Abstract
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 1400 ppm 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.
Highlights
As the Earth’s mean climate changes under increased concentrations of human-emitted greenhouse gases, the intensity and frequency of extreme weather conditions may change as well (Easterling et al, 2000, chap. 11: IPCC, 2013, and references therein)
The paper is structured as follows: in Sect. 2, we describe the climate model output used in this work; in Sect. 3, we provide a background for the univariate extreme value theory we employ; in Sect. 4, we describe the changes in extreme value distributions and corresponding return levels, and compare them with changes in climate means
C1 and C2) and that changing temperature extremes can be studied by estimating changes in generalized extreme value (GEV) parameters and resulting implied changes in return levels
Summary
As the Earth’s mean climate changes under increased concentrations of human-emitted greenhouse gases, the intensity and frequency of extreme weather conditions may change as well (Easterling et al, 2000, chap. 11: IPCC, 2013, and references therein). Alexander et al (2006), for example, used percentile-based thresholds for various climate metrics from 3 decades of global gridded observations (1961–1990) and considered changes in a subsequent period (1991–2003) They found a significant increase in the occurrence of annual warm nights (defined as the 90th percentile of daily minimum temperature under the past climate) and a significant decrease in the occurrence of cold nights (defined as the 10th percentile of daily minimum temperature under the past climate). The climate model is run for a sufficiently long warmup period to ensure the climate has fully responded to forcing changes, so that any time series of block extremes effectively forms a stationary sequence. We conclude with a discussion of the implications of these results
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