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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*, 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

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  1. Comparison of Three Secondary Organic Aerosol Algorithms Implemented in CMAQ Weimin Jiang*, Éric Giroux, Dazhong Yin, and Helmut Roth National Research Council of Canada

  2. 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

  3. 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.

  4. 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

  5. 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

  6. Modularized aerosol module

  7. 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

  8. Impact on spatial distribution

  9. Impact on temporal variation

  10. Impact on model performance

  11. 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?)

  12. 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*

  13. OAY equation vs. IAY equation • Jiang (2003), Atmos. Environ. (in press)

  14. OAY or IAY: A big deal? Yes, a big deal both conceptually and quantitatively.

  15. 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

  16. Thank you !

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