). The State Key Program of the National Natural Science
Foundation of China is under Grant 91537211 and NCAR Water System. Kuo-Nan Liou
was supported by NSF Grant AGS-1660587. The contribution of Yun Qian in this
study was supported as part of the Energy Exascale Earth System Model (E3SM)
project, funded by the U.S. Department of Energy, Office of Science, Office
of Biological and Environmental Research's Earth System Modeling program. The
Pacific Northwest National Laboratory (PNNL) is operated for DOE by the Battelle
Memorial Institute under contract DE-AC06-76RLO 1830. The National
Center for Atmospheric Research is sponsored by the National Science
Foundation.Edited by: Yan Yin Reviewed
by: three anonymous referees
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