Simulation of the Aerosol Indirect Effect in GCM

Simulation of the Aerosol Indirect Effect in GCM

CLOUDNET J. L. Brenguier Météo-France

Cloud Dynamics PrécipitationEvaporation

Microphysics

Onset ofPrécipitation

2ndand

1st Aerosol Indirect Effect

CCN Activation

RadiativeTransfer

ACE-2 Field Campaign June-July 1997


Morocco

SurfaceSite

Tenerife

Experimental StrategyTo select cloud systemswith similar LWP andmorphology, but withdifferent aerosol prop.To sample an area of60 km, about the GCMspatial resolutionTo synchronize in situand remote sensingfor column closureexperiments

Nact=50 cm-3 Nact=250 cm-3



Multi-disciplinary: aerosol chemistry and physics, cloud microphysics cloud radiative transfer First experiment with detailed characterization of aerosols and simultaneous measurements of cloud microphysics in situ and radiative properties from above Observation area of 60 km side sampled during 4 hours: robust statistics BUT Measurements at noon local time No radar/lidar No vertical profile, No diurnal cycle

ACE-2 Special Issue of Tellus 2000

JLB et al. JAS 2000


CLOUDNET J. L. Brenguier Météo-FranceJLB et al. 2003

Meteo-France: J.L. Brenguier FUBerlin: L. Schüller

U Warsaw: H. Pawlowska U Wyoming: J. Snider

MPI: J. Feichter Hadley: D. Roberts

U Dalhousie: U. Lohmann U Columbia: S. Menon

PNNL: S. Ghan U Michigan: J. Penner LMD: J. Quaas


PACECooperative Study between

ACE-2 Experimentalists and GCM Modellersfor Testing/Developing GCM Parameterizations

on the ACE-2 data set

PACE Special Issue of JGR August 2003 (Data Analysis

Menon et al. JGR 2004 (Model Parameterizations)

Series of Closure experiments at a scale relevant to GCM (60 km) on - aerosol activation

- radiative transfer- precipitation formation

Identify variables relevant to GCM parameterization of AIE and establish relationship with physical variables Build a data base for initialisation and validation of SCM versions of the GCMs (8 ACE-2 case studies) Examine the predictability of the selected variables Test parameterizations and examine feedback processes


PACE METHODOLOGY


Microphysics


CCN Activation

Well known since 50 yearsWell known since 50 yearsfrom from - in situ measurements- in situ measurements- & 1D to 3D modelling- & 1D to 3D modelling

51

208 256

134

178

114

134

Droplet Mean Volume Diameter versus Height above Cloud Base(Pawlowska et al. 2000)

N=75cm-3



Can we predict CDNC at the GCM resolution scale from predicted aerosol properties and a diagnostic of updraft intensity

qc(h)>0.9 qcad(h)

Ndrizzle< 2cm-3

0.4H < h < 0.6H

Pawlowska et al. 2000


MicrophysicsCCN Activation

Closure Experiments State of the ArtClosure Experiments State of the Art

CDNC predicted CDNC predicted with detailed process modelswith detailed process models initialised with measured initialised with measured

aerosol physico-chemical aerosol physico-chemical properties and wproperties and w

OVERESTIMATESOVERESTIMATES

measured CDNCmeasured CDNC 0

200

400

600

CD

NC

, cm

-3

18-July

Snider et al. 2003

State of the art in GCM simulation of AIE


Menon et al. 2004


MicrophysicsCCN Activation

CONCLUSIONS : NOT SO BADCONCLUSIONS : NOT SO BAD

Discrepancies attributed to Discrepancies attributed to - incomplete characterization of aerosol propertiesincomplete characterization of aerosol properties- and biased vertical velocityand biased vertical velocity

GCM Parameterization of activation is NOT AN ISSUEGCM Parameterization of activation is NOT AN ISSUE except subgrid scheme to derive local w from grid TKEexcept subgrid scheme to derive local w from grid TKEand better representation of aerosol physico-chemical propertiesand better representation of aerosol physico-chemical properties


Microphysics


Numerous experimental evidencesNumerous experimental evidencesfrom multispectral radiancesfrom multispectral radiances- satellites- satellites- close remote sensingclose remote sensing BUTBUT- Very few that discriminate betweenVery few that discriminate between impact of microphysicsimpact of microphysics and impact of LWPand impact of LWP

Radiative Transfer


Measured reflectances in VIS and NIR, with H-N gridN remotely retrieved values versus Nact

Microphysics


Radiative Transfer




Microphysics


CDNC predicted CDNC predicted with detailed radiative with detailed radiative

(inverse) transfer models(inverse) transfer models initialised with measured initialised with measured

multispectral radiancesmultispectral radiances

AGREE withAGREE with

measured CDNCmeasured CDNC

Radiative Transfer

Schüller et al. 2003


Satellite Monitoring of the AIE Correlation between Cloud Optical Thickness and Droplet Effective Radius

- 1


CONCLUSIONS : QUITE GOODCONCLUSIONS : QUITE GOOD

Discrepancies attributed to Discrepancies attributed to - heterogeneities of the microphysical fieldsheterogeneities of the microphysical fields- optical properties of aerosols and dropletsoptical properties of aerosols and droplets

GCM Parameterization of radiative transfer is AN ISSUEGCM Parameterization of radiative transfer is AN ISSUE because of the poor representation of clouds in GCM,because of the poor representation of clouds in GCM,not because of the susceptibility to CDNCnot because of the susceptibility to CDNC

Microphysics Radiative Transfer

Measurements of the Aerosol Indirect Effect


Microphysics

Aerosol Indirect Effect

Well known since 50 yearsWell known since 50 yearsfrom from - in situ measurements- in situ measurements- & 1D to 3D modelling& 1D to 3D modellinghas been carefully explored for weather has been carefully explored for weather

modification, without success though!!! modification, without success though!!!


PrécipitationEvaporation

2nd


Microphysics


Measured drizzle Measured drizzle concentration depends concentration depends on the maximum radius on the maximum radius droplets can reach in a droplets can reach in a cloud layer and the cloud layer and the threshold (10µm) threshold (10µm) corresponds to the corresponds to the value derived from value derived from detailed modelling of detailed modelling of droplet coalescencedroplet coalescence

Onset of Précipitation


Aerosol-CNES September 2003 J. L. Brenguier Météo-France

CONCLUSIONS : Very badCONCLUSIONS : Very bad

Microphysics Précipitation Evaporation


Menon et al. 2003

Parameterization of precipitation in GCM

EGS 7 April 2003 Nice J. L. Brenguier Météo-France

Detailed microphysics 1 to 3-D (50 to 200 variables)

3-D CRM Runs (diverse conditions)Tripoli-Cotton, Beheng, Khairoutdinov-Kogan

Bulk microphysics for CRM (3 variables: N, qc, qr)Auto-conversion (N, qc) and Accretion (N, qc, qr)

Tuning bulk coefficients to accountfor GCM grid smoothing effects

Bulk microphysics for GCM (2 variables : N, qc)Auto-conversion (N, qc) (Accretion diagnosed)

3-D bulk CRM Runs (meso-scale)

Bulk microphysics for GCM (2 variables : N, H)Average precipitation rate from multi-cells in stationary state


CRM versus GCM PARAMETERIZATIONCRM versus GCM PARAMETERIZATIONOF PRECIPITATIONOF PRECIPITATION

H. PawlowskaH. Pawlowska


Model variability attributed toModel variability attributed tothe use of CRM bulkthe use of CRM bulkparameterizations ofparameterizations ofauto-conversion and accretionauto-conversion and accretionin GCM.in GCM.

New bulk parameterizationsNew bulk parameterizationsare tested (ACE2, DYCOMS),are tested (ACE2, DYCOMS),where precipitation rate onlywhere precipitation rate onlydepends on LWP and CDNCdepends on LWP and CDNC

Microphysics Précipitation Evaporation



Microphysics Cloud Life Cycle

Experimental strategy for BL cloudsExperimental strategy for BL cloudsAerosols have a strong impact on the onset of precipitationAerosols have a strong impact on the onset of precipitationand precipitation rate, but it is not obvious that they have anand precipitation rate, but it is not obvious that they have animpact on the cloud life cycle.impact on the cloud life cycle.

In BL clouds, the contribution of precipitation to the water budgetIn BL clouds, the contribution of precipitation to the water budgetis comparable to the contributions of radiative & turbulent fluxes.is comparable to the contributions of radiative & turbulent fluxes.

The uncertainty in measurements of these fluxes are still higherThe uncertainty in measurements of these fluxes are still higherthan the fluxes themselve.than the fluxes themselve.

Requires long term monitoring of the cloud life cycle at a super siteRequires long term monitoring of the cloud life cycle at a super siteequipped with surface fluxes, radar and lidar remote sensingequipped with surface fluxes, radar and lidar remote sensingand concomitant aerosol characterizationand concomitant aerosol characterization

State of the art in GCM simulation of AIE

EGS 7 April 2003 Nice J. L. Brenguier Météo-France

CONCLUSIONMost of the uncertainty comes from the coarse representation

of thin BL clouds in GCMsMVDR at cloud top H1/3, Optical depth H5/3, Precipitation rate H4

Priorities- Finer vertical resolution and sub-grid vertical schemes - « GCM Bulk » parameterization of rain formation - Reduce the bias in the prediction of Nact - Better understand the heterogenous bias in relation with

the second AIE - Parameterizations of the aerosol processing in clouds

Turbulent Fluxes

Entrainment-Mixing

rv=20 g kg-1

rl=0.2 g kg-1

PrécipitationEvaporation

A=0.05

A=0.50

Microphysics

Trv


1st 2ndand


Aerosol Indirect Effect

Boundary Layer Clouds

CCN Activation

RadiativeTransfer

Documents

Simulation of the Aerosol Indirect Effect in GCM