0. Sub-grid Parameterization Based on Probability Density Functions in GFDL Atmospheric General Circulation Model: Global Tests
double-Gaussian distribution
Huan Guo (GFDL/UCAR)
Guo H., J.-C. Golaz, L. J. Donner, P. Ginoux, and R. S. Hemler, 2014: Multi-variate probability functions with dynamics in the GFDL atmospheric general circulation model: Global Tests. J. Climate, 27(5), , doi: /JCLI-D

1. outline Motivation: strato-cumulus, aerosol effect
CLUBB: cloud layer unified by bi-normal
AM3-CLUBB: better strato-cu and aerosol
Summary: pros and cons of AM3-CLUBB

2. negative strato-cumulus cloud forcing
cloud cover
shortwave cloud forcing
Insufficient data
(%)
Wood (MWR, 2012), Hahn &Warren (2007)
Hwang & Frierson (PNAS, 2013)
high cover & albedo  strong negative cloud forcing

3. deficient strato-cumulus in GCMs
CMIP5
(Hwang & Frierson, PNAS, 2013)
positive shortwave cloud forcing bias
low-bias in strato-cumulus
GFDL AM2
GFDL AM3
(W/m2)

4. outline unified cloud and turbulence parameterization
Motivation
CLUBB (cloud layer unified by bi-normal):
unified cloud and turbulence parameterization
based on assumed sub-grid probability density
function
AM3-CLUBB global results
Summary

5. why choose double-Gaussian
Double-Gaussian is a linear combination of two Gaussians, and offers unified treatment for various cloud regimes:
Variable skewness
Reasonable tails
Multi-dimensional
Analytical cloud properties
Agreement with observations
Golaz et al. (JAS, 2002)

6. implementation of AM3-CLUBB
Deep conv.
Donner
Shallow conv.
UW shallow
PBL
Lock
Macro-physics
Tiedtke
Micro-physics
1-moment
Rotstayn-Klein
Donner
CLUBB
2-moment
Morrison-Gettelman
Donner et al. (J. Climate, 2011); Guo et al. (J. Climate, 2014)

7. outline better strato-cu and aerosol simulation Motivation CLUBB
AM3-CLUBB global results:
better strato-cu and aerosol simulation
Summary

8. 1-degree AMIP simulations (1981-2000) shortwave cloud forcing
Observation
(CERES-EBAF)
(W/m2)
AM3-CLUBB minus Obs.
AM3 minus Obs.
(W/m2)

9. better strato-cumulus simulation
(W/m2)
AM3-CLUBB has smaller shortwave cloud forcing bias in strato-cumulus regions studied by Klein & Hartmann (J. Climate, 1993)

10. better SW cloud forcing seasonal cycle
Obs. (CERES-EBAF)
SWCF (W m-2)
more realistic phase and amplitude in AM3-CLUBB

11. sharper inversion in AM3-CLUBB

12. Longwave cloud forcing
Observation
ice clouds underestimate
(W/m2)
AM3-CLUBB minus Obs.
AM3 minus Obs.
(W/m2)

13. precipitation excessive precipitation
AM3-CLUBB
AM3
excessive precipitation
(mm/day) 7
AM3-CLUBB 6
AM3 5
more deep convection in AM3-CLUBB 4 3 2 1
60S 30S EQ 30N 60N
surface precip. from deep convection (mm/day)

14. smaller bias in aerosol optical depth (AOD)
AM3-CLUBB minus MISR Obs.
AM3 minus MISR Obs.
monthly AOD (BeiJing)
annual mean and seasonal cycles agree better with observations in AM3-CLUBB
AM3-CLUBB
AM3
Observation
(AERONET)

15. higher correlation with observation
relative difference: (model/AERONET-1)
aerosol optical depth (AOD) correlations with Aerosol Robotic Network (AERONET) are 0.8 in CLUBB,
and only 0.56
in non-CLUBB.
CLUBB
non-CLUBB
Figure courtesy Paul Ginoux

16. take-home message AM3-CLUBB issues and future work:
longstanding issue in many GCMs:
deficient strato-cumulus (e.g, in GFDL AM2, AM3)
AM3-CLUBB improves coastal strato-cumulus
simulations, especially at 1-degree resolution or higher.
AM3-CLUBB issues and future work:
more expensive computationally
insufficient ice-phase cloud representations
prognostic precipitation in microphysics
. . .

17. Strato-cumulus simulation is challenging
subtle balance among radiative cooling/heating, microphysics, entrainment, turbulence mixing, surface fluxes …
Wood (MWR, 2012)

18. AM3-CLUBB coupled run drifts

19. Strato-Cu cloud thickness
Shallower strato-cu near California

20. CLUBB in AM3: AM3-CLUBB flowchart
Prognose u, v, qt,θl, u’2, v’2, w’2,
qt’2, θl’2, qt’θl’, w’qt’, w’θl’, w’3
Activate
aerosols
w’2
Input
2-moment
MG microphys. Δt
Close
higher-order moments
buoyancy
Select
PDF
Diagnose
CF, qc
from PDF
Golaz et al. (JAS, 2002a, 2002b); Guo et al. (J. Climate, 2014)