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Convergence and precipitation in AGCM simulations

Convergence and precipitation in AGCM simulations. Baode Chen, Caterina Tassone, Pete Robertson, Max Suarez, Larry Takacs. Outline Motivation AGCM precipitation biases are sensitive (or not) to various parameters Are there “deeper” similarities between models with similar biases?

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Convergence and precipitation in AGCM simulations

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  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 DJF Total Precip. water (TPW) DJF

  5. SWCF DJF LWCF DJF

  6. Taylor diagrams

  7. Experiments 2 GEOS-5 experiments, 1.25ox1.0o 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. Modified GEOS-5 w/ strong double ITCZ (1 JJA) Standard GEOS-5 w/ weak or no double ITCZ (4 JJAs)

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

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

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

  12. Correlation of daily precipitation with daily as a function of latitude and longitudew850 - w850(i,j) precip(i,j)

  13. Weak re-evap moderate re-evap Strong re-evap 0. 0.4 0.8 1.0 r Correlation of daily precipitation withw850in NSIPP-2 (May 1 –Sep 1) (2x)

  14. Correlation of daily precipitation withw850in GEOS-5 (March 1 –Sep 1) Standard GEOS5 – weak bias modified – strong bias SSMI divergence vs GPCP precip (2001) SSMI winds binned to 1x1 then divergence calculated

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

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

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

  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 modified – strong bias Standard GEOS5 – weak bias P-Esfc Standard GEOS5 – weak bias modified – strong bias

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

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

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

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

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

  31. PDFs of w850 in different regions and periods for standard and modified exps 168W - 155W; 5N-15N Mar 15-Apr 4 Apr 15-May 5 May 15-Jun 4 Jun 15-Jul 5 Jul 15-Aug 4 Cen. Pac. N. ITCZ 110W - 98W; 5N-15N S-West of Mexico Time 168W - 155W; 15S-5S Cen. Pac. S. ITCZ 118W - 105W; 15S-5S West of Peru

  32. PDFs of w850 in different regions and periods for standard and modified exps 168W - 155W; 5N-15N Mar 15-Apr 4 Apr 15-May 5 May 15-Jun 4 Jun 15-Jul 5 Jul 15-Aug 4 Cen. Pac. N. ITCZ 110W - 98W; 5N-15N S-West of Mexico 168W - 155W; 15S-5S Cen. Pac. S. ITCZ 118W - 105W; 15S-5S West of Peru red – PDFs for standard GEOS5 Black – PDFs for modified GEOS5

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

  34. Overall statistics for PDFs in 10ox12ox20d subdomains mode Modified GEOS5 Standard GEOS5 mean Fluctuation RMS

  35. 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

  36. PDFs of w850 for standard exp compared with SSMI surface divergence 168W - 155W; 5N-15N Mar 15-Apr 4 Apr 15-May 5 May 15-Jun 4 Jun 15-Jul 5 Jul 15-Aug 4 Cen. Pac. N. ITCZ 110W - 98W; 5N-15N S-West of Mexico 168W - 155W; 15S-5S Cen. Pac. S. ITCZ 118W - 105W; 15S-5S West of Peru red – PDFs for standard GEOS5 Blue – PDFs for scaled SSMI surface divergence

  37. Wavelet transforms of w850 105W,10N (SW of Mexico) Standard GEOS5 105W,10N (SW of Mexico) modified GEOS5 w850 period (d) period (d) 155W,10S (South ITCZ) Standard GEOS5 155W,10S (South ITCZ) modified GEOS5 period (d) period (d) Mar 1 Aug 31 Mar 1 Aug 31 days days

  38. Slow/seasonal correlation of low level divergence Map shows correlation of smoothed w850 time series in standard and modified experiments. (20-day running mean, 10o smoother in space ).

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

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

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

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

  43. 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

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