Comparison of three secondary organic aerosol algorithms implemented in cmaq
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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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