Spatial Predictions of Surface Hoar and Crust Formation

International Snow Science Workshop Grenoble – Chamonix Mont-Blanc - 2013

Spatial predictions of surface hoar and crust formation

Simon Horton*, Michael Schirmer, and Bruce Jamieson Department of Civil Engineering, University of Calgary, Canada

ABSTRACT: Understanding the distribution of critical snowpack layers is important when assessing avalanche hazard. Two common critical layers, surface hoar and melt-freeze crusts, form under specif- ic weather conditions. This study explores the possibility of modelling the formation of these layers with forecasted weather data. Surface hoar and sun crusts were tracked at study sites on two mountains in the Columbia Mountains of Canada. Weather data from automated stations near these sites were compared to forecast data from two numeric weather prediction (NWP) models (15 and 2.5 km grids). The latent heat flux and net shortwave radiation were modelled with the snow cover model SNOWPACK and related to observed surface hoar crystal size and sun crust thickness. Surface hoar formation was then predicted across western Canada with NWP data. Comparing these predictions with observations made by avalanche professionals at 112 study plots found that surface hoar occur- rence was generally over-predicted. Spatial predictions with forecast data could help avalanche forecasting in data sparse areas.

KEYWORDS: surface hoar; sun crust; surface energy balance; avalanche forecasting; numeric weather prediction

1 INTRODUCTION such as precipitation and cloud cover. Haegeli et al. (2003) showed the spatial extent of layers Destructive snow slab avalanches often res- that caused avalanches was on the order of ult from failure in persistent layers such as sur- hundreds of kilometres. Formation on a slope face hoar, facets on crust and depth hoar. These scale is affected by local vegetation, ground layers are spatially variable making their pres- roughness, radiation and wind patterns. Vari- ence and stability hard to predict. Most persist- ations in weather and terrain make modelling ent layers in Columbia Mountains of Canada are difficult (Feick et al., 2007). associated with surface hoar or melt-freeze Processes that form and change these lay- crusts (Haegeli et al., 2003). ers over time can be simulated with weather Surface hoar often forms when near surface driven snow cover models (e.g. Brun et al., air is cooled below its dew-point. This process is 1989; Lehning et al., 2002). These models are often simulated by modelling the energy associ- operationally used in Europe to simulate snow ated with phase change, known as the latent profiles with data from automated weather sta- heat flux (Föhn, 2001). This process is sensitive tions. In Canada, there are large data sparse re- to wind speed making it difficult to model gions without weather stations. Using forecasted (Hachikubo, 1997). Crystal size is believed to be data from numeric weather prediction (NWP) an important property of surface hoar layers, models in these regions has shown promise and has been shown to be an indicator of weak- (Bellaire et al., 2011; 2013). The overall snow ness and persistence (Horton et al., 2013). stratigraphy can be simulated well, but thin weak Large crystals often bond poorly and are attrib- layers were sensitive to weather inputs (Bellaire uted to high fracture propagation propensity. and Jamieson, 2013). In this study we examined Melt-freeze crusts usually form when near how well coupled weather and snow cover mod- surface snow is melted by the sun, rain, or warm els could simulate the surface energy balance, air and then refreezes. Phase changes and surface hoar, and sun crust formation. To test movement of liquid water make this process dif- the models we used detailed weather and snow ficult to model as well (Mitterer et al., 2011). Wet observations from two sites in the Columbia snow forming crusts can provide favourable con- Mountains, then used snow observations from ditions for facet growth including a vapour sup- mountain ranges across western Canada. ply and strong temperature gradients (Jamieson, 2006). Crusts can also provide a bed surface for avalanche release and flow. 2 METHODS Formation of these layers on a regional scale is affected by synoptic weather patterns 2.1 Field methods *Corresponding author address: Simon Horton, Field studies were done on Mt. Fidelity and Department of Civil Engineering, University of Mt. St. Anne in the Columbia Mountains of Calgary, Alberta, Canada, T2N 1N4; Canada (Figure 1). Layers of surface hoar and email: [email protected] melt-freeze crusts were tracked during the 2011-

017 International Snow Science Workshop Grenoble – Chamonix Mont-Blanc - 2013

2012 and 2012-2013 winters. Each mountain points near Fidelity and St. Anne by comparing had three uniform open study sites at treeline el- station measurements (STN) with forecasted evation (south aspect, north aspect and flat). values (GEM). Standard errors (SE) and biases The flat sites were equipped with automated were calculated over N time steps using: weather stations that took measurements of 1/ 2 N 2 (GEM i −STNi ) 2 snow surface temperature and shortwave and SE = − BIAS (1) ∑ N longwave radiation. [ i =1 ( )] Each site was visited one to three times per 1 N (2) week to monitor surface hoar and melt-freeze BIAS = GEM −STN N ∑ ( i i ) crust formation. When surface hoar was present i =1 on the surface, the average and maximum crystal sizes were measured (CAA, 2007). Over two 2.4 Surface energy balance winters we observed 17 layers of surface hoar The snow surface energy balance was mod- and made 31 crystal size measurements at flat elled with the snow cover model SNOWPACK sites. When a frozen melt-freeze crust was (Lehning et al., 2002). This was done with both present at or near the surface its thickness and weather station data and forecasted data from hand hardness were measured. We observed the GEM15 and GEM2.5 models. SNOWPACK 13 sun crusts and made 30 thickness and hard- uses weather and radiation data to simulate the ness measurements at south sites. formation and evolution of snowpack layers. It also simulates the surface temperature, albedo, 2.2 InfoEx dataset and surface heat fluxes (assuming a neutral at- Avalanche operations in western Canada mosphere as done by Stössel et al. (2010)). share daily observations on the Canadian Ava- Since the GEM models do not forecast surface lanche Association's daily industrial information temperature, SNOWPACK was set to predict it exchange (InfoEx). A database of InfoEx obser- using modelled fluxes and radiation (Neumann vations was queried to find the presence or ab- boundary conditions). To verify how well the sur- sence of surface hoar at 112 InfoEx study plots face energy balance was modelled, we com- over the 2012-2013 winter (Figure 1). pared the snow surface temperatures measured at Fidelity and St. Anne with temperatures mod- 2.3 Numeric weather prediction models elled by SNOWPACK (e.g. Fierz et al., 2003).

Forecasted weather data for western 2.5 Surface hoar formation Canada was taken from two NWP models - the Canadian regional 15 km Global Environmental Surface hoar formation was modelled using Multiscale model (GEM15) and the high-resolu- the latent heat flux predicted by SNOWPACK. tion 2.5 km Limited Area Model (GEM2.5) (Mail- To estimate the amount of sublimation, the latent hot et al., 2006; 2012). Gridded data from each heat flux was accumulated over clear weather model were collected twice per day over the periods (defined as periods with no course of the 2012-2013 winter. The four closest precipitation). The accumulated latent heat was grid points to each study plot were identified for compared to crystal size observations from flat each model and time series were made for each sites at Fidelity and St. Anne. Crystal size was grid point. Forecast quality was assessed at grid estimated by multiplying the latent heat by the heat of sublimation (2.83 x 106 J kg-1) and then dividing by density value for surface hoar.

2.6 Sun crust formation The net absorbed shortwave radiation on south facing 30º slopes was predicted by SNOWPACK. This corresponded with our south sites at Fidelity and St. Anne. We correlated the thickness of sun crusts in the field with the accumulated net shortwave radiation.

3 RESULTS

3.1 Measured and forecasted weather A clear weather period between 6 and 12 Figure 1. Location of sites used in this study including February 2013 caused surface hoar to form at Mt. Fidelity and St. Anne (triangles), and study plots both Mt. Fidelity and Mt. St. Anne. A sun crust that report on the InfoEx (circles).

018 International Snow Science Workshop Grenoble – Chamonix Mont-Blanc - 2013

formed beneath the surface hoar at the south Table 1. Average standard error of forecasted values sites. Figure 2 shows the precipitation, incoming at two weather stations (each with four grid points). shortwave radiation, surface temperature, wind GEM15 GEM2.5 speed, and modelled latent heat flux over this period at Fidelity. Cold nighttime surface temper- Air temperature (ºC) 2.8 2.7 Relative humidity (%) 10 11.5 atures and strong daytime solar radiation Wind speed (m s-1) 1.7 1.7 provided good conditions for surface hoar and Incoming shortwave (W m-2) 45 59 sun crust formation. The majority of surface hoar Incoming longwave (W m-2) 28 33 growth occurred on the nights of 9 and 10 Feb- Hourly precipitation (mm) 0.2 0.3 ruary when the surface was cold and the latent Surface temperature (ºC) 3.3 3.7 heat flux was positive. This resulted in 12 to 15 mm crystals at each site on Fidelity. Figure 2 Table 2. Average bias of forecasted values at two also shows forecasted values from the four weather stations (each with four grid points). nearest GEM15 grid points. Values from GEM15 GEM2.5 GEM2.5 are not shown. Discrepancies between Air temperature (ºC) -0.3 1.1 measured and forecasted values are evident Relative humidity (%) -3.6 -6.5 over this period. Wind speed (m s-1) -1.8 -1.8 Incoming shortwave (W m-2) 16 11 Standard errors and biases for the GEM -2 models were calculated for the entire 2012-2013 Incoming longwave (W m ) -17 -8.5 Hourly precipitation (mm) 0.03 0.09 winter at grid points near Fidelity and St. Anne. Surface temperature (ºC) -2.0 -0.7 Most variables were directly forecasted by the models, with the exception of snow surface temperature, which was modelled with SNOWPACK. Average errors and biases for each model are given in Tables 1 and 2. These do not show some of the systematic differences between Fidelity and St. Anne (e.g. wind speed and directions were forecasted better at Fidelity than St. Anne) or variations between adjacent grid points (which were mostly minor). Grid point elevations were often lower than station elevations by 200 to 600 m. This explains the warm bias observed for GEM2.5, but not the cold bias for GEM15. A cold bias with GEM15 was also reported by Mailhot et al. (2012) and Bellaire and Jamieson (2013). Relative humidity was substantially under- predicted by both models. Wind speeds were over-predicted at Fidelity and under-predicted at St. Anne. Incoming shortwave radiation was over-predicted and by both models and longwave radiation was under-predicted. This was also found by Mailhot et al. (2005) and suggests that cloud cover was under-predicted. The surface energy balance depends on many of these parameters. Modelled surface temperatures had cold biases and standard errors of 3.3 ºC with GEM15 data and 3.7 ºC with GEM2.5 data. Temperatures modelled with GEM15 data captured strong cooling on 9 and 10 February (Figure 2), which was important for surface hoar formation. However, the latent heat flux was over-predicted because in this case forecasted wind speeds were too high.

Figure 2. Weather at Mt. Fidelity between February 5 3.2 Surface hoar size and sun crust thickness at and 13 2013 (thick lines) and forecasted values from Fidelity and St. Anne four nearby GEM15 grid points (thin lines). Variables Field observations of surface hoar and sun include precipitation, incoming shortwave radiation, crust from Fidelity and St. Anne we compared snow surface temperature (modelled with SNOWPACK for GEM15 data), wind speed and mod- with modelled fluxes and radiation. The accumu- elled latent heat flux. lated latent heat over clear weather periods was

019 International Snow Science Workshop Grenoble – Chamonix Mont-Blanc - 2013

Table 3. Pearson correlations between surface hoar size (sun crust thickness) and accumulated latent heat (shortwave radiation). Station GEM15 GEM2.5 Surface hoar size (n=31) 0.77 0.50 0.68 (a) (b) Sun crust thickness (n=8) 0.63 0.62 0.86

Figure 3. Relationship between (a) surface hoar crystal size and accumulated latent heat and (b) sun crust thickness and accumulated solar radiation. Surface hoar observations from Mt. Fidelity are shown as circles and Mt. St. Anne as squares. Sun crusts are (a) OBSERVED labelled according to hand hardness (1F = one finger, P = pencil, and K = knife). compared to surface hoar crystal size at Fidelity and St. Anne (Figure 3a). A Pearson correlation of 0.77 resulted when comparing 31 observations with the latent heat modelled from station data over two winters. (b) GEM15 A density value is needed to predict crystal size from latent heat. Horton et al. (2013) found a density of 30 kg m-3 using a large dataset from Fidelity, while this dataset found a density of 20 kg m-3. St. Anne is typically windier than Fidelity, so the different values suggest an effect of wind climate on modelled fluxes. (c) GEM2.5 Accumulated shortwave radiation on south facing slopes was compared to the thickness Figure 4. Maps of (a) the presence or absence of sur- and hardness of sun crusts (Figure 3b). A Pear- face hoar for a layer that formed in early February son correlation of 0.63 resulted when comparing 2013 and crystal size in millimetres modelled with (b) eight thickness observations from south sites GEM15 and (c) GEM2.5 data. Presence and absence with shortwave measurements from weather is based on study plots that reported observations the stations. Thin crusts were generally softer than day before burial. Crystal size was modelled at grid points near all study plots, but were grouped by thick crusts. mountain range with the median size reported. The same observations were also compared when NWP data were used instead of weather verify these sizes, they appear to highlight station data (Table 3). The correlations suggest regions where formation was more developed the models were less accurate at modelling (e.g. south central regions). surface hoar formation, but were comparable at The presence or absence of surface hoar at modelling sun crust formation. In both cases each study plot was compared to whether it was GEM2.5 performed better than GEM15. GEM2.5 predicted at nearby GEM grid points (Tables 4 actually had a stronger correlation than station and 5). Over the entire 2012-2013 winter measurements for sun crusts, but the sample surface hoar was observed 11 % of the time size was small. (base rate). The probability of detecting surface hoar was 81 % with GEM15 and 79 % with 3.3 Surface hoar presence at InfoEx sites GEM2.5. The probability of a false alarm was 80 Forecasted NWP data were used to predict % with GEM15 and 79 % with GEM2.5. This surface hoar formation at InfoEx sites. Predic- suggest both models could predict surface hoar, tions of crystal size for a layer that formed in but often over-predicted it. February 2012 is shown in Figure 4. Over this period surface hoar was reported at 42 % of the 4 DISCUSSION InfoEx study plots, but crystal size was not reported with most observations. The GEM mod- The surface energy balance and heat fluxes els predicted surface hoar at 92 % of the study are important drivers for surface hoar and melt- plots, with a median size of 5 mm for GEM15 freeze crust formation. Coupling SNOWPACK and 9 mm for GEM2.5. Although it is difficult to with the GEM15 and GEM2.5 models resulted in

020 International Snow Science Workshop Grenoble – Chamonix Mont-Blanc - 2013

Table 4. Contingency table for the presence of surface models in the Coast Mountains of Canada. hoar (SH) predicted by GEM15 at 112 study plots in Validation at our two stations supported many of western Canada. their findings. While the higher resolution model SH present No SH present (GEM2.5) did not necessarily improve the forecast of individual weather variables (Tables SH modelled 411 1665 1 and 2), it did improve predictions of surface No SH modelled 96 2344 hoar and sun crust (Table 3).

5 CONCLUSIONS Table 5. Contingency table for the presence of surface hoar (SH) predicted by GEM2.5 at 112 study plots in Properties of surface hoar and sun crust lay- western Canada. ers are closely coupled with meteorological influ- SH present No SH present ences. We used weather data from automated SH modelled 403 1499 stations and NWP models on 15 and 2.5 km grids to model the surface energy balance with No SH modelled 104 2510 SNOWPACK. Surface hoar crystal size was related to accumulated latent heat (correlation of modelled snow surface temperatures with stand- 0.77 with station data) and sun crust thickness ard errors of 3.3 and 3.7 ºC. This may lead to was related to accumulated shortwave radiation reasonable simulations of surface processes. (0.63). Coupling NWP models with SNOWPACK The GEM-SNOWPACK chain did a fair job of tended to over-predict surface hoar due to cold modelling surface cooling at night. There was a surface temperature biases and poor wind fore- slight cold bias that usually resulted in larger lat- casts. Sun crusts were predicted well with NWP ent heat fluxes and over-prediction of surface data. In both cases the higher resolution model hoar. Wind speed errors also resulted in inaccur- performed better. By studying these errors and ate fluxes. Despite these limitations, GEM data biases we hope to improve predictions of critical appeared to provide reasonable predictions of avalanche layers in data sparse regions. surface hoar (Table 3). The resolution of NWP grids (15 and 2.5 km) are far too coarse to re- 6 ACKNOWLEDGEMENTS solve a process like surface hoar formation. However, nighttime cooling can usually be pre- For collecting data we thank the subscribers dicted on a widespread scale during high pres- of the InfoEX, the ASARC field staff, and Ben sure systems. This makes it possible for predic- Shaw. For logistic support we thank the Ava- tions at NWP grid points to be representative of lanche Control Section of Glacier National Park surrounding areas. We found surface hoar form- and Mike Wiegele Helicopter Skiing. For their ation was generally over-predicted at InfoEx help with the GEM and SNOWPACK models we study plots (Tables 4 and 5). This could be due thank Sascha Bellaire, Charles Fierz and Mathi- to model errors and biases (e.g. cold surface as Bavay. For their support of this research we temperatures) or possibly because some sites thank Tecterra, Canadian Pacific Railways, were less prone to surface hoar formation. NSERC, HeliCat Canada, Canadian Avalanche The net absorbed shortwave radiation on Association, Canadian Avalanche Foundation, south slopes was related to sun crust thickness. Parks Canada, Mike Wiegele Helicopter Skiing, The thickness and hardness of sun crusts Canada West Ski Areas Association, Backcoun- should be related to the amount of melting, and try Lodges of BC Association, Association of Ca- therefore net solar radiation. Greater solar radi- nadian Mountain Guides, Teck Mining Company, ation generally resulted in thicker and harder Canadian Ski Guide Association, Backcountry crusts (Figure 3b), which was also reported by Access and the BC Ministry of Transportation Buhler (2013). Solar radiation forecasted by the and Infrastructure Avalanche and Weather Pro- GEM models was also closely related to crust grams. thickness (Table 3). Other factors including the initial snow type, density, and the melting and 7 REFERENCES flow of liquid water affect crust properties. Other types of melt-freeze crusts (e.g. rain and tem- Bellaire, S., Jamieson, B., Fierz, C., 2011. Forcing the perature crusts) are likely more challenging to snow-cover model SNOWPACK with forecasted model with NWP data because their formation is weather data. The Cryosphere 5, 1115-1125. affected by local atmospheric conditions (e.g. in- Bellaire, S., Jamieson, B., Fierz, C., 2013. Corri- versions). gendum to "Forcing the snow-cover model SNOWPACK with forecasted weather data" pub- Validations of NWP models in mountainous lished in The Cryosphere 5, 1115-1125, 2011. regions are valuable to snowcover simulations. The Cryosphere 7, 511-513. Mailhot et al. (2012) found appreciable errors in Bellaire, S., Jamieson, B., 2013. Forecasting the humidity and wind when validating several formation of critical snow layers using a coupled

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