1. Interannual variability in biogenic emissions driven by dynamic vegetation conditions Daniel Cohan (PI), Adetutu Aghedo, Erin Chavez-Figueroa, and Ben Lash, Rice University Loretta Mickley and Lei Zhu, Harvard University Bryan Duncan, NASA Christine Wiedinmyer and Alex Guenther, NCAR Agency partners: TCEQ and CARB

2. Motivation and Objective Biogenic emissions strongly impact air quality and its responsiveness to control measures Regulatory modeling often uses static, outdated vegetation conditions Could dynamic vegetation conditions influence air quality decision making? OBJECTIVE: Explore how interannual variability in biogenic emissions is driven by changing meteorology and vegetation conditions

3. Δ Biogenics Model (MEGAN) ΔΔ isoprene HCHO OMI (& GOME-2)MODIS Leaf Area Index (LAI)

4. Interannual variability in OMI HCHO Duncan et al., GRL 2009 2007 drought

5. Monthly CA-statewide biogenic reactive organic gas (ROG) emissions (essentially BVOC): Meteorology is held constant at 2005 levels and 8-day LAI (MODIS) is allowed to change from year-to-year **** Data is preliminary **** Average July LAI (from 8-day MODIS) Slide from Jeremy Avise, CARB Influence of ΔLAI on MEGAN BVOC

6. ΔLAI and ΔMeteorology drive ΔBVOC Gulden et al., JGR, 2007

7. Progress to Date Analysis of interannual variability in MODIS LAI and NARR reanalysis meteorology fields – Processed LAI data obtained from Myneni group (Boston U.) MEGAN simulations of ΔBVOC due to ΔLAI – Will extend to variability in meteorology Correlations of isoprene with temperature and rainfall Correlations of OMI HCHO data with meteorology and EVI vegetation index – Extension to GOME-2 HCHO and to LAI in progress

8. Biome Types and mean August LAI in MODIS LAI m2 per m 2 Biome Types used by MODIS for LAI determination (2004) Data from R. Myneni group, Boston U.

9. (Std dev/Mean) ≈ 0.1 for MODIS LAI in August over most forested regions

10. August 2005: Cool, wet, high LAI over Southeast Drought Data from NOAA at http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.phphttp://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.php

11. June 2007: Dry, hot, low LAI over SE US; Cool, wet, high LAI over Texas Drought Data from NOAA at http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.phphttp://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.php

12. August 2011: Extreme drought, low LAI over Texas, parts of Southeast Drought Data from NOAA at http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.phphttp://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers.php

13. LAI ____ Temperature _____ 90W to 78W and 32N to 36N (SE US) LAI (MODIS) and Temperature (NARR) anomalies over Southeast US

14. Surface isoprene highly correlated with T, but shifts lower in drought year 2007: drought 2008: typical

15. 15 OMI HCHO Slant Column Density during the growing season For Background correction 1 High HCHO in the SE U.S. 2 Wild fires 3 Some anthropogenic sources: Houston

16. 16 OMI HCHO Slant Column Density during the winter Human Activity: in circle High albedo from Snow: High values in the North However, in Texas, especially Houston, HCHO is very low 1 1 From Nicole Downey Drill Rigs & Wells

17. 17 Temperature drives HCHO levels Three methods for variable selection: all show temperature is driving factor (1)(2) (3) (1)

18. HCHO and Temperature anomalies HCHO correlated with T in growing season, but not in winter

19. 19 HCHO densities turn-over at high temperature HCHO: OMI, Daily, 0.5 by 0.5 degree T: 2m above the ground, GOES-5, Daily, 0.5 by 0.5 degree Then, HCHO was binned by T in a 3 K step-wise: 19 2851543396540371575631 181 HCHO Column Density Turn-over ~ 34˚C

20. 20 HCHO and EVI anomalies in Houston, 2005 - 2008 R 2 =0.003 When seasonal trends are removed, basically HCHO and EVI are independent from each other.

21. Short-term Next Steps Correlations among meteorology, LAI, satellite HCHO, and ground-level isoprene –How to quantify LAI-influencing meteorology, drought? –OMI and GOME-2 HCHO observations jointly? MEGAN model predictions of BVOC response to Δmeteorology & LAI, separately and jointly –How to generate or obtain multiple summers of MEGAN-ready (WRF?) meteorology data?

22. Long-term Modeling Plan Model multiple pathways by which variable vegetation and soil influence air quality –ΔVegetation  ΔBVOC (MEGAN) –ΔVegetation  ΔDeposition of OVOC & O 3 (Karl et al., 2010) –ΔFertilizer, rain, etc.  ΔSoil NO x (Hudman et al. scheme) Extend to HONO emissions (Su et al., Science, 2011) Compare with satellite and ground observations of HCHO, NO 2, and isoprene Model the influences on ozone and PM and their sensitivities to emissions Influence of climate change?

23. Hudman linked ΔSoil NO with ΔOMI NO 2 ; 2x soil NO of previous models Hudman et al., ACP 2010

24. Karl et al.: Faster than expected vegetative uptake of OVOC; Function of LAI Karl et al., Science, 2010