Alpine snow cover in a changing climate: a regional climate model perspective
An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951–2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971–2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40–80 % by mid century relative to 1971–2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000–2,500 m, SWE reductions amount to 10–60 % by mid century and to 30–80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.
KeywordsClimate change Regional climate projections European Alps Snow water equivalent Snow cover duration ENSEMBLES
Snow cover is a key component of the climate system. The low thermal conductivity of snow insulates the underlying ground from atmospheric temperatures and the relatively high albedo of snow alters the ground energy fluxes considerably compared to snow-free ground (Armstrong and Brun 2008). The related snow-albedo feedback is considered as one of the most important feedback mechanisms in the global climate system (Hall 2004). The occurrence of snow cover is crucial for ecology as hibernating animals and the seasonal vegetation cycle strongly depend on the timing of the snow season (Jonas et al. 2008; Marchand 1996). In regions with widespread human activities such as the European Alps snow cover also has a high economical significance. The storage of water in form of snow is relevant for water resources and hydropower production (Armstrong and Brun 2008; Voigt et al. 2010) and snow reliability is of major importance for winter tourism (Elsasser and Buerki 2002).
Snow cover dynamics in the Alps in the twentieth century reveal non-uniform trends and patterns. The mean snow depth and the duration of continuous snow cover in the Swiss Alps showed a gradual increase until the early 1980s, followed by a significant decrease towards the end of the century. These changes were most pronounced at mid and low altitudes (Laternser and Schneebeli 2003). The number of snow days in the Swiss Alps showed a step-like decrease in the late 1980s at all altitudes with no clear trend towards the end of the century (Marty 2008). Observed reductions in Alpine snow cover can mainly be attributed to local temperature increases whereas the impact of precipitation changes is comparably small (Scherrer and Appenzeller 2006). Accordingly, Hantel and Maurer (2011) found that snow cover duration in the Alps seems to exhibit a strong sensitivity to mean European temperatures. The highest sensitivity of snow cover to temperature variations is found at low altitudes, which can be explained by a general temperature level at these elevations close to the melting point. Large-scale phenomena such as the North Atlantic Oscillation and the transition from solar dimming to solar brightening may be responsible for the pronounced snow cover changes in the 1980s (Henderson and Leathers 2010; Marty 2008; Norris and Wild 2007).
Available climate projections for the Alpine area for the twenty first century mostly agree on the sign of changes in snow parameters. The majority of global climate models from the World Climate Research Programme CMIP3 multi-model dataset indicate a significant decrease in snow cover duration in Central Europe during the twenty first century. Also decreases in the maximum snow water equivalent (SWE) are significant for more than 50 % of the models (Brown and Mote 2009). Studies based on high-resolution energy balance and land surface models support these findings: The projected reduction of snow volume in the Swiss Alps by the end of the twenty first century amounts to about 90 % at elevations close to 1,000 m and to about 35 % at elevations close to 3,000 m (Beniston et al. 2003). For two alpine catchments in eastern Switzerland Bavay et al. (2009) found a reduction of the maximal SWE of more than 30 % by 2100. The relative reduction of snow depth was found to be more pronounced at lower elevations. In addition to changes of mean and maximum SWE, climate change is also expected to lead to changes in the timing of peak SWE (Bavay et al. 2009; Martin and Etchevers 2005) and to a general shortening of the snow cover season (Beniston et al. 2003; Magnusson et al. 2010). The mentioned studies consistently show that the response of snow cover to climatic changes can be subject to a pronounced and complex elevation dependency with low-elevation regions typically showing the strongest sensitivity. This can partly be explained by the warmer temperature level at low elevations, but for parameters such as maximum SWE also non-linear interactions between the duration of the snow season and snow accumulation rates are of importance (e.g. Brown and Mote 2009). The elevation-dependent response of snow cover is also connected to the elevation dependency of changes in the atmospheric forcing parameters themselves (e.g. Fyfe and Flato 1999; Giorgi et al. 1997; Kotlarski et al. 2012; Rangwala and Miller 2012). Projected twenty first century snow cover changes are expected to have strong implications for hydropower production (Kobierska et al. 2012) as well as for winter tourism (Abegg et al. 2007). Simulated snow reliability for three ski areas in western Austria has been assessed by Steiger (2010) who found that only the ski area with the highest mean elevation (1,900 m) is snow reliable beyond 2,050 without artificial snowmaking.
The results of the mentioned studies emphasize that the sensitivity of snow parameters to climatic variability and climatic changes does not only depend on elevation but also on site-specific topographic and climatic conditions (e.g. exposition and terrain shading). It is thus unsurprising that horizontal resolution was identified to play a crucial role in characterizing and modeling snow cover over complex terrain (e.g. Dutra et al. 2011). Recent studies investigating future snow cover changes thus use dedicated high-resolution land surface models or energy balance models driven by prescribed changes of climatic parameters provided by a General Circulation Model (GCM) or a Regional Climate Model (RCM). The advantage of such an offline coupling is the ability to capture some of the small-scale processes of snow physics. These processes strongly determine snow accumulation and ablation on a local scale and, at the same time, strongly depend on the topography and further physiographic features. The latter are only approximately captured by climate models due to their relatively coarse spatial resolution. There are, however, a number of disadvantages when evaluating such an offline model chain. First of all, the downscaling methods that are applied to GCM or RCM results in order to bridge the scale gap and to correct for model biases introduce new uncertainties into the data (e.g. Bosshard et al. 2011; Lenderink et al. 2007; Wood et al. 2004). Furthermore, downscaling and post-processing of climate model output can lead to inconsistencies between different atmospheric forcing parameters: While, for instance, temperature and precipitation data in raw climate model output can be expected to be physically consistent with each other, this is not necessarily the case for downscaled and/or post-processed climate model output (e.g. Fowler et al. 2007). A further issue is the double accounting of land surface processes including snow physics. Climate models make use of simplified hydrological and land surface sub-models which potentially feed back to the model’s atmosphere. Linking a climate model’s atmospheric output to a land surface model that strongly differs from the climate model’s online scheme might therefore generate inconsistencies between the applied atmospheric forcing and the state of the land surface.
As an alternative, the analysis of snow cover characteristics as directly simulated by the land surface schemes of climate models (e.g. Brown and Mote 2009; Dutra et al. 2011; Räisänen and Eklund 2012; Salzmann and Mearns 2011) ensures inter-parameter consistency and becomes increasingly attractive given the continuously increasing resolution of both global and regional climate models. In the latter case, current operational resolutions for century-long simulations range between 10 and 50 km. This already allows to describe important topographic features in mountainous terrain and to cover high elevations that are not represented by the strongly smoothed topography of GCMs. Still, small scale topographic variability is not accounted for which obviously limits the applicability to regional and continental scales and often requires an additional downscaling of RCM results to the site scale. Furthermore, although based on energy balance approaches, snow parameterization schemes of current global and regional climate models are often of a strongly simplified nature compared to dedicated models of the surface snow pack. Their main purpose is to provide a realistic surface forcing for the model’s atmospheric component in terms of snow-covered area, surface albedo and surface temperature. In many cases, simple one- or two-layer schemes are applied that do not allow to represent details of snow metamorphism. Important processes such as the refreezing of melt water within the snow pack are typically not accounted for. For illustration and as an example, Figure ESM 1 of the Electronic Supplementary Material (ESM) schematically presents the one-layer snow scheme of the regional climate model COSMO-CLM (Rockel et al. 2008). Given the inherent imperfections of climate models it also has to be considered that the land surface scheme of a climate model might be exposed to a strongly biased atmospheric forcing. Hence, any analysis of snow cover characteristics as simulated by climate models needs to be related to the ability of the climate model to generate a realistic atmospheric forcing.
In a recent study, Räisänen and Eklund (2012) analyzed regional climate model output of the ENSEMBLES project (van der Linden and Mitchell 2009) to assess future snow cover changes in northern Europe. The authors found that all models collectively indicate a future decrease in SWE, particularly in regions with a relatively mild winter climate. In the present study we focus on the European Alps, a topographically strongly structured region in central Europe. Analyzing an extended set of RCM experiments of the ENSEMBLES project at 25 km horizontal resolution we try to answer the following questions: (1) How reliably can current RCMs reproduce snow cover characteristics in the mountainous terrain of the European Alps if driven by a realistic boundary forcing (reanalysis) and by free-running GCMs? (2) How do the same RCMs simulate snow cover changes in this region during the twenty first century? (3) To what extent do the simulated snow cover changes depend on elevation, and finally (4) how large is the model uncertainty with respect to snow cover projections and which signals are robust? In addressing these questions we will mostly consider the parameters (1) mean winter SWE (2) seasonal cycle of SWE and (3) the duration of the snow season.
The following chapter gives an overview on the data sets used and on the methodological details. Chapter 3 presents the model validation for the period 1971–2000, and Chapter 4 provides climate change projections for the twenty first century. The study is concluded in Chapter 5. Supporting figures, mainly concerning the SWE validation of the GCM-driven experiments and the analysis of temperature and precipitation, are provided in the Electronic Supplementary Material (referred to as ESM hereafter).
2 Data and methods
2.1 Regional climate model data
The RCMs used for this study
Climate projection period
2.2 Observational data
To mitigate the problem of comparing station data (point scale) with coarse resolution model output (25 km resolution), all observational data were first mapped to the common RCM grid and the ensemble mean orography using concepts specifically developed for snow cover (Foppa et al. 2007). Calculating non-linear SWE lapse rates allowed adapting station data to be representative of the ensemble mean orography. A 3-dimensional Gaussian filter weighting approach was used for spatial interpolation of detrended data. Given 110 stations relative to 70 grid cells, the mapping procedure is considered to allow appropriate validation, except for grid cells above 2,100 m. The direct use of the coarse RCM ensemble mean orography for spatial interpolation is motivated by the better comparability to the simulated SWE. The coarse RCM resolution and strongly smoothed RCM orography do not account for the high spatial variability of snow distribution in complex alpine terrain and the non-linear height dependence of SWE. Averaging a high-resolution observational SWE product for each RCM grid cell would therefore make little sense. In our approach the gridded observational data represents orography-adjusted SWE values referring to the mean RCM grid cell elevation, which is ideal for comparison against the simulated SWE.
Given the restriction of the observational SWE reference data to the area of Switzerland, our model validation exercise is limited to this region and conclusions can, strictly speaking, only be drawn for the Swiss part of the Alps. However, as the topography of Switzerland covers almost the entire elevation range of the Alps and as previous studies have shown that snow cover sensitivities in Switzerland are not fundamentally different from other parts of the Alps (Hantel and Hirtl-Wielke 2007; Wielke et al. 2004) we assume that the results of our model validation are transferable to an Alpine-wide scale to some degree.
Observational data of temperature and precipitation was provided by the gridded E-OBS data set (Version 4; Haylock et al. 2008). Note that E-OBS precipitation is not corrected for systematic undercatch, known to severely affect snow precipitation measurements in mountain environments (e.g. Egli et al. 2009; Adam and Lettenmaier 2003).
As most of the analyzed RCMs were operated on a rotated latitude-longitude grid (0.22°, corresponding to a grid cell size of approx. 25 km), all model data with other grid specifications were bilinearly interpolated onto this reference grid. Analysis of the SWE data showed that variability introduced by the horizontal distribution is rather small compared to the variability introduced by the altitude. Therefore, most of the analyses are carried out for separate altitude range classes (ARCs) rather than for different sub-regions of the analysis domain. Ensemble mean values of the RCMs are always calculated applying equal weights for all ensemble members considered (simple arithmetic mean). For model validation and as control period for the climate scenarios we’re considering the period 1971–2000. The assessment of future snow cover changes is for the most part carried out for the two scenario periods 2020–2049 and 2070–2099 (subsequently called first and second scenario period) with respect to the control period. All elevation information refers to meters above sea level (m asl).
An inter-model comparison of the RCM orographies yielded large differences within the Alpine region. Especially the orographies of the METO and OURANOS models show large deviations from the ensemble mean orography (Figs. ESM 2 and ESM 3). Presumably, these discrepancies have to be attributed to the use of different digital elevation models when computing mean grid cell orographies in the individual RCMs. For our analysis the different RCM orographies are problematic as SWE strongly depends on altitude (e.g. Schär et al. 1998) and a grid cell-by-grid cell comparison of model data and observations would be influenced by elevation differences and would not reveal a true model bias. Therefore, in the model validation part of this study, all modeled SWE data were adapted to represent the ensemble mean orography using observed monthly mean SWE lapse rates (mean values for Switzerland). Due to the temporal and spatial limitations in the availability of the observational data, the validation could only be carried out for the winter months December to April and for elevations below 2,100 m. Grid cells with elevations above 2,100 m in either the individual RCMs or in the ensemble mean orography were excluded from the validation exercise. The three ARCs defined for the validation part encompass regions below 1,000 m, between 1,000 and 1,500 m and between 1,500 and 2,100 m.
An in-depth analysis of the GCM-driven RCMs revealed snow accumulation deficiencies in some models (DMI-ECHAM5, DMI-BCM, METNO-BCM, METNO-HadCM3Q0 and UCLM-HadCM3Q0) with constantly accumulating SWE (i.e. ongoing glaciation) at altitudes above 2,000 m. Such elevations are mostly far below today’s equilibrium line altitudes in the Alps (e.g. Zemp et al. 2007) and these models have to be considered as strongly biased regarding their representation of high-elevation SWE. They were therefore excluded from the analysis at elevations above 2,000 m. In contrast to the validation part, the winter season considered for the climate projections encompasses the months November to April and five (instead of three) ARCs are defined. The two lowest ARCs are identical to the ones used for the validation part whereas the three higher ARCs encompass elevations from 1,500 to 2,000 m, 2,000 to 2,500 m and elevations above 2,500 m. Note that ARC 5 encompasses only very few grid cells in some RCMs and that its geographical location within the analysis domain can differ considerably between the models; the inter-model comparison in this ARC has therefore to be interpreted with care. The duration of continuous snow cover was calculated by searching the begin and the end of the longest succession of days with a minimum SWE value of 2 cm for each year, each grid cell and each model, before averaging over time (30 years) and over elevation classes. Similarly, the timing of peak SWE was determined for each individual year and grid cell before averaging over time and elevation. A non-parametric Wilcoxon rank sum test was used to test for significant (0.05 level) changes in snow cover variables between the current and future periods (Sect. 4.3). Linear trends in mean winter SWE were computed by least-squares regression (Sect. 3.3).
In the climate projection part, we also analyzed the so-called hundred-days rule. According to this rule, a typical Swiss ski-region is snow reliable (for alpine skiing) in winters with a minimum of 100 days of snow depth larger than 30 cm between the first of December and the fifteenth of April (Abegg 1996). A useful addition is the definition of a second threshold of 50 cm for elevations above 1,500 to 2,000 m. At these elevations, the terrain often features a higher roughness and therefore larger amounts of snow are needed for slope preparation. Analysis of the financial situation of cable-car companies furthermore revealed that 7 out of 10 winters have to be snow reliable to operate a ski-region profitably (Buerki 2000). The application of the hundred-days rule with these two extensions (in the following simply referred to as hundred-days rule) required the de-biasing of the RCM data. The climate projection SWE data was therefore bias-corrected applying monthly mean correction factors that were obtained by a comparison against observations in the period 1971–2000 for 500 m elevation bands. The correction factors were then applied for the entire scenario period until 2099, implicitly assuming an SWE model bias that is constant in time. In a second step the SWE-values were converted to snow depth assuming elevation dependent mean snow densities for each day of the year based on Jonas et al. (2009), i.e. based on the same snow density model that was used for constructing the gridded observational SWE dataset (see above). Finally, snow depths were averaged over 200 m elevation bins for each individual model and the number of models that indicate a fulfillment of the hundred-days rule was counted for each elevation bin and each decade. Altogether, 11 RCM scenarios were considered (those extending until 2099 and not showing accumulation issues).
3.1 Mean winter SWE
Deviation of the simulated mean winter (DJFMA) SWE for the period 1971–2000 from the observed values
ARC 1 (%)
ARC 2 (%)
ARC 3 (%)
ARC 1 (%)
ARC 2 (%)
ARC 3 (%)
The GCM-driven RCMs have a similar spatial SWE distribution and a similar elevation-dependent bias as the ERA40-driven models with a general shift towards higher SWE values in all ARCs (Table 2 and Figs. ESM 5 and ESM 6). The ensemble mean SWE biases for the three ARCs in ascending order amount to −6, +13 and +64 %, respectively. The relative bias of the OURANOS model is positive in all ARCs with an even higher magnitude as in case of the reanalysis-driven experiment. At low elevations this leads to a high standard deviation of the individual model biases and, again, to compensating effects in the ensemble mean (Fig. ESM 6, lower right panels). Only MPI-ECHAM5 and DMI-ARPEGE underestimate mean winter SWE in all ARCs. RCMs with the same GCM as driver show a high inter-model variability (Fig. ESM 6), indicating that SWE biases cannot be explained by inaccuracies of the driving GCM data alone.
3.2 Seasonal evolution of SWE
In the GCM-driven experiments, the ensemble mean matches the seasonal evolution of the observed SWE quite accurately in the two lower ARCs (Fig. ESM 7). In the highest ARC the positive SWE bias increases with time and reaches its maximum in late winter. The inter-model range for the two lower ARCs is higher than in case of the ERA40-driven RCMs, which partly reflects the differing driving GCMs. In the highest ARC, the majority of models reaches peak SWE too late in the season.
3.3 Interannual variability and trend
The model evaluation presented above shows that state-of-the-art RCMs are able to capture the general snow cover characteristics in a region of high topographic variability such as Switzerland. This is true for both the ERA40-driven and the GCM-driven experiments. The spatial variability of mean winter SWE is well represented in most models (Figs. ESM 4 and ESM 5) and also the shape of the mean seasonal SWE evolution at different elevations (Fig. 4 and Fig. ESM 7). In the ERA40-driven experiments peak SWE below 1,000 m mostly occurs too early but is rather well captured at higher elevations. Partly due to compensating biases, the multi-RCM ensemble mean accurately represents mean winter SWE at elevations below 1,500 m (biases between −32 and +13 %). The interannual variability of observed mean winter SWE is well captured by the ERA40-driven experiments with correlation coefficients typically exceeding a value of 0.7 (Table 1 of the ESM).
However, both the ERA40- as well as the GCM-driven RCMs have difficulties in simulating the observed SWE lapse rates. The simulated SWE values at low elevations are generally too small whereas the models tend to overestimate snow at higher elevations (Fig. 3 and Fig. ESM 6). Additional supporting figures in the ESM allow to relate these SWE biases to biases in the atmospheric forcing (Figs. ESM 8 and ESM 9) and provide additional insight into the responsible processes by a separate analysis of snow accumulation and snow ablation (Fig. ESM 10). The reasons for the positive SWE biases in the two higher ARCs are probably linked to the pronounced overestimation of precipitation at these elevations (Figs. ESM 8 and ESM 9, right columns). In a number of RCMs, particularly in the METO models, this results in an overestimation of the mean daily accumulation rate (Fig. ESM 10, lower left panel). CNRM and DMI considerably underestimate the mean accumulation rate in all ARCs and, consequently, show a strong negative SWE bias at all elevations. Please note that precipitation biases have to be interpreted carefully as the observational values are not corrected for the systematic measurement error of rain gauges. This error is especially large at high elevations that encounter higher wind speeds and a higher fraction of solid precipitation compared to lower elevations (Adam and Lettenmaier 2003). The annual correction values for precipitation in Switzerland are in the range of 5–25 %, depending on region and altitude (Sevruk 1997). The contribution of the distinctive negative temperature bias in higher elevations (Figs. ESM 8 and ESM 9, left column) to the overestimation of SWE is probably limited to early and late winter where the observed temperature is near the freezing point and a cold model bias leads to an overestimation of the snowfall fraction and, correspondingly, of snow accumulation. This overestimation of snow mass is, however, carried on through the season and also affects mid-winter peak SWE. In the investigated winter period December–April the high-elevation cold bias has only little effect on the number of accumulation days which are accurately captured by the models (Fig. ESM 10, upper left panel).
The underestimation of SWE in the lowest ARC is not readily explainable by considering temperature and precipitation biases alone and would require a more detailed analysis with the inclusion of further variables relevant for snow cover. It seems, however, to be connected to an underestimation of the mean daily accumulation rate while the length of the accumulation period is rather well captured by the models (Fig. ESM 10, left panels). As regions below 1,000 m represent approximately half of Switzerland’s surface area, the negative SWE bias at these elevations might be especially relevant for the snow albedo feedback. The overestimation of SWE in the OURANOS model, which is strongest at elevations below 1,000 m, is in line with the strong cold bias of several degrees Celsius at all elevations and throughout the entire winter (Figs. ESM 8 and ESM 9, left column). This cold bias leads to a considerable overestimation of the number of accumulation days at all elevations (Fig. ESM 10, upper left panel). Regarding the influence of the large-scale circulation on Alpine snow cover, the good agreement between the temporal patterns of observed and simulated mean winter SWE (ERA40-driven RCMs) indicates that (1) winter snow cover in the European Alps is strongly conditioned by the prevailing large-scale circulation (which is imposed onto the RCMs by the ERA40 re-analysis at the lateral boundaries) and that (2) most RCMs are capable of translating temporal variabilities in the imposed boundary forcing rather accurately into Alpine snow cover variability.
A comparison of regional temperature and precipitation biases in the ERA40- and GCM-driven RCMs in the three ARCs yields similar results for temperature but larger positive precipitation biases in the GCM-driven RCMs. These larger amounts of precipitation are likely to be responsible for the larger amounts of SWE in the GCM-driven RCMs. Another apparent feature is the larger inter-model spread of temperature and precipitation in the GCM-driven experiments which can be explained by the different boundary forcing of the individual experiments (compared to an identical boundary forcing in case of the ERA40-driven runs). For the GCM-driven experiments an important influence of the driving GCM on the temperature bias can be identified which, however, is typically associated with different bias characteristics of SWE (color scheme in Figs. ESM 7 and ESM 9). This again indicates that SWE biases cannot be explained by temperature biases alone and that at least precipitation biases (which are less consistent among experiments with identical boundary forcing; right panels in Fig. ESM 9) have to be taken into account. Indeed, the seasonal evolution of simulated SWE in combination with simulated temperature and precipitation indicates two obvious features at elevations above 1,000 m: Firstly, the monthly mean temperature in early winter is below zero degrees for all experiments (ERA40- and GCM-driven). This suggests that the snow accumulation rate in the RCMs is primarily a matter of precipitation (although temperature variations can certainly have an influence and rainfall/snowmelt can still occur in months with mean temperatures below zero). Accordingly, models with highest precipitation biases typically have the highest SWE accumulation rate and vice versa (Fig. 4, right columns of Figs. ESM 7, ESM 8 and ESM 9, Fig. ESM 10). Secondly, snow melt in late winter is mostly a matter of temperature. Hence the delayed start of snow melting may partially be related to the cold temperature biases of some models. These simple considerations cannot be made for elevations below 1,000 m where both temperature and precipitation (as well as biases thereof) have a strong influence on simulated snow cover over the entire winter season.
A validation of a similar set of GCM-driven RCMs for mean March SWE in Finland by Räisänen and Eklund (2012) revealed a better agreement between simulated absolute values and interpolated observations. A possible reason for the larger SWE biases in our study is the more complex orography of the analysis domain that is only partially resolved by the horizontal resolution of the RCMs. Indeed, studies that coupled high resolving land-surface-models to RCM output generally reached better agreements between simulated and observed snow parameters (Bavay et al. 2009; Magnusson et al. 2010).
4 Climate projections
4.1 Mean winter SWE change
4.2 Change of the seasonal SWE evolution
4.3 Significance of snow cover changes
4.4 Hundred-days rule
Our analysis of future snow cover changes in the GCM-driven RCM ensemble reveals pronounced reductions of both mean winter SWE and the length of the snow season at all elevations. It should, however, be stressed that the derived changes in snow parameters (especially absolute changes) have to be interpreted with care as the validation of the GCM-driven RCMs indicated considerable biases in ensemble mean SWE with a general underestimation of SWE in regions below 1,000 m and an overestimation above. Still, a comparison with previous studies in which alpine snow cover was simulated with high-resolution land surface models reveals results that are mostly in line with our own findings: The reduction of mean SWE by the end of the twenty first century amounts to –40 to –70 % (depending on elevation) which is similar to projected snow volume reductions in Switzerland reported by Beniston et al. (2003). Another finding of the latter study is the asymmetrical shortening of the snow season that concerns more the end than the beginning—a result that is confirmed by our analysis. Previous studies focusing on subareas of Switzerland and applying more complex snowpack models suggested that snow cover duration will shorten by about one month at the beginning and one and a half month at the end (Bavay et al. 2009; Magnusson et al. 2010)—similar values for the shortening were obtained in our study.
A comparison of the changes in snow reliability with previous studies is difficult as these studies were mostly carried out for specific ski areas. Steiger (2010) found that under the A1B emission scenario three ski areas in Austria with mean elevations ranging from 1,100 to 1,900 m will lose their natural snow reliability by the end of the twenty first century. This finding is in approximate agreement with the results of our study. Steiger (2010) also stress that the snow reliability of a ski area strongly depends on the local climate conditions. Such local features as well as local topographic effects are important factors in the evaluation of snow reliability and are only approximately or not at all resolved by an RCM. Also potential benefits from artificial snow making (e.g. Scott et al. 2003; Scott and McBoyle 2007) are not considered in our study. Hence, Fig. 10 only shows a general and spatially averaged picture and its applicability to individual ski areas is limited. Nevertheless the direct evaluation of an RCM has various advantages compared to the use of high-resolution land surface models as discussed in the introduction.
The reason for the asymmetric shortening of the snow cover season is probably related to the different processes relevant for changes in the seasonal course of SWE. In autumn and early winter the dominant process is accumulation of snow by snowfall; this process is primarily influenced by solid precipitation and any snowfall decrease will generally result in a later onset of snow cover. Also temperature plays a role since conditions have to be cold enough for fallen snow to remain on the ground. In spring, the dominant factor is ablation that is (besides radiation) mainly influenced by temperature. Atmospheric warming will generally lead to a faster melt of the snow cover and to an earlier meltout. This meltout will occur even earlier if less snow has been accumulated during the first half of the winter and, hence, the shortening of the snow season in spring is influenced by the combined effects of less accumulation and faster snow melt. This effect is analogous to the more pronounced change in the timing of snow cover meltout compared to the timing of snow cover onset when moving from (colder) high to (warmer) low elevations (see Fig. 8 and Fig. ESM 14).
In the present study, the simulated daily SWE in an ensemble of regional climate simulations was analyzed for mean winter SWE including its interannual variability, seasonal evolution of SWE and continuous snow cover duration in the European Alps. Most RCMs are capable of simulating the general characteristics of these parameters in the past decades (1971–2000). Among others, the spatial variability of mean winter SWE is well represented in most models and also the shape of the mean seasonal SWE evolution at different elevations. The multi-RCM ensemble mean accurately represents mean winter SWE at elevations below 1,500 m which is partly due to compensating biases. The temporal variability of observed mean winter SWE is well captured by the ERA40-driven experiments. However, both the ERA40- and the GCM-driven simulations underestimate snow mass at low elevations (below 1,500 and 1,000 m, respectively) and overestimate it at high elevations (above 1,500 and 1,000 m, respectively). The positive SWE biases at higher elevations could originate from a positive precipitation bias in these areas whereas the reason for the negative SWE biases in lower regions is not readily explainable by only considering precipitation and temperature as proxies for snow. The underestimation of snow at low elevations might, in contrast, be related to the poor resolution of topographical structures by the RCM orographies and the neglect of subgrid topographic variability by their land surface schemes. These simplifications prevent the accumulation of snow in the (subgrid-scale) upper parts of low-elevation grid cells and potentially lead to an underestimation of snow cover. This is consistent with the apparent underestimation of the mean daily accumulation rate at low elevations in the ERA40-driven RCMs. Indeed, Giorgi et al. (2003) showed that the inclusion of a subgrid topography scheme in an RCM can lead to an increase of simulated snow cover and a more realistic simulation of snow cover characteristics in complex Alpine terrain.
The projections for the climate of the twenty first century indicate the strongest reduction of mean winter SWE at low elevations (about −70 % for elevations below 1,000 m by the end of the century). The changes of the analyzed snow parameters appear to be strongest in regions where temperatures are close to the melting point for large parts of the winter. A strong influence of the winter temperature change, which considerably depends on the driving GCM, on the relative change of mean winter SWE can be identified. Evaluation of the continuous snow cover duration indicates an asymmetrical shortening of the snow cover season with a stronger reduction at the end of the winter. The peak values of SWE are shifted towards earlier times in winter. Please note that, although a large model ensemble consisting of several GCMs and RCMs is used in our study, the identified uncertainty ranges of future snow cover changes are likely to underestimate the full uncertainty as only SRES A1B is considered and emission scenario uncertainty is not accounted for.
The strong reduction of mean winter SWE in the Alps is expected to have major impacts for winter tourism. Many ski-regions have mean elevations below 2,000 m and are therefore especially vulnerable to climate change. The shortening of the snow season and the temporal shift of peak SWE to earlier times may lead to larger alpine river discharge in spring and reduced summer discharge (e.g. Bosshard et al. 2011), which is also likely to affect hydropower generation (e.g. Hänggi et al. 2011; Stähli et al. 2011). A shortening of the snow cover season can also be expected to have strong impacts on Alpine ecology, e.g. on hibernating mammals and on the timing of the vegetation cycle.
Obviously, a limitation of the analyzed RCM output is the comparably low horizontal resolution and the simple parameterization of some snow-related processes (concerning, for example, the influence of forests or refreezing of meltwater). The coarse RCM resolution does not allow to accurately capture topographically controlled processes that exert an important influence on snow cover, such as shading, exposition and the elevation-distribution of snow on a subgrid scale. Also the biased atmospheric forcing provided by the atmospheric model components in fully coupled RCM experiments will ultimately introduce biases in the simulated SWE. These biases, in turn, can again partly be attributed to the comparatively coarse horizontal resolution at which RCMs operate and which does not allow to resolve, for instance, winter inversions and cold air pools relevant for snow preservation. Despite these limitations our results closely agree with previous studies applying more complex land surface models at higher resolution, indicating that the direct analysis of surface snow cover in high-resolution RCMs is feasible even in regions of high topographic variability such as the Alps. A further limitation of our approach is the fact that the orography of a 25 km RCM grid does not represent elevations above approximately 2,700 m in the Alps. Ergo the RCMs cannot provide climate change information for elevations beyond this limit. This is a major disadvantage for cryospheric impact research as a considerable amount of snow, permafrost and glacier ice is stored in high elevations and climate change can be expected to considerably depend on surface elevation (e.g. Kotlarski et al. 2012). However, the resolution of RCMs is constantly increasing (it will soon reach to 10 km limit for decadal-scale experiments) and snow-related processes are represented in more and more detail. These improvements can be expected to lead to a more accurate representation of snow cover and its spatial and temporal variability in RCMs and will allow evaluations of future changes in snow cover at altitudes above 2,700 m.
The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged. We also acknowledge the E-OBS dataset from the ENSEMBLES project and the data providers in the ECA&D project (http://eca.knmi.nl). Partial funding for this study has been provided by the Swiss National Science Foundation via NCCR Climate. We would also like to thank Daniel Lüthi and C2SM for model and data support, and three anonymous reviewers for their very helpful and constructive input.
- Abegg B (1996) Klimaänderung und Tourismus. Klimafolgenforschung am Beispiel des Wintertourismus in den Schweizer Alpen. Vdf Zurich, ZurichGoogle Scholar
- Abegg B, Agrawala S, Crick F, de Montfalcon A (2007) Climate change impacts and adaptation in winter tourism. In: Agrawala S (ed) Climate change in the European Alps. Adapting winter tourism and natural hazards management. OECD, Paris, pp 25–60Google Scholar
- Armstrong RL, Brun E (2008) Snow and climate: physical processes, surface energy exchange and modeling. Cambridge University Press, CambridgeGoogle Scholar
- Bosshard T, Carambia M, Goergen K, Kotlarski S, Krahe P, Zappa M, Schär C (2011) Quantifying uncertainty sources in hydrological climate impact scenarios. Water Resour Res (submitted)Google Scholar
- Buerki R (2000) Klimaänderung und Wintertourismus im Obertoggenburg. In: Natur Forschung in der Region St. Gallen, Berichte der St. Gallischen (eds) Naturwissenschaftlichen Gesellschaft. 89:97–109Google Scholar
- CH2011 (2011) Swiss climate change scenarios CH2011. Published by C2SM, MeteoSwiss, ETH, NCCR Climate, and OcCC. Zurich, Switzerland, ISBN 978-3 033-03065-7, 88 pp [available at http://www.ch2011.ch]
- Giorgi F, Hurrell JW, Marinucci MR (1997) Elevation dependency of the surface climate change signal: a model study. Clim Change 10:288–296Google Scholar
- Hall A (2004) The role of surface albedo feedback in climate. Am Meteorol Soc 17:1550–1568Google Scholar
- Hänggi P, Angehrn S, Bosshard T, Helland E, Job D, Rietmann D, Schädler B, Schneider R, Weingartner R (2011) Einfluss der Klimaänderung auf die Stromproduktion der Wasserkraftwerke Löntsch und Prättigau. Wasser Energie Luft 103(4):292–299Google Scholar
- Kobierska F, Jonas T, Zappa M, Bavay M, Magnusson J, Bernasconi SM (2012) Future runoff from a partly glacierized watershed in Central Switzerland: a two-model approach. Adv Water Resour (submitted)Google Scholar
- Kotlarski S, Bosshard T, Lüthi D, Pall P, Schär C (2012) Elevation gradients of European climate change in the regional climate model COSMO-CLM. Clim Change (in press). doi:10.1007/s10584-011-0195-5
- Marchand PJ (1996) Life in the cold: an introduction to winter ecology. University Press of New England, HanoverGoogle Scholar
- Nakicenovic N, Swart R (eds) (2000) Emission scenarios. A special report of working group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, EnglandGoogle Scholar
- Rangwala I, Miller JR (2012) Climate change in mountains: a review of elevation-dependent warming and its possible causes. Clim Change (in press). doi:10.1007/s10584-012-0419-3
- Salzmann N, Mearns L (2011) Assessing the performance of multiple regional climate model simulations for seasonal mountain snow in the Upper Colorado River Basin. J Hydrometeor (in press). doi:10.1175/2011JHM1371.1
- Schär C, Davis TD, Frei C, Wanner H, Widmann M, Wild M, Davis HC (1998) Views from the Alps: regional perspectives on climate. MIT press, BostonGoogle Scholar
- Stähli M, Raymond-Pralong M, Zappa M, Ludwig A, Paul F, Bosshard T, Dupraz C (2011) Auswirkungen auf die Wasserverfügbarkeit und Stromproduktion an den Beispielen Oberhasli und Mattmark. Wasser Energie Luft 103(4):285–291Google Scholar
- Uppala SM, Kallberg PW, Simmons AJ, Andrae U, Da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Van de Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Holm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf J-F, Morcrette J-J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Quart J Roy Meteor Soc 131:2961–3012CrossRefGoogle Scholar
- van der Linden P, Mitchell JFB (2009) ENSEMBLES: climate change and its Impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Exeter 160 ppGoogle Scholar
- Voigt T, Fuessel HM, Gaertner-Roer I, Huggel C, Marty C, Zemp M (2010) Impacts of climate change on snow, ice, and permafrost in Europe: observed trends, future projections, and socio-economic relevance—ETC./ACC Technical Paper 2010/13Google Scholar