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A Comparative Performance Evaluation of the AURAMS and CMAQ Air Quality Modelling Systems Steven C. Smyth, Weimin Jiang, Helmut Roth, and Fuquan Yang ICPET,

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Presentation on theme: "A Comparative Performance Evaluation of the AURAMS and CMAQ Air Quality Modelling Systems Steven C. Smyth, Weimin Jiang, Helmut Roth, and Fuquan Yang ICPET,"— Presentation transcript:

1 A Comparative Performance Evaluation of the AURAMS and CMAQ Air Quality Modelling Systems Steven C. Smyth, Weimin Jiang, Helmut Roth, and Fuquan Yang ICPET, National Research Council of Canada, Ottawa, Ontario Michael D. Moran and Paul A. Makar MSC, Environment Canada, Toronto, Ontario Véronique S. Bouchet and Hugo Landry CMC, Environment Canada, Dorval, Québec

2 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 2 Outline Introduction AURAMS vs. CMAQ – Differences in science, input file preparation, etc. O 3, total PM 2.5, and speciated PM 2.5 performance comparison Summary and Conclusions

3 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 3 Introduction Many aspects of the AURAMS and CMAQ simulations were “aligned” to reduce some of the common sources of differences: –same input meteorology from Environment Canada’s GEM model –same raw emissions inventories processed by SMOKE –same biogenic emissions model –same grid resolution Confidence that the differences in model results are caused by the AQ models themselves rather than by meteorological and/or emissions inputs

4 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 4 Modelling Systems CMAQ v4.6 –SAPRC-99 chemical mechanism; AERO4; NRC PMx post- processor AURAMS v1.3.1b –A Unified Regional Air-quality Modelling System –AQ modelling system with size- and composition-resolved PM –Designed to be a “one” atmosphere or “unified” model in order to address a variety of interconnected tropospheric air pollution problems ranging from ground level O 3 to PM to acid rain

5 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 5 AURAMS v1.3.1b (cont.) 9 PM species/components: sulphate (SU), nitrate (NI), ammonium (AM), black carbon (EC), primary organic aerosols (PC), secondary organic aerosols (OC), crustal material (CM), sea-salt (SE), and particle bound water (WA) 12 PM size distribution bins: 0.01 to 40.96 µm in diameter –Bins 1 thru 8 – PM 2.5 –Bins 9 and 10 – PMC; where PM 10 -PM 2.5 = PMC –Bins 11 and 12 – PM greater than 10 µm in diameter Gas phase chemistry – modified version of ADOM-II Includes sea-salt emissions but not chemistry at this time Zero-gradient lateral boundary conditions

6 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 6 Domains and Simulation period AURAMS - Polar Stereographic; true scale at 60°N; 150 x 106 grid; 42-km resolution CMAQ - Lambert conformal conic; standard parallels of 50°N and 70°N; 139 x 99 grid; 42-km resolution 01:00 July 1, 2002 to 00:00 July 30, 2002 UTC

7 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 7 Model Inputs - Meteorology GEM v3.2 –AURAMS meteorological pre-processor –GEM-MCIP (based on MCIP v3.1) Overlapping grid cell comparison of surface fields – NMEs of 0.25% for pressure; 0.4% for temperature; 3.8% for specific humidity (HU)

8 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 8 Model Inputs - Emissions SMOKE v2.2 Canadian Emissions –2000 CAC inventory U.S. Emissions –2001 CAIR Mexican Emissions –1999 inventory Biogenic Emissions –BEISv3.09 AURAMS – online CMAQ – offline using SMOKE

9 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 9 Model Inputs - Emissions (cont.) Point source processing –AURAMS – plume-rise of major point sources calculated within CTM –CMAQ – meteorological data used to calculate plume rise within SMOKE Emissions files –AURAMS: grams/sec representative week of emissions for each month of simulation 3 emissions files (non-mobile, mobile, minor-point) in RPN format 1 emissions file (major-point sources) in ASCII format –CMAQ: gaseous - moles/sec; PM - grams/sec daily emissions files single comprehensive file in I/O API format

10 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 10 Measurement Data O 3 - hourly measurements from: the EC NAPS network (190 sites) and U.S. EPA AQS network (1087 sites) PM 2.5 - hourly measurements from: NAPS (92 sites) and AQS (262 sites) Speciated PM 2.5 - daily averaged measurements from: NAPS (17 sites) and U.S. EPA STN network (205 sites) O 3 Measurement SitesPM Measurement Sites

11 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 11 O 3 Performance statistics O 3 (ppb)daily peak O 3 (ppb)daily low O 3 (ppb) AURAMSCMAQAURAMSCMAQAURAMSCMAQ meas. mean 35.6 60.860.711.7 mod. mean 42.051.466.770.216.432.7 MB 6.415.85.99.44.620.9 NMB (%) 18 %44 %10 %16 %39 %178 % ME 16.218.816.414.311.021.9 NME (%) 46 %53 %27 %24 %94 %187 % r2r2 0.3930.4380.3500.5050.1040.077 AURAMS lower bias Similar levels of error CMAQ over prediction mainly due to inability in predicting daily lows

12 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 12 O 3 Performance (cont.) Both AURAMS and CMAQ over-predict daily peaks AURAMS much better at predicting daily lows Both models show correct diurnal patterns and overall trends in concentration level

13 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 13 Total PM 2.5 Performance statistics total PM 2.5 (µg m -3 ) daily peak PM 2.5 (µg m -3 ) AURAMSCMAQAURAMSCMAQ meas. mean 14.4 28.328.1 mod. mean 12.25.121.69.1 MB -2.2-9.3-6.6-19.0 NMB - 15 %- 64 %- 23 %- 68 % ME 9.710.216.319.5 NME 67 %71 %58 %69 % r2r2 0.0740.1510.0400.086 AURAMS lower bias Similar levels of error

14 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 14 Total PM 2.5 Performance (cont.) Both models under- predict PM 2.5 Forest-fires not included in emissions contributes to under-prediction in both models Much more PM 2.5 sea- salt in AURAMS

15 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 15 PM 2.5 Species Performance statistics PM 2.5 SO4 (µg m -3 ) PM 2.5 NO3 (µg m -3 ) PM 2.5 NH4 (µg m -3 ) AURAMSCMAQAURAMSCMAQAURAMSCMAQ meas. mean 5.00.901.6 mod. mean 5.32.42.00.261.60.82 MB 0.3-2.61.1-0.640.0-0.81 NMB (%) 6 %-51 %121 %-71 %1 %-50 % ME 3.02.81.60.70.9 NME (%) 60 %56 %175 %79 %54 %57 % r2r2 0.3670.5240.3970.4370.4280.458 AURAMS better bias for SO4 and NH4; similar levels of error CMAQ better correlation

16 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 16 PM 2.5 Species Performance (cont.) statistics PM 2.5 EC (µg m -3 ) PM 2.5 TOA (µg m -3 ) AURAMSCMAQAURAMSCMAQ meas. mean 0.5210.6 mod. mean 0.280.343.90.9 MB -0.24-0.18-6.6-9.6 NMB - 46 %- 36 %- 63 %- 91 % ME 0.30 6.99.6 NME 58 % 66 %91 % r2r2 0.1660.2040.00050.007 CMAQ better bias for EC; similar levels of error AURAMS much better performance for TOA –Due to difference in SOA algorithms Poor TOA correlation for both models impacts overall correlation for total PM 2.5 (AURAMS – 0.074; CMAQ = 0.151)

17 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 17 PM 2.5 Species – Temporal Comparison SO4 SOAPOA NO3 EC NH4 Other PM 2.5 Sea- salt

18 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 18 PM 2.5 Species – Spatial Comparison

19 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 19 PM 2.5 Species – Spatial Comparison (cont.) Similar spatial and temporal patterns for most species Sea-salt aerosols vastly different Concentration levels quite different

20 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 20 PM Composition PM 2.5 sea-salt contributes over half to AURAMS total PM 2.5 mass for all grid cells; only 5% in CMAQ For land grid cells only, PM 2.5 sea-salt contributes 15% in AURAMS and 2% in CMAQ If sea-salt is excluded from model results, total PM 2.5 performance still better in AURAMS results PM composition avg. over all grid cells PM composition avg. over land grid cells only

21 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 21 Discussion and Summary Similar levels of error for O 3, total PM 2.5, and most PM 2.5 species AURAMS better bias for all species except PM 2.5 nitrate and elemental carbon Enhanced AURAMS bias due to cancellation of positive and negative biases Sea-salts contribute much more to overall PM composition in AURAMS than CMAQ –Does not impact overall conclusions regarding relative PM 2.5 performance of the models Spatial and temporal patterns similar, but overall concentration levels quite different

22 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 22 Acknowledgements Radenko Pavlovic and Sylvain Ménard of Environment Canada –transfer of AURAMS code and help in understanding and compiling various AURAMS related material Wanmin Gong of EC –help in identifying problem in AURAMS land-use file Pollution Data Division of EC –2000 Canadian raw emissions inventories U.S. EPA and CMAS –U.S. emissions data, SMOKE, CMAQ, MCIP Colorado State University –VIEWS database for measurement data Meteorological Service of Canada –NAtChem database for measurement data Environment Canada and the Program of Energy Research and Development (PERD) for funding support

23 6th Annual Models-3 Conference, Chapel Hill, NC, October 1-3, 2007 23

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