Dendroclimatologists often approach field work with the intent of reconstructing a particular climate variable (e.g. temperature, streamflow, precipitation). Although guidelines exist for species and site selection, isolating the signal of interest is difficult in areas with complex terrain or a lack of ideal sites. In this case study, I suggest climatological techniques for a more efficient sampling scheme and apply these techniques to identify criteria for selecting sites sensitive to winter precipitation in the north-central Rocky Mountains. These techniques include examining factors influencing the regional response of tree growth to climate by utilizing the International Tree-Ring Databank (ITRDB), using eigenvector analyses to identify modes of variability between sites, and delineating climate regions based on the variable of interest through climate regionalization. Results suggest that low- or mid-elevation Pseudotsuga menziesii sites should be targeted for maximizing the winter precipitation signal in the case study area. The season of precipitation impacting growth was found to be a major component of the overall variability between sites.

Central New York is located in one of the snowiest regions in the United States, with the city of Syracuse, New York the snowiest metropolis in the nation. Snowfall in the region generally begins in mid-November and lasts until late-March. Snow accumulation occurs from a multitude of conditions: frontal systems, mid-latitude cyclones, Nor'easters, and most notably lake-effect storms. Lake effect snowfall (LES) is a difficult parameter to forecast due to the isolated and highly variable nature of the storm. Consequently, studies have attempted to determine changes in snowfall for lake-effect dominated regions. Annual snowfall patterns are of particular concern as seasonal snowfall totals are vital for water resources, winter businesses, agriculture, government and state agencies, and much more. Through the use of snowfall, temperature, precipitation, and location data from the National Weather Service's Cooperative Observer Program (COOP), spatial and temporal changes in snowfall for Central New York were determined. In order to determine climatic changes in snowfall, statistical analyses were performed (i.e. least squares estimation, correlations, principal component analyses, etc.) and spatial maps analyzed. Once snowfall trends were determined, factors influencing the trends were examined. Long-term snowfall trends for CNY were positive for original stations (~0.46 +/- 0.20 in. yr-1) and homogenously filtered stations (0.23 +/- 0.20 in. yr-1). However, snowfall trends for shorter time-increments within the long-term period were not consistent, as positive, negative, and neutral trends were calculated. Regional differences in snowfall trends were observed for CNY as typical lake-effect areas (northern counties, the Tug Hill Plateau and the Southern Hills) experienced larger snowfall trends than areas less dominated by LES. Typical lake-effect months (December - February) experienced the greatest snowfall trend in CNY compared to other winter months. The influence of teleconnections on seasonal snowfall in CNY was not pronounced; however, there was a slight significant (5%) correlation (< 0.35) with the Atlantic Multidecadal Oscillation. It was not clear if changes in air temperature or changes in precipitation were the cause of variations in snowfall trends. It was also inconclusive if the elevation or distance from Lake Ontario resulted in increased snowfall trends. Results from this study will aid in seasonal snowfall forecasts in CNY, which can be used to predict future snowfall. Even though the study area is regionally specific, the methods may be applied to other lake effect dominated areas to determine temporal and spatial variations in snowfall. This study will enhance climatologists and operational forecasters' awareness and understanding of snowfall, especially lake effect snowfall in CNY.