Table 1  Average monthly values of selected climate variables for Sasquatch Mountain Ski Resort at an elevation of 1174 meters. Data is derived for the period of 1991 to 2020 as determined by ClimateBC (version 7.70). 

Figure 2  Yearly observations of winter mean temperatures (°C) from 1901 to 2025 at Sasquatch Mountain Ski Resort (elevation 1174 m) as derived from the climate database ClimateBC. The orange line is the best-fit linear regression line and the green dash lines show the 5% and 95% prediction thresholds.    

Figure 5  Predicted winter degree days <0°C for the period 2030 to 2090 at Sasquatch Mountain Ski Resort (elevation 1174 m). These predictions are based on an eight climate model ensemble using the SSP2-4.5 and SSP5-8.5 emission scenarios, as determined from the climate database ClimateBC. Additionally, the figure includes the observed winter degree days <0°C from 1901 to 2025. The orange line is the best-fit linear regression line and the green dash lines show the 5% and 95% prediction thresholds.   

Figure 1  Base, middle, and peak elevations of the British Columbian ski resorts discussed on this website. The ski resorts are ordered along the X-axis based on their longitude. The resorts are labeled as follows: A = Mount Washington, B = Cypress Mountain, C = Whistler Blackcomb, D = Sasquatch Mountain, E = Sun Peaks, F = Silver Star, G = Big White, H = Revelstoke Mountain, I = Red Mountain, J = Whitewater Mountain, K = Panorama, L = Sunshine Banff, and M = Fernie. The grey dotted line represents the average height of the ski resorts, measured by linear regression from west to east. Note that the base elevation for Whistler Blackcomb and Revelstoke Mountain is considered to be at the top of their Gondola lifts, which are located at  roughly 1080 meters and 790 meters, respectively, rather than the village elevation.

Table 2  Global climate models available in ClimateBC (version 7.70). * Identifies the climate models available in the eight member ensemble used for the future forecasts shown on this website.

Figure 9  Predicted winter rainfall (mm) for the period 2030 to 2090 at Sasquatch Mountain Ski Resort (elevation 1174 m). These predictions are based on an eight climate model ensemble using the SSP2-4.5 and SSP5-8.5 emission scenarios, as determined from the climate database ClimateBC. Additionally, the figure includes the observed winter rainfall from 1901 to 2025. The orange line is the best-fit linear regression line and the green dash lines show the 5% and 95% prediction thresholds.   

Location and Geography

            Sasquatch Mountain Ski Resort is located at latitude 49.380185° North and longitude 121.935646° West. It is located in the Douglas Mountain Range, a subrange of the more extensive Coast Mountains.  The resort is located about 24 kilometers north of Chilliwack and about 80 kilometers west of Vancouver, British Columbia. Two weather stations are located about 20 kilometers from the resorts at Agassiz and Chilliwack, British Columbia. Both of these stations have been in operation for more than 100 years. Data from these stations were used in constructing extrapolated estimates of several meteorological measurements for the winter of 2014-2015.

            The base elevation of the Sasquatch Resort is at 975 meters above sea level, with the summit climbing to a peak altitude of 1372 meters (Figure 1).  Total vertical relief is about 396 meters. Hemlock ski area is located in a topographic bowl and is surrounded by Douglas-Fir Forest. The resort contains 121 hectares of inbounds skiing area, 4 lifts, and 35 runs of varying difficulty. Most of the ski area faces a southeast direction.

            The climate at Sasquatch Resort in winter is relatively mild due to its proximity to the Pacific Ocean (about 250 kilometers from the west coast of Vancouver Island). The relatively large Harrison Lake, located about six kilometers away, may also influence Hemlock's weather by enhancing precipitation and making temperatures milder. Winter precipitation results from a combination of frontal activity, mid-latitude cyclones, and orographic lifting. Table 1 provides monthly statistics for temperature and precipitation at mid-elevation (1174 meters) from November to April.

Climate Indicator Variables

            Four indicator variables were generated from ClimateBC to evaluate whether human induced climate change is having an impact on Sasquatch Mountain Ski Resort at its mid-elevation 1174 m. 

• Winter Mean Temperature - the average of the daily temperature means for the winter months of December, January and February.
  
  
• Winter Degree Days < 0°C - the cumulative sum of days with mean temperatures below (less than) zero taken from the winter months of December, January and February. This calculation uses the absolute values of daily mean temperatures < (below) 0°C. For example, four days with temperatures +5°C, -2°C, -11°C and -6°C would produce a degree day < 0°C value of 19°C (2+11+6).
  
  
• Winter Snowfall – accumulated snowfall for the winter months of December, January and February. This variable is calculated as the equivalent depth of liquid water in millimeters that will remain after a known volume of snow is entirely melted. This estimate is usually made at meteorological stations using known quantities for the water density of snow and measured depth of the snow. Alternatively, some meteorological stations employ the use of a Nipher snow gauge that melts any captured snow, converting it into a water measurement in millimeters.
  
  
• Winter Rainfall – accumulated rainfall for the winter months of December, January and February. This variable is calculated in millimeters.

ClimateBC Software – Historic Data

            This website uses a high-quality, spatially interpolated climate database program, ClimateBC version 7.70, to compute directly calculated and derived climate variables for the various British Columbian ski resorts based on latitude, longitude, and elevation (Wang et al., 2016 and Wang et al., 2025). Climate databases of this type are very useful for studies in which climate scientists seek to determine the impact of climate change on a particular human socio-economic system. ClimateBC uses numerical downscaling to produce output at the local-scale and has historic datasets for the period 1901 to 2025. 

ClimateBC Software – Future Climate Model Forecasts

            ClimateBC also includes future simulated climate forecasts for the 21st century (Mahoney et al., 2022). These forecasts are generated by various Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) used in the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Reports on climate change. However, ClimateBC contains the output of a subset of 13 of the over 44 global climate models used in the most recent IPCC assessment report. The researchers who developed ClimateBC carefully selected this group to ensure that the forecasts made by these 13 models best replicate the range of results produced by the models used in the latest IPCC reports (Mahoney et al., 2022). The output on this webpage used an ensemble of eight models with an equilibrium climate sensitivity of 3.4°C to create a single averaged forecast. The 13 global climate models available in ClimateBC are shown in Table 2. 

Winter Mean Temperature

            Winter mean temperatures have steadily increased from 1901 to 2025, with a rate of 0.16°C per decade, as shown in Figure 2 based on linear regression analysis. The best-fit regression line predicts an average winter mean temperature of approximately -1.8°C in 2025. Notably, the graph reveals that most of the warmest winters at Sasquatch Mounain Ski Resort over the last 45 years have been associated with El Niño events.

            Figure 3 indicates that winter mean temperatures will continue to increase in the 21st century, reaching 0.0 and 1.8°C, respectively, under the SSP2-4.5 and SSP5-8.5 emission scenarios by 2090. 

Winter Degree Days < 0°C

                Winter degree days <0°C have steadily decrease from 1901 to 2025, at a rate of 10.6°C per decade according to linear regression analysis, as shown in Figure 4. The best-fit regression line predicts an average winter degree days of approximately 302°C in 2025. 

            Figure 5 shows that winter degree days <0°C will continue to decrease in the 21st century, falling to 197 and 130°C, respectively, under the SSP2-4.5 and SSP5-8.5 emission scenarios by 2090. 

Winter Snowfall

                Winter snowfall has declined from 1901 to 2025, at a rate of 23.8 mm water equivalent per decade, as shown in Figure 6 based on linear regression analysis. The best-fit regression line predicts an average winter snowfall of about 708 mm water equivalent in 2025. 

            Figure 7 displays that winter snowfall (mm water equivalent) will continue to decrease in the 21st century, dropping to 477 and 266 mm, respectively, under the SSP2-4.5 and SSP5-8.5 emission scenarios by 2090. 

Winter Rainfall

            Winter rainfall has increased from 1901 to 2025, at a rate of 14.5 mm  per decade, as described in Figure 8 based on linear regression analysis. The best-fit regression line predicts an average winter rainfall of about 876 mm water equivalent in 2025. 

            Figure 9 indicates that winter rainfall (mm) will continue to increase in the 21st century, reaching to 1376 and 1657 mm, respectively, under the SSP2-4.5 and SSP5-8.5 emission scenarios by 2090. 

References

Knutti, R., D. Masson, and A. Gettelman. 2013. Climate model genealogy: Generation CMIP5 and how we got there. Geophysical Research Letters 40, 1194–1199. DOI:10.1002/grl.50256

Mahony, C.R., T. Wang, A. Hamann, and A.J. Cannon. 2022. A CMIP6 ensemble for downscaled monthly climate normals over North America. International Journal of Climatology 42 (11), 5871-5891. DOI:10.1002/joc.7566

Wang, T., A. Hamann, and Z. Sang. 2025. Monthly high‐resolution historical climate data for North America since 1901. International Journal of Climatology 45 (3), e8726. DOI: 10.1002/joc.8726

Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11(6): e0156720. DOI:10.1371/journal.pone.0156720

Figure 3  Predicted winter mean temperatures (°C) for the period 2030 to 2090 at Sasquatch Mountain Ski Resort (elevation 1174 m). These predictions are based on an eight climate model ensemble using the SSP2-4.5 and SSP5-8.5 emission scenarios, as derived from the climate database ClimateBC. Additionally, the figure includes the observed winter mean temperatures from 1901 to 2025. The orange line is the best-fit linear regression line and the green dash lines show the 5% and 95% prediction thresholds.   

Figure 4  Yearly observations of winter degree days <0°C from 1901 to 2025 at Sasquatch Mountain Ski Resort (elevation 1174 m) as derived from the climate database ClimateBC. The orange line is the best-fit linear regression line and the green dash lines show the 5% and 95% prediction thresholds.   

Figure 8  Yearly observations of winter rainfall (mm) from 1901 to 2025 at Sasquatch Mountain Ski Resort (elevation 1174 m) as derived from the climate database ClimateBC. The orange line is the best-fit linear regression line and the green dash lines show the 5% and 95% prediction thresholds.   

Figure 7  Predicted winter snowfall (mm water equivalent) for the period 2030 to 2090 at Sasquatch Mountain Ski Resort (elevation 1174 m). These predictions are based on an eight climate model ensemble using the SSP2-4.5 and SSP5-8.5 emission scenarios, as determined from the climate database ClimateBC. Additionally, the figure includes the observed winter snowfall from 1901 to 2025. The orange line is the best-fit linear regression line and the green dash lines show the 5% and 95% prediction thresholds.