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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 - PowerPoint PPT Presentation
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Seasonal Predictability of
SMIP and SMIP/HFP
In-Sik KangJin-Ho Yoo, Kyung Jin, June-Yi Lee
Climate Environment System Research CenterSeoul 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 (1979-2000), 6 or more ensembles- 4 institute 5 models have been participated. : NCEP (USA), CCCma (Canada), SNU/KMA (Korea), MRI/JMA (Japan)
Model Institute Resolution Experiment Type
NCEP NCEP T62L28 SMIP (10 member)
GDAPS KMA T106L21 SMIP (10 member)
GCPS SNU/KMA T63L21 SMIP (10 member)
NSIPP NASA 2ox2.5o L43 AMIP (9 member)
JMA JAPAN T63L40 SMIP (10 member)
Participating Models
Total Variance of JJA Precipitation Anomalies
(a) CMAP (21yr)
(d) NASA (21yr×9member)
(b) SNU (21yr×10member)
(e) NCEP (21yr×10member)
(c) KMA (21yr×10member)
(f) JMA (21yr×6member)
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
N
ii XX
N 1
2)(1
1
N
i
n
jiij XX
nN 1 1
2)()1(
1
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
Preferable Pattern for Asian Monsoon Rainfall Prediction in Model
(a) Good Prediction
(b) Bad Prediction
(c) (a) - (b)
OISSTMME1 CMAP
(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)
5/1
6/1
7/1
8/1
8/31
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
1st and 2nd Season
Potential predictability
1st Season
Actual predictability
Characteristics of Prescribed SST and Predictability
(a) Temporal Correlation
(b) Ratio of Standard Deviation (c) RMS error
Comparison with OISST
Forced Variance Free 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 Variance Error 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
1st Mode
2nd Mode
3rd 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 1979-2002
Global Pattern Correlation (0-360E, 60S-60N)
KMA
SNU
Cor=0.30 Cor=0.08Cor=0.22 Cor=0.08
Cor=0.23 Cor=0.02Cor=0.08 Cor=0.03
Monsoon Pattern Correlation (40-160E, 20S-40N)
KMA
SNU
Cor=0.04 Cor=0.09Cor=0.03 Cor=0.05
Cor=0.06 Cor=-0.22Cor=0.01 Cor=-0.20
Prediction skill of JJA Precipitation during 1979-2002
Perfect Model Correlation of JJA Precipitation during 1979-1999
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
ObservationX (x , t)
Forecast FieldY* (x*, t)
EOFei (x) , ti (t)
SVDi = cor [Ti , Yi]
Si , Ti (t)
EOFtj (t) , ej (x*)
Yi (t) , Pi
Ri (x)
projection of Ti(t) into X
Reproduction of Systematic ErrorX (x,t) = i Yi(t) Ri (x)
Statistical Correction Procedure
Systematic bias correction
GCM prediction
GCM prediction
GCM prediction
GCM prediction
GCM prediction
MME1(composite)
MME2 (SVD based super ensemble)
Correctedprediction
Corrected prediction
Corrected prediction
Corrected prediction
Corrected prediction
Statistical Correction (Post-processing)
MME3
Specio-Ensemble prediction
Model Institute Resolution Experiment Type
NCEP NCEP T63L17 SMIP (10 member)
GDAPS KMA T106L21 SMIP (10 member)
GCPS SNU/KMA T63L21 SMIP (10 member)
NSIPP NASA 2ox2.5o L43 AMIP (9 member)
JMA JAPAN T63L40 SMIP (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)
MME3 MME2 MME1 SNU KMA NASA NCEP JMA
Avg. Skill
79-99
0.45 0.39 0.250.20 0.15 0.25 0.26 0.21
0.42 0.39 0.35 0.32 0.40
00-02
0.41 0.22 0.150.10 -0.21 0.31 0.31 N/A
0.26 0.15 0.31 -0.22 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 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