Réf. Scherrer & Appenzeller 2006 - A

Référence bibliographique complète
SCHERRER S. C., APPENZELLER C. Swiss Alpine snow pack variability: major patterns and links to local climate and large-scale flow. Climate Research, 2006, Vol. 32, p. 187-199.

Abstract: 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.

Mots-clés
Snow, variability, trends, temperature, precipitation, large-scale flow, Alps, Switzerland.

Organismes / Contact
Climate Services, Federal Office of Meteorology and Climatology (MeteoSwiss), Krähbühlstrasse 58, Postfach 514, 8044 Zürich, Switzerland. scherrer@ucar.edu
Climate and Global Dynamics Division, National Center for Atmospheric Research (NCAR), 1850 Table Mesa Drive, Boulder, Colorado 80305, USA.

(1) - Paramètre(s) atmosphérique(s) modifié(s)
(2) - Elément(s) du milieu impacté(s)
(3) - Type(s) d'aléa impacté(s)
(3) - Sous-type(s) d'aléa
  Snow cover    

Pays / Zone
Massif / Secteur
Site(s) d'étude
Exposition
Altitude
Période(s) d'observation
Switzerland Alps     275-2540 m a.s.l. 1958-1999

(1) - Modifications des paramètres atmosphériques
Reconstitutions
 
Observations
A significant increase of the seasonal 0°C isotherm from ~600 m in the 1960s to ~900 m a.s.l. in the 1990s.
Modélisations
 
Hypothèses
 

Informations complémentaires (données utilisées, méthode, scénarios, etc.)
 

(2) - Effets du changement climatique sur le milieu naturel
Reconstitutions
 
Observations
For new snow sums and snow depths 3 well separated patterns of variability were identified. The first (uniform) pattern explains >50% of total variance and the spatial pattern is almost uniform over the entire Swiss area with exception of the southern Alps, where the loading is small. The second (north–south) pattern (explaining ~15% of total variance) shows a north–south dipole with positive (negative) loadings on the northern (southern) slopes of the Alps. The third (low–high) pattern (explaining ~9% of total variance) shows positive loadings at low altitude stations, negative loadings at high altitude stations and a weak, decreasing trend.

The snow patterns are highly correlated with local temperature and precipitation. However, the correlation strongly depends on the snow variable considered. New snow sum anomalies of the leading pattern are primarily related to seasonal precipitation anomalies. Snow day sum leading pattern anomalies on the other hand are primarily related to seasonal temperature anomalies and only weakly to precipitation anomalies, as melt processes are almost linearly related to seasonal temperature (e.g. Ohmura 2001). The change from precipitation dominance for new snow sums to temperature dominance for snow days can be explained by the different character of the variables: new snow sums are only influenced by processes during snow accumulation (i.e. during snowfall events). Evidently these can be described reasonably by seasonal precipitation sums. On the other hand, the leading snow day variability is influenced by processes that control snow accumulation and ablation, which are highly related to seasonal temperature. The north–south snow pattern, which explains most of snow variability in southern Switzerland, correlates excellently with southern Alpine precipitation. The low–high snow pattern correlates with well local temperature.

Also the large-scale flow explains substantial amounts of variance in the Swiss Alpine snow pattern data. The leading pattern of snow variability is primarily linked with a low (high) pressure anomaly pattern centred over central and southeastern Europe. It bears some resemblance to the third pattern of European sea level pressure variability (BLO) often referred to as Euro-Atlantic blocking (D'Andrea et al. 1998, Scherrer et al. 2006). Similarities also exist with the mid-latitude anomaly train pattern (Massacand & Davies 2001), the sea level patterns associated with the leading Swiss Alpine winter precipitation variability (Widmann 1996), the leading European winter precipitation variability pattern (Qian et al. 2000), the second canonical pattern in extreme winter wet days (Haylock & Goodess 2004) and the pattern connected to the second EOF of European and northern African winter precipitation (Rodriguez-Fonseca et al. 2006). The second (north–south) snow pattern is mainly influenced by the East Atlantic anomaly pattern. Only the low–high snow pattern variability, which explains substantial amounts of interannual snow variance at low-lying stations, is primarily linked with the interannual variability of the NAO. The authors find several indications that this low–high pattern could be related to ongoing climate change. It shows no similarity with known precipitation patterns, is strongly linked with the increase of the 0°C isotherm, exhibits a negative trend, and its correlation with NAO decreases from near zero to a highly significant negative value towards the end of the 20th century.
Modélisations
 
Hypothèses
 

Sensibilité du milieu à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)
 
Daily snow depth and new snow sum measurements from MeteoSwiss and the Swiss Federal Institute for Snow and Avalanche Research (SLF) were used. The data were collected and compiled by Laternser (2002). Quality-checked December–January–February (DJF) data from Alpine and close foreland stations in the period from 1958–1999 were considered for most parts of the analysis. For comparison with the NAO index the snow dataset was extended back to 1931.

Three seasonal snow variables were computed from the daily data: averaged snow depths (SDEPTH), cumulated new snow sums (NEW_SUM) and the number of snow days (SDAY). Snow days were defined as days with snow depth = 5 cm. A seasonal value was computed only if data for all 90 days in DJF were available and set to missing values otherwise. In total 89 new snow sum and 110 snow depth stations were used in the analysis. The station altitudes range from 275 to 2540 m a.s.l. with the highest station density between 1250 and 1750 m a.s.l.

For most snow measurement sites local temperature and precipitation values are not available. In these cases seasonal mean temperature and precipitation values were linearly interpolated to the snow station coordinates and altitudes using the 5 of 67 (10 of 360) nearest surrounding homogenised Swiss temperature (precipitation) stations from MeteoSwiss as predictors (Scherrer et al. 2004, Begert et al. 2005). The altitude of the seasonal 0°C isotherm was determined via a linear regression between seasonal mean temperature values and station altitude.

Mean sea level pressure (MSLP) data from the European Centre for Medium-Range Weather forecasts (ECMWF) reanalysis project ERA-40 dataset (Uppala et al. 2005) was used to determine patterns of largescale flow. The domain was the Euro-North Atlantic region from 30°N–80°N and 80°W eastward to 60°E. The horizontal resolution of the field was 1°. The seasonal mean values were computed from 6 hourly values. DJF principal component based NAO index values prior to 1958 were provided by the Climate Analysis Section at the National Center for Atmospheric Research (NCAR) (Hurrell 1995).

Unrotated Principle Component Analysis (PCA) (Preisendorfer 1988) was applied to determine the major patterns of seasonal mean snow variability. The resulting spatial loadings are called Empirical Orthogonal Functions (EOFs) hereafter. The temporal scores are principal components (PCs) below. North's rule of thumb (North et al. 1982) was applied to decide whether an EOF is likely to be subject to large sampling fluctuations and to determine the maximum number of PCs that are well separated from each other.

The fully objective method proposed by Beckers & Rixen (2003) was used to compute PCA with missing seasonal values. As a sign convention for the interpretations, positive values of PCs are defined as positive contributions on the northern slope of the Alps. PCA was also conducted to determine the major patterns of the local temperature and precipitation at the snow stations and large-scale flow variability (sea level pressure) in the Euro-Atlantic sector. The large-scale sea level pressure fields were area weighted in order to give similar weights to all geographical regions.

Standard statistical techniques such as Pearson correlation analysis and stepwise multiple linear models were used to relate local variables such as snow, temperature and precipitation with Euro-Atlantic scale flow fields (Junge & Stephenson 2003). Significance levels were determined using a Monte-Carlo approach of first order auto-regressive (AR1) processes to mimic the redness of the processes investigated.

(3) - Effets du changement climatique sur l'aléa
Reconstitutions
 
Observations
 
Modélisations
 
Hypothèses
 

Paramètre de l'aléa
Sensibilité des paramètres de l'aléa à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)
 
 

(4) - Remarques générales
 

(5) - Syntèses et préconisations
 

Références citées :

Beckers JM, Rixen M (2003) EOF calculations and data filling from incomplete oceanographic datasets. J Atmos Ocean Technol 20:1839–1856

Begert M, Schlegel T, Kirchhofer W (2005) Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. Int J Climatol 25:65–80

D'Andrea F, Tibaldi S, Blackburn M, Boer G and 13 others (1998) Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979–1988. Clim Dyn 14:385–407

Haylock MR, Goodess CM (2004) Interannual variability of European extreme winter rainfall and links with mean large-scale circulation. Int J Climatol 24:759–776

Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation regional temperatures and precipitation. Science 269:676–679

Junge MM, Stephenson DB (2003) Mediated and direct effects of the North Atlantic Ocean on winter temperatures in northwest Europe. Int J Climatol 23:245–261

Laternser MC (2002) Snow and avalanche climatology of Switzerland. PhD thesis no. 14493, Eidgenössisch Technische Hochschule (ETH), Zürich

Massacand AC, Davies HC (2001) Interannual variability of European winter weather: the potential vorticity insight. Atmos Sci Lett 2:52–60

North GR, Bell TL, Cahalan RF, Moeng FJ (1982) Sampling errors in the estimation of empirical orthogonal functions. Mon Weather Rev 110:699–706

Ohmura A (2001) Physical basis for the temperature-based melt-index method. J Appl Meteorol 40:753–761

Preisendorfer RW (1988) Principle component analysis in meteorology and oceanography, Vol 17. Elsevier, Amsterdam

Qian BD, Corte-Real J, Xu H (2000) Is the North Atlantic Oscillation the most important atmospheric pattern for precipitation in Europe? J Geophys Res Atmos 105: 11901–11910

Rodriguez-Fonseca B, Polo I, Serrano E, Castro M (2006) Evaluation of the North Atlantic SST forcing on the European and northern African winter climate. Int J Climatol 26:179–191, doi: 10.1002/joc.1234

Scherrer SC, Appenzeller C, Laternser MC (2004) Trends in Swiss Alpine snow days—the role of local- and largescale climate variability. Geophys Res Lett 31:L13215, doi:10.1029/2004GL020255

Scherrer SC, Croci-Maspoli M, Schwierz C, Appenzeller C (2006) Two dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro-Atlantic region. Int J Climatol 26: 233–249, doi:10.1002/joc.1250

Uppala SM, Kallberg PW, Simmons AJ, Andrae U and 7 others (2005) The ERA-40 reanalysis. Q J R Meteorol Soc 131: 2961 – 3012

Widmann M (1996) Mesoscale variability and long-term trends of Alpine precipitation and their relation to the synoptic scale flow. PhD thesis no. 11769, Eidgenössisch Technische Hochschule (ETH), Zürich