Indirect forcing

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AeroCom working group Indirect forcing


  • "In-cloud" 2D fields (LWP, IWP, COD): Please compute them from grid-box mean values at each level and divide finally by the total (2D) cloud cover. A better name would probably be "cloud-weighted" instead of "in-cloud", which can only be defined in 3D.
  • The three months 1 October - 31 December 1999 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: tcc(i) has to be initialized to 1 for random or maximum-random overlap assumptions in the "satellite simulator"
  • CCN definition: Please use this field as a proxy for what your activation scheme uses (e.g., aerosol mass for a simple diagnostic scheme). If you actually do compute CCN, please compute it at 0.1 % supersaturation.
  • ATTENTION: bug in maximum-random version of cloud-top sampling

Indirect forcing experiment

Data submission deadline

  • Submission of results by 30 April 2008

Simulation setup

  • Simulation start 1 October 1999
  • Forcing by AMIP2 sea surface temperature and sea-ice extent
  • Greenhouse gas concentrations for year 2000
  • Aerosol direct, semi-direct, and indirect effects taken into account.
  • simulation PD (present-day): year 2000 AEROCOM aerosol emissions
  • simulation PI (pre-industrial): pre-industrial AEROCOM aerosol emissions (year 2000 GHG concentration)
  • option (a) (recommended if possible)

nudged to ECMWF re-analysis wind and temperature fields (1 year simulations for each, PD and PI)

  • option (b)

free run (5 years simulations for each, PD and PI)


  • Data is to be collected at the AEROCOM server.
  • Data in NetCDF format
  • All data are 3-dimensional ( lon x lat x time )

(optional ISCCP-simulator output 5-dimensional ( lon x lat x time x COD x PTOP ). For the ISCCP simulator please refer to

  • 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) For evaluation with satellite data

1 year (year 2000) of daily data from the PD run, taken at the overpass time of the Aqua Train satellite constellation (about 13.30 p.m. local time) (or, alternatively, at an arbitrary instant, 13.30 UTC).

# name long_name (CF if possible) units description
1 od550 atmosphere_optical_thickness_due_to_aerosol 1 Aerosol optical depth (@ 550 nm)
2 cdr liquid_cloud-top_droplet_effective_radius m Droplet effective radius at top of liquid water clouds
3 cdnc liquid_cloud_droplet_number_concentration m-3 Droplet number concentration in top layer of liquid water clouds
4 tcc cloud_area_fraction 1 Fractional cover by all clouds
5 lcc liquid_cloud_area_fraction 1 Fractional cover by liquid water clouds
6 lwp atmosphere_cloud_ice_content kg m-2 In-cloud liquid water path for liquid water clouds
7 albs planetary_albedo 1 TOA broadband SW planetary albedo, all-sky
8 rst toa_net_downward_shortwave_flux W m-2 Net TOA downward SW flux, all-sky
9 rstcs toa_net_downward_shortwave_flux_assuming_clear_sky W m-2 Net TOA downward SW flux, clear-sky
10 rlt toa_net_downward_longwave_flux W m-2 Net TOA downward LW flux, all-sky
11 rltcs toa_net_downward_longwave_flux_assuming_clear_sky W m-2 Net TOA downward LW flux, clear-sky
12 ttop air_temperature_at_cloud_top K Temperature at top of clouds
13 lts lower_tropospheric_stability K Difference in potential temperature between 700 hPa and 1000 hPa
14 iwp atmosphere_cloud_ice_content kg m-2 In-cloud ice water path for ice clouds
15 icr cloud-top_ice_crystal_effective_radius m Effective radius of crystals at top of ice clouds
16 icc ice_cloud_area_fraction 1 Fractional cover by ice clouds
17 cod atmosphere_optical_thickness_due_to_clouds 1 In-cloud optical depth
18 ccn cloud_condensation_nuclei m-3 Cloud condensation nuclei number concentration for liquid water clouds where activation corresponding to CDR and CDNC occurs (cloud base or top-layer of liquid water clouds)
19 isccp isccp_cloud_area_fraction 1 Joint histogram of the fractional cover by clouds for 49 bins of cloud optical thickness and cloud top pressure
20 hfls surface_upward_latent_heat_flux W m-2 Surface latent heat flux
21 hfss surface_upward_sensible_heat_flux W m-2 Surface sensible heat flux
22 rls surface_net_downward_longwave_flux_in_air W m-2 Net surface LW downward flux
23 rss surface_net_downward_shortwave_flux W m-2 Net surface SW downward flux
24 rsds surface_downwelling_shortwave_flux_in_air W m-2 Surface SW downward flux (in order to estimate the model's 'true' surface albedo)

(2) For forcing estimates

  • as (1), but monthly-mean fields for both PD and PI simulations
  • if option (a): for year 2000
  • if option (b): for five years (one average seasonal cycle)

“Satellite simulator”

(1) 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 apporach (going from bottom up) to samle 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)
  • cdr3d(nx,nz) cloud droplet effective radius
  • icr3d(nx,nz) ice crystal effective radius
  • cdnc3d(nx,nz) cloud droplet number concentration
thres_cld = 0.001
thres_cod = 0.3
IF ( iovl = random OR iovl = maximum-random ) THEN
  tcc(i) = 1.
  tcc(:) = 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, tcc is the actual cloud cover in the level, for the two others, the actual cloud cover is 1 - tcc
			! ftmp is total cloud cover seen from above down to the current level
			! tcc is ftmp from the level just above
			! ftmp – tcc is thus the additional cloud fraction seen from above in this level

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

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

	IF ( iovl = random OR iovl = maximum-random ) THEN
		tcc(i) = 1. - tcc(i)
ENDDO ! loop over I

(2) Sampling of the satellite-overpass-time

To sample the overpass time of the satellite (13.30 h local time), the idea is to create a mask (satmask) indicating whether or not at the grid-box the local time is 13.30 h ± ½ model-timestep. Then, all output fields are weighted with this mask (field * satmask), and in the output, the diurnal mean is taken. The physical fields at 13.30 h local time are obtained in post-processing by dividing each field by the mask (field / satmask). So, the diurnal mean of satmask must be stored as well!

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°
sat_mask(:) = 0
overpasstime = 48600 ! 13.30 p.m. local time

DO i=1,nx
	localtime(i) = utctime + 240 * lon ! for each degree of longitude east, 4 min earlier local time
	IF ( localtime(i) > 86400 ) THEN ! this is still the previous day
		localtime(i) = localtime(i) - 86400

	! Select 10.30 a.m. ± dt/2
	IF ( ABS( localtime(i) - overpasstime ) <= time_step_len/2 )
		sat_mask(i) = 1

	! Weight the output fields with this mask
	aod(i) = aodd(i) * sat_mask(i) 
	cdr(i) = cdr(i) * sat_mask(i) 
	cdnc(i) = cdnc(i) * sat_mask(i) 
	tcc(i) = tcc(i) * sat_mask(i) 
	lcc(i) = lcc(i) * sat_mask(i) 
	lwp(i) = lwp(i) * sat_mask(i) 
	albs(i) = albs(i) * sat_mask(i) 
	ssw(i) = ssw(i) * sat_mask(i) 
	sswclr(i) = sswclr(i) * sat_mask(i) 
	slw(i) = slw(i) * sat_mask(i) 
	slwclr(i) = slwclr(i) * sat_mask(i) 
	ttop(i) = ttop(i) * sat_mask(i) 
	lts(i) = lts(i) * sat_mask(i) 
	iwp(i) = iwp(i) * sat_mask(i) 
	icr(i) = icr(i) * sat_mask(i) 
	icc(i) = icc(i) * sat_mask(i) 
	cod(i) = cod(i) * sat_mask(i) 	 
	ccn(i) = ccn(i) * sat_mask(i) 

In the diurnal-mean output files, the actual in-cloud fields are derived by

cdr' = cdr / lcc
cdnc' = cdnc / lcc
lwp' = lwp / lcc

ttop' = ttop / tcc

iwp' = iwp / icc
icr' = icr / icc

For all other fields, the actual values are derived by

aod' = aod / sat_mask


Potential participants as of December 2007

  • Yi Ming, GFDL (GFDL GCM)
  • Yves Balkanski, LSCE (LMDZ-INCA)
  • Ulrike Lohmann, ETH (ECHAM5 with convection microphysics)
  • Philip Stier, Univ Oxford (ECHAM5, HadGEM)
  • Leon Rotstayn, CSIRO (CSIRO GCM, subject to clarification)
  • Andrew Gettelman, NCAR (CCM)
  • Toshi Takemura, Kyushu University (SPRINTARS)
  • Surabi Menon, LBL (GISS GCM)
  • Jon-Egill Kristjànsson, Univ Oslo (CCM-Oslo)
  • Trude Storelvmo, ETH (ECEarth)
  • Thanos Nenes, Georgia Tech (GMI)
  • Nicolas Bellouin, Met Office (HadGEM)

Status and Idea (Lille meeting October 2007)


An AEROCOM-IND model intercomparison has taken place under lead of Joyce Penner in 2004/2005. Fourteen simulations have been carried out with each model, each one for five years with the atmospheric GCM, for seven model configurations under present-day and pre-industrial aerosol emission scenarios. The seven configurations varied the degree of freedom of the individual models for the aerosol indirect effect from a common treatment of all relevant parametrisations to individual treatments of all (see [Protocol] for details). Three modelling groups participated (U Oslo, Trude Storelvmo; U Kyushu, Toshi Takemura; LMD Paris, Johannes Quaas). The main result was that the different treatments of aerosol life cycles and of the parameterizations of CDNC and of autoconversion rate lead to large inter-model differences in the simulated all-sky short-wave radiative effects of anthropogenic aerosols, while the influence of the differently simulated cloud fields and aerosol direct effects is minor (Penner et al., 2006).

At the 2004 AEROCOM meeting where the AEROCOM-IND experiment has been proposed, 14 groups expressed interest in participating. Only one out of these, plus two others, actually contributed. This fact, and the experience of the three participating groups, suggest that the requirements were possibly too demanding.

Ideas for a new initiative

  • Focus on the cloud albedo indirect effect for low-level liquid water clouds (models may include other effects, but evaluation would focus on the cloud albedo effect)
  • Use of cloud-aerosol statistical relationships as metrics of the aerosol indirect effect
  • Propose few additional model diagnostics for comparison to data-tied constraints

At present, various aerosol indirect effects have been proposed. These include the cloud albedo effect (increase in CDNC at constant cloud liquid water content), the cloud lifetime effect (delay of collision/coalescence processes due to smaller CDR, thus enhancement of cloud cover and cloud liquid water path) and the semi-direct effect (evaporation of cloud water or inhibition of cloud formation due to warming or stabilisation of the atmosphere due to absorption of solar radiation). So far, these effects have mostly been studied for low-level liquid water clouds. More recently, effects on convective clouds, ice- and mixed-phase clouds have been proposed, too (Lohmann and Feichter, 2005). Modelling studies suggest that effects of anthropogenic aerosols on ice- and mixed-phase clouds are small compared to the ones on liquid water clouds (Lohmann et al., 2007). However, the former effects are even more uncertain than the latter ones. Models also suggest that the semi-direct effect is smaller than the cloud albedo and cloud lifetime effects (Lohmann and Feichter, 2001). The cloud albedo effect is a prerequisite for the cloud lifetime effect. For these reasons, the proposed initiative should focus on the cloud albedo effect on low-level liquid water clouds as the probably most fundamental and best-understood one.

Parametrizations of the cloud albedo effect (in the following just called the aerosol indirect effect, AIE) for GCMs have been derived usually from aircraft data (e.g., Boucher and Lohmann, 1995; Lin and Leaitch, 1997). Comparisons of satellite-derived statistics and GCM simulations show that the parametrisations derived at the small scale are not suitable to be applied directly at the large GCM grid scale making adjustments and evaluation necessary (Lohmann et al., 2007).

The AIE can be defined as

Eq. 1 <math>AIE=\frac{\partial\ln r_e}{\partial\ln\tau_a}</math>

with <math>r_e</math> the CDR and <math>\tau_a</math> the AOD (Feingold et al., 2003). In this way, the AIE has been derived from satellite data (Nakajima et al., 2001; Bréon et al., 2002; Sekiguchi et al., 2003; Quaas et al., 2004; Kaufman et al., 2005; Quaas and Boucher, 2005) and from ground-based remote sensing data (Feingold et al., 2003). An even more appropriate proxy for the AIE might be the relationship between CDNC and AOD as proposed by Quaas et al. (2006). Assuming adiabaticity for low-level liquid-water clouds, CDNC can be derived from satellite data. Such satellite-derived relationships have served in a few studies with single GCMs to examine parameterizations of the AIE (Lohmann and Lesins, 2002; Sekiguchi et al., 2003; Quaas et al., 2004; Quaas and Boucher, 2005; Storelvmo et al., 2006; Quaas et al., 2006).

These AIE metrics are available from POLDER (CDR – aerosol index) and MODIS (CDR – AOD, CDNC – AOD), where MODIS retrievals from NASA-LaRC (Minnis et al., 2004) and NASA-Goddard (Platnick et al., 2003) are both used. MODIS-derived statistics are available at a regional and seasonal basis. As part of the proposed initiative, the metrics could be derived from other satellite sensors and ground-based remote sensing as well.

Model diagnostics requirements


  • AOD
  • Cloud-top droplet effective radius for low-level liquid water clouds
  • Cloud droplet number concentration for low-level liquid water clouds
  • Cloud fraction
  • Fractional coverage by low-level liquid water clouds
  • Cloud liquid water path for low-level liquid water clouds
  • Planetary albedo
  • SW and LW ToA radiative fluxes
  • SW and LW cloud-free ToA radiative fluxes
  • Cloud-top temperature
  • Potential temperature @ 700 hPa and surface
  • Total IWP
  • Total LWP
  • Cloud-top ice crystal radius
  • Cloud-top droplet effective radius for all liquid clouds
  • Angstrom exponent


  • ISCCP simulator output
  • CICCS simulator output
  • CCN number concentration at cloud base (or in-cloud CCN concentration)
  • Aerosol mass concentration at cloud base (or in-cloud aerosol concentration)

CICCS: CFMIP ISCCP Calipso-Cloudsat Simulator (CFMIP: cloud forcing model intercomparison project, lead by Mark Webb, UK Met Office; ISCCP: International Cloud Climatology Project) available as beta release spring 2008 from the UK Met Office (via AEROCOM).

Output is needed as daily averages. Ideally, the overpass time of the polar-orbiting satellite is sampled to provide a diurnal coverage. Simple codes for the cloud-top sampling and for the sampling of the satellite overpass can be provided.

Modelling groups which expressed interest to participate

  • Georgia Tech (Thanos Nenes)
  • ETH Zurich (Trude Storelvmo, Ulrike Lohmann)
  • U Oslo (Jón-Egill Kristjánsson)
  • NCAR (Andrew Gettelman)
  • U Leeds (David Ridley)
  • LSCE/IPSL (Yves Balkanski)
  • UK Met Office (Nicolas Bellouin)
  • MPI Hamburg (Johannes Quaas)
  • NOAA/GFDL (Yi Ming)


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