1. Evaluation of CMAQ soil-NO emissions via comparison of CMAQ output and satellite-retrieved NO 2 columns Dale Allen (University of Maryland; AOSC) Lisa Silverman (UMD; Civil & Environmental Eng) Sheryl Ehrman (UMD ; Chemical & Biomolecular Eng) Ken Pickering (NASA-GSFC) Heidi Plata (UMD; Chemical & Biomolecular Eng)

2. Objectives Develop a better understanding of soil based sources of nitrogen oxides Evaluate whether satellite observations of NO 2 can be used to improve emissions estimates for soil derived NO x over the United States Use this understanding and satellite observations to improve model estimates of NO x emissions in BEIS3, which is the biogenic emissions module used in CMAQ

3. NO x Emissions Sources over the U.S. (approximate values) Six Tropospheric Sources: Emission Quantities: Fossil Fuel Combustion 12-14 Tg N/yr (EPA trends report) Soil-Biogenic Emission 0.5-1.5 Tg N/yr (10-15% of ff source) Biomass Burning 1-2 Tg N/yr (Leenhouts, 1998) Lightning Discharge 1-2 Tg N/yr (~10 f s -1 ) Upper Tropospheric Aircraft Emissions 0.5-1 Tg N/yr Stratospheric Injection <0.5 Tg N/yr

4. BEIS-3 Biogenic NO emissions (YL method) E = R 2.5 x T adj x P adj x F adj x C adj E = Time varying NO emission flux R 2.5 = Baseline NO emission flux (assumes 2.5% of fertilizer N is emitted as NO during growing season) T adj = temperature adjustment factor P adj = precipitation adjustment factor (1-15). Heavy rain activates nitrifying bacteria F adj = fertilizer adjustment factor (1 during April and then decreases over growing season) C adj = canopy adjustment factor (1 during April and then decreases linearly to 0.5) Yienger & Levy (1995)

5. Magnitude and duration of YL precipitation pulse is function of rainfall amount Yienger and Levy(1995) >1.5 cm day -1 0.5<P<1.5 0.1<P<0.5 For showers & heavy rain, substantial enhancement even 4 days after event

6. Evaluation of soil NO source Comparisons of models and satellite observations reveal a factor of 2-4 underestimate of soil-NO emissions wrt to the YL a priori estimate (Martin et al., 2003; Jaegle et al., 2005; Wang et al., 2007; Boersma et al., 2008) YL scheme overestimates pulse duration and underestimates role of soil moisture (Hudman et al., 2010;Yan et al., 2005) Mean 8-hr O 3 enhancement of 3-5 ppbv over agricultural Great Plains during June; Hudman et al., 2010 CMAQ simulations were performed for March – May 2006 –nosoilNO emissions –YL (standard) soilNO emissions –Doubled YL soilNO emissions Resulting tropospheric NO 2 columns are compared to columns from the OMI instrument aboard the Aura satellite

7. OMI tropospheric NO 2 products 1. v1.0 OMI standard product [Bucsela et al., 2008; Celarier et al., 2008] 2. v2.0 DOMINO product [Boersma et al., 2007; Boersma et al., 2011] Each algorithm begins with same slant columns (red lines) Different methods used to remove stratospheric columns Different methods used to convert tropospheric slant cols to overhead cols  Yield different tropospheric vertical column amounts tropopause

8. LNO x contribution est using output from GMI model (Allen et al., 2010) OMI NASA Std product CMAQ nosoilNO CMAQ Dbl YL soilNO CMAQ YL soilNO

9. CMAQ nosoilNO CMAQ Dbl-YL Soil-NO CMAQ YL Soil-no OMI DOMINO LNOx contribution est using output from GMI model (Allen et al., 2010)

10. Percent of tropospheric NO 2 column with a soil-NO source (April-May 2006 mean) Standard YL Source (peak Contribution ~35%) Doubled YL Source (peak Contribution ~60%) Lightning-NO contribution to column from GMI model (Allen et al., 2010)

11. Model columns and 30% threshold calculated w/o LNO x

12. LNO x contribution estimated using NASA’s GMI model (Allen et al., 2010)

13. Soil-NO emissions were examined following precipitation events in the Great Plains and Midwest Screen out events if: Lightning influenced (HYSPLIT & NLDN) Biomass burning (OMI AI > 1) soilNO/totalNO emissions < 0.5

14. CaseDay of Precip. EventPrecipitation on Day 0 (cm)Location 131-Mar-061.60Ravenna, NE 231-Mar-060.70Chambers, NE 32-Apr-060.83Great Bend, KS 42-Apr-061.67Wayne, NE 52-Apr-061.63Ravenna, NE 62-Apr-060.57Briscoe, TX 77-Apr-062.55Brady, NE 816-Apr-061.73Wayne, NE 916-Apr-060.58Brady, NE 1025-Apr-060.97Brady, NE 1125-Apr-061.41Great Bend, KS 1229-Apr-061.82Linn, KS 1329-Apr-061.86Briscoe, TX 1424-May-061.19Hulett, WY 1524-May-060.89Pierre, SD 1624-May-061.37Wayne, NE 16 Cases and Their Locations

15. Locations of Cases

16. Soil Moisture CMAQ Column soilNO CMAQ Column YL soil emissions Precip CMAQ Column OMI NASA Not surprisingly, cloud cover often hinders analysis

17. PRECIP YL soil emissions CMAQ column Soil moisture CMAQ Column soilNO OMI Column NASA product

18. Time series examined: Precipitation soil-NO emissions Tropospheric NO 2 column (NASA standard and DOMINO) Tropospheric NO 2 column (YL and doubled YL emissions) Time period examined: Day preceding precipitation event (day-1), day precipitation began (day0) to 4-days after event (day4) Impact of precipitation pulsing on tropospheric NO 2 columns was examined using mean time series from the 16 case studies

21. OMI NASA cols days3-4 exceed NASA cols days0-2 by ~0.7 pmol cm-2 Considerable noise as cloud cover reduces number of cases

22. DOMINO column days 3&4 exceed DOMINO col days 0-2 by ~0.5 pmol cm -2 Considerable noise as cloud cover reduces number of cases

23. CMAQ column with std YL emissions increases by ~0.3 pmol cm -2 between days 0-1 and 2-4 (Noisy!)

24. CMAQ column with dbl YL emissions increases by ~0.5 pmol cm -2 between days 0-1 and 2-4

25. Conclusions Soil-NO adds 8-22% to tropospheric NO 2 column over US (east of 110°W) Doubling YL soil-NO source decreases bias between model and satellite tropospheric NO 2 cols over US (e of 110°W) from ~-10% to ~-2% (Effect of smoothing by averaging kernel not considered) Over central Plains, peak soil-NO contribution to column ranges from 35-60% Examining 16 precipitation events over central Plains regions, precipitation-pulsing increases satellite-retrieved columns by ~0.5 to 0.7 peta molecules cm -2. CMAQ columns increase by 0.3 to 0.5 (0.5 to 0.7) peta molecules cm -2 for standard YL (doubled-YL) source. However, uncertainty bars on changes are large.

26. Acknowledgements Thomas Pierce of EPA George Pouliot of EPA Ana Prados of UMBC Funding from NASA’s DSS Applied Science Air Quality Program

28. References Eskes, Henk, et al. “A combined retrieval, modelling and assimilation approach to estimate tropospheric NO 2 from OMI measurements.” KNMI, De Bilt, The Netherlands. 10-12 Sept. 2010. Troposperic NO 2 Measured by Satellites. J. J. Yienger and H. Levy II. “Empirical Model of Global Oil-biogenic NOx emissions.” Journal of Physical Research. 100.D6:11,447-11,464. 1995. Plata, Heidi. “Evaluating Satellite Observations to Improve Soil NOx Emissions Estimates.” 2010. Research Report. Plata, Heidi. “Towards improved emission inventories of soil NOx via model/satellite measurement intercomparisons.” 2010. Powerpoint Presentation.

30. Percent of tropospheric NO 2 column with a soil-NO source

38. nosoilNO Dbl YL soilNO YL soilNO V1.0 NASA Std OMI product Note: Low-bias at least partially due to lack of lightning-NO emissions

39. nosoilNO Dbl YL soilNO YL soilNO V2.0 DOMINO Low-bias at least partially due to lack of lightning-NO emissions (LNO x ) Avg kernel not applied as model profile unrealistic due to lack of LNO x

40. Percent of CMAQ’s mean April-May 2006 tropospheric NO 2 column with a soil-NO source YL source Doubled YL source Note: Addition of LNOx would reduce mean percent contribution values by ~25%

41. Soil Moisture CMAQ Col soilNO OMI Col (NASA) CMAQ Col (total) Soil-NO Emission Precip