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Winter Climate Variability in the Southern Appalachian Mountains, 1910–2017

International Journal of Climatology(2018)

Cited 7|Views22
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Abstract
Located in the mid‐latitudes and exhibiting the greatest topographic relief in the eastern United States, the southern Appalachian Mountains (SAM) contain some of the most diverse climatological environments in the United States. This diversity is most pronounced in the winter season when temperature and snowfall can vary drastically across the region. In this study, we identify long‐term trends and variation of temperature and snowfall in the SAM of the southeastern United States during climatological winter (DJF) from 1910 to 2017. Along with recognizing statistically significant climatic trends, we also identify the influence of several teleconnection patterns, namely the El Niño‐Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO) that further scientific understanding of how this region has remained a climatic anomaly. Results of this study indicate the SAM have experienced a statistically significant long‐term cooling trend since the early 20th century, with recent decades suggesting a reversal towards a warming pattern. Snowfall exhibited high interannual variability, with the 1960s and 1970s producing anomalously high amounts of snowfall. Several atmospheric forcing couplings are identified that align with anomalous conditions in the region. Most notably, negative temperature anomalies and higher snowfall amounts were frequently found during El Niño and negative NAO seasons, with the opposite being true during La Niña and positive NAO winters. The influence of these teleconnection patterns was spatially dependent, with lower elevations and eastern‐facing slopes being highly dependent on the phase of ENSO for snowfall, whereas higher elevations and western‐facing slopes were more reliant on the NAO. The identification of pattern couplings is critical to improving understanding of the anomalous climate of the SAM, enhancing seasonal forecasting, and predicting future climate change in the region.
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Key words
climate variability,mid-latitudes,mountains,snow,teleconnections
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