Surface albedo of snow and ice is substantially reduced
by inorganic impurities, such as aeolian mineral dust (MD) and black carbon
(BC), and also by organic impurities, such as microbes that live in the
snow. In this paper, we present the temporal changes of surface albedo, snow
grain size, MD, BC and snow algal cell concentration observed on a snowpack
in northwest Greenland during the ablation season of 2014 and our attempt to
reproduce the changes in albedo with a physically based snow albedo model.
We also attempt to reproduce the effects of inorganic impurities and the red
snow algae (Sanguina nivaloides) on albedo. Concentrations of MD and red snow algae in the
surface snow were found to increase in early August, while snow grain size
and BC were found to not significantly change throughout the ablation
season. Surface albedo was found to have decreased by 0.08 from late July to
early August. The albedo simulated by the model agreed with the albedo
observed during the study period. However, red snow algae exerted little
effect on surface albedo in early August. This is probably owing to the
abundance of smaller cells (4.9×104 cells L-1) when
compared with the cell abundance of red snow reported by previous studies in the
Arctic region (∼108 cells L-1). The simulation of
snow albedo until the end of the melting season, with a snow algae model,
revealed that the reduction in albedo attributed to red snow algae could
equal 0.004, out of a total reduction of 0.102 arising from the three
impurities on a snowpack in northwest Greenland. Finally, we conducted
scenario simulations using the snow albedo model, coupled with the snow
algae model, in order to simulate the possible effects of red snow blooming
on snow albedo under warm conditions in northwest Greenland. The result
suggests that albedo reduction by red snow algal growth under warm
conditions (surface snow temperature of +1.5 ∘C) reached 0.04,
equivalent to a radiative forcing of 7.5 W m-2 during the ablation
season of 2014. This coupled albedo model has the potential to dynamically
simulate snow albedo, including the effect of organic and inorganic
impurities, leading to proper estimates of the surface albedo of snow cover
in Greenland.
Introduction
The Greenland Ice Sheet, which is the largest continuous body of ice in the
Northern Hemisphere, has been losing mass rapidly since the 2000s (Rignot et
al., 2008). The increase in the melting of snow and ice is likely to be
caused by reduction of surface albedo as well as temperature rise (Tedesco
et al., 2011; Box et al., 2012; Yallop et al., 2012). Therefore, it is
important to understand the physical processes causing the reduction in
albedo and to estimate current and future snow and ice albedo accurately on
the Greenland Ice Sheet.
Surface albedo plays an important role in the balance of energy over the
snow surface. Snow albedo is approximately 0.9 for fresh snow and gradually
decreases to approximately 0.5 for granular snow during the melting season
(Wiscombe and Warren, 1980). Because a reduction of snow albedo increases
the absorption of solar radiation by a snowpack, the reduction in albedo
accelerates the melting of snow. The major factors affecting surface albedo
are snow grain size and abundance of light-absorbing impurities in the snow
(Warren and Wiscombe, 1980; Aoki et al., 2011). An increase in the number of
snow impurities and in the snow grain size causes more absorption of solar
radiation in the visible (400–700 nm) and near-infrared (700–3000 nm)
regions (Warren and Wiscombe, 1980; Aoki et al., 2000). Major light-absorbing impurities in a snowpack are black carbon (BC), which is derived
from the combustion of fossil and solid fuels and from biomass burning (Bond
et al., 2013), and mineral dust (MD), which is transported by wind from
local or distant arid terrestrial surfaces (Bøggild et al., 2010). For
example, a mass concentration of 10 µg kg-1 of BC in wet snow can
reduce the albedo by 0.01 (Warren and Wiscombe, 1985). Although
light absorption by MD in the visible region was reported to be lower by
approximately 0.7 % than that by BC (Aoki et al., 2011), the mass
concentration of MD was 10 times greater or more than that of BC in the
snowpack of the Greenland Ice Sheet. Thus, the impact of MD on snow albedo
cannot be ignored (Steffensen, 1997). In addition, organic carbon (OC) is an
impurity that absorbs light in the visible spectrum (Kirchstetter et al.,
2004; Andreae and Gelencsér, 2006). OCs in atmospheric aerosols,
consisting of burned fossil fuel, plant materials, viable microbes
(bacteria, viruses, fungal spores and algae), soil organic matter and
marine aerosol (Jacobson et al., 2000; Cerqueira et al., 2010), might be
present on the surface snow and reduce its albedo.
Physical models of snow surface albedo have been developed to calculate the
surface albedo of snow containing various impurities. Physical models can
reproduce snow albedo as a function of snow grain size, impurities (BC and
MD), and direct and diffuse solar radiation (Wiscombe and Warren, 1980).
Such models have been developed in recent years. For example, Flanner and
Zender (2005, 2006) proposed a multilayer snow albedo model, which is
incorporated into a land surface model in a general circulation model, in
order to simulate the microphysics and radiative properties of snow at a
global scale. The physically based snow albedo model (PBSAM) developed by
Aoki et al. (2011) separately calculates broadband albedos for the
ultraviolet–visible (200–700 nm) and near-infrared (700–3000 nm)
wavebands, at the snow surface, taking into account the spectral radiation
properties of impurities (BC and MD) in those spectra. This model can
efficiently simulate snow albedo using a look-up table, which consists of an
albedo dataset calculated with a radiative transfer model for various
environmental variables (snow grain size, snow water equivalent, snow
impurity concentration and solar zenith angle). Global climate simulation
(e.g., of surface net shortwave flux) using an Earth system model with a snow
albedo module, including light-absorbing impurities (BC, MD and OC),
suggests that the contribution of aerosol OC to total visible absorption in
the snow surface was smaller than that of BC and MD (Yasunari et al., 2015).
Although many albedo models have been developed to include inorganic
impurities (BC and MD) and aerosol organic impurity, recent studies have
suggested that microbes growing in snow, such as snow algae, also affect
snow albedo (Takeuchi, 2013; Aoki et al., 2013; Lutz et al., 2016; Cook et
al., 2017a, b). Snow algae are cold-tolerant photosynthetic
microbes growing on snow and are commonly found globally on glaciers and
snowfields. Blooms of snow algae occur on thawing snow surfaces and change
the color of snow to red or green (Thomas and Duval, 1995; Takeuchi et al.,
2006; Hoham and Remias, 2020). Red-colored snow results from a bloom of snow
algae, which are typically Sanguina (S.) nivaloides (renamed recently from Chlamydomonas nivalis), and can be observed
widely in polar and alpine snow fields (Hoham and Duval, 2001; Segawa et
al., 2005, 2018; Takeuchi, 2013; Hisakawa et al., 2015; Lutz et al., 2016; Tanaka
et al., 2016; Ganey et al., 2017; Procházková
et al., 2019). Many observational studies have reported the quantitative
effect of the red snow blooming on the surface albedo. For example, algal
blooms have a potential to reduce snow albedo by 0.13 on Arctic glaciers
(Lutz et al., 2016). Because the impact of red snow blooming on albedo is
comparable to that of BC and MD, albedo models including the effect of snow
algae have been established recently. For example, a bio-albedo model
proposed by Cook et al. (2017a, b) can simulate spectral albedo using
calculations of radiative transfer that incorporate biological variables
(cell concentration, cell size and cellular pigment composition), snow
physical properties (specific surface area, density and layer thickness)
and irradiance. This model satisfactorily reproduced the spectral albedo of
red snow blooming and temporal change of surface albedo on a glacier in
Greenland. However, the physical properties and inorganic impurities (BC and
MD) in the snow used in the simulation were not representative field values
but assumed constant values based on previous studies at other sites of
Greenland. Thus, the effect of snow algae on intact surface albedo has yet
to be quantitatively assessed. In addition, temporal changes in algal
abundance were not used at the model calculation. Snow algal abundance can
change significantly because of their growth, accumulation and removal of
their cells over time (Müller et al., 2001; Takeuchi, 2013; Onuma et
al., 2016, 2018). Therefore, snow albedo simulations should incorporate a
numerical model of snow algae.
In this study, we aimed to reproduce the temporal changes of snow albedo
observed on a snowpack during the melt season of Qaanaaq Glacier in
northwest Greenland. We used the PBSAM and included the effects of snow
algae, as well as inorganic impurities (BC and MD). Snow physical properties
(snow grain size, temperature, density and spectral reflectance in the
visible band) and the abundances of the three impurities (BC, MD and snow
algae) in the surface and subsurface snow were periodically quantified in
the snowpack from June to August in 2014. The PBSAM was updated to
incorporate the effect of snow algae, and temporal changes of the surface
albedo were calculated by using the model and incorporating the observed
meteorological conditions, snow physical properties and three
impurities. The impacts of the observed and simulated blooms of red snow on
the surface albedo were quantified using the updated PBSAM. In addition, we
simulated temporal change in snow algal abundance by using a snow algae
model to reproduce the change in albedo throughout the entire melting
season.
Map of Greenland (left) and Qaanaaq Ice Cap in northwest Greenland
(right). The figure to the right shows the sampling site on the glacier.
MethodStudy sites and observation methods
Field investigations were conducted at the Qaanaaq Ice Cap in northwest
Greenland (Fig. 1) from June to August in 2014. The Qaanaaq Ice Cap, which
lies on a small peninsula in northwest Greenland, covers an area of 286 km2 and has an elevation of approximately 1110 m a.s.l. (Takeuchi et
al., 2014; Sugiyama et al., 2014). We selected a study site close to the
SIGMA-B automatic weather station (AWS) (77∘32′ N, 69∘04′ W, 944 m a.s.l., Aoki et al., 2014a) on the ice cap, which is easily
accessible on foot from the Qaanaaq village. The site is located near the
equilibrium line of the glacier, which was 1001 m a.s.l. in 2014, as
determined by a mass balance study (Tsutaki et al., 2017). As reported
previously, snow algae visibly bloomed on the snowpack at the study site
from late July to August in 2014. They consisted mostly of the spherical red
cells of S. nivaloides, and their mean diameter was 21.3±2.3µm (Onuma et
al., 2018), although there was no molecular analysis of all species present.
The meteorological conditions considered in this study were measured at the
SIGMA-B AWS, which was installed in 2012 as part of the project Snow
Impurity and Glacier Microbe effects on abrupt warming in the Arctic (SIGMA;
Aoki et al., 2014a). Air temperature and radiation fluxes of the upward and
downward shortwave and upward and downward longwave radiation were collected hourly
from June to August 2014 using the AWS. The temperature sensor and
pyranometers of the AWS were placed at heights of 3.0 and 2.5 m above the
snow surface, respectively. Surface albedo was calculated from the ratio of
upward to downward shortwave radiation. The surface albedo was corrected to
include the effect of local slope (4∘) on snow albedo, according
to Jonsell et al. (2003). The time used in this study was Greenland local
time (LT), which is 2 h later than Greenwich mean time. Detailed
settings of the other meteorological sensors have been described by Aoki et
al. (2014a).
Collection of snow samples and measurement of snow properties were
periodically conducted at the site from days 168 (17 June 2014) to 215
(3 August 2014) to obtain the level of abundance of the impurities and the
physical properties of the surface snow. The collection was conducted on
sunny or cloudy days. The optically equivalent snow grain size was measured
with a handheld lens according to Aoki et al. (2007). Snow temperature was
measured with a thermistor sensor (CT-430WP, Custom Ltd, Tokyo, Japan). The
snow density was measured using a density sampler (volume: 100 cm3).
Variation in snow layer thickness at each observational date relative to
that on day 168 was defined as the relative snow surface level for
estimating snow melting. The snow properties (the optically equivalent snow
grain size, snow temperature, snow density and snow layer thickness) were
measured to simulate snow albedo with a physically based snow albedo model.
Spectral reflectance of the snow surface in the visible wavelength range
(350–700 nm) was measured with a portable spectroradiometer (MS-720, Eiko
Seiki, Japan). Surface snow was collected after the measurement to quantify
the impurity content.
Snow samples were collected from one to three surfaces selected randomly in
the spatial scale of approximately 15 m × 15 m at the study site in
order to estimate spatial mean concentration of each snow impurity at the site.
They were collected from two snow layers, at depths of 0 to 2 cm (surface)
and 2 to 10 cm (subsurface), using a stainless-steel spatula. At each layer,
snow samples were collected separately to quantify the algal cell, MD
particles, and OC and BC contents. The amounts of snow sample used for algal
cell analysis ranged from 22 to 36 g for the surface and 21 to 33 g for the
subsurface. Samples for analysis of MD particle and BC and OC concentrations
ranged from 500 to 2200 g for the surface and 500 to 2100 g for the
subsurface.
Quantification of snow impurities
Mass concentrations of MD in the snow were quantified by the combustion
method (Takeuchi and Li, 2008; Onuma et al., 2018). Snow samples were
collected at the site in dust-free plastic bags from the surface and
subsurface, as described in the previous section. These samples were melted
at room temperature in Qaanaaq village, and their mass was measured with a
weight scale. The dust precipitated in the bag was preserved in clean 30 mL
polyethylene bottles, which were then transported to Chiba University,
Japan, for analysis. The samples were dried (60 ∘C, 24 h) in
pre-weighed crucibles and then combusted (500 ∘C, 3 h) in an
electric furnace to remove organic matter. The mass of mineral particles per
melt water volume (mg L-1) was obtained from the combusted sample
weight and sample volume, since only mineral particles remained after
combustion. Mass concentrations of MD in surface snow at the study site
during the observational period have been observed in previous studies
(Onuma et al., 2018).
Mass concentrations of OC and elemental carbon (EC) in snow were quantified
from filtered snow samples by the thermal optical reflectance method (Chow
et al., 1993; Kuchiki et al., 2015). Snow samples were collected in
dust-free plastic bags from the snow surface and subsurface, as described in
the previous section, at the site. The samples were melted at room
temperature and pre-filtered through a 150 µm mesh filter to remove
large particles, including leaves, insects and clothing fibers. The melted
samples were combined with ammonium dihydrogen phosphate
(NH4H2PO4) of 1.5 g 100 mL-1 as a coagulant and were then magnetically stirred and sonicated for 10 min. Previous studies have
reported that the mean collection efficiency of a quartz fiber filter for BC
particles increased to 95 % when NH4H2PO4 coagulant was
added to the sample solution, compared with 5 % efficiency without the
addition of the coagulant (Torres et al., 2014). In addition, adding the
NH4H2PO4 coagulant to melted snow samples had no apparent
effect on the measurement of OC by the thermal reflectance optical method
(Kuchiki et al., 2015). The melted samples were filtered through a quartz
fiber filter (pore size: 0.45 µm, 2500QAT-UP; Pall Corp., MI, USA) on a
clean bench by atmospheric pressure. The filters were maintained in plastic
cases before being transported to the Meteorological Research Institute in
Tsukuba, Japan, for analysis. Mass concentrations of OC and EC were measured
with an OC–EC aerosol analyzer (Sunset Laboratory Inc., OR, USA) using the
thermal optical reflectance method. Samples were volatilized at 120, 250,
450 and 550 ∘C in a pure helium atmosphere and then combusted at
550, 700 and 800 ∘C in a 10 % oxygen–90 % helium atmosphere,
in accordance with the Interagency Monitoring of Protected Visual
Environments (IMPROVE) thermal evolution protocol (Chow et al., 2001).
Throughout the analysis, laser reflectance from the filter deposit was
continuously monitored to correct the OC pyrolysis. The filter reflectance
usually decreased with increasing temperature in the helium atmosphere due
to pyrolysis of organic material. When oxygen was added, the remaining
light-absorbing carbon combusted, and the reflectance increased. The split
point between OC and EC was identified by the timing of drastic change in
reflectance. EC concentrations measured with the thermal optical method
agree with the BC concentrations measured with an optical method using a
particle soot absorption photometer to within 2 % (Miyazaki et al., 2007).
In the present study, we assumed that the component of EC was equal to BC in
order to estimate the mass concentration of BC in snow. The mass
concentrations of OC and BC per meltwater volume (mg L-1) were
obtained from the volatilization volume of carbon before and after the OC / EC
split point, respectively. Kuchiki et al. (2015) provide a more detailed
description of the method.
Abundance of snow algae was quantified by the direct cell count method
(Takeuchi, 2013; Tanaka et al., 2016; Onuma et al., 2018). Snow samples were
preserved in Whirl-Pak® bags (Nasco, Fort Atkinson, Wisconsin,
USA) and then melted in Qaanaaq village. The melted samples were preserved
in 3 % formalin in clean 30 mL polyethylene bottles before being
transported to Chiba University, Japan, for analysis. Algal abundance was
represented as algal cell concentration per unit of meltwater volume of the
snowpack. Water samples of 50–1000 µL were filtered through a
hydrophilized PTFE membrane filter (pore size 0.45 µm; Omnipore JHWP,
Millipore, Japan), and the number of algal cells on the filter was counted
two to five times for each sample using an optical microscope (BX51,
OLYMPUS, Japan), and cell concentrations (cells L-1) were obtained from
mean cell counts and filtered sample volumes. Cell concentrations for S. nivaloides in
surface snow at the study site have been published previously (Onuma et al.,
2018).
Mass absorption coefficients (MACs) of snow impurities
parameterized in the PBSAM (Aoki et al., 2011). The MAC for red snow algae
was assumed in our study.
A PBSAM was used to simulate snow albedo, including the effect of inorganic
and organic impurities, in this study. Broadband albedos in the snowpack
were calculated by the PBSAM as functions of snow grain size and
concentrations of impurities in a maximum of five layers of snow under solar
illumination conditions (Aoki et al., 2011). In addition, PBSAM can
calculate visible and near-infrared albedos using downward solar radiation
in the visible and near-infrared regions, respectively. In order to include
impurities of different optical properties (BC and MD in the case of Aoki et
al., 2011), a snow impurity factor (SIF) was defined in the model. SIFi for
sub-band of wavelength i was calculated as follows:
SIFi=ki,BCCBC+ki,MDCMD,
where ki,BC and ki,MD are the mass absorption cross sections (MACs)
of BC and MD for sub-band i, respectively. MACs represent the absorption
characteristic (absorption coefficient) for the sub-band of different
wavelengths (Table 1). CBC and CMD are the mass concentrations of BC
and MD in snow, respectively. MACs and the mass concentrations of each
impurity were used as the parameters and variables for simulation by PBSAM.
Aoki et al. (2011) further added the OC to Eq. (1) as follows:
SIFi=ki,BCCBC+ki,MDCMD+ki,OCCOC,
where ki,OC and COC are the MAC and the mass concentration of OC
for sub-band i, respectively. In their study, OC was assumed to be aerosol OC derived from the atmosphere. The MAC of the OC, as well as that of MD and
BC, was assumed using an aerosol model (Hess et al., 1998), as reported by
Aoki et al. (2011).
In the present study, red snow algae were included in the model as part of
the OC. In order to convert the algal cell concentration (cells L-1)
into COC (mg L-1), a regression was applied to a scatter diagram of
observed algal cell versus OC concentration of snow samples in this study.
Because the MAC of snow algae is unlikely to be equal to that of aerosol OC,
as has previously been used in PBSAM, we assumed the MAC for snow algae
based on cell size and pigment composition (Table 1). To calculate the MAC
for snow algae, a lognormal size distribution (Hess et al., 1998, Eq. 3d) was assumed, based on the measurement of algal cell size based on algae
from this study site. The sizes of 100 S. nivaloides cells, which were obtained from snow
samples stored in a freezer, were measured directly using Image-J for
estimation of the normal distribution curve (mode radius = 11.4 µm;
standard deviation = 1.18 µm). Because the effect of light
absorption of snow algae on snow albedo should be calculated quantitatively
in an albedo model, we calculated the imaginary part of refractive indices
for S. nivaloides according to Cook et al. (2017a, Eqs. 2 and 3). The imaginary
part of refractive indices for the spectral region from 400 to 750 nm was
calculated based on the pigment composition (chlorophyll a, chlorophyll b,
primary carotenoids and secondary carotenoids) that were assumed as the
compositions of S. nivaloides by them. The concentrations of each pigment in the algal
cells were based on their study (Cook et al., 2017a, Table 2; “High
End-Member Scenario”). The imaginary part of the refractive index for
non-absorption spectral regions by red snow algae, which are 200–400 and
750–3000 nm, was assumed to be that for pure water. The real part of the
refractive index for the entire spectra from 200 to 3000 nm was assumed to
be the same as pure water. The MAC for snow algae was calculated from Mie
theory by assuming the spherical particles, using the lognormal size
distribution previously calculated and the spectral refractive indices
following the protocol of Aoki et al. (2011).
The information for model validation with field
observations. The wavelength band of the simulated snow albedo was adjusted
to that of the observed snow albedo.
CellWavelength band ofAlgalAlgalconcentrationthe simulated andalbedo reductionalbedo reductionSiteof red snow algaeobserved albedos(observation)(model)Mt. Conness in California (Painter et al., 2001, Fig. 1)2.1 ×107 (cells L-1)Broadband0.070.062Harding Ice Field in Alaska (Takeuchi et al., 2006, Figs. 3 and 4)1.1 ×107 (cells L-1)Visible band0.10.072Gulkana Glacier in Alaska (Takeuchi, 2013, at site S5)1.9 ×107 (cells L-1)Visible band0.120.105Mittivakkat glacier in SE Greenland (Lutz et al., 2014, at site Mit-17)1.8 ×106 (cells L-1)Visible band0.090.015
Snow albedo was calculated with PBSAM, using CBC, CMD, COC,
physical properties in surface (0–2 cm) and subsurface (2–10 cm) snow, and
meteorological conditions recorded in this study. The observed thickness of
the snow layer, snow density, and temperature and grain size were used as
input data in PBSAM. Downward shortwave radiation measured at the AWS, the
direct-to-diffuse insolation ratio and the visible-to-near-infrared
insolation ratio were also used as input variables. These two ratios were
calculated from observed downward shortwave radiation, upward and downward
longwave radiation, and air temperature at the AWS following the protocol of
Niwano et al. (2012). Meteorological variables measured from 10:00 to
12:00 LT for each observation date were used for the model simulation to
calculate snow albedo at 10:00, 11:00 and 12:00 LT.
In this study, four kinds of snow albedo simulations were conducted based on
four assumptions of snow impurity: (1) snow albedo without any impurities
present (Alb-C), (2) snow albedo with the effect of MD only (Alb-D), (3) snow
albedo with the effects of MD and BC only (Alb-DB), and (4) snow albedo with
the effects of all impurities, i.e., MD, BC and algae (Alb-DBA).
Temporal changes in observed snow physical properties on surface
and subsurface snow at the study site. (a) Snow albedo, (b) snow grain size
(radius), (c) mass concentration of MD, (d) mass concentration of BC and (e) mass concentration of OC. Snow albedo was calculated from the ratio of
upward and downward shortwave radiation at AWS. Error bars: standard
deviation.
ResultsTemporal changes in physical properties of the snow
Surface albedo of the snowpack at the study site gradually decreased with
snow melting from late June to early August (Fig. 2a, b). The snowpack
melted continuously from day 176 (25 June 2014) to day 215 (3 August 2014), as previously described by Onuma et al. (2018). For example, surface
snow was fresh snow on day 168, and then it became granular snow on day 176
and remained so until day 215. Mean optically equivalent snow grain size
(radius) of the surface was 0.3±0.1 mm (mean ± SD) on day 168,
was 0.6±0.4 mm on day 176, and then it varied between 0.7 and 0.9 mm
until day 215. The properties of subsurface snow changed similarly. Relative
snow surface level (0 cm on day 168) gradually decreased by 123 cm during
the study period (from day 168 to day 215). Snow albedo was 0.791 on day
168, and then it gradually decreased until day 209 (from 0.791 to 0.698).
Finally, it decreased rapidly by 0.08 from day 209 to day 215.
Temporal changes in impurities in snow
The mass concentration of MD in both surface and subsurface snow gradually
increased from mid-June to early August at the study site and reached the
maximum in early August. The concentration in surface snow was 2.7×10-1 mg L-1 on day 176 (25 June 2014) and gradually
increased, with a slight temporary decrease on day 209, but it increased again
and finally reached 7.5±2.9×10 mg L-1 (mean ± SD) on day 215 (Fig. 2c). A statistical test (one-way analysis of variance
– one-way ANOVA) demonstrated that the temporal change in the mass
concentration of MD was significant (F=4.95, P=0.03<0.05).
The concentration in the subsurface snow was generally lower than that in
surface snow. It was 3.3×10-1 mg L-1 on day 176 and
gradually increased to 1.4×10 mg L-1 on day 215 (Fig. 2c).
In contrast to MD, the mass concentration of BC did not show seasonal trends
in either surface or subsurface snow during the study period. The BC
concentrations in surface and subsurface snow ranged from 5.4×10-5 to 2.5×10-2 mg L-1 (mean: 9.5×10-3 mg L-1) and 1.2×10-5 to 1.8×10-2 mg L-1 (mean: 3.5×10-3 mg L-1),
respectively (Fig. 2d). The temporal change of BC was not statistically
significant for either surface or subsurface snow (one-way ANOVA, surface:
F=3.14, P=0.11>0.05; subsurface: F=8.89, P=0.37>0.05).
The mass concentrations of OC in surface snow gradually increased from
mid-June to early August, and they were maximal in early August. The OC
concentration in surface snow was 3.2×10-2 mg L-1 on
day 168 and gradually increased to 3.4±0.3×10-1 mg L-1 on day 215, although the concentration decreased temporally on days
197 and 209 (Fig. 2e). This temporal change was significant in surface snow
(one-way ANOVA, F=3.14, P=9.8×10-7<0.01).
Mass concentration of OC in the subsurface snow ranged from 3.8×10-2 to 2.0×10-1 mg L-1 (mean: 8.4×10-2 mg L-1; Fig. 2e). There was no significant difference in
concentration from day 168 to day 215 (one-way ANOVA, F=8.89, P=0.18>0.05).
Correlation chart between mass concentration of OC and algal cell
concentration from filed measurements at the study site. The correlation
coefficient is 0.93 (P=8.0×10-4<0.05). Error
bars: standard deviation.
The concentration of OC was positively correlated to algal cell
concentration of S. nivaloides, as previously quantified by Onuma et al. (2018). The
concentration of algal cells ranged from 0 to 4.9×104 cells L-1 in the surface snow from day 168 to day 215 (Fig. 3). The
relationship between algal cell and OC concentrations exhibited a
significant positive linear correlation (r=0.93, P=8.0×10-4<0.05). Based on the relationship, a regression line was
obtained as follows:
COC=5.3×10-6×CAlgae+0.0826,
where CAlgae is algal cell concentration (cells L-1) in surface
snow. This COC (mg L-1) was used as an input variable for
simulation of PBSAM in this study.
Temporal changes in observed and calculated snow albedo at the
study site. Solid symbols indicate observed snow albedo. Cross symbols
indicate four simulations of snow albedo based on the assumption of snow
impurity inclusions. Error bars indicate the albedo range simulated using
the meteorological conditions at 10:00, 11:00 and 12:00 LT.
Temporal changes in snow albedo simulated with PBSAM
Snow albedos simulated with the effect of only MD (Alb-D), with MD and BC
(Alb-DB), and with MD, BC and snow algae (Alb-DBA) gradually decreased from
mid-June to early August, whereas the snow albedo calculated without the
effect of any impurity (Alb-C) did not change significantly (Fig. 4).
Physical properties of snow, snow impurities and meteorological conditions
from 10:00 to 12:00 LT and used as input data for the simulation are
presented in the Supplement (Tables S1–S5). Mean Alb-C ranged from
0.709 to 0.753 during the study period, but the change was not statistically
significant. Mean Alb-DBA was 0.755 on day 168 and gradually decreased to
0.687 until day 209, followed by a large decrease to 0.616 on day 215. The
temporal changes of Alb-D and Alb-DB were similar to that of Alb-DBA.
Coefficients of determination for the regression (R2) between the
calculated and observed albedo from day 168 to day 215 were 0.38 for Alb-C,
0.94 for Alb-D, 0.93 for Alb-DB and 0.93 for Alb-DBA. The root-mean-square
errors (RMSEs) were 0.04 for Alb-C, 0.02 for Alb-D, 0.02 for Alb-DB and 0.02
for Alb-DBA. These results indicate that the three albedo simulations
including snow impurities (Alb-D, Alb-DB and Alb-DBA) exhibited good
performance in representing temporal changes of measured albedo.
DiscussionsTemporal changes of MD and BC on the snowpack
Differences in temporal changes in mass concentrations of MD and BC suggest
that they were transported to the snow surface through different processes.
Aoki et al. (2014b) have reported temporal changes in MD and BC
concentrations in surface snow located at an elevation of 1490 m a.s.l. in
northwest Greenland (SIGMA-A site, 78∘03′ N, 67∘38′ W). During their observations, from 28 June to 12 July 2012, the MD
concentration in surface snow increased by a factor of 349 and reached 1.3 mg L-1 while BC concentration increased by only a factor of 5.4 and reached 4.9×10-3 mg L-1. Their study suggested that the main
factors explaining the increment were deposition from the atmosphere for MD
and an enrichment following sublimation and evaporation of snow for BC.
Geochemical analyses of MD on glaciers in the Arctic region, including
Qaanaaq Glacier, suggested that it is likely to be supplied mainly from
local ground surfaces (e.g., moraine near the glacier) rather than more
distant areas (Nagatsuka et al., 2014, 2016; Tobo et al., 2019). The mass
concentration of MD at the study site increased by a factor of 57 from 2.7×10-1 mg L-1 on day 176 (25 June 2014) to 15.2 mg L-1 on day
190 (9 July 2014). The increase in MD concentration at the study site was
likely due to exposure of the ground surface during the melting season. The
mean BC concentration at the study site was 9.5×10-3 mg L-1, which is the same order as those (from 0.9×10-3 to 4.9×10-3 mg L-1) at the SIGMA-A site, suggesting that BC at
both sites was supplied from distant sources.
Temporal changes of calculated albedo suggest that MD is the main factor
causing the reduction in albedo at the study site during the melting season.
Alb-D and Alb-DB gradually decreased from mid-June to early August, whereas
Alb-C did not change significantly during that period (Fig. 4). Thus, the
reduction in albedo at the study site was due to the increase in snow
impurities, rather than the changing of the snow grain size. The snow grain
size did not change significantly after day 168. The differences of surface
albedo between the Alb-C and Alb-D and between the Alb-D and Alb-DB were
0.1 and almost 0 on day 215, respectively, which were equivalent to the
reduction in albedo caused by MD and BC, respectively. Although the MAC of
BC used was larger than that of MD at any wavelength (Table 1), the effect
of BC on albedo was smaller than the effect of MD. This was due to the
greater concentration of MD on the snow surface.
Reproduction of temporal change in snow albedo using PBSAM, including the effects of snow algae
Temporal changes in algal cell concentration were positively correlated with
that in the mass concentration of OC in surface snow, suggesting that snow
algae can be regarded as the main constituent of OC in snow (Fig. 3). The
positive correlation between the observed algal cell and OC concentrations
in surface snow suggests that OC in snow can be approximated using the algal
cell concentration at the study site. Indeed, S. nivaloides was the dominant species in
snowpack at the study site throughout the summer season of 2014 (Onuma et
al., 2018). However, significant amounts of OC were detected in snow samples
without cells of S. nivaloides, indicating that these snow samples contained organic
matter originating from other organisms (for example, bacteria and
yeast-like fungi) and atmospheric OC aerosol. The intercept of 0.0826 of
Eq. (3) can be interpreted as being contributed from the other
organisms and the atmospheric OC aerosol. In fact, Chroococcaceae
cyanobacterium, which is a cyanobacterium found commonly on glaciers and
snowpacks in Greenland, was observed on the surface snow of the study site
from mid-June to early August in 2014 (Onuma et al., 2018). However, its
effect was neglected in the present study because the concentration was much
smaller than the abundance of S. nivaloides at the study site.
The MACs for snow algae in this study were likely to reproduce
characteristics of light absorption caused by a snow algal bloom. In order
to calculate the effects of light absorption by snow algae on snow albedo,
we assumed that four pigments accounted for absorption in each cell. These
pigments are major light absorption pigments for red snow algae (Cook et
al., 2017a). Spectral variation in the imaginary part of refractive indices
for wavelengths in this study, calculated from the four algal pigments,
agreed with the spectral variation in that estimated from observed spectral
reflectance of the red snow surface on the Qaanaaq Glacier (Aoki et al.,
2013). The result suggests that the imaginary part of refractive indices in
this study can reproduce the light absorption based on theoretical optical
characteristics of S. nivaloides.
Temporal change of snow albedo (Alb-DBA) on the snowpack at the study site
was simulated with the PBSAM, including the effects of the three impurities
(MD, BC and snow algae), for the study period (from day 168 to 215). The
result indicates that the model that included the total effect of MD, BC
and snow algae was the best in reproducing temporal changes throughout the
study period. The values of R2 and RMSE between the observed albedo and
modeled Alb-DBA from days 168 to 215 were 0.93 and 0.016, respectively.
Furthermore, the Alb-DBA exhibited good performance in simulations with MD
and BC only (Alb-DB). However, there was no significant difference in model
performance among these simulations. This is probably due to the lower cell
concentration at the study site, which was 4.9±1.7×104 cells L-1 (mean ± SD) on day 215, when compared with
that of typical red snow appearing on oligotrophic polar or alpine snow,
which ranges from 3.2×106 to 2.0×108 cells L-1 (Thomas and Duval, 1995; Takeuchi and Koshima, 2004; Takeuchi et
al., 2006; Stibal et al., 2007; Takeuchi, 2013; Lutz et al., 2014; Tanaka
et al., 2016; Onuma et al., 2018; Procházková et al., 2018). In
fact, visible red snow was not seen on day 215 at the study site. The
spectral reflectance of the surface snow was consistent with this; it did
not show the typical spectral absorption of the algal pigments (carotenoids
and chlorophyll), which have absorption peaks in the wavebands of 400–600
and 670–680 nm (Painter et al., 2001; Takeuchi et al., 2006). The snow
albedo simulation by Cook et al. (2017a) suggested that algal abundance of
10 µg of algae per gram of snow, which is equivalent to 5.9×104 cells L-1 for a snow density of 600 kg m-3, has little
effect on the spectral absorption between 400 and 2200 nm.
Simulation of temporal changes in surface snow albedo using PBSAM and a snow algae model
Although our field observations ended on day 215, snow algal abundance could
further increase until the end of the melting season. In order to infer
temporal changes in snow albedo for the whole melting season, we calculated
snow albedo using the PBSAM and a snow algae model proposed by Onuma et al. (2018). Temporal changes in abundance of S. nivaloides on surface snow of Qaanaaq Glacier
can simply be expressed by a differential logistic growth equation.
Microbial growth was therefore calculated as follows (Onuma et al., 2018):
X=K1+K-X0X0eμt0-t,t=d-df,
where X and X0 are population densities of microbes at t and t0,
respectively, and μ is the growth rate of microbes in t-1. K is the carrying capacity of algae in the snow surface and t0 is the day of the
first appearance of algae on the snow surface. t represents the number of the
days during which the snow surface temperature was above 0 ∘C,
because snow algal growth mainly occurs on the melting snow surface.
Although this model assumes algal growth on the snow surface, the algal
cells observed in the surface snow were mostly cyst stage, which do not
divide and thus do not actively increase their population. The algae may divide
at the subsurface or deeper layers in the snowpack. Therefore, the increase
in algal cells at the snow surface may be due to not only their growth but also
accumulation at the surface as snow melts. However, their actual life cycle
is still uncertain on this glacier. In this study, we use this model, which
may include growth and/or accumulation of the algal cells but can reasonably
reconstruct the observation of their seasonal change on the snow surface of
the study site (Onuma et al., 2018).
Snow surface temperatures at the study site were obtained from the AWS data
(Onuma et al., 2018). Parameters for the algae, including the initial cell
concentration, algal growth rate and carrying capacity, were also the same
as those in the previous study (9.0×10-1 cells L-1,
0.39 d-1 and 3.2×106 cells L-1, respectively;
Onuma et al., 2018). Because the snowpack at the site was unlikely to have
disappeared after day 215, snow algae possibly increased on the surface snow
beyond day 215. The calculation showed that algal cell concentration
significantly increased from days 216 to 233 (1.5×105 to 1.6×106 cells L-1) and then remained constant until the
end of the melting season (Fig. 5).
Temporal changes in algal cell concentration and albedo reduction
from days 215 to 243 at the study site; algal cell concentration (red solid
line) and albedo reduction (green dotted line).
Simulation of snow albedo using PBSAM, coupled with the snow algae model,
was conducted on the snowpack at the study site for the entire melting season in
2014 (Fig. 5). The meteorological conditions, snow physical properties and
inorganic impurities were assumed to be constant after the last observation
on day 215 (Tables S1–S5). Consequently, Alb-DBA was 0.616 on day 216 and
0.612 at the end of the melting season (day 233). The effects of the snow algae
(the difference between Alb-DB and Alb-DBA) were 0.001 and 0.004 on days 216
and 233, respectively, indicating that snow albedo was significantly
decreased owing to the blooms of red snow late in the melting season
(August).
Possible albedo reduction in the presence of red snow blooming
We validated the albedo reduction for high algal abundance using the snow
albedos of red snow surface on oligotrophic polar or alpine snow reported by
previous studies (Painter et al., 2001; Takeuchi et al., 2006; Takeuchi,
2013; Lutz et al., 2014) (Table 2). The algal cell concentrations obtained
from their field measurements were used as input variables in surface (0–2 cm) and subsurface (2–10 cm) snow. These algal cell concentrations were
converted into COC using Eq. (3). Our observational data on day 215
(meteorological, snow physical and impurity conditions) were used as other
input data of these simulations. The simulation using the cell concentration
observed by Painter et al. (2001) demonstrated that the difference between
Alb-DB and Alb-DBA was 0.062, which is equivalent to the albedo reduction by
snow algae and in agreement with the algal albedo reduction (0.07) observed
by Painter et al. (2001). This reduction in albedo was also close to the
result of another simulation with the bio-albedo model proposed by Cook et
al. (2017a, algal albedo reduction = 0.07). Thus, both our PBSAM and the
bio-albedo model can consistently reproduce the reduction in albedo based on
the optical properties of S. nivaloides. The simulation using the cell concentration
observed by Takeuchi (2013) suggested that the simulated albedo reduction
was close to the observed one (model: 0.105; observation: 0.12). In contrast,
the simulation using the cell concentration reported by Lutz et al. (2014)
produced an albedo reduction by snow algae of 0.015, which was lower than
that observed by them (0.09) and calculated with the bio-albedo model
(0.09). This is probably owing to different algal pigments in the ice
surfaces. Lutz et al. (2014) reported that glacier algae (filamentous cells:
6.1×106 cells L-1) were found in addition to snow algae
(spherical cells: 1.8×106 cells L-1) in the samples
collected at their study site (MIT-17). The phenolic pigments of glacier
algae have a broader bandwidth of spectral absorption than the carotenoids
and chlorophyll of S. nivaloides (Remias, 2012; Williamson et al., 2020). In the albedo
simulation with the bio-albedo model, measured pigment compositions (total
chlorophyll, primary and secondary carotenoids) were used as model
parameters while our simulation only used MAC for snow algae (S. nivaloides). The
simulation using the cell concentration reported by Takeuchi et al. (2006)
showed that the simulated albedo reduction underestimated the observed
albedo reduction (model: 0.072, observation: 0.099). This may be due to the
difference between the observed and parameterized cell size. Our PBSAM
assumed that the cell size of S. nivaloides is 11.4 µm, whereas that measured by
Takeuchi et al. (2006) was 17.5 µm. Because the MACs for red snow
were estimated using the cell size of 11.4 µm, the simulated mass
absorption might underestimate the intact mass absorption for red snow
algae. Unfortunately, we have only the validation data in the study site
(MD, BC and OC concentrations and snow physical properties in surface and
subsurface snow layers). The detailed time series observation, including
analysis of cell size, pigment composition and algal community, should be
conducted at other sites to evaluate our albedo model. Moreover, the
detailed spatial measurements of algal cell abundance and snow albedo would
also be needed because the patchy red color caused by the blooms of snow algae
often appears on oligotrophic polar and alpine snow.
The relationship between reduction in snow albedo and algal cell
concentration. Albedo reductions were calculated from the difference in
Alb-DB and Alb-DBA simulated with an assumed algal cell density and (a) snow
grain size or (b) MD concentrations. Grey shades indicate the range of the
albedo reduction simulated with snow grain size ranging from 0.3 to 1.5 mm
or MD concentration from 0 to 150 mg L-1, respectively.
Potential for albedo reduction caused by blooms of red snow
Using the PBSAM, we conducted sensitivity analyses to quantify the reduction
in albedo with different concentrations of snow algae. Figure 6 shows the
albedo reduction by snow algae (Alb-DB minus Alb-DBA) as a function of algal
cell concentration for various snow grain sizes or MD concentrations on the
surface snow. Algal cell concentrations ranged from 4.9×104
to 2.0×108 cells L-1, which cover the range of cell
concentrations for typical red snow reported previously on oligotrophic
polar and alpine snow (Thomas and Duval, 1995; Takeuchi and Koshima, 2004;
Takeuchi et al., 2006; Stibal et al., 2007; Takeuchi, 2013; Lutz et al.,
2014; Tanaka et al., 2016; Onuma et al., 2018; Procházková et al.,
2018). Various snow grain sizes and MD concentrations were used in the
simulation (0.3–1.5 mm and 0–150 mg L-1, respectively), which were
based on observations on day 215 in this study (0.87 mm and 75 mg L-1,
respectively). Observational data on day 215 (meteorological, snow physical
and snow impurity conditions) were used as other input data for this
simulation. The simulation demonstrated that the albedo reduction by snow
algae ranged from 0 to 0.196 (algal cell concentration: 4.9×104 to 2.0×108 cells L-1) with an MD concentration
of 75 mg L-1, consistent with the algal albedo reduction estimated for
red algal abundances observed on various arctic glaciers (a maximum
reduction of 0.2; Lutz et al., 2016). Thus, the simulation with our albedo
model was comparable to the visible blooms of red snow.
Light absorption of algal cells in different snow grain sizes and
inorganic impurity concentrations in snow
Sensitivity analyses with PBSAM using different snow algal cell
concentrations and grain sizes suggested that increased snow grain size in a
snow layer can enhance light absorption by snow algal cells arising from
deeper penetration of incident radiation through snowpack. The difference
between Alb-DB and Alb-DBA was larger when snow grain size was large (1.2 mm, albedo reduction: 0–0.21; Fig. 6a). Conversely, the difference was
smaller when snow grain size was smaller (0.6 mm, albedo reduction:
0–0.18). Aoki et al. (2011) suggested that light penetration depth in snow
composed of coarse grains is deeper, enhancing light absorption by inorganic
impurities due to increased scattering of light. Therefore, increased snow
grain size possibly enhances albedo reduction by snow algae. Blooms of red
snow algae could accelerate the increase in snow grain size because of the
increase in penetration of incoming radiation within the snowpack, leading
to further reduction in snow albedo.
Increases in MD and BC concentrations in a snowpack possibly weaken the
scattering of light in the snowpack and reduce the amount of light absorbed
by snow algae. The difference between Alb-DB and Alb-DBA was smaller when MD
concentrations were larger (100 mg L-1, albedo reduction: 0–0.18; Fig. 6b). Conversely, the difference was larger when MD concentrations were
reduced (50 mg L-1, albedo reduction: 0–0.21). These results suggest
that algal cells absorb more light when MD concentrations were smaller,
compared with a higher MD concentration. The albedos calculated with
different BC concentrations also confirmed this result. The higher
concentrations of MD or BC may decrease the intensity of light scattered in
snow layers, thereby resulting in reduced light absorption by algae. There
is limited information about the effect of MD and BC on algal light
absorption in snowpacks, and a recent study suggested that the interaction
between algal cells and other impurities in snow should be investigated
(Cook et al., 2017a). Although further study is necessary to investigate in
situ interactions among snow algae and inorganic impurities in snowpack, our
simulation suggests that increased concentrations of inorganic snow
impurities weaken algal light absorption due to a reduction of the intensity
of scattered light in snow.
Temporal changes in (a) cumulative days of snow melting (b) algal
growth, (c) albedo reduction by snow algae and (d) radiative forcing by
snow algae for 30 d under various surface snow temperature (SST)
conditions at the study site.
Radiative forcing of algal cells
To quantitatively assess the effects of red snow blooming on the net
shortwave radiation, we calculated the radiative forcing from the observed
downward shortwave radiation multiplied by the difference of Alb-DB and
Alb-DBA, following the method of Niwano et al. (2012). Meteorological
conditions measured from 10:00 to 12:00 LT on day 215 were used to
calculate the radiative forcing. The calculations demonstrated that the
radiative forcings ranged from 0 to 0.1 W m-2 (mean: 0.1 W m-2)
and 18.0 to 63.6 W m-2 (mean: 37.5 W m-2) when cell concentrations
were 4.9×104 and 2.0×108 cells L-1,
respectively. The difference of Alb-DB and Alb-C was used to calculate the
radiative forcing of total MD and BC, and on day 215 at the study site the
forcing ranged from 8.4 to 33.9 W m-2 (mean: 19.4 W m-2). Cell
concentrations of S. nivaloides in visibly red snow surfaces ranged from 1.0 ×106 to 5.0 ×107 cells L-1 in Greenland (Lutz et
al., 2014, 2016; Onuma et al., 2018). The radiative forcings
calculated with these cell concentrations were equivalent to the range from
0.2 to 8.3 W m-2. Our calculations suggest that prominent blooms of red
snow (5.0×107 cells L-1, equivalent to
300 mg L-1) have the potential to increase radiative
forcing equal to that caused by total MD (75 mg L-1) and BC (3.7×10-3 mg L-1), although further field observation and
model validation in various snowfields are needed to discuss the potential
for studying albedo reduction arising from blooms of red snow.
Temporal changes in snow albedo reduction caused by red snow algae under warming conditions
The sensitivity test of PBSAM, coupled with the snow algae model, suggested
that albedo reduction by snow algae reached a maximum of 0.04, equivalent to
a radiative forcing of 7.5 W m-2, when surface snow temperature was
increased by 1.5 ∘C in August 2014, at the study site. Monthly
mean surface air temperature at the study site, from 2012 to 2017, which was
measured with the SIGMA-B AWS, ranged from -2.9 to 0.2 ∘C in
August (2014 season: -1.2∘C). Because the cell concentration of
S. nivaloides continuously increased during snow melting (Onuma et al., 2018), the
abundance of snow algae at the study site could differ each year. In this
study, we simulated temporal changes in snow albedo during the late summer
season using PBSAM coupled with a snow algae model, while assuming various
surface snow temperatures to estimate the impact of red snow algal growth on
snow albedo under climate change (Fig. 7). The simulation was conducted for
30 d, starting on day 215. The surface snow temperature assumed was the
observation plus or minus 0.5, 1.0 and 1.5 ∘C, but we kept
0 ∘C in case the temperature exceeded 0 ∘C. The
initial cell concentration, algal growth rate and carrying capacity were
4.9×104 cells L-1, 0.39 d-1 and 2.0×108 cells L-1, respectively. The algal cell concentration obtained
from the snow algae model was used in PBSAM to calculate surface albedo
(Fig. 7b). The other variables used in the PBSAM are from observational data
on day 215. Our simulations suggested that snow algae can exhibit additional
growth in warmer conditions, resulting in a larger reduction in albedo,
equivalent to larger radiative forcing. In particular, simulations with
surface snow temperature of plus 1.5 ∘C demonstrated that the
reduction in albedo and radiative forcing significantly increased for 30 d (red lines in Fig. 7). Although there is little information pertaining
to blooms of red snow on surface snow in Greenland, satellite observations
have detected the blooms on surface snow caused by growth of S. nivaloides in southeast
Greenland (Hisakawa et al., 2015). Ganey et al. (2017) suggested that the
red snow area detected by Landsat 8 extended over about 700 km2 on an
Alaskan ice field, and the red snow was responsible for 17% of the total
snowmelt there. Further study is necessary to simulate snow albedo with the
inclusion of the effect of red algal growth over the Greenland Ice Sheet.
Future climate warming in Greenland may expand the area of red snow in the
near future, leading to accelerated loss in mass of the Greenland Ice Sheet.
Conclusions
Temporal changes in snow albedo of Qaanaaq Glacier in northwest Greenland
were calculated with a physical snow albedo model that incorporated the
effect of three snow impurities (MD, BC and snow algae). A PBSAM (Aoki et
al., 2011) can calculate snow albedo using meteorological conditions, snow
physical properties and snow inorganic impurities. To quantify the effect of
red snow blooming on snow albedo, we calculated a light absorption
coefficient for red snow algae, based on geometry and the pigment
composition of red snow algae (Sanguina nivaloides, which was renamed recently from
Chlamydomonas nivalis) and introduced this coefficient into PBSAM. In addition, we simulated a
temporal change in snow albedo using this PBSAM coupled with a simple
numerical model for snow algal abundance (Onuma et al., 2018). The
calculated albedo agreed with the observed albedo during the algal growing
period, from late June to early August, although the algal cell
concentration did not reach the level of typical red snow blooming during
the observation period. We also calculated the snow albedo of the typical
red snow blooming surface previously reported and demonstrated that it
agreed with the observed snow albedo. Our simulation suggests that typical
red snow blooming has the potential to reduce snow albedo by 0.21, equivalent
to a radiative forcing of 40 W m-2. Finally, we conducted scenario
simulations (surface snow temperature of plus or minus 1.5 ∘C) in
order to estimate a possible albedo reduction by snow algae in the near
future. The albedo reduction by snow algae only equaled 0.04 (radiative
forcing: 7.5 W m-2) during a warmer ablation season (surface snow
temperature of +1.5 ∘C) in northwest Greenland, suggesting that
climate warming in the near future of Greenland Ice Sheet may expand the
area of red snow and further accelerate a loss of the mass balance. Our
model can simulate surface albedo in the broadband wavelength (300–3000 nm)
range, including the effects of both organic and inorganic impurities, and
it can independently estimate the reductions in albedo arising from each
impurity (MD, BC and snow algae). Intercomparison with other albedo models
(e.g., the bio-albedo model proposed by Cook et al., 2017a) would be useful
to develop the albedo model and to further understand the process of albedo
reduction arising from microbial activities on snow and ice. Although
further study is necessary to understand dynamics of organic and inorganic
impurities in the snowpack, the physical model of snow albedo coupled with
the snow algae model have the potential to provide a mechanistic understanding of
temporal changes of snow albedo over the Greenland Ice Sheet by
incorporating microbial activity on the snow and ice. In the future, coupling a
regional climate model NHM-SMAP (Niwano et al., 2018), which uses PBSAM as the
snow albedo scheme, and the snow algae model will enable us to estimate the
effect of red snow blooming on the melting of snow.
Data availability
All of the observation and model input and output data presented in this
study are available upon request to the corresponding author (Yukihiko
Onuma, onuma@iis.u-tokyo.ac.jp).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-14-2087-2020-supplement.
Author contributions
YO and NT designed the study and wrote the paper. YO and TA established the
light absorption coefficient for red snow algae and simulated snow albedo
with PBSAM. YO, ST and NN collected snow samples and observed snow physical
properties. YO and ST analyzed the collected data. MN and TA prepared the
SIGMA AWS data and provided technical support.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank the filed campaign members of the SIGMA (Snow Impurity and
Glacial Microbe effects on abrupt warming in the Arctic) project and GRENE
(the Green Network of Excellence) Arctic Climate Change Research Project in
Greenland in 2014. We also thank the two reviewers (Daniel Remias and Marian Yallop) and the editor (Elizabeth Bagshaw) for helpful suggestions that
greatly improved this paper.
Financial support
This research has been supported by JSPS-KAKENHI (grant nos. 23221004,26247078, 26241020, 16H01772, 18H03363, 19H01143 and 20K19955) and the Arctic Challenge for Sustainability (ArCS and ArCS II) projects.
Review statement
This paper was edited by Elizabeth Bagshaw and reviewed by Daniel Remias and Marian Yallop.
ReferencesAndreae, M. O. and Gelencsér, A.: Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols, Atmos. Chem. Phys., 6, 3131–3148, 10.5194/acp-6-3131-2006, 2006.Aoki, Te., Aoki, Ta., Fukabori, M., Hachikubo, A., Tachibana, Y., and Nishio,
F.: Effects of snow physical parameters on spectral albedo and bidirectional
reflectance of snow surface, J. Geophys. Res., 105, 10219–10236,
10.1029/1999JD901122, 2000.Aoki, T., Hori, M., Motoyoshi, H., Tanikawa, T., Hachikubo, A., Sugiura, K.,
Yasunari, T. J., Storvold, R., Eide, H. A., Stamnes, K., Li, W., Nieke, J.,
Nakajima, Y., and Takahashi, F.: ADEOS-II/GLI snow/ice products – Part II:
Validation results using GLI and MODIS data, Remote Sens. Environ., 111, 274–290,
10.1016/j.rse.2007b.02.035, 2007.Aoki, T., Kuchiki, K., Niwano, M., Kodama, Y., Hosaka, M., and Tanaka, T.:
Physically based snow albedo model for calculating broadband albedos and the
solar heating profile in snowpack for general circulation models, J. Geophys. Res., 116,
D11114, 10.1029/2010JD015507, 2011.Aoki, T., Kuchiki, K., Niwano, M., Matoba, S., Uetake, J., Masuda K., and
Ishimoto, H.: Numerical Simulation of Spectral Albedos of Glacier Surfaces
Covered with Glacial Microbes in Northwestern Greenland, Radiation Processes
In The Atmosphere And Ocean (IRS2012), edited by: Cahalan, R. and Fischer, ALP Conf. Proc., 1531, 176, 10.1063/1.4804735, 2013.Aoki, T., Matoba, S., Uetake, J., Takeuchi, N., and Motoyama, H.: Field
activities of the “Snow Impurity and Glacial Microbe effects on abrupt
warming in the Arctic” (SIGMA) project in Greenland in 2011–2013, Bull. Glaciol. Res., 32,
3–20, 10.5331/bgr.32.3, 2014a.Aoki, T., Matoba, S., Yamaguchi, S., Tanikawa, T., Niwano, N., Kuchiki, K.,
Adachi, K., Uetake, J., Motoyama, H., and Hori, M.: Light-absorbing snow
impurity concentrations measured on Northwest Greenland ice sheet in 2011
and 2012, Bull. Glaciol. Res., 32, 21–31, 10.5331/bgr.32.21, 2014b.Bøggild, C. E., Brandt, R. E., Brown, K. J., and Warren, S. G.: The
ablation zone in northeast Greenland: ice types, albedos and impurities, J. Glaciol.,
56, 101–113, 10.3189/002214310791190776, 2010.Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate
system: A scientific assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, 10.1002/jgrd.50171,
2013.Box, J. E., Fettweis, X., Stroeve, J. C., Tedesco, M., Hall, D. K., and Steffen, K.: Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers, The Cryosphere, 6, 821–839, 10.5194/tc-6-821-2012, 2012.Cerqueira, M., Pioa, C., Legrandb, M., Puxbaumc, H., Gieblc, A., Afonsoa,
J., Preunkertb, S., Gelencsérd, A., and Fialhoe, P.: Particulate carbon
in precipitation at European background sites, Aerosol Sci., 41, 51–61,
10.1016/j.jaerosci.2009.08.002, 2010.Chow, J. C., Watson, J. G., Pritchett, L. C., Pierson, W. R., Frazier, C. A.,
and Purcell, R. G.: The DRI thermal/optical reflectance carbon analysis
system: Description, evaluation and applications in U.S. air quality
studies, Atmos. Environ., 27, 1185–1201, 10.1016/0960-1686(93)90245-T, 1993.
Chow, J. C., Watson, J. G., Crow, D., Lowenthal, D. H., and Merrifield, T.:
Comparison of IMPROVE and NIOSH carbon measurements, Aerosol Sci. Technol., 34, 23–34, 2001.Cook. J. M., Hodson, A. J., Taggart, A. J., Mernild, S. H., and Tranter, M.:
A predictive model for the spectral “bioalbedo” of snow. J. Geophys. Res.-Earth Surf., 122,
10.1002/2016JF003932, 2017a.Cook, J. M., Hodson, A. J., Gardner, A. S., Flanner, M., Tedstone, A. J., Williamson, C., Irvine-Fynn, T. D. L., Nilsson, J., Bryant, R., and Tranter, M.: Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo, The Cryosphere, 11, 2611–2632, 10.5194/tc-11-2611-2017, 2017b.Flanner, M. G. and Zender, C. S.: Snowpack radiative heating: Influence on
Tibetan Plateau climate, Geophys. Res. Lett., 32, L06501, 10.1029/2004GL022076, 2005.Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo
evolution, J. Geophys. Res., 111, D12208, 10.1029/2005JD006834, 2006.Ganey, G. Q., Loso, M. G., Burgess, A. B., and Dial, R. J.: The role of
microbes in snowmelt and radiative forcing on an Alaskan icefield, Nature Geosci., 10, 754–759,
10.1038/NGEO3027, 2017.Hess, M., Koepke, P., and Schult, I.: Optical properties of aerosols and
clouds: The software package OPAC, B. Am. Meteorol. Soc., 79, 831–844,
10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2,
1998.Hisakawa, N., Quistad, S. D., Hestler, E. R., Martynova, D., Maughan, H.,
Sala, E., Gavrilo, M. V., and Rowher, F.: Metagenomic and satellite analyses
of red snow in the Russian Arctic, Peer J., 3, e1491, 10.7717/peerj.1491, 2015.
Hoham, R. W. and Duval, B.: Microbial ecology of snow and freshwater ice.
in:
Snow Ecology, edited by: Jones, H. G., Pomeloy, J. W., Walker, D. A., and Hoham, R. W., Cambridge University Press, 168–228, 2001.Hoham, R. W. and Remias, D.: Snow And Glacial Algae: A Review, J. Phycol., 56, 264–282, 10.1111/jpy.12952, 2020.Jacobson, M. C., Hansson, H. C., Noone, K. J., and Charlson, R. J.: Organic
atmospheric aerosols: Review and state of the science, Rev. Geophys., 38, 267–294,
10.1029/1998RG000045, 2000.
Jonsell, U., Hock, R., and Holmgren, B.: Spatial and temporal variations in
albedo on Storglaciären, Sweden, J. Glaciol., 49,
59–68, 2003.Kirchstetter, T. W., Novakov, T., and Hobbs, P. V.: Evidence that the
spectral dependence of light absorption by aerosols is affected by organic
carbon, J. Geophys. Res., 109, D21208, 10.1029/2004JD004999, 2004.Kuchiki, K., Aoki, T., Niwano, M., Matoba, S., Kodama, Y., and Adachi, K.:
Elemental carbon, organic carbon, and dust concentrations in snow measured
with thermal optical and gravimetric methods: Variations during the
2007–2013 winters at Sapporo, Japan, J. Geophys. Res.-Atmos., 120, 868–882,
10.1002/2014JD022144, 2015.Lutz, S., Anesio, A. M., Jorge Villar, S. E., and Benning, L. G.: Variations
of algal communities cause darkening of a Greenland glacier, FEMS Microbiol. Ecol., 89, 402–414,
10.1111/1574-6941.12351, 2014.Lutz, S., Anesio, A. M., Raiswell, R., Edwards, A., Newton, R. J., Gill, F.,
and Benning, L. G.: The biogeography of red snow microbiomes and their role
in melting arctic glaciers, Nat. Commun., 7, 11968, 10.1038/ncomms11968, 2016.Miyazaki, Y., Kondo, Y., Han, S., Koike, M., Kodama, D., Komazaki, Y.,
Tanimoto, H., and Matsueda, H.: Chemical characteristics of water-soluble
organic carbon in the Asian outflow, J. Geophys. Res., 112, D22S30,
10.1029/2007JD009116, 2007.Müller, T., Leya, T., and Fuhr, G.: Persistent Snow Algal Fields in
Spitsbergen: Field Observations and a Hypothesis about the Annual Cell
Circulation, Arct. Antarct. Alp. Res., 33, 42–51, 10.2307/1552276,
2001.Nagatsuka, N., Takeuchi, N., Uetake, J., and Shimada, R.: Mineralogical
composition of cryoconite on glaciers in northwest Greenland, Bull. Glaciol. Res., 32,
107–114, 10.5331/bgr.32.107, 2014.Nagatsuka, N., Takeuchi, N., Uetake, J., Shimada, R., Onuma, Y., Tanaka, S.,
and Nakano, T.: Variations in Sr and Nd isotopic ratios of mineral particles
in cryoconite in western Greenland, Front. Earth Sci., 4, 93, 10.3389/feart.2016.00093,
2016.Niwano, M., Aoki, T., Kuchiki, K., Hosaka, M., and Kodama, Y.: Snow
Metamorphism and Albedo Process (SMAP) model for climate studies: Model
validation using meteorological and snow impurity data measured at Sapporo,
Japan, J. Geophys. Res., 117, F03008, 10.1029/2011JF002239, 2012.Niwano, M., Aoki, T., Hashimoto, A., Matoba, S., Yamaguchi, S., Tanikawa, T., Fujita, K., Tsushima, A., Iizuka, Y., Shimada, R., and Hori, M.: NHM–SMAP: spatially and temporally high-resolution nonhydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet, The Cryosphere, 12, 635–655, 10.5194/tc-12-635-2018, 2018.Onuma, Y., Takeuchi, N., and Takeuchi, Y.: Temporal changes in snow algal
abundance on surface snow in Tohkamachi, Japan, Bull. Glaciol. Res., 34,
21–31, 10.5331/bgr.16A02, 2016.Onuma, Y., Takeuchi, N., Tanaka, S., Nagatsuka, N., Niwano, M., and Aoki, T.: Observations and modelling of algal growth on a snowpack in north-western Greenland, The Cryosphere, 12, 2147–2158, 10.5194/tc-12-2147-2018, 2018.Painter, T. H., Duval, B., and Thomas, W. H.: Detection and quantification
of snow algae with an airborne imaging spectrometer, Appl. Environ. Microbiol., 67, 5267–5272,
10.1128/AEM.67.11.5267-5272.2001, 2001.Procházková, L., Remias, D., Holzinger, A., Řezanka, T., and
Nedbalová, L.: Ecophysiological and morphological comparison of two
populations of Chlainomonas sp. (Chlorophyta) causing red snow on
ice-covered lakes in the High Tatras and Austrian Alps, Eur. J. Phycol., 53, 230–43,
10.1080/09670262.2018.1426789, 2018.Procházková, L., Leya, T., Křížková, H., and
Nedbalová, L.: Sanguina nivaloides and Sanguina aurantia gen. et spp.
nov. (Chlorophyta): the taxonomy, phylogeny, biogeography and ecology of two
newly recognised algae causing red and orange snow., FEMS Microbiol. Ecol., 95, fiz064, 10.1093/femsec/fiz064, 2019.Remias, D.: Cell structure and physiology of alpine snow and ice algae, in:
Plants in alpine regions, Cell physiology of adaption and survival
strategies, edited by: Lütz, C., Springer Wien, 202, 175–186, 10.1007/978-3-7091-0136-0_13, 2012.Rignot, E., Box, J. E., Burgess, E., and Hanna, E.: Mass balance of the
Greenland ice sheet from 1958 to 2007, Geophys. Res. Let., 35, L20502, 10.1029/2008gl035417, 2008.Segawa, T., Miyamoto, K., Ushida, K., Agata, K., Okada, N., and Koshima, S.:
Seasonal Change in Bacterial Flora and Biomass in Mountain Snow from the
Tateyama Mountains, Japan, Analyzed by 16S rRNA Gene Sequencing and
Real-Time PCR, App. Environ. Microbiol., 71, 123–130, 10.1128/AEM.71.1.123–130, 2005.Segawa, T., Matsuzaki, R., Takeuchi, N., Akiyoshi, A., Navarro, F.,
Sugiyama, S., Yonezawa, T., and Mori, H.: Bipolar dispersal of red-snow
algae, Nat. Commun., 9, 3094, 10.1038/s41467-018-05521-w, 2018.Steffensen, J. P.: The size distribution of microparticles from selected
segments of the Greenland Ice Core Project ice core representing different
climate periods, J. Geophys. Res., 102, 26755–26763, 10.1029/97JC01490, 1997.Stibal, M., Elster, J., Ŝabacká, M., and Kaŝtovská, K.:
Seasonal and diel changes in photosynthetic activity of the snow alga
Chlamydomonas nivalis (Chlorophyceae) from Svalbard determined by pulse
amplitude modulation fluorometry, FEMS. Microbiol. Ecol., 59, 265–273,
10.1111/j.1574-6941.2006.00264.x, 2007.Sugiyama, S., Sakakibara, D., Matsuno S., Yamaguchi, S., Matoba, S., and Aoki
T.: Initial field observations on Qaanaaq ice cap, northwestern Greenland,
Ann. Glaciol., 55, 25–33, 10.3189/2014AoG66A102, 2014.Takeuchi, N.: Seasonal and altitudinal variations in snow algal communities
on an Alaskan glacier (Gulkana glacier in the Alaska range), Environ. Res. Lett., 8, 035002,
10.1088/1748-9326/8/3/035002, 2013.
Takeuchi, N. and Kohshima, S.: snow algal community on a Patagonian glacier,
Tyndall glacier in the Southern Patagonia Icefield, Arct. Antarct. Alp. Res., 36, 91–8, 2004.
Takeuchi, N. and Li, Z.: Characteristics of surface dust on
Ürümqi glacier No. 1 in the Tien Shan
mountains, China Arct. Antarct. Alp. Res., 40, 744–750, 2008.Takeuchi, N., Dial, R., Kohshima, S., Segawa, T., and Uetake, J.: Spatial
distribution and abundance of red snow algae on the Harding Icefield, Alaska
derived from a satellite image, Geophys. Res. Lett., 33, L21502, 10.1029/2006GL027819, 2006.Takeuchi, N., Nagatsuka, N., Uetake, J., and Shimada, R.: Spatial variations
in impurities (cryoconite) on glaciers in northwest Greenland, Bull. Glaciol. Res., 32, 85–94,
10.5331/bgr.32.85, 2014.Tanaka, S., Takeuchi, N., Miyairi, M., Fujisawa, Y., Kadota, T., Shirakawa,
T., Kusaka, R., Takahashi, S., Enomoto, H., Ohata, T., Yabuki, H., Konya,
K., Fedorov, A., Konstantinov, P.: Snow algal communities on glaciers in the
Suntar-Khayata Mountain Range in eastern Siberia, Russia, Polar Sci., 10, 227–238,
10.1016/j.polar.2016.03.004, 2016.Tedesco, M., Fettweis, X., Van den Broeke, M. R., Van de Wal, R. S. W.,
Smeets, C. J. P. P., van de Berg, W. J., Serreze, M. C., and Box, J. E.: The
role of albedo and accumulation in the 2010 melting record in Greenland,
Environ. Res. Let., 6, 014005, 10.1088/1748-9326/6/1/014005, 2011.
Thomas, W. H. and Duval, B.: Sierra Nevada, California, USA, snow algae:
snow albedo changes, algal-bacterial interrelationships, and ultraviolet
radiation effects, Arct. Alp. Res., 27, 389–99, 1995.Tobo, Y., Adachi, K., DeMott, P. J., Hill, T. C. J., Hamilton, D. S.,
Mahowald, N. M., Nagatsuka, N., Ohata, S., Uetake, J., Kondo, Y., and Koike,
M.: Glacially sourced dust as a potentially significant source of ice
nucleating particles, Nat. Geosci., 12, 253–258 10.1038/s41561-019-0314-x, 2019.Torres, A., Bond, T. C., Lehmann, C. M. B., Subramanian, R., and Hadley, O.
L.: Measuring organic carbon and black carbon in rainwater: Evaluation of
methods, Aerosol Sci. Technol., 48, 238–249, 10.1080/02786826.2013.868596, 2014.Tsutaki S., Sugiyama, S. Sakakibara, D., Aoki, T., and Niwano, M.: Surface
mass balance, ice velocity and near-surface ice temperature on Qaanaaq Ice
Cap, northwestern Greenland, from 2012 to 2016, Ann. Glaciol., 58, 181–192, 10.1017/aog.2017.7, 2017.Warren, S. G. and Wiscombe, W. J.: A model for the spectral albedo of snow,
II: Snow containing atmospheric aerosols, J. Atmos. Sci., 37, 2734–2745,
10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2,
1980.Warren, S. G. and Wiscombe, W. J.: Dirty snow after nuclear war, Nature, 313, 467–470, 10.1038/313467a0, 1985.Wiscombe, W. J. and Warren, S. G.: A model for the spectral albedo of
snow: I. Pure snow, J. Atmos. Sci., 37, 2712–2733,
10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2,
1980.Williamson, C. J., Cook, J., Tedstone, A., Yallop, M., McCutcheon, J.,
Poniecka, E., Campbell, D., Irvine-Fynn, T., McQuaid, J., Tranter, M.,
Perkins, R., and Anesio, A.: Algal photophysiology drives darkening and melt
of the Greenland Ice Sheet, P. Natl. Acad. Sci., 117, 5694–5705,
10.1073/pnas.1918412117, 2020.Yallop, M. L., Anesio, A. M., Perkins, R. G., Cook, J., Telling, J., Fagan, D.,
MacFarlane, J., Stibal, M., Barker, G., and Bellas, C.: Photophysiology and
albedo-changing potential of the ice algal community on the surface of the
Greenland ice sheet, ISME J., 6, 2302–2313, 10.1038/ismej.2012.107, 2012.Yasunari, T. J., Koster, R. D., Lau, W. K. M., and Kim, K. M.: Impact of snow
darkening via dust, black carbon, and organic carbon on boreal spring
climate in the Earth system, J. Geophys. Res.-Atmos., 120, 5485–5503, 10.1002/2014jd022977,
2015.