• \
    Start Submission Become a Reviewer

    Reading: Statistical properties of the temperature maxima in an intermediate order Quasi-Geostrophic ...

    Download

     A- A+
    Alt. Display

    Original Research Papers

    Statistical properties of the temperature maxima in an intermediate order Quasi-Geostrophic model

    Author:

    S. Vannitsem

    Institut Royal Météorologique de Belgique Avenue Circulaire, 3, 1180 Brussels, BE
    X close

    Abstract

    The statistical properties of temperature maxima are investigated in a very long integration (2 400 000 months) of an intermediate order Quasi-Geostrophic model displaying deterministic chaos. The variations of the moments of the extreme value distributions as a function of the time window, n, are first explored and compared with the behaviours expected from the classical Generalized Extreme Value (GEV) theory. The analysis reveals a very slow convergence (if any) toward the asymptotic GEV distributions. This highlights the difficulty in assessing the parameters of the GEV distribution in the context of deterministic chaotic (bounded) atmospheric flows.

    The properties of bivariate extremes located at different sites are then explored, with emphasis on their spatial dependences. Several measures are used indicating: (i) a complex dependence of the covariance between the extremes as a function of the time window; (ii) the presence of spatial (non-trivial) teleconnections for low-amplitude extremes and (iii) the progressive decrease of the spatial dependence as a function of the amplitude of the maxima. An interpretation of these different characteristics in terms of the internal dynamics of the model is advanced.

    How to Cite: Vannitsem, S., 2007. Statistical properties of the temperature maxima in an intermediate order Quasi-Geostrophic model. Tellus A: Dynamic Meteorology and Oceanography, 59(1), pp.80–95. DOI: http://doi.org/10.1111/j.1600-0870.2006.00206.x
      Published on 01 Jan 2007
    Peer Reviewed
     CC BY 4.0
     Accepted on 28 Aug 2006            Submitted on 29 Mar 2006

    References

    1. Balakrishnan , V. , Nicolis , C. and Nicolis , G. 1995 . Extreme value dis-tributions in chaotic dynamics . J. Stat. Phys . 80 , 307 – 336 .  

    2. Beersma , J. J. and Buishand , T. A. 2004 . Joint probability of precipitation and discharge deficits in the Netherlands . Water Res. Research 40 , W12508 .  

    3. Bell , J. L. , Sloan , L. C. and Snyder , M. K. 2004 . Regional changes in extreme climate events: A future climate scenario . J. Climate 17 , 81 – 87 .  

    4. Beniston , M. and Stephenson , D. B. 2004 . Extreme climate events and their evolution under changing climatic conditions . Glob. Planet. Change 44 , 1 – 9 .  

    5. Buishand , T. A. 1984 . Bivariate extreme-value data and the station-year method . J. Hydrol . 69 , 77 – 95 .  

    6. Buishand , T. A. 1989 . Statistics of extremes in Climatology . Statistica Neerlandica 43 , 1 – 30 .  

    7. Cassou , Ch. , Terray , L. and Phillips , A. S. 2005 . Tropical atlantic influ-ence on European heat waves . J. Climate 18 , 2805 – 2811 .  

    8. Chamey , J. G. and Straus , D. M. 1980 . Form-drag instability, multiple equilibria and propagating planetary waves in baroclinic, orographi-cally forced, planetary wave systems. J. Atmos. Sc i . 37 , 1157 – 1176 .  

    9. Coles , S. 2001 . An introduction to statistical modeling of extreme values . Springer Series in Statistics , Springer , New York , 208 p .  

    10. Coles , S. , Heffernan , J. and Tawn , J. 1999 . Dependence measure for extreme value analyses . Extremes 2 , 339 – 365 .  

    11. Elomer , Y. B. , Yagger , T. H. and Tsonis , A. A. 2006 . Estimated return periods for hurricane Katrina . Geophys. Res. Lett . 33 , L08704 .  

    12. Emanuel , K. 2005 . Increasing destructiveness of tropical cyclones over the past 30 years . Nature 436 , 686 – 688 .  

    13. Embrechts , P. , Klfippelburg , C. and Mikosch , T. 1997 . Modeling extremal events for insurance and finance . Springer , New York .  

    14. Favre , A.-C. , El Adlouni , S. , Perreault , L. , Thiemonge , N. and Bobee , B. 2004 . Multivariate hydrological frequency analysis using copulas . Water Res. Research 40 , W01101 .  

    15. Gershunov , A. and Barnett , T. P. 1998 . ENSO influence on intraseasonal extreme rainfall and temperature frequencies in the contiguous United States: Observations and model results . J. Climate 11 , 1575 – 1586 .  

    16. Gumbel , E. J. 1958 . Statistics of extremes . Columbia University Press , New York .  

    17. Hosking , J. R. M. , Wallis , J. R. and Wood , E. E 1984 . Estimation of the generalised extreme-value distribution by the method of probability weighted moments . Technometrics 27 , 252 – 261 .  

    18. Karl , T. R. and Knight , R. W. 1997 . The 1995 Chicago heat wave: How likely is a recurrence? Bull. Amer. Meteor. Soc . 78 , 1107 – 1119 .  

    19. Kharin , V. V. and Zwiers , E W. 2000 . Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere-ocean GCM . J. Climate 13 , 3760 – 3788 .  

    20. Klein Tank , A. M. G. , Peterson , T. C. , Quadir , D. A. , Dorji , S. Zhou , X. , and co-authors . 2006. Changes in daily temperature and precipitation extremes in Central and South Asia.I. Geophys. Res . 111 , D16105 .  

    21. Kunkel , K. E. , Pielke , R. A. and Changnon , S. A. 1999 . Temporal fluc-tuations in weather amd climate extremes that cause economic and human health impacts: A review . Bull. Amer. Meteor. Soc . 80 , 1077 – 1098 .  

    22. Malguzzi , P. , Speranza , A. , Sutera , A. and Caballero , R. 1996 . Nonlin-ear amplification of stationary Rossby waves near resonance. Part I. J. Atmos. Sc i . 53 , 298 – 311 .  

    23. Marshall , J. and Molteni , E 1993 . Toward a dynamical understanding of planetary-scale flow regimes. J. Atmos. Sc i . 50 , 1792 – 1818 .  

    24. Meehl , G. A. , Zwiers , E , Evans , J. , Knutson , T. , Means , L. , and co-authors. 2000. Trends in extreme weather and climate events: Is-sues related to modeling extremes in projections of future climate changes. Bull. Amer. Meteor. Soc . 81 , 427 - 436 .  

    25. Naveau , Ph. , Nogaj , M. , Ammann , C. , Yiou , P. , Cooley , D., and co-authors . 2005 . Statistical analysis of climate extremes. C. R. Geo-sciences Acad. Sci . 337 , 1013-102 2 .  

    26. Schär , Ch. et al 2004 . The role of increasing temperature variability in European summer heatwaves . Nature 427 , 332 – 336 .  

    27. Shepherd , T. G. 1987 . A spectral view of nonlinear fluxes and stationary-transient interaction in the atmopshere. J. Atmos. Sc i . 44 , 1166 – 1178 .  

    28. Sneyers , R. 1979 . L’ intensite et la duree maximale des precipitations sur la Belgique. Publ. Ser. B, 99, Inst. Roy. Meteor. Belgique, ‘in French’.  

    29. Sparks , J. , Changnon , D. and Starke , J. 2002 . Changes in the frequency of extreme warm-season surface dewpoints in Northeastern Illinois: Im-plications for cooling-system design and operations . J. Appl. Meteor . 41 , 890 – 898 .  

    30. Stedinger , J. R. , Vogel , R. M. and Foufoula-Georgiou , E. 1993 . Fre-quency analysis of extreme events. In: Handbook of Hydrology, (ed. D.R. Maidment ), McGraw-Hill, New-York, pp. 181 - 186  

    31. Tung , K. K. and Lindzen , R. S. 1979 . A theory of stationary long waves. Part I: A simple theory of blocking. Mon. Wea. Rev. 107 , 714 - 734 .  

    32. Van den Brink , H. W. , Können , G. P. and Opsteegh , J. D. 2004 . Statistics of extreme synoptic-scale wind speeds in ensemble simulations of current and future climate./ . Climate 17 , 4564 – 4574 .  

    33. Vannitsem , S. and Nicolis , C., 1997 . Lyapunov vectors and error growth patterns in a T21L3 quasi-geostrophic model. J. Atmos. Sc i . 54 , 347 – 361 .  

    34. Yiou , P. and Nogaj , M. 2004 . Extreme climate events and weather regimes over the North Atlantic: when and where? . Geophys. Res. Let . 31 , L07202 .  

    Jump to Discussions Related content
    comments powered by Disqus