1. Global isoprene sources and chemistry: constraints from atmospheric observations Daniel J. Jacob with Emily Fischer, Fabien Paulot, Lei Zhu, Eloïse Marais, Chris Miller and funding from NASA, HUCE

2. Volatile organic compounds (VOCs) in the atmosphere: carbon oxidation chain VOCRO 2 NO 2 O3O3 organic peroxy radical NO h carbonyl R’O 2 h OH + products organic aerosol ROOH organic peroxide OH HO 2 OH, h OH products EARTH SURFACE biosphere combustion industry deposition Increasing functionality & cleavage sources of organic aerosol sources/sinks of oxidants (ozone, OH)

3. Volatile organic compounds (VOCs) in the atmosphere: effect on nitrogen cycle NO x CH 3 C(O)OO OH EARTH SURFACE combustion deposition Reservoirs for long-range transport of NO x lightning deposition HNO 3 peroxyacetylnitrate (PAN) other organic nitrates NO x OH deposition HNO 3 Long-range atmospheric transport RO 2 N fixation hours

4. Why is isoprene such an important VOC? Global emission, Tg C a -1 1. Large emission: 2. Oxidation generates suite of volatile reactive products: Isoprene OH ~1 h multistep Formaldehyde Other carbonyls Dicarbonyls Peroxides Epoxides Isoprene nitrates

5. Contribution of isoprene to PAN from GEOS-Chem global 3-D chemical transport model Emily Fischer, Harvard Anthropogenic Open fires Isoprene Other biogenic VOCs % January July

6. Sensitivity of nitrogen deposition to isoprene emission Sensitivity for Cayuhoga National Park (Ohio) computed with the GEOS-Chem adjoint Local isoprene emission suppresses N deposition, upwind emission increases it Fabien Paulot, Harvard of local NO x emission)

7. Estimating isoprene emissions: bottom-up and top-down approaches Bottom-up estimate from plant model: E ISOP = f(plant type, phenology, LAI, T, PAR, water stress, …) Isoprene oxidation products Ecosystem observations Atmospheric observations Top-down estimate from Inversion of chemical transport model: E ISOP = f(atmospheric concentrations, transport, chemistry)

8. Observing isoprene oxidation products from space: formaldehyde (HCHO) and glyoxal (CHOCHO) Scattering by atmosphere and Earth surface   HCHO or CHOCHO absorption spectrum   GOME (1995-2001), SCIAMACHY (2002-2012), OMI (2004-), GOME-2 (2006-) instruments Spectral fitting yields “slant” columns of HCHO, CHOCHO along light path Air mass factor from radiative transfer model converts slant to vertical columns HCHO CHOCHO Annual mean vertical columns from GOME-2, 2007-2008 HCHO CHOCHO

9. Relating HCHO columns to VOC emission VOC i HCHO h (340 nm), OH oxidation k ~ 0.5 h -1 Emission E i displacement In absence of horizontal wind, mass balance for HCHO column  HCHO : yield y i but wind smears this relationship depending on VOC lifetime wrt HCHO production: Local linear relationship between HCHO column and E VOCsource Distance downwind  HCHO Isoprene  -pinene methanol 100 km detection limit  HCHO is mainly sensitive to isoprene emission with smearing ~ 10-100 km

10. Past use of  HCHO vs. E ISOP relationship over US to constrain isoprene emission with OMI data OMI HCHO (Jun-Aug 2006) OMI-constrained isoprene emission GEOS-Chem local relationship between HCHO column and isoprene emission Model slope (2400 s) agrees with INTEX-A vertical profiles (2300), PROPHET Michigan site (2100) Palmer et al. [2003, 2006}, Millet et al. [2006, 2008]

11. Temperature dominates variability of E ISOP seen by OMI can’t pick up any other variable from multivariate correlations, case studies Lei Zhu, Harvard 1 5 10 15 10 15 molecules cm -2 HCHO column, Jun-Aug 2005 2006 2007 2008 Correlation of monthly mean HCHO with air T NE Texas, JJA 2005-2008 Exponential fit MEGAN Daily data in Southeast US binned by air temperature 290 295 300 305 310 K 285 290 295 300 K turnover at 307 K

12. After 2009 it’s curtains for OMI …but GOME-2 provides consistent continuity GOME-2 HCHO, 2007 OMI June July August GOME-2 vs. OMI correlation monthly data in SE US JJA 2007-2008 Lei Zhu, Harvard OMI 13x24 km 2 13:30 GOME-2 40x80 km 2 9:30 nadir pixel time slope = 0.91 r 2 = 0.82

13. Using OMI HCHO to constrain isoprene emissions in Africa MODIS leaf area index MODIS fire counts Earth lights AATSR gas flares 10 15 molecules cm -2 OMI annual mean HCHO slant columns 2005-2009 Observed HCHO distribution over Africa points to sources from (1) biosphere, (2) open fires, (3) oil and gas industry Africa accounts for 20% of global biogenic isoprene emissions in MEGAN inventory…but based on little in situ data Aug-Sep Marais et al., in press

14. 10 15 molecules cm -2 Isolating biogenic HCHO in the OMI data Exclude open fire (and dust) influence using MODIS fire counts, OMI absorbing aerosol optical depth Exclude oil/gas industry influence using AATSR gas flare product Marais et al., in press HCHO slant column original data HCHO vertical column biogenic only air mass factor HCHO slant column HCHO biogenic vertical column; 8-day product with 1 o x1 o resolution

15. OH NO HO 2  -IEPOX formaldehyde h Pathways for HCHO formation from isoprene oxidation RO 2 OH Isomerization C 1,5 -shift ROOH high-NO x branch (RO 2 +NO) yields fast HCHO as 1 st generation product Peeters Paulot MVKMACR Epoxydiols [Paulot et al., 2009] More recently proposed low-NO x pathways regenerate OH, produce HCHO: Isomerization [Peeters and Muller, 2010] standard GEOS-Chem mechanism first-generation high-NO x low-NO x low-NO x branch (RO 2 +HO 2 ) yields slower HCHO, depletes OH OH

16. Time-dependent HCHO yield from isoprene oxidation DSMACC box model calculations aging/smearing Yield is sensitive to NO x, not so much to mechanism except at very low NO x Marais et al., in press

17. Boundary layer NO x levels over Africa Annual NO 2 tropospheric columns, fire influences excluded Satellite observations Model % isoprene RO 2 reacting with NO (GEOS-Chem, July) Boundary layer NO x over Africa is typically 0.1-1 ppbv Expect NO x dependence of HCHO yield, moderate smearing Marais et al., in press boundary layer

18. Testing HCHO-isoprene smearing with AMMA aircraft data Flight tracks (Jul-Aug 2006) and MODIS leaf area index Latitudinal profiles below 1 km WIND HCHO tracks isoprene with only ~50 km smearing But NO x measured in AMMA was relatively high (mean 0.3 ppb) OMI HCHO Marais et al., in press WIND

19. Smearing produces“shadow” region 200-300 km downwind of rainforest Marais et al., in press OMI HCHO column 10 15 molecules cm -2 WIND July Testing HCHO-isoprene smearing in longitudinal transect across Congo: high isoprene and low NO x shadow

20. Relationship between HCHO column and isoprene emission Model sensitivity S of HCHO column (Δ  HCHO ) to isoprene emission (ΔE ISOP ) as function of tropospheric NO 2 column (  NO2 ) Standard Paulot Use S = Δ  HCHO / ΔE ISOP for local OMI NO 2 to derive isoprene emission Exclude “shadow” regions on basis of anomalously high S values Marais et al., in press

21. Error analysis on inferring E ISOP from satellite HCHO data Slant HCHO column 20% (spectral fitting) Vertical HCHO column 20% (clouds, vertical distribution, albedo) Isoprene emission Estimated errors (8-day data, 1 o x1 o resolution) 15% (chemical mechanism) 25-60% (smearing) 15% (NO 2 column) Total error: 40% (high-NO x ), 40-90% (low-NO x ). Can be reduced by averaging Smearing is dominant error component. Need to resolve transport! Marais et al., in press

22. Isoprene emission (12-15 local time annual mean, 2006) Comparison of OMI isoprene emissions to MEGAN MEGAN is too low for equatorial forest, too high for savanna Marais et al., in press

23. 2005-2009 monthly variability of isoprene emission for evergreen broadleaf forest of central Africa Eloïse Marais, Harvard Variability is small and weakly correlated to temperature and LAI Need to address uncertainty in meteorological and LAI products! E ISOP, temperature E ISOP, LAI AVHRR

24. 2005-2009 monthly variability of isoprene emission in open deciduous broadleaf forest of s. Africa May-Sept dry season; LAI drops below 1 in Aug, driving E ISOP down Sept-Nov increase in LAI (greening) causes spike in E ISOP Wet season cloudiness causes T to decrease after Nov, driving E ISOP down even though LAI continues to increase Suggests saturation of E ISOP when LAI exceeds 1.5 Eloïse Marais, Harvard E ISOP, temperature E ISOP, LAI Jan AVHRR

25. Glyoxal from space as additional constraint on VOC sources GOME-2 Glyoxal sources in GEOS-Chem: 55% isoprene, 24% acetylene, 7% aromatics, 8% fire emission, 2% monoterpenes Glyoxal lifetime ~1 h (photolysis) Chris Miller, Harvard Operational data available from SCIAMACHY, GOME-2 OMI retrieval in progress (Chris Miller, Harvard) GEOS-Chem

26. Does glyoxal provide information complementary to HCHO? GOME-2 GEOS- Chem Glyoxal columns (Jun-Aug 2007) Glyoxal/HCHO column ratio GOME-2 shows variability in glyoxal/HCHO ratio that GEOS-Chem doesn’t capture Chris Miller, Harvard

27. Glyoxal production from isoprene Observed fast production with 2-3% yield [Galloway 2011] – Dibble isomerization? Chris Miller, Harvard Dibble isomerization first-generation

28. Tower data from CABINEX, northern Michigan (Jul-Aug 09) Measured GEOS-Chem with E ISOP /2 isoprene Glyoxal Pathways for glyoxal formation Dibble Observations by Frank Keutsch Dibble isomerization is dominant model pathway for glyoxal formation Chris Miller, Harvard OH-aldehydes

29. Vision for the future: ecosystem monitoring Adjoint inversion of isoprene emission using geostationary satellite observations of HCHO and glyoxal HCHO, glyoxal measurement  (x, t) 1-km chemical transport model inverse model Emission E( x’, t’) Geostationary observation  diurnal information, higher precision daily data GEMS (Korea), 2017; Sentinel-4 (Europe), 2019; GEO-CAPE (US), 2020+ Adjoint inversion  solve smearing problem, allow isoprene emission monitoring need to properly represent chemistry-transport coupling on scales of PBL mixing Wind boundary layer mixing (~1 h)