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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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 |
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Copernicus Publications: E-Journals |
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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 |
_version_ |
1766330512855531520 |