Abstract
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