1. Sunseasonal (Extended-Range) Forecast of the Asian Summer Monsoon Rainfall Song Yang （杨崧） Department of Atmospheric Sciences Sun Yat-sen University Guangzhou, China yangsong3@mail.sysu.edu.cn

2. Model, Data and Others 4 3 2 1 5 Quasi-Biweekly Oscillation Regional Monsoons Tropical Land and Ocean Rainfalls Southern China Early-Season Rainfall CONTENT

3. 3 Coauthors and Reference 3 1. Jia, X. and S. Yang*, 2013: Impacts of the quasi-biweekly oscillation over the western North Pacific on East Asian subtropical monsoon during early summer. J. Geophys. Res., 118, 1-14. 2. Jia, X., S. Yang* & Coauthors, 2013: Prediction of global patterns of dominant quasi- biweekly oscillation by the NCEP Climate Forecast System version 2. Climate Dyn., 41, 1635-1650. 3. Liu, X., S. Yang* & Coauthors, 2013: Diagnostics of sub-seasonal prediction biases of the Asian summer monsoon by the NCEP Climate Forecast System. Climate Dyn., 41, 1453-1474. 4. Liu, X, S. Yang* & Coauthors, 2014: Subseasonal forecast skills of global summer monsoons in the NCEP Climate Forecast System version 2. Climate Dyn., 42, 1487-1508. 5. Liu, X., S. Yang* & Coauthors, 2015: Subseasonal predictions of regional summer monsoon rainfalls over tropical Asian oceans and land. J. Climate, submitted. 6. Zhao, S. and S. Yang*, 2014: Dynamical prediction of the early-season rainfall over southern China by the NCEP Climate Forecast System. Wea. Forecasting, 29, 1391-1401. 7. Zhao, S., S. Yang* & Coauthors, 2015: Skills of yearly prediction of the early-season rainfall over southern China by the NCEP Climate Forecast System. Theor. & Appl. Climatol., in press.

4. 4 US NCEP Climate Forecast System (CFSv2) 4 Atmosphere GFS2009 (T126/L64) Land NOAH 4-L Ocean MOM4 Sea ice Predicted CO 2 Evolving with time Initial conditions CFS Reanalysis (CFSR) Hindcast ~24/month (4 runs / 5 days) Forecast 4 runs/day (seasonal) 16 runs/day (45 days) Hindcast Data Used: Daily data, 1999-2011 Four members every day, integrated for 45 days

5. Model, Data and Others 4 3 2 1 5 Quasi-Biweekly Oscillation Regional Monsoons Tropical Land and Ocean Rainfalls Southern China Early-Season Rainfall CONTENT

6. 6 QBWO in AVHRR, CFSR and CFSv2 10-20-day variance of OLR in boreal summer for NOAA AVHRR, CFSR, and CFSv2 predictions at various leads

7. 7 South Asia High & E-SE Asia Convection Quasi-biweekly variability of the South Asia High is important for convergence/divergence over East & Southeast Asia Contours: H200 Shading: Difference in 200- hPa Divergence

8. 8 QBWO Variance Kikuchi and Wang (2009) Three JJA Domains Five DJF Domains

9. 9 Prediction of Eight QBWS Modes High skills for predicting the North Pacific and South Pacific Modes, but low skill for predicting the Asian summer monsoon Best: North Pacific South Pacific Worst: Asian Monsoon Central America South Africa

10. 10 Prediction of QBWO for El Nino & La Nina Years High skill for El Nino years, but low skill for La Nina years

11. Model, Data and Others 4 3 2 1 5 Quasi-Biweekly Oscillation Regional Monsoons Tropical Land and Ocean Rainfalls Southern China Early-Season Rainfall CONTENT

12. Indian Monsoon & SCS Monsoon 12 Precipitation (70ºE-90ºE) Precipitation (110ºE-130ºE ) More Effect from the Tropical Indian Ocean SST (Boundary Forcing) More Effect from the Subtropical Western Pacific High (Internal Dynamics) SST forcing is important for skills of subseasonal (extended-range) forecast of regional monsoon rainfalls

13. Webster-Yang Index & Goswami et al. Index 13 Prediction skill is high when regional monsoon is strongly related to large-scale features

14. Multivariate EOF Analysis (Rainfall & V850) 14 PCs: From short leads (red) to longer leads (blue) (1)Prediction skill is a function of the stage of monsoons (2)An abrupt turning point of bias in late June and early July

15. Model, Data and Others 4 3 2 1 5 Quasi-Biweekly Oscillation Regional Monsoons Tropical Land and Ocean Rainfalls Southern China Early-Season Rainfall CONTENT

16. Six Ocean and Land Domains 16

17. Pattern Correlation 17 Multi-year Average Individual Years Higher skills over ocean domains, esp. the Arabian Sea Lower skills over land domains, esp. the Indo- China Peninsula

18. Temporal Correlation 18 Again, higher skills are over ocean domains, esp. the Arabian Sea, and lower skills are over land domains, esp. the Indo-China Peninsula

19. Corr. between Precip & Ts 19 Positive: Enhanced radiation => increased Ts => unstable => convection Negative: Enhanced rainfall => declined radiation => decreased Ts Overestimation worsens with lead time (e.g. Arabian Sea) Positively significant over Arabian Sea Negatively significant over land except southern China

20. Corr with Ts, and Reg of V850 on P Indices 20 1.Overestimated relationships of rainfall with Ts and atmospheric circulation 2.Problem worsens with increased lead time

21. Cross Correlations: Supporting Evidence 21 1.Larger correlation appears for neighboring regions compared to more remote regions 2.Longer range predictions capture larger scale features

22. Lag Corr (Precip/Ts) 22 1.Largest correlations at leads or lags by 1-2 pentads 2.Ts forcing in AS, but Ts response in BOB and SCS 3.Ts response in Indian Peninsula, but weak air-land interaction in southern China 4.Changes with lead time?

23. Other Features 23 Regional rainfall over oceans is related to larger-scale circulation patterns, compared to that over land As lead time increases, strengthening connections between regional rainfall and large-scale circulation are found over extensive regions, and the regional independence of rainfall variability is gradually obscured by uniform large-scale features. Comparisons between skillful and unskillful forecasts indicate that the regional characteristics of rainfall and model’s deficiencies in capturing the relationship between small- and large-scale features are responsible for the regional discrepancies of actual subseasonal predictability.

24. Model, Data and Others 4 3 2 1 5 Quasi-Biweekly Oscillation Regional Monsoons Tropical Land and Ocean Rainfalls Southern China Early-Season Rainfall CONTENT

25. Simulations of SC Early-Season Rainfall 25 Rainfall for Pentads 19-36 Pentad Rainfall and Model-Obs Difference

26. Prediction of SC Rainfall (Skill of 8-14 Days) 26 Higher Skill for April-May than for June Higher Skill for Southern China than for Other Regions

27. Prediction of SC Early-Season Rainfall 27 Difference in Predicted Rainfalls (Lead Time 15-29 Days Minus Lead Time 0-14 Dyas)

28. Yearly Prediction of SC Early-Season Rainfall 28 Higher Skills in 2005 & 2006 Lower Skills in 2001 & 2010 Small Difference in LD 0-4 Prediction and LD 0-14 Prediction

29. Detailed Features for Years of High & Low Skills 29 Years of High Skills 2005 & 2006 Years of Low Skills 2001 & 2010

30. 30 Detailed Features for Years of High & Low Skills High Skills Low Skills

31. Prediction of Surface Temp for Pentads 1-12 31 High Skills for Rainfall Prediction Low Skills for Rainfall Prediction In the years with high (low) skills of predicting SC early-season rainfall, the skills of predicting the previous surface temperature over the tropical western Pacific are high (low).

32. Summary 32