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Abstract

Estimating oceanic and atmospheric extremes from global climate models is not trivial as these
models
often poorly represent extreme events. However, these models do tend to capture the central climate
sta- tistics well (e.g., the mean temperature, variances, etc.). Here, we develop a Bayesian
hierarchical model (BHM) to improve estimates of extremes from ocean and climate models. This is
performed by first mod- eling observed extremes using an extreme value distribution (EVD). Then, the
parameters of the EVD are modeled as a function of climate variables simulated by the ocean or
atmosphere model over the same time period as the observations. By assuming stationarity of the
model parameters, we can estimate extreme values in a projected future climate given the climate
statistics of the projected climate (e.g., a climate model projection under a specified carbon
emissions scenario). The model is demonstrated for extreme sea surface temperatures off
southeastern Australia using satellite-derived observations and downscaled global climate model
output for the 1990s and the 2060s under an A1B emissions sce- nario. Using this case study we
present a suite of statistics that can be used to summarize the probabi- listic results of the BHM
including posterior means, 95% credible intervals, and probabilities of exceedance. We also present
a method for determining the statistical significance of the modeled changes in extreme value
statistics. Finally, we demonstrate the utility of the BHM to test the response of extreme
values to prescribed changes in climate.

Item Type:	Article
Authors/Creators:	Oliver, ECJ and Wotherspoon, SJ and Holbrook, NJ
Journal or Publication Title:	Progress in Oceanography
ISSN:	0079-6611
DOI / ID Number:	10.1016/j.pocean.2013.12.004
Additional Information:	Copyright 2014 Elsevier
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