Return times of hot and cold days via recurrences and extreme value theory
- 261 Downloads
In this paper we introduce a model evaluation and comparison metric based on the methodology introduced in Faranda et al. (Geophys Res Lett 40(21):5782–5786, 2013) to assess biases and their potential origins in a historical model simulation against long-term reanalysis. The metric is constructed by exploiting recent results of dynamical systems theory linking rare recurrences to the classical statistical theories of extreme events for time series. We compute rare recurrences for 100 years daily mean temperatures data obtained in a model with historical greenhouse forcing (the Institut Pierre-Simon Laplace, IPSL-CM5 model) and compare them with the same quantities obtained from two datasets of reanalysis (twentieth Century Reanalysis and ERA 20C). The period chosen for the comparison is 1900–2000 and the focus is on the European region. We show that with respect to the traditional approaches, the recurrence technique is sensitive to the change in the size of the selection window of extremes due to the conditions imposed by the dynamics.
KeywordsClimate Dynamical systems Extreme events Recurrences Temperature
D. Faranda and P. Yiou were supported by ERC Grant No. 338965-A2C2 and M. Carmen Alvarez-Castro was supported by the Swedish Research Council Grant: “Euro-Atlantic climate variability during the last millennium: atmospheric circulation and extreme events (MILEX)”.
- Brown S, Caesar J, Ferro CA (2008) Global changes in extreme daily temperature since 1950. J Geophys Res Atmos 113(D5) (1984–2012) Google Scholar
- Galambos J (1980) The asymptotic theory of extreme order statistics. Wiley, New YorkGoogle Scholar
- Gnedenko B (1943) Sur la distribution limite du terme maximum d’une série aléatoire. Ann Math pp 423–453Google Scholar
- Gumbel EJ (1958) Statistics of extremes. Columbia University Press, New YorkGoogle Scholar
- Hourdin F, Foujols MA, Codron F, Guemas V, Dufresne JL, Bony S, Denvil S, Guez L, Lott F, Ghattas J, Braconnot P, Marti O, Meurdesoif Y, Bopp L (2013a) Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Clim Dyn 40(9–10):2167–2192CrossRefGoogle Scholar
- Hourdin F, Grandpeix JY, Rio C, Bony S, Jam A, Cheruy F, Rochetin N, Fairhead L, Idelkadi A, Musat I, Dufresne JL, Lahellec A, Lefebvre MP, Roehrig R (2013b) LMDZ5b: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Clim Dyn 40(9–10):2193–2222CrossRefGoogle Scholar
- Pickands III J (1975) Statistical inference using extreme order statistics. Ann Stat pp 119–131Google Scholar
- Poli P, Hersbach H, Tan D, Dee D, Thepaut JN, Simmons A, Peubey C, Laloyaux P, Komori T, Berrisford P, et al (2013) The data assimilation system and initial performance evaluation of the ECMWF pilot reanalysis of the 20th-century assimilating surface observations only (ERA-20C)Google Scholar
- Yiou P, Nogaj M (2004) Extreme climatic events and weather regimes over the north atlantic: When and where? Geophys Res Lett 31(7):Google Scholar
- Yiou P, Dacunha-Castelle D, Parey S, Huong Hoang T (2009) Statistical representation of temperature mean and variability in Europe. Geophys Res Lett 36(4)Google Scholar