Tropical Biases in GFDL Models: Past, Present, and Future Trends

1. Tropical Biases in GFDL atmospheric and coupled models Where are we? How did we get there? Where are we going? (GAMDT/LMDT/OMDT/CMDT)

2. Where are we? (cm2a11o2) • Atmosphere (in AMIP mode) • Mean • Regressions against ENSO • Coupled • Mean • ENSO Variability

3. AM2/LM2: comparison to other models Differences in annual mean precipitation from CMAP (Xie-Arkin)

4. MAM precip (mm/day)

5. Low cloud amount in JJA Observations

6. Nino3 regressions in am2p11 AMIP integrations Zonal stress – all seasons Precip- all seasons ECMWF am2p11 difference

7. Regression of Nino3 vs Z200(m) DJF: 10 runs 1950-2000 NH SH

8. Cm2a11o2 SST bias

9. Coupled model cm2a11o2 Annual precip MAM precip

10. Wavelet analysis of Nino3 SST anomalies

11. El Nino variability in AM2 coupled to OM2

12. Nino3 regressions in am2p11 AMIP and cm2 Zonal stress – all seasons Precip- all seasons ECMWF am2p11 cm2

13. How did we get here? AM2p2 AM2p8 AM2p12 AM2p6 AM2p10

14. OM2 • MOM4 (fully integrated into FMS) • Tripolar grid; • 2 deg Mercator south of 60N outside of equatorial zone • 50 vertical levels • 10m vertical resolution near surface - 2/3 degree meridional resolution at equator • Explicit free surface • Uniform GM thickness diffusion • Prescribed, spatially varying “color” (solar radiation penetration depth)

15. AM2p11 • B-grid Dynamical Core for am2p11 (Wyman): • 2.5° lon X 2.0° lat X 18 vertical levels • top at ~ 30 km • Split time stepping: 200, 600, and 1800 seconds for gravity waves, advection, and all physics except radiation • Piecewise-parabolic finite volume vertical advection of tracers (S. J. Lin) • Finite volume form of pressure gradient force calculation (S. J. Lin)

16. T42 N90 = 1 degree

17. Tropical SST bias cm2_a11_o2 (1K ) cm2_a10_o2 old pressure gradient (1K) dT : 11 –10 (0.5K)

18. Effect of pressure gradient form on % Low cloud am2p11 (5% contour) am2p10 (5% contour) am2p11 –am2p10 2% contour

19. Changes in oceanic heating due to pressure gradient 4 w/m2 contour total p11 – p10 sensible + evap radiation

20. AM2p11 • Prognostic cloud scheme (Klein) • 3 prognostic cloud tracers which are advected and diffused: cloud liquid, cloud ice and cloud fraction • Cloud fraction parameterization from Tiedtke (1993) as is used in ECMWF model • Cloud microphysics from Rotstayn (1997) as is used in CSIRO model • Precipitation macrophysics (large-scale rain and snow areas) from Jakob and Klein (2000)

21. AM2p11 • Relaxed Arakawa Schubert (RAS) Convection (Moorthi/Suarez) (Sirutis) • Ensemble of cumulus updrafts – no downdrafts • Specified precipitation efficiencies as a function of the depth of the updrafts. Non-precipitated fraction is a source of condensate for cloud scheme • Closure: relax cloud work function to a threshold value with a timescale dependent upon cloud type • Simple diffusive cumulus momentum transport (Held) • For deep convection, a minimum bound on lateral entrainment rates is imposed (Tokioka modification)

22. cumulus momentum transport • Parameterize cumulus momentum transport (CMT) as a simple vertical diffusion of horizontal momentum where convection occurs • Km~McDz~ acwupDz

23. AM2p11: without CMT AM2p11: with CMT

24. El Nino variability in zonal wind stress in AMIP integrations AM2p11: without CMT AM2p11: with CMT

25. zonal wind stress regressed on NINO3 in AMIP integrations AM2p11: w/o cmt and w/o Tokioka AM2p11: w/o CMT AM2p11

26. Wavelet analysis of Nino3 SST anomalies AM2p11 without CMT biannual peak Phase locked to seasons

27. Moist static energy budget • changes in evaporation more important than changes in radiative fluxes(clouds) In ENSO regressions, changes in stress larger than changes in precip • diffusion directly affects vorticity budget Dominance of baroclinic mode • precip increases strength of low level damping

28. Dominant feedback determining response to cumulus momentum transport? precip (divergence) cmt evaporation vorticity

29. AM2p11 • Planetary Boundary Layer • Mellor-Yamada (1982) 2.5 order dry parameterization with prognostic turbulent kinetic energy • “Gustiness” enhancement to wind speed used in surface flux calculations (Beljaars 1995) • Oceanic roughness lengths enhanced at low wind speed (Beljaars 1995) • Gravity Wave Drag (Stern) • Orographic drag from Stern and Pierrehumbert

30. In Development: • New boundary layer turbulence parameteriziation based upon UK Meteorological Office PBL (Klein) • Stability based upon moist thermodynamics • K-profile mixing for surface driven and cloud top radiatively driven mixing • Explicit entrainment parameterization based upon Large-eddy simulations and observations • Enhanced momentum drag in regions of variable orography (“orographic roughness”) • Enhanced mixing in very stable conditions • 6 more vertical levels in the PBL – 9 levels beneath 1500 m

31. Trade inversion height (annual mean) L18 – Mellor-Yamada L24 – Mellor-Yamada L24 – UKMO PBL meters

32. Low cloud amount in JJA Observations New PBL parameterization

33. Changes in latent heat flux and relative humidity (annual mean) due to new PBL latent heat fluxes (w/m2, colors) 2 meter relative humidity (%, contours)

34. Changes in precipitation (annual mean)

35. In Development: AM3 • New convection scheme to replace RAS (Donner et al. 2002) • Cloud microphysics in an ensemble of updrafts with prognostic vertical velocity • Parameterized heating from a mesoscale anvil based upon Leary and Houze (1980) observations • Radiative impact of convective towers and mesoscale anvils included • Convective and mesoscale downdrafts

36. In Development: AM3 • Enhanced stratosphere (Wilson) • Raise the model top and add 5 to 10 more vertical levels • Replace Pierrehumbert-Stern orographic gravity wave drag with anisotropic gravity wave drag parameterization from Garner • Add convectively generated gravity waves from the parameterization of Alexander and Dunkerton (NWRA)

37. Thanks to: Steve Klein Paul Kushner Tony Rosati Andrew Wittenberg