1. Convergence and precipitation in AGCM simulations Baode Chen, Caterina Tassone, Pete Robertson, Max Suarez, Larry Takacs

2. Outline Motivation AGCM precipitation biases are sensitive (or not) to various parameters Are there “deeper” similarities between models with similar biases? Understand budgets of ITCZs Model and experiment descriptions Correlations of low-level convergence and precipitation Analysis of water vapor budgets A look at high-frequency (daily) statistics of low-level convergence Conclusions

3. Models All have shown same basic sensitivity – more rain reevaporation => less double ITCZ bias NSIPP-1 (1997- RAS w/ simple microphysics Diagnostic clouds Louis PBL UCLA dynamical core NSIPP-2 (2002-2005) RAS w/ Sundquist Prognostic Clouds Louis PBL UCLA dynamical core GEOS-5 (2004 -- ) “MERRA tag” -1/2007 RAS w/ Sundquist Prognostic clouds Lock PBL FV dynamical core run 2.5x2.0 to 0.333x0.25 resolution – analysis and climate mode still looking at anvil ice sedimentation rates

4. Zonal wind at 200 mb DJFTotal Precip. water (TPW) DJF

5. SWCF DJF LWCF DJF

6. Taylor diagrams

7. Experiments 2 GEOS-5 experiments, 1.25 o x1.0 o resolution, AMIP-style, forced w/ weekly SSTs 1) Standard GEOS-5 “MERRA tag”: weak or no double ITCZ bias 2) Modified MERRA tag less reevaporation increased RAS relaxation rate at low-levels strong double ITCZ Focus on 6 months of daily model output from 1994 Occasional comparisons with NSIPP-2 (2.5x2) simulations as well as GPCP daily precipitation estimates and surface divergence derived from SSMI scatterometer winds

8. Standard GEOS-5 w/ weak or no double ITCZ (4 JJAs) Modified GEOS-5 w/ strong double ITCZ (1 JJA)

9. Standard GEOS-5 w/ weak or no double ITCZ Modified GEOS-5 w/ strong double ITCZ

10. Standard GEOS-5 w/ weak or no double ITCZ (DJF) Modified GEOS-5 w/ strong double ITCZ (DJF) Equatorial water vapor profiles

11. dd Basic sensitivity – more rain reevaporation => less double ITCZ bias NSIPP-2 seasonal mean precipitation JJA Low reevap medium reevap high reevap Observations

12. Correlation of daily precipitation with daily as a function of latitude and longitude  850 precip(i,j)  850 (i,j)

13. Strong re-evap Weak re-evapmoderate re-evap 0. 0.4 0.8 1.0 r Correlation of daily precipitation with  850 in NSIPP-2 (May 1 –Sep 1) (2x)

14. Correlation of daily precipitation with  850 in GEOS-5 (March 1 –Sep 1) Standard GEOS5 – weak biasmodified – strong bias SSMI divergence vs GPCP precip (2001) SSMI winds binned to 1x1 then divergence calculated

15. Very/Pretty Good Not so good??? Vertically-integrated water budget TPW change (storage) Total moisture convergence surface evaporation

16. P-E Seasonal mean integrated moisture convergence and P - E Standard GEOS5 – weak bias modified – strong bias Standard GEOS5 – weak biasmodified – strong bias “sloppy” calculation for integral term - still pretty close

17. Correlation of daily In the tropics where precipitation is high, storage is not an issue Standard GEOS5 – weak biasmodified – strong bias

18. precipitation surface evaporation Evaporation - flat, bland field compared with precipitation

19. precipitation Evaporation (bold) and precipitation time series for boxes

20. Local evaporation not a significant factor in time-space structure of integrated water vapor budget Storage term (TPW) is not a factor in tropics where precipitation is high

21. Integrated moisture convergence and P - E Standard GEOS5 – weak bias modified – strong bias Standard GEOS5 – weak bias modified – strong bias P-E sfc

22. Standard GEOS5 – weak biasmodified – strong bias Standard GEOS5 – weak bias modified – strong bias Total moisture convergence compared with PBL convergence

23. Standard GEOS5 – weak biasmodified – strong bias Standard GEOS5 – weak bias modified – strong bias Total moisture convergence compared with PBL convergence

24. red - blue dashed - black - Mean moisture budget terms along 8S modfied – strong bias standard – weak bias

25. red - blue dashed - black - Mean moisture budget terms along 8S modfied – strong bias standard – weak bias Mid-level moisture divergence low-level moisture convergence

26. Overall, low level moisture convergence oversupplies precipitation in ITCZs. Excess gotten rid of by divergence in free troposphere. Still, low-level convergence explains basic structure of precipitation field

27. Standard GEOS5 – weak biasmodified – strong bias Standard GEOS5 – weak bias modified – strong bias Total moisture convergence compared with PBL convergence Interesting exception to “oversupply”. Also the case in NSIPP-2

28. Hovmueller of W850 along 8S

29. Hovmueller of W850 along 8N

30. Instantaneous w850 fields from standard(left) and modified (right) GEOS5

32. 168W - 155W; 5N-15N 110W - 98W; 5N-15N 168W - 155W; 15S-5S 118W - 105W; 15S-5S Mar 15-Apr 4Apr 15-May 5May 15-Jun 4Jun 15-Jul 5Jul 15-Aug 4 West of Peru S-West of Mexico Cen. Pac. S. ITCZ Cen. Pac. N. ITCZ Time PDFs of  850 in different regions and periods for standard and modified exps

33. 168W - 155W; 5N-15N 110W - 98W; 5N-15N 168W - 155W; 15S-5S 118W - 105W; 15S-5S Mar 15-Apr 4Apr 15-May 5May 15-Jun 4Jun 15-Jul 5Jul 15-Aug 4 West of Peru S-West of Mexico Cen. Pac. S. ITCZ Cen. Pac. N. ITCZ PDFs of  850 in different regions and periods for standard and modified exps red – PDFs for standard GEOS5Black – PDFs for modified GEOS5

34. PDFs of daily w850 in three regions: Black curve shows weak re- evaporation case, red-dashed shows strong re-evaporation case

35. mode mean Fluctuation RMS Overall statistics for PDFs in 10 o x12 o x20d subdomains Standard GEOS5 Modified GEOS5

36. Standard GEOS5 has more symmetric PDFs of w850 Modified GEOS5 with stronger double ITCZ bias has highly-skewed asymmetric PDFs, mode shifted towards weak descent (divergence) with long tail representing rare but intense convergence episodes. Similar patterns found in NSIPP-2 model => double ITCZ coincides with skewed PDFs

37. 168W - 155W; 5N-15N 110W - 98W; 5N-15N 168W - 155W; 15S-5S 118W - 105W; 15S-5S Mar 15-Apr 4Apr 15-May 5May 15-Jun 4Jun 15-Jul 5Jul 15-Aug 4 West of Peru S-West of Mexico Cen. Pac. S. ITCZ Cen. Pac. N. ITCZ red – PDFs for standard GEOS5Blue – PDFs for scaled SSMI surface divergence PDFs of  850 for standard exp compared with SSMI surface divergence

39. Wavelet transforms of  850 days period (d) Mar 1 Aug 31days Mar 1 Aug 31 period (d)  850 105W,10N (SW of Mexico) Standard GEOS5 155W,10S (South ITCZ) Standard GEOS5155W,10S (South ITCZ) modified GEOS5 105W,10N (SW of Mexico) modified GEOS5

40. Slow/seasonal correlation of low level divergence Map shows correlation of smoothed  850 time series in standard and modified experiments. (20-day running mean, 10 o smoother in space ).

41. Slow/seasonal correlation of low level divergence Boundary control on convergence?

42. Rapid transient motions generating convergence Slowly varying convergence Schematic diagram of differences between moisture convergence in northern and soiuthern ITCZs (JJA)

43. Zonal mean moist heating (JJA) Standard GEOS-5 w/ weak or no double ITCZ Modified GEOS-5 w/ strong double ITCZ

44. JJA 1984/85 precip. Pt.-by-pt. correlation of  and precip 850mbar moistening original cooling modified cooling Experiment with arbitrarily rearranged heating and strong re-evaporation (NSIPP-2 model)

45. Summary (~Conclusions) ITCZ biases and high frequency behavior of convergence may be related Models we looked at with double ITCZs also have: highly skewed PDFs of w strong temporal correlation between PBL convergence and precip (bottom heavy heating??) Model without double ITCZ bias also in better agreement with statistics derived from SSMI and GPCP Convergence in “correct” ITCZs may be more subject to boundary control – so more resilient to parameterization changes