Tiger Team project: Processes contributing to model differences in North American background ozone estimates AQAST PIs: Arlene Fiore (Columbia/LDEO) and Daniel Jacob (Harvard) Co-I: 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 University of Wisconsin-Madison June 14, 2012

Objective: Improved error estimates of simulated North American background O3 (NAB) Problem: Poorly quantified errors in NAB distributions complicate NAAQS-setting and interpreting SIP attainment simulations 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 YEAR 2006 GEOS-Chem GFDL AM3 Resolution ½°x⅔° (and 2°x2.5°) ~2°x2° Meteorology Offline (GEOS-5) Coupled, nudged to NCEP U and V Strat. O3 & STE Parameterized (Linoz) Full strat. chem & dynamics Isoprene nitrate chemistry 18% yield w/ zero NOx recycling 8% yield w/ 40% NOx recycling (obs based; Horowitz et al, 2007) Lightning NOx tied to model convective clouds, scaled to obs. flash climat; higher NOx at N. mid-lat tied to model convective clouds Emissions NEI 2005 + 2006 fires (emitted at surface) ACCMIP historical + RCP4.5 (2005, 2010); vert. dist. climatological fires ALL DIFFERENT! 2

Seasonal mean North American background in 2006 (estimated by simulations with N. American anth. emissions set to zero) North American background (MDA8) O3 in model surface layer AM3 (~2°x2°) GEOS-Chem (½°x⅔°) AM3: More O3-strat + PBL-FT exchange? Spring (MAM) GC: More lightning NOx (~10x over SWUS column) + spatial differences Different contributions from summertime Canadian wildfires? (use of 2006 in GC vs climatology in AM3) Summer (JJA) ppb J. Oberman 3

Space-based constraints on mid-trop O3? Comparison with OMI & TES “500 hPa” in spring Bias vs. N mid-latitude sondes subtracted from retrievals Masked out where products disagree by > 10 ppb L. Zhang Models bracket retrievals Qualitative constraints where the retrievals agree in sign 4

Large differences in day-to-day and seasonal variability of N. American background: Eastern USA, Mar-Aug 2006 Voyageurs NP, MN: 93W, 48N, 429m GEOS-Chem ( ½°x⅔° ) AM3 (~2°x2°) OBS. Mean(σ) Total model O3 Model NAB O3 GC NAB declines into summer AM3 NAB too high in summer: Excessive fire influence? Both models too high in summer Similar correlations with obs GC captures mean AM3 +11 ppb bias: isop. chem.? Georgia Station, GA: 84W, 33N, 270m AM3 NAB declines in Jul/Aug (when total O3 bias is worst) GC NAB varies less than AM3 (total O3 has similar variability) Does model horizontal resolution matter? 5

Horizontal resolution not a major source of difference in model NAB estimates Between LARGEST DIFFERENCES OCCUR IN SUMMER at CASTNET SITES < 1.5 km (CONUS except CA) GC Higher resolution broadens distribution + shifts closer to observed mean (lower) GC 2°x2.5° GC ½°x⅔° GC High-res shows slight shift towards higher NAB (vertical eddies [Wang et al., JGR, 2004]) GC NAB 2°x2.5° GC NAB ½°x⅔° AM3 ~2°x2° AM3 NAB ~2°x2° AM3 represents distribution shape but biased high OBS SPRING (MAM) CASTNet sites above1.5 km GC ½°x⅔° similar to GC 2°x2.5° Much larger differences between AM3 and GC distributions (both total and NAB O3) than between the 2 GC resolutions 6

Large differences in day-to-day and seasonal variability of N. American background: Western USA, Mar-Aug 2006 Gothic, CO: 107W, 39N, 2.9km GEOS-Chem ( ½°x⅔° ) AM3 (~2°x2°) OBS. Mean(σ) Total model O3 Model NAB O3 Models bracket Obs. AM3 larger σ than GC (matches obs) Mean NAB is similar GC NAB ~2x smaller σ than AM3 AM3 NAB > GC NAB in MAM (strat. O3?); reverses in JJA (lightning) Fig 3-58 of O3 Integrated Science Assessment Grand Canyon NP, AZ: 112W, 36N, 2.1km 7

How much does N. American background vary year-to-year? NORTH AMERICAN BACKGROUND IN AM3 (ZERO N. Amer. emissions 1981-2007) MEAN OVER 27 YEARS STANDARD DEVIATION Western CO experiences largest year-to-year variability: What drives this? ppb ppb 8

Stratospheric O3: key driver of daily (+ inter-annual) variability, particularly late spring – e.g. 1999 shown here r2=0.44 (vs. obs) r2=0.31 (vs. obs) OBS AM3 O3-strat r2=0.45 (vs. obs) r2=0.50 (vs. obs) For GTH: r(obs, total) = 0.67 r(obs, o3s)  = 0.71 r(total,o3s) = 0.85 For GRC: r(obs,total) = 0.66 r(obs,o3s) = 0.56 r(total,o3s)= 0.81 Langford et al., 2009 Examine observational constraints on strat. influence (M. Lin) M. Lin 9

Improved error estimates of simulated North American background O3 (NAB) that inform EPA analyses AQ management outcomes: Improved NAB error estimates to support: ongoing review of ozone NAAQS (EPA ISA for O3), SIP simulations focused on attaining NAAQS, development of criteria for identifying exceptional events Deliverables: Report to EPA on confidence and errors in NAB estimates & key factors leading to model differences (peer-reviewed publication) Guidance for future efforts to deliver estimates of sources contributing to U.S. surface O3 What next?  satellite constraints: how quantitative?  multi-model effort (more robust; error characterization)? -- focus on specific components of NAB tied to multi-platform observations -- choose a common study period (2008? 2010-2011)? -- leverage AQAST IP + other TT projects where possible 10