Evaluation of the tropical intraseasonal oscillation in a coupled climate model

1. NOAA 32nd Climate Diagnostics & Prediction Workshop22-26 October 2007 Evaluation of the tropical intraseasonal oscillation in a coupled climate model Suhee Park, Young-Hwa Byun, Han-Cheol Lim, and Won-Tae Kwon National Institute of Meteorological Research (METRI) /KMA, Korea • Contents • Introduction • Development of METRI CGCM • Intraseasonal oscillations in model simulations • The effect of air-sea coupling • Summary

2. Introduction (1/2) • Background • The development of the METRI CGCM • The METRI coupled GCM is developed for the seasonal prediction (2007) • East Asian Summer Monsoon (EASM) • An accurate prediction of the EASM is important for economy, water management and human life in East Asian region. • It has complex space and time structures, which covers both subtropics and mid-latitudes and from subseasonal to interdecadal time scale. • Deficiencies in the skill of dynamical seasonal prediction • The limited predictability of the EASM may be due to the fact that the contribution from the external variability over the region is relatively weak and comparable to that from internal variability (e.g., Stern and Miyakoda 1995; Goswami et al. 2006). • Goswami et al. (2006) proposed that the internal interannual variability of the monsoon annual cycle is primarily due to interaction between the monsoon annual cycle and the summer intraseasonal oscillations. • Therefore, the ability of the dynamical model for the seasonal prediction to represent the intraseasonal oscillation (ISO) is important.

3. Introduction (2/2) • Previous studies about simulation of the ISO • AGCM • - Poor simulation of the ISO is a generic problem in GCMs. Typically, model ISOs are too weak and propagate too fast (Slingo et al. 1996) • CGCM • Current state-of-the-art coulped GCMs still have significant problems and display a wide range of skill in simulating the tropical intraseasonal variability. The total intraseasonal (2–128 day) variance of precipitation is too weak in most of the models. (Lin et al. 2006) • Sensitivity to model ISOs • Factors expected to be important for ISO simulations: model physics, model resolution and air–sea coupling. • Slingo et al. (1996) found that convection schemes with convective available potential energy (CAPE) type closure tend to produce more realistic MJO signals. Improvements of ISO simulations were also found by adding moisture triggers to the deep convection schemes (e.g., Tokioka et al. 1988; Wang and Schlesinger 1999; Lee et al. 2003), or by including convective downdrafts and rain evaporation (Maloney and Hartmann 2001). • ISO simulation was found to be improved when using higher horizontal resolution (e.g., Kuma 1994) and/or vertical resolution (Inness et al. 2001). • Coupling to the ocean has been found by many studies to improve the MJO signals (e.g., Waliser et al. 1999; Inness and Slingo 2003, Sperber et al. 2005)

4. Objectives • The purpose of this study is to evaluate the intraseasonal oscillation in a coupled climate model • Comparison of the simulated ISO by coupled model with those of uncoupled model • Analysis of the effect of air-sea coupling on the ISO

5. Development of Coupled Climate Model (1/2) • METRI CGCM • Component models: AGCM and OGCM • The atmospheric component model • YOURS-GSM • Hor. Res. : T62 (about 2˚ x 2˚) • Ver. Res. : L28 • PBL : nonlocal scheme (Hong and Pan 1996) • Land model : two layer soil model (Mart and Pan 1984) • Cumulus convection : • Relaxed Arakawa-Schubert (Moorthi and Suarez 1992) • The ocean component model • MOM3 (Pacanowski and Griffies 1998) • Hor. Res. : Tropics 1/3˚, Extratropics 1˚ • Ver. Res. : L40 • domain : 75˚S - 65˚N, 0-360˚E • Ver. mixing: nonlocal K-profile (Larger et al. 1994) • Hor. Mixing: • - tracers; isoneutral method (Gent and Mcwilliams 1990) • - momentum; nonlinear scheme (Smagorinsky 1963) • * Explicit free surface, Partial cell • YOnsei University Research model System (YOURS) Global Spectral Model (GSM)

6. Development of Coupled Climate Model (2/2) Atmospheric model run YUORS-GSM Integration time: 24 hours Ocean model run MOM3 Integration time: 24 hours SST FLUX Zonal/meridional Momentum flux Short/long wave radiation flux Precipitation Latent/sensible heat flux • METRI CGCM • Coupling Strategy • Initial data: • I.C. for the atmosphere : NCEP/DOE Reanalysis 2 data • (R2, Kanamitsu et al. 2002) • I.C. for the ocean : Global Ocean Data Assimilation System data • (GODAS, Dehringer et al. 2005)

7. Experiments • Experimental Design • Simulation period • May-June-July-August-September 1997-2004 (5 months, 8 years) • Two runsare designed to investigate the characteristics of coupled model. * SMIP: Seasonal Prediction Model Intercomparison Project • Observed data • Precipitation : CMAP: monthly data GPCP Satellite-Derived (IR) GPI Daily Rainfall Estimates : daily data • Atmospheric variables : NCEP/DOE Reanalysis (R2) data

8. Seasonal mean climate • Performance – Seasonal mean climate (JJA) • AGCM is forced by OISST OBS AGCM CGCM Prec VP200 U850 • AGCM : strong convection => overestimated precipitation with strong U850 and VP200 • CGCM : Systematic errors are reduced.

9. Intraseasonal Variability • Performance – Intraseasonal variability • Standard deviation of 20-70-day filtered variables OBS AGCM CGCM SST Prec VP200 U850

10. Intraseasonal Variability • Strong ISO case: May 1 ~ June 30, 2002 *20-70-day filtered data U850 VP200 Prec

11. Intraseasonal Variability • Wavenumber-frequency spectra of precipitation • Meridional spectra computed over the Western Pacific (10°S–37.5°N, 100°E-150°E) time-latitude data • Zonal spectra computed over equatorial (2.5°S–2.5°N, 0–360°E) time–longitude data OBS AGCM CGCM

12. Intraseasonal Variability • Complex EOF Composite to capture the evolution of the ISO • CEOF analysis is performed in order to find the dominant propagating mode • 20-70-day bandpass filtered200 hPa velocity potential fileds • Composite maps • Separated by 30 deg over 360 deg phase range corresponding to periods of the dominants mode. (from phase-1 to phase-13) OBS AGCM CGCM Phase 1 0° Phase 4 90° Phase 7 180° Phase 10 270°

13. Intraseasonal Variability • Complex EOF composite one cycle : Precip. & V850 OBS AGCM CGCM Phase 1 0° Phase 4 90° Phase 7 180° Phase 10 270°

14. Air-sea Coupling • Lag Correlation of precipitation with surface variables (Woolnough et al. 2000) Observation Shortwave radiation flux SST Zonal Wind stress Latent Heat flux • Observations show a coherent relationship between convection and surface variables. Warm SSTs lead convection by 5-8 days. These result from increased SW radiation and reduced evaporation by weaker winds during suppressed phase of the ISO.

15. Air-sea Coupling • The ISO and Coupling with the ocean : the Air-sea interaction in the ISO over the Indian Ocean and the Western Pacific Suppressed convection phase • Easterly wind anomaly • Reduced Mean state westerly wind • Stable condition  No cloud • Evaporation decrease •  LH flux to ocean increase • Downward SW flux increase  SW flux to ocean increase SST increase Enhanced convection phase • It plays an important role in the evolution of the ISO.

16. Air-sea Coupling • Lag Correlation AGCM Shortwave radiation flux SST Zonal Wind stress Latent Heat flux • AGCM simulations show a poor relationship between convection and SST.

17. Air-sea Coupling • Lag Correlation CGCM Shortwave radiation flux SST Zonal Wind stress Latent Heat flux • CGCM simulations show a coherent relationship between convection and SST.

18. Summary • In the CGCM, simulated seasonal mean climate is more similar to observation than that in the AGCM. • The evolution of convective activity over the Indian Ocean and the eastward propagation from the Indian Ocean to the western Pacific is clearly better in the CGCM than in the AGCM. • It is found that the coupled model improve the ISO simulation and it seems to be due to the effect of the air-sea interaction. Suppressed convections make SSTs warmer and warm SSTs initiate enhanced convection. • It is noted that, although the relation between convection and SST in the CGCM experiment is similar to observation, the magnitude of correlation is lower than observation, about half. It seems to be results of the unrealistic latent heat flux anomalies, which are related not only with the error in wind anomalies, but also with the error in the low level mean state wind.

19. Thank you very much!

20. Seasonal mean climate • The effect of air-sea interaction on seasonal climate CGCM minus AGCM • Air-sea interaction in CGCM SST Overestimated convection AGCM minus OBS Less fluxes to ocean Prec Decreasing SST VP200 Reducing precipitation U850 • Differences between two model simulations can be attributed to the effect of air-sea interaction in CGCM. - Overestimated convection from AGCM leads to decreasing flux to ocean  less flux to ocean results in decreasing SST cold SSTs over tropical Indian Ocean and Pacific region reduce precipitation  more realistic precipitation

21. Interannual Variability • Performance – Interannual variability : ENSO • El Nino (1997,2002,2004) minus La Nina (1998,1999) Prec SST OBS OBS AGCM CGCM CGCM • Interannual variability in CGCM is comparable to that in AGCM forced with observed SST!

22. Intraseasonal Variability • [CASE] May 1 ~ 30, 2002 SST SWF LHF

23. Air-sea Coupling • The Air-sea interaction in the ISO simulated by CGCM Suppressed convection phase • Easterly wind anomaly • Reduced Mean state westerly wind • Stable condition  No cloud • Evaporation decrease •  LH flux to ocean increase • Downward SW flux increase  SW flux to ocean increase SST increase Enhanced convection phase