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Detection and prediction of mean and extreme European summer temperatures with a multimodel ensemble

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    Rights statement: The final edited version of this paper was published in the Journal of Geophysical Research. Copyright (2013) American Geophysical Union.

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http://onlinelibrary.wiley.com/doi/10.1002/jgrd.50703/abstract;jsessionid=E151C5F369DE1F7583DAC2630476D98F.f01t01
Original languageEnglish
Pages (from-to)9631-9641
Number of pages11
JournalJournal of Geophysical Research: Atmospheres
Volume118
Issue number17
DOIs
Publication statusPublished - 16 Sep 2013

Abstract

We analyze observed mean to extreme summer temperature indices across Europe in order to determine whether there is evidence for a detectable climate change signal and whether these indices show evidence for predictability. Observations from 1960 to 2011, taken from E-OBS an observational dataset created for the European Commission funded project (ENSEMBLES), are compared with the model simulations from the global coupled climate models CanCM4, HadCM3, MIROC5, and MPI-ESM-LR, as published on the CMIP5 archive. Indices are examined that span a moderate to extreme range of the summer temperature distribution by including the summer average, the hottest 5day average, and the hottest daily maximum and daily minimum temperatures during summer. The region of interest is Europe; however, a number of subregions are also studied, which include Western Europe, the British Isles, the Mediterranean, and Central Europe. The observed changes in the analyzed indices are well represented by the multimodel mean and are within the range of the multimodel ensemble for most regions, with the exception of 1 and 5day average daily maximum temperature extremes across the UK. Observed changes are detectable against estimates of internal climate variability for both moderate and extreme temperature indices across all regions in almost all cases. Exceptions are the hottest 5day average daily maximum temperature in the UK and Central Europe, for which results are not conclusive. An analysis of the skill in decadal hindcasts of these indices shows that there is significant prediction skill across these indices for three of the four models for some regions and some models. This skill exceeds the skill of forecasts based on observed climatology and random noise and is largely due to external forcing. However, there is some evidence that there is additional skill originating from the assimilation of observations into the initialization in some cases.

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