Preprints
https://doi.org/10.5194/nhess-2020-117
https://doi.org/10.5194/nhess-2020-117

  22 Apr 2020

22 Apr 2020

Review status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.

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

Tamás Bódai1,2 and Torben Schmith3 Tamás Bódai and Torben Schmith
  • 1Center for Climate Physics, Institute for Basic Science, Busan, Republic of Korea, 46241
  • 2Pusan National University, Busan, Republic of Korea, 46241
  • 3Danish Meteorological Institute, Copenhagen, Denmark

Abstract. 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.

Tamás Bódai and Torben Schmith

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Tamás Bódai and Torben Schmith

Tamás Bódai and Torben Schmith

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Short summary
A lot of people work outdoors year-round and their work safety is of basic concern. For example, in shipping route planning, it is very important to be able to know well in advance how long time crew can stay on deck to carry out their task, which depends on the temperature. We examine one element of a forecast system with respect to the choice of the quantity that it relies on. The forecast of cold extremes can be much more precise when relying on a local quantity rather than a nonlocal one.
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