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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) - PowerPoint PPT Presentation

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  • Tiger Team project:Model intercomparison of background ozone to inform NAAQS setting and implementationNASA AQAST Meeting U.S. EPA, Research Triangle Park, NCNovember 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)

  • Objective: Improved error estimates of simulated North American background O3 (NAB) that inform EPA analyses Problem: Poorly quantified errors in NAB distributions complicate1) 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 differencesInitial project results c/o NOAA Hollings Scholar Jacob Oberman at GFDL summer 2011

    GEOS-ChemGFDL AM3MeteorologyOffline (GEOS-5)Coupled, nudged to NCEP U and VStrat. O3 Parameterized (Linoz)Full strat. chem & dynamicsIsoprene nitrate chemistry18% yield w/ zero NOx recycling8% yield w/ 40% NOx recycling (obs constrained; Horowitz et al, 2007)Lightning NOxtied to obs. flash climat. w/higher NOx yield at N. mid-lat tied to model convective cloudsEmissionsEMEP, Streets, NEI 2005, 2006 fires (emitted at surface), MECAN 2.0ACCMIP emissions w/ climat. fires, vertically distributed

  • 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 AugustAM3 predicts rising total and NAB for some observed high-O3 events in spring, GC predicts a decline Can NASA satellites offer constraints?2006Thick lines: base-caseThin lines: NA Background (zero out NA anthrop. emissions)OBS: 58.37.0OBS: 55.87.0

  • Stratospheric ozone intrusions: May 26-31 exampleGFDL AM3GEOS-CHEM500 hPa NA background (ppb)Bias in surface MDA8 (ppb) vs. CASTNet obsOMI Total Column O3OMI/MLS Trop. Column O3DUDU 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 forNorth American background AM3GEOS-ChemSummer (JJA)North American background (MDA8) O3 in model surface layerSpring (MAM)AM3: MoreO3-strat + PBL-FT exchange?

    GC: Morelightning NOx (~10x over SWUS column)+ spatial differencesRole for differences in O3 from wildfires? Biogenic emissions?

  • Model treatment of wildfires can contribute to model differences in NAB estimates: June 28, 2006 eventAM3Elevated PAN above PBL (750 mb)AM3[ppt]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 GCNorth American background (MDA8) [ppb]

  • Two models show different strengths in capturing distributions of base-case and N. American background O3 Observed GEOS-Chem total AM3 total GEOS-Chem NAB AM3 NABSurface MDA8 O3 [ppb] below 1.5 km+ above 1.5 kmZhang et. al.,2011U.S. CASTNet sites20 40 60 800.08

    0.06

    0.04

    0.02

    0.002006 MAM sites > 1.5 km 0 20 40 60 80 1000.06

    0.04

    0.02

    0.002006 JJA sites < 1.5 km Capitalize on model strengths to inform policyDevelop 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 peakedFrequency per ppb

  • Improved error estimates of simulated North American background O3 (NAB) that inform EPA analyses

    AQ management outcomes: Improved NAB error estimates to supportthe 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 publicationGuidance 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

  • Extra Slides

  • Transport event driven by biomass burning emissionsCO biomass burning emissions June 2006 (log-scale)AM3GCmoles / 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

  • 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 sitesexcluding CA sites
  • Biogenic isoprene emissions in AM3MEGAN 2.1 emission factors [Guenther et al., 2006]AVHRR and MODIS PFT and LAI mapped to MEGAN vegetation typesTied to model surface air temperature24-30 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 chemistryNested GEOS-ChemAM3/C48 (~200 km)

  • Transects along the 40N parallel

  • Compare w.r.t. satellite products?

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

    * STE driven by meteorology in GEOS-Chem. If by meteorology, why GC can not produce the events? Global total lightning NOx is 6.0 Tg N/yr in GEOS-Chem and 3.0 Tg N/yr AM3. Differ mainly in spatial distribution, 4x higher NOx yield per flash in GEOS-Chem over the Northern mid-latitudes than over the tropics

    *Can NASA satellites offer constraints on conditions (fires, lightning, stratosphere) under which models agree/disagree most, and thus help refine best estimates?*OMI/MLS tropospheric column ozone confirms the penetration of stratospheric ozone into the troposphere*High-altitude southwest US is more exposed to ozone produced from lightning NOx than the lower elevation southeastern US. *Low PAN for the same event in GC? Higher vertical distribution in AM3 affects transport and chemistry (PAN only forms at low temperatures).

    *Frequency unit?*TO DATE, EPA HAS RELIED ON PRB ESTIMATES FROM ONE MODEL. Can NASA satellites offer constraints on conditions (fires, lightning, stratosphere) under which models agree/disagree most, and thus help refine best estimates?*

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