Parameterizing convective organization Brian Mapes, University
of Miami Richard Neale, NCAR
Slide 2
What is organization? Deviations from random parcel/ uniform
environment/ no history assumptions embodied in a GCMs convection
treatment.
Slide 3
Worth parameterizing?...to the degree that errors attributable
to those assumptions can be reduced.
Slide 4
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
Slide 5
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
Slide 6
Example: organization increases during diurnal convective rain
development Khairoutdinov and Randall 2006
Slide 7
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.
Slide 8
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 its worth its weight
Slide 9
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 more, deeper convection precipitation forced,
decaying, advected org (lat,lo n,t) forced, decaying, advected org
(lat,lo n,t) shear rolls, deformation filaments subgrid geography
and breezes stochastic component
Slide 10
b) implementations tested so far wider 2 nd plume plume overlap
more frequent rain evap. rain evap. 2 nd plume closure plume base T
convection + precipitation org evap2org org2Tpert org2cbmf2
org2rkm2 (appendix) CAM5 with UWens 2- plume ensemble
Slide 11
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 more, deeper convection precipitation forced, decaying,
advected org (lat,lo n,t) forced, decaying, advected org (lat,lo
n,t) evap2org =2 org2rkm =5 org2Tpert = 1 + shear (rolls,
deformation lines, etc.) subgrid geography and breezes stochastic
component
Slide 12
Only enough time for one result: Org escapes entrainment
dilemma An old tradeoff of GCM errors more mixing dilutes updraft
buoyancy unstable (e.g. cold aloft) climate biases permitting
undilute plumes too unconditional convection, too little
variability there is no just right only tradeoffs & compromises
not a Goldilocks problem as formulated
Slide 13
The Entrainment Dilemma: a well-trod track precip variability
unstable mean state stable too undilute (ZM) (CCM3/CAM3) obs.
dilemma axis: (ZM-Hack-LScond trade-offs) too diluted (CCM2/ Hack,
UW shallow only)
Slide 14
Entrainment dilemma: tropical sounding UWens with an undilute
member: too stable UW only: too dilute unstable state
Slide 15
Dilemma: a well-trod track precip variability unstable mean
state stable too undilute (ZM) (CCM3/CAM3) obs. dilemma axis:
(ZM-Hack-LScond trade-offs) dilution +freezing CAM3.5+ dilution
+freezing CAM3.5+ too diluted (CCM2/ Hack, UW shallow only)
Slide 16
Entrainment dilemma: tropical sounding
Slide 17
UWens with an undilute member: too stable UW only: too dilute
unstable state Entrainment dilemma: tropical sounding
Slide 18
UWonly: unstable bias, excess variance UW_ens_org: about right
Org and the entrainment dilemma
Slide 19
Slide 20
UWonly: unstable bias, excess variance UW_ens_org: about right
Org and the entrainment dilemma
Slide 21
Dilemma: a well-trod track precip variability unstable mean
state stable 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)
Slide 22
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
Slide 23
Hysteresis involving org? DEEP CONVECTION STABILITY 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
Slide 24
? Hysteresis on longer time scales from org timescale of ~3h ?
DEEP CONVECTION STABILITY low org beginning of rain drives org
increase high org convection persists stabilization, rain
decreases, so org begins to decrease
Slide 25
Summary 1.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 orgs timescale
(~3h) is a virtue in itself. 6.Further characterization is
underway.