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Tiger Team project : Model intercomparison of background ozone to inform NAAQS setting and implementation. AQAST PIs: Arlene Fiore (Columbia/LDEO) and Daniel Jacob (Harvard) Co-I (presenter) : Meiyun Lin (Princeton/GFDL) Project personnel: Jacob Oberman (U Wisconsin)
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Tiger Team project: Model intercomparison of background ozone to inform NAAQS setting and implementation 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) NASA AQAST Meeting U.S. EPA, Research Triangle Park, NC November 16, 2011
Objective: Improved error estimates of simulated North American background O3 (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 O3 NAAQS. • To date, EPA NAB estimates have been provided by one model. • Approach: • Compare GFDL AM3 and GEOS-Chem NAB (regional, seasonal, daily) • Process-oriented analysis of factors contributing to model differences Initial project results c/o NOAA Hollings Scholar Jacob Oberman at GFDL summer 2011
Two models differ widely in day-to-day variability and seasonal cycle: CASTNet Mtn. West Sites • AM3 predicts rising total and NAB for some observed high-O3 events in spring, GC predicts a decline • AM3 predicts seasonal cycle in background,GC predicts ~ constant and biased high total in August OBS: 58.3±7.0 Can NASA satellites offer constraints? OBS: 55.8±7.0 2006 Thick lines: base-case Thin lines: NA Background (zero out NA anthrop. emissions)
Stratospheric ozone intrusions: May 26-31 example GFDL AM3 GEOS-CHEM OMI Total Column O3 500 hPa NA background (ppb) DU OMI/MLS Trop. Column O3 Bias in surface MDA8 (ppb) vs. CASTNet obs DU AM3 better captures the variability due to stratospheric influence, but the magnitude represents an upper limit (biased high w.r.t. surface obs)
Two models differ in seasonal mean estimates for North American background North American background (MDA8) O3 in model surface layer AM3 GEOS-Chem AM3: More O3-strat + PBL-FT exchange? Spring (MAM) GC: More lightning NOx (~10x over SWUScolumn) + spatial differences Summer (JJA) Role for differences in O3 from wildfires? Biogenic emissions?
Model treatment of wildfires can contribute to model differences in NAB estimates: June 28, 2006 “event” GC AM3 North American background (MDA8) [ppb] AM3 • Need to use event-specific wildfire emissions (satellites) • Uncertainties will remain from • vertical distribution of emissions • (lower temp., higher PAN prod.) • (2) fire plume chemistry Elevated PAN above PBL (750 mb) [ppt]
Two models show different strengths in capturing distributions of base-case and N. American background O3 U.S. CASTNet sites Observed GEOS-Chem total AM3 total GEOS-Chem NABAM3 NAB • below 1.5 km • + above 1.5 km Zhang et. al.,2011 2006 MAM sites > 1.5 km 2006 JJA sites < 1.5 km 0.08 0.06 0.04 0.02 0.00 0.06 0.04 0.02 0.00 Frequency per ppb 0 20 40 60 80 100 20 40 60 80 Surface MDA8 O3[ppb] • Isop. nitrate chem may play a role • AM3 biased highbut may better represent distribution shape (wider background range) • GC and AM3bracket observed distribution • GC NABlower, more peaked • Capitalize on model strengths to inform policy • Develop bias-corrections to harness info on variability / process-level
Improved error estimates of simulated North American background O3 (NAB) that inform EPA analyses • AQ management outcomes: • Improved NAB error estimates to support • the next revision of the ozone NAAQS, • SIP simulations focused on attaining the current ozone NAAQS, • development of criteria for identifying exceptional events. • Deliverables (Sept. 30, 2012): • Report to EPA on confidence and errors in NAB estimates & key factors leading to model differences; documented in peer-reviewed publication • 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
Transport event driven by biomass burning emissions CO biomass burning emissions June 2006 (log-scale) AM3 GC moles / km2 / 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
>1.5 km sites excluding CA sites • Neither model fully captures trend in observations • AM3 predicts seasonal cycle in PRB, GC predicts ~constant <1.5 km sites • Overestimate of total ozone by AM3 • Models agree on trend in PRB
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
Two models have similar isoprene emissions, but differ in isoprene nitrate chemistry Nested GEOS-Chem AM3/C48 (~200 km)
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)