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Global warming has strong impacts on snow cover, which in turn affects ecosystems, hydrological regimes and winter tourism. Only a few long-term snow series are available worldwide, especially at high elevation. Here, we analyzed several snowpack characteristics over the period 1970–2015 at eleven meteorological stations, spanning elevations from 1139 to 2540 m asl in the Swiss Alps. Snow cover duration has significantly shortened at all sites, on average by 8.9 days decade−1. This shortening was largely driven by earlier snowmelt (on average 5.8 days decade−1) and partly by later snow onset but the latter was significant in only ~30 % of the stations. On average, the snow season now starts 12 days later and ends 26 days earlier than in 1970. Overall, the annual maximum snow depth has declined from 3.9 to 10.6 % decade−1 and was reached 7.8 ± 0.4 to 12.0 ± 0.4 days decade−1 earlier, though these trends hide a high inter-annual and decadal variability. The number of days with snow on the ground has also significantly decreased at all elevations, in all regions and for all thresholds from 1 to 100 cm. Overall, our results demonstrate a marked decline in all snowpack parameters, irrespective of elevation and region, and whether for drier or wetter locations, with a pronounced shift of the snowmelt in spring, in connection with reinforced warming during this season.
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Shorter snow cover duration since 1970 in the Swiss Alps
due to earlier snowmelt more than to later snow onset
Geoffrey Klein
&Yann Vitasse
&Christian Rixen
Christoph Marty
&Martine Rebetez
Received: 12 May 2016/Accepted: 4 September 2016 /Published online: 21 September 2016
#Springer Science+Business Media Dordrecht 2016
Abstract Global warming has strong impacts on snow cover, which in turn affects ecosystems,
hydrological regimes and winter tourism. Only a few long-term snow series are available
worldwide, especially at high elevation. Here, we analyzed several snowpack characteristics
over the period 19702015 at eleven meteorological stations, spanning elevations from 1139 to
2540 m asl in the Swiss Alps. Snow cover duration has significantly shortened at all sites, on
average by 8.9 days decade
. This shortening was largely driven by earlier snowmelt (on
average 5.8 days decade
) and partly by later snow onset but the latter was significant in only
~30 % of the stations. On average, the snow season now starts 12 days later and ends 26 days
earlier than in 1970. Overall, the annual maximum snow depth has declined from 3.9 to 10.6 %
and was reached 7.8 ± 0.4 to 12.0 ± 0.4 days decade
earlier, though these trends hide
a high inter-annual and decadal variability. The number of days with snow on the
ground has also significantly decreased at all elevations, in all regions and for all
thresholds from 1 to 100 cm. Overall, our results demonstrate a marked decline in all
snowpack parameters, irrespective of elevation and region, and whether for drier or wetter
locations, with a pronounced shift of the snowmelt in spring, in connection with reinforced
warming during this season.
Climatic Change (2016) 139:637649
DOI 10.1007/s10584-016-1806-y
*Geoffrey Klein
Institute of Geography, University of Neuchatel, Espace Louis Agassiz 1, CH-2000 Neuchatel,
WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Neuchatel, Switzerland
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
1 Introduction
Increasing temperatures during the 20th and early 21st centuries have caused a global
reduction of the cryosphere (IPCC 2013). The snow cover has declined worldwide both in
lowland areas (IPCC 2013) and in mountainous regions (Park et al. 2012;Pederson
et al. 2013;Xuetal.2015), including the European Alps (Durand et al. 2009;Marty
2008;ValtandCianfarra2010). The snow cover is projected to further decrease in the
Northern Hemisphere from 7to25 % by the end of the century, depending on
climate scenarios (IPCC 2013),andsimilarchangesarealsoexpectedintheEuropean
The seasonal year-to year variability of the snow cover is mainly driven by large-scale
weather pattern anomalies (Scherrer and Appenzeller 2006;Seageretal.2010), but also by
regional and altitudinal patterns (Laternser and Schneebeli 2003). Long-term measurement
series of snow cover in the Swiss Alps were shown to follow the same significant decreasing
trend, both in the mean snow depth (or height of snowpack, HS, according to (Fierz et al.
2009)) and in the duration of the snow season at elevations below 1600 m asl, with only a
slight decrease documented above this elevation (Laternser and Schneebeli 2003).
Since the 1980s, a rapid temperature increase has caused numerous abrupt environmental
changes worldwide (Reid et al. 2015). This rapid warming was also observed in the European
Alps (Marty 2008) and has been particularly pronounced in spring (Acquaotta et al. 2015;
Rebetez and Reinhard 2008). Warmer temperatures were shown to be the major cause of the
shorter snow cover duration in the European Alps (Hantel and Hirtl-Wielke 2007; Scherrer
et al. 2004; Serquet et al. 2011). In fact, the reduction of the snow cover duration can be caused
either by accelerated snowmelt due to warmer temperatures in spring (Rixen et al. 2012;
Wielke et al. 2004) and/or reduced snowfalls during the winter season (Marty and Blanchet
2012; Scherrer et al. 2013). The latter could be the consequence of a decrease in the snow/rain
ratio, as observed in the Swiss Alps in winter (Serquet et al. 2011), late autumn (Serquet et al.
2013) and spring (Marty and Meister 2012; Serquet et al. 2013).
In snow-dominated regions, the snow cover has serious implications for hydrological
regimes and ecosystems (Callaghan et al. 2011), as well as for winter tourism (Damm et al.
2016). An upward shift of the snow line was already observed over the last decades in different
mountain regions, as for instance in Tanzania at Mt. Kilimanjaro (Park et al. 2012)orincentral
Chile (Casassa et al. 2003). Similarly, the upper limit of the treeline, mainly driven by warmer
temperatures during the growing season (Körner 2012), has increased at half of the 166 study
sites worldwide (Harsch et al. 2009), and a significant upward shift of the vegetation was
observed in mountainous areas in Europe (Lenoir et al. 2008). As vegetation and snow are
tightly linked in cold biomes, especially the beginning of growth and snowmelt (Vitasse et al.
2016), it is crucial to assess to what extent the duration and amount of snow cover is currently
affected by ongoing climate change at higher elevations to better anticipate future conse-
quences for mountain ecosystems.
The majority of the studies that examine changes in snow conditions in relation to global
warming focus on the period of the meteorological winter (DJF) and often ignore changes
occurring in autumn and spring. In this study, we examine long-term series of HS measure-
ments in the Swiss Alps over the whole year, using data from weather stations with manual
snow observations at elevations ranging between 1139 and 2540 m asl. We studied how the
snowpack has changed in the Swiss Alps over the period 19702015, with the aims (i) to test
whether changes could be detected in different snow parameters related to HS and to snow
638 Climatic Change (2016) 139:637649
cover duration and if snow cover duration has declined, (ii) to determine whether this reduction
is mainly the cause of earlier snowmelt or later snow onset or both.
2.1 Selection of the study sites
We used daily manual HS data provided by the Swiss Federal Office of Meteorology and
Climatology (MeteoSwiss). We selected stations from the Swiss Alps with a continuous snow
cover for at least 90 % of the years during the longest available common period, being 1970
2015. All data series were manually controlled and tested for outliers and missing data.
Metadata stored by MeteoSwiss were checked for all events reported which might have
impacted the data quality. The measurement method remained unchanged during the period
taken into account. A continuous snow cover was defined as at least 40 consecutive days (~
6 weeks) with a minimum of 1 cm of HS on the ground. Defining the threshold at either
minimum 30 or 50 consecutive days did not change the number of qualifying stations.
We analyzed nine snowpack parameters (see below, section 2.2), for all stations and years
providing at least 90 % of available data. We considered stations having at least 90 %
of annual resulting data for each parameter over the study period. This threshold was
verified and found to be robust for all stations using a bootstrap method. In detail, we tested
trend significances of 1000 samples for which 5, 10, 15 and 20 % of the annual resulting data
was randomly removed.
Most of the selected stations were slightly relocated during the study period (maximum
90 m of elevation and 2.2 km of distance on the same slope), except one station (Säntis) which
experienced a significant relocation in 1978 (Marty and Meister 2012). Data for all snow
parameters were checked for consistency at the dates of relocations for each station, and no
changes in trends were found, except for one station (Säntis) which was therefore discarded.
Following this filtering procedure, eleven stations were finally selected for our study. Their
elevations range from 1139 to 2540 m asl, with a mean snow cover duration from 108 to
260 days and a mean maximum HS from 65 to 353 cm over the study period (Table 1).
2.2 Snow data
The annual snow parameters were considered from 1 September until 31 August of the
following year. This period was chosen based on the earliest snow onset date (6 September
1984), and latest snowmelt date (16 August 1980) of all our stations, which wereboth found at
the highest site (Weissfluhjoch). For all snow parameters, years are designated by the calendar
year when the snow season ends. Beforeextracting all snow parameters, data gaps equal toone
day were filled by linear interpolation for a better homogeneity in the raw data.
For each snow year and station, we looked at nine parameters: the maximum HS, the times
of snow onset, snowmelt and maximum HS, the snow cover duration and the number of days
with HS 1, 20, 50 and 100 cm (days with snowpack, called hereafter DSP). The highest
threshold was not meaningful for three stations (Scuol, Sta. Maria Val Müstair, and Grächen),
due to their dryer climate and on average lower maximum HS (65, 70 and 74 cm respectively,
see Table 1). The snow onset date was defined as the first day of the first continuous snow
cover period and the snowmelt date as the first snow-free day after the last continuous snow
Climatic Change (2016) 139:637649 639
cover period. The snow cover duration corresponds to the number of days between
snow onset date and snowmelt date. In case of multiple identical values of maximum
HS (78 occurrences, i.e. 15 % of the station-years), we identified the latest occurrence
as the date with maximum HS. Using the earliest occurrence showed no significant
changes in the trend results.
Among the 506 station-years considered, only three had a continuous snow cover shorter
than 40 consecutive days (Airolo 1989 and 1993, Scuol 2002). To avoid excluding these
extreme cases due to a lack of snow, we computed the snow onset and snowmelt dates
according to their longest continuous snow cover for these three occurrences (34, 36 and
15 days respectively).
In addition, two distinct continuous snow covered periods of more than 40 days during the
same season were found for 5 station-years. For these specific cases, the snow cover duration
therefore includes snow free days (4, 3, 40 and 2 days in 1990, respectively at Andermatt,
Arosa, Bosco-Gurin and Sta. Maria Val Müstair, as well as 7 days in 2013 at Airolo).
2.3 Statistics
Because none of the analyzed snow parameters were following a normal distribution
(verified using Shapiro tests), temporal trends were therefore calculated on the original
annual values for each parameter and each station by applying the non-parametric
Theil-Sen estimator slope, combined with a Mann-Kendall significance test over the
common temporal period for all stations (19702015). No significant breaks in the
slopes of the temporal trends were detected for any parameters and stations over the
study period (tested by stepwise regression methods).
All analyses, tables and figures were performed using R 3.2 (Team RC 2015) and the
following R-packages: EnvStats, kendall reshape2 and zoo.
Tab l e 1 Selected stations for the long-term trend analysis of the snow parameters. Coordinates, elevation, mean
snow cover duration and mean maximum HS over the study period (19702015) are reported. Coordinates and
elevation correspond to the snow measurement location (often slightly apart from the other meteorological
Station Code Coordinates Elevation
[m asl]
Mean snow
cover duration
Mean maximum
HS [cm]
Airolo AIR 46°3134N/08°3551E 1139 111 111
Scuol SCU 46°4736N/10°1659E 1298 108 65
Sta. Maria Val Müstair SMM 46°3555N/10°2534E 1418 124 70
Andermatt ANT 46°3800N/08°3540E 1442 158 150
Bosco-Gurin BOS 46°1900N/08°2919E 1486 148 165
Grächen GRC 46°1209N/07°5028E 1550 120 74
Davos DAV 46°4845N/09°5050E 1560 156 110
Arosa ARO 46°4731N/09°4059E 1750 176 138
Segl-Maria SIA 46°2621N/09°4556E 1798 161 124
Grimsel Hospiz GRH 46°3417N/08°1958E1970 220 353
Weissfluhjoch WFJ 46°4947N/09°4833E 2540 260 250
640 Climatic Change (2016) 139:637649
3.1 Time of snow onset, snowmelt and snow cover duration
The time of snow onset was delayed at all eleven stations, and significantly at four of them by
3.1 ± 0.2 to 4.0 ± 0.2 days decade
(Fig. 1a and Table 2). All stations showed a significantly
earlier snowmelt, on average 5.8 days decade
, ranging from 3.6 ± 0.2 to 7.5 ± 0.2 days
(Fig. 1bandTable2). As a result, the snow cover duration was significantly reduced
at all stations by 6.2 ± 0.2 to 11.2 ± 0.2 days decade
(Fig. 1c and Table 2), on average by
8.9 days decade
, corresponding to a shortening of 2.6 to 7.5 % decade
. No pattern or
significant correlations were found neither between elevation and the changes in the timing of
snow onset, snowmelt or snow cover duration (Pearson correlation p-values of 0.24, 0.65 and
0.63, respectively), nor with geographic coordinates (p-values 0.88, 0.25 and 0.11, respective-
ly, extracted from the multiple linear regression based on the geographic coordinates of the
Fig. 1 7-year simple moving average of the snow onset, snowmelt dates and snow cover duration of the eleven
study stations over the period 19702015 (with half-windows on both edges). The bold line corresponds to the mean
Sen slope of the eleven stations and was calculated by averaging all annual values across stations and then,
computing the slope of the mean series. For station names, see Table 1, for individual slope values, see Table 2
Climatic Change (2016) 139:637649 641
Tab l e 2 Estimated trends (slope per decade) for each snow parameter over the study period (19702015), calculated from the Theil-Sen test. Significant slopes are marked in bold. The
significance level, calculated with the Mann-Kendall test, is indicated with stars (* p< 0.05, ** p< 0.01 and *** p< 0.001)
Code Onset
[days decade
[days decade
Snow cover duration
[days decade
Max HS
[cm decade
Max HS
[days decade
DSP 1cm
[days decade
DSP 20 cm
[days decade
DSP 50 cm
[days decade
DSP 100 cm
[days decade
AIR 3.4 -6.7** -10.0** -13.2* -7.8* -10.0*** -10.9** -6.5 -0.7
SCU 4.0* -3.6* -8.1** -5.4 -5.2 -10.0*** -10.1** -0.3 NA
SMM 2.1 -4.2* -6.3** -8.3** -7.8* -4.5* -6.2 -2.4* NA
ANT 2.4 -5.0* -7.5** -8.0 -0.7 -6.8*** -5.0 -4.0 -5.0
BOS 2.4 -7.3** -10.4** -21.8* -8.8* -10.0*** -7.7* -13.4** -12.5**
GRC 3.5** -7.0*** -11.2*** -8.0** -12.0** -10.0*** -9.4* -4.4 NA
DAV 3.1* -4.4** -8.3*** -8.1* 3.0 -6.5** -8.8* -6.7 -0.8*
ARO 2.4 -6.9*** -10.0*** -11.5* -8.1* -7.2*** -10.0*** -14.2** -13.2**
SIA 4.0* -7.5*** -10.4*** -15.9** -11.7** -11.1*** -14.3*** -13.3** -5.3***
GRH 1.7 -5.8* -6.2* -22.2 -8.0** -6.2** -8.7*** -9.7** -6.0
WFJ 2.3 -5.6*** -8.1** -11.2 -1.7 -7.5** -6.2* -7.9** -5.7
Mean 2.8 -5.8 -8.9 -12.1 -6.3 -8.2 -8.8 -7.5 -6.2
642 Climatic Change (2016) 139:637649
3.2 Maximum HS (snow depth)
The stationsaverage maximum HS ranged from 65 to 353 cm, and decreased at all stations over
the study period irrespective of elevation or geographical location. The decrease of the maximum
HS was significant for seven stations with a rate from 8.0 ± 0.6 to 21.8 ± 0.8 cm decade
(Fig. 2a
and Table 2), corresponding to a reduction from 3.9 to 10.6 % decade
. The day of maximum HS
occurred earlier at all sites except one (Davos), and significantly for seven stations with a rate
from 7.8 ± 0.4 to 12.0 ± 0.4 days decade
(Fig. 2b and Table 2). A highly significant correlation
was found between maximum HS values and snowmelt date for all eleven stations (mean Pearson
coefficient r= 0.68, p< 0.0001). Significant correlations were also found between maximum HS
values and the snow onset date for five stations. No significant correlations were found between
the times of maximum HS and snow onset, whereas significant correlations were found for six
stations between the times of maximum HS and snowmelt.
3.3 Frequency of DSP (frequency of days with snowpack)
The frequency of DSP has decreased at all sites and for all thresholds (1, 20, 50 or 100 cm). This
decrease was significant at all stations for DSP 1 cm from 4.5 ± 0.2 to 11.1 ± 0.2 days decade
(Fig. 3a and Table 2), at nine stations for DSP 20 cm from 6.2 ± 0.3 to 14.3 ± 0.3 days decade
(Fig. 3b and Table 2), at six stations for DSP 50 cm from 2.4 ± 0.2 to 14.2 ± 0.4 days decade
(Fig. 3c and Table 2), and at four stations for DSP 100 cm (out of only eight stations for this last
threshold as three were disregarded, see Data and methods section) from 0.8 ± 0.2 to
13.2 ± 0.5 days decade
(Fig. 3d and Table 2). No significant correlations could be found
between the reduction in the number of DSP and elevation (p-values 0.54, 0.67, 0.24 and 0.65 for
Fig. 2 7-year simple moving average of the maximum snow depth (HS) and its day of occurrence of the eleven
study stations over the period 19702015 (with half-windows on both edges). The bold line corresponds to the mean
Sen slope of the eleven stations and was calculated by averaging all annual values across stations and then, computing
the slope of the mean series. For station names, see Table 1, for individual slope values, see Table 2
Climatic Change (2016) 139:637649 643
the thresholds 1, 20, 50 and 100 cm respectively) or geographical location of the study sites (p-
values 0.33, 0.65, 0.70 and 0.89 respectively).
4 Discussion
Our study shows that snow cover duration, as well as the maximum HS and the frequency of
DSP have all clearly been declining in the Swiss Alps, irrespective of elevation (1139 to
Fig. 3 7-year simple moving average of the days with snowpack (DSP) 1, 20, 50 and 100 cm of the eleven
study stations over the period 19702015 (with half-windows on both edges). The bold line corresponds to the
mean Sen slope of the eleven stations and was calculated by averaging all annual valuesacross stations and then,
computing the slope of the mean series. For the DSP 100 cm, the three driest stations (SCU, SMM and GRC)
were omitted from the plot and the mean Sen slope calculation. Note that the y-axes are not all the same. For
station names, see Table 1, for individual slope values, see Table 2
644 Climatic Change (2016) 139:637649
2540 m asl) and location, and whether for stations with much or little snow. Complementary to
numerous previous studies showing stronger declines in snow cover at elevations below
1600 m asl in the Alps (Durand et al. 2009; Laternser and Schneebeli 2003; Valt and
Cianfarra 2010), during our study period 19702015, we found a significant snow cover
decline at all elevations up to the highest elevation site (2540 m asl). Here, we analyzed the
whole snow season including autumn and spring, whereas previous studies mainly considered
the winter season, usually from December to February or March. The observed snowpack
reduction is most likely related to the general increase in temperatures observed at all
elevations in the Swiss Alps, especially during spring (Rebetez and Reinhard 2008). The
impact of global warming on snowpack may have been additionally enhanced by an
increasing trend in sunshine duration, observed at both low and high elevations in the
European Alps from 1975 to 2000 (Auer et al. 2007) and in solar surface radiation in
Switzerland from 1981 to 2010, particularly in spring (Sanchez-Lorenzo and Wild 2012).
Our results do not show any regional or elevation-dependent trends but rather a clear decrease
in all snow parameters at all stations.
The strong reduction in the snow cover duration was more the result of an earlier snowmelt
than a later snow onset: the earlier snowmelt date was approximately twice as important as the
delayed snow onset date, with a contribution accounting on average for 67 % of the shortening
of the snow season against 33 % for snow onset. On average, among our stations, the
snowmelt now takes place around 16 March at the lowest station (Airolo, at 1139 m asl on
the southern side of the Swiss Alps) and around 25 June at the highest station (Weissfluhjoch,
at 2540 m asl on the eastern part of the Swiss Alps), compared to 15 April and 20 July,
respectively based on the linear trends. In autumn, the snow onset now takes place on average
on 7 December at the lowest station and on 25 October at the highest station, whereas it was
respectively on 22 November and 15 October in 1970.
The differences in observed changes in snow onset and snowmelt dates are coherent with
the differences in seasonal temperature trends in the Swiss Alps, showing a stronger increase in
spring (+0.84 °C decade
) than in autumn (+0.21 °C decade
) since the 1970s (Rebetez and
Reinhard 2008; Serquet et al. 2013). The observed increasing trend in sunshine duration (Auer
et al. 2007; Sanchez-Lorenzo and Wild 2012) may have had a particularly strong impact on
snowmelt, as the sunrays are much higher in MarchJuly compared to OctoberDecember, the
time of snow onset. Thus, in addition to stronger temperature increasing trends in spring, the
increasing sunshine duration may have contributed to the stronger shift observed in the time of
snowmelt compared to the one detected in the time of snow onset. Particularly in spring, when
the sun is higher than in late autumn, the changing albedo may also enhance the warming and
melting process: the white snow cover disappeared earlier due to global warming, resulting
into a decrease of the albedo. More energy was thus available on the ground, further increasing
air temperature (Rebetez and Reinhard 2008), and accelerating the melting of snow. The
resulting snow cover duration was consequently reduced both in the beginning and end of the
season, lasting now only 98 days at the lowest station and 239 days at the highest, compared to
143 and 275 days respectively in 1970.
The observed decrease in maximum HS is consistent with previous studies, showing a
decrease in maximum HS at all elevations in the Swiss Alps (Marty and Blanchet 2012). The
significant correlations found between the snow onset date and the maximum HS values show
that warmer temperatures and later snow onset in autumn contribute significantly to the
reduction of the maximum snow amounts which can then be reached during the winter.
Warmer temperatures in September to December have had an impact at the elevations where
Climatic Change (2016) 139:637649 645
they were just below the zero-degree level (Serquet et al. 2013), resulting into rainfall instead
of snowfall. These previous early snowfalls are now missing in the yearly maximum amount
of HS. The high correlation between maximum HS and snowmelt date observed at all stations
shows that these parameters are strongly interdependent. Although the time of snow onset is
less variable than the time of snowmelt, there is no correlation between the times of maximum
HS and snow onset. Our results clearly suggest that the time of maximum HS is mainly
governed by warmer and possibly sunnier spring conditions when the snowpack starts melting.
These relationships show how the temperature increase observed in spring has had a strong
impact on the snow cover. Increasing temperatures in late autumn and spring have contributed
to the snowfall/precipitation-day ratio decrease when the temperatures were not far below the
zero-degree limit, which in turn has also contributed to the decrease in all snowpack param-
eters. The decline in the number of DSP was also found when using high thresholds of HS,
which supports that the warmer past decades have also impacted the snowpack at locations
having large amounts of snow precipitation, typically at higher elevations where colder
temperatures and longer snow seasons occur. The highest thresholds used are relevant to
confirm that a prominent decline in snowpack has also occurred in wetter regions with
abundant precipitation. Former studies often did not look at these melting months and
therefore concluded that there is little change in winter snowpack at higher elevations
(Marty 2008;Scherreretal.2004).
Water resources in mountainous areas are tightly connected to the snow cover cycle.
Numerous regions may be affected because our study area stands in a central position in
Europe, at the source of three major drainage basins, the Rhine, Danubeand Rhone rivers, with
alpine basins having a strong influence on major distant downstream catchments crossing
several countries. Earlier snowmelt and reduced snow accumulation during winter, as a
consequence of global warming, can have a commensurable impact on plant and animals of
alpine ecosystems, runoff regimes, soil moisture and water availability in the drainage basins
(Barnett et al. 2005; Zierl and Bugmann 2005). For example, in the alpine belt, earlier
snowmelts and warmer temperatures were found to cause earlier plant development
(Ernakovich et al. 2014; Vitasse et al. 2016), which can put them at higher risk to be damaged
by frost (Wipf et al. 2009). The combination of an earlier snowmelt and a shortening of the
snow cover duration might also alter the spatial pattern of suitable habitats of some snowbed
plant communities (Carbognani et al. 2014). Similarly, the reduction in the snow cover has
already affected the reproduction of the alpine fauna, as for example the decreasing litter size
of the Alpine marmot (Tafani et al. 2013).
The natural snow cover duration and HS is also crucial for winter tourism in mountain
regions, because their reduction can drastically shift upward the snow-reliability limit for ski
resorts and shorten the winter sports season (Pons et al. 2015). If snowpack continues to
decline, artificial snow may also progressively become critical to produce for winter sports
(Gajić-Čapka 2011;Steiger2010), particularly at the beginning and at the end of the ski season
(Rixen et al. 2011), due to temperatures more and more frequently above the freezing threshold
at sensitive elevations and times of the year (Rixen et al. 2011; Serquet et al. 2011;Serquet
et al. 2013). A relationship between HS and the overnight stays of tourists in ski resorts in
Austria has been demonstrated for low and mid-elevation ski resorts, whereas the two
parameters were independent for the high-elevation resorts (Falk 2010). Our results show that
stations located at elevations higher than 1700 m asl still have more than 79 days (and 206 days
at the highest station at 2540 m asl) with at least 50 cm of HS, irrespective of the timing of the
ski season. Model results show that the reduction of the snow season and the stronger
646 Climatic Change (2016) 139:637649
reduction of the snow cover at the end of the season will likely continue in the coming decades
and that the snow reliability for winter tourism will become critical at elevations up to 1800 m
asl and 2000 m asl by mid and end of the century, respectively (Steger et al. 2013). Specific
results from climatic models have shown that the decline of snowpack could be moderate until
2050, but will likely accelerate during the second half of the century (Bavay et al. 2013). This
could result into millions of overnight stays lost during the winter seasons (Damm et al. 2016).
Our results show that this issue must be taken seriously into account for future prospects, even
at higher elevations.
5 Conclusions
Our results show a clear reduction of the snowpack over the period 19702015 in the Swiss
Alps, based on eleven stations from 1139 to 2540 m asl, irrespective of elevation or region. In
particular, they show that snowpack has been decreasing at higher elevation to the same extent
as it has declined at lower elevations. We found a clear shortening of the continuous snow
cover duration, on average by 8.9 days decade
, irrespective of the region or elevation. The
reduction in snow cover duration is mostly the result of earlier snowmelt (on average by
5.8 days decade
), rather than later snow onset, likely mostly due to a higher temperature
increase in spring compared to autumn. On average, the snow season now starts 12 days later
and ends 26 days earlier than in 1970. We also found a general decline in the value of the
annual maximum HS, in connection with the later snow onset. The time of maximum
HS also occurred earlier, and was highly connected to the snowmelt date and much
less to the time of snow onset, illustrating the impact of the spring temperatures and
of the earlier start of the melting period on the snow season. The number of DSP has decreased
at all elevations, in all regions and irrespective of the HS threshold. Our results show particu-
larly strong trends of snow decline in spring, which may progressively lead to increasing
consequences on hydrological regimes and on summer water availability, whether for ecosys-
tems or for society.
Acknowledgments This work was supported by the Swiss National Science Foundation (grant number
200021-152954). We are grateful to MeteoSwiss for providing the snow data, to Stephan Bader and Gergely
Rigo for their help concerning the snow stationsmetadata, to Christophe Randin for his help with data analysis
and to William Doehler for his editorial improvements of the manuscript.
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... Climate change effects are particularly strong in mountain regions which have experienced a faster rate of ambient temperature increase compared to the global average (Auer et al. 2007, Pepin et al. 2015. Higher temperatures in spring alter the timing and duration of the snowmelt process (Steger et al. 2013, Klein et al. 2016, thus modifying the availability of food sources during the reproduction period (Barras et al. 2021). Shifts in peak food availability might lead to a phenological mismatch or enlarge the distance between high quality foraging habitat and suitable nest sites and consequently increase time and energy necessary for provisioning. ...
... Moreover, invertebrate fall-out on snow can additionally offer easily accessible food (Antor 1995). Climate change is expected to alter the snow cover extent and the timing of the snowmelt (Steger et al. 2013, Klein et al. 2016. Snow cover changes and therewith potential changes in food availability may affect the timing and duration of the breeding period. ...
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Timing and location of reproduction are central to reproductive success across taxa. Among birds, many species have evolved specific strategies to cope with environmental variability including shifts in timing of reproduction to track resource availability or selecting suitable nest location. In mountain ecosystems, complex topography and pronounced seasonality result in particularly high spatiotemporal variability of environmental conditions. Moreover, the risk of climate‐induced resource mismatches is particularly acute in mountain regions given that temperature is increasing more rapidly than in the lowlands. We investigated how a high‐elevation passerine, the white‐winged snowfinch Montifringilla nivalis, selects its nest site in relation to nest cavity characteristics, habitat composition and snow condition. We used a combination of field habitat mapping and satellite remote sensing to compare occupied nest sites with randomly selected pseudo‐absence sites. In the first half of the breeding season, snowfinches preferred nest cavities oriented towards the morning sun while they used cavities proportional to their availability later on. This preference might relate to the nest microclimate offering eco‐physiological advantages, namely thermoregulatory benefits for incubating adults and nestlings under the harsh conditions typically encountered in the alpine environment. Nest sites were consistently located in areas with greater‐than‐average snow cover at hatching date, likely mirroring the foraging preferences for tipulid larvae developing in meltwater along snowfields. Due to the particularly rapid climate shifts typical of mountain ecosystems, spatiotemporal mismatches between foraging grounds and nest sites are expected in the future. This may negatively influence demographic trajectories of the white‐winged snowfinch. The installation of well‐designed nest boxes in optimal habitat configurations could to some extent help mitigate this risk.
... Fontrodona Bach et al. (2018) studied changes in mean and maximum snow cover depths based on observed data in Europe from 1951. Many other papers have analysed different aspects of snow patterns at regional and country scales, for example, in central Europe (Dong and Menzel, 2020), Austria (Olefs et al., 2020), Bulgaria (Brown and Petkova, 2007), Great Britain (Kay, 2016;Brown, 2019), Poland (Szwed et al., 2017;Tomczyk et al., 2021), Italy (Diodato et al., 2021), Slovakia (Siman and Polč ak, 2017;Siman and Sl avkov a, 2019), the Swiss-Austrian Alps (Klein et al., 2016;Schöner et al., 2018), Switzerland (Marty and Blanchet, 2012), and others. ...
... For example, Fontrodona Bach et al. (2018), based on observational evidence over Europe in 1951-2017, reported widespread decreases of −12.2%/10 years for mean snow depth and −11.4%/10 years for maximum snow depth, both accelerating after the 1980s. Klein et al. (2016) mentioned shortening of snow cover duration in the Swiss Alps between 1970 and 2015, attributed more to earlier snowmelt than to later snow onset. Schöner et al. (2018), analysing 139 stations within the Swiss-Austrian Alps in 1961-2012, reported a clear decrease in snow depths in southern regions, but almost no change in the northeast. ...
This paper analyzes temperature and snow patterns of winters (December–February) averaged for the territory of the Czech Republic during the 1961–2021 period and their broad environmental impacts and responses. Series of mean, maximum, minimum, absolute maximum and absolute minimum temperatures show significant increasing linear trends, while decreasing trends were detected in numbers of frost, ice and extremely cold days, duration of cold waves, snowfall days, sums of heights of new snow, days with snow depths ≥1 cm, mean and maximum snow depths. The winter severity, derived from five temperature and five snow variables and expressed by temperature/snow scores, indicates decreasing severity of winters for 1961–2021, in which temperature severity is more pronounced than that of snow. Decreasing winter severity is in line with decreasing frequency of cyclonic and directional circulation types according to objective classification, while the trend in anticyclonic types was opposite. Types with maritime airflow from the Atlantic Ocean and the Mediterranean contribute to milder and types with continental airflow from the east to colder winters. The coldest winters, 1962/63 and 1984/85, and the mildest winters, 2006/07 and 2019/20, were analyzed in greater detail. Concerning of different analyzed environmental impacts, they are influenced not only by severe winter weather, but also by political, socioeconomic and general environmental changes in the country. In line with decreasing winter severity were only statistically significant decreasing trends in proportions of traffic accidents connected with snow and glaze ice on the roads and volumes of damaged wood due to high weights of snow and ice deposit, expressed as salvage felling. Series of other environmental impacts (e.g. fatalities attributed to weather, impacts on the economy and society) reflect rather severity of individual winters and express high inter‐annual variability without any representative trends. This article is protected by copyright. All rights reserved.
... (2019) of -0 days/dec to -10 days/dec, also close to the -8.9 days/dec over the 1970-2015 period found by Klein et al. (2016) 675 over 11 stations in the Swiss Alps, the average -7 days/dec simulated over Austria for the 1960-2019 period by Olefs et al. (2020), as well as the range of -4.5days/dec to -7.0 days/dec find by Matiu et al. (2021) in the 1971-2019 period using a large set of in-situ observations over the whole Alps. 1955Alps. ...
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Assessing past distributions, variability and trends of the mountain snow cover and its first order drivers, temperature and precipitation, is key for a wide range of studies and applications. In this study, we compare the results of various modelling systems (global and regional reanalyses ERA5, ERA5-Land, ERA5-Crocus, CERRA-Land, UERRA MESCAN-SURFEX, MTMSI, and regional climate model simulations CNRM-ALADIN and CNRM-AROME driven by the global reanalysis ERA-Interim) against observational references (in-situ, kriged datasets and satellite observations) across the European Alps, from 1950 to 2020. The comparisons are performed in terms of monthly and seasonal snow cover variables (snow depth and snow cover duration) and their main atmospherical drivers (near-surface temperature and precipitation). We assess multi-annual averages of regional and sub-regional mean values, their inter-annual variations, and trends over various time scales. CNRM-AROME and CNRM-ALADIN simulations, and ERA5-Land exhibit an overestimation of the snow accumulation during winter, increasing with elevations. ERA5, ERA5-Crocus, MESCAN-SURFEX, CERRA-Land and MTMSI offer a satisfying description of the monthly snow evolution albeit a spatial comparison against satellite observation indicates that all datasets overestimate the snow cover duration of the snow cover, especially the melt-out date. The analysis of the inter-annual variability and trends indicate that modelling snow cover dynamics remain complex across multiple scales, that none of the models evaluated here fully succeed to reproduce, compared to observational reference datasets. Indeed, while most of the evaluated model outputs perform well at representing the inter-annual to multi-decadal winter temperature and precipitation variability, they often fail to address the variability of the snow depth and snow cover duration. We discuss several artifacts potentially responsible for incorrect long-term climate trends in several reanalysis products (ERA5 and MESCAN-SURFEX), which we attribute primarily to the heterogeneities of the observation datasets assimilated. Reference datasets and some of the evaluated datasets provides past trends in line with current available literature. Over the last 50 years (1968–2017) at a regional scale, the European Alps have experienced a winter warming of 0.3 to 0.4 °C per decade, a weak reduction of winter precipitation, and a substantial decrease of the snowpack characteristics, with a decline of the winter snow depth and the snow cover duration reaching −10 % per decade and −10 days per decade, respectively, especially at low and intermediate elevations. Overall, we show that no modelling strategy outperforms all others within our sample, and that upstream choices (horizontal resolution, heterogeneity of the observations used for data assimilation in reanalyses, coupling between surface and atmosphere, level of complexity and configuration of the snow scheme etc.) have great consequences on the quality of the datasets and their potential use. Despite their limitations, in many cases these modeling outputs can be used to characterize the main features of the mountain snow cover for a range of applications.
... However, local measurements of snowpack often extend back only a few decades 6 , while the long resting period during the cold season joined with the mostly negligible moisture-limiting conditions of the Alps, prevents adopting tree rings as an effective proxy to reconstruct snow conditions 17 . As short-term series of observations are not adequate to describe snowpack dynamics and the full amplitude of mountain wintertime conditions 18 , the absence of any continuous long-term (centennial or more) instrumental or reconstructed record of snowpack duration for the Alps so far, prevents it being correctly placed in the right context and assessing the true nature of the present-day shrinking 6,[19][20][21][22][23][24] . ...
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Snow cover in high-latitude and high-altitude regions has strong effects on the Earth’s climate, environmental processes and socio-economic activities. Over the last 50 years, the Alps experienced a 5.6% reduction per decade in snow cover duration, which already affects a region where economy and culture revolve, to a large extent, around winter. Here we present evidence from 572 ring-width series extracted from a prostrate shrub (Juniperus communis L.) growing at high elevation in the Val Ventina, Italy. These ring-width records show that the duration of current snowpack cover is 36 days shorter than the long-term mean, a decline that is unprecedented over the last six centuries. These findings highlight the urgent need to develop adaptation strategies for some of the most sensitive environmental and socio-economic sectors in this region. Snow is an important component of the environment and climate of mountain regions, but providing a long-term historical context for recent changes is challenging. Here, the authors use ring-width data from shrubs to show that recent snow loss in the central Alps is unprecedented over the last 600 years.
... Chen et al. (2018) believed that the shortened SCD was dominated by SCS, which may be caused by different study periods. Our results are similar to those of previous studies in the Tianshan Mountains in Central Asia (Yang et al., 2019b) and the Northern Hemisphere (Peng et al., 2013;Chen et al., 2015b;Klein et al., 2016;Chen et al., 2021). It can be seen that the dominant factors of seasonal snow cover change may be different for different study periods and areas. ...
... Delayed melt-out timing in late spring and early summer, when air temperature and photoperiod peakby decisively shortening the growing seasonhas strong effects on growing degree days and eventually on shrub or tree cambial initiation, cell division and growth (Carrer et al., 2019;Francon et al., 2020a;Franke et al., 2017;Hoch, 2015;Kirdyanov et al., 2003;Schmidt et al., 2010;Vaganov et al., 1999;Wheeler et al., 2016). Finally, we interpret the May temperature signal, also found at our site for Jc and Pu, by the rushing effect of higher spring temperatures on melt-out dates (Carrer et al., 2019;Durand et al., 1999;Francon et al., 2017;Klein et al., 2016). However, as a tree species, Pu has vegetative parts that are not covered by snow in winter (unlike Jc). ...
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Mountain ecosystems are particularly sensitive to climate change, which in part causes encroachment of woody plants at the treeline ecotone, with repercussions on treeline advance and ecosystem carbon balance. Yet, studies investigating the long-term trends in radial growth as well as year-to-year response of several tree and shrub species to climate change are scarce, especially in the Pyrenees where dendroecological studies are hitherto critically lacking. Here, we estimate and compare the long-term growth trends of two shrub (Rhododendron ferrug-ineum and Juniperus communis) and one tree (Pinus uncinata) species, and investigate their year-to-year growth response to changing climatic conditions and advancing snow melt-out timings. We used the Age-Class Isolation method (ACI) to derive growth trends from the ring width series of trees and shrubs. Climate-growth relationships were evaluated using fixed-and moving-window bootstrap correlation functions with the aim to determine the effects of changing climate and snowpack on shrub and tree growth. Overall, our results show that all species at our site, especially shrubs, have grown increasingly well over at least the last century, probably in response to increasing temperatures during the growing season and earlier snow melt-out dates. Nevertheless, the two shrub species differ quite strongly in their response to climate. Whereas the climate signal of J. communis has been relatively stable in recent decades despite the persistent and significant warming trend, R. ferrugineum shows a strong shift in climate sensitivity and is increasingly affected negatively by climate change. Altogether, our results address the different climate sensitivity of the two most common shrubs in the Pyrenees. They also contribute to a better understanding of vegetation dynamics in the Pyrenean treeline ecotone in the context of global change.
... Water resource availability is undergoing strong seasonal modifications due to climate warming, with rapid glacier retreat worldwide , e.g., an estimated volume loss of 84 ± 15 % by 2100 in the European Alps . Peak annual runoff from glacier melt will be reached between 2010 and 2060 across the world (Huss and Hock, together with more liquid precipitation and earlier snowmelt (Lane and Nienow, 2019;Klein et al., 2016), will cause a change in streamflow regimes, with a shift in the flow magnitude and in the timing of high flows to earlier months (Berghuijs et al., 2014;Beniston et al., 2018;Gabbi et al., 2012;Lane and Nienow, 2019). ...
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Proglacial margins form when glaciers retreat and create zones with distinctive ecological, geomorphological and hydrological properties in Alpine environments. There is extensive literature on the geomorphology and sediment transport in such areas as well as on glacial hydrology, but there is much less research into the specific hydrological behavior of the landforms that develop after glacier retreat in and close to proglacial margins. Recent reviews have highlighted the presence of groundwater stores even in such rapidly draining environments. Here, we describe the hydrological functioning of different superficial landforms within and around the proglacial margin of the Otemma glacier, a temperate Alpine glacier in the Swiss Alps; we characterize the timing and amount of the transmission of different water sources (rain, snowmelt, ice melt) to the landforms and between them, and we compare the relationship between these processes and the catchment-scale discharge. The latter is based upon a recession-analysis-based framework. In quantifying the relative groundwater storage volumes of different superficial landforms, we show that steep zones only store water on the timescale of days, while flatter areas maintain baseflow on the order of several weeks. These landforms themselves fail to explain the catchment-scale recession patterns; our results point towards the presence of an unidentified storage compartment on the order of 40 mm, which releases water during the cold months. We suggest attributing this missing storage to deeper bedrock flowpaths. Finally, the key insights gained here into the interplay of different landforms as well as the proposed analysis framework are readily transferable to other similar proglacial margins and should contribute to a better understanding of the future hydrogeological behavior of such catchments.
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Using Earth observation to accurately extract snow phenology changes is of great significance for deepening the understanding of the ecological environment and hydrological process, agricultural and animal husbandry production, and high-quality development of the social economy in Xinjiang. Considering snow cover phenology based on MODIS product MOD10A1 data, this paper constructed a method for automatically extracting key phenological parameters in Xinjiang and calculated three key phenological parameters in Xinjiang from 2001 to 2020, including SCD (snow cover duration), SOD (snow onset date), and SED (snow end date). The daily data of four field camera observation points during an overlapping period from 2017 to 2019 were used to evaluate the snow cover phenological parameters extracted by MOD10A1, and the mean absolute error (MAE) and root mean square error (RMSE) values were 0.65 and 1.07, respectively. The results showed the following: 1. The spatiotemporal variation in snow phenology was highly altitude dependent. The mean gradients of SCD in the Altai Mountains, Tienshan Mountains, and Kunlun Mountains is 2.6, 2.1, and 1.2 d/100 m, respectively. The variation trend of snow phenology with latitude and longitude was mainly related to the topography of Xinjiang. Snow phenological parameters of different land-use types were different. The SCD values in wasteland were the lowest and the SED was the earliest, while forest land was the first to enter SOD accumulation. According to the study, the mean annual values of SCD, SOD, and SED were 25, 342 (8 December), and 51 (8 February) as day of year (DOY), respectively. The snow cover area was mainly distributed in the Altai Mountains, Junggar Basin, Tianshan Mountains, and Kunlun Mountains. 2. The variation trend and significance of snow cover phenological parameters in different regions are different, and the variation trend of snow cover phenological parameters in most regions of Xinjiang is non-significant.
Global climate change gives rise to changing spatial patterns of snow and ice, especially over mountain blocks where orographic and synoptic circulation effects play significant roles in creating patterns of precipitation and glacier development. The presence of snow and ice results in heat balance changes and other land surface feedbacks that have implications for patterns of mountain glacier retreat and the dynamics of mountain geomorphic systems. This study considers the sensitivity of the mountain cryosphere (snow, ice, permafrost) to global climate change, and the implications of this sensitivity analysis for evaluating mountain surface stability, geomorphic change and the generation of mountain geohazards. Consideration of these issues is informed by evidence from case studies reported in the literature and by field observations of mountain system dynamics worldwide. Results show that ‘sensitivity’ to climate forcing has been interpreted and defined in different ways in mountain snow, ice and permafrost systems, with respect to properties such as albedo, mass balance or rapidity of system change. There are also significant spatial differences in sensitivity between different mountain blocks for snow, ice and permafrost, and these regions are therefore likely to follow different trajectories of geomorphic change in response to climate forcing, related to their physiographic properties and the extent of cryospheric coverage. Within glaciated mountains in particular, the relative timing of different geomorphic events, and the interplay between slope, glacier front and proglacial sediment sources and environments, may vary depending on glacier size, geomorphic setting and microclimate. By contrast, responses to permafrost warming (increased surface instability and mass movements) and changes in snow patterns (avalanche risk, floods) may have quite different spatial and temporal patterns and influenced by different environmental controls. An integrated evolutionary model for mountain system development under a changing cryosphere is proposed, highlighting the critical role of energy balance as a forcing factor that then triggers downstream mountain system responses. This suggests that different elements of mountain systems exhibit different sensitivities, and furthermore that these sensitivities change over time and space through the period of anthropogenic global warming and paraglacial relaxation.
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The response of the cryosphere to a warmer climate is spatially diversified and requires accurate monitoring and understanding. The study analyses the changes in snow cover phenology (the first and last snow cover days - FSC and LSC), duration (SCD, SCDmax) and snow-free days (SFD) in Romania, which includes wide parts of the Carpathian Mountain range (the Eastern, the Southern and Southwestern Carpathians), using daily snow depth observations from 114 weather stations (WS), with long-term and gap-free time series, over the 1961–2020 period. The results are discussed over five elevation bands (< 500, 501-1,000, 1,001–1,500, 1,501-2,000, and > 2,000 m) and over the major Köppen-Geiger climate regions of the country. A delay in FSC and a retreat in LSC was systematically observed throughout the country, with average rates from 1.6 to 2.2 days decade − 1 . The observed trends in snow cover phenology are prominent at mid-elevations (500-1,500 m) and in the lowlands (especially below 500 m), feedbacking the intense warming process. Consequently, declines in SCD and SCDmax have been also observed country-wide, especially at mid-elevations (500-1,000 and 1,000–1,500 m) and lowlands below 500 m, with an average rate exceeding 2.0 days decade − 1 . Furthermore, the number of snow-free days have a significant growth in most areas, with an important contribution of significant trends (44% of WS). In mountain areas the SFD positive trends are weak and not statistically significant. The elevation dependency of the detected snow cover trends was not systematically observed throughout the major climate regions of the country. Accelerated snow cover changes with elevation were found specific only to the Dfc and Dfb climate regions, within narrow elevation bands (1,000–1,500 m). The high-elevation areas show weak changes in snow cover phenology and duration, mostly not statistically significant. The long-term variability in snow cover parameters shows breakpoints grouped during the 1980s, and mostly in the 1990s period. The analysis of the possible links with the large-scale atmospheric circulation (North Atlantic Oscillation - NAO) revealed that NAO has a significant negative correlation with LSC, SCD, and SCDmax for 30% of stations, mostly located in the north-eastern lowlands of Romania. NAO showed no statistically significant influence on FSC trends.
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Himalayan mountain glaciers and the snowpack over the Tibetan Plateau provide the headwater of several major rivers in Asia. In situ observations of snow cover extent since the 1960s suggest that the snowpack in the region have retreated significantly, accompanied by a surface warming of 2–2.5 °C observed over the peak altitudes (5000 m). Using a high-resolution ocean–atmosphere global climate model and an observationally constrained black carbon (BC) aerosol forcing, we attribute the observed altitude dependence of the warming trends as well as the spatial pattern of reductions in snow depths and snow cover extent to various anthropogenic factors. At the Tibetan Plateau altitudes, the increase in atmospheric CO2 concentration exerted a warming of 1.7 °C, BC 1.3 °C where as cooling aerosols cause about 0.7 °C cooling, bringing the net simulated warming consistent with the anomalously large observed warming. We therefore conclude that BC together with CO2 has contributed to the snow retreat trends. In particular, BC increase is the major factor in the strong elevation dependence of the observed surface warming. The atmospheric warming by BC as well as its surface darkening of snow is coupled with the positive snow albedo feedbacks to account for the disproportionately large role of BC in high-elevation regions. These findings reveal that BC impact needs to be properly accounted for in future regional climate projections, in particular on high-altitude cryosphere.
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In alpine environments, the growing season is severely constrained by low temperature and snow. Here, we aim at determining the climatic factors that best explain the interannual variation in spring growth onset of alpine plants, and at examining whether photoperiod might limit their phe- nological response during exceptionally warm springs and early snowmelts. We analysed 17 years of data (1998–2014) from 35 automatic weather stations located in subalpine and alpine zones ranging from 1560 to 2450 m asl in the Swiss Alps. These stations are equipped with ultrasonic sensors for snow depth measurements that are also able to detect plant growth in spring and summer, giving a unique opportunity to analyse snow and climate effects on alpine plant phenology. Our analysis showed high phenological variation among years, with one exceptionally early and late spring, namely 2011 and 2013. Overall, the timing of snowmelt and the beginning of plant growth were tightly linked irrespective of the elevation of the station. Snowmelt date was the best predictor of plant growth onset with air temperature after snowmelt modulating the plants’ development rate. This multiple series of alpine plant phenology suggests that currently alpine plants are directly tracking climate change with no major photoperiod limitation.
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Increasing temperatures and snow scarce winter seasons challenge the winter tourism industry. In this study the impacts of +2 °C global warming on winter tourism demand in Europe’s ski tourism related NUTS-3 regions are quantified. Using time series regression models, the relationship between natural snow conditions and monthly overnight stays is estimated. Based on these model results, we quantify the risk of tourism demand losses due to weather variability and assess the potential impacts of climate change. Hereby, the concept of Weather-Value at Risk (0.95) is applied. Snow data are provided by the hydrological model VIC, which is forced by E-OBS data to obtain historical snow values for tourism model calibration and forced by EURO-CORDEX climate simulations to obtain snow projections until 2100. Under +2 °C warming, the weather-induced risk of losses in winter overnight stays related to skiing tourism in Europe amounts to up to 10.1 million nights per winter season, which is +7.3 million overnight stays additionally at risk compared to the reference period (1971–2000). Among the top four European skiing tourism nations – Austria, France, Italy and Switzerland – France and Switzerland show the lowest increase in risk of losses in winter overnight stays. The highest weather-induced risk of losses in winter overnight stays – in the reference period as well as in the +2 °C scenarios – is found in Austria, followed by Italy. These two countries account for the largest fraction of winter overnight stays in skiing related NUTS-3 regions.
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Snow is a key element for many socioeconomic activities in mountainous regions. Due to the sensitivity of the snow cover to variations of temperature and precipitation, major changes caused by climate change are expected to happen. We analyze the evolution of some key snow indices under future climatic conditions. Ten downscaled and postprocessed climate scenarios from the ENSEMBLES database have been used to feed the physics-based snow model SNOWPACK. The projected snow cover has been calculated for 11 stations representing the diverse climates found in Switzerland. For the first time, such a setup is used to reveal changes in frequently applied snow indices and their implications on various socioeconomic sectors. Toward the end of the twenty-first century, a continuous snow cover is likely only guaranteed at high elevations above 2000 m a.s.l., whereas at mid elevations (1000–1700 m a.s.l.), roughly 50 % of all winters might be characterized by an ephemeral snow cover. Low elevations (below 500 m a.s.l.) are projected to experience only 2 days with snowfall per year and show the strongest relative reductions in mean winter snow depth of around 90 %. The range of the mean relative reductions of the snow indices is dominated by uncertainties from different GCM-RCM projections and amounts to approximately 30 %. Despite these uncertainties, all snow indices show a clear decrease in all scenario periods and the relative reductions increase toward lower elevations. These strong reductions can serve as a basis for policy makers in the fields of tourism, ecology, and hydropower.
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Himalayan mountain glaciers and the snowpack over the Tibetan Plateau provide the headwater of several major rivers in Asia. In-situ observations of snow cover fraction since the 1960s suggest that the snow pack in the region have retreated significantly, accompanied by a surface warming of 2–2.5 °C observed over the peak altitudes (5000 m). Using a high-resolution ocean–atmosphere global climate model and an observationally constrained black carbon (BC) aerosol forcing, we attribute the observed altitude dependence of the warming trends as well as the spatial pattern of reductions in snow depths and snow cover fraction to various anthropogenic factors. At the Tibetan Plateau altitudes, the increase of atmospheric CO2 concentration exerted a warming of 1.7 °C, BC 1.3 °C where as cooling aerosols cause about 0.7 °C cooling, bringing the net simulated warming consistent with the anomalously large observed warming. We therefore conclude that BC together with CO2 has contributed to the snow retreat trends. Especially, BC increase is the major factor in the strong elevation dependence of the observed surface warming. The atmospheric warming by BC as well as its surface darkening of snow are coupled with the positive snow albedo feedbacks to account for the disproportionately large role of BC in high-elevation regions. These findings reveal that BC impact needs to be properly accounted for in future regional climate projections, in particular on high-altitude cryosphere.
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The major patterns of interannual Swiss Alpine snow pack variability were determined and their relation to local and large-scale climate variability and recent trends was investigated. The snow variables considered were the seasonally averaged new snow sum, snow depth and snow days for winter (DJF) in the period 1958-1999. Three major patterns of large-scale snow variability were identified. The first pattern explains ∼50% of total variance and extends over the entire area except the southernmost parts. The second pattern explains ∼15 % of total variance and has a dipole structure with a maximum on the northern and a strong minimum on the southern slope of the Alps. The third pattern (∼10 % of total variance) is height dependent with a strong maximum at lowland stations and a minimum at high stations. In contrast to the first and second pattern, the third pattern's time component shows a distinct trend. It is well correlated with the 0°C isotherm which increased from ∼600 m a.s.l. in the 1960s to ∼900 m a.s.l. in the late 1990s and could be related to climate change. Variability in the first new snow sum pattern was primarily related to total precipitation anomalies. In contrast, variability in the first snow day pattern was primarily related to temperature anomalies. The dominance of precipitation for new snow sums and the dominance of temperature for snow days is physically consistent with the former being controlled by accumulation only and the latter by accumulation and ablation. The surface pressure anomaly pattern linked to the first new snow sum pattern is centred over southeastern Europe, resembling the Euro-Atlantic blocking pattern. For snow days the corresponding pressure anomaly is shifted further southeastward. The second snow pattern is mainly influenced by an East Atlantic like pattern, whereas only the third (height and temperature dependent) pattern is strongly linked to the North Atlantic Oscillation index.
Alpine treelines mark the low-temperature limit of tree growth and occur in mountains world-wide. Presenting a companion to his book Alpine Plant Life, Christian Körner provides a global synthesis of the treeline phenomenon from sub-arctic to equatorial latitudes and a functional explanation based on the biology of trees. The comprehensive text approaches the subject in a multi-disciplinary way by exploring forest patterns at the edge of tree life, tree morphology, anatomy, climatology and, based on this, modelling treeline position, describing reproduction and population processes, development, phenology, evolutionary aspects, as well as summarizing evidence on the physiology of carbon, water and nutrient relations, and stress physiology. It closes with an account on treelines in the past (palaeo-ecology) and a section on global change effects on treelines, now and in the future. With more than 100 illustrations, many of them in colour, the book shows alpine treelines from around the globe and offers a wealth of scientific information in the form of diagrams and tables.