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WRAP Experience: Investigation of Model Biases

WRAP Experience: Investigation of Model Biases. Uma Shankar, Rohit Mathur and Francis Binkowski MCNC –Environmental Modeling Center Research Triangle Park, NC 27709. Acknowledgements. Studies performed under contract with the Western Regional Air Partnership

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WRAP Experience: Investigation of Model Biases

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  1. WRAP Experience:Investigation of Model Biases Uma Shankar, Rohit Mathur and Francis Binkowski MCNC–Environmental Modeling Center Research Triangle Park, NC 27709

  2. Acknowledgements • Studies performed under contract with the Western Regional Air Partnership • Model results provided by the WRAP Regional Modeling Center (Gail Tonnesen, Chao-Jung Chien, Mohammed Omary)

  3. Outline • Overview of Simulations • Analysis of Modeling Results • January nitrate overprediction • Planetary Boundary Layer (PBL) heights and nitrate bias • Role of ammonia emissions reduction: nitrate bias in different chemical regimes • Coarse mass (CM) underprediction • Comparison of CM emission and deposition fluxes • Summary • Recommendations

  4. CMAQ Configuration • Advection: Piecewise-Parabolic Method (PPM) • Diffusion: K-theory • Gas-phase Chemistry: Carbon Bond Mechanism – 4 • extensions include SO2 oxidation to particulate SO4, secondary organic aerosol formation by oxidation of 6 VOC groups including monoterpenes • Gas-phase Solver: Modified Euler Backwards Integration • Particulate dynamics using the modal approach • Kuo-Anthes cloud scheme for deep convection • Shallow convection scheme and aqueous chemistry in clouds as in the Regional Acid Deposition Model (RADM ) • Size-dependent dry and wet removal algorithms

  5. Overview of the Simulations • Analysis Period: • 62 days of CMAQ simulations (January and July, 1996) • Compared model predictions for all PM species and visibility metrics with IMPROVE network measurements to evaluate model performance • on days for which measurements are reported (January and July 10, 13, 17, 20, 24, and 27, 1996) • on an event average basis • excluded 31st due to lack of 24-hr output (output time-shifted to PST)

  6. Overview of the Simulations (cont’d) • Boundary Conditions (BCs) • default BCs from the REgulatory Modeling System for Aerosols and Deposition (REMSAD) • choice of BCs based on earlier sensitivity tests for better inter-model comparison between REMSAD and CMAQ • Time-independent • SO42- reduced from 1.2 mg/m3 to 0.3 mg/m3 based on CARB measurements of background aerosol in coastal areas, and NH3 reduced from 0.3 ppb to 0.1 ppb • Emissions • Wildfires included • NH3 reduced by 50% over the whole domain for the winter months based on reported uncertainties from prior studies by the EPA ORD

  7. Surface Level CMAQ NH3 Emissions January Average 1996 - Base

  8. Sulfate Response to NH3 and BC Changes Base NH3 Emissions, BCs 50% Base NH3 Emissions, New BCs

  9. Aerosol NO3 to Total NO3 Ratio in January Base NH3 Emissions, BCs 50% Base NH3 Emissions, New BCs

  10. Bias vs. IMPROVE SO4 and NO3 January 1996

  11. Daily Average Nitrate January 1996 January 13 January 17 January 24 January 27

  12. PBL Heights and Total Nitrate January 13 1996 Columbia River Gorge Yellowstone Bridger W PBL Height (m) Nitrate x 100 (mg/m3)

  13. PBL Heights and Total Nitrate January 13 1996 (cont’d) Upper Buffalo Lone Peak Pinnacles NM PBL Height (m) Nitrate x 100 (mg/m3)

  14. PBL Height vs. Nitrate Bias January 1996 January 17 3000 Nighttime avg. Daytime avg. 2500 2 y = 1e+03 - 5.4e+02x R = 0.28 2 y = 1.2e+03 - 5e+02x R = 0.17 2000 1500 1000 500 0 -2 -1.5 -1 -0.5 0 0.5 1 1.5 3 D m NO (CMAQ - Obs) ( g/m ) 3

  15. PBL Height vs. Nitrate Bias January 1996 (cont’d)

  16. MM5 Wintertime PBL Height Predictions • Wintertime PBL heights not well-examined against obs data in previous analyses • MM5 simulations performed in 5-day chunks • Snow cover fields have crude spatial resolution, are updated only once a week, and remain in effect through each five-day period • Could contribute to varying degrees of underestimation in PBL heights at different periods; most significant on the worst days of overprediction • Simulations used MRF – improved land-surface models available in MM5 and could provide better surface temperature and PBL predictions over water bodies and snow cover

  17. January NO3 Bias in Different Chemical Regimes “Free” NHx / Total Nitrate = ([NH3] + [NH4+] – 2*[SO42-]) / ([HNO3] + [NO3-]) Ratio > 1.0 NO3 formation limited by HNO3 < 1.0 NO3 formation limited by NH3

  18. Surface Level NHx/Total Nitrate in January Base NH3 Emissions, BCs 50% Base NH3 Emissions, New BCs

  19. SO4 Response to Change in Emissions, BCs January Avg DSO4 January Avg Cloud Fraction

  20. Event-Average NO3 and Bias January 1996

  21. Understanding the NO3 Bias • NHx/total nitrate ratio best applies to closed systems • Biases highest for high values of the ratio, i.e., HNO3-limited regime HNO3too high in some locations • Some NH3 source regions become more HNO3-limited: possible offsetting role of SO4 reductions • Need observations of NH4, NH3 and HNO3 to help further evaluation (compute observed ratio) • Need to isolate effects of BC changes from the effects of NH3 emissions reductions • Aerosol nitrate to total nitrate ratio should be compared with observations (e.g., CASTNet)

  22. Who are the Bad Guys?

  23. Comparison with IMPROVE: PM2.5 and PM-Coarse

  24. Comparison of Area PM10 Emissions from WRAP and NEI Inventories

  25. PM-Coarse Deposition and Emission Fluxes (Domain Average) January 13 July 13 Deposition Flux (gm/s) Emission Flux (gm/s)

  26. PM-Coarse Deposition and Emission Fluxes(Domain Average) January 27 July 27

  27. Summary • Biases in nitrate tend to be anti-correlated with PBL height for large biases; less of a trend for smaller biases • PBL height and ground temperature show anomalous behavior at one location; nitrate bias correspondingly very high • Ammonia emission reductions have a strong impact on both the SO4 and NO3 concentrations, and on the chemical regime • Ammonia reductions have less of an impact on the nitrate bias if the regime is severely HNO3-limited • Positive nitrate bias is not systematic, and may be due to transport or overestimates of NOx emissions at such locations

  28. Summary (cont’d) • Coarse mode deposition and emission fluxes are consistent with predicted concentrations on a domain-average basis • Little or no day-to-day variability in emission fluxes, probably due to exclusion of wind-blown dust • More variability in deposition fluxes during the daytime in January, and between January and July

  29. Recommendations • Future MM5simulations shoulduse a land surface model option to better predict ground temperature and PBL heights over water and snow cover • NOx emission sensitivity studies, along with comparisons of total nitrate and NHx against measurements would help characterize the source of the most severe overpredictions in nitrate • Additional sensitivities could examine the effect of NH3 emissions reductions without the confounding influences of BC changes on the nitrate bias

  30. Recommendations (cont’d) • Coarse mass dry deposition measurements should be compared with model predictions to determine the source of the coarse mass underprediction • The effect of including wind-blown dust emissions on the model predictions should be evaluated

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