1. UNIVERSITY OF ALASKA FAIRBANKS 1 CMAQ – Jan/Feb Episode Results/Challenges Nicole Mölders, Ketsiri Leelasakultum Acknowledgements: Heather Angelhoff, Jim Conner, Deanna Huff, Ron Lowell, Jim McCormick, Nicole Svengaard, and Todd Thompson, WRCC for observational data Catherine F. Cahill, Tom Carlson, Bob Dulla, Hendrik Elbern, Rob Ellemann, Elmar Friese, Mark Hixson, Deanna Huff, Gerhard Kramm, Chris Nolte, George Pouliot, N.Q. Tran, T.T. Tran and Barbara Trost for fruitful discussion

2. UNIVERSITY OF ALASKA FAIRBANKS 2 Simulations are performed on domain 3 using background concentrations as initial and boundary conditions From: Gaudet and Stauffer (2010)From: Mölders and Leelasakultum (2011)

3. UNIVERSITY OF ALASKA FAIRBANKS 3 January 23, 2008 0000UTC to February 12, 2008 0000UTC episode meteorological data produced by Gaudet and Staufer (2010) with WRF-ARW version 3.1 3 one-way nested domains with 12, 4 and 1.3km grid- increments Used 1.3km data, 201  201 grid points, 38 layers <= Sierra Research Inc. emission data is available for 199  199 grid points single layer emission data <= 4 layer emission data (soon) MCIP version 3.6 with slight modifications CMAQ version 4.7.1 with slight modifications

4. UNIVERSITY OF ALASKA FAIRBANKS 4 From: Mölders and Leelasakultum (2011) Spatial distribution of simulated 24h-average PM 2.5 -concentrations at breathing level for January 23, 2008 to February 10, 2008 Preliminary results CMAQ as is The only changes made are to get the model run for the domain CMAQ with Sierra EI permits a very detailed look at the heterogeneity of concentrations in the nonattainment area

5. UNIVERSITY OF ALASKA FAIRBANKS 5 Preliminary results without Alaska adaptation (CMAQ as is) CMAQ simulated PM 2.5 concentrations are too low Similar occurred with the first WRF/Chem simulations, i.e. we are confident that we know how to fix the problem (been there done that) From: Mölders and Leelasakultum (2011) Daily PM 2.5 (left) and hourly PM 2.5 (right) for January 23, 2008 (Day 1) to February 10, 2008 (Day 19) at the grid cell holding the official monitoring site at the State Building

6. UNIVERSITY OF ALASKA FAIRBANKS 6 24h-averages broadly align linearly, but with offset due to huge CMAQ’s underestimation of concentrations Daily PM 2.5 (left) and hourly PM 2.5 (right) for January 23, 2008 (Day 1) to February 10, 2008 (Day 19) at the grid cell holding the official monitoring site at the State Building From: Mölders and Leelasakultum (2011) Preliminary results of non-adapted CMAQ

7. UNIVERSITY OF ALASKA FAIRBANKS 7 Soccer goal plot shows that many values are outside the EPA goal/criteria From: Mölders and Leelasakultum (2011) Preliminary results of non-adapted CMAQ

8. UNIVERSITY OF ALASKA FAIRBANKS 8 As is, CMAQ does not meet EPA’s performance criteria => we will Alaska-adapt CMAQ good news, performance curves look like they should except for offset Based on data from FNSB and CMAQ simulation valid at State Building From: Mölders and Leelasakultum (2011) --- performance criterion __ performance goal --- performance criterion __ performance goal Preliminary results of non-adapted CMAQ

9. UNIVERSITY OF ALASKA FAIRBANKS 9 Good news: CMAQ behavior of errors suggests that we can fix it Hourly data clearly show errors are high for low and low for high concentrations as we would expect Hint at offset that may have various reasons From: Mölders and Leelasakultum (2011) --- performance criterion __ performance goal --- performance criterion __ performance goal Preliminary results of non-adapted CMAQ

10. UNIVERSITY OF ALASKA FAIRBANKS 10 Performance statistics of non-adapted CMAQ official monito ring site # of Ob s. Mean CMAQ (μg/m 3 ) Mean observed (μg/m 3 ) Ratio of means (sim/obs) Mean bias (μg/ m 3 ) Mean fracti onal bias (%) Mean error (μg/m 3 ) Mean fractio nal error (%) Correl ation coeffici ent Daily193.947.10.11-43.2-16143.21610.05 Hourly4543.947.20.16-43.1-15443.21550.03 Preliminary results of non-adapted CMAQ

11. UNIVERSITY OF ALASKA FAIRBANKS 11 CMAQ simulated OC fraction of PM 2.5 is about half of the observed OC From: Mölders and Leelasakultum (2011) Preliminary results of non-adapted CMAQ

12. UNIVERSITY OF ALASKA FAIRBANKS 12 Ratio of 24h-average concentration to primary PM 2.5 emission indicates that concentration is emission-driven From: Mölders and Leelasakultum (2011) Preliminary results of non-adapted CMAQ

13. UNIVERSITY OF ALASKA FAIRBANKS 13 Aerosol physics and chemistry must be adapted for extreme temperatures 2 REU students (NSF-funded) currently look into aerosol module behavior at extreme temperatures Aerosol module has const. activity coefficients valid at 20 o C Actually the activity coefficient is temperature dependent via the Debye-Hückel coefficient Currently literature search for other aerosol relevant species Plan: implement them in aerosol module, test in WRF/Chem first From: Fleming and Mölders 2011

14. UNIVERSITY OF ALASKA FAIRBANKS 14 Gas-to-particle conversion differs strongly at 20 o C and -40 o C Simulations are performed with current state of the aerosol module same initial meteorological and chemical conditions except for T From: Mölders et al. (2011)

15. UNIVERSITY OF ALASKA FAIRBANKS 15 Comparison of meteorological model performance between FNSB WRF/Chem scenario and DEC CMAQ scenario Different WRF/Chem is already Alaska adapted, CMAQ is not Domain, horizontal and vertical grid increments Winter conditions compared to long-term mean EI top-down vs. bottom-up Model domain and increment (4km vs. 1.33km) are different Advection/diffusion schemes WRF/Chem considers feedback of chemistry on meteorology, while the WRF-CMAQ package does not (inline vs. offline) Some meteorological options in WRF part Gas phase chemistry modules WRF/Chem run in forecast mode, WRF run in data assimilation mode

16. UNIVERSITY OF ALASKA FAIRBANKS 16 Zoom in on some differences in the WRF part WRF-CMAQ 1.33km physics options mp_physics = 10 ra_lw_physics = 4 ra_sw_physics = 4 radt = 30 min sf_sfclay_physics = 2 sf_surface_physics = 3 bl_pbl_physics = 2 bldt = 0 cu_physics = 0 isfflx = 1 ifsnow = 1 icloud = 1 surface_input_source = 1 num_soil_layers = 6 WRF/Chem 4km physics options mp_physics = 4 ra_lw_physics = 1 ra_sw_physics = 2 radt = every time step sf_sfclay_physics = 2 sf_surface_physics = 3 bl_pbl_physics = 2 bldt = 0 cu_physics = 5 isfflx = 1 ifsnow = 1 icloud = 1 surface_input_source = 1 num_soil_layers = 6

17. UNIVERSITY OF ALASKA FAIRBANKS 17 Comparison of meteorological model performance between FNSB WRF/Chem scenario and DEC CMAQ scenario (cont.) Same Both use WRF for meteorological part Both assume background concentrations as initial condition at start of the episode and boundary conditions throughout the simulation Both take chemical fields of previous run as initial concentration for the next run

18. UNIVERSITY OF ALASKA FAIRBANKS 18 Especially under cloudy conditions chemistry has strong impact on meteorology WRF/Chem simulations using RETRO/EDGAR emission data Model domain encompasses entire Alaska, North Pacific, and parts of Asia, 30km increment Example of how chemistry affects meteorology in Interior Alaska in 2000 From: Tran et al. (2011)

19. UNIVERSITY OF ALASKA FAIRBANKS 19 Comparison of bottom-up and top-down EI shows differences in the nonattainment area Attention: Model domains and grid-increment differ Sierra Research EINEI2008-AkEM From: Mölders and Leelasakultum (2011)

20. UNIVERSITY OF ALASKA FAIRBANKS 20 Emissions in the nonattainment area look similar except for more details due to the fine resolution of the Sierra Research EI Emissions from traffic, point sources slightly higher in Sierra due to finer increment Zoom-in Sierra Research EI 1.333km Zoom- in NEI2008 AkEM 4km From: Mölders and Leelasakultum (2011)

21. UNIVERSITY OF ALASKA FAIRBANKS 21 Comparison of CMAQ and WRF/Chem simulated concentrations at breathing level for the same days (1-23 to 1-27) hints at too diffusive advection scheme, too high background concentrations in CMAQ WRF-CMAQ (non-adapted)WRF/Chem Alaska adapted From: Mölders and Leelasakultum (2011)

22. UNIVERSITY OF ALASKA FAIRBANKS 22 Zoom-in into concentration distribution as obtained with the non-adapted CMAQ for the day with the highest 24h-average PM 2.5 concentration State Building monitoring site Point-source locations X Grid-cell with maximum concentration __ terrain height __ schematic view of nonattainment area From: Mölders and Leelasakultum (2011) Preliminary results

23. UNIVERSITY OF ALASKA FAIRBANKS 23 We have ideas how to improve CMAQ and get it “Alaska adapted” WRF/Chem had similar problems when using the NEI Deposition velocities Background concentrations Stomata resistances Winter settings … fixes that were successful in adapting WRF/Chem for Alaska work for CMAQ and work from there Background concentrations are still for Lower 48 Adapt deposition velocities for snow Aerosol module: activity coefficients (coop with Kramm, Dlugi, EURAD group) …. Change advection scheme: experience with WRF-Chem shows that advection scheme may dig “wholes” and yield fault gradients As many data we needed to collect for adapting WRF/Chem are already collected Alaska-adaptation of CMAQ will be less work

24. UNIVERSITY OF ALASKA FAIRBANKS 24 Some of the next steps are to 1. Finish evaluation for speciation and State Building site 2. Evaluation of PM 2.5 and assessment of errors related to simulated meteorology by PM 2.5 and met data from the Rams trailer trailer was moved weekly that winter (Bonner St 1-18-2008 – 1-24-2008, Toolik Dr 1-24-2008 – 1-31-2008, Red Fox Drive 1-31-2008 – 2-7-2008, van Horn Rd 2-7-2008 – 2-14-2008) two locations (Bonner St, Red Fox Dr) are above the inversion Toolik Dr is between North Pole and Fairbanks 3. Modify CMAQ as needed, starting out with WRF/Chem experience 4. Perform simulations with adapted CMAQ 5. Evaluate new performances of adapted CMAQ with speciation, State Building and Rams data 6. Assess improvement 7. Go to back to step 3 if needed otherwise get emission data for scenarios and start scenario simulations 8. …

25. UNIVERSITY OF ALASKA FAIRBANKS 25 Changes in IC/BC conditions and model setup for next run 1. Alaska relevant background profiles for initial and boundary conditions Data are already collected Work on implementing them in CMAQ 2. Modified dry deposition velocities Adapt stomata resistances for Alaska species (may have 2 different impacts) Snow modifications 3. Use Piecewise Parabolic Method instead of global mass-conserving scheme as advection scheme 4. Change K Z,min (we used default) may be too high and may have contributed to the over-prediction in rural areas 5. Multi-layer area emission data if already available

26. UNIVERSITY OF ALASKA FAIRBANKS 26 Preliminary conclusions CMAQ as is does not meet the performance criteria, but we think we know how to fix it From our seat the only option is to Alaska-adapt CMAQ From our experience with various photo- chemical models the behavior shown is “normal” when you transfer a model to a new region the performance can be improved so CMAQ like WRF/Chem meets the EPA performance criteria we can improve CMAQ to get there There is a lot of work to do – let’s start doing it