Journal cover Journal topic
Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
Journal topic
Volume 2, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79-103, 2016
https://doi.org/10.5194/ascmo-2-79-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79-103, 2016
https://doi.org/10.5194/ascmo-2-79-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Jul 2016

01 Jul 2016

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Whitney K. Huang1, Michael L. Stein2, David J. McInerney3, Shanshan Sun4, and Elisabeth J. Moyer4 Whitney K. Huang et al.
  • 1Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
  • 2Department of Statistics, University of Chicago, Chicago, IL 60637, USA
  • 3School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, 5005, Australia
  • 4Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637, USA

Abstract. Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400ppm CO2) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that warm extremes largely change in accordance with mean shifts in the distribution of summertime temperatures. Cold extremes warm more than mean shifts in the distribution of wintertime temperatures, but changes in GEV location parameters are generally well explained by the combination of mean shifts and reduced wintertime temperature variability. For cold extremes at inland locations, return levels at long recurrence intervals show additional effects related to changes in the spread and shape of GEV distributions. We then examine uncertainties that result from using shorter model runs. In theory, the GEV distribution can allow prediction of infrequent events using time series shorter than the recurrence interval of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.

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