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Comparison of Three Secondary Organic Aerosol Algorithms Implemented in CMAQ. Weimin Jiang*, É ric Giroux, Dazhong Yin, and Helmut Roth National Research Council of Canada. Outline. SOA calculation in CMAQ The three CMAQ SOA algorithms Model set-up

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comparison of three secondary organic aerosol algorithms implemented in cmaq

Comparison of Three Secondary Organic Aerosol Algorithms Implemented in CMAQ

Weimin Jiang*, Éric Giroux, Dazhong Yin, and Helmut Roth

National Research Council of Canada

outline
Outline
  • SOA calculation in CMAQ
  • The three CMAQ SOA algorithms
  • Model set-up
  • Impact on organic aerosol modelling results:
  • spatial, temporal, SOA/fine ratios, algorithm correlations
  • Impact on organic aerosol modelling performance:
  • comparison with measurements
  • Conclusions and discussion
soa calculation in cmaq
SOA calculation in CMAQ
  • Three major steps
  • Steps 1 and 3: Binkowski and Roselle (2003); Binkowski and Shankar (1995); US EPA (1999)
  • Implementation details: Jiang and Roth (2003)
  • Step 2: SOA algorithm to calculate SOA mass formation rate.
three cmaq soa algorithms
Three CMAQ SOA algorithms
  • Pandis: constant AYs for 6 pseudo SOA precursor species
  • Odum: AYs for 4 pseudo species from
  • Schell: system of equations for 10 condensable species derived from 6 pseudo species, with T correction for gas phase saturation concentrations
model set up the model
Model set-up: the model
  • Base model: CMAQ 4.1
  • Modularized AERO2 by NRC (Jiang and Roth, 2002)
  • Schell extracted from AERO3 in CMAQ 4.2 and converted to a submodule in AERO2
  • Three CMAQ executables: different only in SOA submodule; all other science and code the same
model set up domain period inputs
Model set-up: domain, period, inputs
  • Nested LFV domain, Pacific ’93 episode (July 31 – August 7, 1993): see H. Roth’s presentation
  • All model inputs are the same except for organic aerosol species:
    • clean IC and BC for the study of algorithm impact on modeling results
    • observation-base IC and BC for the study of algorithm impact on model performance
conclusions and discussion
Conclusions and discussion
    • SchellPandisOdum
  • Science best among three simplified not usable
  • SOA-generationn x Pandis 10n x Odum very low
  • performance good on average underestimate dramatic underestimate
  • Note wide range of norm.bias
  • Deficiency/problem no partitioning of org. OAY, not IAY
  • aerosol to gas phase
  •  overestimate SOA
  • (corrected in CMAQ 4.3?)
odum algorithm problem oay vs iay
Odum algorithm problem: OAY vs. IAY
  • OAY = Overall AY
  • = average AY
  • from DROG=0 and M0=0
  • to DROG= DROG* and M0=M0*
  • IAY = Instantaneous AY
  • = AY at DROG* and M0*
oay equation vs iay equation
OAY equation vs. IAY equation
  • Jiang (2003), Atmos. Environ. (in press)
oay or iay a big deal
OAY or IAY: A big deal?

Yes, a big deal both conceptually and quantitatively.

acknowledgment
Acknowledgment
  • US EPA: Original Models–3/CMAQ
  • Environment Canada Pollution Data Branch, Air Quality Research Branch, Pacific & Yukon Region:
  • Raw emissions and ambient measurement data
  • Dr. D. G. Steyn of the University of British Columbia: Pacific ’93 data set
  • Program of Energy Research and Development (PERD) in Canada:
    • Funding support
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