1 / 35

On Climate Predictability of the Summer Monsoon Rainfall Bin Wang

On Climate Predictability of the Summer Monsoon Rainfall Bin Wang Department of Meteorology and IPRC University of Hawaii Acknowledging contribution from Q. H. Ding, X. H. Fu, I.-S. Kang, J.-Y. Lee, K. Jin. JJA precipitation, 850 hPa winds, 200hPa STR. MCZ: BOB-SCS-PS.

nalani
Download Presentation

On Climate Predictability of the Summer Monsoon Rainfall Bin Wang

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. On Climate Predictability of the Summer Monsoon Rainfall Bin Wang Department of Meteorology and IPRC University of Hawaii Acknowledging contribution from Q. H. Ding, X. H. Fu, I.-S. Kang, J.-Y. Lee, K. Jin

  2. JJA precipitation, 850 hPa winds, 200hPa STR MCZ: BOB-SCS-PS

  3. Why do we care about the rainfall in MCZ? Source of predictability for EASM Wang, Wu and Lau 2001

  4. Assessment of 11 AGCMs ensemble simulations of summer monsoon rainfall Data: CLIVAR/ Monsoon panel Intercomparison project) (Kang et al. 2002) AMIP type design 10-member ensemble Focus on 1997 ElNino (Sept 1 1996-August 31 1998)

  5. ISM (5-30N, 65-105E) WNPSM (5-25N, 110-150E) AGCMs climatology is poor in WNP heat source region

  6. AAM and El Nino domain

  7. El Nino region A-AM region Wang, Kang, Lee 2003, JC

  8. MCZ Rest of A-AM

  9. Prediction skill for JJA rainfall (2 years) 11-model ensemble mean

  10. Prediction skill for JJA rainfall (21 years) 5-model ensemble mean

  11. Why do Nearly All Atmospheric Models Fail to Simulate Seasonal Rainfall Anomalies in Summer Monsoon Convergence Zones? Bad model? Poor strategy?

  12. Concurrent Rainfall Leads SST by 1-month SST leads Rainfall by 1-month Observed Rainfall-SST correlation (1979-2002) Fig.3

  13. Simultatious Rainfall Leads SST by 1-month SST leads Rainfall by 1-month Rainfall-SST correlation fromCoupled model ) Fig.4

  14. Concurrent Rainfall Leads SST by 1-month SST leads Rainfall by 1-month Rainfall-SST correlation from AMIP-type run Fig.5

  15. Predictability of the ISO Are MJO or boreal summer ISO reproducible in forced AGCM simulations (AMIP-type)? How important is the air-sea interaction in prediction of ISO?

  16. 1979

  17. CMAP Rainfall Coupled Daily Forced Mean Forced

  18. Arabian Sea Bay of Bengal Phase Relationships between Rainfall and SST

  19. Kemball-Cook and Wang (2001)

  20. Summary AGCM alone can not reproduce realistic seasonal rainfall anomalies in summer Monsoon Convergence Zone (MCZ). Caution should be taken when validating model or determining upper limit of predictability using AMIP approach. Two-tier approach may be inherently inadequate for monsoon rainfall anomalies. Atmospheric only model may loss significant amount of predictability on MJO. Coupled and forced ISO solutions are two distinguished solutions. Chaos can be induced by both IC and BC errors.

  21. Thank You

  22. Main Points Current AGCMs forced by SSTA have little skill in simulation and prediction of seasonal rainfall anomalies over summer Monsoon Convergence Zone (MCZ). Cautions must be taken when validating model or determining the upper limit of the predictability using AMIP approach. Two-tier approach may be inherently inadequate for monsoon rainfall anomalies. Atmospheric only model may loss significant amount of predictability of MJO.

  23. Fig.2

  24. Monsoon climate prediction must deal with ISO Cadet 1986 Is ISO a noise or signal?

  25. OBS-Model correlation: sample size 22 Anomalous SST-Model precipitation Correlation

  26. Correlation coefficients: Local SST-Precipitation Anomalies In the MCZ region: sample size: 222 or 2220 OBS 11-COMPOS. COLA DNM GEOS GFDL IAP IITM MRI NCAR NCEP SNU SUNY JJA 97 -0.15 0.59 0.42 0.49 0.38 0.19 0.02 0.15 0.49 0.43 0.39 0.36 0.33 SON 97 -0.33 0.71 0.59 0.7 0.5 0.35 0.45 0.49 0.44 0.66 0.37 0.34 0.37 JJA 98 -0.45 0.56 0.19 0.77 0.24 0.52 0.59 0.44 0.5 0.51 0.57 -0.12 0.38 TO- TAL -0.35 0.58 0.33 0.65 0.32 0.42 0.42 0.37 0.47 0.51 0.47 0.04 0.35

  27. OBS-Model correlation: sample size 22 Anomalous SST-Model precipitation Correlation

  28. (a) MME1(Model Composite) (b) SNU (c) KMA (d) NASA (e) NCEP (f) JMA Prediction Skill of JJA Precipitation during 21 years Temporal Correlation with Observed Rainfall

  29. Fig. 4. Same as in Fig. 2 except the results are obtained from MME (5-model) output for the period 1979-1999.

  30. Regional Coupled Model: ECHAM-UHIO • Atmospheric Model: ECHAM4.6 T30 (3.75o). • Ocean Model: UH 2.5-layer Intermediate Model, 2ox1o • Coupling: daily, Full, No flux correction; Warm pool only

  31. UH 2.5 layer Ocean Model (Wang, Li, Chang 1995, JPO; Fu and Wang 2001, JC)

More Related