The Backyard Effect – Climate Change And The Ski Industry
Extensive field and experimental evidence in a variety of environments show that behavior depends on a reference point. For the decision to ski it is the urban, not the global weather that counts: time series model analysis predicts day-to-day variations in skier attendance from a combination of mountain and urban weather, snow cover and cyclical factors.
As such, city dwellers are less likely to head for skiing when their backyards are bare, even if New England ski resorts have many feet of packed power and ideal skiing conditions, according to new research from the University of New Hampshire.
The researchers found that the New England ski industry is directly impacted by the “backyard effect,” which means that urban snow conditions significantly affect skier activity. Snow in urban backyards can be as important to ski businesses as snow in the mountains, according to the researchers.
Because snow and weather follow deeper climate trends, the research also has implications for understanding the potential consequences of climate change and how it could impact the New England ski industry. Since 1970, Northeast winters have warmed by an average of 0.70 °C/decade.
“Ski areas, emblematic of winter tourism, provide the economic engine for many rural regions. Their importance extends beyond employment and revenues of the ski area itself. Real estate booms in second homes and condominiums, and in migration by retirees and others, raise housing prices and transform communities in fundamental ways. Tax revenues, businesses, and the needs for infrastructure and social services change as well. If climate shifts directly affect ski areas, their indirect impacts ripple as well,” according to the researchers.
The researchers studied two of the nation’s oldest alpine resorts, both in New Hampshire: Cannon Mountain in the northwestern White Mountains and Gunstock Mountain Resort near Lake Winnipesaukee.
With the assistance of resort personnel, researchers obtained records of daily attendance through seven winter seasons at Cannon and nine winters at Gunstock. Weather and snow-condition indicators include daily snowfall, snow-depth and temperature for Boston, and Lakeport and Bethlehem, NH.
They found that attendance at the ski areas is more influenced by snowfall in Boston than at the resorts themselves. For example, a one centimeter increase in the previous day’s snow-depth at Bethlehem, near Cannon, increases the predicted attendance by 11 skiers/snowboarders. On the other hand, a one centimeter increase in the previous day’s snow-depth in Boston increases predicted attendance somewhat more, by 18 skiers, even though Boston snow might have no bearing on Cannon-area conditions.
“If this backyard effect reflects ignorance, then education is the cure — skiers could be persuaded that great skiing exists in the mountains, even when their backyard is bare. The backyard effect might also partly reflect subtler dynamics, such as people who feel less like skiing, or perceive more activity choices, when conditions are not wintry near home,” the researchers said.
Weekends and holidays also play a crucial role in ski resort attendance. The highest spikes in attendance on weekends and holidays occur at different times from one year to the next, however, because they are influenced by snow conditions and weather.
The results are particularly interesting in light of the continued investments made by ski resorts in snow-making infrastructure. “Snowmaking costs millions, but has become a competitive and climatic necessity in many places. Smaller, less capitalized resorts, and those in marginal climates, have trouble making the necessary investments — a factor in their high failure rate, and the industry’s consolidation into a smaller number of larger resorts,” the researchers said.
Volume 27, Issue 15 , Pages 2113 – 2124 DOI 10.1002/joc.1502
Ski areas, weather and climate: time series models for New England case studies
Lawrence C. Hamilton 1 *, Cliff Brown 1, Barry D. Keim 2
Sociology Department University of New Hampshire Durham, NH 03824, USA
Department of Geography and Anthropology Louisiana State University Baton Rouge, LA 70803, USA
email: Lawrence C. Hamilton (Lawrence.Hamilton@unh.edu)*Correspondence to Lawrence C. Hamilton, Sociology Department University of New Hampshire Durham, NH 03824, USA.
Wintertime warming trends experienced in recent decades, and predicted to increase in the future, present serious challenges for ski areas and whole regions that depend on winter tourism. Most research on this topic examines past or future climate-change impacts at yearly to decadal resolution, to obtain a perspective on climate-change impacts. We focus instead on local-scale impacts of climate variability, using detailed daily data from two individual ski areas. Our analysis fits ARMAX (autoregressive moving average with exogenous variables) time series models that predict day-to-day variations in skier attendance from a combination of mountain and urban weather, snow cover and cyclical factors. They explain half to two-thirds of the variation in these highly erratic series, with no residual autocorrelation. Substantively, model results confirm the backyard hypothesis that urban snow conditions significantly affect skier activity; quantify these effects alongside those of mountain snow and weather; show that previous-day conditions provide a practical time window; find no monthly effects net of weather; and underline the importance of a handful of high-attendance days in making or breaking the season. Viewed in the larger context of climate change, our findings suggest caution regarding the efficacy of artificial snowmaking as an adaptive strategy, and of smoothed yearly summaries to characterize the timing-sensitive impacts of weather (and hence, high-variance climate change) on skier activity. These results elaborate conclusions from our previous annual-level analysis. More broadly, they illustrate the potential for using ARMAX models to conduct integrated, dynamic analysis across environmental and social domains. Copyright © 2007 Royal Meteorological Society