The Cryosphere, 9, 1879–1893, 2015
www.the-cryosphere.net/9/1879/2015/
doi:10.5194/tc-9-1879-2015
© Author(s) 2015. CC Attribution 3.0 License.
Satellite observations of changes in snow-covered land surface
albedo during spring in the Northern Hemisphere
K. Atlaskina1, F. Berninger2, and G. de Leeuw1,3
1Department of Physics, University of Helsinki, Helsinki, Finland
2Department of Forest Sciences, University of Helsinki, Helsinki, Finland
3Finnish Meteorological Institute, Helsinki, Finland
Correspondence to: K. Atlaskina (ksenia.atlaskina@helsinki.ﬁ)
Received: 13 April 2015 – Published in The Cryosphere Discuss.: 7 May 2015
Revised: 9 September 2015 – Accepted: 11 September 2015 – Published: 24 September 2015
Abstract. Thirteen years of Moderate Resolution Imaging
Spectroradiometer (MODIS) surface albedo data for the
Northern Hemisphere during the spring months (March–
May) were analyzed to determine temporal and spatial
changes over snow-covered land surfaces. Tendencies in land
surface albedo change north of 50◦ N were analyzed using
data on snow cover fraction, air temperature, vegetation index
and precipitation. To this end, the study domain was divided
into six smaller areas, based on their geographical position
and climate similarity. Strong differences were observed
between these areas. As expected, snow cover fraction (SCF)
has a strong inﬂuence on the albedo in the study area and can
explain 56 % of variation of albedo in March, 76 % in April
and 92 % in May. Therefore the effects of other parameters
were investigated only for areas with 100 % SCF. The second
largest driver for snow-covered land surface albedo changes
is the air temperature when it exceeds a value between −15
and −10 ◦C, depending on the region. At monthly mean air
temperatures below this value no albedo changes are observed.
The Enhanced Vegetation Index (EVI) and precipitation
amount and frequency were independently examined
as possible candidates to explain observed changes in albedo
for areas with 100 % SCF. Amount and frequency of precipitation
were identiﬁed to inﬂuence the albedo over some areas
in Eurasia and North America, but no clear effects were observed
in other areas. EVI is positively correlated with albedo
in Chukotka Peninsula and negatively in eastern Siberia. For
other regions the spatial variability of the correlation ﬁelds is
too high to reach any conclusions.
1 Introduction
Broadband surface albedo, deﬁned as the fraction of the solar
energy (short-wave radiation in spectral range from 0.3 to
5 µm) reaching the Earth surface and reﬂected upward, plays
an important role in the Earth energy balance (IPCC, 2013).
It determines the amount of radiation absorbed by the surface
and, consequently, the energy available for heating of
the surface and the atmosphere. Albedo varies with surface
type, vegetation density and growth conditions, moisture, etc.
The short-wave (SW) snow albedo varies from 0.9 to 0.95 for
fresh snow (Wuttke et al., 2006) and decreases gradually to
values down to 0.5–0.6 for aged snow. The albedo of snowfree
surfaces is much lower, for instance 0.08–0.3 for soil, depending
on soil type and wetness (Idso et al., 1975; Matthias
et al., 1999), 0.17–0.28 for grassland (Song, 1999), or 0.08–
0.13 for boreal forest (Baldocchi et al., 2000; Lukeš et al.,
2014). The greatest interannual variability occurs at high latitudes
in the Northern Hemisphere (NH) due to seasonal variations
in snow cover, which is further augmented by snow
age and other snow properties, and deposition of absorbing
material. The snow cover extent in the NH has been observed
to decrease during the past decades (Brown and Robinson,
2011), in particular in the spring season. Observations by
Peng et al. (2013) at 400 stations in the NH show that the
snow cover duration decreased between 1979 and 2006. At
216 of these stations the number of snow cover days decreased
on average by as many as 5 days decade−1. Earlier
snowmelt was detected at 210 Eurasian stations; 84 of them
experienced a shift in snowmelt of 5 days decade−1 (Peng et
al., 2013).
Published by Copernicus Publications on behalf of the European Geosciences Union.
1880 K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo
Loss of snow-covered areas is related to the increase in air
temperature. Brown and Robinson (2011) estimated that for
each 1 ◦C of warming in the NH (40–60◦ N) the loss of snow
is 1.44 million km2 in March and 2.00 million km2 in April.
Air temperatures at northern latitudes increased several times
faster than that in the tropics (Screen, 2014) and affected the
snow cover extent in the NH. The change in air temperature
can explain 50 % of the variability in the NH spring snow
cover extent during the years 1922–2010 (Brown and Robinson,
2011). Decrease in snow mass leads in turn to an increase
of absorption of solar radiation by darker snow-free
surfaces, causing a faster increase in temperature, thus triggering
a positive feedback: larger absorption of radiation enhances
further warming. This feedback is usually referred to
as snow-albedo feedback (SAF). The inﬂuence of snow cover
on the radiative balance is the strongest during the spring
months, and warming observed in the 20th century was most
likely enhanced by the retreat of snow cover (Groisman et
al., 1994).
Air temperature can affect snow properties, which in turn
change snow albedo (Wiscombe and Warren, 1980; Taillandier
et al, 2007). At higher temperatures snow grains are
larger as the air temperature is higher (e.g., Marbouty, 1980;
Flanner and Zender, 2006) which results in a decrease of the
single scattering albedo (SSA). In the larger snow grains the
chances of a photon to be absorbed are greater due to the increased
optical path within an ice crystal (Warren, 1982). A
decrease in the SSA is the primary reason for the decrease
of snow albedo for wavelengths shorter than 2.5 µm (Wiscombe
and Warren, 1980; Nakamura et al., 2001). Grain size
is the main physical factor responsible for snow-albedo variations
(Domine et al., 2006). The snow grain growth process
contributes to the positive snow-albedo feedback loop,
increasing the absorption of radiation by the snow in the surface
layer. It results in the increase of snowmelt and thus in
a decrease of the surface albedo which consequently results
in stronger absorption of solar radiation. These processes
clearly affect the Arctic climate and augment the effect of
temperature changes at high latitudes.
An experimental study by Aoki et al. (2003) shows that
snow albedo is very stable at temperatures below −10 ◦C and
decreases when the surface temperature increases to above
−5 ◦C. Based on measurements at four Russian stations,
Roesch et al. (1999) found that the snow albedo could be parameterized
in terms of a polynomial function of the surface
temperature for temperatures between −10 and 0 ◦C, while
at lower temperatures the albedo is independent of temperature.
A number of climate models parameterize the snow
albedo as a function of the surface temperature, where snow
albedo varies at temperatures below zero and, for instance,
−1 ◦C (Collins et al., 2004), −2 ◦C (Best et al., 2011) and
−5 ◦C (Roeckner et al., 2003).
In addition to air temperature, other factors may affect
snow albedo, such as the amount and frequency of precipitation,
changes in vegetation or darkening of the snow surface
by deposition of absorbing material such as absorbing
aerosol particles. Climate models predict a decrease of the
spring snowfall during the 21st century over most of the
North American territory and Europe, and an increase in the
Siberian and Canadian tundra, in the Arctic Archipelago and
in Greenland (Krasting et al., 2013). However, winter snowfall
is projected to increase over much of Siberia and the
northern part of North America, potentially increasing the
snow-covered area in the NH. Changes in the amount and
frequency of precipitation can alter the snow properties and,
therefore, the surface albedo: fresh snow has a higher albedo
than aged snow with larger grain size (e.g., Nakamura et al.,
2001). As snow ages, it has a longer exposure to deposition of
impurities, such as dark aerosol particles or vegetation litter,
which reduces the albedo (Melloh et al., 2001; Hansen and
Nazarenko, 2004; Aoki et al., 2006; Winkler et al., 2010).
The net effect of precipitation on the surface albedo will be
a combination of the above-mentioned processes, ultimately
determined by the air temperature – its mean, magnitude and
sign of trends (Knowles et al., 2006).
Vegetation productivity in the subarctic region has increased
recently (Barichivich et al., 2013) and is associated
with the rise in summer warmth, which is connected to sea
ice retreat (Dutrieux et al., 2012). Vegetation is darker than
snow and, hence, the presence of vegetation results in a decrease
of the albedo of snow-covered areas. In the presence
of snow, forested areas have a very low albedo in comparison
with open ground, typically in the range of 0.1–0.2,
with sometimes higher values up to about 0.3, when snow
is present on the top of the canopy (Betts and Ball, 1997).
The inﬂuence of boreal forests on temperature in a changing
climate is not well understood: the effect of low forest albedo
may exceed the effect of increased carbon uptake, and forests
may warm the climate (Bonan, 2008). Chapin III et al. (2005)
have shown that shrub expansion in the tundra (Naito and
Cairns, 2011) is likely to increase substantially and might
play a great role in tundra warming. Sturm et al. (2005) found
that if tall shrubs protrude above the snow and cover 10 % of
the surface, the albedo decreases by 30 %. These authors also
report that the transition from shrub-free tundra to shrubland
can increase the absorbed radiation during periods of snow
by 69–75 %, depending on the latitude.
Surface albedo is a key parameter in climate models and
has to be accurately parameterized. It is not possible to correctly
simulate SAF without knowing how the surface albedo
at high latitudes is modiﬁed by the processes described
above. Currently, the land surface albedo in climate models
is parameterized in terms of land cover or vegetation types,
surface parameters (e.g., snow cover fraction and extent), and
atmospheric conditions simulated in the model. Prescribed
ground and canopy albedo during snow-covered periods, together
with varying vegetation masking, might be responsible
for the large spread in the estimates of SAF in CMIP5
models (Qu and Hall, 2014). Wang et al. (2006) concluded
that uncertainties in snow cover properties are among the key
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K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo 1881
error sources in surface albedo simulations; they dominate
model performance despite the high complexity of the land
model parameterizations.
Ground-based observations of albedo properties at high
latitudes are very sparse. As an alternative, satellite remote
sensing can be used to monitor albedo changes over vast
areas. The frequency of such observations depends on the
swath width of the instrument and the orbit. The footprint
of satellite observations is much larger (hundreds of meters
at nadir) than that of surface-based or tower measurements.
Furthermore, satellite data are often integrated over
many pixels, extending the resolution to several km. Hence,
satellite measurements do not provide as much detail on spatial
and temporal variations as ground-based measurements.
Where in situ measurements might fail to represent regional
estimates (Davidson and Wang, 2004), satellite data may
be more representative. They may therefore be useful as a
source of validation data for land parameterization schemes
in climate models, which usually have an even coarser spatial
resolution (e.g., Bender et al., 2006; Alton, 2009; Hagemann
et al., 2013; He et al., 2014).
In this contribution we present the results from a study on
the use of satellite data to obtain information on the snowcovered
surface albedo. The goal of the study is to identify
systematic changes in the surface albedo during the transition
spring period (March, April and May), and to determine
the effect of factors such as (1) snow cover fraction (SCF),
(2) air temperature, (3) precipitation amount and frequency
and (4) vegetation greenness. The area of interest is north of
50◦ N (NH50) and covers all land territories except Greenland
and Iceland. Our study focused only on the territories
where at least once during the study period land was covered
by snow with SCF > 1 %. We use surface albedo data
which have routinely been provided by the Moderate Resolution
Imaging Spectroradiometer (MODIS) since the year
2000. This data set is not yet sufﬁciently long to be appropriately
utilized to provide climatologically signiﬁcant results.
Nonetheless, it is a consistently validated product readily
available for different research purposes (Schaaf et al.,
2011), and we aim to describe observed changes as they are
seen and give an explanation where possible.
2 Data
2.1 MODIS data
2.1.1 Albedo
The MODIS Bidirectional Reﬂectance Distribution Function
(BRDF) and Albedo algorithm utilizes multiple spectral
bands to retrieve broadband albedo information. The algorithm
uses a semi-empirical kernel-driven bidirectional reﬂectance
model to describe the anisotropy of land surface
reﬂectance (for details see Lucht et al., 2000; Schaaf et al.,
2002). BRDF speciﬁes the angular distribution of the reﬂection
of a target as a function of illumination and viewing
geometries, and it is wavelength-speciﬁc. Cloud-free, snowﬂagged
and atmospherically corrected surface reﬂectances at
seven MODIS bands (1–7, centered at 648, 858, 470, 555,
1240, 1640 and 2130 nm consecutively), accumulated during
a cycle of 16 days, are used to retrieve BRDF parameters.
BRDF functions are angularly integrated and converted to
three broadband albedos (0.3–0.7, 0.7–5.0 and 0.3–5.0 µm).
If fewer than seven observations were collected during a 16day
period, or the collected observations do not ﬁt the BRDF
model well, a backup magnitude inversion algorithm is applied.
The albedo retrieved with the magnitude inverse algorithm
is assigned a lower-quality ﬂag, but the difference
in quality between data from the mainstream and backup algorithms
is minor (Salomon et al., 2006). If the number of
available observations is less than three, a ﬁll value from
an a priori BRDF database is assigned to a given pixel. Retrieval
is conducted every 8 days and is based on the observations
accumulated during 16 days (collection V005). This
data processing overlapping increases the frequency of obtaining
high-quality data, but can lead to an autocorrelation
in retrievals if an observation is used in more than one 16day
processing period (Schaaf et al., 2011). If the majority of
the days in each of the 16-day periods were snow-covered,
only these observations are used in the retrieval. But if only
few observations indicate the presence of snow, these days
are discarded from processing, and the albedo is computed
as if the surface were snow-free (Schaaf et al., 2002). This
may cause bias in estimating the surface albedo during the
snowmelt period, when the transition from snow-covered to
snow-free conditions may greatly affect albedo values.
The MODIS MCD43C3 product (collection V005) provides
information on the directional hemispherical reﬂectance
(black-sky albedo) and the bihemispherical reﬂectance
(white-sky albedo). In this study we used the shortwave
broadband (0.3–5.0 µm) black-sky albedo, which is deﬁned
as the reﬂection of direct radiation in the absence of
a diffuse component of illumination. MCD43C3 (NASA,
2006a) is a combined product from two MODIS sensors onboard
the Terra and Aqua satellites with a spatial resolution
of 0.05◦ (5600 m) in a latitude/longitude Climate Modeling
Grid (CMG).
The albedo product has been extensively validated
(e.g., Stroeve et al., 2005; Salomon et al., 2006; Liu et
al., 2009; Wang et al., 2014) and the algorithm has been
proven to be stable and reliable, and the product is suitable
for climatological studies. The uncertainty of the highestquality
data is reported to be well less than 5 % as compared
to ﬁeld measurements, and less than 10 % for data
of lower quality (http://landval.gsfc.nasa.gov/ProductStatus.
php?ProductID=MOD43).
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1882 K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo
2.1.2 SCF
MODIS MCD43C3 also provides data on SCF. The MODIS
snow mapping algorithm is based on the Normalized Difference
Snow Index (NDSI), which utilizes the fact that snow
has a high reﬂectance in the visible part of the solar spectrum
and low reﬂectance in the infrared (Hall et al., 2002).
MODIS channels 4 (0.55 µm) and 6 (1.6 µm) for Terra or
channel 7 (2.1 µm) for Aqua are used to calculate NDSI as
follows:
NDSI =
Ch4 − Ch6/7
Ch4 + Ch6/7
, (1)
where Ch4 and Ch6/7 are reﬂectances in MODIS channels 4,
and 6 or 7, respectively.
Non-densely vegetated pixels with NDSI ≥ 0.4 and a reﬂectance
in channel 2 (0.841–0.876 µm) of larger than 11 %
are mapped as snow-covered. The 11 % threshold screens
out liquid water pixels that may have high NDSI values. In
forested areas, the NDVI (Normalized Differential Vegetation
Index) is used together with the NDSI to map snow cover
(Klein et al., 1998). NDSI is able to separate snow-covered
pixels from those which are cloud-contaminated and those
which are snow-free.
The determination of SCF in each 0.05◦ CMG cell is based
on the fraction of the number of snow-covered MODIS pixels
(resolution 500 m) to the total number of MODIS pixels
within each 0.05◦ larger pixel. The quality of the SCF within
a MODIS 500 m pixel has been studied by Salomonson and
Appel (2004) who report that the mean absolute error is less
than 0.1. The validation study by Hall and Riggs (2007)
shows that the overall accuracy of the MODIS snow maps
is 93 %, with lower accuracy over forests, complex terrains
and ephemeral snow areas.
2.1.3 EVI
The vegetation index (VI) is a measure of canopy greenness.
It reﬂects properties of the canopy, its structure, leaf area and
chlorophyll content, and can be used as a proxy to fractional
vegetation cover (Ormsby et al., 1987; Carlson and Ripley,
1997). The EVI (Enhanced Vegetation Index) is one of the
two routinely produced vegetation indices from the MODIS
sensors (Huete et al., 2011). In this study we chose EVI over
the more widely used NDVI (Normalized Difference Vegetation
Index) because it is less sensitive to soil and atmospheric
effects. Moreover, its signal does not saturate as fast as that of
NDVI (Huete et al., 2002). The EVI is computed as follows:
EVI = 2.5
ρN − ρR
ρN − C1ρR − C2ρB + L
, (2)
where ρN,R,B are reﬂectances in the near infrared, red and
blue bands; L is a soil adjustment factor; C1 and C2 are
aerosol resistance correction factors (Huete et al., 1999). Coefﬁcients
currently used in the algorithm are, L = 1, C1 = 6
and C2 = 7.5.
In this study we used the MOD13C2 (V005) data set provided
as a monthly product on a 0.05◦ (5600 m) resolution
grid. MOD13C2 (NASA, 2006b) comprises composites of
the 16-day and 1 km resolution MOD13A2 standard product.
In our analysis we chose to use data for June because in this
month the effect of snow cover on the EVI values is minimal.
The presence of snow decreases EVI values due high snow
reﬂectance at wavelengths in the visible and low reﬂectance
in the NIR (near-infrared) band (Huete et al., 2002; Zhang
et al., 2006). Furthermore we assumed that an increase in
the maximum EVI might be associated with an increase in
non-green biomass that might protrude above the snow and
decrease the albedo with time.
2.2 CRU climate data set
The Climatic Research Unit (CRU) TS3.21 data set (Jones
and Harris, 2013) provides homogenized monthly climate
data interpolated from over 4000 weather stations spread
around the world gridded to a spatial resolution of 0.5◦
(Mitchell and Jones, 2005). Among the available parameters
are cloud cover, diurnal temperature range, frost day frequency,
precipitation, daily mean temperature, monthly average
daily maximum temperature, vapor pressure, potential
evapotranspiration and wet day frequency. In this study we
used monthly means of daily mean temperature, precipitation
and wet day frequency.
3 Methods
All data were reprojected and resampled to the 25 km EASEGrid
(Equal-Area Scalable Earth Grid), which is widely used
in the analysis and visualization of data for the polar areas
(Brodzik and Knowles, 2002). MODIS 0.05◦ resolution data
sets were ﬁrst reprojected to the EASE 5km grid with the
nearest neighbor method in such a way that each 25 km grid
would contain 5 × 5 smaller grids. In a second step, data were
aggregated to the 25 km EASE grid, where mean values were
stored. Additionally, monthly means for surface albedo and
SCF were calculated to match the temporal resolutions of
the EVI and CRU climate data. CRU time series were reprojected
and resampled to the EASE 25 km grid with a bilinear
interpolation method. All data pixels for any given month
and year with corresponding SCF less than 1 % in each of
the 25 km × 25 km grid cells were discarded from the analysis.
This masking out was done separately for each month
and year.
We chose Spearman’s rank correlation over linear Pearson
correlation in our calculations, because the response of surface
albedo to changes in the studied variables might not be
linear.
Due to the coarse temporal resolution of the data, it was
problematic to construct maps of correlation coefﬁcients:
each pixel would have a maximum of 13 values to calculate
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K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo 1883
correlations (years 2000–2012). Assuming that the studied
parameters are spatially autocorrelated, we applied a running
window of 3 × 3 pixels, thus increasing the amount of data
by up to 9 times (less on the edges of data matrices).
To study connections between the albedo and air temperature,
precipitation or vegetation under full snow cover conditions,
we eliminated all pixels where snow cover was not
complete (SCF < 100 %) at any time during the study period.
In other words, if for any given month a pixel was assigned
SCF < 100 % at either of the years during the study
period, it was discarded from this part of the analysis for the
given month. For instance, if in April of the year 2001 the
mean SCF of a pixel was < 100 %, this pixel was masked
out for all studied years in April. This selection reduced the
amount of data available for the analysis, but ensures that a
strong effect of SCF variations on albedo is excluded, and
that the main part of the analysis is conducted over territories
fully covered with snow. The size of the 100 % SCF
area decreases from month to month as spring proceeds and
more areas experience snowmelt. In particular, in May this
area includes only Taymyr and subarctic Canada with the
Arctic Archipelago, while more southern territories are cut
out. Applying identical masks would drastically narrow the
study area, or, alternatively, May would need to be left out of
the analysis. Therefore, different masks for March, April and
May were used to maximize the study area and data availability.
Considering that all pixels with incomplete snow cover
were masked out, the mask for any given month was the same
for all years considered. Furthermore, the study domain was
divided into several smaller areas, based on their geographical
position and climate similarity (Fig. 1). The division was
done as follows.
– The Russian Far East and Siberia (in this article referred
to as Siberia) region can be described as an area with
subarctic and arctic climate, with different levels of continentality.
During long cold winters, arctic air masses
dominate. Climate severity increases from west to east
and with distance from the Bering Sea.
– The Taymyr Peninsula is considered in this work separately
due to the fact that no SCF changes occurred
throughout all spring seasons for 13 years in a large part
of the region. Taymyr belongs to the arctic climate belt
with mean winter temperatures of −25 to −35 ◦C and
annual precipitation of 250–300 mm, mainly occurring
in the form of snow. It is a tundra region.
– Most of Fennoscandia, which includes Scandinavia,
Finland and Kola Peninsula (referred to as Scandinavia
in this work), has severe long winters in its northern
part. Snow cover lasts for a long time, and the growing
season is short. The southern part is largely affected by
its closeness to the Atlantic Ocean and is characterized
by milder winters with little snow. Vegetation changes
Figure 1. Different regions considered in the study and their extent
for 100 % SCF in the 3 months of interest: March, April and May.
from tundra in the north to the coniferous and mixed
forests in the south.
– The northern Canada and Alaska area lies in the arctic
and subarctic climate belts. Winters are long and cold.
Most of the precipitation comes in the form of rain during
the summer period. Snow cover can last almost all
year around. Land cover in this area is tundra in the
north, and shrubs and forest in the south.
– The Labrador Peninsula climate can be categorized as
humid continental due to the closeness to the sea. Winters
are mild with a lot of snowfall. Northern Labrador
belongs to the tundra biome, while its southern part is
covered with taiga forest.
– The Canadian Arctic Archipelago has a typical arctic
climate with long, cold winters and very short, cool
summers. Vegetation consists of dwarf shrubs, grasses,
mosses and lichens.
The actual masks applied in the calculations deviate from
those depicted in Fig. 1, depending on the data gaps in data
sets and the SCF < 100 % ﬁlter.
4 Results
4.1 Albedo and SCF changes
Changes in SCF and albedo in the NH50 during the period
2000–2012 for each of the spring months are presented in
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1884 K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo
Fig. 2. The changes have been calculated as a linear regression
in a scatter plot of the parameter of interest in a certain
month versus the years. Blue colors indicate an average
increase over these years, red indicates a decrease, with
the actual values indicated in the color bars at the bottom
of Fig. 2. In March, the snow cover changes (trend different
from zero) inﬂuenced 58 % of all snow-covered areas, while
in April these changes affected 74 % and in May, 91 %.
The March SCF decreased in most of the central East
European Plain, the south of the West Siberian Plain, and
on the Central Siberian Plateau. In Canada the March SCF
decreased in the Ontario province and in northern Alberta.
In contrast, SCF increase is also observed in certain areas,
such as around the Baltic Sea, in parts of eastern Europe and
Russia, the southern part of the West Siberian Plain and the
Transbaikal area. The largest increases are up to +30 %. In
North America, increases occur in the northern Great Plains
in Alberta and in the Saskatchewan provinces. At other territories
changes are less than 1 %.
In April, areas with a negative SCF trend were larger, and
cover a large part of the Eurasian territory from central Scandinavia
to the Far East of Russia, and in North America from
central Alaska to southern Quebec along the Laurentian Upland.
On the other hand, the territories in Europe where SCF
increases are smaller in April than in March. They cover
Estonia and adjacent territories in western Russia. In the
North American sector, however, the areas with increased
SCF were slightly larger, such as east of the Rocky Mountains,
and part of Alaska and the Yukon Plateau.
In May, almost all snow-covered areas are affected by SCF
changes. In the North American domain, the SCF mainly
decreased with a small exception of positive trends on the
Ungava Peninsula, the central Labrador Peninsula, the western
shore of the Hudson Bay and western Alaska. Despite
the fact that SCF increased in March and April in the Great
Plains, it did not affect SCF in May: there the snow is already
completely melted by this time. In the Eurasian sector,
a small part of land, which includes the Northern Urals and
part of the Khabarovsk region of the Russian Far East, experiences
an increase in SCF with a maximum of up to 30 %.
The Taymyr Peninsula and the Yano-Indigirka lowland are
the only areas in Eurasia where land remained fully snow-
covered.
In the areas where the SCF changed, the albedo exhibits
similar spatial patterns. The sign and magnitude of changes
in both variables coincide; maximum albedo changes are
±0.3 12 yr−1. Correlation coefﬁcients for SCF and albedo
are positive and have high values (R > 0.7) over most of
the study domain, excluding some scattered pixels (Fig. 3).
The correlation was found to be signiﬁcant (not shown) with
p values < 0.05 for all pixels with moderate (R = 0.5–0.7)
and strong correlation (R > 0.7). Averaged over the whole
study area, SCF and albedo also correlate strongly. In Fig. 4a,
time series of the mean SCF and the mean albedo in NH50
for each of the spring months are shown. The variations in
Figure 2. Average albedo (left panel) and SCF (right panel) changes
during the years 2000–2012, determined as described in the text.
Snow-free area, Greenland and Iceland are shown in green and are
not considered in this study.
these two parameters trace each other well with extremes in
the same years, indicating a strong connection. The variation
of mean albedo with mean SCF in the NH50 study area
(Fig. 4b) demonstrates the linear relationship between these
two parameters. Correlation coefﬁcients R vary from 0.75 in
March, 0.87 in April to 0.96 in May. Corresponding linear ﬁt
equations are shown on Fig. 4b. Thus SCF can explain 56 %
of variation of the albedo in March, 76 % in April and 92 %
in May.
However, in March and April in regions such as eastern
Siberia, Fennoscandia, the northern part of North America
or the Labrador Peninsula, and in May for the Taymyr
Peninsula and the Canadian Archipelago, no signiﬁcant SCF
changes (less than 1 % 12 yr−1) are observed, but the albedo
did change. This implies that factors other than SCF affect
surface albedo. These other factors have a large impact: in
conditions of unchanged snow cover extent, albedo changes
by up to ±0.2 12 yr−1. To study effects of other parameThe
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K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo 1885
Figure 3. Albedo-SCF correlation coefﬁcient maps. Gray color indicates
areas where SCF is 100 % throughout the study period: R
could not be calculated.
ters that potentially could cause the observed changes, further
analysis was conducted over fully snow-covered pixels,
i.e., pixels with 100 % SCF.
4.2 Regional study
Scatter plots of regionally averaged values of albedo versus
air temperature, precipitation amount, number of precipitating
days and EVI for each of the 3 months are shown in
Fig. 5. Corresponding Spearman correlation coefﬁcients are
listed in Table 1, and coefﬁcients with a p value ≤ 0.05 are
shown in bold. Below we discuss the effects of different parameters
on the 100 % snow-covered land surface albedo in
separate subsections.
4.2.1 Air temperature
Regional averages show that albedo is negatively correlated
with regionally averaged monthly mean air temperature
when this parameter exceeds a threshold value of
about −15 ◦C (Fig. 5, top line). Being the warmest from
the study regions, Scandinavia already exhibits this relation
in March, although the correlation is weak (R = −0.49,
p value > 0.05). Siberia and Labrador show this pattern in
April with R = −0.56 and −0.88, respectively. In May the
correlation coefﬁcient for the northern regions of Taymyr
is −0.8 and for the Arctic Archipelago R = −0.73. For avFigure
4. Time series (top panel) and scatter plot (bottom panel) of
mean NH50 domain albedo and SCF for the years 2000–2013.
erage monthly temperatures in a region colder than −15 ◦C,
there is no or a very weak correlation between albedo and
temperature.
4.2.2 Precipitation amount and wet days
Scatter plots of snow-covered land surface albedo versus the
amount of precipitation (in mm) are presented in Fig. 5 on
the second row, and on the third row, scatter plots of albedo
versus the number of days with precipitation are shown. In
March no signiﬁcant correlation is observed for any of the
studied areas. The possible reason for this might be that areas
that are too large are chosen for averaging, and effects
of precipitation variations inside regions are averaged out.
In April the precipitation amount in Canada correlates negatively
with albedo (R = −0.62, p value < 0.05). For the Arctic
Archipelago (R = 0.7) and Taymyr (R = 0.65), the number
of wet days and albedo are positively correlated with corwww.the-cryosphere.net/9/1879/2015/
The Cryosphere, 9, 1879–1893, 2015
1886 K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo
Figure 5. Snow-covered land area albedo versus mean regional air temperature (top panels), precipitation amount (second row panels),
number of precipitating days (third row panels) and EVI (bottom panels). Straight lines show linear ﬁts to the data. Corresponding Spearman
correlation coefﬁcients are listed in Table 1.
relation coefﬁcients of 0.7 and 0.65, respectively; p values
for both regions are less than 0.05. In May no signiﬁcant correlations
were found.
4.2.3 EVI
Scatter plots of snow-covered land surface albedo versus EVI
are presented in Fig. 5 on the fourth row. Canada is the only
region for which signiﬁcant correlations between EVI and
albedo are observed, with R = −0.81 in March and 0.79 in
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K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo 1887
Table 1. Spearman correlation coefﬁcients corresponding to the values presented in Fig. 5. Prec. represents precipitation; temp. represents
temperature; EVI represents the Enhanced Vegetation Index. Correlation coefﬁcients with corresponding p value < 0.05 are shown in bold.
March April May
Temp. Prec. Wet EVI Temp. Prec. Wet EVI Temp. Prec. Wet EVI
days days days
Arctic Arch. 0.24 −0.4 −0.03 0.22 0.16 0.57 0.7 −0.18 –0.73 −0.15 −0.42 −0.15
Taymyr 0.0 −0.57 −0.28 0.19 −0.28 0.53 0.65 −0.14 –0.8 0.35 0.12 −0.26
Siberia −0.26 −0.14 −0.28 0.22 −0.56 −0.32 −0.39 −0.32 – – – –
Scandinavia −0.49 0.11 0.06 −0.49 – – – – – – – –
Canada −0.44 0.52 0.55 –0.81 −0.26 –0.62 0.01 0.79 – – – –
Labrador 0.08 −0.49 −0.52 −0.22 –0.88 −0.21 −0.07 −0.38 – – – –
April. No signiﬁcant correlation was found for any of the
other regions at any time. Despite the fact that the strongest
greening is observed over subarctic areas (Fig. S1 from Supplement),
on a regional scale we could not ﬁnd a connection
between EVI changes and albedo.
4.3 Correlation maps
Figure 6 shows the spatial distributions of the Spearman
correlation coefﬁcients of albedo with the four climate parameters
considered in this study: temperature, precipitation,
number of wet days and EVI (top to bottom panels in Fig. 6).
Pixels with corresponding p values greater than 0.1 were
screened out from the maps.
4.3.1 Temperature
The previous results did not show a signiﬁcant correlation
between temperature and albedo in any of the considered regions
in March. However, closer examination of the maps
reveals negative correlations in the southern part of the study
domain in North America, i.e., albedo decreases with increasing
air temperature, and positive correlations in the
northern part of this area (increase with increasing air temperature).
This relation has been averaged out in a regional
study. The mean temperature in the area with negative correlation
is higher than −15 ◦C (Fig. S2). In areas with positive
correlation mean monthly temperatures are colder than
−15 ◦C. In Eurasia the only region with monthly mean temperature
above −15 ◦C is Scandinavia, where some areas
exhibit the same relation. There is also a vast area of the
Lena and Yana river basins and the Vilyuy Plateau, where the
temperature–albedo correlation is negative and signiﬁcant.
In North America the temperature and albedo in April are
related in a similar way as in March. In the areas with mean
monthly temperatures warmer than −15 ◦C, correlations are
negative and signiﬁcant. In Eurasia albedo and temperature
are negatively correlated over almost the whole area. The
strongest correlations were found in the northern part of the
West Siberian Plain: R reaches −0.7. Mean temperatures in
the region are higher than −15 ◦C.
In May temperatures are higher than −15 ◦C everywhere
in the study area, which includes a small part of northern
Canada and the Arctic Archipelago, and some areas of the
Taymyr Peninsula. Correlation between albedo and temperature
in May is strongly negative and signiﬁcant for all studied
territories.
4.3.2 Precipitation
In March the correlation maps of albedo and precipitation
amount show very high variability, which is especially
prominent in North America. Due to this fact, no conclusion
can be reached as regards a possible connection between
these variables in North America. However, several
isolated regions with signiﬁcant positive correlation can be
distinguished in Eurasia, such as Chukotka Peninsula, Magadan
area, a few isolated areas in eastern Siberia and the
Gulf of Ob. In contrast, negative correlation is observed in
the Taymyr Peninsula.
In April the correlations between albedo and precipitation
are negative in study areas in North America – Nunavut and
Labrador. In Eurasia the correlation coefﬁcients show that
there is a weaker connection between albedo and precipitation
than in March. Nevertheless, two small regions stand
out: Taymyr with positive correlations and the Ob delta with
negative correlations. It is notable that the correlation sign in
these areas in April is opposite to those in March.
In May a negative signiﬁcant correlation is detected in
Nunavut.
4.3.3 Number of wet days
The correlation maps for the albedo and the number of wet
days show generally similar patterns as for the precipitation
amount. In March, for Chukotka, Magadan, the Central
Siberian Plateau and the Gulf of Ob, both positive and significant
correlations are observed, and on the Taymyr Peninsula,
only negative values occur. High variability is observed over
North America, with the exception of Labrador with negative
correlation, and, therefore, the results are generally inconclu-
sive.
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1888 K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo
Figure 6. Spearman correlation coefﬁcient map for temperature (top row panels), precipitation (second row panels), number of days with
precipitation (third row panels) and EVI (bottom row panels) for March (left column panels), April (middle column panels) and May (right
column panels). Only pixels with corresponding p values greater than 0.1 are shown.
In April and May a strong variability occurs over almost
all areas, excluding Taymyr where wet days and albedo are
positively correlated in April.
4.3.4 EVI
Among all studied variables, the EVI correlation coefﬁcient
and p value ﬁelds are the most variable. Nonetheless, in
March and April, vast territories in Siberia and Far East show
a signiﬁcant correlation, which is predominantly positive in
Chukotka and negative in the rest of eastern Siberia. Due to
the strong variability of the albedo versus EVI correlation coefﬁcient
maps in North America, no conclusion on the effect
of changes in greenness on the surface albedo can be reached
for any of the months.
5 Discussion
Among all tested variables, the surface albedo is most
strongly correlated with SCF during the spring season. Signiﬁcant
high positive correlation between albedo and SCF
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K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo 1889
was observed over most of the territories of the NH50 domain
where SCF changes were detected. This behavior can
be explained by the large contrast in albedo between snowcovered
and the open surface: the albedo of snow usually
signiﬁcantly exceeds that of the underlying surface.
For the rest of the study area, where SCF remained unperturbed,
albedo has changed by up to ±0.2 units 12 yr−1.
To test which climate variables affected these changes, we
chose air temperature, precipitation and vegetation as the
most probable candidates that would affect surface albedo.
Air temperature affects surface albedo most in conditions
of non-changing SCF during spring. The results presented
in Sect. 4 suggest that there is a threshold of about −15 ◦C,
above which the temperature does have a signiﬁcant effect on
the surface albedo, while at lower temperatures there seems
to be no effect. This ﬁnding is supported by the relations
found between regionally averaged albedo and air temperatures
and also by the Spearman correlation maps. Averaging
to the monthly means, however, complicates interpretation
of the results. A long averaging period may level possible
temperature ﬂuctuations and hence the effect of potentially
occurring warmer periods during the month over which the
data are averaged, which favor snow metamorphosis, cannot
be observed. To address this limitation, an independent data
set of ﬁner temporal resolution was analyzed. Data, methods
and results are described in the Supplement. We found that
the temperature threshold value depends on the region and
can depart from −15 ◦C. Notably, the coldest region of the
Arctic Archipelago is characterized by the lowest threshold
value of −15 ◦C, consistent with the one found in the current
analysis.
The effect of precipitation on surface albedo would be an
increase in albedo when it snows, resulting in a positive correlation.
However, there is no clear pattern to conﬁrm such an
effect. The number of wet days and albedo correlated positively
in April in two regions – Arctic Archipelago (R = 0.7)
and Taymyr (R = 0.65); both regions are part of the tundra
ecozone. However, in this month the precipitation amount
and albedo show a signiﬁcant negative correlation in Canada
with R = −0.62. The correlation maps in Fig. 6a support
this ﬁnding. The maps also show that in the Taymyr region
the correlation coefﬁcients change from positive in March
to negative in April. Furthermore, a signiﬁcant correlation
is observed in March in several regions in Eurasia for both
amount and frequency, which was not apparent in the regional
study; averaging over a large domain might obscure
possible relations by the cancellation of positive and negative
effects when there is a strong spatial variability. In general,
in North America the correlations are highly heterogeneously
distributed which renders the interpretation difﬁcult,
especially as regards the relation between the number of precipitation
days and albedo.
The effect of air temperature on snow albedo can be described
by metamorphosis processes changes. The model experiment
by Flanner and Zender (2006) shows that temperature
affects snow-albedo evolution, such that snow albedo
decreases more as temperature is higher. Hachikubo et
al. (2014) examine the effect of temperatures on the rate
at which a speciﬁc snow surface area (SSA), a parameter
depending on the microstructure of the snowpack and related
to the snow albedo, decreases in isothermal conditions.
Their comparison of snow samples stored at temperatures
of −47 and −19 ◦C shows that SSA decays faster at higher
temperatures. In terms of albedo this means that it decreases
faster at warmer temperatures, even if the temperature is relatively
cold. However, the inﬂuence of the air temperature on
snow albedo should be discussed together with that of precipitation
because of its effect on the temperature gradient
in the snowpack and, therefore, the metamorphosis regime.
Taillandier et al. (2007) discusses possible feedbacks between
snow metamorphism and snow albedo in a changing
climate. Because snow crystals grow faster at higher temperatures,
the albedo of a snow-covered surface will decrease
in a warming climate, contributing to the positive feedback
loop. However, there are other parameters, such as snow precipitation
amount and frequency, and wind speed, which can
alter this straightforward temperature–albedo dependency. If
precipitation amount increases together with temperature, the
net effect is such that grain size does not grow as fast as it
would in warmer temperatures without precipitation regime
change or snowpack thickness alterations. This way warming
temperatures can produce negative feedback. If precipitation
increases in isothermal conditions, the metamorphosis rate
can decrease when a certain temperature gradient threshold
in the snowpack is passed. Through this mechanism, the decrease
of the snow albedo is slower. The present study is targeted
to investigating changes in the snow-covered surface
albedo on large spatial and temporal scales. The relatively
large grid size of data and coarse averaging to monthly means
could blend complicated connections between albedo, temperature
and precipitation. For instance, an occasional rise of
the temperatures, favoring snowpack thawing and subsequent
refreezing, leads to the formation of the large amorphous ice
crystals or melt-freeze crust on the snowpack surface (Colbeck,
1982) that is characterized by a decreased albedo (Albert
and Perron, 2000). Decreasing albedo during snowmelt
periods (Meinander et al., 2013) might have been obscured
by averaging over long periods. The analysis presented in
the Supplement studies the albedo–temperature relation using
data with a ﬁner temporal resolution. The results show
that days that are warmer than average did not occur often.
Moreover, even with a low number of warmer days, temperature
threshold of −15 ◦C can be observed in some regions.
Considering the above discussion, we cannot be conclusive
which of the processes can give the best explanation of our
ﬁndings.
The research presented above allowed us to identify some
connection between EVI and albedo. The regional study
shows that only in Canada a signiﬁcant Spearman correlation
is obtained in March, when R is negative and thus albedo dewww.the-cryosphere.net/9/1879/2015/
The Cryosphere, 9, 1879–1893, 2015
1890 K. Atlaskina et al.: Satellite observations of changes in snow-covered land surface albedo
creases with EVI, and April when R is positive and albedo
increases with EVI. The obtained correlation maps, as in the
case of precipitation variables, show a high spatial variability.
Regardless of this, the results show an evident positive
correlation in Chukotka and a negative correlation in large
territories of the Far East and eastern Siberia in March and
April. There might be several possible reasons behind these
results due to various processes involved in the vegetationalbedo
feedback. The effect of arctic shrub in the tundra on
snow accumulation, melt rate and albedo is not clear and
depends on canopy height, density and whether shrubs are
subject to bending under the weight of the snowpack during
winter (Pomeroy et al., 2006). For instance, snow tends
to melt faster under tall shrubs, though snow accumulation
is greater, resulting in a longer snowmelt period in comparison
with sparse tundra. At the same time, exposure of darker
vegetation material causes a reduction of the surface albedo.
Generally, the dynamics of the albedo of tundra during the
spring depends on the dominant vegetation type (Loranty et
al., 2011) and, therefore, detailed tundra vegetation discrimination
would be necessary for better understanding the effect
of vegetation on springtime albedo on a large scale. Expansion
of vegetation or change in vegetation type also alters
wind ﬁelds. Wind speed decreases under the canopy, preventing
densiﬁcation of the snow from wind impact, which
in turn slows metamorphism and, consequently, the decrease
of albedo. On the other hand, the time of the thaw period
is delayed when the forest fraction increases (e.g., Link and
Marks, 1999; Koivusalo and Kokkonen, 2002). Furthermore,
denser canopies in forest can intercept more snow, which
can increase the albedo. For example, Kuusinen et al. (2012)
found that in the boreal Scots pine forests the forest albedo
can increase by as much as 20 %. At the same time, snow
captured by the canopy can sublimate; thus, this portion of
snow does not reach the forest surface, resulting in a shallower
snowpack (Strasser et al., 2011).
In this study, changes in the wind ﬁeld were not considered.
As mentioned above, wind can increase snow density.
In that case the temperature gradient in the snowpack is reduced,
and it slows metamorphism processes and, subsequently,
snow albedo decrease (Flanner and Zender, 2006).
However, if strong wind redistributes old snow with low
SSA, fragmentation and sublimation of snow crystals during
a high wind episode will result in higher SSA and snow
albedo (Domine et al., 2009). Another factor not discussed
in this work is the sedimentation of impurities on the snow,
such as light-absorbing aerosols. The presence of such a kind
of impurities affects the albedo in the visible part of the spectrum
(Meinander et al., 2013); however, it has no inﬂuence
on the albedo at wavelengths beyond 0.9 µm (Warren and
Wiscombe, 1980). In a warming climate, when the snowpack
consists of larger grain sizes, black carbon sedimented on the
snow surface can amplify radiative perturbation caused by
these impurities (Hadley and Kirchstetter, 2011). Moreover,
there is evidence that the presence of absorbing aerosols on
the snowpack surface can reduce the snow density (Meinander
et al., 2014), which in turn inﬂuences the albedo decline
rate in aging snow (Flanner and Zender, 2006).
6 Conclusions
Spring surface albedo has undergone signiﬁcant changes during
recent years. We studied the effects of snow cover, air
temperature, precipitation amount and frequency and vegetation
greenness on surface albedo. There is clear evidence
of a strong connection between albedo and changes in snow
cover fraction in the study domain due to the large difference
in reﬂectance of snow-covered and snow-free surfaces.
We also found that in the territories where the snow cover
fraction did not change, the albedo changed nonetheless by
±0.2 units 12 yr−1. Our results suggest that air temperature
is one of the possible reasons for this albedo change when
it exceeds a value between −15 and −10 ◦C, depending on
the region, above which the albedo and the temperature are
negatively correlated. Furthermore, precipitation and vegetation
are of importance in some areas, such as Chukotka,
Taymyr, and eastern Siberia. The high spatial variability of
the obtained correlation ﬁelds illustrates the complexity of
the feedbacks and mechanisms in the climate system at high
latitudes. It is clear that more detailed information is needed
to identify and quantify relations between precipitation, vegetation
and albedo in a changing climate with conﬁdence.
The Supplement related to this article is available online
at doi:10.5194/tc-9-1879-2015-supplement.
Acknowledgements. Financial support from the Nordic Center
of Excellence CRAICC and the Academy of Finland Center of
Excellence program (grants no. 1118615 and 272041) is gratefully
acknowledged. The MCD43C3 and MOD13C2 data products are
courtesy of the online Data Pool at the NASA Land Processes
Distributed Active Archive Center (LP DAAC), USGS/Earth
Resources Observation and Science (EROS) Center, Sioux Falls,
South Dakota (https://lpdaac.usgs.gov/data_access). We also thank
Jaakko Ikonen and Boyan Tabakov for the data analysis advice and
technical assistance.
Edited by: S. M. Noe
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