Parameterizing Parameterizing convective convective

organizationorganizationa stab, in current a stab, in current

CAMCAM

and whyand whyBrian Mapes and Richard NealeBrian Mapes and Richard Neale

QuickTime™ and ampeg4 decompressor

are needed to see this picture.

Parameterizing Parameterizing convective convective

organizationorganizationa stab, in current a stab, in current

CAMCAM

and whyand whyBrian Mapes and Richard NealeBrian Mapes and Richard Neale

What did the movie show?

• (I claim, for current purposes): – Afternoon timing of rain set by an organization

delay time – This is many cu parcel turnover times– Aided by mtns in this case– More generally, aided by precipitation

• (via its evaporation - cold pools– a positive feedback

»When it rains, it pours

Outline

• Several more slides emphasizing concept and pervasiveness of ”organization”

• What this study isn’t about, and is

• Sensitivities and feedbacks

• WHAT WE DID TO CODE

• WHAT IT DID TO MODEL OUTPUTS

• Discussion type stuff and summary

A model view of diurnal development Khairoutdinov and Randall 2006

high-res simulations of shallow-deep transition (flat perioidc domain, specified diurnal surface flux)

Khairoutdinov and Randall 2006Organization (“upscale growth”) takes time

precip. evaporation is key

Khairoutdinov and Randall 2006

connected cloud

objects sorted by top height

true aspect ratio

connected cloud

objects sorted by top height

true aspect ratio

Mesoscale org. is ubiquitous in deep convectionCloudsat: an unbiased sample from the Asian monsoon

Mesoscale org. is ubiquitous in deep convectionCloudsat: an unbiased sample from the Asian monsoon

Mesoscale org is ubiquitous, II: (100s km, many hours scales)

Composite 10x10 deg 3-hourly evolution of IR, PW, 10m divergence around 1st appearance of cold

clouds (< 210K) on 0.5 deg grid

t=0t=0-12h-12h +9h+9h

Meso scale & lifetime clear even in this equal-weight composite

IRIR

PWPW

divdiv

Mapes Milliff Morzel in prep.

(strong rotation cases excluded)

A time domain view in tropical shipborne Doppler radar

data lag regression of vertical mass

flux w.r.t. surface rain

Mapes and Lin 2005 MWR

1000

800

600

400

200

p (mb)

Generic view of organized convection

shallow -> deep -> stratiform

shallowcu

deepcb

humid, cloudy heatedupdrafts

dry,rainy

downdraftcold conv. outflow

stratiform rain anvil

---(hours, days, even weeks)---->

So What? asks a Climo-Globo-Dynamician

So What? asks a Climo-Globo-Dynamician

Mean state implications Spatial patterns of convxn (tropical biases) Transient variability

– diurnal timing over land– PDF and nonlinear impacts

like ground hydrology

– MJO and other tropical waves apparently impacts ENSO

Mean state implications Spatial patterns of convxn (tropical biases) Transient variability

– diurnal timing over land– PDF and nonlinear impacts

like ground hydrology

– MJO and other tropical waves apparently impacts ENSO

Issues in GCM precipitationDai 2006: ”Precipitation Characteristics in Eighteen Coupled Climate Models”

Issues in GCM precipitationDai 2006: ”Precipitation Characteristics in Eighteen Coupled Climate Models”

...unrealistic double-ITCZ pattern over the tropical Pacific ...models fail to capture ... large intraseasonal variations ...too much convective (over 95% of total) and too little stratiform

precipitation* ...underestimate the contribution and frequency for heavy (>20 mm

day−1) and overestimate them for light (<10 mm day−1) precipitation...rains too frequently...

...Intensity... in storm tracks off eastern coasts... too weak... ...warm-season convection starts too early [in the day]...

– *is TRMM conv/strat = convection.F90 / stratiform.F90 ?? focus on the real process issues: profile, location, timing...

...unrealistic double-ITCZ pattern over the tropical Pacific ...models fail to capture ... large intraseasonal variations ...too much convective (over 95% of total) and too little stratiform

precipitation* ...underestimate the contribution and frequency for heavy (>20 mm

day−1) and overestimate them for light (<10 mm day−1) precipitation...rains too frequently...

...Intensity... in storm tracks off eastern coasts... too weak... ...warm-season convection starts too early [in the day]...

– *is TRMM conv/strat = convection.F90 / stratiform.F90 ?? focus on the real process issues: profile, location, timing...

Might “organization” help?Might “organization” help?

shallowcu

deepcb

stratiform rain anvil

developmentsensitivities &

feedbacks

continuationpast mean-state stabilization

late stageimpacts

What this talk ISN’T about, 1What this talk ISN’T about, 1 We are NOT attempting to parameterize the

impacts of mesoscale anvils (stratiform precipitation) as another category of moist vertical eddy– a la Donner (1993, 2001)

{other authors literally compare TRMM “stratiform” rain to model “large-scale” rain, presuming that this is the job of the cloud scheme, NOT convection scheme}

– philosophical debate needed? elsewhere.

We are NOT attempting to parameterize the impacts of mesoscale anvils (stratiform precipitation) as another category of moist vertical eddy– a la Donner (1993, 2001)

{other authors literally compare TRMM “stratiform” rain to model “large-scale” rain, presuming that this is the job of the cloud scheme, NOT convection scheme}

– philosophical debate needed? elsewhere.

We are NOT attempting to parameterize the impacts of two-dimensionality or steadiness (“organization” as used in some contexts), e.g. on vertical momentum flux

We are NOT attempting to parameterize the impacts of two-dimensionality or steadiness (“organization” as used in some contexts), e.g. on vertical momentum flux

What this talk ISN’T about, 2What this talk ISN’T about, 2

What this talk IS aboutWhat this talk IS about Organization here means sub-grid variations of

(mainly) thermodynamic variables, correlated* with convective updraft occurrence

Organization here means sub-grid variations of (mainly) thermodynamic variables, correlated* with convective updraft occurrence

“organized” by

precipitation

“random”

*this makes it sound too unlikely -- convection is a highly systematically self-selection process for the most buoyant parcels - “special” parcels are inevitable, common, essential even if the weather doesn’t look “organized” (2D, etc.)

Considering convection-LS interaction

in param’z’n framework

Q1

local column impacts

Q1(T tend.)

Q2(q tend.)

Q3(u tend.)

z

z

z

SC DC ST

PBLmean

e

lower trop. q

upper trop. q

T in lower trop.

inversions

T upper

shearaerosolother e.g. “dyn”?

PBLsubgridvar.

(SST)

sensitivities of convection(? unknown ?)

Sensitivity problems (CCM3-CAM2,3)

deep convection closed on undilute parcel CAPEsensitivities of convection

(? unknown ?)

PBLmean

e

lower trop. q

upper trop. q

lower trop T

inversions

deep T

shearaerosolother e.g. “dyn”?

PBLsubgridvar.

(SST)

PBLmean

e

deep layer mean T

(SST)

sensitivities of CAPE

q aloft

Consequences rediscovered repeatedly over past several years in CCSM comm...

Sensitivity determines local feedbacks

sensitivities

Q1

impacts

Q1

Q2

Q3

z

z

z

SC DC ST

PBLmean

e

deep layer mean T q aloft

negative

negative

Rain & downdraft feedbacks: not all negative!

sensitivities

Q1

impacts

Q1

Q2

Q3

z

z

z

SC DC ST

PBL mean e

PBLsubgridvar.

lower trop. q

upper trop. q

lower trop. T

inversions

deep T

shearaerosol

other

negative

POSITIVE

(Updrafts don’t sample a (Updrafts don’t sample a homogeneous mean state homogeneous mean state

every 20 minutes with downdraft every 20 minutes with downdraft outflows blended into PBL)outflows blended into PBL)

Sensitivity update (CAM3.5)deep convection closed on dilute

parcel CAPE

PBLmean

e

deep layer mean T

sensitivities of dCAPE

q aloft

Better variability, incl. ENSO improvement (Neale et al. in prep.)

PBLmean

e

deep layer mean T

sensitivities of CAPE

q aloft

Q3Also impacts update - Richter

Sensitivity update (CAM3.5)deep convection closed on dilute

parcel CAPE

PBLmean

e

deep layer mean T

sensitivities of dCAPE

q aloft

• latent heat of freezing also added, to compensate loss of mean CAPE

• convection now clusters more in moister parts of space-time (increasing variability)

• still, negative feedbacks locally (deep conv. chills PBL, dries, heats)

What we did for summer vacationWhat we did for summer vacation Add “org” as local postive feedbacks Add “org” as local postive feedbacks

ordinary PBL T’ (Ok-ARM)

org (tied to recent (3h) rain

Convective closure entrains moister air: (capped at saturation)

params chosen so org ~1 for rainrate of P = 3mm/d

Didn’t do enough to make its impact clear: try a bigger hammer!Perturb initial parcel T:

Compensating for downdraft chilling...Compensating for downdraft chilling...

mean heating from ZM convection scheme in single column test

DASH: tests doubling alfa, a downdraft mass flux valve (to its documentation value, then 2x)

DOT: doubling Ke, a “stratiform” rain evaporation parameter, then 4x

mean heating from ZM convection scheme in single column test

DASH: tests doubling alfa, a downdraft mass flux valve (to its documentation value, then 2x)

DOT: doubling Ke, a “stratiform” rain evaporation parameter, then 4x several

K/d in month mean

Single column model testsSingle column model tests

When it rains, it pours... (increased variance)

When it rains, it pours... (increased variance)

diurnal delaydiurnal delay

Diurnal delay as expected (tau = 3h)

at least in SCM

Diurnal delay as expected (tau = 3h)

at least in SCM

What does it do? Mean stateWhat does it do? Mean state

Mean state can be more stable (warmer aloft) since convection is happening in org-enhanced areas

Mean state can be more stable (warmer aloft) since convection is happening in org-enhanced areas

control

diff

Mean stateMean state

Warmer tropics, higher Z300 in tropics,...

Warmer tropics, higher Z300 in tropics,...

Mean Mean

...westerly jet stream changes

...westerly jet stream changes

Drier tooDrier tooMean Mean

Drier, since deep convection is occurring in special org-enhanced places and buffered from entrainment

Drier, since deep convection is occurring in special org-enhanced places and buffered from entrainment

Mean Mean

Drier, and less cloudy -

except in stratus regions (due to enhanced stability?)

Drier, and less cloudy -

except in stratus regions (due to enhanced stability?)

Variability Variability

When it rains, it pours

When it rains, it pours

Variability Variability

Where it rains, it pours (& the converse)

Where it rains, it pours (& the converse)

Focus on Asian

monsoon

Focus on Asian

monsoonHotter in the desert(2m T)

Time variations

Time variations 10 days in

July

Where it rains it pours

(“noise?”)

10 days in July

Where it rains it pours

(“noise?”)

QuickTime™ and aCinepak decompressor

are needed to see this picture.

PDF viewpointPDF viewpointreference CAM with ORG

Rather extreme, but maybe Rather extreme, but maybe a step in a useful a step in a useful

direction?direction?

Improvements requiredImprovements required

org should be able to move– advect w/ low level wind? spread, upshear enhancement, etc.?

(more than we could do here, in parallel code...no neighbors!)

other sources besides precip– precip evaporation, really– subgrid geography?– deformation (gradient tightening)?

impacts on convection deciders should be calibrated– e.g. downdrafts don’t really heat inflow; they just don’t cool it

Ideally, seek consistency w/ PBL and cloud scheme subgrid assumptions - but dist. tails are key to convxn

Resolution dependent (to make whole system more resolution independent)?

org should be able to move– advect w/ low level wind? spread, upshear enhancement, etc.?

(more than we could do here, in parallel code...no neighbors!)

other sources besides precip– precip evaporation, really– subgrid geography?– deformation (gradient tightening)?

impacts on convection deciders should be calibrated– e.g. downdrafts don’t really heat inflow; they just don’t cool it

Ideally, seek consistency w/ PBL and cloud scheme subgrid assumptions - but dist. tails are key to convxn

Resolution dependent (to make whole system more resolution independent)?

Beyond CAM-lineage constraintsBeyond CAM-lineage constraints I wish it governed shallow-deep transition, not just

the strength of deep convection– Park-Bretherton unified PBL-convx suite?

We still need to get late-stage impacts right– top-heavy Q1 profile, i.e. impacts observed during

stratiform precipitation “meso” subroutine in convect.f ? water passed to stratiform.f ?

should we care ? really governed by bottom-heavy Q1 elsewhere?

– (rain in cu)

I wish it governed shallow-deep transition, not just the strength of deep convection– Park-Bretherton unified PBL-convx suite?

We still need to get late-stage impacts right– top-heavy Q1 profile, i.e. impacts observed during

stratiform precipitation “meso” subroutine in convect.f ? water passed to stratiform.f ?

should we care ? really governed by bottom-heavy Q1 elsewhere?

– (rain in cu)

Too heuristic? Too heuristic? Literalists will want org to be a quantity that can

be objectively measured (e.g. in CRMs), not just tuned for impact.

Internal inconsistencies with other subgrid schemes pinch over time – but subgrid dist. & overlap assumptions devised for

area (radiation) may not be good for the small but important (systematically self-selecting) buoyant “tail” parcels, especially a key subset (deciders) driving development...

Literalists will want org to be a quantity that can be objectively measured (e.g. in CRMs), not just tuned for impact.

Internal inconsistencies with other subgrid schemes pinch over time – but subgrid dist. & overlap assumptions devised for

area (radiation) may not be good for the small but important (systematically self-selecting) buoyant “tail” parcels, especially a key subset (deciders) driving development...

SummarySummary ORG variable used to enhance local positive

feedbacks (opposing some excessive local negative feedbacks)

Effect: when it rains, it pours– implication: rest of atm more stable

Bigger dynamic range, delayed diurnal development, more variability, rain persists past marginal stability (discharge-recharge), strong dev. sensitivity to moisture yet without mean-state unstable (cold) biases

some of these seem potentially desirable

ORG variable used to enhance local positive feedbacks (opposing some excessive local negative feedbacks)

Effect: when it rains, it pours– implication: rest of atm more stable

Bigger dynamic range, delayed diurnal development, more variability, rain persists past marginal stability (discharge-recharge), strong dev. sensitivity to moisture yet without mean-state unstable (cold) biases

some of these seem potentially desirable

Convection: a 2-scale circulation

time

Time mean

Deformation radius R

Gravity wave speed c

Cloud C

Convection in a low-res gridtime

Time mean

R

Grid ScaleC<G<R

G< G/2c

G/c

2G/c

3G/c

c c

C

Convection’s tendencies in model

process & scale categoriesAdiabatic transport

Phase changes

G-sized area averages

Advection.fAdvection.f ConvectionConvection.f.f

Deviations therefrom

ConvectioConvection.fn.f

(No net (No net effect on effect on resolved resolved scales)scales)

Example: evaporation and downdraftsExample: evaporation and downdrafts

mean heating from ZM convection scheme in single column test

DASH: tests doubling alfa, a downdraft mass flux valve (to its documentation value, then 2x)

DOT: doubling Ke, a “stratiform” rain evaporation parameter, then 4x

mean heating from ZM convection scheme in single column test

DASH: tests doubling alfa, a downdraft mass flux valve (to its documentation value, then 2x)

DOT: doubling Ke, a “stratiform” rain evaporation parameter, then 4x

Effects of minimum in ensemble

Arakawa & Schubert (1974): an ensemble of plumes with different entrainment rates i

• Low- and high- clouds compete for PBL moisture

• Competition decided on Aintegral of b over height of b>0 layer

• Lowest- cloud very deep & buoyant: dominates

• Separate shallow scheme may have to be used (or can tune by specifying critical work function for each cloud type)

Parameterization priorities, 5-10y

Plain old Q1 and Q2 are still big Q’s

e.g. model MJO Q1 profilesMJO heating (anomaly) profile

Lin et al. 2004

Point scale (5’ vertically pointing cloud radar vs. gauge

rain, EPIC 2001):

cudyn.

(multi-cellular)

anvil dyn. &micro-physics…

…includingstratiform

rain

Synoptic scale (6h 1000-km humidity vs.

budget rain, COARE)

days

Problems with weak entrainment in closure

– Deep convection dries the lower troposphere, but feels no feedback (brake): dry bias in mean state

– Moisture storage/xport/discharge mechanisms weak:

too little variability: P too much like E– All sens. in PBL: premature diurnal rainfall over land– Over warm SST, but dry aloft, scheme too active

(“double ITCZ” in SE Pac ?)

Entrainment and large-scale convective weather variability

(aqua-planet, rad.-conv. equilibrium,

prescribed “radiation” (cooling), uniform warm SST)

Low entrainment rate .125/HPBL

Virginie Lorant Ph.D. 2001

High entrainment rate .185/HPBL

Longitude (repeated)

Tim

e

Longitude (repeated)

Rain Rain

Entrainment: a Good Thing?• Entrainment restrains too-easy deep

convectionBUT…• makes top height of convection too low

– (in realistic stratification)

• ---> unstable sounding bias – (in climate)

» e.g. cold tropical upper troposphere

• Unwanted trade-off: variability Unwanted trade-off: variability (encouraged by entrainment constraint) (encouraged by entrainment constraint) vs. mean state (better with less dilution)vs. mean state (better with less dilution)

A solution: entrainment, but possibly of non-average air

• All plumes entrain strongly. First clouds entrain clear air, so are very sensitive to q, but are typically shallow.

• Later convection may entrain air pre-moistened by prior clouds, becoming deeper.

• This gives deep clouds an indirect q dependence, and opens up questions of cloud-field organization.

1

3

2

Discretize mixing by generationsrather than by entrainment rate

1 1 12

A minimal model: 4 levels

Trade layer

Middle trop.

Upper trop.

humidity density

Two (or 3) vertical modes indynamics

Trimodal clouddistribution: cu, cg, cb(good # for microphysics/precip. knobs)

PBL

4 subcolumn typeseach w/area fraction & prior rain rate

env. RH

plume model

1

anorm

1

cloudbase area

inhibition

energy

New values ofL(c-e), div[w’X’],rain & downdrafts,for each of the 4prior subcolumn types (which mayproduce any of

{cu, cg, cb} at thepresent time step)

4-level toy model summary

PBL

LT

MT

UT

b1 b2 b4b3

PBL

LT

MT

UT

w1 w2 w4w3

PBL

LT

MT

UT

PBL

LT

MT

UT

pure env cu cg cb 1 2 3 4

cb4

cg3

cu2

RAINPROD

1 1

RH=100%

random trigger E: random trigger E: TKETKE

OrgE: amt OrgE: amt T;T;by type by type rainrain

cu

cu

cg

cb

4-layer atm on warm beta-plane channel

Sensitivities: lifting

subsidence

control

T’ effect only (~50%)

unimodal LSD param. flawedunimodal LSD param. flawed

low-level lift, subsidence above

low-level subsidence, lift above

1. Lifting good for rain (duh)

2. Linear for ~1K stimuli

3. T’ ~half of total (q’ rest)

4. Low levels dominate5. Strong initial

sensitivity, but vertical structure changes stretch LSD param. too far

6. Shear of 10 m/s has no significant effect on rain

Moisture sensitivity

(courtesy Tetsuya Takemi)

5 6 8

568

568

Need to map & calibrate sensitivities

Example: Convecting CRM’s Q1 response to observed T’, q’

T

q

lower half of T’

upper half of T’

lower q’

upper q’

perturb

COARE sounding regressionsjust beforerain

Response not totally deterministic, even with

128 x 128 km domain.Stochastic aspect to convection must

be recognized.

Top heaviness of deep conv. heating

Lack of stratiform processes per se, or of cumulus showers?

“representing organization” - start with, say,

prognostic plume entrainment?GCM

Deep convectionheating in GCM

Lee Kang Mapes 2001

20N-20S cooling

Deep convectionheating

obs

Earth

Mapes 2000

Example of successive entraining

plume buoyancies (equatorial

EPAC)

qsat(T,p)q(p)

very dry (no deep convection)

undilute

12 3

4