Proceedings, International Snow Science Workshop, Banff, 2014

Transcription

1 EVALUATING THE LOCAL AVALANCHE DANGER IN TROMSØ, NORTHERN NORWAY USING FIELD MONITORING, FIELD INVESTIGATIONS AND THE SURFEX/ISBA-CROCUS SNOWPACK MODEL. Markus Eckerstorfer 1 *, Dagrun Vikhamar-Schuler 2, Eirik Malnes 1, Yngve Antonsen 1 1 Earth Observation, Norut, Tromsø, Norway 2 Norwegian Meteorological Institute, Oslo, Norway ABSTRACT: During the winter 2013/2014 we conducted frequent fieldwork, collecting snowpack property and stability data from five monitoring sites along a vertical transect. This transect stretched from 250 m to 750 m a.s.l. on a mountain in the southern part of the forecasting region Tromsø, located in the county of Troms, Northern Norway. At these five snowpit sites, we collected snow- and air temperature data using ibutton temperature sensors, placed on plastic stakes. These data were used to assess the local avalanche danger level as well as the avalanche problem on each of the 12 field days. In this paper, we compare the local avalanche danger rating and the avalanche problem assessed by us with the regional avalanche danger rating and avalanche problem given by the Norwegian Avalanche Centre. Our results show, that local and regional avalanche danger levels correlated well, while the local and regional avalanche problem did not. Overall, however, our vertical transect of snowpit sites seems to be a representative location to assess the regional avalanche danger in the forecasting region Tromsø. This is good news for avalanche forecasting in Tromsø using the SURFEX/ISBA-CROCUS snowpack model to represent snowpack conditions at different locations. Even forced with weather forecasting data from the AROME-Norway model, the model output correlates highly with the field data. KEYWORDS: avalanche danger scale, stability test, ECT, SURFEX/ISBA-CROCUS snowpack model, Norway 1. INTRODUCTION Avalanche forecasting is the prediction of current and future snow instability in space and time relative to a given triggering level for avalanche initiation (McClung, 2002). The product of avalanche forecasting is an avalanche danger rating, expressed as a number on a danger scale. Each avalanche danger level holds information about snowpack stability, the probability of avalanche triggering and the resulting consequences. Such avalanche danger rating is usually applied on a regional scale, for example for a particular forecasting region. A regional avalanche danger level is often produced for large forecasting regions, with considerable variation in snowpack stability (Schweizer et al., 2008), and it can thus only be a general assessment of the situation. This is because avalanche danger level assessment is traditionally * Corresponding author address: Markus Eckerstorfer, Earth Observation, Norut P.O. Box 6434, Tromsø Science Park, 9294 Tromsø, Norway; tel: ; markus.eckerstorfer@norut.no based on point-scale field observations that are then upscaled using a meteorological station network, as well as snowpack models (Hirashima et al., 2008). In Norway, avalanche bulletins are produced daily for 25 forecast regions at the Norwegian Avalanche Centre. Several data sources are used for the production: snow- and weather observations collected by an educated team of observers and laypersons together with data from weather stations and snow- and weather models. 2. SCOPE OF THE STUDY In this study, we present field data used to evaluate the local avalanche danger and compare it with modeling data from the SURFEX/ISBA- CROCUS snowpack model, as well as the regional avalanche danger on 12 selected days during the winter 2013/2014. The county of Troms in Northern Norway has a total area of about km 2, and is divided into seven forecasting regions. The region Tromsø has an area of 6100 km 2 and contains a maritime, mountainous fjord landscape with the open Barents Sea to the North and to the West (Fig. 1). Towards its southern boarder, we established our 425

2 field site (Fig. 1), with a vertical transect of snowpack monitoring sites, spanning from 250 m to 750 m a.s.l. The highest peaks in the area reach slightly above 1200 m a.s.l.; the local tree line is at around m a.s.l. 3. STUDY AREA Fig. 1: Topographic map of the county of Troms with its capital Tromsø located in its center. The dashed purple line delimits the forecasting region Tromsø. The field site is visualized with a pink rectangle. Note the major roads located between the mountainous landscape and the fjords. 4. METHODS 4.1 Snowpack temperature monitoring Along a vertical transect, we deployed five monitoring sites, each equipped with a plastic stake with ibutton temperature sensors. The ibuttons were placed from the ground surface up to a maximum depth of 220 cm, using a 10 cm spacing interval. These ibutton stakes were deployed before the winter and autonomously logged either air- or snow temperatures. During each field investigation, the snow depth next to the stake was measured by probing. The stakes were then collected in July so the data could be read-off. 4.2 Snow profiles and stability tests During 12 field days, between 22 October 2013 and 5 May 2014, we conducted standard snow profiles near each of the five deployed ibutton stakes. Snow profiles were done according to standard observation guidelines (Greene et al., 2010). Snow temperatures were measured every 10 cm, snow density was measured in each layer. In each snow pit, we conducted a minimum of two Extended Column Tests, following the guidelines by Simenhois and Birkeland (2009). Multiple test results were averaged to receive one stability score. During the field days, we further noted obvious signs of instability such as recent avalanching, whumpfing and shooting cracks. By using the Bavarian Matrix, we developed the local avalanche danger level for that given day. We also assigned a prevailing Norwegian avalanche problem according to Landrø et al. (2013). The field data was used to construct diagrams showing the seasonal development of the different snowpack properties at different depths SURFEX/ISBA-CROCUS snowpack model We applied the multilayer SURFEX/ISBA- CROCUS model (Vionnet et al., 2012) and the Snowtools Python scripts (Morin and Willemet, 2010) to assess the development of the snowpack for the monitoring site no. 3 from September 2013 to August The model simulates snow properties for up to 50 snow layers, where each snowfall event is modeled as a new snow layer. Forcing data for the simulations were extracted from the numerical weather prediction model Harmonie cycle 36 h1.1. This includes air temperature, precipitation, incoming short- and long-wave radiation, surface pressure, wind speed and wind direction. The Harmonie model includes Arome physics and the forecasts are produced at 2.5 km spatial resolution (Seity et al., 2011). The simulated snow conditions were compared with the snow measurements carried out at monitoring site no. 3, representing the location of the above tree line monitoring site (snow temperature, snow depth, snow density). 5. RESULTS AND DISCUSSION 5.1 Seasonal snowpack development from field data We choose to only present data from snow pit location Nr. 3, which is representative for snow conditions above tree line. Fig. 2 shows a field data derived model of snow depth, inferred from ibutton temperature sensors and manual depth probing. The snow depth model ends abruptly on the last field day (6 May 2014), the entire melting period is thus not shown. However, the temperature data at different depths suggest that snow had entirely melted around 15 July above tree line. The snowpit location is highly 426

3 wind influenced, which can be seen by the fast onset of the snow cover. meltforms (MF) (Fig. 4). Fig. 4 shows a field data derived model of the snowpacks grain form development and layering. Note that grain forms in between field days (yellow stars) are just simple interpolations between two observations. The snowpack was highly influenced by numerous rain-on-snow events during large parts of December and January. Especially in January and February, the snowpack at both sites almost entirely consisted of persistent meltforms (MF). In between these meltforms, solid ice formations (IF) developed, which prevailed throughout the season, causing problems in the form of melt layer recrystallization (Birkeland, 1998). These buried facets (FC) below or above crusts became reactive from the beginning of March 2014, when finally significant new snow fall accumulated, adding stress to the entire early season snowpack. Fig. 2: Field data derived model of snow depth development over time above tree line. Temperatures are from ibutton sensors at different depths, and depict either air or snow temperature. The yellow stars mark the field days. Fig. 4: Field data derived model of the snowpack development over time regarding its layering and grain forms above tree line. The yellow stars mark the field days. Fig. 3: Field data derived model of the snowpacks density development over time at different depths. The yellow stars mark the field days. Fig. 3 shows a field data derived model of the snowpacks density development over time at different depths. From the bottom up to about 100 cm, densities were in the order of kg/m 3. This solid base consisted largely of persistent 5.2 Comparison between field and model data The output from SURFEX/ISBA-CROCUS model was post processed with the Snowtools python scripts (Morin and Willemet, 2010) to produce plots of the seasonal development of snow temperature, densities and grain forms, presented in Figs When comparing the model output with the field data, one should note that the model outputs clearly simulates both the accumulation period and the melting period. Maximum modeled snow depth is approximately 250 cm (Fig. 5), which is an overestimation as compared to the observed maximum snow depth 427

4 of 190 cm (which was not constant in May as it appears in the field model). This result is quite reasonable because the ground topography at the field site is spatially highly variable which results in spatially variable snow depths as well in a wind influenced area. parameters (precipiation, temperatures, surface pressure) and only a few stations observe radiation parameters, which are also needed. Experience therefore shows that a combination of data from weather station observations and atmospheric model prognoses provide the best results. However, this study shows that the model performs very well for a location without meteorological data input, forced solely by weather forecast data from the AROME-Norway model. The test location represents coastal areas in Northern Norway where verification analysis of the AROME-Norway model generally show high quality of the daily temperature prognoses. Fig. 5: Modeled development of snow temperatures derived from the SURFEX/ISBA- CROCUS snowpack model. Note that the color bar differs from Fig. 2. Note also that the snowpack model considers the melting period. Fig. 5 shows modeled snow temperature data, with a different color bar than Fig. 2. There is a good agreement in snow temperatures, showing that the snowpack base remained close to 0 C during large parts of the season. The persistent cold during January and February penetrated the entire snowpack, cooling it effectively down. Temperature gradients were high during the transition phase, enabling the growth of facets. The modeled seasonal development in snow density (Fig. 6) and grain forms (Fig. 7) is largely comparable to the field data. The most significant difference is the missing layer of persistent facets in the lower 50 cm of the snowpack, which is not present in the model. However, this layer was not always detectable in other snowpit locations. During the last two forecasting seasons in Norway, the CROCUS snowpack model was daily run for the location of meteorological stations and forced with both weather data from these stations and prognoses from operational atmospheric models run at MET Norway. The model is very data demanding, since it models the full surface energy balance. Most operational weather stations from MET Norway measure only a few weather Fig. 6: Modeled development of snow depth and densities over time derived from the SURFEX/ISBA-CROCUS snowpack model. Note that the snowpack model considers also the melting period. Fig. 7: Modeled development of snow depth and grain forms derived from the SURFEX/ISBA-CROCUS snowpack model. Note that the snowpack model considers also the melting period. 428

5 5.3 Snowpack stability above tree line Tbl. 1: Extended Column Test results, danger signs and local avalanche danger level Date ECT Score Q Danger signs ECTN 24 Graupel ECTN 22 Avalanches, Whumpfing, cracks ECTX Wind ECTP 13 1 Wind ECTP 11 1 WL in upper 50cm ECTN 18 Wind ECTN 18 WL in upper 50cm ECTN 22 Avalanches, wind, cracks ECTP 12 1 Avalanches, wind ECTN 15 Avalanches, wind ECTX Wind 2 Local avalanche danger Local vs. regional avalanche danger In Fig. 7 we compare the local avalanche danger level determined by us during one of our 12 field days, with the regional avalanche danger level by the Norwegian Avalanche Centre ( Regional avalanche warning started in December On three out of twelve days, the danger levels differed. Two times, the local avalanche danger level was lower, one time it was higher. The otherwise good agreement is somewhat surprising to us, since we expected the regional avalanche danger level being most of the time at the more conservative end, and thus slightly higher. It is also important, that the seasonal trend in avalanche danger levels from these twelve random days of the season does only to some degree depict the overall season trend in avalanche danger. The most active avalanche periods were in the beginning of December 2013, as well as during the month of March During our field days in December and March, we classified considerable (3) avalanche danger, however, during both periods, avalanche danger rose up to high (4) during some days. The low local avalanche danger during January-February 2014 was confirmed by the regional avalanche danger ECTX Wet snow 2 The most comprehensive snowpack stability data set comes from the study site above tree line. Tbl. 1 presents the scores as well as obvious danger signs or lemons. It is not possible to infer any relationship between ECT score, danger signs and resulting local avalanche danger level, given the small dataset. The local avalanche danger level was determined in the field, taking also potential avalanche size and resulting consequence into account. So even though a fracture propagated in the ECT on 23 January 2014, and a danger sign was found, the local avalanche danger level was rated as 1 low (Tbl.1). The reason was that the probability of an avalanche was low, resulting only in a small avalanche. Moreover, only single hazard sites existed. This clearly shows that a couple of stability tests alone only make for a poor stability rating, resulting in both false stabilities and instabilities. Fig. 7: Local avalanche danger level determined from field data vs. regional avalanche danger level by the Norwegian Avalanche Centre on the days of our field work. 429

6 Tbl. 2: Comparison between local and regional avalanche problems according to the Norwegian avalanche problem classification (Landrø et al. 2013). Date Local avalanche problem Poor bonding of wind Regional avalanche problem No forecast Buried facets No forecast Buried facets above crust Buried facets above crusts Poor bonding of wind Buried loose new snow Buried facets Poor bonding of crust and wind slab Buried facets Poor bonding of wind Buried facets Poor bonding of crust and wind slab Buried facets below crust Buried facets Buried facets Poor bonding of wind Poor bonding of wind Poor bonding of wind Buried facets Poor bonding of wind Unstable saturated top layers Unstable saturated top layers In Tbl. 2 we compare the local avalanche problem, assessed during the field day and the regional avalanche problem by the Norwegian Avalanche Centre ( Only on two out of twelve days, the avalanche problem was rated similar. This is surprising, as the local and regional avalanche danger are much more similar. This is moreover surprising as the Norwegian avalanche problems play an important role in the daily bulletin, as the principal problem a backcountry user has to deal with. The local avalanche problems were mostly buried facets (Norwegian avalanche problem Nr. 4: Buried weak layer of facets), which were a persistent problem throughout the season, and became consequently active in March These buried facets were also the most reactive in the ECT s. The regional avalanche problems on the other hand were in five out of 12 cases poor bonding of wind (Norwegian avalanche problem Nr. 2). This problem is definitely given at most days during the winter in a wind influenced, alpine landscape, however, the spatial distribution of pockets of wind is confined to lee areas, that are easily recognizable. It should be, however, also noted that the problem of buried facets was most of the time either mentioned in the bulletin text, or given as a secondary avalanche problem. 6. CONCLUSION In this paper, we presented field and model data from a snow pit location within a vertical transect of five snow pit location. We used this snow pit location as our representative site for snow conditions above tree line in the forecasting region Tromsø. Still, data from the other snowpit locations, as well as signs of instability and meteorological conditions during the field data were also used as data input for our assessment of the local avalanche danger level. All observations from our 12 field days were submitted to the observation platform This made these observations accessible for all other backcountry users as well as for the Norwegian Avalanche Centre. This means that our given local avalanche danger rating, together with the avalanche problem was used by the Norwegian Avalanche Centre as data input. The comparison between the local avalanche danger level given by us, and the regional avalanche danger level showed somewhat good agreement, however, differing significantly when it comes to the prevailing avalanche problem. This means for us that our field site is somewhat representative for the forecasting region. As the snow conditions can be modeled quite well, the Norwegian Avalanche Centre can use the model output from different locations within the forecasting region with good confidence. We are, however, unsure, if this would also apply to other forecasting regions with different climatic conditions. The use of field data, acquired both by professionals and layperson backcountry users is, however, still of great importance, mostly in the assessment of snow stability and associated avalanche problem. ACKNOWLEDGEMENTS This study is part of the project SeFaS Centre for remote sensing of avalanches, financed by the regional development fund of the county of Troms. This work was also financially supported by the Research Council of Norway (contract no /E10 430

7 REFERENCES Greene, E., T. Wiesinger, K. W. Birkeland, C. Coleou, A. Jones, and G. Statham, 2006: Fatal avalanche accidents and forecasted danger levels: Patterns in the United States, Canada, Switzerland and France. Proceedings of the International Snow Science Workshop, Telluride, CO, McClung, D. M. and P. A. Schaerer, 2006: The Avalanche Handbook. 3rd ed The Mountaineers, 347 pp. Schweizer, J. and J. B. Jamieson, 2003: Snowpack properties for snow profile analysis. Cold Regions Science and Technology, 37, Birkeland, K.W., Terminology and predominant processes associated with the formation of weak layers of near-surface faceted crystals in the mountain snowpack. Arctic and Alpine Research, 30(2): Greene, E.M., Atkins, D., Birkeland, K.W., Elder, K., Landry, C., Lazar, B., McCammon, I., Moore, M., Sharaf, D., Sternenz, B., Tremper, B. and Williams, K., Snow, weather, and avalanches: Observational guidelines for avalanche programs in the United States. American Avalanche Association, Pagosa Springs, CO, Second Printing Fall Hirashima, H., Nishimura, K., Yamaguchi, S., Sato, A. and Lehning, M., Avalanche forecasting in a heavy snowfall area using the snowpack model. Cold Regions Science and Technology, 51(2-3): Landrø, M., Kosberg, S. and Müller, K., Avalanche problems; an important part of the Norwegian forecast, and a ueful tool for the users, Proceedings of the International Snow Science Workshop 2013, Grenoble - Chamonix Mont-Blanc. McClung, D.M., The Elements of Applied Avalanche Forecasting, Part I: The Human Issues. Natural Hazards, 26: Morin, S. and Willemet, J.M., Snowtools. Technical report, France. Schweizer, J., Kronholm, K., Jamieson, B.J. and Birkeland, K.W., Review of spatial variability of snowpack properties and its importance for avalanche formation. Cold Regions Science and Technology, 51(2-3): Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C. and Masson, V., The AROME- France Convective-Scale Operational Model. Monthly Weather Review, 139: Simenhois, R. and Birkeland, K.W., The Extended Column Test: Test effectiveness, spatial variability, and comparison with the Propagation Saw Test. Cold Regions Science and Technology, 59(2-3): Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Moigne, P.L., Martin, E. and Willemet, J.-M., The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2. Geoscientific Model Development, 5: