Operational MJO prediction at ECMWF

1. Operational MJO prediction at ECMWF Frédéric Vitart With contributions from A. Beljaar, P. Bechtold and M . Miller European Centre for Medium-Range Weather Forecasts

2. Outline • MJO prediction using the ECMWF monthly forecasting system 2. Skill of the monthly forecasting system to predict the MJO • Some sensitivity experiments • Impact of changes in physic parameterization • Conclusion

3. Forecasting at ECMWF ECMWF: Weather and Climate Dynamical Forecasts Product Atmospheric model Atmospheric model Tool Ocean model Ocean model Forecasting systems at ECMWF Medium-Range Forecasts Day 1-15 Monthly Forecast Day 1-32 Seasonal Forecasts Month 1-7 (13) Atmospheric model

4. The ECMWF monthly forecasting system • Coupled ocean-atmosphere integrations: a 51-member ensemble is integrated for 32 days every Thursday • Atmospheric component: IFS with the latest operational cycle and with a T159L62 resolution (about 125 km) • Oceanic component: HOPE (from Max Plank Institute) with a zonal resolution of 1.4 degrees and 29 vertical levels • Coupling: OASIS (CERFACS). Coupling every ocean time step (1 hour) • Calibration: a 5-member ensemble is integrated at the same day and same month as the real-time time forecast over the past 12 years.

5. Prediction of the Madden Julian Oscillation (MJO) Monthly Forecast starting on 7 December 2007 VP200

6. Prediction of the Madden Julian Oscillation (MJO) Monthly Forecast starting on 7 December 2007

7. MJO metric (similar to Wheeler and Hendon (2004)) • Forecast anomalies of OLR, U850, & U200 are obtained by subtracting the hindcast climatology of the respective fields from the forecast. • The forecast anomalies are then averaged over latitudes (-15, 15). • The forecast anomalies of the 3 fields are divided by the respective global standard deviations calculated from the NCEP reanalyses. • The resulting anomalies are projected onto the first two EOF patterns, the latter being computed by Matt Wheeler from NCEP reanalyses. • The two projection coefficient time series are further divided by the standard deviations of first two PC (also from NCEP reanalyses), to obtain the RMM12. • To remove the interannual variability, a 120-day running mean of RMM12 is subtracted.

8. Operational MJO Prediction

9. Coupled forecast at TL159 (~125 km) Initial condition Day 32 Planned unified 32-day VAREPS/monthly system Present TL159 monthly system: + 12-year climatology Future 32-day VAREPS/monthly system: EPS Integration at T399 (~50km) Coupled forecast at TL255 (~80km) Initial condition Day 32 Day 10 Heat flux, Wind stress, P-E Ocean only integration + 18-year climatology

10. Skill of the MJO Prediction • EXPERIMENT: A 5-member ensemble has been run every day from 15 December 1992 to 31st January 1993. • The MJO diagnostic is based on the same method as in the real-time forecasts, except for: • Velocity potential anomalies at 200 hPa instead of U200 • Fields are averaged between 10N and 10S instead of 15N and 15S.

11. ERA40: 15/12/92-31/01/93 Skill of the MJO Prediction Velocity Potential 200 hPa U850 OLR

12. Skill of the MJO Prediction

13. Skill of the MJO Prediction Current operational version of the monthly forecasting system (CY32R2) Anomaly correlation Amplitude PC1 PC2

14. Impact of ocean-atmosphere coupling Cycle CY32R2 PC2 PC1 VAREPS Coupled Ocean ML

15. Warm ocean (skin ) layer model to represent diurnal time scale Similarity temperature profile is assumed with depth scale d representing penetration depth of solar radiation: (surface/skin temperature) (deep/bulk temperature) Prognostic equation for temperature difference between skin and bulk ocean temperature: Energy forcing from surface and solar absorption Wind and stability dependent mixing term (reduces temperature difference to 0 in strong winds) Zeng and Beljaars (2005), Geophys. Res. Lett. 32, L14605

16. Tsk difference between 20 UTC and 12 UTC averaged from 20 to 22 May 1988 Model Tsk difference; forecast from 19880519 12 UTC Wind speed (m/s) Tsk difference retrieved from GOES-8(Wu et al. 1999; BAMS, 80, 1127-1138)

17. Impact of the Ocean Slab Model PC1 PC2 Coupled+slab model Mixed-layer model Coupled

18. Evolution of SST anomalies. 5-day means Control Coupled Analysis Coupled + Slab pentad2-pentad1 pentad3-pentad2 pentad4-pentad3 pentad5-pentad4 pentad6-pentad5

19. Impact of changes in model parameterization OLR- Forecast range: day 15 ERA40 28R3 29R1 31R1 32R2 32R3 29/12 05/01 12/01 days 20/01 28/01 04/04 12/02 10/04 04/05 09/06 06/07 11/07

20. Amplitude of the MJO PC1 PC2 28R3 29R1 30R2 31r1 32r2 32r3

21. MJO Propagation PC1 PC2 28R3 29R1 30R2 31r1 32r2 32r3

22. CY32R3: velocity Potential at 200hPa Starting date: 29/12/1992

23. Recent or planned changes to the ECMWF physical parametrizations • Improved treatment of ice sedimentation, auto- conversion to snow in cloud scheme and super-saturation with respect to ice (CY31R1) • Change in the short-wave radiation code (more wavelengths) + McICA • MODIS albedo + revised cloud optical properties (CY32R2) • New formulation of convective entrainment • Variable relaxation timescale for closure • Reduction in background free atmosphere vertical diffusion (CY32R3)

24. Impact on relative humidity (RH) climatology 31r1 – 30r1 annual mean difference Largest changes in the tropical upper troposphere

25. Convection changes to operational massflux scheme New formulation of convective entrainment: Previously linked to moisture convergence – Now more dependent on the relative humidity of the environment New formulation of relaxation timescale used in massflux closure: Previously only varied with horizontal resolution – Now a variable that is dependent on the convective turnover timescale i.e. variable in both space and time also Impact of these changes is large including a major increase in tropical variability. Midlatitude synoptic variability is also increased and is more realistic.

26. Composite vertical profile for west pac, JJA Minimum cloud top heights distributions (CloudSat) precipitating clouds • Of note: • Trimodality (quadra-modal) heights • precipitating clouds are deeper than non precipitating clouds non- precip clouds

27. Average tropical cloud profiles In the Tropics now a tri-modal cloud distribution becomes apparent, with a strong increase in mid-level clouds. CloudSat data will be used to verify these results.

28. Forecast Biases and Convection Precipitation for DJF against GPCP for different cycles: from 15 year 5 months integrations for 1990-2005. CY31R1 Sep 2006 CY32R2 June 2007 CY32R3 Nov 2007

29. Tropical variability in wave-number frequency space of OLR (DJFM) Obs (NOAA) 32R3 32R1

30. MJO Prediction Current system CY32R2 VarEPS/Monthly + CY32R3

31. Conclusion • The current monthly forecasting system has some skill to predict the amplitude of the MJO up to about 14 days. However the amplitude of the MJO is significantly reduced after a few days of forecasts, but this will change after the new model operational implementation on 3 November. • Sensitivity experiments: • Strong impact of ocean-atmosphere coupling • No significant impact when increasing the horizontal resolution to about 80 kilometres • Strong sensitivity to changes in the model parameterization, and in particular to the mass flux scheme.