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.
Context Detecting biotic resistance to biological invasions across large geographic areas may require acknowledging multiple metrics of niche usage and potential spatial heterogeneity in associations between invasive and native species diversity and dominance.Objectives Determine (1) if native communities are resistant to biological invasions at macroscales; (2) the metrics that best quantify biotic resistance at these scales; and (3) the degree to which the direction and magnitude of invader-native associations vary with scale and/or location. Methods Using a mixed-effects modeling framework to account for potential sub-regional and crossscale variability in invader-native associations, we modeled the species richness and cover of invasive plants in 42,626 plots located throughout Eastern USA forests in relationship to plot-level estimates of native tree biomass, species richness, and evolutionary diversity.Results We found (1) native tree biomass and evolutionary diversity, but not species richness, to be negatively associated with invader establishment and dominance, and thus indicative of biotic resistance; (2) evidence that evolutionary diversity limits invader dominance more than it does invader establishment; Special issue: Macrosystems ecology: Novel methods and new understanding of multi-scale patterns and processes.(3) evidence of greater invasion resistance in parts of the agriculturally-dominated Midwest and in and around the more-contiguous forests of the Appalachian Mountains; and (4) the magnitude to which native tree biomass and evolutionary diversity limit invasion varies across the ranges of these metrics. Conclusions These findings illustrate the improved understanding of biotic resistance to invasions that is gained by accounting for sub-regional variability in ecological processes, and underscores the need to determine the factors leading to spatial heterogeneity in biotic resistance.
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