Advertisement

Meteorology and Atmospheric Physics

, Volume 100, Issue 1–4, pp 23–36 | Cite as

Estimating ensemble size requirements of AGCM simulations

  • A. S. TaschettoEmail author
  • M. H. England
Article

Summary

This study investigates the statistical methods for determining the minimum sample size necessary for an ensemble set generated with an atmospheric general circulation model. Due to the limits imposed by computational cost, an improved and a priori estimation of ensemble size is highly desirable. In this context, the methodology shown here is an important step for defining the number of integrations required in a numerical experiment. We show that the global distribution of ensemble size has a spatial and seasonal dependence. In addition, the ensemble size is dependent on the variable being analyzed and the geographic region of interest. For example, we show that a relatively large number of integrations are required to simulate the seasonal mean air temperature at 925 hPa and the sea-level pressure at mid to high latitudes. The seasonal mean precipitation, however, can be well represented with relatively few integrations at high latitudes, but it requires a large ensemble size at the tropics, particularly over the monsoon regions. These latitudinal differences in the number of integrations are associated with the internal variability in the model. Furthermore, differences among the variable fields partly arise due to the distinct shapes of the associated property distributions. Both Gaussian and nonparametric statistics are considered here. The Wilcoxon Rank Sum test reveals that the air temperature, sea-level pressure and precipitation do not follow a Gaussian distribution in some regions of the globe. Thus, we suggest the nonparametric approach be used whenever the normal assumption is violated or cannot be assessed. This study is based on the determination of the sampling size of ensemble simulations using the statistical Gaussian method of Wehner (2000). We extend this previous by considering a nonparametric approach.

Keywords

Internal Variability Atmospheric General Circulation Model Ensemble Size South Atlantic Convergence Zone Probability Density Func 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brankovic, C, Palmer, TN 1997Atmospheric seasonal predictability and estimates of ensemble sizeMon Wea Rev125859874CrossRefGoogle Scholar
  2. Charney, JG, Shukla, J 1981Predictability of monsoonsLighthill, JPearce, R eds. Monsoon dynamicsCambridge Univ PressCambridge, UK99109Google Scholar
  3. Collins WD, Rasch PJ, Boville BA, Hack JJ, McCaa JR, Williamson DL, Kiehl JT, Briegleb B, Bitz C, Lin S-J, Zhang M, Dai Y (2004) Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR/TN-464 + str. National Center for Atmospheric Research, Boulder, CO, 210 ppGoogle Scholar
  4. Groisman, PY, Karl, TR, Easterling, DR, Knight, RW, Jamason, PF, Hennessy, KJ, Suppiah, R, Page, CM, Wibig, J, Fortuniak, K, Razuvaer, VN, Douglas, A, Forland, E, Zhai, P-M 1999Changes in the probability of heavy precipitation: important indicators of climate changeClim Change42243283CrossRefGoogle Scholar
  5. Harzallah, A, Sadourny, R 1995Internal versus SST-forced variability as simulated by an atmospheric general circulation modelJ Clim8474495CrossRefGoogle Scholar
  6. Hodges, JJ, Lehmann, E 1956The efficiency of some nonparametric competitors of the t-testAnn Math Statist27324335CrossRefGoogle Scholar
  7. Kumar, A, Barnston, AG, Hoerling, MP 2001Seasonal predictions, probabilistic verifications, and ensemble sizeJ Clim1416711676CrossRefGoogle Scholar
  8. Lilliefors, HW 1967On the Kolmogorov-Smirnov test for normality with mean and variance unknownJ Am Stat Assoc62399402CrossRefGoogle Scholar
  9. Lorenz, EN 1963Deterministic nonperiodic flowJ Atmos Sci20130141CrossRefGoogle Scholar
  10. Lorenz, EN 1973On the existence of extended range predictabilityJ Appl Meteor12543546CrossRefGoogle Scholar
  11. Mooley, D 1973Gamma distribution probability model for Asian summer monsoon monthly rainfallMon Wea Rev101160176CrossRefGoogle Scholar
  12. Oleson KW, Dai Y, Bonan G, Bosilovich M, Dickinson R, Dirmeyer P, Hoffman F, Houser P, Levis S, Niu G-Y, Thornton P, Vertenstein M, Yang Z-L, Zeng X (2004) Technical description of the Community Land Model (CLM). NCAR/TN-461+str. National Center for Atmospheric Research, Boulder, COGoogle Scholar
  13. Palmer, TN, Anderson, LT 1994The prospects for seasonal forecasting – a review paperQuart J R Meteorol Soc120755793Google Scholar
  14. Peng, P, Kumar, A 2005A large ensemble analysis of the influence of tropical SSTs on seasonal atmospheric variabilityJ Clim1810681085CrossRefGoogle Scholar
  15. Pitman, EJG 1949Lecture notes on nonparametric statistical inferenceColumbia UniversityNew YorkGoogle Scholar
  16. Quan, XW, Webster, PJ, Moore, AM, Chang, HR 2004Seasonality in SST-forced atmospheric short-term climate predictabilityJ Clim1730903108CrossRefGoogle Scholar
  17. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of SST, sea ice and night marine air temperature since the late nineteenth century. J Geophys Res 108; DOI: 10.1029/2002JD002670Google Scholar
  18. Rowell, DP 1998Assessing potential seasonal predictability with an ensemble of multi-decadal GCM simulationsJ Clim11109120CrossRefGoogle Scholar
  19. Shukla, J 1989Tropical forecasting: Predictability perspectiveAust Meteor Mag37141153Google Scholar
  20. Stern, W, Miyakoda, K 1995Feasibility of seasonal forecasts inferred from multiple GCM simulationsJ Clim810711085CrossRefGoogle Scholar
  21. Storch, HV, Zwiers, FW 1999Statistical analysis in climate researchCambridge University PressCambridgeGoogle Scholar
  22. Straus, D, Shukla, J, Paolino, D, Schubert, S, Suarez, M, Kumar, A, Peigon, P 2003Predictability of the seasonal mean atmospheric circulationJ Clim2236293649CrossRefGoogle Scholar
  23. Taschetto, AS, Wainer, I 2008Reproducibility of South American precipitation due to subtropical South Atlantic SSTsJ Clim2128352851CrossRefGoogle Scholar
  24. Wang, XL, Zwiers, FW 1999Interannual variability of precipitation in an ensemble of AMIP climate simulations conducted with the CCC GCM2J Clim1213221335CrossRefGoogle Scholar
  25. Wehner, MF 2000A method to aid in the determination of the sampling of AGCM ensemble simulationsClim Dyn16321331CrossRefGoogle Scholar
  26. Wilcoxon, F 1945Individual comparisons by ranking methodsBiometrika18083Google Scholar
  27. Yang, X-Q, Anderson, JL, Stern, WF 1998Reproducible forced modes in AGCM ensemble integrations and potential predictability of atmospheric seasonal variations in the extratropicsJ Clim1129422959CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Climate Change Research Centre (CCRC) UNSWSydneyAustralia

Personalised recommendations