Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate... AbstractThis study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0442
D.O.I.
10.1175/JCLI-D-17-0106.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThis study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Jan 19, 2018

References

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