Comparisons of CMAQ and AURAMS modeling runs over coastal British Columbia

Transcription

1 Comparisons of CMAQ and AURAMS modeling runs over coastal British Columbia Robert Nissen Environment Canada Contact: Collaborators: NW-AIRQUEST Meeting, Seattle, February 2, 2012 Collaborators: Paul Makar, Andrew Teakles, Colin di Cenzo, Harry Yau, Junhua Zhang, Qiong Zheng, Michael Moran, Curtis Mooney

2 A simple comparison with unexpected results Compare two off-line models: CMAQ v4.6 (Community Multiscale Air Quality; US EPA & NOAA) AURAMS v1.4.2 (A Unified Regional Air-quality Modelling System; Environment Canada). Progenitor of GEM-MACH s chemistry code. Use the same inputs: Meteorology (GEM, v3.2.2) Emission inventory (2005/2006 USA/Canada) Emissions processing system (SMOKE, speciation differs) Horizontal coordinate system (polar stereographic) Model grid (co-located model gridpoints). Run models in native mode (default model settings used, at the start) How well do the models perform? What can be learned? A by-product: tech transfer to GEM-MACH15 (on-line model)

3 Model AURAMS CMAQ Version Horizontal Projection Chemical Mechanism (gas phase) Particle size distribution Diffusion Polar stereographic true at 60 o N, 93X93 gridpoints; 12 km resolution ADOM-II Bin approach; 12 size bins Laasonen numerics, diffusion constants from GEM, internal eddy diffusivity minimum of 0.1 m 2 s -1. Vertical levels SAPRC-99 Modal approach, 3 modes Internal calculation of eddy diffusivity, with internal minimum of 1.0 m 2 s -1 Plume rise Calculated on-line Pre-calculated by SMOKE Driving meteorology Global Environmental Multscale (GEM) model, version 3.2.2, 30 hour simulations, including 6-hour spin-up; final 24 hours used for consecutive days. Simulation periods (focus on last two) July 19 August 19, 2004 January 28 February 28, 2005 July 15 August 15, 2005

4 Weather inputs: GEM 3.2.2, 15km weather forecasting domain. Off-line AQ models domain: 93 x 93, 12km, Polar Stereographic

5 Comparisons to observations: observation stations, hourly O3 and PM2.5 Comparison with locations of stations All stations in domain

6 Summer 2005: stats from >200 stations, BC, Washington, Oregon, initial comparison Statistic Ozone PM2.5 Obs. AURAMS CMAQ Obs. AURAMS CMAQ Number of pairs Mean Maximum Minimum Y intercept (of obs vs model) Slope (of obs vs model) Correlation coefficient (R) Mean Bias Root Mean Square Error Normalized Mean Bias (%) Normalized Mean Error (%) The green boxes are the better statistical values of the two. AURAMS did better for ozone, and for slope and R of PM 2.5. CMAQ did better for the rest of the PM statistics. Why?

7 Model Ozone vs. Observations AURAMS CMAQ

8 Model PM 2.5 vs. Observations AURAMS CMAQ

9 Vancouver Airport O 3 : AURAMS is close to Observations, CMAQ created a nighttime peak that was not observed, and got the daytime peak too high.

10 Abbotsford Airport O 3 : Farther inland, CMAQ has more difficulty with night time titration of ozone.

11 Hope Airport PM 2.5 : AURAMS and CMAQ both have negative bias further up the valley; some of this may be due to wildfire smoke.

12 Vancouver Airport PM 2.5 : For Vancouver, CMAQ is better for peak values, AURAMS is biased very high. Previous comparisons (North American domain, Smyth et al, several EC/EPA comparisons) showed similar results for both models why?

13 Tried modifying CMAQ vertical structure to make it similar to AURAMS (couldn t go down to AURAMS lowest level, 7 m deep). Native mode vertical levels Sensitivity of CMAQ towards its vertical level structure Base Case: solid lines, scenario: dashed Comparison of CMAQ O3, NOx, and PM levels vs. 27 levels O 3 NOx,O3 [ppbv] PM2.5 [ug/m3] PM 2.5 NO x Nox_15 [ppbv] PM2.5_15 [μg/m3] O3_15 [ppbv] Nox_27 [ppbv] PM2.5_27 [μg/m3] O3_27 [ppbv] T06: T08: T10: T12: T14: T16: T18: T20:00 Date:Time [UTC] T22: T00: T02: T04:00 moved in the right direction, but was a relatively small effect, compared to

14 Tried running AURAMS with CMAQ s diffusion cutoff of 1 m 2 s -1 Comparison of O3, NOx, and PM2.5, Base Case AURAMS versus 1m2/s diffusion constant minimum ppbv (O3, NO2) and ug/m3 (PM2.5) NOx (ppbv) Base Case O3 (ppbv) Base Case PM2.5 (ug/m3) Base Case NOx (ppbv) 1m2/sec O3 (ppbv) 1m2/sec PM2.5 (ug/m3) 1m2/sec 0 UTC: 7/17/05 6:00 7/17/05 12:00 7/17/05 18:00 7/18/05 0:00 7/18/05 6:00 A large effect! AURAMS acts like CMAQ: O 3 doesn t Time titrate (UTC) properly at night, PM improves at night. Problem: these lower limits are arbitrary, and not physically realistic. Diffusion constants generated by CMAQ v4.6 similar to GEM v3.2.2 (aside from cutoff) part of the problem could be the diffusion, but increasing diffusion would improve PM 2.5 at the expense of O 3 at night. So, what else could be causing these problems?

15 Look for clues in the PM speciation: The simulated PM 2.5 in Vancouver peaks at night, and it s mostly primary (crustal material, primary organic carbon). Implies emissions and/or transport aren t right. Ok, so let s look at the emissions Temporal Allocation? Spatial Allocation? Total amounts?

16 Temporal allocation of PM 2.5 emissions on Canadian side Mobile sources; Marine vessels-commercial; Residual;Ocean-going vessels Miscellaneous area sources; Other combustion; Charcoal grilling residential (see commercial); Total Nighttime

17 Peculiar Emission patterns at night 2 nd biggest source of primary PM 2.5 at 4 am: people using their barbeque grills. 4 th biggest source of primary PM 2.5 at 4 am: farm tractors. Many other issues like this NO and PM 2.5 from the similar source types had dissimilar splitting factors. Temporal splitting for mobile emissions were inconsistent Some Ocean-going vessel types assumed constant, others have same time splitting profile as railroad diesel engines. Etc., etc. These are default SMOKE profiles.

18 Emissions issues: spatial allocations also had errors A hole in the emissions corresponding to Fraser Valley Regional District. GIS mapping errors : 26 spatial allocation factors affected. No biogenic emissions from Texada, some other major islands in Strait of Georgia.

19 Two parallel efforts ensued: Emissions (Moran, Zhang, Qiong) Fixes for the above problems. More detailed Canadian mobile emissions spatial allocation factors. AURAMS operator splitting, etc. (Makar, Nissen, Mooney) Default AURAMS uses 1 step forward operator splitting: try ½ step centred. Improvements and corrections to particle settling and deposition algorithm.

20 Operator splitting: a long-standing problem with AURAMS (and GEM-MACH): sea-salt positive bias; factor of 3 too high compared to observations Base Case Scenario was fixed by using better operator splitting.

21 Vancouver Airport: PM2.5 base case and modified models PM 2.5 : Both models decrease magnitudes, peaks better but still high for AURAMS. O 3 : very similar to base case.

22 General results from the revised emissions - O3, CMAQ: New emissions improve Mean, Minimum, MB, RMSE, NMB, NME, relative to its own base case. Maximum, y-intercept, Slope and R all worse than base case. - PM2.5, CMAQ: New emissions improve y-intercept. Slope unchanged, all other stats worse. - O3, AURAMS: new operator splitting and other code changes leave R unchanged, remaining stats improve except except Maximum and Minimum (slight increases for both). - PM2.5, AURAMS: new operator splitting and other code changes improve Minimum and Slope, R unchanged, all other stats worse. - PM2.5, AURAMS: new emissions + operator splitting: improves PM2.5 maximum, RMSE, NME compared to base case. O3 still better than base case. - O3, AURAMS, new emissions: Slope, R, Maximum and Minimum improve, rest get worse. - PM2.5, AURAMS, new emissions: Mean, Maximum, MB, RMSE, NMB, NME improve, R unchanged. Minimum, Slope and y-intercept get worse. - The net effect of all of the changes, which model is doing better (after emissions and model changes): O3: AURAMS better than CMAQ for all stats. PM2.5: CMAQ better for all stats except max, slope and R.

23 Conclusions (1) A comparison of CMAQv4.6 and AURAMSv1.4.2 at 12km resolution has been completed. Statistics showed base case AURAMS performance was better for O 3, base case CMAQ was better for PM 2.5 (except for correlation coefficient and slope). The PM biases occurred at night, and were due to primary PM, potentially implicating emissions. At large part of CMAQ's improved PM 2.5 bias was due to the use of 1m 2 s -1 as a diffusion minimum. Adding this limit to AURAMS resulted in similar performance to CMAQ: improved PM at the expense of insufficient night-time O3 titration.

24 Conclusions (2) Emissions inventory analysis: half of nighttime primary PM emissions should not have been there due to temporal errors, and spatial allocation also had problems. Operator splitting improvements gets rid of the sea-salt bias in AURAMS, improves AURAMS O 3 predictions. Revised emissions: improves some, but not all, of the statistics, for both models. But grid totals were used in temporal allocation study what about looking at urban gridpoints? Current work: Repeating analysis for urban gridpoints (as opposed to entire grid)): are they realistic? Operator splitting changes ported to operational GEM-MACH model: runs completed, need to score them.