1. Tiger Team project : Model intercomparison of background ozone to inform NAAQS setting and implementation NASA AQAST Meeting U.S. EPA, Research Triangle Park, NC November 16, 2011 AQAST PIs: Arlene Fiore (Columbia/LDEO) and Daniel Jacob (Harvard) Co-I (presenter): Meiyun Lin (Princeton/GFDL) Project personnel: Jacob Oberman (U Wisconsin) Lin Zhang (Harvard) AQ management contacts: Joe Pinto (EPA/NCEA) Pat Dolwick (EPA/OAR/OAQPS)

2. Objective: Improved error estimates of simulated North American background O 3 (NAB) that inform EPA analyses Problem: Poorly quantified errors in NAB distributions complicate 1) quantifying uncertainties in risk assessments for NAAQS-setting 2) interpreting SIP simulations aimed at attaining the O 3 NAAQS. To date, EPA NAB estimates have been provided by one model. Approach: 1)Compare GFDL AM3 and GEOS-Chem NAB (regional, seasonal, daily) 2)Process-oriented analysis of factors contributing to model differences Initial project results c/o NOAA Hollings Scholar Jacob Oberman at GFDL summer 2011 GEOS-ChemGFDL AM3 Meteorology Offline (GEOS-5)Coupled, nudged to NCEP U and V Strat. O 3 Parameterized (Linoz)Full strat. chem & dynamics Isoprene nitrate chemistry 18% yield w/ zero NO x recycling 8% yield w/ 40% NO x recycling (obs constrained; Horowitz et al, 2007) Lightning NO x tied to obs. flash climat. w/ higher NOx yield at N. mid-lat tied to model convective clouds Emissions EMEP, Streets, NEI 2005, 2006 fires (emitted at surface), MECAN 2.0 ACCMIP emissions w/ climat. fires, vertically distributed <6 km, MEGAN2.1

3. Two models differ widely in day-to-day variability and seasonal cycle: CASTNet Mtn. West Sites AM3 predicts seasonal cycle in background, GC predicts ~ constant and biased high total in August AM3 predicts rising total and NAB for some observed high- O 3 events in spring, GC predicts a decline  Can NASA satellites offer constraints? 2006 Thick lines: base-case Thin lines: NA Background (zero out NA anthrop. emissions) OBS: 58.3±7.0 OBS: 55.8±7.0

4. Stratospheric ozone intrusions: May 26-31 example GFDL AM3GEOS-CHEM 500 hPa NA background (ppb) Bias in surface MDA8 (ppb) vs. CASTNet obs OMI Total Column O 3 OMI/MLS Trop. Column O 3 DU  AM3 better captures the variability due to stratospheric influence, but the magnitude represents an upper limit (biased high w.r.t. surface obs)

5. Two models differ in seasonal mean estimates for North American background AM3GEOS-Chem Summer (JJA) North American background (MDA8) O 3 in model surface layer Spring (MAM) AM3: More O 3 -strat + PBL-FT exchange? GC: More lightning NO x (~10x over SWUS column) + spatial differences Role for differences in O 3 from wildfires? Biogenic emissions?

6. Model treatment of wildfires can contribute to model differences in NAB estimates: June 28, 2006 “event” AM3 Elevated PAN above PBL (750 mb) AM3 [ppt]  Need to use event-specific wildfire emissions (satellites)  Uncertainties will remain from (1)vertical distribution of emissions (lower temp., higher PAN prod.) (2) fire plume chemistry GC North American background (MDA8) [ppb]

7. Two models show different strengths in capturing distributions of base-case and N. American background O 3 Observed GEOS-Chem total AM3 total GEOS-Chem NAB AM3 NAB Surface MDA8 O 3 [ppb] below 1.5 km + above 1.5 km Zhang et. al.,2011 U.S. CASTNet sites 20 40 60 80 0.08 0.06 0.04 0.02 0.00 2006 MAM sites > 1.5 km 0 20 40 60 80 100 0.06 0.04 0.02 0.00 2006 JJA sites < 1.5 km  Capitalize on model strengths to inform policy  Develop bias-corrections to harness info on variability / process-level Isop. nitrate chem may play a role AM3 biased high but may better represent distribution shape (wider background range) GC and AM3 bracket observed distribution GC NAB lower, more peaked Frequency per ppb

8. Improved error estimates of simulated North American background O 3 (NAB) that inform EPA analyses AQ management outcomes: Improved NAB error estimates to support (1)the next revision of the ozone NAAQS, (2)SIP simulations focused on attaining the current ozone NAAQS, (3)development of criteria for identifying exceptional events. Deliverables (Sept. 30, 2012): 1)Report to EPA on confidence and errors in NAB estimates & key factors leading to model differences; documented in peer-reviewed publication 2)Guidance for future efforts to deliver more robust NAB  satellite constraints (next step, OMI/TES c/o L. Zhang)  design multi-model effort (more robust, as in climate research) Possible Long-term Goal: Establish an integrated multi-model and observational analysis framework to inform policy on a sustained basis

9. Extra Slides

10. Transport event driven by biomass burning emissions CO biomass burning emissions June 2006 (log-scale) AM3 GC moles / km 2 / day Why is event only in AM3? Hypothesis: Higher vertical distribution in AM3 affects transport and chemistry (PAN only forms at low temperatures) Case 3: Biomass burning

11. Neither model fully captures trend in observations AM3 predicts seasonal cycle in PRB, GC predicts ~constant Overestimate of total ozone by AM3 Models agree on trend in PRB >1.5 km sites excluding CA sites <1.5 km sites

12. Biogenic isoprene emissions in AM3 MEGAN 2.1 emission factors [Guenther et al., 2006] AVHRR and MODIS PFT and LAI mapped to MEGAN vegetation types Tied to model surface air temperature  24-30 Tg C/yr within NA (235-300E, 15-55N)  16-23 Tg C/yr within the United States  366-405 Tg C/yr globally

13. Two models have similar isoprene emissions, but differ in isoprene nitrate chemistry Nested GEOS-Chem AM3/C48 (~200 km)

14. Transects along the 40N parallel

15. Compare w.r.t. satellite products?

17. Nested GEOS-Chem Zhang et al., 2011 Distribution merged for March-August, canceling GEOS-Chem low distribution in spring (MAM) and high distribution in summer (JJA)