parameterizing convective organization

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.