Introduction
The world's snow cover is concentrated in the Northern Hemisphere and plays a crucial role in
climate change and regional water supply (Pardé et al., 2007; Takala et al., 2011; Barnett
et al., 2005; Hancock et al., 2013; Immerzeel et al., 2010). Snow cover extent (SCE) shrank over the
Northern Hemisphere between the 1970s and 2006 (Karl et al., 1993; Gao et al., 2012; Déry and
Brown, 2007; Armstrong and Brodzik, 2001). Snow water equivalent (SWE) is a more comprehensive
parameter that takes into account snow extent, density and depth, and represents the corresponding
depth of water that would result were the snow to melt in place. The pan-Arctic average trend in
peak seasonal SWE was -0.17 cmdecade-1, with maximum and minimum trends of 5.70 and
-2.5 cmdecade-1, respectively, for the period 1979–2009 (Liston and Hiemstra,
2011). However, the variation in SWE differed according to location and time. During the period
covering the last 30 years and more, three passive microwave radiometers have been used to generate
the SWE data. The products that have been used to estimate the SWE at the scale of the Northern
Hemisphere consist of:
“Global Monthly EASE-Grid Snow Water Equivalent Climatology” product,
which covers the period from November 1978 to May 2007 (Armstrong et al., 2007). The passive
radiometers used to produce this were the Scanning Multichannel Microwave Radiometer (SMMR) and
selected Special Sensor Microwave/Imagers (SSM/I). The AMSR-E L3 Global SWE product, which covers
the period June 2002 to October 2011 (Tedesco et al., 2004; Chang et al.,1987; Armstrong and
Brodzik, 2001). Both the two products are released by the National Snow and Ice Data Center (NSIDC).
The GlobSnow SWE product (v1.3), covering the period from 1978 to the present (Takala et al.,
2011; Pulliainen, 2006; Takala et al., 2009), which is released by the European Space Agency (ESA).
Due to their different inversion algorithms, the three SWE products have different estimated
accuracies and no comparison between them, or error analysis for them, has been produced at the
scale of the Northern Hemisphere during their period of operation.
Methods
Data preprocessing
SMMR and SSM/I are passive radiometers and their brightness temperatures at frequencies of 18(19)
and 37 GHz are employed to estimate SWE. In the inversion algorithm, two important
approximations have been made: firstly, the snow grain size is assumed to be a constant
0.3 mm; secondly, snow density is set to a constant 0.3 gcm-3 (Tedesco and
Narvekar, 2010). This data set is projected on to the Equal Area Scalable Earth Grid (EASE-grid) at
the hemisphere scale. Pixels from the 25 km EASE-Grid version of the BU-MODIS Land Cover
data that had more than 50 % ice cover were set as permanent ice (ice sheets, ice shelves, and
large glaciers) and without SWE inversion values. As any vegetation scatters microwaves in the same
way as snow, the presence of vegetation will affect the SWE estimates. Therefore, pixels covered by
forest were adjusted using the forest fraction.
The AMSR-E L3 Global SWE is also projected on to a 25km×25km EASE-grid on
the hemisphere scale. The algorithm used to produce this product is derived from the spectral
difference method and is similar to the Global Monthly SWE Product of SMMR & SSM/I. With the
availability of the special X-band channels on AMSR-E at 10.7 GHz, the algorithm's ability
to detect deep snow (using the difference between 10.7 and 18.7 GHz) is improved and its
dynamic range to help with forest attenuation is enhanced (Tedesco and Narvekar, 2010). Before the
SWE values can be retrieved, wet snow pixels have to be excluded. The forest effect is corrected
with the IGBP (International Geosphere Biosphere Programme) data obtained from the Boston
University. It should be noted that the snow density employed in the AMSR-E L3 product is no longer
a constant; instead, its value is obtained from a global snow density map, in which the average snow
density values for each of the six seasonal classes (Sturm and Holmgren, 1995) are calculated
according to in situ measurements made in Canada (Brown and Braaten, 1998) and the former Soviet
Union (Krenke, 2004). In addition, the land cover, ocean, and ice-sheet masks are based on the
MOD12Q1 data, re-gridded as a 25 km EASE-Grid version of the land cover data (MOD12Q1 IGBP,
collection V004). The method of defining
“ice” pixels is the same as for the SMMR & SSM/I Global Monthly SWE product discussed earlier.
The GlobSnow SWE product is retrieved using an assimilation scheme based on a semi-empirical snow
emission model from satellite data combined with ground-based weather station measurements and
covers all land surface areas except for mountainous regions and Greenland. The data are stored in
the EASE-Grid format. The snow density is treated as a constant with a value of
0.24 gcm-3, a reasonable “global” value given by the analysis by Sturm (2010). This
inversion algorithm is developed further and is fully described in the article by Pulliainen
et al. (2006), where considerations of the temporal continuity of the SWE are added. The mountain
mask applied is derived from a 2′×2′ grid of ETOP2 data containing global
elevation and bathymetry from the National Geophysical Data Center (NGDC). Due to the saturated
brightness temperature response, wet snow or temporary thin snow layers are not reliably detected
and are masked by observed brightness temperature values using an empirical equation.
Ground station data validation
We selected coarse snow depth measurements from 7388 weather stations (Fig. 1) – each of them with
at least 15 days' snow cover – from Global Historical Climatology Network-Daily (GHCN-DAILY) to be
used as ground truth data, in order to validate monthly SWE estimate products. Accumulations are
displayed in 5 mm steps in Fig. 2, in order to show the trends more clearly. The results of
the comparison, used as ground truth for SWE values between 0 and 200 mm (Fig. 2), suggest
that in the Northern Hemisphere, for values of SWE over 30 mm, the linear relationship
between the estimated GlobSnow SWE and the ground truth SWE is better than for the SWE products
released by the NSIDC. For GlobSnow the correlation index is over 0.5, while for the other products
the correlation indexes are only 0.22 and 0.12. This is largely because a large number of in situ
measurements of snow depth have been introduced into GlobSnow's inversion algorithm and improved its
accuracy (Luojus et al., 2010). However, since the microwave upwelling radiation emitted from the
ground by scattering no longer decreases with increasing snow depth at higher frequency microwaves
(such as 37 GHz), passive microwave SWE retrieval algorithms do not have the ability to
detect deep snow. The assimilation retrieval process of GlobSnow is also influenced by this
limitation. In previous studies, the observable scattering at higher frequencies saturates for thick
snow packs after a certain threshold, often between 120 and 180 mm. While SWE values exceed
the threshold, the accuracy of all the inversion procedures drops sharply, as the situations shown
in Fig. 2.
Note that for thin snow – that is for values of SWE less than 30 mm (equivalent to a snow
depth of 12 cm for a snow density of 0.24 gcm-3, and a snow depth of less than
10 cm for a snow density of 0.3 gcm-3) – the GlobSnow SWE values are clearly
overestimates, which is the same as the result obtained from the accuracy testing experiments for
Eurasia (Luojus et al., 2010).
Data merge
According to the results of comparing the three estimated SWE products and the station measurements,
it was seen that for SWE >30 mm, the GlobSnow product is more accurate than products
produced by the NSIDC and that for SWE <30 mm, the situation is reversed. In order to
optimize the SWE dataset, we chose pixels that had monthly SWE values continuous above 30 mm
in winter (December to March) for the period 1979/80 to 2010/11 from GlobSnow, and the other pixels
that had monthly SWE values no more than 30 mm in the same period from the two NSIDC
products. However, the period covered by the sets of pixels from the two NSIDC products is
different: SMMR & SSM/I end in May 2007, whereas the AMSR-E data cover the period from June 2002
to October 2011. We built linear fitting equations for each pixel in every month during the period
of overlap (2002–2007) in order to simulate winter SWEs in 2007–2011 using AMSR-E products. For
each pixel,
SWEij,mon′=αSWEij,mon+β
where SWE′ is obtained from NSIDC SWE products derived from SSMR & SMM/I data
at location (i,j) in one of the winter months and SWE is obtained from the NSIDC SWE product
derived from AMSR-E data, α and β are linear coefficient. Considering the differences
in the algorithms used to produce these products, this kind of forecasting is not effective in all
areas. Simulated data was added only for pixels that proved to be statistically significant at
p=0.05 under both a t test and an F test. For the remainder, the SWE series from SSMR & SMM/I
(ending in 2007) was retained in the pixels and the simulated data is not added, in order to avoid
the introduction of errors into the trend analysis. However, in the calculation of the total snow
mass, the values of SWE in the non-simulated pixels were still replaced by those from the NSIDC
AMSR-E product as the low number of small values has relatively little influence.
In the merged products, the distribution maps of the three different products are shown in
Fig. 3. The GlobSnow product accounts for the largest proportion (more than 50 %) in the merged
data in all the four months, and it mainly concentrates in high altitude regions. The NSIDC
(SMMR & SSM/I and the linear fitted AMSR-E) product accounts for a little larger proportion than
the NSIDC (SMMR/SSM/I) product in all the four months except in February, and both of the two
products mainly distributed in low altitude regions.
Results
The distribution map of the average optimized SWE product for the 32 years is presented in
Fig. 4. In December, snow cover is mainly located between 55 and 70∘ N in
Eurasia and between 60–70∘ N in North America. Snow cover extends to
mid-latitude regions near 50∘ N in January in Eurasia and North America, and there is
little difference between January, February and March in these areas. Large areas in central and
western Siberia have the biggest and deepest snow accumulations of snow in all four months.
Using the optimized SWE product (1979/80–2010/11), and the trend-free pre-whitening Mann–Kendall
(TFPW-MK) (Yue et al., 2002) method, which has been widely used for hydro-meteorological trend
assessments (Gao et al., 2012), we analyzed the SWE changes in the past 32 years in the Northern
Hemisphere for different regions and different months, as shown in Fig. 5.
According to the SWE change map (Fig. 5), in December, there was a significant change in central
Siberia (60–70∘ N, 110–130∘ E) of about -1 cmdecade-1. Over the
West Siberian Plain and parts of the Eurasian continent near 60∘ N there was a slight
increase, but the most significant rise was concentrated around 66∘ N, 90∘ E –
the rate of increase here was over 1 cmdecade-1. In January, the area where there was
a significant decrease in SWE increases several fold, and is not only concentrated in the same
regions as December but also extends to Baffin Island and northern Europe. In February, the rate of
SWE decrease in Siberia accelerates, reaching over -2 cmdecade-1; the areas where
there is a decrease also grows further to include most of the Hudson Bay coastline. Meanwhile, the
rising trend around 60∘ N in Siberia weakens, with the maximum rate being
0.7 cmdecade-1. In March, the significant reduction in SWE in North America is still
obvious even though the pattern is variable with some small areas where there is an increase. In
Northern Europe, there is a reduction in the area where there is a significant decrease, whereas
there is a clear expansion in areas where there is an increase and this area of increase extends to
the Eastern European Plain.
Overall, then, significant drops in SWE are concentrated at high latitudes of around 65∘ N
in Siberia and in North America; areas where there was a rise are mainly scattered around
60∘ N in Eurasia. In Eurasia there was a significant decline in SWE in northern areas but
an increasing trend in areas towards the south of the snow-covered areas, except for the “edges”
of the snow-covered area below approximately 58∘ N. In North America the overall SWE
declined and significant changes are concentrated along the coast of Hudson Bay and parts of Alaska
in January and February.
From the temporal view, although the monthly total snow mass fluctuates from year to year, there is
a declining trend in the Northern Hemisphere for all winter months in the period 1979/80–2010/11
(Fig. 6). The monthly total snow mass is calculated by summing the total mass of snow in all
pixels. The most serious snow mass change occurred in January and February, at rates of -16.45±6.68 and -13.55±7.80 Gtyear-1, respectively, corresponding
to -0.67 and -0.45 % per year, whereas the rate in March was -12.58±6.88 Gtyear-1, or nearly -0.42 % per year. The smallest change was in December
– only -10.70±5.62 Gtyear-1, taking up to a change rate of -0.64 % per
year. There is high confidence in these monthly total snow mass changes (significance level p<0.01). From the perspective of monthly differences, at less than 2000 Gt the total snow mass in
December is the lowest for the four winter months; the value increases to about 2500 Gt in January,
and reaches a peak of about 3000 Gt in February, with this value lasting into March.
Focusing on the more recent 15 years from 1996/97 to 2010/11, we found that the rate of decrease in
the SWE slows down for the four months studied except in March, and the trend becomes less
significant. During this period, the changing rate was -9.08±6.90 Gtyear-1 in
December, -13.15±8.74 Gtyear-1 in January and -10.87±10.56 Gtyear-1 in February (p<0.1). It should be noted that the February results
are not statistically significant. In March the rate accelerated to -13.16±6.6 Gtyear-1 (p<0.1).
To validate the results of this work, the results of the merged data are compared with the SWE
changes in North America (Gan et al., 2013) which uses the SSMR and SSM/I data only, as shown in
Fig. 7. Both Gan et al.'s (2013) and our results use the Mann–Kendall trend analysis method, which
guarantees the differences are caused by the data source only. In the comparison, both the two data
sets show decreasing trend in the studied four months, but in different decreasing rate. Compared to
the results of Gan et al., the decreasing rate acquired from the merged data is faster in December
and January, but slower in February and March.
Discussions
Accuracy analysis
Due to different data sources and different processing procedures, the existing three SWE products
may differ greatly in different regions (Liu et al., 2014). The accuracy of the three existing SWE
products is tested with ground station data in the Northern Hemisphere, and the 30 mm
threshold is acquired. Theoretically, the fusion of the GlobSnow products thicker than 30 mm
and the NSIDC products thinner than 30 mm is the best choice. However, it should be noted
that, the conclusion is based on about 30 years ground station validation in the whole Northern
Hemisphere, so it is only a statistical result. The merged product is superior in the Northern
Hemisphere for the past 30 years, and it is more suitable for total SWE or average SWE calculation
in the whole Northern Hemisphere, but it is not necessary the most accurate choice for a certain
region or a certain time.
Relation to climate changes
As a feature that is responsive to climate change, SWE is affected by changes in air temperature and
also by changes in precipitation timing and seasonality (Immerzeel et al., 2010). To find out the
relation between changes in the value of SWE and climate change, we collected the global monthly
gridded datasets of air temperature and precipitation, and analyzed the trends in these data for the
past 32 years. Snowfall and rainfall cannot be separated in the precipitation records; however, the
study areas used in this work are mostly at latitudes higher than 50∘ N and so all the
precipitation in the four months being considered was treated as snow.
The average winter temperature and precipitation changes in the snow-covered areas found using the
TFPW-MK method are shown in Fig. 8. In the past 32 years, the temperature has increased at a rate of
0.17 ∘Cdecade-1 and the precipitation increased at a rate of
0.4 mmdecade-1.
On a global scale, most climate data and models agree that there is a near-surface warming trend due
to the rising levels of greenhouse gases in the atmosphere (Barnett et al., 2005). According to the
fifth IPCC report (IPCC, 2013), the global warming rate over the past 15 years (1998–2012) has been
0.05 (-0.05 to +0.15) ∘Cdecade-1 and for the period since 1951 (1951–2012)
the rate is calculated as 0.12 (0.08 to 0.14) ∘Cdecade-1. In a warmer world,
the melting and sublimating of winter snow accelerates (Cox et al., 2000). Even though the
precipitation is also increasing, the total snow mass on the ground decreased.
The decrease in SWE directly reduces water supply in the spring (Barnett et al., 2005; Diffenbaugh
et al., 2012) and also influences vegetation growth (Wahrent et al., 2005; Wang et al., 2013). In
addition, it has a complex influence on the ground climate. On the one hand, the snowpack insulates
soil from cold air so the thinning of the snow weakens the insulating effect and cools the soil
(Zhang et al., 2005) in winter; on the other hand, thinning of the snow is positively related with
snow-season shortening and soil without snow cover absorbs more solar energy and warms up in spring
(Lawrence et al., 2010).
Conclusions
Three SWE products, NSIDC (SSMR & SSMI), NSIDC (AMSR-E L3) and GlobSnow are verified using global
weather station data. The results show that for values of SWE less than 30 mm, the NSIDC
product has the best correlation with the ground truth data; when the SWE is between 30 and
200 mm, the GlobSnow product shows the best correlation. Based on the validation results, we
choose pixels (of size 25km×25km) that have monthly SWE values above
30 mm in the GlobSnow SWE product for the period 1979/80–2010/11 and the remaining pixels
from the NSIDC products to optimize the SWE product.
Using this new SWE product, we show that, although the snow mass fluctuates from year to year, there
has been a significant decreasing trend in the total mass of snow in the Northern Hemisphere for the
period 1979/80–2010/11. The most notable declines in total snow mass are -16.45±6.68 and -13.55±7.80 Gtyear-1 in January and February,
respectively. These are followed by March and December, which have decreasing trends of -12.58±6.88 and -10.70±5.62 Gtyear-1, respectively, from 1979/80
to 2010/11. The most significant decline is concentrated between 65 and 75∘ N
in Siberia and North America; in contrast, there has been an increase in snow mass at latitudes near
60∘ N in Eurasia. Both the temperature and precipitation increased in the study area, but
the temperature rise is speculated to play a more important on snow cover.