Titlebook: Extremes in a Changing Climate; Detection, Analysis Amir AghaKouchak,David Easterling,Soroosh Sorooshi Book 2013 Springer Science+Business

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Gian Paolo Clemente,Nino Savellierence offers an attractive framework to estimate non-stationary models and, importantly, to quantify estimation and predictive uncertainties..This chapter therefore focuses on the application of Bayesian inference to non-stationary extreme models. It is organized as a step-by-step building of non-s

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https://doi.org/10.1007/978-3-642-22435-5irst eschews the term return period and instead communicates yearly risk in terms of a probability of exceedance. The second extends the notion of return period to the non-stationary setting. We examine two different definitions of return period under non-stationarity. The first, which appears in Ol

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Gambling on My Life and My Right to Lives various parametric and nonparametric tail dependence coefficient estimators. The tail dependence coefficient describes the dependence (degree of association) between concurrent extremes at different locations. Accurate and reliable knowledge of the spatial characteristics of extremes can help impr

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Some Examples of Discriminationitation provide measures of moderately rare weather events that are straightforward to calculate. Drought indices provide measures of both agricultural and hydrological drought that are especially suitable for constructing multi-model ensemble projections of future change. Extreme value statistical

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https://doi.org/10.1007/978-3-030-38185-1 climate system; inhomogeneities in the climate station records, and incomplete sampling of the climate system. There is no known theoretical basis for characterizing uncertainties arising from the chaotic nature of the climate system. Ensemble simulations as well as long control simulations from cl