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 SO 2 oxidation to particulate SO 4, 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 31 st due to lack of 24-hr output (output time-shifted to PST)

6. 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 –SO 4 2- reduced from 1.2  g/m 3 to 0.3  g/m 3 based on CARB measurements of background aerosol in coastal areas, and NH 3 reduced from 0.3 ppb to 0.1 ppb Emissions –Wildfires included –NH 3 reduced by 50% over the whole domain for the winter months based on reported uncertainties from prior studies by the EPA ORD Overview of the Simulations (cont’d)

7. Surface Level CMAQ NH 3 Emissions January Average 1996 - Base

8. Sulfate Response to NH 3 and BC Changes Base NH 3 Emissions, BCs50% Base NH 3 Emissions, New BCs

9. Aerosol NO 3 to Total NO 3 Ratio in January Base NH 3 Emissions, BCs50% Base NH 3 Emissions, New BCs

10. Bias vs. IMPROVE SO 4 and NO 3 January 1996

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

12. PBL Heights and Total Nitrate January 13 1996 Yellowstone PBL Height (m) Nitrate x 100 (  g/m 3 ) Bridger W Columbia River Gorge

13. PBL Heights and Total Nitrate January 13 1996 (cont’d) PBL Height (m) Nitrate x 100 (  g/m 3 ) Upper BuffaloLone PeakPinnacles NM

14. PBL Height vs. Nitrate Bias January 1996 0 500 1000 1500 2000 2500 3000 -2-1.5-0.500.511.5 January 17 Nighttime avg. Daytime avg. y = 1e+03 - 5.4e+02x R 2 = 0.28 y = 1.2e+03 - 5e+02x R 2 = 0.17  NO 3 (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 NO 3 Bias in Different Chemical Regimes “Free” NH x / Total Nitrate = ([NH 3 ] + [NH 4 + ] – 2*[SO 4 2- ]) / ([HNO 3 ] + [NO 3 - ]) Ratio > 1.0 NO 3 formation limited by HNO 3 < 1.0 NO 3 formation limited by NH 3

18. Surface Level NH x /Total Nitrate in January Base NH 3 Emissions, BCs50% Base NH 3 Emissions, New BCs

19. SO 4 Response to Change in Emissions, BCs January Avg  SO 4 January Avg Cloud Fraction

20. Event-Average NO 3 and Bias January 1996

21. Understanding the NO 3 Bias NHx/total nitrate ratio best applies to closed systems Biases highest for high values of the ratio, i.e., HNO 3 -limited regime HNO 3 too high in some locations Some NH 3 source regions become more HNO 3 - limited: possible offsetting role of SO 4 reductions Need observations of NH 4, NH 3 and HNO 3 to help further evaluation (compute observed ratio) Need to isolate effects of BC changes from the effects of NH 3 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: PM 2.5 and PM-Coarse

24. Comparison of Area PM 10 Emissions from WRAP and NEI Inventories

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

26. PM-Coarse Deposition and Emission Fluxes (Domain Average) January 27July 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 SO 4 and NO 3 concentrations, and on the chemical regime Ammonia reductions have less of an impact on the nitrate bias if the regime is severely HNO 3 -limited Positive nitrate bias is not systematic, and may be due to transport or overestimates of NO x 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 MM5 simulations should use a land surface model option to better predict ground temperature and PBL heights over water and snow cover NO x emission sensitivity studies, along with comparisons of total nitrate and NH x against measurements would help characterize the source of the most severe overpredictions in nitrate Additional sensitivities could examine the effect of NH 3 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