ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
Shorter snow cover duration since 1970 in the Swiss Alps
due to earlier snowmelt more than to later snow onset
Geoffrey Klein
1,2
&Yann Vitasse
1,2,3
&Christian Rixen
3
&
Christoph Marty
3
&Martine Rebetez
1,2
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
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.
Climatic Change (2016) 139:637649
DOI 10.1007/s10584-016-1806-y
*Geoffrey Klein
geoffrey.klein@unine.ch
1
Institute of Geography, University of Neuchatel, Espace Louis Agassiz 1, CH-2000 Neuchatel,
Switzerland
2
WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Neuchatel, Switzerland
3
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
Alps(Schmuckietal.2015).
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.
2Dataandmethods
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
measurements)
Station Code Coordinates Elevation
[m asl]
Mean snow
cover duration
[days]
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
3Results
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
1
(Fig. 1a and Table 2). All stations showed a significantly
earlier snowmelt, on average 5.8 days decade
1
, ranging from 3.6 ± 0.2 to 7.5 ± 0.2 days
decade
1
(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
1
(Fig. 1c and Table 2), on average by
8.9 days decade
1
, corresponding to a shortening of 2.6 to 7.5 % decade
1
. 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
stations).
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
1
]
Snowmelt
[days decade
1
]
Snow cover duration
[days decade
1
]
Max HS
[cm decade
1
]
Max HS
[days decade
1
]
DSP 1cm
[days decade
1
]
DSP 20 cm
[days decade
1
]
DSP 50 cm
[days decade
1
]
DSP 100 cm
[days decade
1
]
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
1
(Fig. 2a
and Table 2), corresponding to a reduction from 3.9 to 10.6 % decade
1
. 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
1
(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
1
(Fig. 3a and Table 2), at nine stations for DSP 20 cm from 6.2 ± 0.3 to 14.3 ± 0.3 days decade
1
(Fig. 3b and Table 2), at six stations for DSP 50 cm from 2.4 ± 0.2 to 14.2 ± 0.4 days decade
1
(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
1
(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
1
) than in autumn (+0.21 °C decade
1
) 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
1
, 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
1
), 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.
References
Acquaotta F, Fratianni S, Garzena D (2015) Temperature changes in the north-western Italian alps from 1961 to
2010. Theor Appl Climatol 122:619634
Auer I, Böhm R, Jurkovic A, Lipa W, Orlik A, Potzmann R, Schöner W, Ungersböck M, Matulla C, Briffa K
(2007) HISTALPhistorical instrumental climatological surface time series of the greater alpine region. Int
J Climatol 27:1746
Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in
snow-dominated regions. Nature 438:303309
Bavay M, Grünewald T, Lehning M (2013) Response of snow cover and runoff to climate change in high Alpine
catchments of Eastern Switzerland. Adv Water Resour 55:416
Climatic Change (2016) 139:637649 647
Callaghan TV, Johansson M, Brown RD, Groisman PY, Labba N, Radionov V, Bradley RS, Blangy S, Bulygina
ON, Christensen TR (2011) Multiple effects of changes in Arctic snow cover. Ambio 40:3245
Carbognani M, Tomaselli M, Petraglia A (2014) Current vegetation changes in an alpine late snowbed
community in the south-eastern alps (N-Italy. Alp Bot 124:105113
Casassa G, Rivera A, Escobar F, Acuña C, Carrasco J,Quintana J (2003) Snow line risein Central Chile in recent
decades and its correlation with climate. in EGS-AGU-EUG Joint Assembly, p. 14395.
Damm A, Greuell W, Landgren O, Prettenthaler F (2016) Impacts of + 2 °C global warming on winter tourism
demand in Europe. Climate Services.
Durand Y, Giraud G, Laternser M, Etchevers P, Mérindol L, Lesaffre B (2009) Reanalysis of 47 years of climate
in the French Alps (19582005): Climatology and trends for snow cover. J Appl Meteorol Climatol 48:
24872512
Ernakovich JG, Hopping KA, Berdanier AB, Simpson RT, Kachergis EJ, Steltzer H, Wallenstein MD (2014)
Predictedresponses of arctic and alpineecosystems to altered seasonality under climate change. Glob Chang
Biol 20:32563269
Falk M (2010) A dynamic panel data analysis of snow depth and winter tourism. Tour Manag 31:912924
Fierz C, Armstrong RL, Durand Y, Etchevers P, Greene E, McClung DM, Nishimura K, Satyawali PK, Sokratov
SA D2009]The international classification for seasonal snow on the ground. UNESCO/IHP Paris.
Gajić-Čapka M (2011) Snow climate baseline conditions and trends in Croatia relevant to winter tourism. Theor
Appl Climatol 105:181191
Hantel M, Hirtl-Wielke LM (2007) Sensitivity of alpine snow cover to European temperature. Int J Climatol 27:
12651275
Harsch MA, Hulme PE, McGlone MS, Duncan RP (2009) Are treelines advancing? A global meta-analysis of
treeline response to climate warming. Ecol Lett 12:10401049
IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA
Körner C D2012]Alpine treelines: functional ecology of the global high elevation tree limits. Springer Science &
Business Media.
Laternser M, Schneebeli M (2003) Long-term snow climate trends of the Swiss Alps (193199). Int J Climatol
23:733750
Lenoir J, Gégout J-C, Marquet P, De Ruffray P, Brisse H (2008) A significant upward shift in plant species
optimum elevation during the twentieth century. Science 320:17681771
Marty C (2008) Regime shift of snow days in Switzerland. Geophys Res Lett 35
Marty C, Blanchet J (2012) Long-term changes in annual maximum snow depth and snowfall in Switzerland
based on extreme value statistics. Clim Chang 111:705721
Marty C, Meister R (2012) Long-term snow and weather observations at Weissfluhjoch and its relation to other
high-altitude observatories in the alps. Theor Appl Climatol 110:573583
Park S-H, Lee M-J, Jung H-S (2012) Analysis on the snow cover variations at Mt. Kilimanjaro using Landsat
satellite images. Korean Journal of Remote Sensing 28.
Pederson GT, Betancourt JL, McCabe GJ (2013) Regional patterns and proximal causes of the recent snowpack
decline in the Rocky Mountains, US. Geophys Res Lett 40:18111816
Pons M, López-Moreno JI, Rosas-Casals M, Jover È (2015) The vulnerability of Pyrenean ski resorts to climate-
induced changes in the snowpack. Clim Chang 131:591605
Rebetez M, Reinhard M (2008) Monthly air temperature trends in Switzerland 19012000 and 19752004.
Theor Appl Climatol 91:2734
Reid PC, Hari RE, Beaugrand G, Livingstone DM, Marty C, Straile D, Barichivich J, Goberville E, Adrian R,
Aono Y D2015]Global impacts of the 1980s regime shift. Global change biology.
Rixen C, Teich M, Lardelli C, Gallati D, Pohl M, Pütz M, Bebi P (2011) Winter tourism and climate change in
the alps: an assessment of resource consumption, snow reliability, and future snowmaking potential. Mt Res
Dev 31:229236
Rixen C, Dawes MA, Wipf S, Hagedorn F (2012) Evidence of enhanced freezing damage in treeline plants
during six years of CO2 enrichment and soil warming. Oikos 121:15321543
Sanchez-Lorenzo A, Wild M (2012) Decadal variations in estimated surface solar radiation over Switzerland
since the late nineteenth century. Atmos Chem Phys 12:86358644
Scherrer SC, Appenzeller C (2006) Swiss alpine snow pack variability: major patterns and links to local climate
and large-scale flow. Clim Res 32
Scherrer SC, Appenzeller C, Laternser M (2004) Trends in Swiss alpine snow days: the role of local-and large-
scale climate variability. Geophys Res Lett 31
Scherrer SC, Wüthrich C, Croci-Maspoli M, Weingartner R, Appenzeller C (2013) Snow variability in the Swiss
alps 18642009. Int J Climatol 33:31623173
648 Climatic Change (2016) 139:637649
Schmucki E, Marty C, Fierz C, Weingartner R, Lehning M (2015) Impact of climate change in Switzerland on
socioeconomic snow indices. Theor Appl Climatol:115
Seager R, Kushnir Y, Nakamura J, Ting M, Naik N (2010) Northern Hemisphere winter snow anomalies: ENSO,
NAO and the winter of 2009/10. Geophysical research letters 37.
Serquet G, Marty C, Dulex JP, Rebetez M (2011) Seasonal trends and temperature dependence of the snowfall/
precipitation-day ratio in Switzerland. Geophys Res Lett 38
Serquet G, Marty C, Rebetez M (2013) Monthly trends and the corresponding altitudinal shift in the snowfall/
precipitation day ratio. Theor Appl Climatol 114:437444
Steger C, Kotlarski S, Jonas T, Schär C (2013) Alpine snow cover in a changing climate: a regional climate
model perspective. Clim Dyn 41:735754
Steiger R (2010) The impact of climate change on ski season length and snowmaking requirements in Tyrol,
Austria. Climate research (Open Access for articles 4 years old and older) 43:251.
Tafani M, Cohas A, Bonenfant C, Gaillard J-M, Allainé D (2013) Decreasing litter size of marmots over time: a
life history response to climate change? Ecology 94:580586
Team RC (2015) R: A Language and Environment for Statistical Computing (R Foundation for Statistical
Computing, Vienna, 2012). URL: http:// www.R-project. org.
Valt M, Cianfarra P (2010) Recent snow cover variability in the Italian alps. Cold Reg Sci Technol 64:146157
Vitasse Y, Rebetez M, Filippa G, Cremonese E, Klein G, Rixen C (2016) Hearingalpine plants
growing after snowmelt: ultrasonic snow sensors provide long-term series of alpine plant phenology. Int J
Biometeorol:113
Wielke L-M, Haimberger L, Hantel M (2004) Snow cover duration in Switzerland compared to Austria.
Meteorol Z 13:1317
Wipf S, Stoeckli V, Bebi P (2009) Winter climate change in alpine tundra: plant responses to changes in snow
depth and snowmelt timing. Clim Chang 94:105121
Xu Y, Ramanathan V, Washington W (2015) Observed high-altitude warming and snow cover retreat over Tibet
and the Himalayas enhanced by black carbon aerosols. Atmos Chem Phys Discuss 15:1907919109
Zierl B, Bugmann H (2005) Global change impacts on hydrological processes in alpine catchments. Water
Resour Res 41
Climatic Change (2016) 139:637649 649
... for 10% and 30%, respectively, of the seasonal snow ablation (Boudhar et al., 2016). In the western United-States and in the Australian Alps, Rn was found to be also the main driver of snow ablation Bilish et al., 2018 Durand et al., 2009;Valt and Cianfarra et al., 2010;Klein et al., 2016;Marcolini et al., 2017;Schöner et al., 2019;Matiu et al., 2021;Vorkauf et al., 2021). Here, the sudden increase of temperature observed since the 1980s (Begert et al., 2005;Ceppi et al., 2012), accentuated during spring (+ 0.84 ºC decade -1 , 1975Rebetez and Reinhard, 2008) led a reduction of 20-60% of the snow days at < 1800 m (mean value of 1947Marty, 2008). ...
... Here, the sudden increase of temperature observed since the 1980s (Begert et al., 2005;Ceppi et al., 2012), accentuated during spring (+ 0.84 ºC decade -1 , 1975Rebetez and Reinhard, 2008) led a reduction of 20-60% of the snow days at < 1800 m (mean value of 1947Marty, 2008). The period 1980 -1995 recorded the highest negative HS anomalies of the 20 th century; this pattern was observed in the Swiss (e.g., Scherrer and Appezeller, 2006;Klein et al., 2016;Vorkauf et al., 2021), Austrian (Schöner et al., 2019), Italian (Valt and Cianfarra et al., 2010) and French ...
Article
Mid-latitude mountain snowpacks are highly vulnerable to climate warming. Past and future hydroclimate changes require a thorougout knowledge of snow ablation physical processes and the associate climate drivers. In this work we provide the first spatio-temporal characteritzation of the energy available for snow ablation (Qm) in the Pyrenees for the period 1959–2020, during the main ablation season (March to June) for low (1200 m), mid (1800 m) and high (2400 m) elevations. We analyze the role of the the main Circulation Weather Types (CTs) in the Qm components for the entire mountain range. Finally, we train and tune a machine learning algorithm, the Random Forest, with atmospheric data (Surface Level Pressure and 500 hPa Geopotential Height) as an independent variable and Qm as the dependent one, in order to determine how much of the observed changes in Qm can be related with atmospheric circulation variability. The largest contribution of Qm is Net Radiation (Rn), increasing with elevation. Qm has shown a statistically significant increase since the 1980s. The comparison between the period 1959–1980 and the 2000–2020 revealed that positive Qm fluxes have been anticipated 22 and 12 days at mid and high elevations, respectively, showing evidence of an advance in the timing of the ablation season and faster snow ablation in high-elevation areas of the Pyrenees. The Qm is principally driven by Rn during the prevailing antyciclonic situations, characterized by the extension of high-pressure systems, low-barometric gradients and the SE advection of hot and dry air masses. The positive frequency of these anticyclonic CTs explains the majority (75%) of the Qm variability, the upward Qm trends and the earlier snow ablation onset since the 1980s.
... In the current context of climate change, the study area undergoes deep modifications. Since the end of the Little Ice Age (LIA), there has been a clear decrease in the number of snowfall days relative to the total precipitation days in the Alps (Serquet et al. 2011), together with earlier snow melting (Klein et al. 2016). In the Swiss Alps between 1139 and 2540 m a.s.l., snow cover duration shortened by 8.9 days/decades during the period 1970-2015, with a snow season starting 12 days later and ending 26 days earlier than in 1970 (Klein et al. 2016). ...
... Since the end of the Little Ice Age (LIA), there has been a clear decrease in the number of snowfall days relative to the total precipitation days in the Alps (Serquet et al. 2011), together with earlier snow melting (Klein et al. 2016). In the Swiss Alps between 1139 and 2540 m a.s.l., snow cover duration shortened by 8.9 days/decades during the period 1970-2015, with a snow season starting 12 days later and ending 26 days earlier than in 1970 (Klein et al. 2016). This decrease is dependent on the altitude and therefore less significant at high altitudes. ...
Article
Full-text available
Climate change leads to deep modifications of high Alpine environments. Those modifications have significant consequences on mountaineering itineraries and make them technically more difficult and more dangerous. Although a growing number of studies have recently documented this issue, they only list the processes affecting the itineraries and do not document their characteristics. Therefore, the acquired data lack relevance to be spread and for prevention making among climbers. In the present study, on the basis of the processes identified in previous studies in the Mont Blanc massif, we developed a legend in order to map the processes related to climate change that affect the itineraries and modify their climbing parameters. Following the UNIL geomorphological legend and using the same color code, 21 symbols were defined to map 23 processes. In order to evaluate the applicability and interest of the legend proposed, we present a first application in the Valais Alps (Switzerland), based on 21 semi-structured interviews with local Alpine guides and hut keepers. The map then allowed to list the processes affecting each of the 36 itineraries studied. On average, an itinerary is affected by 9 different processes and 25% of the itineraries have greatly evolved, which means their ascent in summer cannot be recommended anymore because of climate change. More generally, this legend would provide a common methodological basis, destined to be completed within future studies and to be relevant beyond the European Alps. This basis would also ease the comparability and compilation of results from different future studies. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
... They found that the Alps present negative trends below 2000 m, whereas no clear trend is found above this altitude. On the other side, in the Swiss Alps, Klein et al. [93] found a decline at both lower and higher elevations. Some contrasting trends were identified, as well in the Fennoscandian mountain areas. ...
Article
Full-text available
This study detected the spatial changes in Snow Cover Area (SCA) over the Snowy Mountains in New South Wales, Australia. We applied a combination of Object-Based Image Analysis (OBIA) algorithms by segmentation, classification, and thresholding rules to extract the snow, water, vegetation, and non-vegetation land covers. For validation, the Maximum Snow Depths (MSDs) were collected at three local snow observation sites (namely Three Mile Dam, Spencer Creek, and Deep Creek) from 1984 to 2020. Multiple Landsat 5, 7, and 8 imageries extracted daily MSDs. The process was followed by applying an Estimation Scale Parameter (ESP) tool to build the local variance (LV) of object heterogeneity for each satellite scene. By matching the required segmentation parameters, the optimal separation step of the image objects was weighted for each of the image bands and the Digital Elevation Model (DEM). In the classification stage, a few land cover classes were initially assigned, and three different indices—Normalized Differential Vegetation Index (NDVI), Surface Water Index (SWI), and a Normalized Differential Snow Index (NDSI)—were created. These indices were used to adjust a few classification thresholds and ruleset functions. The resulting MSDs in all snow observation sites proves noticeable reduction trends during the study period. The SCA classified maps, with an overall accuracy of nearly 0.96, reveal non-significant trends, although with considerable fluctuations over the past 37 years. The variations concentrate in the north and south-east directions, to some extent with a similar pattern each year. Although the long-term changes in SCA are not significant, since 2006, the pattern of maximum values has decreased, with fewer fluctuations in wet and dry episodes. A preliminary analysis of climate drivers’ influences on MSD and SCA variability has also been performed. A dynamic indexing OBIA indicated that continuous processing of satellite images is an effective method of obtaining accurate spatial–temporal SCA information, which is critical for managing water resources and other geo-environmental investigations.
... Therefore, weather and climate play a crucial role for millions of people, not only in the Alps themselves, but also in vast surrounding lowland areas (e.g., EEA 2009).Observed warming in the Alps is about twice the global mean (+ 1.8 °C since 1880; Auer et al. 2007;Begert and Frei 2018). Particularly rapid warming has been observed since the 1980s and seasonal snow (Klein et al. 2016;Marty et al. 2017;Matiu et al. 2021;Olefs et al. 2020;Schöner et al. 2018) and glaciers (Beniston et al. 2018;Huss 2012) are retreating. Summer droughts have been shown to intensify . ...
Article
Full-text available
A comprehensive assessment of twenty-first century climate change in the European Alps is presented. The analysis is based on the EURO-CORDEX regional climate model ensemble available at two grid spacings (12.5 and 50 km) and for three different greenhouse gas emission scenarios (RCPs 2.6, 4.5 and 8.5). The core simulation ensemble has been subject to a dedicated evaluation exercise carried out in the frame of the CH2018 Climate Scenarios for Switzerland. Results reveal that the entire Alpine region will face a warmer climate in the course of the twenty-first century for all emission scenarios considered. Strongest warming is projected for the summer season, for regions south of the main Alpine ridge and for the high-end RCP 8.5 scenario. Depending on the season, medium to high elevations might experience an amplified warming. Model uncertainty can be considerable, but the major warming patterns are consistent across the ensemble. For precipitation, a seasonal shift of precipitation amounts from summer to winter over most parts of the domain is projected. However, model uncertainty is high and individual simulations can show change signals of opposite sign. Daily precipitation intensity is projected to increase in all seasons and all sub-domains, while the wet-day frequency will decrease in the summer season. The projected temperature change in summer is negatively correlated with the precipitation change, i.e. simulations and/or regions with a strong seasonal mean warming typically show a stronger precipitation decrease. By contrast, a positive correlation between temperature change and precipitation change is found for winter. Among other indicators, snow cover will be strongly affected by the projected climatic changes and will be subject to a widespread decrease except for very high elevation settings. In general and for all indicators, the magnitude of the change signals increases with the assumed greenhouse gas forcing, i.e., is smallest for RCP 2.6 and largest for RCP 8.5 with RCP 4.5 being located in between. These results largely agree with previous works based on older generations of RCM ensembles but, due to the comparatively large ensemble size and the high spatial resolution, allow for a more decent assessment of inherent projection uncertainties and of spatial details of future Alpine climate change.
... Previous studies have shown that both the large-scale distribution Brambilla et al., 2020;de Gabriel et al., 2021) and the fine-scale habitat use Brambilla et al., 2017;Brambilla et al., 2019;Brambilla, Capelli, et al., 2018;Brambilla, Resano-Mayor, et al., 2018;Resano-Mayor et al., 2019) are strongly affected by climate or climate-dependent, often ephemeral, resources. We focused on snowfinch habitat use during the critical nestling-rearing phase, when breeding birds are highly dependent on snow patches, their melting margins and short-sward or low-density alpine grasslands, which are all microhabitats highly vulnerable to climate warming (Choler et al., 2021;Klein et al., 2016). ...
Article
Full-text available
Fine‐scale habitat selection modelling can allow a mechanistic understanding of habitat selection processes, enabling better assessments of the effects of climate and habitat changes on biodiversity. Remotely sensed data provide an ever‐increasing amount of environmental and climatic variables at high spatio‐temporal resolutions, and a unique opportunity to produce fine‐scale habitat models particularly useful in challenging environments, such as high‐elevation areas. Working at a 10‐m spatial resolution, we assessed the value of remotely sensed data for investigating foraging habitat selection (in relation to topography, microclimate, land cover) in nestling‐rearing white‐winged snowfinch (Montifringilla nivalis), a high‐elevation species highly sensitive to climate change. Adult snowfinches foraged at locations with intermediate vegetation cover and higher habitat heterogeneity, also avoiding extremely warm or extremely cold microclimates. Temperature interacted with other environmental drivers in defining habitat selection, highlighting trade‐offs between habitat profitability and thermoregulation: snowfinches likely adopted mechanisms of behavioural buffering against physiologically stressful conditions by selecting for cooler, shaded and more snowy foraging grounds at higher temperatures. Our results matched those from previous studies based on accurate field measurements, confirming the species' reliance on climate‐sensitive microhabitats (snow patches and low‐sward grassland, in heterogeneous patches) and the usefulness of satellite‐derived fine‐scale modelling. Habitat suitability models built on remotely sensed predictors can provide a cost‐effective method for periodic monitoring of species' habitats both at fine grain and over large extents. Fine‐scale models also enhance our understanding of the actual drivers of (micro)habitat selection and of possible buffering behaviours against warming, allowing more accurate and robust distribution models, finer predictions of potential future changes and carefully targeted conservation strategies and habitat management. We modelled the fine‐scale foraging habitat selection of nestling‐rearing white‐winged snowfinches (Montifringilla nivalis) by means of environmental predictors (topography, land‐cover, and microclimate) exclusively derived from high‐resolution remote sensing (digital elevation models, Sentinel‐2 multispectral images, and microclimatic models, respectively). Results were entirely coherent with those previously obtained by field‐collected predictors, confirming the species' vulnerability to climate change due to its selection for climate‐sensitive foraging microhabitats. Additionally, new mechanisms of behavioural buffering against physiologically stressful conditions were disclosed owing to remotely‐derived temperature estimates. Such modelling workflow is a promising first step toward a more timely and effective monitoring of breeding populations for this and other climate‐threatened species.
Article
The northern wheatear Oenanthe oenanthe has an almost circumpolar breeding distribution in the Northern Hemisphere, but all populations migrate to sub‐Saharan Africa in winter. Currently, tracking data suggest two main access routes to the northern continents via the Middle East and the Iberian Peninsula. These routes would require detours for birds breeding in the European Alps. Our aim was to map the migration routes and determine annual schedules for birds breeding in Switzerland and Austria, using light level geolocators. We compared their migration patterns with birds from a lowland breeding population in Germany. Birds from the Alps cross the Mediterranean Sea directly heading straight to their non‐breeding sites. In contrast, birds from Germany travelled further west via the Iberian Peninsula. While the German population initiated autumn migration relatively early, arrival on the wintering sites was nearly synchronous across the three populations. During spring migration, German birds arrived earlier at their breeding grounds than birds from the Alps. A comparison with the literature indicated that the breeding populations in the Alps use their own route and are among the latest to arrive in spring, showing resemblance to the phenology of Arctic breeding populations. Our results indicate that the annual cycle of Alps‐breeding wheatears is influenced primarily by breeding ground conditions, and not solely by migration distance.
Article
Summer mountain pastures (also called alpages ) are a central element for many agro-pastoral livestock systems in the alpine region, by providing the feedstock for herds during the summer transhumance. However, vegetation phenology and productivity in mountain pastures are increasingly affected by climate hazards exacerbated by climate change, such as early snow removal, late frost events, or droughts. Difficulties can then arise to match animal demand with forage resource on alpages and, in the long term, threaten the sustainable management of these highly multifunctional socio-ecological systems. To help agro-pastoral actors adapt, an essential step is to quantify the risk of impacts on the forage resource, due to an increased occurrence or intensity of climate hazards. Exposure to climate hazards on alpages is defined locally by topographic aspects in combination with the broader influence of the regional climate. Our work therefore aimed at providing a tailored assessment of potential climate risk for the forage resource at the individual scale of each alpage in the French Alps. To this end, we developed agro-climatic indicators based on atmospheric and snow cover data accounting for geographic and topographic conditions, and applied them to a database providing unique spatially explicit information at the alpage level. For the first time, we introduce a description of agro-climatic conditions and provide a classification of agro-climatic profiles of alpages in the French Alps, ranging from low to high potential risk for the forage resource, mainly following a North-South gradient combined with altitude. We also bring insights on the evolutions of the climate risk with climate change and discuss management implications for agro-pastoral livestock systems using alpages . We finally present a web-based visualization tool that aim at communicating agro-climatic profiles and their evolution to practitioners and at assisting decision makers in understanding climate-related risks on the alpages of the French Alps.
Thesis
Après avoir été perçus négativement par les habitants des territoires de montagne, les glaciers ont, depuis plus de deux siècles, été l’objet d’une mise en tourisme. Aux premières visites de l’Arche de l’Arveyron (Chamonix) au XVIIIe siècle ont succédé trains à crémaillères et téléphériques permettant d’accéder et de contempler, en seulement quelques dizaines de minutes, les plus grands glaciers des Alpes et du monde. Ainsi, le tourisme glaciaire regroupe aujourd’hui des pratiques et des sites touristiques emblématiques de certains territoires de montagne. Cependant, l’augmentation des températures et le retrait extrêmement rapide des glaciers font également de ces sites glaciaires des marqueurs du changement climatique. La Mer de Glace en France, le glacier du Rhône en Suisse ou encore celui du Pasterze en Autriche, font parties de ces grands sites touristiques glaciaires qui subissent de plein fouet les changements paysagers liés au retrait de la cryosphère. Qu’est-ce que ces changements impliquent pour les acteurs de ces sites touristiques glaciaires ? pour leurs visiteurs ? À travers des méthodologies mixtes, cette thèse de doctorat tente d’apporter une réponse à ces deux questions pour six grands sites touristiques glaciaires alpins. En substance, les résultats montrent que les sites touristiques glaciaires sont largement impactés par le changement climatique et les modifications glaciologiques et géomorphologiques qu’il engendre pour les territoires de montagne. Ces impacts entrainent des difficultés de gestion des sites, des problématiques d’itinéraires, des difficultés à réaliser certaines activités et qui peuvent devenir plus dangereuses ou encore une baisse d’attractivité des sites par des activités glaciaires moins attrayantes ou par une « dégradation du paysage » redoutée par les gestionnaires des sites. Nos résultats auprès des visiteurs des sites montrent cependant que cette « dégradation » paysagère n’induit pas une baisse drastique de la satisfaction des visiteurs à l’égard du paysage glaciaire : les jugements négatifs se cantonnent aux glaciers ou aux formes paraglaciaires mais ne ternissent que très peu l’appréciation générale des visiteurs vis-à-vis du paysage. Dans le même temps, une nouvelle forme de tourisme – le tourisme de la dernière chance – se développe autour des glaciers et montre que ceux-ci sont aujourd’hui considérés comme des « espèces en voie de disparition ». Par ailleurs, les gestionnaires de sites en question mettent en place des stratégies d’adaptation au changement climatique qui sont principalement réactives et qui pose la question de leur soutenabilité long termes. Question d’autant plus importante que les modélisations glaciaires à l’horizon 2050 laissent penser que les adaptations actuelles ne seront pas suffisantes.
Article
Breeding success of an Alpine black grouse Lyrurus tetrix population in southern Switzerland was monitored from 1981 to 2020. This long‐term dataset allows exploring relationships of reproductive rates with climate and habitat, which have shown marked changes during this period. Over the 40 years, the average elevation of black grouse breeding sites increased by around 100 m in central/southern Ticino but showed only a slight increase in northern Ticino, where black grouse occur at higher elevations. Average reproductive rates in northern Ticino remained constant throughout the study period but declined in central/southern Ticino. Relationships between reproductive success and weather as well as habitat variables were analysed with a multiple regression model. Temperature during the early chick‐rearing phase and the time of egg‐laying was positively correlated with reproductive rate. Correlations between reproductive rates and precipitation were less clear, and only small proportions of the variance in reproductive rates could be explained by precipitation. Brush forest explained the greatest amount of variation in reproductive rate (6.2%). Forest, alpine agricultural areas and unproductive vegetation all showed a positive relationship with reproductive rate, but the proportion of the variance explained was small. Year (5.1%) and its interaction with region (2.3%) explained considerable amounts of the variance. While in northern Ticino reproductive success did not show a negative trend when correcting for weather and habitat changes, there remained a negative trend over the years in central/southern Ticino. Despite the positive correlations of reproductive rate with temperature, increasing temperatures do not appear to have improved reproductive success, likely as a result of habitat changes that forced black grouse towards higher elevations. Changes in reproductive success were limited to the southern region, indicating deteriorating conditions at the edge of the distribution range.
Article
Vegetative traits of plants can respond directly to changes in the environment, such as those occurring under climate change. That phenotypic plasticity could be adaptive, maladaptive, or neutral. ●We manipulated the timing of spring snowmelt and amount of summer precipitation in factorial combination and examined responses of specific leaf area (SLA), trichome density, leaf water content (LWC), photosynthetic rate, stomatal conductance, and intrinsic water‐use efficiency (iWUE) in the subalpine herb Ipomopsis aggregata. The experiment was repeated in three years differing in natural timing of snowmelt. To examine natural selection, we used survival, relative growth rate, and flowering as fitness indices. ●A 50% reduction in summer precipitation reduced stomatal conductance and iWUE, and doubled precipitation increased LWC. Combining natural and experimental variation, earlier snowmelt reduced soil moisture, photosynthetic rate and stomatal conductance, and increased trichome density and iWUE. Precipitation reduction reversed the mortality selection favoring high stomatal conductance under normal and doubled precipitation, and higher LWC improved growth. ●Earlier snowmelt is a strong signal of climate change and can change expression of leaf morphology and gas exchange traits, just as reduced precipitation can. Stomatal conductance and SLA showed adaptive plasticity under some conditions.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Book
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.