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CES/SNU AGCM Intercomparison Project WCRP/CLIVAR Predictability of SST forced signals in ensemble simulations of multiple AGCMs during El Niño.

Published byMarion Barker Modified over 8 years ago

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Presentation on theme: "CES/SNU AGCM Intercomparison Project WCRP/CLIVAR Predictability of SST forced signals in ensemble simulations of multiple AGCMs during El Niño."— Presentation transcript:

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

2 (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. Pattern Correlation and RMS for precipitation anomalies CDL/SNU (a) Pattern Correlation DJF96/97 JJA97 DJF97/98 JJA98 Fig. 4. (a) Pattern correlation coefficient between the simulated and observed precipitation anomalies for each model and each season over the Monsoon- ENSO region, 30 o S – 30 o N and 60 o E – 90 o W.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

18 CES/SNU ANOVA for DJF97/98 Monsoon Predictability

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

20 CES/SNU Forecast Skill Potential Predictability

21 CDL/SNU Range of Pattern correlations for difference number of the models Number of Models Correlation Model combinations for the highest 10 correlations for the case of three model composite. 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. 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 A better composite is not made by a combination of best models but can be made by a combination of various kinds of model. 0.85 0.56 0.86 0.66 0.83

22 CES/SNU Autocorrelations of each model Monsoon Predictability

23 CES/SNU Independency of each model Monsoon Predictability

24 CES/SNU Forecast Skill Potential Predictability

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

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

27 CES/SNU Analysis of Variance for JJA97 Precipitation Anomalies 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

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