Influences of In-cloud Scavenging Parameterizations on Aerosol Concentrations and Deposition in the ECHAM5-HAM GCM

1
Influences of In-cloud Scavenging
Parameterizations on Aerosol Concentrations and
Deposition in the ECHAM5-HAM GCM
Betty Croft - Dalhousie University, Halifax,
Canada Ulrike Lohmann - ETH Zurich, Zurich,
Switzerland Randall Martin - Dalhousie
University, Halifax, Canada Philip Stier -
University of Oxford, Oxford, U.K. Johann
Feichter - MPI for Meteorology, Hamburg,
Germany Sabine Wurzler LANUV, Recklinghausen,
Germany Corinna Hoose - University of Oslo, Oslo,
Norway Ulla Heikkilä - Bjerknes Centre for
Climate Research, Bergen, Norway Aaron van
Donkelaar Dalhousie University, Halifax, Canada

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--------------------------- CAFC
Winter Meeting, Toronto, February 1, 2010

2
Do global models agree on predicted aerosol
profiles?
Koch et al. (2009), ACP Black carbon profiles
differ by 2 orders of magnitude among global
models.
? Deficits in our understanding of the processes
involved and their interactions
3
The aerosol-cloud-precipitation interaction
puzzle
AEROSOLS
CLOUDS
PRECIPITATION
This problem involves many processes, and
isolating the effects of one or the other is
difficult.
4
Aerosol Scavenging Processes
Sedimentation and dry deposition
(Figure adapted from Hoose et al. (2008))
Wet scavenging accounts for 50-95 of aerosol
deposition, and strongly controls aerosol
3-dimensional distributions, which influence
climate both directly and indirectly.
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Aerosol wet scavenging processes
Aerosols ? Cloud Droplets / Ice Crystals ?
Precipitation

• Processes
• Nucleation of droplets/crystals
• Impaction with droplets/crystals

• Processes
• In-cloud (tuning parameters)
• Autoconversion
• Accretion
• Aggregation
• Below-cloud
• 1) Impaction with rain/snow

We examine the relative contributions of
nucleation and impaction to in-cloud scavenging.
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Modeling of Aerosol In-Cloud Scavenging

• Methodologies -
• Prescribed scavenging ratios (e.g., Stier et al.
  (2005))
• Diagnostic - cloud droplet and ice crystal number
  concentrations are used to diagnose nucleation
  scavenging size-dependent impaction scavenging
  (e.g. Croft et al. (2009))
• 3) Prognostic - in-droplet and in-crystal
  aerosol concentrations are prognostic species
  that are passed between model time-steps (e.g.,
  Hoose et al. (2008))

Using the ECHAM5-HAM GCM, we compare the
strength/weaknesses of these 3 fundamental
approaches, and examine the sensitivity of
predicted aerosol profiles to differences in the
parameterization of in-cloud scavenging.
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ECHAM5 GCM coupled to HAM (Hamburg Aerosol
Module) (Stier et al. (2005))
All results shown are for a 1-year simulation of
the ECHAM5-HAM global aerosol-climate model, at
T42 resolution, nudged to the meteorological
conditions of the year 2001, and following a 3
months spin-up period. SUsulfate BCblack
carbon POMparticulate organic matter DUdust
SSsea salt
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1) Prescribed in-cloud scavenging ratios
standard ECHAM5-HAM (nucleationimpaction)
Tgt273K 238ltTlt273K Tlt238K
NS
KS
AS
CS
KI
AI
CI
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2) Diagnostic scheme Size-Dependent Nucleation
Scavenging
Assume each cloud droplet or ice crystal
scavenges 1 aerosol by nucleation, and apportion
this number between the j1-4 soluble modes,
based on the fractional contribution of each
mode to the total number of soluble aerosols
having radii gt35 nm, which are the aerosols that
participate in the Ghan et al. (1993) activation
scheme.
Find rcrit that contains Nscav,j in the
lognormal tail.
From the cumulative lognormal size-distribution,
Scavenge all mass above this radius for
nucleation scavenging. Thus, we typically
scavenge a higher fraction of the mass versus
number distribution.
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Size-Dependent Impaction Scavenging by Cloud
Droplets
Example for CDNC 40 cm-3, assuming a gamma
distribution
Prescribed coefficients of Hoose et al. (2008)
prognostic scheme are shown with red steps
Solid lines Number scavenging coefficients

Dashed lines Mass scavenging coefficients
Data sources described in Croft
et al. (2009)
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Impaction Scavenging by Column and Plate Ice
Crystals
Prescribed coefficients of Hoose et al. (2008)
(red steps)
Assume columns for Tlt238.15K
Assume plates for 238.15ltTlt273.15 K
(Data from Miller and Wang, (1991), and following
Croft et al. (2009))
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3) Prognostic scheme Aerosol-cloud processing
approach (Hoose et al. (2008))
Stratiform in-droplet and in-crystal aerosol
concentrations are additional prognostic
variables.
Two new aerosol modes ? In-droplet
(CD) In-crystal (IC)
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Histograms of diagnosed vs. prescribed scavenging
ratios
Aitken mode ?
Accumulation mode ?
Coarse mode ?
Tgt273 K
238ltTlt273 K
Tlt238 K
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Uncertainty in global and annual mean mass
burdens

SO4
BC
POM
DUST
SS
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Uncertainties in Aerosol Mass Mixing Ratios
Zonal and annual mean black carbon mass is
increased by near to one order of magnitude in
regions of mixed and ice phase clouds relative to
the simulation with prescribed scavenging ratios.
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Uncertainties in Accumulation Mode Number
Assuming 100 of the in-cloud aerosol is cloud
borne reduces the accumulation mode number
burden by up to 0.7, but the diagnostic and
prognostic scheme give increases up to 2 and 5
times, respectively relative to the prescribed
fractions.
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Uncertainties for Nucleation Mode Number
Increased new particle nucleation is found for
the simulation that assumes 100 of the in-cloud
aerosol is cloud-borne.
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(nm)
Uncertainties in Aerosol Size The size of the
accumulation mode particles changes by up to 100.
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Contributions of nucleation vs. impaction to
annual and global mean stratiform in-cloud
scavenging Diag. scheme

SO4
BC
POM
Dust
SS
Number
gt90 of mass scavenging by nucleation (dust50)
gt90 of number scavenging by impaction.
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Influence of impaction on dust scavenged mass
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Influence of impaction on black carbon scavenged
mass
22
Observed black carbon profiles from aircraft
(Koch et al. 2009)
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Observations of MBL size distributions
(Heintzenberg et al. (2000))
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Observations of AOD from MODIS MISR composite
(van Donkelaar et al., subm.)
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Observations of sulfate wet deposition (Dentener
et al. (2006))
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Observed 210Pb and 7Be concentrations and
deposition (Heikkilä et al. (2008))
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Current work Coupled Stratiform-Convective
Aerosol Processing
Stratiform Clouds
Convective Clouds
Detrainment
CDCV
Detrainment
IC CV
CDVC and ICCV will not be prognostic variables
since the convective clouds entirely evaporate or
sublimate after the above processes for each GCM
timestep.
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Aerosol Processing by Convective Clouds
CCN0.6/CN
CN Solid CCN0.6 Dotted
Evidence for dust coating by sulfate above the
boundary layer as a result of cloud processing.
Red 12 hours before convective system Blue 12
hours after convective system Figure from
Crumeyrolle et al. (2008), ACP - case study
from Niger.
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• Summary and Outlook
• Mixed /ice phase cloud scavenging was most
  uncertain between the parameterizations.
  Middle/upper troposphere black carbon
  concentrations differed by more than 1 order of
  magnitude between the scavenging schemes.
  Recommend ? understanding nucleation and
  impaction processes for cloud temperatures
  Tlt273K.
• In stratiform clouds, number scavenging is
  primarily (gt90) by impaction, and largely in
  mixed and ice phase clouds (gt99). Mass
  scavenging is primarily (gt90) by nucleation,
  except for dust (50). Recommend ?
  understanding of impaction processes for cloud
  temperatures lt273K, and for dust at all cloud
  temperatures.
• Better agreement with black carbon profiles for
  diagnostic and prognostic schemes. ? ? prescribed
  ratios for mixed phase clouds.
• Recommend diagnostic and prognostic schemes over
  the prescribed ratio scheme, which can not
  represent variability of scavenging ratios.
• Recommend further development of the prognostic
  aerosol cloud processing approach for convective
  clouds.

Thanks! Questions?
Acknowledgements