Tuesday, 14 January 2020
Hall B1 (Boston Convention and Exhibition Center)
Changes in climate can result in wide-ranging economic impacts, especially for businesses that rely on consistent weather patterns. The North Carolina ski resorts (Beech Mountain, Appalachian, Sugar Mountain, Wolf Ridge, Cataloochee, and Sapphire Valley) are the southernmost resorts in the eastern US. The diverse terrain and elevation of the Appalachian Mountains result in colder temperatures and traditionally higher snowfall amounts compared to other areas of the Southeast, making these businesses viable. Within the past two decades, resorts have extended their snowmaking period to compensate for reduced natural snowfall. This generates concern for businesses that may be in areas that are not entirely snow reliable. To understand what may happen in the future, it is pertinent to examine past and ongoing trends. Yearly snowfall data from fall 2010 to spring 2018 were obtained from the Community Collaborative Rain, Hail & Snow Network (CoCoRaHS) to observe weather trends and short-term climate variability. The snowfall data was interpolated using co-kriging, a Gaussian process using covariances along with other variables of altitude, minimum temperature, and slope. Various teleconnections (Arctic Oscillation, El Niño-Southern Oscillation, and North Atlantic Oscillation) were examined to compare years in similar phases to negate influences and observe snowfall trends. The stations with complete records within the study period were spatially analyzed by universally kriging their regression slopes to determine how climate change may affect those areas. A kernel density surface using the Fotheringham Bandwidth Formula was created from active CoCoRaHS stations throughout the eight year study period. This aided in observing which areas need more stations to generate better interpolation data and locations that have experienced a decline in stations. Precipitable water, sea level pressure, air temperature, and vector winds were observed to see which conditions generate worst case and best case scenarios for natural snowfall. The results can aid in the analysis of weather trends for the Southern Appalachians and contribute to long-term climate analysis while also promoting the use of citizen science to advance research efforts.
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