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Assessing the Relationship between Low-Frequency Oscillations of Global Hydroclimate Indices and Long-Term Precipitation throughout the United States

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Abstract

Because many locations throughout the United States have recently experienced periods of extreme wet and dry conditions, an attempt is made to better understand the relationships between long-term total precipitation and climate variability. Correlations between total precipitation at over 1200 U.S. sites and low-frequency oscillations of the mean activity of 30 hydroclimate indices (HCIs) are analyzed using correlation analysis and sliding window sizes on the order of years to reduce the effects of high-frequency variability in the time series. The strength and significance of each relationship are assessed using the Pearson’s correlation coefficient r, leave-one-out cross validation, and a Monte Carlo approach. The sliding window size, lag time, and beginning month were varied to produce the optimal correlation at each site; a 60-month sliding window and lag times of 12 and 48 months resulted in the strongest correlations. Correlations with 7 and 8 HCIs at each lag time, respectively, were regionally delineated. The Madden–Julian oscillation represents the dominant HCI at the 12-month lag time throughout most of the western half of the United States, whereas El Niño–Southern Oscillation revealed strong links to annual and longer-term total precipitation in the eastern and western United States, respectively. Other HCIs, such as the North Atlantic Oscillation and the Pacific decadal oscillation, demonstrated dominance over much smaller and more well-defined regions within the Southwest and the South, respectively. The final results of this study allow a greater understanding of potential links between climate variability and long-term precipitation in the United States, leading to potentially improved predictions of the onset and persistence of future extreme meteorological events at longer lead times.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jason Giovannettone, jpgiovannettone@gmail.com

Abstract

Because many locations throughout the United States have recently experienced periods of extreme wet and dry conditions, an attempt is made to better understand the relationships between long-term total precipitation and climate variability. Correlations between total precipitation at over 1200 U.S. sites and low-frequency oscillations of the mean activity of 30 hydroclimate indices (HCIs) are analyzed using correlation analysis and sliding window sizes on the order of years to reduce the effects of high-frequency variability in the time series. The strength and significance of each relationship are assessed using the Pearson’s correlation coefficient r, leave-one-out cross validation, and a Monte Carlo approach. The sliding window size, lag time, and beginning month were varied to produce the optimal correlation at each site; a 60-month sliding window and lag times of 12 and 48 months resulted in the strongest correlations. Correlations with 7 and 8 HCIs at each lag time, respectively, were regionally delineated. The Madden–Julian oscillation represents the dominant HCI at the 12-month lag time throughout most of the western half of the United States, whereas El Niño–Southern Oscillation revealed strong links to annual and longer-term total precipitation in the eastern and western United States, respectively. Other HCIs, such as the North Atlantic Oscillation and the Pacific decadal oscillation, demonstrated dominance over much smaller and more well-defined regions within the Southwest and the South, respectively. The final results of this study allow a greater understanding of potential links between climate variability and long-term precipitation in the United States, leading to potentially improved predictions of the onset and persistence of future extreme meteorological events at longer lead times.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jason Giovannettone, jpgiovannettone@gmail.com
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