Transcript Document

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
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
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.
Aerosol Scavenging Processes:
Sedimentation and
dry deposition
Wet scavenging accounts for 50-95% of
aerosol deposition, and strongly controls
aerosol 3-dimensional distributions, which
influence climate both directly and indirectly.
(Figure adapted
from Hoose et al.
(2008))
Aerosol wet scavenging processes:
Aerosols  Cloud Droplets / Ice Crystals  Precipitation
Processes:
Processes:
1) Nucleation of
droplets/crystals
In-cloud: (tuning parameters)
2) Impaction with
droplets/crystals
1) Autoconversion
2) Accretion
3) Aggregation
We examine the relative
contributions of nucleation and
impaction to in-cloud scavenging.
Below-cloud:
1) Impaction with rain/snow
Modeling of Aerosol In-Cloud Scavenging:
Methodologies -
1) Prescribed scavenging ratios (e.g., Stier et al. (2005))
2) 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.
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.
SU:sulfate; BC:black carbon; POM:particulate organic matter; DU:dust;
SS:sea salt
Prescribed In-Cloud Scavenging Ratio
1) Prescribed in-cloud scavenging ratios:
standard ECHAM5-HAM (nucleation+impaction)
1.2
1
0.8
0.6
0.4
0.2
0
NS
KS
AS
CS
KI
AI
CI
Liquid
T>273K
Mixed
238<T<273K
Ice
T<238K
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 j=1-4 soluble modes,
based on the fractional contribution of each mode
to the total number of soluble aerosols having radii
>35 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.
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)
Impaction Scavenging by Column and Plate Ice Crystals:
Prescribed
coefficients of
Hoose et al.
(2008)
(red steps)
Assume plates for 238.15<T<273.15 K
Assume columns for T<238.15K
(Data from Miller and Wang, (1991), and following Croft et al. (2009))
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)
Histograms of diagnosed vs. prescribed scavenging ratios:
Aitken
mode 
Accumulation
mode 
Coarse
mode 
T>273 K
238<T<273 K
T<238 K
Uncertainty in global and annual mean mass burdens:
50
40
30
F100-CTL
20
DIAG-CTL
10
PROG-CTL
[%] 0
-10
-20
SO4
BC
POM
DUST
SS
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.
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.
Uncertainties for Nucleation
Mode Number:
Increased new particle
nucleation is found for the
simulation that assumes 100% of
the in-cloud aerosol is cloudborne.
(nm)
Uncertainties in
Aerosol Size:
The size of the
accumulation
mode particles
changes by up to
100%.
Contributions of nucleation vs. impaction to annual and
global mean stratiform in-cloud scavenging: Diag. scheme
80
70
60
Nuc (Warm)
50
Nuc (Mixed)
Nuc (Ice)
40
[%]
30
Imp (Warm)
Imp (Mixed)
Imp (Ice)
20
10
0
SO4
BC
POM
Dust
SS
Number
>90% of mass scavenging by nucleation (dust:50%); >90% of number scavenging by impaction.
Influence of impaction on dust scavenged mass:
Influence of impaction on black carbon scavenged mass:
Observed black carbon profiles from aircraft (Koch et al. 2009)
Observations of MBL size distributions (Heintzenberg et al. (2000))
Observations of AOD from MODIS MISR composite (van Donkelaar et al., subm.)
Observations of sulfate wet deposition (Dentener et al. (2006))
Observed 210Pb and 7Be concentrations and deposition (Heikkilä et al. (2008))
Current work: Coupled Stratiform-Convective Aerosol Processing:
Stratiform Clouds
Convective Clouds
Detrainment
CD
CV
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.
Aerosol Processing by Convective Clouds:
CN: Solid
CCN0.6/CN
CCN0.6: Dotted
Red: 12 hours before convective system
Blue: 12 hours after convective system
Figure from Crumeyrolle et al. (2008), ACP
- case study from Niger.
Evidence for dust coating by
sulfate above the boundary
layer as a result of cloud
processing.
Summary and Outlook:
1) 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 T<273K.
2) In stratiform clouds, number scavenging is primarily (>90%) by
impaction, and largely in mixed and ice phase clouds (>99%). Mass
scavenging is primarily (>90%) by nucleation, except for dust (50%).
Recommend:  understanding of impaction processes for cloud
temperatures <273K, and for dust at all cloud temperatures.
3) Better agreement with black carbon profiles for diagnostic and
prognostic schemes.  ↓ prescribed ratios for mixed phase clouds.
4) Recommend diagnostic and prognostic schemes over the prescribed
ratio scheme, which can not represent variability of scavenging ratios.
5) Recommend further development of the prognostic aerosol cloud
processing approach for convective clouds.
Acknowledgements:
Thanks!
Questions?