Tropical Biases in GFDL atmospheric and coupled models

Where are we?

How did we get there?

Where are we going?

(GAMDT/LMDT/OMDT/CMDT)

• Where are we? (cm2a11o2) Atmosphere (in AMIP mode)

• Mean

• Regressions against ENSO Coupled

• Mean

• ENSO Variability

AM2/LM2:comparison to other models

Differences in annual mean precipitation from CMAP (Xie-Arkin)

MAM precip (mm/day)

Low cloud amount in JJA

Observations

Nino3 regressions in am2p11 AMIP integrations

Zonal stress – all seasons Precip- all seasons

ECMWF

am2p11

difference

Regression of Nino3 vs Z200(m) DJF: 10 runs 1950-2000

NH SH

Cm2a11o2 SST bias

Coupled model cm2a11o2Annual precip MAM precip

Wavelet analysis of Nino3 SST anomalies

El Nino variability in AM2 coupled to OM2

Nino3 regressions in am2p11 AMIP and cm2

Zonal stress – all seasons Precip- all seasons

ECMWF

am2p11

cm2

How did we get here?

AM2p2 AM2p

6

AM2p8 AM2p1

0

AM2p12

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)

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)

T42

N90 = 1 degree

Tropical SST bias

cm2_a11_o2(1K )

cm2_a10_o2old pressure gradient

(1K)

T : 11 –10(0.5K)

am2p11 (5% contour) am2p10 (5% contour)

Effect of pressure gradient form on % Low cloud

am2p11 –am2p102% contour

Changes in oceanic heating due to pressure gradient

4 w/m2 contour

total

sensible + evap radiation

p11 – p10

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)

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)

cumulus momentum transport

• Parameterize cumulus momentum transport (CMT) as a simple vertical diffusion of horizontal momentum where convection occurs

• Km ~ Mcz ~ acwupz

AM2p11: without CMT

AM2p11: with CMT

El Nino variability in zonal wind stress in AMIP integrations

AM2p11: with CMT

AM2p11: without CMT

zonal wind stress regressed on NINO3 in AMIP integrations

AM2p11AM2p11: w/o CMT AM2p11:

w/o cmt and w/o Tokioka

Wavelet analysis of Nino3 SST anomalies

AM2p11 without CMT

biannual peakPhase locked to seasons

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

precip(divergence)

vorticity evaporation

Dominant feedback determining response to cumulus momentum transport?

cmt

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

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

Trade inversion height (annual mean)

L18 – Mellor-Yamada

L24 – Mellor-Yamada

L24 – UKMO PBL

meters

Low cloud amount in JJA

Observations

New PBL parameterization

latent heat fluxes (w/m2, colors)

2 meter relative humidity

(%, contours)

Changes in latent heat flux and relative humidity(annual mean) due to new PBL

Changes in precipitation (annual mean)

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

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)

Thanks to:

Steve Klein

Paul Kushner

Tony Rosati

Andrew Wittenberg