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.
| Original language | English |
|---|---|
| Pages (from-to) | 1001-1024 |
| Number of pages | 24 |
| Journal | Climatic Change |
| Volume | 113 |
| Issue number | 3-4 |
| DOIs | |
| Publication status | Published - Aug 2012 |
Bibliographical note
Funding Information: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.
