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Long-Term Evaluation of Water-Year Anomalies Using an Updated Tree-Ring Reconstruction of Seasonal Precipitation in the Southwestern United States
AuthorMiley, Nicholas L.
Natural Resources and Environmental Science
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Precipitation patterns in the southwestern United States are characterized by a bimodal distribution. Teleconnections linked to the El Niño-Southern Oscillation (ENSO) determine frequency and intensity of cool-season precipitation, while the North American Monsoon (NAM) brings warm-season rainfall over the entire region. Warm-season precipitation on average accounts for a larger portion of annual precipitation than cool-season, but the variability of cool-season precipitation is tightly linked to total water-year precipitation (previous October to current September) outcomes. While warm-season precipitation plays an important but secondary role in water-year totals, the synchronicity of the two seasons is a primary feature of both wet and dry water-year anomalies. In 2016 we collected 209 stem increment cores from Douglas-fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa) at eight sites along the southern edge of the Colorado Plateau in Arizona and New Mexico. These samples were combined with existing chronologies from the International Tree Ring Data Bank (ITRDB) to reconstruct seasonal and inter-annual regional precipitation patterns back to the late 17th century. Total ring-width presented a strong water-year signal. Latewood-width was strongly correlated with the July-August monsoonal rainfall, while earlywood chronologies carried a November-February precipitation signal matching the Southern Oscillation Index. Including total ring-width as a proxy for water-year provides a holistic framework that accounts for precipitation accumulating outside of the cool- and warm-seasons. This study highlights the importance of a multi-proxy approach to paleoclimatic reconstructions aimed at understanding the linkages between seasonal and water-year precipitation patterns and large scale climatic modes (i.e. ENSO).