Evolution of MJO in ECMWF and GFS Precipitation Forecasts

1. Evolution of MJO in ECMWF and GFS Precipitation Forecasts John Janowiak1, Peter Bauer2 , P. Arkin1, J. Gottschalck3 1Cooperative Institute for Climate and Satellites (CICS) Earth Systems Science Interdisciplinary Center (ESSIC) University of Maryland, College Park, Maryland, USA 2ECMWF Reading, U. K. 3 Climate Prediction Center Camp Springs, Maryland, USA “Satellites” 34th Climate Diagnostics and Prediction Workshop, Monterey,CA Oct 29, 2009

2. Outline • Motivation • CMORPH Background (“observations”) • Case Study of MJO as represented in precip. field from: - CMORPH - ECMWF forecasts (1-10 day) - GFS forecasts (1-15 day) • Conjecture … and a Forecast

3. Janowiak: MWR, 1990 Note: 12-36h forecasts • Models circa1989: • Some MJO behavior in • dynamic fields … but not • reflected in precipitation • … so, let’s reexamine • using today’s models “observed” (GPI) Model fcsts

4. Outline • Motivation • CMORPH Background (“observations”) • Case Studies of MJO as represented in precip. field from: - CMORPH - ECMWF forecasts (1-10 day) - GFS forecasts (1-15 day) • Conjecture and … a Forecast

5. CMORPH* NOAA/CPC “Morphing” technique Provides quantitativeestimates of precip @ 0.07o x 0.07o lat/lon / ½ hr ( ~ 8 km @ equator) Uses IR or model winds to propagate & ‘morph’ precip. identified by passive microwave Dec 2002 – present; extending back to ~1998 Hourly Precipitation Loops: 15Z 8Jun2008 – 06Z9Jun2008 RADAR CMORPH 0.25o lat/lon 0.07o lat/lon “morphing”: spatial/temporal interpolation * Joyce et al. (J. Hydromet 2004)

6. CMORPH mm/hr Yields confidence that satellite estimates are useful over water Note: estimates are theoretically better over water than land

7. Outline • Motivation • CMORPH Background (“observations”) • Case Studies of MJO as represented in precip. field from: - CMORPH - ECMWF forecasts (1–10 day) - GFS forecasts (1-15 day) • Conjecture and … a Forecast

8. Anomaly from Period Mean 15N-15S Case Study: Mod-Stg MJO Nov 2007 – Feb 2008 (CPC: Jon Gottschalck) CMORPH Precipitation from Indian Ocean across the Pacific to Greenwich Seasonal mean removed MJO signatures clearly evident Diagonal lines subjectively drawn to identify axis of MJO (and intervening dry periods) & eastward progression of features T I M E

9. Anomaly from Period Mean 15N-15S Case Study: Mod-Stg MJO Nov 2007 – Feb 2008 CMORPH Arrows identify westward moving elements within MJO envelope (Nakazawa, 1988)

13. ~10days

15. ~10 days

17. Difference from Nov 2007 – Feb 2008 Period Mean Dec 4-15, 2007 Dec 16 – Jan 3 Jan 5-20, 2008

18. W W Excellent Difference from Nov 2007 – Feb 2008 Period Mean Dec 4-15, 2007 Dec 16 – Jan 3 Jan 5-20, 2008

19. Excellent Difference from Nov 2007 – Feb 2008 Period Mean Dec 4-15, 2007 Dec 16 – Jan 3 W Jan 5-20, 2008

20. Difference from Nov 2007 – Feb 2008 Period Mean Dec 4-15, 2007 Dec 16 – Jan 3 W Jan 5-20, 2008 Excellent

21. CMORPH GFS 10 dy ECMWF 10 dy Difference from Nov 2007 – Feb 2008 Period Mean Dec 4-15 A (5 dy smoothed) B • Models clearly show MJO signal • But late compared to obs • More spread out in time C

22. A CMORPH GFS 10 dy ECMWF 10 dy B C Difference from Nov 2007 – Feb 2008 Period Mean (5 dy smoothed) Dec 16-Jan 3

23. A CMORPH GFS 10 dy ECMWF 10 dy B C Difference from Nov 2007 – Feb 2008 Period Mean (5 dy smoothed) Jan 5-20

24. Model beats persistence:3-4 days “Persistence” Corr: 0.51 0 1 2 3 4 5 6 7 These show pattern correlations over the region between forecasts and observations for different lags (the different colored lines) and for different forecast lead time (the horizontal axis) The green line labeled “1” represents the correlation between forecasts initialized one day later than the observations they are compared to, etc.

25. Conjecture and … a Forecast … • Model forecasts of MJO precip. evolution can be helped by ocean-atmosphere coupling • Plans: perform same analyses on CFSRR ‘hindcasts’

26. “Tomorrow” (within a decade or so) CMORPH CMORPH GFS 10 dy GFS 10 dy ECMWF 10 dy ECMWF 10 dy “observed” “obs” Model fcsts “today” (2007) “yesterday” (1989)

27. Thank you … johnj@essic.umd.edu

28. EXTRA

29. Jan-May 2005 (weak-mod) ~10 days `

30. Same, except for global Tropics

31. 1 0 2 3 4 5 6 7 These show pattern correlations over the region between forecasts and observations for different lags (the different colored lines) and for different forecast lead time (the horizontal axis) The green line labeled “1” represents the correlation between forecasts initialized one day later than the observations they are compared to “Interesting if true” – we are working to figure out what this might mean

32. Conclusions • Both the GFS and (particularly) ECMWF exhibit realistic MJO precipitation patterns and variability • At longer leads, both models lose details and lag behind the observations • Perhaps the initialization is imperfect in some fashion – or these results make a case for more effective precipitation initialization? • These advances (relative to ~1990) suggest that useful skill in predicting MJO-related precipitation may be close to being attained