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Climate change: a new metric to measure changes in the frequency of extreme temperatures using record data

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Abstract

Consensus on global warming is the result of multiple and varying lines of evidence, and one key ramification is the increase in frequency of extreme climate events including record high temperatures. Here we develop a metric—called “record equivalent draws” (RED)—based on record high (low) temperature observations, and show that changes in RED approximate changes in the likelihood of extreme high (low) temperatures. Since we also show that this metric is independent of the specifics of the underlying temperature distributions, RED estimates can be aggregated across different climates to provide a genuinely global assessment of climate change. Using data on monthly average temperatures across the global landmass we find that the frequency of extreme high temperatures increased 10-fold between the first three decades of the last century (1900–1929) and the most recent decade (1999–2008). A more disaggregated analysis shows that the increase in frequency of extreme high temperatures is greater in the tropics than in higher latitudes, a pattern that is not indicated by changes in mean temperature. Our RED estimates also suggest concurrent increases in the frequency of both extreme high and extreme low temperatures during 2002–2008, a period when we observe a plateauing of global mean temperature. Using daily extreme temperature observations, we find that the frequency of extreme high temperatures is greater in the daily minimum temperature time-series compared to the daily maximum temperature time-series. There is no such observable difference in the frequency of extreme low temperatures between the daily minimum and daily maximum.

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Notes

  1. The term “draw” in this paper refers to the sampling from a distribution as opposed to the sometimes British English usage of the term to refer to a tie.

  2. We assume that the temperature distribution is continuous. Otherwise we need to deal with ties of temperature observations that happen with positive probability, which unnecessarily complicates the presentation of Rényi (1962) result below.

  3. As mentioned earlier, although we assume that the n draws come from the same initial distribution the transformed distribution by taking the maximum of n such draws clearly represents a change in the underlying distribution.

  4. http://climate.geog.udel.edu/~climate/html_pages/Global2_Ts_2009/README.global_t_ts_2009.

  5. As defined earlier, the record high (low) temperature at time t is the highest (lowest) temperature observed up to time t.

  6. Although one can also estimate RED-H for the minimum of monthly average temperatures and vice versa, here we are interested in how the “extreme” monthly average temperatures become more extreme over time in order to examine changes in the tails of the temperature distribution.

  7. Although the use of the same time series for both high and low record samples implies that for any month-grid pair we cannot set both a high and low temperature record in any given time period, this restriction does not qualitatively change our results as long as the time-series is sufficiently long.

  8. The grid resolutions we tested include 2° × 2°, 5° × 5°, 8° × 8° and 10° × 10°.

  9. Prior to the 1950s longer time series data on extreme temperatures are available only for a few countries, including the US, Canada and the former USSR. In this limited geographic context (prior to the 1950s) there is neither a strong upward nor downward trend, nor any uniform trends across the different regions (Karl et al. 1984, 1991, 1993; Kumar et al. 1994; Weber et al. 1994; Bonsal et al. 2001).

  10. The comparison of DelCCR2 and HadCRUT3v (on landmass) from 1930 to 1969 is found in the online supplementary material.

  11. For comparison purposes, we reconstruct the record high and record low indicators from the single time-series for each month-grid pair in DelCCR2 as in HadCRUT3v.

  12. Implementing multiple simulations for random tie-breaks in temperature observations is computationally costly when the data set is an unbalanced panel.

  13. All estimates of RED are statistically significant at less than 1% level of significance even in the case of the coarsest data set with 8° × 8° resolution.

  14. The exception is RED-L Mn for 2007 and 2008 where it is higher in the 8° × 8° grid resolution than it is across the other grid resolutions.

  15. In more technical terms, recall that a RED-H Mx estimate of 16 in the tropics means that the transformed distribution is generated by taking the maximum of 16 draws from its base distribution, and a RED-H Mx estimate of 7 in extratropics means that the transformed distribution is only from a maximum of 7 draws (See Eq. 1).

  16. Cochrane-Orcutt AR(1) regression (Cochrane and Orcutt 1949; Breusch and Pagan 1979) is simply the generalized least-squares method to estimate the parameters in a linear regression model in which the errors are assumed to follow a first-order autoregressive process AR(1). For the time series of our RED, the partial autocorrelation for lags larger than one is generally not significant and so we restricted it to an AR(1) process. Our procedure uses an iterative process to find the best autocorrelation coefficient. A similar procedure is used in Wang and Swail (2001), which is applied in Zhang and Zwiers (2004) for climate data.

  17. Note that the R-squared in Cochrane-Orcutt AR(1) cannot be compared with the counterpart in the OLS since it comes from the transformed dependent variable on the transformed independent variables.

  18. This observation is robust when the time window is restricted to 2002–2007.

  19. This is also consistent with findings based on other indices. For example, days of warm summer nights have increased while frosty days have decreased (Frich et al. 2002; Griffiths et al. 2005; Alexander et al. 2006; Choi et al. 2009).

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Acknowledgements

DelCCR2, HadCRUT3v, and ETCCDI data were provided by the Center for Climatic Research at the University of Delaware, Climate Research Unit at the University of East Anglia, and Expert Team on Climate Change Detection and Indices, respectively. We gratefully acknowledge financial support from the Kyung Hee University Research Grant (2009) and Barnard College Presidential Research Award (2010). The primary research was conducted while the second author was a Research Fellow at Barnard College, and on sabbatical leave from Kyung Hee University. He would like to thank both institutions for providing invaluable support to conduct research on climate change. We thank three anonymous referees for their constructive criticisms and many helpful suggestions.We also thank Cynthia Howells, Brendan O’Flaherty, Stephanie Pfirman, Sanjay Tikku, Moonkyoung Um, and Jerry Welch for encouragement and generous feedback on earlier versions of the paper. Finally, we thank Claire Fram and Britt Johnson for excellent research assistance.

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Authors and Affiliations

  1. Department of Economics, Barnard College, Columbia University, 3009 Broadway, New York, NY, 10027, USA

    Lalith Munasinghe

  2. Department of Economics, Kyung Hee University, 1 Hoegi-dong, Dongdaemoon-gu, Seoul, 130-701, Korea

    Tackseung Jun

  3. NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY, 10025, USA

    David H. Rind

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  1. Lalith Munasinghe
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  2. Tackseung Jun
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Correspondence to Tackseung Jun.

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Munasinghe, L., Jun, T. & Rind, D.H. Climate change: a new metric to measure changes in the frequency of extreme temperatures using record data. Climatic Change 113, 1001–1024 (2012). https://doi.org/10.1007/s10584-011-0370-8

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