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aerocom:indirect

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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 atmosphere_optical_thickness_due_to_clouds 1 Grid cell mean cloud optical depth
codliq atmosphere_optical_thickness_due_to_liquid_clouds 1 Grid cell mean cloud optical depth
codice atmosphere_optical_thickness_due_to_ice_clouds 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
hus specific_humidity kg/kg
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
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 3-month (which?) PD simulation only.

name long_name (CF if possible) units description comment notes
t temperature K each layer
z altitude m 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
radar 94GHz_radar_subcolumn dBZe Radar reflectivity in 70 subcolumns
fracout fracout_cloud_flag_subcolumn 1 subcolumn cloud flag 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.

aerocom/indirect.1380656246.txt.gz · Last modified: 2013-10-01 19:37:26 by steve.ghan@pnnl.gov