World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

Weather Regime Dependence of Extreme Value Statistics for Summer Temperature and Precipitation : Volume 15, Issue 3 (06/05/2008)

By Yiou, P.

Click here to view

Book Id: WPLBN0003972983
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: Weather Regime Dependence of Extreme Value Statistics for Summer Temperature and Precipitation : Volume 15, Issue 3 (06/05/2008)  
Author: Yiou, P.
Volume: Vol. 15, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Li, Z. X., Nogaj, M., Yiou, P., & Goubanova, K. (2008). Weather Regime Dependence of Extreme Value Statistics for Summer Temperature and Precipitation : Volume 15, Issue 3 (06/05/2008). Retrieved from

Description: Laboratoire des Sciences du Climat et de l'Environnement/IPSL, CE Saclay l'Orme des Merisiers, 91191 Gif-sur-Yvette, France. Extreme Value Theory (EVT) is a useful tool to describe the statistical properties of extreme events. Its underlying assumptions include some form of temporal stationarity in the data. Previous studies have been able to treat long-term trends in datasets, to obtain the time dependence of EVT parameters in a parametric form. Since there is also a dependence of surface temperature and precipitation to weather patterns obtained from pressure data, we determine the EVT parameters of those meteorological variables over France conditional to the occurrence of North Atlantic weather patterns in the summer. We use a clustering algorithm on geopotential height data over the North Atlantic to obtain those patterns. This approach refines the straightforward application of EVT on climate data by allowing us to assess the role of atmospheric variability on temperature and precipitation extreme parameters. This study also investigates the statistical robustness of this relation. Our results show how weather regimes can modulate the different behavior of mean climate variables and their extremes. Such a modulation can be very different for the mean and extreme precipitation.

Weather regime dependence of extreme value statistics for summer temperature and precipitation

Beck, C., Jacobeit, J., and Jones, P.: Frequency and within-type variations of large-scale circulation types and their effects on low-frequency climate variability in Central Europe since 1780, Int. J. Climatol., 27, 473–491, 2007.; Bernacchia, A. and Naveau, P.: Detecting spatial patterns with the cumulant function. Part I: The theory, Nonlin. Processes Geophys., 15, 159–167, 2007.; Bradley, R S.: Paleoclimatology: Reconstructing Climates of the Quaternary, Academic Press, San Diego, 1999.; Cassou, C., Terray, L., and Phillips, A.: Tropical Atlantic influence on European heat waves, J. Climate, 18, 2805–2811, 2005.; Cheng, X H. and Wallace, J M.: Cluster analysis of the northern-hemisphere wintertime 50-hPa height field: spatial patterns, J. Atmos. Sci., 50, 2674–2696, 1993.; Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, Springer-Verlag, London, 2001.; Cooley, D., Naveau, P., Jomelli, V., Rabatel, A., and Grancher, D.: A Bayesian hierarchical extreme value model for lichenometry, Environmetrics, 17, 555–574, 2006.; Corti, S., Molteni, F., and Palmer, T N.: Signature of recent climate change in frequencies of natural atmospheric circulation regimes, Nature, 398, 799–802, 1999.; Efron, B. and Tibshirani, R.: An introduction to the bootstrap, Monographs on statistics and applied probability; 57, Chapman and Hall, New York, 1993.; Embrechts, P., Klüppelberg, C., and Mikosch, T.: Modelling Extremal Events for Insurance and Finance, Applications of mathematics, 33, Springer, Berlin; New York, 1997.; Palmer, T N.: A nonlinear dynamical perspective on climate prediction, J. Climate, 12, 575–591, 1999.; Fischer, E., Seneviratne, S., Luthi, D., and Schaer, C.: Contribution of land-atmosphere coupling to recent European summer heat waves, Geophys. Res. Lett., 34, L05707, doi:10.1029/2006GL029068, 2007.; Ghil, M. and Robertson, A W.: Waves vs. particles in the atmosphere's phase space: A pathway to long-range forecasting?, Proc. Natl. Acad. Sci., 99, 2493–2500, 2002.; Green, P J. and Silverman, B W.: Nonparametric regression and generalized linear models: a roughness penalty approach, Monographs on statistics and applied probability; 58, Chapman and Hall, London; New York, 1st edn., 1994.; Gumbel, E J.: Statistics of Extremes, Columbia University Press, New York, 1958.; Hartigan, J A. and Wong, M A.: A K-means clustering algorithm., Appl. Statist., 28, 100–108, 1979.; Hastie, T. and Tibshirani, R.: Generalized additive models, Monographs on statistics and applied probability; 43, Chapman and Hall, London, New York, 1st edn., 1990.; Hurrell, J., Kushnir, Y., Ottersen, G., and Visbeck, M. (Eds.): An Overview of the North Atlantic Oscillation. The North Atlantic Oscillation: Climate Significance and Environmental Impact, vol. 134 of Geophysical Monograph Series, American Geophysical Union, 2003.; Kageyama, M., D'Andrea, F., Ramstein, G., Valdes, P J., and Vautard, R.: Weather regimes in past climate atmospheric general circulation model simulations, Clim. Dynam., 15, 773–793, 1999.; Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., W.Higgins, Janowiak, J., Mo, K C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-Year Reanalysis Project, B. Am. Meteorol. Soc, 77, 437–471, 1996.; Katz, R., Parlange, M., and Naveau, P.: Statistics of extremes in hydrology, Adv. Water Res., 25, 1287–1304, 2002.; Katz, R W.: Extreme value theory for precipitation: sensitivity analysis for climate change, Adv. Water Res., 23, 133–139, 1999.; Kharin, V V. and Zwiers, F W.: Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere–ocean GCM, J. Climate, 13, 3760–37


Click To View

Additional Books

  • The Quasi-static Approximation of the Sp... (by )
  • Is the Atlantic Multidecadal Oscillation... (by )
  • On the Three-dimensional Configuration o... (by )
  • Using Mssa to Determine Explicitly the O... (by )
  • Flip-mhd-based Model Sensitivity Analysi... (by )
  • Scaling for Lobe and Cleft Patterns in P... (by )
  • On the Influence of a Large Scale Cohere... (by )
  • Non-gaussian Interaction Information: Es... (by )
  • Four-dimensional Energy Spectrum for Spa... (by )
  • Conditioning Model Output Statistics of ... (by )
  • Diffusive Draining and Growth of Eddies ... (by )
  • Granulometric Characterization of Sedime... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Fair are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.