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PREDICTABILITY of WPSH and EA SUMMER MONSSON and WNP TS PREDICTION

This paper explores the predictability of the Western Pacific Subtropical High (WPSH) and East Asian Summer Monsoon (EASM) and discusses methods to improve rainfall and tropical cyclone (TC) predictions. The study investigates the interannual variability of WPSH, identifies its controlling factors, and evaluates its predictability. It also proposes a physically-based empirical model for predicting WPSH intensity and examines the application of subtropical predictability to forecast EASM rainfall and WNP TC activity. Overall, the findings highlight the importance of understanding WPSH variability for accurate seasonal predictions.

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PREDICTABILITY of WPSH and EA SUMMER MONSSON and WNP TS PREDICTION

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  1. PREDICTABILITY of WPSH and EA SUMMER MONSSON and WNPTS 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)

  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. Variations of WPSH is of utmost importance for seasonalforecast 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) Sui et al. 2007 Wu and Zhou 2008: 500 hPa (120-140E, 10-30N) 80,83,87,88,93,95,96,98,03 81, 82, 84,85,86,90,94,01,02,04

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

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

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

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

  15. WPSHI can be reproduced by PC1 and PC2 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.

  16. Origin of EOF2?

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

  18. Numerical Exp: EOF 2: Forced response to CP cooling ECHAM ensemble experiments Anomalies forced by ECP SST 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 WPSH warming cooling Positive A-O thermodynamic feedback between the WPSH and the Indo-Pacific warm pool ST dipole Wang et al. 2000, Wang et al. 2003

  22. Coupled Model (POEM) Experiments

  23. Orgin of WPSH variability1. 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. Prediction of EASM strength, Tropical storm days (TSDs) and the TS numbers influencing EA EASM Intensity index Tropical storm days (WNP) Number of TS impacting EA coasts

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

  31. Real forecast test (2010-2012) 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.

  32. Conclusions 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). Dynamics of WPSH: The IAV of WNPSH is primarily controlled by equatorial central Pacific warming/cooling and apositive atmosphere-ocean feedback between the WNPSH and Indo-Pacific warm pool ocean (SST dipole). Predictability: The WNPSH is highly predictable. 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? 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. Xiang et al. 2013, GRL

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

  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

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