1. PREDICTABILITY of WPSH and EA SUMMER MONSSON and WNP TS PREDICTION Bin Wang, Baoqiang Xiang, June-Yi Lee Department of Meteorology and IPRC, University of Hawaii PNAS 2013 International Conference on S2S Prediction, College Park, Maryland, USA 10-13 February, 2014

2. The CliPAS-ENSEMBLES MME System (9 models) Institute Model Name AGCMOGCM Ensemble member Reference ABOM POAMA1.5 BAM 3.0d T47 L17 ACOM3 0.5-1.5 o lat x 2.0 o lon L31 10Zhong et al (2005) GFDL CM2.1 AM2.1 2 o lat x 2.5 o lon L24 MOM4 1/3 o lat x 1 o lon L50 10Delworth et al (2006) NCEP CFS GFS T62 L64 MOM3 1/3 o lat x 5/8 o lon L27 15Saha et al (2006) SNU T42L21 MOM2.2 1/3 o lat x 1 o lon L40 6Kug et al (2008) CMCC-INGV CMCC ECHAM5 T63 L19 OPA 8.2 2.0 o x2.0 o L31 9 Alessandri et al (2011) Pietro and Masina (2009) ECMWF IFS CY31R1 T159 L62 HOPE-E 1.4 o x 0.3 o -1.4 o L29 9 Stockdale et al. (2011) Balmaseda et al. (2008) IFM-GEOMAR IFM ECHAM4 T42 L19 OPA 8.2 2.0 o lat x 2.0 o lon L31 9 Keenlyside et al. (2005) Jungclaus et al. (2006) MF IFS T95 L40 OPA 8.0 182GPx152GP L31 9 Daget et al. (2009) Salas Melia (2002) UKMO ECHAM5 T42 L19 MPI-OM1 2.5 o lat x 0.5 o -2.5 o lon L23 9Collins et al. (2008)

3. Current skills for Prediction of SM rainfall is far from satisfactory

4. Given the limited skills of the current models, Is there anything else we can do beside MME? A new thrill: Use the high predictability of the controlling circulation system to improve rainfall prediction and TC activity prediction that the models cannot do

5. Asian summer monsoon Circulation system Asian summer monsoon circulation system

6. V ariations of WPSH is of utmost importance for seasonal forecast of EASM rainfall and WNP TS activity, (Tao and Chen 1987, Nita 1987, Wu et al. 2002, Wang et al. 2001, Lau and Weng 2002), Yet the causes of the WPSH variability remains debated and the predictability of the WPSH has not been established.

7. I. How to measure the year-to- year variation of the WPSH? II. What determines WPSH interannual variability? III. How predictable is the WPSH? IV. Can WPSH predictability benefit EASM rainfall and WNP TS predictions?

8. I. WPSH Index and its representation of the EASM and WNP tropical storms (TSs)

9. WNPSH has largest variability in the global subtropics WNPSH index : Normalized JJA mean H850 anomaly (15°N-25°N, 115°E-150°E) 80,83,87,88,93,95,96,98,03 81, 82, 84,85,86,90,94,01,02,04 Sui et al. 2007 Wu and Zhou 2008: 500 hPa (120-140E, 10-30N)

10. Wang et al. 2009 MV-EOF1: Precipitation, winds at 850 hPa and 200 hPa, and SLP. WPSH Index and the EA- WPSM PC1: R=0.92 WPSHI represents the leading mode of EA-WPSM

11. WPSHI reflects WNP TS Days (TSD) 4 extremely strong (left) vs. 4 extremely weak (right) years WPSHI and Subtropical WNP TSD r=-0.81

12. WPSHI reflects the TS numbers that affect EA coasts WPSHI and reversed TS numbers in six coastal regions (R=0.76 ). JJA TSDs: Climatology (contours) and the difference (shading) between 9 strong and 10 weak WPSH cases.

13. II. What control interannual variability of the WPSH? Approach to answer: What controls major modes of H850 variability?

14. r=0.60 r=0.73 Cor with WPSHI First two leading EOF modes of JJA 850 hPa GPH anomaly They account for 75% of WNP H850 variance

15. The normalized WNPSH index (black) and the reconstructed index based on PC1 and PC2 ( 1.226×EOF-1+1.245×EOF-2 ) from the EOF analysis of H850 anomaly (red). They have a correlation of about 0.94. WPSHI can be reproduced by PC1 and PC2

16. Origin of EOF2?

17. Anomalies associated with EOF 2 H850 & precipitation SST & 850 winds

18. Anomalies forced by ECP SST cooling ECHAM ensemble experiments Numerical Exp: EOF 2: Forced response to CP cooling

19. Origin of EOF 1?

20. Anomalies associated with EOF 1 H 850 & rainfall Winds 850 & SST A

21. EOF 1: WPSH and Indo-Pacific SST coupled Mode Positive A-O thermodynamic feedback between the WPSH and the Indo-Pacific warm pool ST dipole warming WPSH Wang et al. 2000, Wang et al. 2003

22. Coupled Model (POEM) Experiments

23. Orgin of WPSH variability 1. remote cooling/warming in the equatorial central Pacific (EOF2) 2. local positive thermodynamic feedback between the WNPSH and the Indo-Pacific warm pool SST dipole. (EOF1)

24. III. How predictable is the interannual variation of the WPSH ? Estimate the practical predictability by Physically based Empirical (P-E) model Multi-dynamical models ensemble

25. Predictors selected based on the major modes dynamics IO-WNP = SSTA in IO (10°S- 10°N, 50°E-110°E) minus SSTA in WNP (0°-15°N, 120°E-160°E) during April- May. r= 0.76 CPSST = May minus March (SSTA (5°S-5°N, 170°W- 130°W) r = - 0.50 NAOI during April-May is a precursor for EOF2 r = - 0.41

26. WPSH intensity reproduced by a Physically based empirical model WPSH index =1.756×(IO-WNP) –0.435×CPSST–0.282×NAOI

27. Hindcast the WPSH intensity by coupled climate models (1982-2005) The WPSH intensity is highly predictable

28. IV. Using Subtropical Predictability to forecast EASM rainfall and WNP TS prediction

29. EASM Intensity index Tropical storm days (WNP) Number of TS impacting EA coasts Prediction of EASM strength, Tropical storm days (TSDs) and the TS numbers influencing EA

30. The P-E model prediction is constructed by the product of the predicted WPSH index and the regressed precipitation onto the observed WPSH index. 3 coupled models’ MMEP-E model Prediction Temporal correlation skill of JJA rainfall

31. Observed WPSH index (blue) and simulated and predicted WPSH (red) The empirical model is constructed based on the time period between 1979- 2009 and the last three years (2010-2012) are predicted. Real forecast test (2010-2012)

32. Conclusions 1.Importance: The IAV of the WPSH faithfully represents the strength of EASM (r=–0.92), the TS days over the subtropical WNP (r=–0.81), and the total number of TSs impacting East Asian coasts (r=–0.76). 2.Dynamics of WPSH: The IAV of WNPSH is primarily controlled by equatorial central Pacific warming/cooling and a positive atmosphere-ocean feedback between the WNPSH and Indo-Pacific warm pool ocean (SST dipole). 3.Predictability: The WNPSH is highly predictable. 4.Prediction of precipitation and TS: Predictability of the WNPSH paves a promising pathway to predict EASM, ASM and WNP tropical storm activity.

33. Implications Positive monsoon-ocean interaction can provide a new source of climate predictability. Subtropical dynamics is important for understanding monsoon/TS predictability. Making use of the global models’ strength in prediction of large scale circulation may substantially improve direct rainfall and TC prediction

34. Thank You Comments?

35. How can anomalous western North Pacific Subtropical High intensify in late summer? Xiang et al. 2013, GRL 1) The majority of strong WNPSH cases exhibit anomalous intensification in late summer (August), which is dominantly determined by the seasonal march of the mean state. 2) The local convection-wind-evaporation-SST (CWES) feedback relying on both mean flows and mean precipitation. This mechanism is more effective in August.

36. Coupled mode of WPSH intensify from June to August 14 extreme WPSH composite evolution from June to August Xiang et al. 2013 GRL

37. Mean precipitation (shading) and 850 hPa wind

38. Model simulated response in June and August June August Xiang et al. 2013 GRL The local convection-wind-evaporation-SST (CWES) feedback relying on both mean flows and mean precipitation is more effective in August.

39. Observed intensification of WNPSH in August

40. EOF-2 (forced mode)

41. Modeling Evidence for EOF-2 June August Precipitation (shading), H850 (black contours) and SST forcing (blue contours)

42. Why Intensified? Simply answer: ---- Mean state change

43. Remarks

44. Two leading EOF modes of H850 in JJA