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Theoretical and Applied Climatology

, Volume 125, Issue 1–2, pp 197–211 | Cite as

Six temperature and precipitation regimes of the contiguous United States between 1895 and 2010: a statistical inference study

  • Samuel S. P. Shen
  • Olaf Wied
  • Alexander Weithmann
  • Tobias Regele
  • Barbara A. Bailey
  • Jay H. Lawrimore
Original Paper

Abstract

This paper describes six different temporal climate regimes of the contiguous United States (CONUS) according to interdecadal variations of surface air temperature (SAT) and precipitation using the United States Historical Climatology Network (USHCN) monthly data (Tmax, Tmin, Tmean, and precipitation) from 1895 to 2010. Our analysis is based on the probability distribution, mean, standard deviation, skewness, kurtosis, Kolmogorov-Smirnov (KS) test, and Welch’s t test. The relevant statistical parameters are computed from gridded monthly SAT and precipitation data. SAT variations lead to classification of four regimes: 1895–1930 (cool), 1931–1960 (warm), 1961–1985 (cool), and 1986–2010 (warm), while precipitation variations lead to a classification of two regimes: 1895–1975 (dry) and 1976–2010 (wet). The KS test shows that any two regimes of the above six are statistically significantly different from each other due to clear shifts of the probability density functions. Extremes of SAT and precipitation identify the ten hottest, coldest, driest, and wettest years. Welch’s t test is used to discern significant differences among these extremes. The spatial patterns of the six climate regimes and some years of extreme climate are analyzed. Although the recent two decades are the warmest among the other decades since 1895 and many hottest years measured by CONUS Tmin and Tmean are in these two decades, the hottest year according to the CONUS Tmax anomalies is 1934 (1.37 °C), which is very close to the second Tmax hottest year 2006 (1.35 °C).

Keywords

Climate Regime Interdecadal Variation Dust Bowl Climate Regime Change Warm Regime 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This study was supported in part by the US National Oceanographic and Atmospheric Administration (Award No. EL133E09SE4048), the US National Science Foundation (Awards No. AGS-1015926 and AGS-1015957), the US Department of Energy (Award No. DE-SC002763), and a contract from US NASA Jet Propulsion Laboratory. The comments from the anonymous reviewers have helped improve the clarity and overall quality of the paper.

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Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Samuel S. P. Shen
    • 1
    • 2
  • Olaf Wied
    • 1
  • Alexander Weithmann
    • 1
  • Tobias Regele
    • 1
  • Barbara A. Bailey
    • 1
  • Jay H. Lawrimore
    • 3
  1. 1.Department of Mathematics and StatisticsSan Diego State UniversitySan DiegoUSA
  2. 2.Scripps Institution of OceanographyUniversity of California San DiegoLa JollaUSA
  3. 3.NOAA/National Climatic Data CenterAshevilleUSA

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