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aerocom:indirect

Indirect Effect Experiment Remarks

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

Motivation

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 steve.ghan@pnnl.gov and xiaohong.liu@pnnl.gov 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.

Diagnostics

* All data except COSP diagnostics is to be collected at the AEROCOM server.
* Groups hold COSP diagnostics and contact Kenta Suzuki (Kentaro.Suzuki@jpl.nasa.gov) for analysis
* 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", "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

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 od550aer*angstrm
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_liquid_path kg m-2 grid cell mean liquid water path for liquid water clouds
iwp atmosphere_cloud_ice_path 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 CCN number concentration at S=0.1% at 1 km above the surface
ccn0.3bl cloud_condensation_nuclei_0.3_pbl m-3 CCN number concentration at S=0.3% at 1 km above the surface
colccn.1 column_cloud_condensation_nuclei_0.1 m-2 column-integrated CCN number concentration at S=0.1%
colccn.3 column_cloud_condensation_nuclei_0.3 m-2 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 (to estimate the model's 'true' surface albedo)
ttop air_temperature_at_cloud_top K Temperature at top of clouds, weighted by cloud cover
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, plus a land-ocean mask (0 land, 1 ocean).

(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 each layer (S=0.1%)
ccn0.3 cloud_condensation_nuclei_0.3 m-3 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 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
mmrdu mass_fraction_of_dust_dry_aerosol_in_air kg/kg each layer
mmrbc mass_fraction_of_black_carbon_dry_aerosol_in_air kg/kg each layer
mmrso4 mass_fraction_of_sulfate_dry_aerosol_in_air kg/kg each layer
cirrus_nso4 sulfate_aerosol_number_for_homogeneous m-3 grid cell mean sulfate aerosol number used for homogeneous aerosol freezing even if ice not nucleated
cirrus_ndust dust_aerosol_number_for_heterogeneous m-3 grid cell mean dust aerosol number used for heterogeneous aerosol freezing even if ice not nucleated
cirrus_nbc BC_aerosol_number_for_heterogeneous m-3 grid cell mean BC aerosol number used for heterogeneous aerosol freezing even if ice not nucleated
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 for models with COSP
3-hr snapshots and daily means for January-March 2008 PD simulation only.
(a) 2D

name long_name (CF if possible) units description comment notes
clwmodis modis_liquid_cloud_fraction 1 Column fractional cover by liquid water clouds from modis simulator
reffclwmodis modis_droplet_effective_radius*clwmodis m grid cell mean from modis simulator
climodis modis_ice_cloud_fraction 1 Column fractional cover by ice water clouds from modis simulator
reffclimodis modis_ice_effective_radius*climodis m grid cell mean from modis simulator
tauwmodis modis_liquid_cloud_optical_thickness*clwmodis 1 grid cell mean from modis simulator
tauimodis modis_ice_cloud_optical_thickness*climodis 1 grid cell mean from modis simulator
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

(b) 3D

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 each layer (S=0.1%)
ccn0.3 cloud_condensation_nuclei_0.3 m-3 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 stratiform cld only
rel droplet_effective_radius m grid cell mean each layer stratiform cld only
lccl layer_liquid_cloud_fraction 1 Fractional cover by liquid water stratiform 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 stratiform cld only
rei ice_effective_radius m grid cell mean each layer stratiform cld only
iccl layer_ice_cloud_fraction 1 Fractional cover by ice water stratiform clouds each layer
dbze94 94GHz_radar_reflectivity_subcolumn dBZe Radar reflectivity each model layer in 100 subcolumns
fracout fracout_cloud_flag_subcolumn 1 subcolumn cloud flag each model layer in 100 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
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.

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, 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.

aerocom/indirect.txt · Last modified: 2015-12-09 13:15:31 by steve.ghan@pnnl.gov