TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-2147-2018Observations and modelling of algal growth on a snowpack in north-western
GreenlandObservations and modelling of algal growthOnumaYukihikoonuma@iis.u-tokyo.ac.jpTakeuchiNozomuhttps://orcid.org/0000-0002-3267-5534TanakaSotaNagatsukaNaokoNiwanoMasashihttps://orcid.org/0000-0003-3121-3802AokiTeruohttps://orcid.org/0000-0003-1007-986XInstitute of Industrial Science, The University of Tokyo, Kashiwa,
277-8574, JapanGraduate School of Science, Chiba University, Chiba, 263-8522, JapanNational Institute of Polar Research, Tokyo, 190-8518, JapanMeteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, JapanGraduate School of Natural Science and Technology, Okayama
University, Okayama, 700-8530, Japan
Yukihiko Onuma (onuma@iis.u-tokyo.ac.jp)27June20181262147215813November201729November201724April201812June2018This 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://tc.copernicus.org/articles/12/2147/2018/tc-12-2147-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/2147/2018/tc-12-2147-2018.pdf
Snow algal bloom is a common phenomenon on melting
snowpacks in polar and alpine regions and can substantially increase snow melt
rates due to the effect of albedo reduction on the snow surface.
In order to reproduce algal growth on the snow surface using a numerical
model, temporal changes in snow algal abundance were investigated on the
Qaanaaq Glacier in north-western Greenland from June to August 2014. Snow algae
first appeared at the study sites in late June, which was approximately 94 h
after air temperatures exceeded the melting point. Algal abundance increased
exponentially after this appearance, but the increasing rate became slow after
late July, and finally reached 3.5 × 107 cells m-2 in
early August. We applied a logistic model to the algal growth curve and found
that the algae could be reproduced with an initial cell concentration of
6.9 × 102 cells m-2, a growth rate of 0.42 d-1,
and a carrying capacity of 3.5 × 107 cells m-2 on this
glacier. This model has the potential to simulate algal blooms from
meteorological data sets and to evaluate their impact on the melting of
seasonal snowpacks and glaciers.
Introduction
Snow algae are cold-tolerant, photosynthetic microbes growing on snow and ice
and are commonly found on glaciers and snowfields worldwide. Snow algal
blooms occur on thawing snow surfaces and change the colour of the snow to
red or green (Thomas and Duval, 1995; Hoham and Duval, 2001; Takeuchi, 2013).
Red snow algal blooms (usually by Chlamydomonasnivalis) commonly occur in polar and alpine snow fields (Hoham and
Duval, 2001; Segawa et al., 2005; Takeuchi et al., 2006; Lutz et al., 2016;
Tanaka et al., 2016; Ganey et al., 2017).
The conditions required for the growth of snow algae are the occurrence of
liquid water, solar radiation, and nutrients. Snow algal cells are typically
present in the liquid water film surrounding snow grains when the snow melts
(Fukushima, 1963). Field observations showed that snow algae begin to grow
when the air temperature is above the freezing point for several days,
suggesting that algal growth requires a certain amount of water content in
the snow (Pollock, 1972; Onuma et al., 2016). A field study on algal
photosynthesis suggested that algal growth requires at least 1 % of
incident photosynthetically active radiation in the snowpack, promoting
photosynthesis and germination of algae (Curl Jr. et al., 1972). After the algae
appears on the snow surface, nutrient depletion (particularly nitrates) in
the snowpack can cause shifts in life cycle phases and a decrease in the growth
rate (Hoham et al., 1989). Previous studies have shown that the abundance of
snow algae increases as the snow melts. For example, snow algal abundance on
a glacier in Alaska continued to increase during the melt season until the
snowpack completely melted on the glacier surface (Takeuchi, 2013). Snow
algal abundance on a seasonal snowpack in Japan increased exponentially with
snowmelt until the snowpack completely melted (Onuma et al., 2016). Such
temporal changes in snow algal abundance can be affected by the snow
conditions, such as water content, solar radiation, and nutrient
availability.
A numerical model could be utilized to reproduce the seasonal change in algal
abundance on snowpacks, to understand algal growth in the snowfields on a
regional or worldwide scale, and to evaluate their effects on the surface
albedo and resultant melt rate. The effect of algae on surface albedo can
be physically calculated using an albedo model based on algal abundance (Cook
et al., 2017a, b). A temporal change in snow algal abundance could also be
reproduced using a numerical model. Many models were proposed and
applied to temporal changes in the abundance of photosynthetic microbes in
aquatic environments such as lakes or oceans. For example, there has been a
model for cyanobacteria in lakes, which can reproduce their exponential
growth using their initial concentration, growth rate, and nutrient
concentration (Chen et al., 2009). Additionally, a model for algae (diatoms)
growth in sea ice was developed using a sea ice physical model (Pogson et
al., 2011). This model can reproduce the temporal change in chlorophyll a
concentration in Arctic sea ice from the initial chlorophyll a concentration,
algal growth rate, and grazing rate. The exponential growth of snow algae
observed on a seasonal snowpack in Japan was reproduced using a Malthusian
model (Onuma et al., 2016). Although this model might be effective for the
seasonal snowpacks that exist for a short period and disappear in spring or
early summer, it is questionable whether the model is suitable for algae on
permanent snowfields or glaciers.
The Greenland Ice Sheet, the second-largest continuous body of ice in the
world, is known to be inhabited by snow algae. Several studies have reported
the visible red snow caused by blooms of Cd. nivalis over the ice
sheet (Lutz et al., 2014; Uetake et al., 2010; Takeuchi et al., 2014). The
ice sheet is reportedly losing mass due to an increase in temperature and
decrease in surface albedo during the last two decades (Rignot et al., 2008;
Wientjes and Oerlemans, 2010; Box et al., 2012). Decline in surface albedo by
snow and ice algal blooms can increase surface melt rates and thus is
likely one of the factors to cause mass loss of the ice sheet in recent years
(Yallop et al., 2012; Aoki et al., 2013; Lutz et al., 2014, 2016; Tedstone et
al., 2017; Stibal et al., 2017). Observation of a glacier in south-eastern
Greenland showed surface reflectance in the visible wavelengths for red snow
(49 %) to be lower than that of clean snow (75 %), and that snow algal
growth might lead to a positive feedback, increasing the melt rate of the
glacier (Lutz et al., 2014). Quantification of snow algal abundance is
important for estimating the melt rate of snow over the ice sheet. Niwano
et al. (2015) demonstrated that the snow albedo and snowmelt in Greenland
Ice Sheet can be simulated by a snow physical model (Niwano et al., 2012)
that incorporates a physically based snow albedo model (Aoki et al., 2011).
Establishment of a numerical model for algal growth possibly leads to a
simulation of the snowmelt including the effect of algal growth on snow albedo by a
coupled snow microbial–physical model. However, there is little information
on the temporal changes in snow algal abundance on a snowpack in Greenland,
and a numerical model for the snow algal growth has not been established to
date.
In this study, biological and meteorological observations were conducted on
the Qaanaaq Glacier located in north-western Greenland in order to quantify the
temporal change in snow algal abundance and establish a numerical model for
algal growth. Temporal changes in algal abundance on the snow surface were
quantified at two locations on the glacier from June to August in 2014 and
were fitted to a simple numerical equation. Factors affecting the parameters
of the equation are discussed in terms of meteorological data, and physical
and chemical snow data from the study sites.
Study sites and methods
A map of the Qaanaaq ice cap in north-western Greenland, showing the
location of sampling sites on the glacier.
The investigation was conducted at the Qaanaaq ice cap in north-western Greenland
(Fig. 1) from June to August in 2014. The Qaanaaq ice cap, which lies on a
small peninsula of north-western 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 two study sites at different elevations
(sites A and B) on the Qaanaaq Glacier, which is an outlet glacier of the ice
cap and is easily accessible on foot from Qaanaaq village. Site A is a
snowpack located at an elevation of 551 m a.s.l. towards the middle of the
glacier and is likely formed by snowdrift. Since the depth of the snowpack
was deeper than that of surrounding areas, the snow persisted through much of
the melt season. Site B is located at 944 m a.s.l., and was close to the
equilibrium line of the glacier (Tsutaki et al., 2017). Meteorological data
used in the study were collected with an automatic weather station (AWS),
which was installed at Site B in 2012 by the Snow Impurity and Glacier
Microbe effects on abrupt warming in the Arctic project (SIGMA) (Aoki et al.,
2014). Air temperature and solar radiation were collected hourly from April
to August 2014 using the AWS. Aoki et al. (2014) provided a more detailed
description of the AWS. The temperature sensor and pyranometer of the AWS
were placed at heights of 3.0 and 2.5 m above the snow surface. Air
temperature at Site A was calculated from the air
temperature collected at Site B with a temperature lapse rate, which was
assumed to be -7.80 × 10-3 K m-1 (Sugiyama et al., 2014).
Solar radiation at Site A was measured hourly from day 172 (21 June 2014) to
214 (2 August 2014) with a pyranometer (EKO ML-020) installed at 1.5 m above
the snow surface. The measured time of the hourly meteorological data is
defined as local time (LT is Greenwich Mean Time minus 2 h) in summer.
Snow pits were observed once weekly during the study period at both sites to
determine vertical profiles of snow type, temperature, density, and
liquid-water content. The snow temperature was measured with a thermistor
sensor (CT-430WP, Custom Ltd, Tokyo, Japan). The volumetric liquid-water
content in snow layers was obtained from snow density and snow permittivity,
which were measured using a density sampler and dielectric probe (Denoth,
1994), respectively. Snow surface temperature was obtained from direct
measurements and from calculating the observed downward and upward long-wave
radiant fluxes, assuming the emissivity of the snow surface to be 0.98
(Armstrong and Brun, 2008), following the protocol of Niwano et al. (2015).
Surface snow collection and snow pit observation were carried out,
simultaneously from days 162 to 214 (nine times total) at Site A and from
days 168 to 215 (eight times total) at Site B. Samples were collected from
one to five randomly selected surfaces (depth of 0 to 2 cm) using a
stainless-steel scoop. The sampling area ranged from 100 to 900 cm2 and
was recorded for each collection. Snow layers below the surface were also
collected from snow pits at Site A on day 162 and Site B on day 168. The
samples collected were from the surface layer (depth = 0–2 cm), the
subsurface layer (depth = 2–10 cm), and the layers of every 10 cm down
to the previous summer layer (depth = 150 cm for the Site A and 142 cm
for the Site B). All of the samples were preserved in
Whirl-Pak® bags (Nasco, Fort Atkinson,
Wisconsin, USA). Electrical conductivity (EC) and pH for the collected
samples were measured using a portable pH–conductivity meter (F-54, HORIBA,
Japan) after the samples were melted in Qaanaaq village. Samples used for
algal cell analysis were collected separately. These samples were melted and
preserved in 3 % formalin in 30 mL clean polyethylene bottles before being
transported to Chiba University, Japan, for analysis.
Algal abundance was obtained by counting cells and was represented as cell
number per unit surface area of snowpacks. Water samples of
20–1000 µL were filtered through a hydrophilized PTFE membrane
filter (pore size 0.45 µm, Millipore). 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
number per unit area (cells m-2) was calculated using the cell concentration
and area of sample collection. To obtain a cell volume biomass (biovolume),
mean cell volumes were estimated by measuring the size of 5–50 cells for
each species using a microscope and mean cell volume was obtained
geometrically. Total algal volume per unit area (mL m-2) per taxon was
obtained by multiplying the cell count and cell volume.
Abundance of mineral particles in snow was quantified using another set of
samples collected from the snow surface. Melted samples were dried
(60 ∘C, 24 h) in pre-weighed crucibles then combusted
(500 ∘C, 3 h) in an electric furnace to remove organic matter. The
mass of mineral particles per area (g m-2) was obtained from the
combusted sample weight and sampling area, since only mineral particles
remained after combustion.
ResultsMeteorological conditions
Meteorological observations on the Qaanaaq Glacier showed that air
temperature was below 0 ∘C from April through most of June and
increased above 0 ∘C from late June through early August (Fig. 2a).
The daily mean air temperature at Site B ranged from -25.8 to
-11.1 ∘C in April and from -19.7 to -7.9 ∘C in May.
It first exceeded 0 ∘C in the daytime on day 154 (3 June 2014) and
remained above 0 ∘C from late June to early August. This air
temperature record indicates that snowmelt occurred continuously from
late June to August at the study sites.
Meteorological conditions at sites A and B from 1 April to 1 September 2014.
(a) Daily mean air temperature, (b) solar radiation.
Solar radiation gradually increased from April to mid-July, before decreasing
(Fig. 2b). At the location of the Qaanaaq Glacier, the sun never set from day
108 (18 April 2014) to day 241 (29 August 2014). Monthly mean solar radiation
values at Site B for April, May, June, and July were 165, 276, 296, and
244 W m-2. The daily mean solar radiation in July ranged
from 71 to 375 W m-2 (mean: 218 W m-2) and from 90 to
383 W m-2 (mean: 244 W m-2) at sites A and B,
indicating that solar radiation did not significantly vary among sites.
Physical and chemical conditions of surface snow
Meteorological and physical conditions of surface snow at Site A
from 1 June to 1 September 2014. (a) Mean daily air temperature, (b) mean
daily snow surface temperature calculated from observed downward and upward
long-wave radiant fluxes, (c) relative snow surface level at the site (0 cm
on day 162), (d) snow density, (e) volumetric liquid-water content of snow,
and (f) abundance of mineral particles. Melt period in (b) is defined as
the period from the first day to the last day on which mean daily snow surface temperature
was 0 ∘C from 1 June to 1 September 2014. Standard deviation shown
by error bars.
Meteorological and physical conditions on surface snow at Site B
from 1 June to 1 September 2014. (a) Mean daily air temperature, (b) mean
daily snow surface temperature calculated from observed downward and upward
long-wave radiant fluxes, (c) relative snow surface level at the site (0 cm
on day 168), (d) snow density, (e) volumetric liquid-water content of snow,
(f) abundance of mineral particles. Melt period in (b) is defined
as the period from the first day to the last day when the daily mean snow surface
temperature was 0 ∘C from 1 June to 1 September 2014. Standard
deviation shown by error bars.
Snow observations showed that the surface snow was consistently wet from late
June to early August at both sites (Figs. 3 and 4). When the observation was
started on day 168 (17 June 2014), the surface snow at Site B was fresh dry
snow without surface melt. This snow became granular on day 176, implying
that the snow surface began to melt. Surface snow density was
386 kg m-3 on day 168 and gradually increased until day 215
(3 August 2014, 489 kg m-3). The mean snow grain size was 0.3 mm on
day 168, 0.9 mm on day 181, and varied between 0.7 and 0.9 mm until day
215. Snow surface level decreased by 121 cm during the study period
(47 days). Surface snow temperature was –0.2 ∘C on day 168 and
0 ∘C from days 176 to 215, except on day 197 (-0.1 ∘C).
Hourly surface snow temperature was calculated from long-wave radiation and
showed that the temperature was above 0 ∘C for 885 h of
1129 h during the study period. The volumetric liquid-water content of
surface snow was 6.3 % on day 181 and varied between 3.8 and 4.9 % until
day 215. Changes in snow properties were similar between sites. For example,
the surface snow of Site A was granular and the surface snow temperature was
0 ∘C after day 179. The results indicate that the snowpacks at both
sites melted continuously from late June until early August.
The mass of mineral particles in surface snow gradually increased from June
to early August at both sites, and it was consistently greater at the lower
site (Site A) than at the higher site (Site B) (Figs. 3 and 4). The mineral
abundance at Site B was 3.0 × 10-3 g m-2 on day 176 and
gradually increased to 7.6 ± 3.0 × 10-1 g m-2
(mean ± SD) until day 215. The abundance at Site A was 1.4 g m-2
on day 179 and gradually increased to 6.6 ± 1.9 g m-2 until day
214. A statistical test demonstrated that the temporal changes in mineral
abundance were significant at both sites (one-way ANOVA, Site B: F=4.95,
P=0.02<0.05; Site A: F=2.74, P=0.004<0.01). The comparison of
the mineral abundance in August between sites A and B showed that their
difference was statistically significant (6.6 ± 1.9 vs.
7.6 ± 3.0 × 10-1 g m-2; Student's t test, t=4.10, P=0.009<0.01).
The EC and pH of surface snow did not show seasonal trends or differences
between the sites. The EC ranged from 1.5 to 4.0 µS cm-1
(mean: 2.7 µS cm-1) and from 0.4 to
3.2 µS cm-1 (mean: 2.4 µS cm-1) at sites A
and B. The pH ranged from 5.5 to 6.2 (mean: 5.9) and from 5.3
to 6.1 (mean: 5.8) at sites A and B. There was no significant
difference in EC or pH between sites in late June (Student's t test, EC: t=2.47, P=0.13>0.05; pH: t=2.32, P=0.15>0.05). The EC and pH in July and August did not significantly vary among
sites.
Snow algae on snow surface
Photograph of snow algal cells observed on the snow surface at
Site A. An oval red cell with secondary carotenoids, most likely mature
spores of Cd. nivalis, which is the dominant species at both sites.
Scale bar = 20 µm.
Microscopic observation revealed that the red spherical algal cells were
dominant at both study sites. Algal cells (Fig. 5) contained a reddish-orange
and/or green pigment and were 21.3 ± 2.3 µm in diameter. The
cell volume biomass of this alga accounted for over 95 % of the total algal
biomass at both study sites. This alga was likely Chlamydomonas
(C.) nivalis since the shape, size, and pigmentation
(Fig. 5) corresponded with the taxon observed previously in 2007 and 2012 on
this glacier (Uetake et al., 2010; Takeuchi et al., 2014).
The other cell types were present in the samples in trace amounts. One was
spherical in shape with an orange or green pigment, and its cell size was
smaller (9.0 ± 2.2 µm) than the previously described algae.
Another cell was also spherical but with pale blue-green pigments and was
much smaller in size (4.6 ± 1.2 µm). These types of algal
cells were likely the undefined alga and Chroococcaceae cyanobacterium reported by Uetake et al. (2010).
Temporal changes in algal cell concentration of surface snow
Temporal changes in algal abundance on snow surface at (a) Site A
and (b) Site B. Melt periods in (a) and (b) are indicated in Figs. 3b and 4b.
Standard deviation shown by error bars.
Microscopic analysis revealed that the algal cells appeared on the surface
snow in late June and gradually increased until late July (Fig. 6). Algal
abundance was 7.4 cells m-2 at Site B when the algae first appeared on
day 181 and then increased to 5.0 × 105 cells m-2 until
day 215, although its abundance decreased occasionally on days 190 and 197
(Fig. 6b). The algal cells at Site A first appeared on day 179
(3.1 × 103 cells m-2), then their abundance
exponentially increased until day 201
(2.2 × 107 cells m-2) (Fig. 6a). Temporal changes in
algal abundance were significant at both sites (one-way ANOVA, Site A: F=2.45, P=0.006<0.01; Site B: F=2.91, P=5.9 × 10-5<0.01). The snow pit samples collected before the appearance of the algae (on
days 162 at Site A and 168 at Site B) contained no algal cells in all of
the snow layers down to the last summer surface.
The algal abundances on the snow surface at Site B continued increasing
until early August, whereas the abundances at Site A did not significantly
increase between days 201 to 214 (Fig. 6). The mean algal abundance at Site A
was 2.2 × 107 cells m-2 on day 201 and
3.5 × 107 cells m-2 on day 214. The algal abundance on
day 201 was 637 times that of day 195; however, algal abundance on day 214
was only 1.6 times that of day 201. The temporal change in algal abundance at
Site A was not significant between days 201 and 214 (one-way ANOVA, F=4.56, P=0.26>0.01).
DiscussionsOrigin of snow algae and their growth condition on the Qaanaaq
Glacier
The red snow phenomenon observed on the Qaanaaq Glacier is likely to occur
every summer according to previous studies on the glacier (Uetake et al.,
2010; Takeuchi et al., 2014). Additionally, the species causing this
phenomenon are likely the same as those typically occurring in Arctic
snowfields. The dominant algal cell, Cd. nivalis, has been widely reported in Arctic
snowfields (Spijkerman et al., 2012; Takeuchi, 2013; Hisakawa et al., 2015;
Lutz et al., 2016; Tanaka et al., 2016).
The red algal cells appear to have originated from windblown algal spores in
the atmosphere, but they are not likely from the remaining snow of the previous
melt season. Algae growing on the snow surface are usually derived from
spores transported by wind or animals from distant places (up to hundreds or
kilometres) or from motile cells that migrated from the lower layers of the
snowpack (Müller et al., 2001; Remias, 2012). The migration of motile
cells in the snowpack requires solar radiation as well as liquid water
(Hoham, 1980). However, photosynthetically active radiation can only
penetrate to a depth of 1 m in wet snowpacks (Curl Jr. et al., 1972). When snow
algae appeared on the snow surface at the study sites, the previous summer
surface was located deeper than 1 m from the present surface (229 and
110 cm at sites A and B, respectively). The depth appeared to be too great
for these cells to migrate to the surface. Furthermore, there was
superimposed ice over the last summer surface in the snowpack at Site B when
the algae appeared (Aoki et al., 2014). The superimposed ice layers seem to
block algal migration to the surface. The lack of algal cells in the snow pit
samples also suggest that algal cells are not derived from the lower snow
layers. Therefore, the algal cells are unlikely to have originated from
beneath the snow. Alternatively, algal cells might have been transported from
the ground surface surrounding the glacier or from distant sources
atmosphere. Previous studies reported that mineral dust on glaciers in
north-western and south-western Greenland is mainly supplied from local ground
surfaces (e.g. moraine near the glacier) rather than the distant areas
(Nagatsuka et al., 2014, 2016). Therefore, the algal spores, which were
washed out from the glacier and stayed on the ground, may be supplied with
dust around the glacier by wind.
Meteorological records suggest that the initiation of algal growth requires
the air temperature to remain above 0 ∘C for a certain period of
time after the previous snowfall. The snow algae at both sites A and B
appeared two days apart. Prior to algal appearances, the
hourly air temperature remained above 0 ∘C for 94 h from day 175 at
Site A and for 136 h from day 176 at Site B; there was no snowfall during
this time at either site. The period from the last snowfall appears to be
important in initiating snow algal growth, as fresh snow coverage inhibits
photosynthesis of the snow algae under the snow. Additionally, snowmelt is
required for the initiation of algal growth (Fukushima, 1963; Onuma et al.,
2016). Snow algae on a snowpack in Japan has been reported to appear when air
temperatures exceed 0 ∘C for 24 h, which is likely the minimum
requirement for initiating snow algal growth (Onuma et al., 2016). The
duration was longer in this study than that which was observed in Japan. The
longer duration may be due to a difference in algal species or weather
conditions on this glacier. These results suggest that continuous melting for
a minimum of 94 h is required for the initiation of algal growth on the
Qaanaaq Glacier, although further studies are necessary to determine the snow
physical conditions for the initiation.
Approximation of the algal growth curve with a numerical model
In order to reproduce the observed algal growth with a numerical equation,
we applied a logistic model that utilizes a general differential equation of
microbial growth to the observed algal growth curve. An increase in
microbial cells can simply be expressed by a differential equation known as
the Malthusian model, which is defined by an initial cell concentration and
algal growth rate (Lavoie et al., 2005). The Malthusian model is based on
the assumptions that microbial abundance increases by cell division in all
present cells at a constant rate, so that there is no addition or removal of
cells in the habitat, and that light, nutrients, and habitable space are
unlimited. According to this model, the microbial growth curve is calculated
as follows (Cui and Lawson, 1982):
X=X0eμ(t-t0),
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.
The Malthusian model has been applied to observational microbial abundances
in sea ice (Lavoie et al., 2005) and in snowfield (Onuma et al., 2016).
However, the algal abundance at Site A did not significantly increase after
late July, despite the air temperature remaining above 0 ∘C and a
lack of snowfall, indicating that the Malthusian model could not represent
the algal growth curve on the surface snow of the Qaanaaq Glacier. The
decreased growth rate observed on the glacier suggests that algal abundance
has a limited capacity in this habitat. A logistic model is a microbial
growth equation with a carrying capacity, and thus could represent the algal
growth curve observed in this study. The temporal change of the logistic
model is represented as follows (Cui and Lawson, 1982):
X=K1+K-X0X0eμt0-t,t=d-df,
where K is the carrying capacity of algae in the snow surface (depth =
2 cm) and t0 is the day of the first appearance of algae on the snow
surface. Since snow algae can grow only on the melting snow surface, we
assumed that algal growth was interrupted when snow surface temperature was
below 0 ∘C. Thus, t represents the number of days during which
the mean temperature was above 0 ∘C. This equation was fitted to the
observational algal cell concentrations at sites A and B through Poisson
regression. The observational data used are from the day of algal appearance
(days 179 at Site A and 181 at Site B, t0) through the last day of the
study period (days 214 at Site A and 215 at Site B, tmax). This
regression is based on the assumption that there is no inflow or outflow of
algal cells on the snow surface. To fit Poisson regression to the observed
algal cell concentrations, carrying capacity was assumed to be
3.5 × 107 cells m-2 at both sites based on the observed
maximum concentration of algal cells (day 214 at Site A). Although it is
uncertain whether the algal concentration at Site A was greatest on day
214 in the summer of 2014,
the carrying capacity was likely around 3.5×107 cells m-2 since
the cell concentrations hardly increased from day 201 to 214 despite air
temperatures remaining above 0 ∘C. In contrast, the algal cell
concentration at Site B continued to increase significantly until day 215,
suggesting that it did not reach the level of the carrying capacity at this
site. The cell concentration would increase further the day after because the
snow surface temperature calculated at Site B remained above 0 ∘C for a
week. Although the carrying capacity possibly varies on different snow
surfaces, it was assumed to be the same at Site A and B in this study since
they are on the same glacier.
No inflow of algal cells on the snow surface was assumed for this calculation
because wind delivery of algal cells appeared to be smaller compared with the
abundance during the growth period. The initial concentration of algae on the
surface (3.1 × 103 cells m-2 on day 179 at Site A and
7.4 cells m-2 on day 181 at Site B), which is probably equivalent to
the algal cells of the wind delivery, was substantially smaller than the
final concentration (3.5 × 107 cells m-2 on day 214 at
Site A and 5.0×105 cells m-2 on day 215 at Site B).
Outcropping algal cells from the subsurface snow also appear to be
insignificant because no algal cells were detected in all of the snow layers
below the surface. The outflow of algal cells by meltwater is also likely to
be too insignificant to affect the algal cell abundance on the snow surface since
the algal cell concentration kept increasing during the study period.
List of the parameters of a logistic model of snow algal growth
obtained from data from each study site.
Temporal changes in snow temperature and algal abundance on the
surface at Site A. (a) Mean daily snow surface temperature, (b) observed and
calculated algal abundance, and (c) enlargement of the observed and
calculated algal abundance between 0 and 1.0 × 106 cells m-2.
Surface snow temperature was calculated from observed downward and
upward long-wave radiant fluxes. Solid marks indicate observed algal
abundance. Solid lines indicate algal abundance calculated from regression by
logistic model. Standard deviation shown by error bars.
Temporal changes in snow temperature and algal abundance on surface
snow at Site B. (a) Mean daily snow surface temperature, (b) observed and
calculated algal abundance, and (c) enlargement of the observed and
calculated algal abundance between 0 and
1.0 × 106 cells m-2. Surface snow temperature was
calculated from observed downward and upward long-wave radiant fluxes. Solid
marks indicate observed algal abundance. Solid lines indicate algal abundance
calculated from regression by the logistic model. Standard deviation shown by
error bars.
Fitting the data to the model showed that the coefficients of determination
in the regressions (R2) were 0.64 and 0.96 at sites A and B,
respectively, suggesting that the algal growth curve was reproduced well with
the equation (Table 1, Figs. 7 and 8). However, the confidence levels cannot
be used to calculate the regression curve because the standard deviations for the
observed algal cell concentration increased over time. Therefore, the
uncertainty in the calculated algal abundance appears to be larger
late in the melt season. The decline of algal cell concentration observed
from days 201 to 208 at Site A was not reproduced in the calculated growth
curve. This is likely the reason for the lower R2 value at Site A.
However, the calculated cell concentration
(3.4 × 107 cells m-2) was consistent with the observed
abundance (3.5 × 107 cells m-2) on day 214, which was
the day when algal cell concentration on surface snow was greatest during
the observational period; this suggests that the model can accurately
reproduce the cell abundance with the order of magnitude and the time at
which algal cell concentration reached the carrying capacity.
List of maximum algal cell concentrations of red algal
bloom reported from various snow fields across the world. Maximum algal cell
concentrations per area (cells m-2) were obtained by
calculation from reported maximum algal cell concentrations per volume
(cells mL-1) assuming the snow density of granular
snow to be 500 kg m-3 and the depth of collected
samples to be 0.02 m.
Study sitesAlgal speciesMaximum algalReferencescell concentration(cells m-2)Oregon, USAChlamydomonas nivalis2.3 × 109Sutton (1972)Washington, USAChloromonas brevispina5.0 × 109Hoham et al. (1979)AntarcticaMesotaenium berggrenii1.0 × 109Ling and Seppelt (1990)AntarcticaChloromonas rubroleosa2.0 × 109Ling and Seppelt (1993)California, USATrochiscia americana6.3 × 108Thomas (1994)SvalbardChloromonas alpine7.5 × 109Spijkerman et al. (2012)Alaska, USAChlyamidomonas nivalis5.1 × 107Takeuchi (2013)SE GreenlandChlyamidomonas nivalis5.0 × 108Lutz et al. (2014)Factors affecting parameters of algal growth model
The growth rates (μ) obtained from the regression of the algal growth
curve did not significantly differ between the two sites, whereas the initial
cell concentration (X0) at the lower site was 100 times greater than
that of the higher site (Table 1).
The difference in initial cell concentration (X0) between the two sites
was likely attributed to the abundance of initial algal spores supplied from
the atmosphere. The initial cell concentration observed at sites A and B were
6.9 × 102 and 6.3 cells m-2, and the abundance of
mineral particles on the snow surface was also significantly greater at
Site A (1.4 g m-2) than at Site B (7.0 × 10-3 g m-2,
Table 1). The sources of mineral particles on the Qaanaaq Glacier were mostly
from local sediments, such as soil and moraine near the glacier (Nagatsuka et
al., 2014). Therefore, the initial algal spores on surface snow are likely
supplied with mineral particles by wind from the surrounding ground surface.
There is little information on the abundance of initial algal spores on the snow
surface. An estimation of initial algal concentrations from mineral particle
abundance could be applied to this model in the other glaciers or snowfields
since the abundance of mineral particles is widely available by observation,
the surface mass balance model on the Greenland Ice Sheet (Goelles et al.,
2015), and atmospheric circulation models (Ginoux et al., 2001).
The growth rates (μ) obtained from the sites were similar, suggesting
that growth rates on the glacier are constant. The growth rates were 0.39 and
0.42 d-1 at sites A and B, respectively (Table 1). Although solar
radiation might affect algal photosynthesis and thus their growth rate on the
snowpack, the effect is unclear since there was no significant difference in
the July solar radiation among the two sites (218 vs. 244 W m-2 for
sites A and B, respectively; Student's t test, t=-0.99, P=0.32>0.05). According to the measurement of the growth rate of isolated snow
algae, Chloromonas nivalis in the culture, it was 0.6 d-1 in
18 ∘C water (Leya et al., 2009), which is significantly greater than
the growth rate of 0.39–0.42 d-1 in the present study (Table 1). The
lower growth rate in our study is likely due to the lower temperature of the
algal habitat compared to the culture conditions, as growth rate of fresh
water algae is dependent on water temperature (Eppley, 1972). The growth rate
of snow algae may also vary among algal species, although further study is
necessary.
The carrying capacity of snow algae may be determined by nutrient
availability in the snowpack. The carrying capacity was estimated to be
3.5 × 107 cells m-2 in this study, based on the observed
growth curve (Table 1). Although there are no observational data on the
carrying capacity of snow algae, it could be represented by the cell
concentration of snow algal bloom reported late in the melt season in
previous studies. Cell concentrations of snow algal blooms reported
previously in various geographical locations ranged from
5.1 × 107 to 7.5 × 109 cells m-2
(Table 2), suggesting that carrying capacity varies among sites and might be
determined by environmental conditions. There are two possible factors
affecting carrying capacity: (1) the reduction in physical space available
for microbial growth (i.e. the volume of liquid melt water, McKindsey et al.,
2006) and (2) the exhaustion of resources for the algae on the snow surface
(Cui and Lawson, 1982). The maximum algal cell volume on surface snow (depth
= 2 cm) at Site A (day 214) was substantially smaller compared to the
total volume of liquid water in the surface snow obtained from the water
content (0.19 mL m-2 vs. 500 mL m-2); this indicates that the
physical space for algal growth in the habitat is not a factor affecting
carrying capacity in this study. Nutrients such as nitrogen and phosphorus
are essential for algal growth and are usually supplied from the
atmosphere to the snow surface as aerosols. Phosphorus supplied that is
carried by wind to glaciers in the form of phosphate minerals easily becomes
a limiting factor for algal growth compared to nitrogen, as the concentration
of phosphorus was less than that of nitrogen in glaciers (Stibal et al.,
2008). The addition of nutrients from outside likely increases snow algal
abundance in the snowpacks reported by Ganey et al. (2017). Therefore, the
carrying capacity may be determined by approximation using the relationship
between observational abundance of algal cells and mineral dust on the snow
surface, although further study is necessary to substantiate this claim.
Conclusions
The temporal changes in snow algal abundance on snowpacks of the Qaanaaq
Glacier in north-western Greenland were studied. Spherical algal cells filled
with red pigment, which are likely Cd. nivalis, were dominant on the
snowpack. The algal cells first appeared on the snow surface in late June
when the snow had melted and the air temperature remained above 0 ∘C
for approximately 94 h. Algal abundance increased exponentially for
a month, and then the growth rate decreased mid-July, even though the air
temperatures remained above 0 ∘C and no snowfall occurred. A
logistic model was applied to the observed algal growth curves to reproduce
the abundance numerically using three parameters: the initial cell concentration
(X0), growth rate (μ), and carrying capacity (K). The
growth curves were reproduced with coefficients of determination (R2) of
0.64 and 0.96 at the lower and higher sites. Our observational
results suggest that the model parameters can be determined using the
environmental conditions (physical and chemical snow properties and
meteorological conditions) of the glacier; thus this logistic model has the
potential to reproduce the snow algae on glaciers or ice sheets in Greenland,
although further studies are necessary to determine the three parameters of the model.
The parameters determined in this study were based on the observation of a
single glacier and season. More observation data on the algal seasonal growth
could reduce the uncertainty in the model. In order to validate and calibrate
the model parameters in more extensive areas of the glacier or the ice sheet,
satellite images could be useful as a recent study successfully quantified the
red algal abundance on an ice field in Alaska (Ganey et al., 2017).
Furthermore, it is important to understand the life cycle of snow algae
including the process of atmospheric transportation of the algal spores and
effect of nutrient dynamics within the surface snow. Our results demonstrate
that a simple numerical model could simulate the temporal variation in algal
abundance on snow surface on a Greenland glacier. In future, coupling this
algal model with a regional climate model in Greenland, such as the model
proposed by Niwano et al. (2018), would enable us to estimate snowmelt
regarding the effect of algal bloom. In addition, the model would be
useful for understanding the algal life cycle on the ice sheet.
All of the observation and model output data presented in this study are available upon request to the corresponding author (Yukihiko Onuma, onuma@iis.u-tokyo.ac.jp).
YO and NT designed the study and wrote the paper. 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.
The authors declare that they have no conflict of
interest.
Acknowledgements
We would thank to 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 two anonymous reviewers and an editor (Marco
Tedesco) for helpful suggestions that greatly improved this manuscript. This
study was supported in part by Grant-in-Aids (23221004, 26247078, 26241020,
16H01772).Edited by: Marco Tedesco
Reviewed by: two anonymous referees
ReferencesAoki, 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, in: Radiation
processes in the atmosphere and ocean (IRS2012), edited by: Cahalan, R. and
Fischer, J., 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, B.
Glaciol. Res., 32, 3–20, 10.5331/bgr.32.3, 2014.
Armstrong, R. L. and Brun, E. (Eds.): Snow and Climate: Physical Processes, Surface
Energy Exchange and Modeling, Cambridge Univ. Press, Cambridge, UK, 2008.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.Chen, S., Chen, X., Peng, Y., and Peng, K.: A mathematical model of the
effect of nitrogen and phosphorus on the growth of blue-green algae
population, Appl. Math. Model., 33, 1097–1106,
10.1016/j.apm.2008.01.001, 2009.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 122, 434–454, 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.Cui, Q. and Lawson, G. J.: Study on models of single populations: an
expansion of the logistic and exponential equations, J. Theor. Biol., 98,
645–659, 10.1016/0022-5193(82)90143-6, 1982.Curl Jr., H., Hardy, J. T., and Ellermeier, R.: Spectral absorption of solar
radiation in alpine snow fields, Ecology, 53, 1189–1194,
10.2307/1935433, 1972.Denoth, A.: An electronic device for long-term snow wetness recording, Ann.
Glaciol., 19, 104–106, 1994.Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fish. B.-NOAA,
70, 1063–1085, 1972.Fukushima, H.: Studies on cryophytes in Japan, J. Yokohama Munic. Univ. Ser.
C, Nat. Sci., 43, 1–146, 1963.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, Nat.
Geosci., 10, 754–759, 10.1038/NGEO3027, 2017.Ginoux, P., Chin, M., Tegen, I., Prospero, J., Holben, B., Dubovik, O., and
Lin, S.-J.: Sources and global distributions of dust aerosols simulated with
the GOCART model, J. Geophys. Res., 106, 20255–20273,
10.1029/2000JD000053, 2001.Goelles, T., Bøggild, C. E., and Greve, R.: Ice sheet mass loss caused by
dust and black carbon accumulation, The Cryosphere, 9, 1845–1856,
10.5194/tc-9-1845-2015, 2015.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.: Unicellular chlorophytes-snow algae, in: Phytoflagellates, edited by: Cox, E. R.,
Elsevier, New York, 61–84, 1980.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., Roemer, S. C., and Mullet, J. E.: The life history and ecology
of the snow alga Chloromonas brevispina comb. nov. (Chlorophyta, Volvocales),
Phycologia, 18, 55–70, 10.2216/i0031-8884-18-1-55.1, 1979.Hoham, R. W., Yatsko, C. P., Germain, L., and Jones, H. G.: Recent
discoveries of snow algae in upstate New York and Québec Province and
preliminary reports on related snow chemistry, Proc. 46th Ann. Eastern Snow
Conf., 196–200, 1989.Lavoie, D., Denman, K., and Michel, C.: Modeling ice algal growth and
decline in a seasonally ice-covered region of the Arctic (Resolute Passage,
Canadian Archipelago), J. Geophys. Res., 110, C11009,
10.1029/2005JC002922, 2005.Leya, T., Rahn, A., Lütz, C., and Remias, D.: Response of arctic snow
and permafrost algae to high light and nitrogen stress by changes in pigment
composition and applied aspects for biotechnology, FEMS Microbiol. Ecol., 67,
432–443, 10.1111/j.1574-6941.2008.00641.x, 2009.Ling, H. U. and Seppelt, R. D.: Snow algae of the Windmill Island,
continental Antarctica, Mesotaenium berggrenii (Zygnematales, Chlorophyta),
the alga of grey snow, Antarct. Sci., 2, 143–148,
10.1017/S0954102090000189. 1990.Ling, H. U. and Seppelt, R. D.: Snow algae of the Windmill Island,
continental Antarctica. 2. Chloromonas rubroleosa sp. nov. (Volvocales,
Chlorophyta), an alga of red snow, Eur. J. Phycol., 28, 77–84,
10.1080/09670269300650131, 1993.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.McKindsey, C. W., Thetmeyer, H., Landry, T., and Silvert, W.: Review of
recent carrying capacity models for bivalve culture and recommendations for
research and management, Aquaculture, 261, 451–462,
10.1016/j.aquaculture.2006.06.044, 2006.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, B. 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., Matoba, S., Yamaguchi, S., Tanikawa, T., Kuchiki, K.,
and Motoyama, H.: Numerical simulation of extreme snowmelt observed at the
SIGMA-A site, northwest Greenland, during summer 2012, The Cryosphere, 9,
971–988, 10.5194/tc-9-971-2015, 2015.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, B. Glaciol. Res., 34, 21–31,
10.5331/bgr.16A02, 2016.Pogson, L., Tremblay, B., Lavoie, D., Michel, C., and Vancoppenolle, M.:
Development and validation of a one-dimensional snow–ice algae model against
observations in resolute passage, Canadian Arctic Archipelago, J. Geophys.
Res., 116, C04010, 10.1029/2010JC006119, 2011.Pollock, R.: What colors the mountain snow?, Sierra Club Bull., 55, 18–20,
1970.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, Appl. Environ. Microb., 71, 123–130. 10.1128/AEM.71.1.123-130,
2005.Spijkerman, E., Wacker, A., Weithoff, G., and Leya, T.: Elemental and fatty
acid composition of snow algae in Arctic habitats, Front. Microbiol, 3, 380,
10.3389/fmicb.2012.00380, 2012.Stibal, M., Tranter, M., Telling, J., and Benning, L. G.: Speciation, phase
association and potential bioavailability of phosphorus on a Svalbard
glacier, Biogeochemistry, 90, 1–13, 10.1007/s10533-008-9226-3, 2008.Stibal, M., Box, J. E., Cameron, K. A., Langen, P. L., Yallop, M. L.,
Mottram, R. H., and Ahlstrøm, A. P.: Algae drive enhanced darkening of
bare ice on the Greenland ice sheet, Geophys. Res. Lett., 44, 22,
10.1002/2017GL075958, 2017.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.Sutton, F. A.: The physiology and life histories of selected cryophytes of
the Pacific NorthWest, PhD Thesis, Oregon State University, Corvallis, 98,
1972.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., 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, B. 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., and 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.Tedstone, A. J., Bamber, J. L., Cook, J. M., Williamson, C. J., Fettweis, X.,
Hodson, A. J., and Tranter, M.: Dark ice dynamics of the south-west Greenland
Ice Sheet, The Cryosphere, 11, 2491–2506,
10.5194/tc-11-2491-2017, 2017.
Thomas, W. H.: Tioga Pass revisited: Interrelationships between snow algae
and bacteria, Proceedings, 62nd Ann. W. Snow Conf., Santa Fe, NM, 56–62,
1994.Thomas, W. H. and Duval, B.: Sierra Nevada, California, USA, snow algae:
snow albedo changes, algal-ba cterial interrelationships, and ultraviolet
radiation effects, Arctic Alpine Res., 27, 389–99, 10.2307/1552032,
1995.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.Uetake, J., Naganuma, T., Hebsgaard, M. B., and Kanda, H.: Communities of
algae and cyanobacteria on glaciers in west Greenland, Polar Sci., 4, 71–80,
10.1016/j.polar.2010.03.002, 2010.Wientjes, I. G. M. and Oerlemans, J.: An explanation for the dark region in
the western melt zone of the Greenland ice sheet, The Cryosphere, 4,
261–268, 10.5194/tc-4-261-2010, 2010.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.