1. Observational Constraints on Global Organic Aerosol Telluride Science Research Center Workshop on Organic Aerosol July 30, 2014 Colette L. Heald Xuan Wang, Qi Chen *analogous to “peak oil”?

2. My Talk Today Part 1: Brown Carbon Part 2a: Van Krevelen Diagram re-visited Part 2b: Simulating the Global Elemental Composition of OA H:C O:C

3. IPCC AR5 Estimates that Black Carbon is the 2 nd Largest Warming Agent in the Atmosphere. (but that’s not what models say) How can these be reconciled? Top-down constraints from Bond et al. come from absorption measurements. How important are organics to this?

4. Adding Brown Carbon to GEOS-Chem Absorption of BrC is highly uncertain - we choose upper-range estimates Brown Carbon Aromatic SOA 50% of biofuel POA 25% of fire POA Absorption Coefficient Get RI from field measurements Mie calculation MAE=1 m 2 /g MAE=0.3 m 2 /g

5. Including Brown Carbon is Critical to Capturing the Spectral Dependence of AERONET AAOD *AAOD product here using lev2 SSA with lev1.5 AOD

6. Including Brown Carbon is Critical to Capturing the Spectral Dependence of AAOD *AAOD product here using lev2 SSA with lev1.5 AOD

7. Our Work Suggests Brown Carbon is an Important Component of Absorption Radiative Forcing Brown Carbon contributes 35% of the DRF warming from carbonaceous aerosols. (Also: BC DRF=0.21 Wm -2, is less than methane and tropospheric ozone.) [Wang, Heald, et al., ACPD, 2014]

8. The State of Dis-Union [Heald, et al., 2011]

9. The State of Dis-Union: From a Mass Perspective we Need More (Anthropogenic) Sources and More Sinks [Heald, et al., 2011] *Now adding ~100 Tg/yr source of ASOA

10. Van Krevelen Diagram: Insight Into OA Aging [Heald et al., 2010] Need to re-visit: (1) more data (2) corrected AMS elemental ratios (Canagaratna et al., 2014) Total OA (AMS data) fell on -1 slope, suggesting that aging (mixing, chemistry, volatilization) follow consistent path. We noted levelled off at higher O:C (alcohol addition, fragmentation?)

11. Updated Van Krevelen of Ambient Measurements See clear progression in OSc. Fitted slope shallower (-0.6 slope) than Heald et al., 2014 (-1 slope), largely because AMS correction affects O:C more than H:C.

12. But There is Diversity Among Campaigns All individual slopes steeper (-0.7 to -1.1) than bulk …overall fitting compensating for various intercepts

13. A Disconnect Between Laboratory and Ambient Elemental Composition? Most of the laboratory data lies below the ambient line… Except isoprene-derived OA.

14. A Disconnect Between Laboratory and Ambient Elemental Composition? Most of the laboratory data lies below the ambient line… Few aging experiments get to high O:C within a week of aging.

15. Statistical Mixtures Demonstrate the Consistencies and Inconsistencies of Lab and Field Measurements Anthropogenic BiogenicALL

16. A Disconnect Between Lab and Ambient Elemental Composition? Aging Experiments Mis-match suggests that either/both (1)Have not identified important OA source types (2)Laboratory studies are not representative of ambient composition (mixtures?) [Chen et al., 2014a, in prep]

17. Goal: Develop an Observationally-Based Model Simulation of OA Elemental Composition (and Aging) Step 1: Re-fit 2 product SOA yields (I’ll spare you this) Step 2: Assign elemental ratios to POA/SOA types simulated in model based on lab data Simulated surface composition occupies a narrow range (O:C = 0.3 to 0.5), compared to wider range seen in ambient.

18. Updated (Very Simple) Aging Scheme Step 3: Account for semi-volatile POA emissions Step 4: Age gas-phase organics End point: O:C=1.1 H:C=1.4 (defined by field obs)

19. Laboratory-Based Parameterization of Aging Rates Step 5: Estimate all rates from lab photochemical aging experiments k carbon  FF = 1.5 × 10 −11 cm 3 molecule −1 s −1  BB = 6 × 10 −12 cm 3 molecule −1 s −1  SOG = 3 × 10 −13 cm 3 molecule −1 s −1 k age  FF = 2.5 × 10 −12 cm 3 molecule −1 s −1  BB = 1 × 10 −11 cm 3 molecule −1 s −1  SOG = 1 × 10 −10 cm 3 molecule −1 s −1

20. New Scheme Increases Overall OA Burden by 40% µg/m 3

21. New Scheme Dramatically Alters Simulation of Elemental Composition Now simulate a wider range of oxygen content, and also more pronounced seasonality in continental regions. O:C Base O:C Updated AgingOSc Updated Aging

22. Comparison With Surface AMS Observations Aging drastically improves ability to capture high O:C in remote regions - but at the cost of mis-representing urban (low O:C, high H:C)? Missing source (i.e. lab vs. ambient disconnect?) or inherent scale limitation? New scheme also demonstrates better match to observed mass.

23. Vertical Comparison From Airborne Campaigns Similarly, aging is critical to reproducing observed O:C. Cannot simulate O:C>1, or variability in observed H:C. But for airborne measurements, including heterogeneous oxidation helps to reproduce the vertical gradient. [Chen et al., 2014b, in prep]

24. Conclusions Brown Carbon likely contributes important source of UV absorption; ignoring this may artificially inflate BC DRF estimates. There is a disconnect between laboratory and ambient OA elemental composition. Simple, measurement-based aging scheme dramatically improves simulation of elemental composition in remote conditions. Including heterogeneous oxidation important for remote/aloft.