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

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 CO 2 ) 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.

Quantifying the influence of global warming on unprecedented extreme climate events

“…54). Although CMIP provides simulations from many different climate models, the limited number of realizations means that a relatively small number of simulated years are available from each model in each forcing window, which can create substantial errors in the calculation of return intervals of the most extreme events (23). We therefore analyze the National Center for Atmospheric Research (NCAR) LENS ensemble, which generates a large ensemble (∼30 realizations) of a single model in an individual CMIP5 forcing pathway (e.g., refs.…”

“…Because sampling errors tend to be large when data segment lengths are similar to the event return interval (23), the fact that the observational record is limited to several decades is likely to create substantial uncertainty in the calculated return interval of the most extreme events. We therefore follow refs.…”

“…This analysis is similar to that described in The Contribution of the Observed Trend to the Probability of the Event Magnitude for the influence of the historical trend, but performed on the Historical and Pre-Industrial GCM Simulations rather than the observed and detrended time series. The NCAR large ensemble provides >700 y of data in the Historical and PreIndustrial Simulations (Table S1); in contrast to decadal-scale periods, 1,000-y simulations have been shown to "provide fairly accurate estimates of changes in return levels even for long return periods" (23).…”

“…First, although some local observations are centuries old, much of the climate system is observed only sparsely, and only for the past few decades (19-21). As a result, observational samples are small relative to the magnitude of the most extreme events (20), creating substantial uncertainty in the probability (22,23). Second, the historical increase in greenhouse forcing has already altered global climate dynamics (e.g., refs.…”

“…As a result, distinguishing a change in probability between the earlier and later periods poses a challenge that cannot be readily overcome solely through observational analysis (e.g., refs. 27 and 28), or with the relatively small climate model ensembles conventionally analyzed in efforts such as the Intergovernmental Panel on Climate Change (23).…”