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CES/SNU PowerPoint Presentation

CES/SNU

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CES/SNU

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  1. WCRP/CLIVAR CES/SNU AGCM Intercomparison Project Predictability of SST forced signals in ensemble simulations of multiple AGCMs during 1997-98 El Niño event Kyung Jin, In-Sik Kang, Siegfried D. Schubertand June-Yi Lee Climate Environment System Research Center School of Earth and Environmental Science Seoul National University

  2. Pattern Correlation and RMS for precipitation anomalies CDL/SNU (a) Pattern Correlation DJF96/97 JJA97 DJF97/98 JJA98 (b) Root-mean-square DJF96/97 JJA97 DJF97/98 JJA98 (b) Root-mean-square (rms) of the simulated precipitation anomalies over the Monsoon-ENSO region, normalized by the observed rms. The line in the bar indicates the range of correlation and rms values of individual run, and the black square those of the ensemble mean. Fig. 4. (a) Pattern correlation coefficient between the simulated and observed precipitation anomalies for each model and each season over the Monsoon-ENSO region, 30oS – 30oN and 60oE – 90oW.

  3. Precipitation Anomaly with 99% Significance Level CES/SNU Potential Predictability

  4. Precipitation Variability induced by Initial Conditions CES/SNU Potential Predictability

  5. 200hPa GPH Anomaly with 99% Significance Level CES/SNU Potential Predictability

  6. 200hPa GPH Variability induced by Initial Conditions CES/SNU Potential Predictability

  7. 200hPa Zonal Wind Anomaly with 99% Significance Level CES/SNU Potential Predictability

  8. 200hPa Zonal Wind Variability induced by Initial Conditions CES/SNU Potential Predictability

  9. 850hPa Zonal Wind Anomaly with 99% Significance Level CES/SNU Potential Predictability

  10. 850hPa Zonal Wind Variability induced by Initial Conditions CES/SNU Potential Predictability

  11. 500hPa GPH Variability induced by Initial Conditions CES/SNU Potential Predictability

  12. 500hPa GPH Variability induced by Initial Conditions CES/SNU Potential Predictability

  13. Ratio of Region Including 99% Significance Level For all globe CES/SNU Potential Predictability

  14. Ratio of Region Including 99% Significance Level Precipitation over Monsoon-ENSO region vs. 200hPa GPH over PNA region CES/SNU Potential Predictability

  15. Ratio of Region Including 99% Significance Level: DJF97/98 Precipitation over Monsoon-ENSO region vs. 200hPa GPH over PNA region CES/SNU Potential Predictability

  16. Precipitation Anomaly vs.Variability induced by Initial Condition Over Monsoon-ENSO region CES/SNU Potential Predictability

  17. Analysis of Variance(ANOVA) for Multi-model Ensemble CDL/SNU

  18. ANOVA for DJF97/98 CES/SNU Monsoon Predictability

  19. Analysis of Variance for each variable during DJF97/98 CES/SNU Potential Predictability

  20. Forecast Skill CES/SNU Potential Predictability

  21. Range of Pattern correlations for difference number of the models CDL/SNU 0.85 0.86 Correlation 0.66 0.56 Model combinations for the highest 10 correlations for the case of three model composite. Number of Models • The maximum correlation value changes little, while the minimum value increases significantly with an increase of the number of models . • The best model or a composite of a few models can be better than the composite of many models Fig. 7. Range of pattern correlation value between the observed and model composite precipitation anomalies over the Monsoon-ENSO region for different number of the models being composed during the 97/98winter. The black square indicates the average value of the correlation coefficients for various combinations of the models composed. The line is the range of correlations. 0.83 • A better composite is not made by a combination of best models but can be made by a combination of various kinds of model.

  22. Autocorrelations of each model CES/SNU Monsoon Predictability

  23. Independency of each model CES/SNU Monsoon Predictability

  24. Forecast Skill CES/SNU Potential Predictability

  25. Inter-ensemble Variance CES/SNU Potential Predictability JJA97 DJF97/98

  26. Analysis of Variance CDL/SNU Variance due to model bias Ratio of components of variance

  27. Analysis of Variance for JJA97 Precipitation Anomalies CES/SNU Potential Predictability • Intra-ensemble Variance • Inter-ensemble Variance • Total Variance The over-bar: mean over the m =10 ensemble members Square bracket: mean over n independent models