Indirect Effect

The proposed study is designed to address two key areas of uncertainty: 1) the sensitivity of cloud liquid water path to aerosol, and 2) the competition between heterogeneous and homogeneous nucleation of ice crystals.

To address issue 1), we’ve added daily and monthly diagnostics that can be compared with CloudSat and MODIS retrievals of the relationship between the aerosol optical depth and the probability of precipitation (Wang et al., 2012).

To address issue 2), we’ve added experiments in which heterogeneous nucleation is neglected for T<-37C, and in which ice nucleation for T<-37C is a prescribed function of temperature (Cooper, 1986).

We also have added a requirement to nudge toward analyzed winds, which we’ve found greatly reduces the noise due to natural variability without significantly inhibiting the cloud response to the aerosol. (Kooperman et al., 2012). Simulations of six years duration each should be sufficient for all experiments.

To facilitate analysis and comparison before the 2013 AeroCom meeting, the results should be submitted to the AeroCom repository by December 1, 2013. Please contact and when your results have been submitted.

Cooper, W. A.: Ice initiation in natural clouds. precipitation enhancement – a scientific challenge, Meteor. Mon., 43, 29–32, 1986.

Kooperman, G. J., M. S. Pritchard, S. J. Ghan, R. C. J. Sommerville, and L. M. Russell, 2012: Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5. J. Geophys. Res., 117, doi:10.1029/2012JD018588.

Wang, M., S. Ghan, X. Liu, T. L’Ecuyer, K. Zhang, H. Morrison, M. Ovchinnikov, R. Easter, R. Marchand, D. Chand, Y. Qian, and J. E. Penner, 2012: Strong constraints on cloud lifetime effects of aerosol using satellite observations. Geophys. Res. Lett., 39, 15, doi:10.1029/2012GL052204.

Submission of results by 1 December 2013
Steve Ghan,
Xiaohong Liu,

Indirect forcing experiment

Repository: aerocom-users:/metno/aerocom/work/aerocom1/INDIRECT3

Data submission deadline

* Submission of results by 1 December 2013

Simulation setup

* Simulation start 1 October 2005
* Forcing by AMIP2 sea surface temperature and sea-ice extent
* Preferred: Nudge toward ECMWF reanalysis winds (not temperature) through 2010
* Acceptable: Nudge toward winds from one baseline simulation by your model
* Less acceptable: No nudging
* Greenhouse gas concentrations for year 2000
* Aerosol direct, semi-direct, and indirect effects taken into account.

* all_2000: simulation PD (present-day): year 2000 IPCC aerosol emissions
* all_1850: simulation PI (pre-industrial): year 1850 IPCC aerosol emissions (year 2000 GHG concentration)
* hom_2000: present day emissions no heterogeneous nucleation of ice in cirrus clouds with T<-37 C
* hom_1850: as in hom_2000, but for pre-industrial emissions
* fix_2000: present day emissions fixed ice nucleation for T<-37 C using a constant ice number of 383.6 /L, which is from Cooper (1986) at T=-37C
* fix_1850: as in fix_2000, but for pre-industrial emissions


* All data except COSP diagnostics is to be collected at the AEROCOM server.
* Groups hold COSP diagnostics and contact Kenta Suzuki ( for analysis
* follow the aerocom data protocol (
* Data in NetCDF format, one variable and year per file with CMOR variable names
* All data are 3-dimensional ( lon x lat x time )
* filenames aerocom_<ModelName>_<ExperimentName>_<VariableName>_<VerticalCoordinateType>_<Period>_<Frequency>.nc

where <ModelName> can be chosen such that Model Name, Model version and possibly the institution can be identified. No underscores (_) are allowed in <ModelName>. Use (-) instead. Max 20 characters. <ExperimentName> = all_2000, all_1850, hom_2000, hom_1850, fix_2000, or fix_1850 <VariableName> see list below <VerticalCoordinateType> => "Surface", "TOA", "Column", "ModelLevel" <Period> => "2008", "2010", ... <Frequency> => "timeinvariant","hourly", ,"3hourly", "daily", "monthly"

* CFMIP COSP diagnostics provided by COSP do not need to be run through cmor because the names are the same,
* but please separate files for each variable

In addition to the diagnostics below, it is highly recommended to store the AEROCOM standard and forcing diagnostics, so that the simulations can be analysed for the direct forcing as well, and future more in-depth analyses are possible.

(1) 2D diagnostics for evaluation with satellite data

5 years (years 2006-2010) of 3-hourly instantaneous data from the PD run

namelong_name (CF if possible)unitsdescription

od550aeratmosphere_optical_thickness_due_to_aerosol1Aerosol optical depth (@ 550 nm)



cdrliquid_cloud-top_droplet_effective_radiusmGrid cell mean droplet effective radius at top of liquid water clouds

cdncliquid_cloud_droplet_number_concentrationm-3Grid cell mean droplet number concentration in top layer of liquid water clouds

cdnumcolumn_cloud_droplet_number_concentrationm-2grid cell mean column total

icnumcolumn_ice_crystal_number_concentrationm-2grid cell mean column total

cltcloud_area_fraction1Fractional cover by all clouds

lccliquid_cloud_area_fraction1Fractional cover by liquid water clouds

lwpatmosphere_cloud_liquid_pathkg m-2grid cell mean liquid water path for liquid water clouds

iwpatmosphere_cloud_ice_pathkg m-2grid cell mean ice water path for ice clouds

icrcloud-top_ice_crystal_effective_radiusmgrid cell mean effective radius of crystals at top of ice clouds

iccice_cloud_area_fraction1Fractional cover by ice clouds

codcloud_optical_depth1Grid cell mean cloud optical depth

codliqcloud_optical_depth_due_to_liquid1Grid cell mean cloud optical depth

codicecloud_optical_depth_due_to_ice1Grid cell mean cloud optical depth

ccn0.1blcloud_condensation_nuclei_0.1_pblm-3CCN number concentration at S=0.1% at 1 km above the surface

ccn0.3blcloud_condensation_nuclei_0.3_pblm-3CCN number concentration at S=0.3% at 1 km above the surface

colccn.1column_cloud_condensation_nuclei_0.1m-2column-integrated CCN number concentration at S=0.1%

colccn.3column_cloud_condensation_nuclei_0.3m-2column-integrated CCN number concentration at S=0.3%

rsuttoa_upward_shortwave_fluxW m-2TOA upward SW flux, all-sky

rsutcstoa_upward_shortwave_flux_assuming_clear_skyW m-2TOA upward SW flux, clear-sky

rsutnoatoa_upward_shortwave_flux_no_aerosolW m-2TOA upward SW flux, all-sky, aerosol removed from calculation

rsutcsnoatoa_upward_shortwave_flux_clear_sky_no_aerosolW m-2TOA upward SW flux, clear-sky, aerosol removed from calculation

rluttoa_upward_longwave_fluxW m-2TOA upward LW flux, all-sky

rlutcstoa_upward_longwave_flux_assuming_clear_skyW m-2TOA upward LW flux, clear-sky

hflssurface_upward_latent_heat_fluxW m-2Surface latent heat flux

hfsssurface_upward_sensible_heat_fluxW m-2Surface sensible heat flux

rlssurface_net_downward_longwave_flux_in_airW m-2Net surface LW downward flux

rsssurface_net_downward_shortwave_fluxW m-2Net surface SW downward flux

rsdssurface_downwelling_shortwave_flux_in_airW m-2Surface SW downward flux (to estimate the model's 'true' surface albedo)

ttopair_temperature_at_cloud_topKTemperature at top of clouds, weighted by cloud cover

ltslower_tropospheric_stabilityKDifference in potential temperature between 700 hPa and 1000 hPa

w500vertical_velocity_dpdt_at_500_hPahPa s-1 

sprecipstratiform_precipitation_ratekg m-2 s-1grid cell mean at surface

autoconvcolumn_autoconversion_ratekg m-2 s-1grid cell mean column total

accretncolumn_accretion_ratekg m-2 s-1grid cell mean column total

(2) For forcing estimates: as in (1), but monthly-mean fields for both PD and PI simulations, plus a land-ocean mask (0 land, 1 ocean).

(3) 3D monthly mean diagnostics

namelong_name (CF if possible)unitsdescription

ttemperatureKeach layer

husspecific_humiditykg/kgeach layer

zaltitudemeach layer

airmassatmosphere_mass_content_of_airkg m-2each layer

ccn0.1cloud_condensation_nuclei_0.1m-3each layer (S=0.1%)

ccn0.3cloud_condensation_nuclei_0.3m-3each layer (S=0.3%)

ncliquid_cloud_droplet_number_concentrationm-3grid cell mean each layer

lwccloud_liquid_water_contentkg m-3grid cell mean each layer

reldroplet_effective_radiusmgrid cell mean each layer

lcclliquid_cloud_fraction1Fractional cover by liquid water clouds each layer

wsubcsubgrid_vertical_velocity_for_stratiformm s-1 

autoclautoconversion_ratekg m-2 s-1layer total in grid cell

accretlaccretion_ratekg m-2 s-1layer total in grid cell

niice_cloud_crystal_number_concentrationm-3grid cell mean each layer

iwccloud_ice_water_contentkg m-3grid cell mean each layer

reiIce_effective_radiusmgrid cell mean each layer

icclice_cloud_fraction1Fractional cover by ice water clouds each layer

satiice_supersaturation1Supersaturation with respect to ice

wsubisubgrid_vertical_velocity_for_cirrusm s-1 

mmrdumass_fraction_of_dust_dry_aerosol_in_airkg/kgeach layer

mmrbcmass_fraction_of_black_carbon_dry_aerosol_in_airkg/kgeach layer

mmrso4mass_fraction_of_sulfate_dry_aerosol_in_airkg/kgeach layer

cirrus_nso4sulfate_aerosol_number_for_homogeneousm-3grid cell mean sulfate aerosol number used for homogeneous aerosol freezing even if ice not nucleated

cirrus_ndustdust_aerosol_number_for_heterogeneousm-3grid cell mean dust aerosol number used for heterogeneous aerosol freezing even if ice not nucleated

cirrus_nbcBC_aerosol_number_for_heterogeneousm-3grid cell mean BC aerosol number used for heterogeneous aerosol freezing even if ice not nucleated

cirrus_nihomhomogeneous_nucleation_numberm-3grid cell mean ice crystal number production from homogeneous aerosol freezing for T<-37C during one model time step

cirrus_nihetheterogeneous_nucleation_numberm-3grid cell mean ice crystal number production from heterogeneous aerosol freezing for T<-37C during one model time step

cirrus_freqhomhomogeneous_nucleation_frequency1frequency counter of homogeneous aerosol freezing for T<-37C. For each time step, freqhom = 1 if homogeneous ice nucleation happens; otherwise freqhom = 0. Monthly average of this value indicates the homogeneous nucleation frequency.

cirrus_freqhetheterogeneous_nucleation_frequency1frequency counter of heterogeneous aerosol freezing for T<-37C. At each model time step, set freqhom = 1 if heterogeneous ice nucleation happens; otherwise freqhom = 0. Monthly average of this value indicates the heterogeneous nucleation frequency.

mp_hetnucdroplet_freezing_rate_by_heterogeneousm-3 s-1grid cell mean freezing rate of cloud droplets in mixed-phase clouds for T>-37C

mp_homnucdroplet_freezing_rate_by_homogeneousm-3 s-1grid cell mean instantaneous freezing rate of cloud droplets for T⇐-37C

(4) Optional CFMIP COSP diagnostics. Highly desirable for models with COSP
3-hr snapshots and daily means for January-March 2008 PD simulation only.
(a) 2D

namelong_name (CF if possible)unitsdescriptioncommentnotes

clwmodismodis_liquid_cloud_fraction1Column fractional cover by liquid water cloudsfrom modis simulator 

reffclwmodismodis_droplet_effective_radius*clwmodismgrid cell meanfrom modis simulator 

climodismodis_ice_cloud_fraction1Column fractional cover by ice water cloudsfrom modis simulator 

reffclimodismodis_ice_effective_radius*climodismgrid cell meanfrom modis simulator 

tauwmodismodis_liquid_cloud_optical_thickness*clwmodis1grid cell meanfrom modis simulator 

tauimodismodis_ice_cloud_optical_thickness*climodis1grid cell meanfrom modis simulator 

parasolRefltoa_bidirectional_reflectance1PARASOL ReflectanceSimulated reflectance from PARASOL as seen at the top of the atmosphere for 5 solar zenith angles. Valid only over ocean and for one viewing direction (viewing zenith angle of 30 degrees and relative azimuth angle 320 degrees). 

cltcalipsocloud_area_fraction%CALIPSO Total Cloud Fraction  

cllcalipsocloud_area_fraction_in_atmosphere_layer%CALIPSO Low Level Cloud Fraction  

clmcalipsocloud_area_fraction_in_atmosphere_layer%CALIPSO Middle Level Cloud Fraction  

clhcalipsocloud_area_fraction_in_atmosphere_layer%CALIPSO High Level Cloud Fraction  

(b) 3D

namelong_name (CF if possible)unitsdescriptioncommentnotes

ttemperatureKeach layer  

zaltitudemeach layer  

pressureatmospheric_pressurePaeach layer  

airmassatmosphere_mass_content_of_airkg m-2each layer  

ccn0.1cloud_condensation_nuclei_0.1m-3each layer (S=0.1%)  

ccn0.3cloud_condensation_nuclei_0.3m-3each layer (S=0.3%)  

ncliquid_cloud_droplet_number_concentrationm-3grid cell mean each layer  

lwccloud_liquid_water_contentkg m-3grid cell mean each layer stratiform cld only  

reldroplet_effective_radiusmgrid cell mean each layer stratiform cld only  

lccllayer_liquid_cloud_fraction1Fractional cover by liquid water stratiform clouds each layer  

niice_cloud_crystal_number_concentrationm-3grid cell mean each layer  

iwccloud_ice_water_contentkg m-3grid cell mean each layer stratiform cld only  

reiice_effective_radiusmgrid cell mean each layer stratiform cld only  

iccllayer_ice_cloud_fraction1Fractional cover by ice water stratiform clouds each layer  

dbze9494GHz_radar_reflectivity_subcolumndBZeRadar reflectivity each model layer in 100 subcolumns  

fracoutfracout_cloud_flag_subcolumn1subcolumn cloud flag each model layer in 100 subcolumns 0 clear, 1 strat 2 conv  

clcalipsocloud_area_fraction_in_atmosphere_layer%CALIPSO Cloud Area Fraction at 40 height levels

clcalipso2cloud_area_fraction_in_atmosphere_layer%CALIPSO Cloud Fraction Undetected by CloudSatClouds detected by CALIPSO but below the detectability threshold of CloudSatat 40 height levels

cfadDbze94histogram_of_equivalent_reflectivity_factor_over_height_above_reference_ellipsoid1CloudSat Radar Reflectivity CFADCFADs (Cloud Frequency Altitude Diagrams) are joint height - radar reflectivity distributions.40 levels x 15 bins

cfadLidarsr532histogram_of_backscattering_ratio_over_height_above_reference_ellipsoid1CALIPSO Scattering Ratio CFADCFADs (Cloud Frequency Altitude Diagrams) are joint height - lidar scattering ratio distributions.40 levels x 15 bins

Sampling of cloud-top quantities

The idea is to use the cloud overlap assumption (maximum, random, or maximum-random) to estimate which part of the cloud in a
layer can be seen from above.

Note: For the CCN, whether to sample it in the same way as CDNC, or use a similar approach (going from bottom up)
to sample it at cloud base depends on your parameterization of the activation.

let i=1,2,...,nx be the index for the horizontal grid-points let k=1,2,...,nz be the index for the vertial levels, with 1 being the uppermost level, and nz the surface level

naming convention for the 3D input fields:

iovl is the flag to select the overlap hypothesis cod3d(nx,nz) cloud optical thickness f3d(nx,nz) cloud fraction t3d(nx,nz) temperature phase3d(nx,nz) cloud thermodynamic phase (0: entire cloud consists of ice, 1: entire cloud consists of liquid water, between 0 and 1: mixed-phase) phase3d could be from fice3d/f3d where fice3d=ice+mixed phase cloud fraction cdr3d(nx,nz) in-cloud droplet effective radius icr3d(nx,nz) in-cloud ice crystal effective radius cdnc3d(nx,nz) in-cloud droplet number concentration

thres_cld = 0.001
thres_cod = 0.3
IF ( iovl = random OR iovl = maximum-random ) THEN

clt(i) = 1.


clt(:) = 0

icc(:) = 0
lcc(:) = 0
ttop(:) = 0
cdr(:) = 0
icr(:) = 0
cdnc(:) = 0

DO i=1,nx

DO k=2,nz ! assumption: uppermost layer is cloud-free (k=1) IF ( cod3d(i,k) > thres_cod and f3d(i,k) > thres_cld ) THEN ! visible, not-too-small cloud ! flag_max is needed since the vertical integration for maximum overlap is different from the two others: for maximum, clt is the actual cloud cover in the level, for the two others, the actual cloud cover is 1 - clt ! ftmp is total cloud cover seen from above down to the current level ! clt is ftmp from the level just above ! ftmp - clt is thus the additional cloud fraction seen from above in this level

IF ( iovl = maximum ) THEN flag_max = -1. ftmp(i) = MAX( clt(i), f3d(i,k)) ! maximum overlap ELSEIF ( iovl = random ) THEN flag_max = 1. ftmp(i) = clt(i) * ( 1 - f3d(i,k) ) ! random overlap ELSEIF ( iovl = maximum-random ) THEN flag_max = 1. ftmp(i) = clt(i) * ( 1 - MAX( f3d(i,k), f3d(i,k-1) ) ) / & ( 1 - MIN( f3d(i,k-1), 1 - thres_cld ) ) ! maximum-random overlap ENDIF ttop(i) = ttop(i) + t3d(i,k) * ( clt(i) - ftmp(i) )*flag_max

! ice clouds icr(i) = icr(i) + icr3d(i,k) * ( 1 - phase3d(i,k) ) * ( clt(i) - ftmp(i) )*flag_max icc(i) = icc(i) + ( 1 - phase3d(i,k) ) * ( clt(i) - ftmp(i) )*flag_max ! liquid water clouds cdr(i) = cdr(i) + cdr3d(i,j) * phase3d(i,k) * ( clt(i) - ftmp(i) )*flag_max cdnc(i) = cdnc(i) + cdnc3d(i,j) * phase3d(i,k) * ( clt(i) - ftmp(i) )*flag_max lcc(i) = lcc(i) + phase3d(i,k) * ( clt(i) - ftmp(i) )*flag_max clt(i) = ftmp(i) ENDIF ! is there a visible, not-too-small cloud? ENDDO ! loop over k

IF ( iovl = random OR iovl = maximum-random ) THEN clt(i) = 1. - clt(i) ENDIF

ENDDO ! loop over I

naming convention for the input variables:

utctime current time of the day in UTC in seconds time_step_len length of model time-step lon(nx) longitude in degrees from 0 to 360


* 2D cloud fields (lwp, iwp, cdr, cdnc, ttop, cod): Please save them as grid-box mean values but DO NOT divide by the total (2D) cloud cover, which will be done in analysis after averaging in time and space.

* The three months 1 October - 31 December 2005 are thought as spin-up, which can of course be longer. Please choose as overlap assumption the one you use in the radiation scheme.

* ATTENTION: clt(i) has to be initialized to 1 for random or maximum-random overlap assumptions in the “satellite simulator”

* CCN definition: Compute CCN using Kohler theory at 0.1 and 0.3 % supersaturation.