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Seasonal Predictability of SMIP and SMIP/HFP

Seasonal Predictability of SMIP and SMIP/HFP. In-Sik Kang Jin-Ho Yoo, Kyung Jin, June-Yi Lee Climate Environment System Research Center Seoul National University. SMIP (Seasonal prediction Model Intercomparison Project). Organized by World Climate Research Programme

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Seasonal Predictability of SMIP and SMIP/HFP

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  1. Seasonal Predictability of SMIP and SMIP/HFP In-Sik Kang Jin-Ho Yoo, Kyung Jin, June-Yi Lee Climate Environment System Research Center Seoul National University

  2. SMIP (Seasonal prediction Model Intercomparison Project) • Organized by World Climate Research Programme • Climate Variability and Predictability Programme (CLIVAR) Working Group on Seasonal to Interannual Prediction (WGSIP) • CoordinatorsG. Boer(CCCma), M. Davey (UKMO), I.-S. Kang (SNU), and K. R. Sperber (PCMDI) • Purpose • Investigate 1 or 2 season potential predictability based on the initial condition and observed boundary condition • SMIP Experimental Design • - Model Integration : 7 month x 4 season x 22 year (1979-2000), 6 or more ensembles • 4 institute 5 models have been participated. • : NCEP (USA), CCCma (Canada), SNU/KMA (Korea), MRI/JMA (Japan) • Participating Models

  3. (a) CMAP (21yr) (b) SNU (21yr×10member) (c) KMA (21yr×10member) (d) NASA (21yr×9member) (e) NCEP (21yr×10member) (f) JMA (21yr×6member) Total Variance of JJA Precipitation Anomalies

  4. (b) Forced variance Forced variance Climate signals caused by external forcing (a) Total variance (c) Free variance Free variance Intrinsic transients due to natural variability Analysis of Variance of JJA Precipitation Anomalies (SNU case)

  5. Forced Variance Free Variance Signal-to-noise

  6. Forced Variance Error Variance Forced/Error Variance

  7. (a) MME1(Model Composite) (b) SNU (c) KMA (d) NASA (e) NCEP (f) JMA Prediction Skill of JJA Precipitation during 21 years Temporal Correlation with Observed Rainfall

  8. (a) SNU Pattern Cor. for Ensemble mean Pattern Cor. for each member 5 Model Mean  (b) KMA (c) NASA MME1 – Model Composite (d) NCEP NINO3.4 (e) JMA Previous DJF NINO3.4 Recent NINO3.4 Prediction Skill of JJA Precipitation-Global Pattern Correlation

  9. Prediction Skill of JJA Monsoon Rainfall Monsoon Region (40-160E, 20S-40N) Pattern Correlation

  10. (a) Good Prediction (d) Good Prediction (f) Good Prediction (b) Bad Prediction (e) Bad Prediction (g) Bad Prediction (c) (a) - (b) Preferable Pattern for Asian Monsoon Rainfall Prediction in Model OISST MME1 CMAP Selected Cases Good Prediction: 81’ 95’ 96’ 98’ Bad Prediction: 80’ 82’ 85’ 88’

  11. SMIP/HFP (Historical Forecast Project)  SMIP2 To carry out 7-month ensemble integrations of atmospheric GCMs with observed initial conditions and observed (prescribed) boundary conditions 1st and 2nd Season Potential predictability  SMIP2/HFP To carry out 4-month ensemble integrations of atmospheric GCMs with observed initial conditions and predicted boundary conditionsor Coupled GCM 1st Season Actual predictability  HFP Procedure ( ex: prediction for summer: JJA) Global SST prediction Predicted SST 4/1 5/1 6/1 7/1 8/1 8/31 Dynamical prediction AGCM integration (4 month) 6 ensembles : started from 4/28/00,12Z, 4/29/00,12Z 4/30/00,12Z (12hr interval) Initial condition : Atmosphere NCEP Reanalysis anomaly + model climatology Land surface NCEP Reanalysis

  12. (a) Temporal Correlation (b) Ratio of Standard Deviation (c) RMS error Characteristics of Prescribed SST and Predictability Comparison with OISST

  13. Forced Variance Free Variance (b) KMA (a) SNU (b) KMA (a) SNU (c) SNU/HFP (d) KMA/HFP (c) SNU/HFP (d) KMA/HFP (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP Signal-to-noise

  14. Forced Variance Error Variance (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP (b) KMA (a) SNU (b) KMA (a) SNU (c) SNU/HFP (d) KMA/HFP (c) SNU/HFP (d) KMA/HFP Forced/Error Variance

  15. Observation Prediction EOF Analysis of Summer Mean SST Time coefficients Eigen Vectors Observation Prediction 27.7% 37.5% 1st Mode 15.8% 21.3% 2nd Mode 11.1% 8.5% 3rd Mode

  16. (a) SNU (b) KMA (c) SNU (d) KMA Change of SST Influence: Decreased Forced Variance SMIP signal – HFP signal Absolute value of COV of Prcp & CEP. SST Central Equatorial SST : 180E-220E, 5S-5N

  17. (a) Observation MME1 (b) SNU (c) KMA TPAC NPAC WPAC IDO Local Influence of Regional SST on the Asian Monsoon Rainfall Predictability

  18. SNU Cor=0.30 Cor=0.08 Cor=0.22 Cor=0.08 KMA Cor=0.23 Cor=0.02 Cor=0.08 Cor=0.03 Prediction skill of JJA Precipitation during 1979-2002 Global Pattern Correlation (0-360E, 60S-60N)

  19. SNU Cor=0.04 Cor=0.09 Cor=0.03 Cor=0.05 KMA Cor=0.06 Cor=-0.22 Cor=0.01 Cor=-0.20 Prediction skill of JJA Precipitation during 1979-2002 Monsoon Pattern Correlation (40-160E, 20S-40N)

  20. Perfect Model Correlation of JJA Precipitation during 1979-1999 Global Domain (0-360E, 60S-60N) Monsoon Region (40-160E, 20S-40N)

  21. (a) CMAP (b) SNU (c) KMA (d) NASA (e) NCEP (f) JMA (d) MME1 (e) PC time series EOF Analysis of Summer Mean Precipitation

  22. Statistical Correction Procedure Forecast Field Y* (x*, t) Observation X (x , t) EOF ei (x) , ti (t) EOF tj (t) , ej (x*) SVD i = cor [Ti , Yi] Si , Ti (t) Yi (t) , Pi projection of Ti(t) into X Ri (x) Reproduction of Systematic Error X (x,t) =  iYi(t) Ri (x)  Systematic bias correction EOF Analysis Truncation of small scale noise modes by retaining first 10 EOF modes SVD Analysis Couple pattern of observation and model Transfer Function Replace the model SVD mode to the corresponding observation mode

  23. APCN Multi Model Ensemble prediction • Ensemble procedure MME1(composite) MME2 (SVD based super ensemble) GCM prediction GCM prediction GCM prediction GCM prediction GCM prediction Statistical Correction (Post-processing) Corrected prediction Corrected prediction Corrected prediction Corrected prediction Corrected prediction MME3 Specio-Ensemble prediction • Participated Model

  24. Prediction skill of APCN Multi Model predictions Pattern correlation precipitation over monsoon region (40E-160E, 20S-40N) Prediction SST used (real forecast)

  25. SMIP/HFP history after statistical correction MME3 with SMIP type history for statistical correction MME3 with SMIP/HFP type history for statistical correction MME3 with 5 models (only SNU & KMA are different : SMIP vs SMIP/HFP) Prediction SST used (real forecast) Prediction dataset has inconsistency in SST boundary condition. During 1979-1999, observed SST was used for SMIP type simulation. However, the forecast after 2000 used predicted SST in real forecast mode. Thus, SMIP/HFP can be more skillful for later stage due to consistency in boundary condition for statistical correction based on previous forecast history

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