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Parameterizing convective organization. Brian Mapes , University of Miami Richard Neale, NCAR. What is organization?. Deviations from random parcel/ uniform environment/ no history assumptions embodied in a GCM’s convection treatment. Worth parameterizing ?. - PowerPoint PPT Presentation
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Parameterizing convective organization
Brian Mapes, University of MiamiRichard Neale, NCAR
What is organization?
• Deviations from random parcel/ uniform environment/ no history assumptions embodied in a GCM’s convection treatment.
Worth parameterizing?
• ...to the degree that errors attributable to those assumptions can be reduced.
A parsimonious, corrective approach
• Address the biggest possible bundle (‘EOF1’) of the many phenomena that are lacking, at minimum cost/complexity (1 variable, linear)
• Simplicity also commensurate with lack of globally systematic knowledge to base on
A parsimonious, corrective approach
• Correction = Expectation[ reality – model ]
1. depends on model• not just “out there” to be measured in sky or CRMs
2. depends on field realities of convection• not a fiction, not derivable as theory
Example: organization increases during diurnal convective rain development
Khairoutdinov and Randall 2006
What increases?• Variance or magnitude
of fluctuations, of many variables, at many altitudes
• Coherence among above
• Scale of fluctuations (slope of size spectrum)
• Local environment of coherent structures
4 new variables? No.
One.
New model branch: CAM5_UWens_org
1. Disabled Zhang-McFarlane– UW (Bretherton-Park) ”shallow” plume scheme only
» deep convection too dilute, but a functioning climate
2. I extended code to ensemble of UW plumes– unified physical basis for PBL – shallow – deep
» TKE / CIN closure buoyancy driven plume fluxes
3. ORG governs plume ensemble members– now to demonstrate it’s worth its weight
a) full proposed organization scheme
wider plumes with less lateral mixing
plume overlap more likely
(preconditioned local environs)
evaporation of rain
inhibition/closure
updraft base T > grid cell mean
mor
e, d
eepe
r con
vecti
on
precipitation
forced, decaying, advected
org(lat,lon,t)
shear rolls, deformation
filaments
subgrid geography
and breezes
stochastic component
b) implementations tested so far
wider 2nd plume
plume overlap more frequent
rain evap.
2nd plume closure
plume base T’
conv
ectio
n +
precipitation
orgevap2org
org2Tpert
org2cbmf2
org2rkm2
(appendix)
CAM5 with UWens 2-
plume ensemble
Org scheme in CAM5_UWens_org - summer 2010
wider plumes (entrain less)
plume overlap more likely
(preconditioned local environs)
evaporation of rain
inhibition
updraft base warmer than grid
mean
mor
e, d
eepe
r con
vecti
on
precipitation
forced, decaying, advected
org(lat,lon,t)
evap2org=2
org2rkm=5
tau = 10ks
org2Tpert= 1
+shear (rolls, deformation
lines, etc.)
subgrid geography
and breezes
stochastic component
The Entrainment Dilemma: a well-trod track
precip variability
unst
able
mea
n st
ate
st
able
too undilute (ZM) (CCM3/CAM3)
obs.
dilemma axis:
(ZM-Hack-LScond
trade-offs)
too diluted(CCM2/ Hack, UW shallow only)
Entrainment dilemma: tropical sounding
UWens with an undilute member: too stable UW only:
too dilute unstable state
Dilemma: a well-trod track
precip variability
unst
able
mea
n st
ate
st
able
too undilute (ZM) (CCM3/CAM3)
obs.
dilemma axis:
(ZM-Hack-LScond
trade-offs)
dilution +freezing CAM3.5+
too diluted(CCM2/ Hack, UW shallow only)
Entrainment dilemma: tropical sounding
(CAM5: UW+ZM_dil_freez schemes)
UWens with an undilute member: too stable UW only:
too dilute unstable state
Entrainment dilemma: tropical sounding
(CAM5: UW+ZM_dil_freez schemes)
UWonly: unstable bias, excess variance
UW_ens_org: about right
Org and the entrainment dilemma
UWonly: unstable bias, excess variance
UW_ens_org: about right
Org and the entrainment dilemma
Dilemma: a well-trod track
precip variability
unst
able
mea
n st
ate
st
able
too undilute (ZM) (CCM3/CAM3)
too diluted(CCM2/ Hack, UW shallow only)
obs.
IDEA: Org-dependent convection can be restrained by mixing in non-rainy places (increasing variance), while deep convection is less dilute once organized in rainy places (no unstable bias)
dilemma axis:
(ZM-Hack-LScond
trade-offs)
Others have roughly same idea• “A Systematic
Relationship between Intraseasonal Variability and Mean State Bias in AGCM Simulations”
• Daehyun Kim, Adam H. Sobel, Eric D. Maloney, Dargan M. W. Frierson, and In-Sik Kang
Hysteresis involving org?
DEEP CONVECTION
STAB
ILIT
Y
low org
beginning of rain drives org increase
high org convection persists
stabilization, rain decreases,
so org begins to decrease
dawn
NOON afternoon rain peak
? Hysteresis on longer time scales from org timescale of ~3h ?
DEEP CONVECTION
STAB
ILIT
Y
low org
beginning of rain drives org increase
high org convection persists
stabilization, rain decreases,
so org begins to decrease
Summary1. Organization is a set of subgrid variances and
relationships that are lacking in average plume/ uniform environment schemes.
2. Entrainment limits convective development, in unorganized cloud fields.
3. Org scheme allows less-dilute convection, once organized. This avoids mean bias from 2.
4. CAM5-UWens-org models exist, they run, and they appear to escape the Entrainment Dilemma.
5. Diurnal cycle delay by org’s timescale (~3h) is a virtue in itself.
6. Further characterization is underway.
Help
After much delay, hiring postdoc next week for next steps. Unless one of you catches me
fast.