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1 EDF R&D - EDF Recherche et Développement 2 LM-Orsay - Laboratoire de Mathématiques d-Orsay

Abstract : The paper presents a stochastic Seasonal Functional Heteroscedastic Auto-Regressive model developed to simulate daily minimum, maximum or mean temperature time series coherent with observed time series and designed to reliably reproduce extreme values through a careful study of the extremes and their bounded character.
The model is first validated using different daily minimum and maximum weather-station time series over Eurasia and the United-States in different climatic regions.
It is shown that the model is able to produce coherent results both for the bulk of the distribution and for its extremes and especially that it can produce higher or lower extreme values than observed.
Then a possible use in the climate change context is tested.
It consists in fitting the model over the first part of a long temperature time series and in using it to simulate a large number of possible trajectories for the second part when temperature has increased.
Two approaches have been tested to do so, one based on a simple mean change in mean and variance and the other in considering the full seasonalities and trends estimated over the observed second part of the time series.
Both approaches have been found to give good results as well for the bulk as for the extremes of the temperature distribution over the second part of the period.
However, the second approach allows taking interannual variability changes into account, which leads to more realistic results when this occurs.
These results give confidence in the possibility of using this tool as a statistical downscaling tool reliably reproducing temperature extremes.


Keywords : stochastic modeling extremes daily temperature





Author: Sylvie Parey - Thi Thu Huong Hoang - Didier Dacunha-Castelle -

Source: https://hal.archives-ouvertes.fr/



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