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

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 Models circa 1989: Some MJO behavior in dynamic fields … but not reflected in precipitation … so, let’s reexamine using today’s models “observed” (GPI) Model fcsts Note: 12-36h forecasts

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 quantitative estimates of precip @ 0.07 o x 0.07 o 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 * Joyce et al. (J. Hydromet 2004) “morphing”: spatial/temporal interpolation RADARCMORPH Hourly Precipitation Loops: 15Z 8Jun2008 – 06Z9Jun2008 0.25 o lat/lon 0.07 o lat/lon

6. mm/hr CMORPH 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. Case Study: Mod-Stg MJO Nov 2007 – Feb 2008 (CPC: Jon Gottschalck) CMORPH Anomaly from Period Mean 15N-15S 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

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

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

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

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

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

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

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

24. 6 0 1 2 3 4 5 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. “Persistence” Corr: 0.51 Model beats persistence:3-4 days

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

27. Thank you … johnj@essic.umd.edu

28. EXTRA

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

30. Same, except for global Tropics

31. 6 0 1 2 3 4 5 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