Seasonal Predictability in East Asian Region Targeted Training Activity: Seasonal Predictability in Tropical Regions: Research and Applications 『 East Asian Group 』 Juhyun Park (Republic of Korea) Yanju Liu (China), qiaoping Li (China) N. Jyothi (India), A. P. Dimri (India) 『 East Asian Group 』 Juhyun Park (Republic of Korea) Yanju Liu (China), qiaoping Li (China) N. Jyothi (India), A. P. Dimri (India)

Table of Contents 3. Deterministic Forecast skill in DMME 3. Deterministic Forecast skill in DMME 5. Conclusion 5. Conclusion 1. Introduction 1. Introduction 2. Climatological map of DEMETER 7 models 2. Climatological map of DEMETER 7 models 4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME

Period Period 1980 yr ~ 2001 yr (Summer mean/Winter mean) 1980 yr ~ 2001 yr (Summer mean/Winter mean) Region Region Lon. : 40E~160E, Lat. : 20S~60N Lon. : 40E~160E, Lat. : 20S~60N Variable Variable Precipitation, 2m Temperature Precipitation, 2m Temperature 1. Introduction 1. Introduction

CERFECMWINGV LODYMAXPMETF UKMODMMEOBS 850 hPa wind climatology (JJA) 2. Climatological structure of DEMETER models 2. Climatological structure of DEMETER models

CERFECMWINGV LODYMAXPMETF UKMODMMEOBS 2m Temperature climatology (JJA) 2m Temperature climatology (JJA) 2. Climatological structure of DEMETER models 2. Climatological structure of DEMETER models

Precipitation climatology (JJA) Precipitation climatology (JJA) 2. Climatological structure of DEMETER models 2. Climatological structure of DEMETER modelsCERFECMWINGV LODYMAXPMETF UKMODMMEOBS

Correlation between observation and MME of Precipitation

observation and MME of Precipitation over Northeast China

observation and MME of Precipitation over Mid-lower Yangtze River basins

2. Climatological structure of DEMETER models 2. Climatological structure of DEMETER models Effect of MME : Mean Bias reduction CMAP climatology – ECMW model climatology CMAP climatology – Multi-model climatology Effect of MME : Mean Bias reduction CMAP climatology – ECMW model climatology CMAP climatology – Multi-model climatology

3. Deterministic Forecast skill in MME 3. Deterministic Forecast skill in MME Correlation skill between Observation and Multi-Model Correlation skill between Observation and Multi-Model MSLP MSLP JJA DJF PRCP JJA DJF TA2M JJA DJF

3. Deterministic Forecast skill in MME 3. Deterministic Forecast skill in MME Indian ocean index Indian ocean index JJA DJF The Indian Ocean Region Lon. : 40 E ~ 110 E Lat. : 15 S ~ 10 N Var. : SST anomaly Black line : Observation index Red line : the index in DMME

3. Deterministic Forecast skill in MME 3. Deterministic Forecast skill in MME East Asia Summer Monsoon index East Asia Summer Monsoon index The East Asia Region Black line : Observation index Green line : the index in DMME

Correlation between EASM Index and Precipitation Observation(left) MME(right)

Precipitation in strong monsoon year(1997)

Observation(left) MME(right) Precipitation in weak monsoon year(1998)

4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME  First Step : Climatological Probability Distribution Function Climatological PDF 0 Xc-Xc  Second Step : Probability Forecast for particular time - Normalizing all the forecast value In the 3 category case, -Xc and Xc make the below area separate 1/3 value each. Ensemble PDF of particular time BNNNAN Below normal (BN) Near normal (NN) Above normal (AN) Non-parametric approach Parametric approach / For Above normal case / m : Ensemble mean value for particular year

4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME TS2MPRCP 1982 winter mean 1985 winter mean

 Reliability diagram : graphically represent the performance of probability forecasts of dichotomous events for each category  The plot of observed relative frequency as a function of forecast probability :  The 1:1 diagonal perfect reliability line :  A summary of the frequency of use of each forecast value 4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME Black, dashed line : Each model Red, solid line : DMME Above Normal Category ( Global Region ) Precipitation / JJA

4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME  Brier Score (B)  Brier skill score (BSS) n : the number of realizations of the forecasts over which the validation is preformed For each realization i, p i : forecast probability of the occurrence of the event v i : a value equal to 1 or 0 depending on the event occurred / not. B ref : a reference forecast (taken to be the low-skill climatological forecasts) BSS = 1 : a perfect forecast system BSS = 0 (negative) : performs like (poorer than) the reference system

4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME CERFECMWINGV LODYMAXPMETF UKMO Above Normal Category Precipitation / JJA DMME

4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME  Economic value Observation (real event) YesNo Forecast (Action) Yes Hit (h) Cost (C) False (f) Cost (C) No Miss (m) Loss (L) Correct reject 0 V = 1 : a perfect forecast system V = 0 : performs like the reference system * pt : threshold probability

4. Probabilistic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME CERFECMWINGV LODYMAXPMETF UKMO Above Normal Category Precipitation / JJA DMME

 The Multi-model shows the better predictability than the single model following this study.  But, the forecast skill is different about the variables and the target region. This is the same results as in the deterministic forecast.  Probability forecasts show more information for users about future climate than deterministic forecast. Because this contains the uncertainty in the forecast problem. 5. Conclusion 5. Conclusion

Thank you !! ^o^