aerosol mixing
DESCRIPTION
Aersols make the air smell niceTRANSCRIPT
Evaluating the Role of Aerosol Mixing Statein Cloud Droplet Nucleation
using a new activation parameterization
Daniel Rothenberg and Chien Wang
Massachusetts Institute of TechnologyDepartment of Earth, Atmospheric, and Planetary Sciences
Program in Atmospheres, Oceans, and Climate
December 11, 2013
EAPSDepartment ofEarth, Atmospheric,and Planetary Sciences
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 1 / 12
The Mixing State Problem
How do you represent mixtures of aerosols in GCMs/CRMs?
Uniform, homogeneous compositionparticles
Different populations of chemicallyhomogeneous particles
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 2 / 12
The Mixing State Problem
Real aerosol populations arechemically and physicallyheterogeneous
Different particles with varyingoptical and microphysicalproperties
Important to capture thisdiversity in order to resolveanthropogenic aerosoleffects on clouds/climate!
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 3 / 12
The Mixing State Problem
Real aerosol populations arechemically and physicallyheterogeneous
Different particles with varyingoptical and microphysicalproperties
Important to capture thisdiversity in order to resolveanthropogenic aerosoleffects on clouds/climate!
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 3 / 12
Example Effect: Droplet Nucelation
Put aerosols in an updraft...adiabatic cooling
↓supersaturated environment
↓condensational growth of
aerosol/droplets↓
bifurcate aerosol into cloud droplets(”activation”) and haze
Critical factor - Smax (function oftemperature, updraft speed, aerosolproperties)
How does the aerosol mixing statecontribute to potential dropletactivation?
Droplet nucleation simulated with detailed parcel model
cloud droplets
haze
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 4 / 12
Error in Droplet Nucleation due toInternal Mixing Assumption
10−4 10−3 10−2 10−1 100
rp (µm)
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
nN
(rp)
(cm
−3)
Original - internal mixture
10−4 10−3 10−2 10−1 100
rp (µm)
Decomposed
mixed - internalsulfate - externalcarbon - external
α
γ (�)
Internal mixture ofcarbon/sulfate →decompose into spectrumof mixtures preservingnumber and mass of eachspecies
internal 0.2 0.4 0.6 0.8 external
alpha
sulfate
0.2
0.4
0.6
0.8
carbon
gam
ma
Nucleated Droplet Count Error(internal - external)
-250
-200
-150
-100
-50
0
50
100
150
200
250 Error in predicted dropletnumber from 1 m/s updraft,explicitly computed withdetailed parcel model
+100% error when mostlycarbon - important fordownwind of intense biomassburning/industrial emissions?
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 5 / 12
MARC = a Multimode, 2-Moment, and Mixing-state-resolving !Model of Aerosols for Research of Climate!
Log-normal distribution, 2 prognostic moments (Q, N) + BIM & OIM, prescribed σ !
(Kim et al., JGR, 2008)
gaseous oxidation
aqueous oxidation
Condensation
Evap-resuspense
Aging (surface preparation)
Nucleation
Growth Coagulation)
Radiation
Clouds
Meteorology
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 6 / 12
Droplet Nucleation / Activation Parameterization
Smax - the “Activation Equation”
From parcel theory, can derive (Ghan et al, 2011)
αV
γ=
4πρwρa
GSmax
Smax∫0
r2(tact) + 2G
tmax∫tact
Sdt
1/2
dN
dScdSc
Need assumptions,1 aerosol modes have bulk properties (e.g. hygroscopicity)2 instantaneous particle growth in equilibrium with relative humidity3 activation instantly happens when particle sees critical S (Kohler Theory)
Basic equation underlying parameterizations used in GCMs/CRMs to predictdroplet nucleation
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 7 / 12
Droplet Nucleation Errors in CESM+MARC
180°W 120°W 60°W 0° 60°E 120°E 180°E
60°S
30°S
0°
30°N
60°N
Error, predicted droplet nucleation (ARG - explicit)1/cm3, Avg: -36.8 (-597.9 − -0.3)
240
160
80
0
80
160
240
180°W 120°W 60°W 0° 60°E 120°E 180°E
60°S
30°S
0°
30°N
60°N
Error, predicted droplet nucleation (FN - explicit)1/cm3, Avg: -5.2 (-360.7 − 26.1)
240
160
80
0
80
160
240
Severe underprediction inareas with high carbonaceousaerosol loading
Exactly in regions mostimportant for anthropogenicaerosol effects
Need to better parameterizemixing state / competitioneffects on droplet nucleation
Parameterizations:ARG - Abdul-Razzak and Ghan, 2000FN - Fountoukis and Nenes, 2005explicit - numerical parcel model
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 8 / 12
Polynomial Chaos Expansion of Parcel Model
Polynomial emulator of full-complexity model
Computationally-cheap (produce/run), accurate distribution of modeled response
Detailed Parcel Model
Updraft speed, temperature, pressure,
aerosol properties
Smax
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 9 / 12
Polynomial Chaos Expansion of Parcel Model
Polynomial emulator of full-complexity model
Computationally-cheap (produce/run), accurate distribution of modeled response
Detailed Parcel Model
Updraft speed, temperature, pressure,
aerosol properties
Smax
Polynomial Chaos Expansion
Produce sets of model runs based on PDFs of input parameters
Save response function(s)
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 9 / 12
Polynomial Chaos Expansion of Parcel Model
Polynomial emulator of full-complexity model
Computationally-cheap (produce/run), accurate distribution of modeled response
Detailed Parcel Model
Updraft speed, temperature, pressure,
aerosol properties
Smax
Polynomial Chaos Expansion
Produce sets of model runs based on PDFs of input parameters
Save response function(s)
Parcel Model Emulator
Numerical quadrature to compute coefficients of orthogonal basis functions
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 9 / 12
Emulation Results - Single Mode Aerosol
101 102 103 104
Droplet Concentration, cm−3 (Detailed Parcel Model)
101
102
103
104
Dro
ple
t C
on
cen
trati
on
, cm
−3 (
Poly
nom
ial
Ch
aos)
SM1
SM2
SM3
SM4
SM5
10-3 10-2
Supersaturation (Detailed Parcel Model)
10-3
10-2
Su
pers
atu
rati
on
(P
oly
nom
ial
Ch
aos) SM1
SM2
SM3
SM4
SM5
10-2 10-1 100 101
Updraft Speed (m/s)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Su
pers
atu
rati
on
(%
)
ARG
FN
parcel
pce
TOP: Emulator well-calibrated exceptfor large number concentrations ofsmall particles (SM5)
LEFT: Reproduces non-linear responsein Smax due to important variables
Next step - extend to multiple aerosolmodes
Aerosols (SMi) from Nenes and Seinfeld, 2003
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 10 / 12
Emulation Results - Single Mode Aerosol
101 102 103 104
Droplet Concentration, cm−3 (Detailed Parcel Model)
101
102
103
104
Dro
ple
t C
on
cen
trati
on
, cm
−3 (
Poly
nom
ial
Ch
aos)
SM1
SM2
SM3
SM4
SM5
10-3 10-2
Supersaturation (Detailed Parcel Model)
10-3
10-2
Su
pers
atu
rati
on
(P
oly
nom
ial
Ch
aos) SM1
SM2
SM3
SM4
SM5
10-2 10-1 100 101
Updraft Speed (m/s)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Su
pers
atu
rati
on
(%
)
ARG
FN
parcel
pce
TOP: Emulator well-calibrated exceptfor large number concentrations ofsmall particles (SM5)
LEFT: Reproduces non-linear responsein Smax due to important variables
Next step - extend to multiple aerosolmodes
Aerosols (SMi) from Nenes and Seinfeld, 2003
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 10 / 12
Conclusions/Summary
Resolving some degree of the “mixing state” of heterogeneous aerosol calculationswill change the potential for droplet nucleation and calculated cloud droplet burden.
Existing parameterizations for global models may not be well-calibrated for thecomplexity and diversity of aerosol populations predicted from mixing-state resolvingmodels.
Biases in physics calculations due to complex mixing state must be addressed toaccurately simulate anthropogneic aerosol effects on clouds and climate.
Polynomial Chaos and other advanced statistical techniques could help produceefficient activation parameterizations specifically tuned for applications in GCMs andCRMs.
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 11 / 12
Conclusions/Summary
Resolving some degree of the “mixing state” of heterogeneous aerosol calculationswill change the potential for droplet nucleation and calculated cloud droplet burden.
Existing parameterizations for global models may not be well-calibrated for thecomplexity and diversity of aerosol populations predicted from mixing-state resolvingmodels.
Biases in physics calculations due to complex mixing state must be addressed toaccurately simulate anthropogneic aerosol effects on clouds and climate.
Polynomial Chaos and other advanced statistical techniques could help produceefficient activation parameterizations specifically tuned for applications in GCMs andCRMs.
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 11 / 12
Conclusions/Summary
Resolving some degree of the “mixing state” of heterogeneous aerosol calculationswill change the potential for droplet nucleation and calculated cloud droplet burden.
Existing parameterizations for global models may not be well-calibrated for thecomplexity and diversity of aerosol populations predicted from mixing-state resolvingmodels.
Biases in physics calculations due to complex mixing state must be addressed toaccurately simulate anthropogneic aerosol effects on clouds and climate.
Polynomial Chaos and other advanced statistical techniques could help produceefficient activation parameterizations specifically tuned for applications in GCMs andCRMs.
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 11 / 12
Acknowledgments
This material is based upon work supported by the National Science FoundationGraduate Research fellowship under NSF Grant No. 1122374
We would also like to thank Steve Ghan (PNNL) for providing a reference parcel modeland code for his activation scheme; Rotem Bar-Or and Alex Avramov (MIT) for helpingrun the CESM+MARC and for helpful discussion; Dan Czizco, Ron Prinn, and PaulO’Gorman (MIT) for feedback and comments while preparing portions of this work forthe MIT PAOC General Examination.
Coupled CESM+MARC runs performed using the NCAR Yellowstone supercomputer.
Rothenberg/Wang (MIT) AGU Fall 2013 December 11, 2013 12 / 12