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 Climate Variability and Predictability Programme (CLIVAR) Working Group on Seasonal to Interannual Prediction (WGSIP) Coordinators G. 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 ( ), 6 or more ensembles - 4 institute 5 models have been participated. : NCEP (USA), CCCma (Canada), SNU/KMA (Korea), MRI/JMA (Japan) ModelInstituteResolutionExperiment Type NCEP T62L28SMIP (10 member) GDAPSKMAT106L21SMIP (10 member) GCPSSNU/KMAT63L21SMIP (10 member) NSIPPNASA2 o x2.5 o L43AMIP (9 member) JMAJAPANT63L40SMIP (10 member) Participating Models Total Variance of JJA Precipitation Anomalies (a) CMAP (21yr) (d) NASA (21yr9member) (b) SNU (21yr10member) (e) NCEP (21yr10member) (c) KMA (21yr10member) (f) JMA (21yr6member) Analysis of Variance of JJA Precipitation Anomalies (SNU case) (a) Total variance (b) Forced variance (c) Free variance Free variance Intrinsic transients due to natural variability Forced variance Climate signals caused by external forcing Forced VarianceFree VarianceSignal-to-noise Forced VarianceError VarianceForced/Error Variance Prediction Skill of JJA Precipitation during 21 years (a) MME1(Model Composite) (d) NASA (b) SNU (e) NCEP (c) KMA (f) JMA Temporal Correlation with Observed Rainfall Prediction Skill of JJA Precipitation-Global Pattern Correlation (a) SNU (b) KMA (c) NASA (d) NCEP (e) JMA Previous DJF NINO3.4 Recent NINO3.4 Pattern Cor. for Ensemble mean Pattern Cor. for each member 5 Model Mean MME1 Model Composite NINO3.4 Monsoon Region (40-160E, 20S-40N) Pattern Correlation Prediction Skill of JJA Monsoon Rainfall Preferable Pattern for Asian Monsoon Rainfall Prediction in Model (a) Good Prediction (b) Bad Prediction (c) (a) - (b) OISST MME1CMAP (d) Good Prediction (e) Bad Prediction (f) Good Prediction (g) Bad Prediction Selected Cases Good Prediction: 81 95 96 98 Bad Prediction: 80 82 85 88 SMIP/HFP (Historical Forecast Project) HFP Procedure ( ex: prediction for summer: JJA) 5/16/17/18/18/31 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 AGCM integration (4 month) Global SST prediction 4/1 Predicted SST Dynamical prediction To carry out 7-month ensemble integrations of atmospheric GCMs with observed initial conditions and observed (prescribed) boundary conditions SMIP2 To carry out 4-month ensemble integrations of atmospheric GCMs with observed initial conditions and predicted boundary conditions or Coupled GCM SMIP2/HFP 1 st and 2 nd Season Potential predictability 1 st Season Actual predictability Characteristics of Prescribed SST and Predictability (a) Temporal Correlation (b) Ratio of Standard Deviation (c) RMS error Comparison with OISST Forced VarianceFree Variance Signal-to-noise (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP Forced VarianceError Variance Forced/Error Variance (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP (a) SNU (b) KMA (c) SNU/HFP (d) KMA/HFP 37.5% 21.3% 11.1% 27.7% 15.8% 8.5% Observation Prediction Time coefficients Observation Prediction Eigen Vectors 1 st Mode 2 nd Mode 3 rd Mode EOF Analysis of Summer Mean SST 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 (a) SNU (b) KMA (c) SNU (d) KMA Influence of Regional SST on the Asian Monsoon Rainfall Predictability (b) SNU (a) Observation (c) KMA TPAC NPAC WPAC IDO Local MME1 Prediction skill of JJA Precipitation during Global Pattern Correlation (0-360E, 60S-60N) KMA SNU Cor=0.30 Cor=0.08 Cor=0.22 Cor=0.08 Cor=0.23 Cor=0.02 Cor=0.08 Cor=0.03 Monsoon Pattern Correlation (40-160E, 20S-40N) KMA SNU Cor=0.04 Cor=0.09 Cor=0.03 Cor=0.05 Cor=0.06 Cor=-0.22 Cor=0.01 Cor=-0.20 Prediction skill of JJA Precipitation during Perfect Model Correlation of JJA Precipitation during Monsoon Region (40-160E, 20S-40N) Global Domain (0-360E, 60S-60N) EOF Analysis of Summer Mean Precipitation (a) CMAP (d) NASA (b) SNU (e) NCEP (c) KMA (f) JMA (d) MME1 (e) PC time series 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 Observation X (x, t) Forecast Field Y * (x *, t) EOF e i (x), t i (t) SVD i = cor [T i, Y i ] S i, T i (t) EOF t j (t), e j (x * ) Y i (t), P i R i (x) projection of T i (t) into X Reproduction of Systematic Error X (x,t) = i Y i (t) R i (x) Statistical Correction Procedure Systematic bias correction GCM prediction GCM prediction GCM prediction GCM prediction GCM prediction MME1(composite) MME2 (SVD based super ensemble) Corrected prediction Corrected prediction Corrected prediction Corrected prediction Corrected prediction Statistical Correction (Post-processing) MME3 Specio-Ensemble prediction ModelInstituteResolutionExperiment Type NCEP T63L17SMIP (10 member) GDAPSKMAT106L21SMIP (10 member) GCPSSNU/KMAT63L21SMIP (10 member) NSIPPNASA2 o x2.5 o L43AMIP (9 member) JMAJAPANT63L40SMIP (10 member) Participated Model Ensemble procedure APCN Multi Model Ensemble prediction Prediction SST used (real forecast) Prediction skill of APCN Multi Model predictions Pattern correlation precipitation over monsoon region (40E-160E, 20S-40N) MME3MME2MME1SNUKMANASANCEPJMA Avg. Skill N/A N/A SMIP/HFP history after statistical correction MME3 with 5 models (only SNU & KMA are different : SMIP vs SMIP/HFP) MME3 with SMIP type history for statistical correction MME3 with SMIP/HFP type history for statistical correction Prediction SST used (real forecast) Prediction dataset has inconsistency in SST boundary condition. During , 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