Does the AO index have predictive power regarding extreme cold temperatures in Europe?

With a view to seasonal forecasting of extreme value statistics, we apply the method of Nonstationary extreme value statistics to determine the predictive power of large scale quantities. Regarding winter cold extremes over Europe, we find that the monthly mean daily minimum local temperature – whic...

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Main Authors: Bódai, Tamás, Schmith, Torben
Format: Text
Language:English
Published: 2020
Subjects:
Online Access:https://doi.org/10.5194/nhess-2020-117
https://nhess.copernicus.org/preprints/nhess-2020-117/
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spelling ftcopernicus:oai:publications.copernicus.org:nhessd84975 2023-05-15T14:58:25+02:00 Does the AO index have predictive power regarding extreme cold temperatures in Europe? Bódai, Tamás Schmith, Torben 2020-04-22 application/pdf https://doi.org/10.5194/nhess-2020-117 https://nhess.copernicus.org/preprints/nhess-2020-117/ eng eng doi:10.5194/nhess-2020-117 https://nhess.copernicus.org/preprints/nhess-2020-117/ eISSN: 1684-9981 Text 2020 ftcopernicus https://doi.org/10.5194/nhess-2020-117 2020-07-20T16:22:14Z With a view to seasonal forecasting of extreme value statistics, we apply the method of Nonstationary extreme value statistics to determine the predictive power of large scale quantities. Regarding winter cold extremes over Europe, we find that the monthly mean daily minimum local temperature – which we call a native co-variate in the present context – has a much larger predictive power than the nonlocal monthly mean Arctic Oscillation index. Our results also prompt that the exploitation of both co-variates is not possible from 70 years-long data sets. Text Arctic Copernicus Publications: E-Journals Arctic
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description With a view to seasonal forecasting of extreme value statistics, we apply the method of Nonstationary extreme value statistics to determine the predictive power of large scale quantities. Regarding winter cold extremes over Europe, we find that the monthly mean daily minimum local temperature – which we call a native co-variate in the present context – has a much larger predictive power than the nonlocal monthly mean Arctic Oscillation index. Our results also prompt that the exploitation of both co-variates is not possible from 70 years-long data sets.
format Text
author Bódai, Tamás
Schmith, Torben
spellingShingle Bódai, Tamás
Schmith, Torben
Does the AO index have predictive power regarding extreme cold temperatures in Europe?
author_facet Bódai, Tamás
Schmith, Torben
author_sort Bódai, Tamás
title Does the AO index have predictive power regarding extreme cold temperatures in Europe?
title_short Does the AO index have predictive power regarding extreme cold temperatures in Europe?
title_full Does the AO index have predictive power regarding extreme cold temperatures in Europe?
title_fullStr Does the AO index have predictive power regarding extreme cold temperatures in Europe?
title_full_unstemmed Does the AO index have predictive power regarding extreme cold temperatures in Europe?
title_sort does the ao index have predictive power regarding extreme cold temperatures in europe?
publishDate 2020
url https://doi.org/10.5194/nhess-2020-117
https://nhess.copernicus.org/preprints/nhess-2020-117/
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_source eISSN: 1684-9981
op_relation doi:10.5194/nhess-2020-117
https://nhess.copernicus.org/preprints/nhess-2020-117/
op_doi https://doi.org/10.5194/nhess-2020-117
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