CMAQ and REMSAD- Model Performance and Ongoing Improvements

1. CMAQ and REMSAD- Model Performance and Ongoing Improvements Brian Timin, Carey Jang, Pat Dolwick, Norm Possiel, Tom Braverman USEPA/OAQPS December 3, 2002

2. Introduction • USEPA has performed an annual simulation of CMAQ and REMSAD for a 1996 base year • An operational evaluation has been completed for both models • Performance evaluations have uncovered some weaknesses in the model formulation and inventories • OAQPS has identified a list of CMAQ model improvement priorities

3. 1996 National CMAQ and REMSAD- Model Setup • CMAQ- May 2001 release w/MEBI solver • REMSAD- Version 7.01 • Model Setup: • Domain: • CMAQ and REMSAD: 36km, 12 layers, ~38 m surface layer • Emissions: • CMAQ and REMSAD: 1996 NEI w/adjustments, processed via SMOKE • Meteorology: 1996 MM5 • Chemistry: • CMAQ: CB-IV chemical mechanism w/ fast solver (MEBI) • REMSAD: micro-CB-IV chemical mechanism

4. Nationwide Modeling Domains CMAQ Modeling Domain REMSAD Modeling Domain CMAQ National domain is a Lambert conformal projection from 100°W, 40°N REMSAD uses a lat-long projection

5. Notes on Emission Inventory • Base Year 1996 NEI w/adjustments • Removal of wildfires, wind blown dust, and residential on-site incineration • PM Transport Factor • 75% reduction in fugitive dust sources • Adjusted CA NOx and VOC (non-EGU) • Revised Temporal Data • Prescribed burning • Animal husbandry • Used results from ORD inverse modeling (monthly reductions of 20-60%) • Annual NH3 inventory reduced by ~30% • Biogenic Emissions • BEIS 3.09

6. CMAQ and REMSAD Model Performance • Completed statistical comparison against observations for 12 layer REMSAD and CMAQ • Data sources: IMPROVE network; CASTNET dry dep. Network; NADP wet deposition network; CASTNET visibility network • All comparisons paired in time/space • Statistics and scatterplots for seasonal and annual averages • Calculated performance statistics by year and season for each monitoring site • Thousands of individual numbers; only presenting gross summary • Limited data base (in 1996) makes conclusive statements re: model performance difficult

7. IMPROVE Annual Average Performance StatisticsAnnual mean predicted/annual mean observed REMSAD CMAQ

8. Seasonal Average Sulfate Performance

9. July Average Sulfate

10. Seasonal Average Particulate Nitrate Performance

11. January Average Particulate Nitrate

12. Seasonal Average Organic Aerosols Performance

13. July Average Organic Aerosols AE2 Aerosol

14. Seasonal Average Crustal/Other PM2.5 Performance

15. July Average Crustal/Other PM2.5

16. Winter Average NitrateCMAQ 1996 vs. Observed 2001-2002 (IMPROVE and Urban Speciation) Qualitative comparison of spatial patterns with more recent urban speciation data

17. Model Performance- Summary of Individual Species • CMAQ tends to predict higher concentrations than REMSAD; especially in the West • REMSAD slightly underpredicts sulfate in the East; CMAQ slightly overpredicts sulfate • Nitrate is overpredicted in the East • Total nitrate (particulate + nitric acid) is overpredicted in all seasons • Indicates an overestimation of nitric acid • REMSAD underpredicts organic carbon; CMAQ is relatively unbiased • Large uncertainty in the primary organic inventory (no wildfires), the organic measurements, and the secondary organic chemistry • CMAQ is predicting much more biogenic SOA; but it is using an aerosol yield approach (AE2) • Much of the biogenic SOA in REMSAD is being partitioned into the gas phase

18. Model Performance- Individual Species • Elemental carbon is generally unbiased • Large uncertainty in measurement of elemental carbon (EC/OC split) • IMPROVE sites have very low EC concentrations • Soil/other concentrations are overpredicted • Inventory issues • Fugitive dust, unspeciated emissions from construction, paved roads, etc. in urban areas • NADP wet concentration comparisons • Sulfate • CMAQ overpredicts in the East; REMSAD underpredicts • Nitrate • Both models overpredict in the East; REMSAD underpredicts in the West • Ammonium • REMSAD underpredicts; CMAQ slightly overpredicts in the East

19. Next Steps • Additional evaluation techniques can be applied • Further comparisons to more recent urban speciation data • Closer look at individual sites, days, seasons, regions • Time series plots • 20% best/worst days for visibility • Plan to model 2001 base year • Significantly more ambient data available • Continue to look at PM monitoring issues and how they affect model performance evaluation • Uncertainty in nitrate observed data • EC/OC split • Monitoring network protocol differences

21. OAQPS CMAQ Model Improvement Priorities (non-inventory) • Winter nitrate overprediction (general nitric acid overprediction) • Chemistry • Dry deposition • SOA overpredictions (biogenic) with 2002 release • Emission factors • Aerosol yields • Gas/particle partitioning • Horizontal diffusion • Relatively low explicit diffusion • Run times • Decreased run time will allow more refined modeling of longer time periods

22. Model Improvement Priorities(modeling inventory) • Ammonia inventory • Currently using adjusted 1996 inventory based on ORD monthly inverse modeling estimates • Need long term methodological improvements • Primary organic carbon • Need improved fire emissions • May be missing some organic sources • Primary semi-volatiles? • Primary unspeciated PM2.5 (PM-Other) • Modeled concentrations are grossly overestimated • Unspeciated fraction in certain speciation profiles is very high • Solid waste combustion (89% unspeciated) • Coal combustion (85% unspeciated) • Wood waste combustion (65% unspeciated)

23. Primary PM2.5 Emissions CMAQ- Partial Solution • The primary PM emissions in the 2001 CMAQ release were emitted in the wrong module • Emitted in the AERO module • Should be emitted in the VDIFF module • Problem corrected in the 2002 release • Primary PM2.5 concentrations reduced by 5-35%

24. July Average “PM-Other” Concentrations PM2.5-Other 2001 Release Ratio of 2002/2001 Release

25. Winter Nitrate- CMAQ vs. REMSAD • Much of the difference in winter nitrate predictions between CMAQ and REMSAD can be traced to different implementations of the dry deposition routines • Nitrate concentrations were found to be sensitive to dry deposition of NH3, HNO3, and NO2 • Improvements and adjustments are needed in both CMAQ and REMSAD, particularly in the areas of: • Treatment of snowcover and freezing temperatures • Specification of land use and surface roughness • Treatment of soluble species when canopies are wet • January nitrate concentrations agreed to within ~25% after the dry deposition routines were made more similar to each other through a series of sensitivity runs (with REMSAD)

26. January Nitrate Comparison After Dry Deposition Sensitivities

27. Dry Deposition- CMAQ • CMAQ contains 2 dry deposition routines; RADMDRY and M3DRY • M3DRY is a new routine • Many improvements over the old Wesely routine (RADMDRY) • MM5-PX (Pleim/Xiu land surface model) output is needed to take advantage of many of the improvements in M3DRY • Most significant change is enhanced deposition velocities for soluble species when canopy is wet • M3DRY does not currently have a “temperature function” or a specific treatment for snow or frozen ground • ORD is working on improvements to M3DRY • Adding freezing temperature and snowcovertreatment • M3DRY may increase dry deposition of soluble species (e.g NH3)

28. Additional Issues- CMAQ 2002 Release • CMAQ 2002 release contains new AE3 aerosol mechanism • Includes ISORROPIA nitrate partitioning and SOA gas/particle partioning • Ran sensitivity test of 2002 release with AE3 for January and July 1996 • Particulate nitrate increases due to heterogeneous chemistry • Gas phase N2O5 rate constant lowered • Added a heterogeneous N2O5 reaction to aerosol mechanism • N2O5---> HNO3 (particulate nitrate) • Biogenic SOA increases by a factor of 3 to 4 • AE3 biogenic SOA (July) is too high in parts of the country (especially the West) • Aerosol yields increased by a factor of 4 (in new release) • SOA partitioning is dominated by particle phase

29. AE2 vs AE3January Average Particulate Nitrate CMAQ 2001- AE2 CMAQ 2002- AE3 AE3 includes both effect of ISORROPIA and heterogeneous chemistry

30. AE2 vs AE3July Average Biogenic SOA CMAQ 2001- AE2 CMAQ 2002- AE3

31. SOA Gas/Particle PartitioningJuly Average % Biogenic SOA in Particle Phase

32. Horizontal Diffusion • Kh in CMAQ may be too low, especially at 36km resolution • CMAQ Kh is indirectly proportional to grid cell size • REMSAD, UAM-V, and CAMx Kh is directly proportional to grid cell size • At 36km resolution the Kh in CAMx is ~17,000 m2/sec and the Kh in CMAQ is ~25 m2/sec (both using PPM advection) • Which methodology is more scientifically correct?

33. Summary of OAQPS CMAQ Model Improvement Priorities (non-inventory) • Winter nitrate overprediction (general nitric acid overprediction) • Gas phase chemistry (daytime and nighttime) • Daytime NO2 + OH rate constant • SAPRC • CB-IV 2002 • Nighttime • N2O5 gas phase rate constant and heterogeneous reaction • Dry deposition (M3DRY routine) • Snowcover and freezing temperatures • Wet canopy • AE3 SOA overpredictions (biogenic) • Terpene emission factors • Aerosol yields • Gas/particle partitioning

34. Summary of OAQPS CMAQ Model Improvement Priorities (non-inventory) • Horizontal diffusion • Is current methodology OK? • Does CMAQ need more explicit diffusion when using “accurate” advection schemes (PPM and Bott)? • Run times • Can CMAQ be made to run faster? • 2001 release is 3 times slower than REMSAD • 2002 release (with CB-IV) is almost 4 times slower than REMSAD • SAPRC will slow it down even more • OAQPS is working with ORD to address all of the above issues