Indirect Effect Experiment Remarks

Indirect forcing experiment

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
* hom_1850: pre-industrial emissions no heterogeneous nucleation of ice
* fix_2000: present day emissions fixed ice nucleation for T<-37 C using Cooper (1986) as a f(temperature)
* fix_1850: pre-industrial emissions fixed ice nucleation for T<-37 C using Cooper (1986) as f(temperature)

Diagnostics

* All data except COSP diagnostics is to be collected at the AEROCOM server.
* Groups hold COSP diagnostics until analyst is identified
* follow the aerocom data protocol (http://aerocom.met.no/protocol.html)
* 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", "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 data from the PD run

name	long_name (CF if possible)	units	description
od550aer	atmosphere_optical_thickness_due_to_aerosol	1	Aerosol optical depth (@ 550 nm)
angstrm	AOD_Angstrom_exponent	1
aerindex	aerosol_index	1
cdr	liquid_cloud-top_droplet_effective_radius	m	Grid cell mean droplet effective radius at top of liquid water clouds
cdnc	liquid_cloud_droplet_number_concentration	m-3	Grid cell mean droplet number concentration in top layer of liquid water clouds
cdnum	column_cloud_droplet_number_concentration	m-2	grid cell mean column total
icnum	column_ice_crystal_number_concentration	m-2	grid cell mean column total
clt	cloud_area_fraction	1	Fractional cover by all clouds
lcc	liquid_cloud_area_fraction	1	Fractional cover by liquid water clouds
lwp	atmosphere_cloud_ice_content	kg m-2	grid cell mean liquid water path for liquid water clouds
iwp	atmosphere_cloud_ice_content	kg m-2	grid cell mean ice water path for ice clouds
icr	cloud-top_ice_crystal_effective_radius	m	grid cell mean effective radius of crystals at top of ice clouds
icc	ice_cloud_area_fraction	1	Fractional cover by ice clouds
cod	cloud_optical_depth	1	Grid cell mean cloud optical depth
codliq	cloud_optical_depth_due_to_liquid	1	Grid cell mean cloud optical depth
codice	cloud_optical_depth_due_to_ice	1	Grid cell mean cloud optical depth
ccn0.1bl	cloud_condensation_nuclei_0.1_pbl	m-3	grid-cell mean CCN number concentration at S=0.1% at 1 km above the surface
ccn0.3bl	cloud_condensation_nuclei_0.3_pbl	m-3	grid-cell mean CCN number concentration at S=0.3% at 1 km above the surface
colccn.1	column_cloud_condensation_nuclei_0.1	m-2	grid-cell mean column-integrated CCN number concentration at S=0.1%
colccn.3	column_cloud_condensation_nuclei_0.3	m-2	grid-cell mean column-integrated CCN number concentration at S=0.3%
rsut	toa_upward_shortwave_flux	W m-2	TOA upward SW flux, all-sky
rsutcs	toa_upward_shortwave_flux_assuming_clear_sky	W m-2	TOA upward SW flux, clear-sky
rsutnoa	toa_upward_shortwave_flux_no_aerosol	W m-2	TOA upward SW flux, all-sky, aerosol removed from calculation
rsutcsnoa	toa_upward_shortwave_flux_clear_sky_no_aerosol	W m-2	TOA upward SW flux, clear-sky, aerosol removed from calculation
rlut	toa_upward_longwave_flux	W m-2	TOA upward LW flux, all-sky
rlutcs	toa_upward_longwave_flux_assuming_clear_sky	W m-2	TOA upward LW flux, clear-sky
hfls	surface_upward_latent_heat_flux	W m-2	Surface latent heat flux
hfss	surface_upward_sensible_heat_flux	W m-2	Surface sensible heat flux
rls	surface_net_downward_longwave_flux_in_air	W m-2	Net surface LW downward flux
rss	surface_net_downward_shortwave_flux	W m-2	Net surface SW downward flux
rsds	surface_downwelling_shortwave_flux_in_air	W m-2	Surface SW downward flux (in order to estimate the model's 'true' surface albedo)
ttop	air_temperature_at_cloud_top	K	Temperature at top of clouds
lts	lower_tropospheric_stability	K	Difference in potential temperature between 700 hPa and 1000 hPa
w500	vertical_velocity_dpdt_at_500_hPa	hPa s-1
sprecip	stratiform_precipitation_rate	kg m-2 s-1	grid cell mean at surface
autoconv	column_autoconversion_rate	kg m-2 s-1	grid cell mean column total
accretn	column_accretion_rate	kg m-2 s-1	grid cell mean column total

(2) For forcing estimates: as in (1), but monthly-mean fields for both PD and PI simulations

(3) 3D monthly mean diagnostics

name	long_name (CF if possible)	units	description
t	temperature	K	each layer
hus	specific_humidity	kg/kg	each layer
z	altitude	m	each layer
airmass	atmosphere_mass_content_of_air	kg m-2	each layer
ccn0.1	cloud_condensation_nuclei_0.1	m-3	grid cell mean each layer (S=0.1%)
ccn0.3	cloud_condensation_nuclei_0.3	m-3	grid cell mean each layer (S=0.3%)
nc	liquid_cloud_droplet_number_concentration	m-3	grid cell mean each layer
lwc	cloud_liquid_water_content	kg m-3	grid cell mean each layer
rel	droplet_effective_radius	m	grid cell mean each layer
lccl	liquid_cloud_fraction	1	Fractional cover by liquid water clouds each layer
wsubc	subgrid_vertical_velocity_for_stratiform	wsubc	m s-1
autocl	autoconversion_rate	kg m-2 s-1	layer total in grid cell
accretl	accretion_rate	kg m-2 s-1	layer total in grid cell
ni	ice_cloud_crystal_number_concentration	m-3	grid cell mean each layer
iwc	cloud_ice_water_content	kg m-3	grid cell mean each layer
rei	Ice_effective_radius	m	grid cell mean each layer
iccl	ice_cloud_fraction	1	Fractional cover by ice water clouds each layer
sati	ice_supersaturation	1	Supersaturation with respect to ice
wsubi	subgrid_vertical_velocity_for_cirrus	m s-1
cirrus_nso4	sulfate_aerosol_number_for_homogeneous	m-3	grid cell mean sulfate aerosol number used for homogeneous aerosol freezing for T<-37C
cirrus_ndust	dust_aerosol_number_for_heterogeneous	m-3	grid cell mean dust aerosol number used for heterogeneous aerosol freezing for T<-37C
cirrus_nbc	BC_aerosol_number_for_heterogeneous	m-3	grid cell mean BC aerosol number used for heterogeneous aerosol freezing for T<-37C
cirrus_nihom	homogeneous_nucleation_number	m-3	grid cell mean ice crystal number production from homogeneous aerosol freezing for T<-37C during one model time step
cirrus_nihet	heterogeneous_nucleation_number	m-3	grid cell mean ice crystal number production from heterogeneous aerosol freezing for T<-37C during one model time step
cirrus_freqhom	homogeneous_nucleation_frequency	1	frequency 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_freqhet	heterogeneous_nucleation_frequency	1	frequency 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_hetnuc	droplet_freezing_rate_by_heterogeneous	m-3 s-1	grid cell mean freezing rate of cloud droplets in mixed-phase clouds for T>-37C
mp_homnuc	droplet_freezing_rate_by_homogeneous	m-3 s-1	grid cell mean instantaneous freezing rate of cloud droplets for T<=-37C

(4) Optional CFMIP COSP diagnostics. Highly desirable but optional for now
3-hr snapshots and daily means for January-March 2008 PD simulation only.

name	long_name (CF if possible)	units	description	comment	notes
t	temperature	K	each layer
z	altitude	m	each layer
pressure	atmospheric_pressure	Pa	each layer
airmass	atmosphere_mass_content_of_air	kg m-2	each layer
ccn0.1	cloud_condensation_nuclei_0.1	m-3	grid cell mean each layer (S=0.1%)
ccn0.3	cloud_condensation_nuclei_0.3	m-3	grid cell mean each layer (S=0.3%)
nc	liquid_cloud_droplet_number_concentration	m-3	grid cell mean each layer
lwc	cloud_liquid_water_content	kg m-3	grid cell mean each layer
ewl	droplet_effective_radius	m	grid cell mean each layer
lccl	liquid_cloud_fraction	1	Fractional cover by liquid water clouds each layer
ni	ice_cloud_crystal_number_concentration	m-3	grid cell mean each layer
iwc	cloud_ice_water_content	kg m-3	grid cell mean each layer
rei	Ice_effective_radius	m	grid cell mean each layer
iccl	ice_cloud_fraction	1	Fractional cover by ice water clouds each layer
dbze94	94GHz_radar_reflectivity_subcolumn	dBZe	Radar reflectivity each model layer in 70 subcolumns
fracout	fracout_cloud_flag_subcolumn	1	subcolumn cloud flag each model layer in 70 subcolumns 0 clear, 1 strat 2 conv
clcalipso	cloud_area_fraction_in_atmosphere_layer	%	CALIPSO Cloud Area Fraction	at 40 height levels
clcalipso2	cloud_area_fraction_in_atmosphere_layer	%	CALIPSO Cloud Fraction Undetected by CloudSat	Clouds detected by CALIPSO but below the detectability threshold of CloudSat	at 40 height levels
cfadDbze94	histogram_of_equivalent_reflectivity_factor_over_height_above_reference_ellipsoid	1	CloudSat Radar Reflectivity CFAD	CFADs (Cloud Frequency Altitude Diagrams) are joint height - radar reflectivity distributions.	40 levels x 15 bins
cfadLidarsr532	histogram_of_backscattering_ratio_over_height_above_reference_ellipsoid	1	CALIPSO Scattering Ratio CFAD	CFADs (Cloud Frequency Altitude Diagrams) are joint height - lidar scattering ratio distributions.	40 levels x 15 bins
parasolRefl	toa_bidirectional_reflectance	1	PARASOL Reflectance	Simulated 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).
cltcalipso	cloud_area_fraction	%	CALIPSO Total Cloud Fraction
cllcalipso	cloud_area_fraction_in_atmosphere_layer	%	CALIPSO Low Level Cloud Fraction
clmcalipso	cloud_area_fraction_in_atmosphere_layer	%	CALIPSO Middle Level Cloud Fraction
clhcalipso	cloud_area_fraction_in_atmosphere_layer	%	CALIPSO High Level Cloud Fraction

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 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)
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

clt(i) = 1.

ELSE

clt(:) = 0

ENDIF
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

Q/A

* 2D cloud fields (lwp, iwp, cdr, cdnc, cod): Please compute them from grid-box mean values at each level 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.