Multi-model estimates for

Multi-model estimates for
intercontinental transport of ozone pollution in the northern hemisphere (and uncertainties therein) Arlene M. Fiore F. Dentener, O. Wild, C. Cuvelier, M. Schultz, D. Reidmiller, C. Textor, M. Schulz, C. Atherton, D. Bergmann, I. Bey, G. Carmichael, W. Collins, R. Doherty, B. Duncan, G. Faluvegi, G. Folberth, M. Garcia Vivanco, M. Gauss, S. Gong, D. Hauglustaine, P. Hess, T. Holloway, L. Horowitz, I. Isaksen, D. Jacob, D. Jaffe, J. Jonson, J. Kaminski, T. Keating, A. Lupu, I. MacKenzie, E. Marmer, V. Montanaro, R. Park, K. Pringle, J. Pyle, M. Sanderson, S. Schroeder, D. Shindell, D. Stevenson, S. Szopa, R. Van Dingenen, P. Wind, G. Wojcik, S. Wu, G. Zeng, A. Zuber GFDL lunchtime seminar, January 21, 2009

Reduced Visibility from Transpacific Transport of Asian Dust
Evidence of intercontinental transport at northern midlatitudes: 2001 Asian dust event Dust leaving the Asian coast in April 2001 Image c/o NASA SeaWiFS Project and ORBIMAGE Reduced Visibility from Transpacific Transport of Asian Dust Clear Day April 16, 2001 Glen Canyon, Arizona, USA

difficult (impossible?) to observe directly
Hemispheric scale contribution of major source regions to summertime surface ozone: difficult (impossible?) to observe directly North America Estimated with model simulations that zero out anthropogenic emissions of O3 precursors within the source region Europe GEOS-Chem Model 4°x5° horizontal resolution Li et al., JGR, 2002 Asia

U.N. Economic Commission for Europe Convention on Long-Range Transboundary Air Pollution (CLRTAP; established 1979) 51 parties in Europe, North America, and Central Asia Co-chairs: Terry Keating (U.S. EPA) and André Zuber (EC) TF HTAP Mission: Develop a fuller understanding of hemispheric transport of air pollution to inform future negotiations under CLRTAP

Multi-model assessment involving >25 modeling groups
OBJECTIVES: Quantify S-R relationships for HTAP regions and assess uncertainties in these estimates HTAP S-R Regions Focus species: Ozone and precursors Aerosols and precursors Mercury Persistent Organic Pollutants Idealized Tracers Oxidized Nitrogen NA EU SA EA PRODUCTS: 2007 Interim Report to inform the review of the CLRTAP Gothenburg Protocol to abate acidification, eutrophication, and tropospheric ozone ( 2010 Assessment Report to inform the CLRTAP on hemispheric air pollution and S-R relationships.

Wide range in prior estimates of intercontinental surface ozone source-receptor (S-R) relationships
ASIA  NORTH AMERICA NORTH AMERICA  EUROPE annual mean events events seasonal mean from foreign region (ppbv) Surface O3 contribution seasonal mean 2 example S-R pairs annual mean Studies in TF HTAP [2007] + Holloway et al., 2008; Duncan et al., 2008; Lin et al., 2008 Assessment hindered by different (1) methods, (2) reported metrics, (3) meteorological years, (4) regional definitions Few studies examined all seasons

Objective: Quantify & assess uncertainties in. N
Objective: Quantify & assess uncertainties in N. mid-latitude S-R relationships for ozone TF HTAP REGIONS CASTNet EMEP EANET BASE SIMULATION (21 models):  horizontal resolution of 5°x5° or finer  2001 meteorology  each group’s best estimate for 2001 emissions  methane set to 1760 ppb SENSITIVITY SIMULATIONS (13-18 models):  -20% regional anthrop. NOx, CO, NMVOC emissions, individually + all together (=16 simulations)  -20% global methane (to 1408 ppb)

 Many models biased low at altitude, high over EUS+Japan in summer
Large inter-model range; multi-model mean generally captures observed monthly mean surface O3 Mediterranean Central Europe < 1km Central Europe > 1km NE USA SW USA SE USA  Many models biased low at altitude, high over EUS+Japan in summer  Good springtime/late fall simulation Surface Ozone (ppb) Great Lakes USA Mountainous W USA Japan

Annual mean surface O3 decrease from
North America as a receptor of ozone pollution: Annual mean foreign vs. domestic influences Annual mean surface O3 decrease from -20% NOx+CO+NMVOC regional anthrop. emissions Source region: (1.64) Full range of 15 individual models “import sensitivity” domestic = 0.45 ∑ 3 foreign Seasonality? ppbv “domestic” “Sum 3 foreign”

North America as a receptor of ozone pollution: Seasonality of response to -20% foreign anthrop. emissions SUM OF 3 FOREIGN REGIONS Spring max Similar response to EU & EA emissions Apr-Nov (varies by model) 15- MODEL MEAN SURFACE O3 DECREASE (PPBV) EA EU Springtime: 10 models equivalent; 4 EA 30-55% > EU; annual mean that is 8 predict equivalent & 6 suggest 25-50% greater from EA SA Spring (fall) max due to longer O3 lifetime, efficient transport [e.g., Wang et al., 1998; Wild and Akimoto, 2001; Stohl et al., 2002; TF HTAP 2007]

MODEL ENSEMBLE MEAN SURFACE
North America as a receptor of ozone pollution: Seasonality of response to -20% foreign anthrop. emissions NOx+NMVOC+CO Comparable response to NOx & NMVOC emissions (but varies by model) MODEL ENSEMBLE MEAN SURFACE O3 DECREASE (PPBV) NMVOC NOx CO Wide range in EU anthrop. NMVOC inventories  large uncertainty in the estimated response of NA O3

“high transport season”
North America as a receptor of ozone pollution: Seasonality in “import sensitivity” Surface O3 response to -20% domestic (NA NOx+NMVOC+CO) anthrop. emis. Response to -20% Foreign > Domestic (winter) RATIO PPB IMPORT SENSITIVITY: -20% domestic ∑ (-20% 3 foreign) during spring “high transport season” Conclude that response to foreign, although 10ths of ppb is not trivial when compared with response to equivalent % reductions in domestic anthrop. Emissions… Point out “shoulder” = “high-transport” seasons, where future growth in foreign emissions may extend season of poor air quality ~0.2 when domestic O3 production peaks in summer

often > SA/EA on each other
Surface O3 response to decreases in foreign anthropogenic emissions of O3 precursors EU receptor Source region: SUM3 NA EA EU SA NA>EA>SA over EU (robust across models) response typically smallest to SA emis. (robust across models) Surface O3 decrease (ppb) EA receptor SA receptor NA & EU often > SA/EA on each other (dominant region varies by model) Mention: SA emis smallest in annual mean / springtime; EA spring, most models EU/NA > SA; SA: many models annual mean equivalent responses to at least 2 regions

Monthly mean import sensitivities
1.1 (EA), 0.7 (EU) during month with max response to foreign emissions SA fairly constant ~0.5 during month of max response to domestic emissions

Surface ozone response to -20% global [CH4]: similar decrease over all regions
Full range of 18 models ppb 1 ppbv O3 decrease over all regions [Dentener et al.,2005; Fiore et al., 2002, 2008; West et al., 2007] EU NA E Asia S Asia 20% global CH4  -25% global anthrop. Emissions (accounting for feedback of methane on its own lifetime (F = 1.33)+ anthrop = 60% total CH4 emissions [IPCC 2007]) Estimate O3 response to -20% regional CH4 anthrop. emissions to compare with O3 response to NOx+NMVOC+CO: -20% global [CH4] ≈ -25% global anthrop. CH4 emissions Anthrop. CH4 emis. inventory [Olivier et al., 2005] for regional emissions Scale O3 response diagnosed with change in global burden to obtain O3 response due to regional CH4 emission change

Tropospheric O3 responds approximately linearly to anthropogenic CH4 emission changes across models
MOZART-2 [this work] MOZART-2 [West et al., 2006,2007] TM3 [Dentener et al., 2005] GISS [Shindell et al., 2005] GEOS-CHEM [Fiore et al., 2002] IPCC TAR [Prather et al., 2001] Harvard [Wang and Jacob, 1998] X + Anthropogenic CH4 contributes ~50 Tg (~15%) to tropospheric O3 burden ~5 ppbv to surface O3 Fiore et al., JGR, 2008

to estimate O3 response to -20% regional anthrop. CH4 emissions)
Comparable annual mean surface O3 response to -20% foreign anthropogenic emissions of CH4 vs. NOx+NMVOC+CO Sum of annual mean ozone decreases from 20% reductions of anthropogenic emissions in the 3 foreign regions ppb Receptor: NA EU E Asia S Asia (Uses CH4 simulation + anthrop. CH4 emission inventory [Olivier et al., 2005] to estimate O3 response to -20% regional anthrop. CH4 emissions)

Summary: Hemispheric Transport of O3
Benchmark for future: Robust estimates + key areas of uncertainty “Import Sensitivities” (D O3 from anthrop. emis. in the 3 foreign vs. domestic regions): during month of max response to foreign emis; during month of max response to domestic emissions Comparable O3 decrease from reducing equivalent % of CH4 and NOx+NMVOC+CO over foreign regions ( ppb for 20% reductions) More info at TF HTAP Interim Report TF HTAP multi-model publications: Shindell et al., ACP, 2008: Transport to the Arctic Sanderson et al., GRL, 2008: NOy transport Fiore et al., JGR, in press: Surface O3 Reidmiller et al., in prep: U.S. surface O3 Jonson et al., in prep: Evaluation with O3 sondes Casper et al., in prep: Health impacts (O3) Schultz et al., in prep: Idealized tracers

Some Remaining Questions…
What is the contribution of hemispheric transport to metrics relevant to attainment of O3 air quality standards? How important is interannual variability? What is causing model spread? Can we tie to specific processes and use observational constraints to reduce uncertainty? Can we scale O3 responses to other combinations and magnitudes of emission changes? How will climate change affect hemispheric transport of air pollution? .. And much more TF HTAP work ongoing to inform 2010 report!

Wide model range in “AQ-relevant metrics”: MAM average daily max 8-hour (MDA8) surface O3 over the USA (HTAP models) ppb

CASTNet sites used in model evaluation
Northwest California Plains Southeast Great Lakes North-east Mtn. West Florida / Gulf Far NE Reidmiller, AGU, 2008; in prep for JGR

Model Evaluation Wide spread in individual models, but ensemble represents obs quite well Correlations are generally strongest in East & weaker in West Models have large (+) biases in summer that are largest in the East Reidmiller, AGU, 2008; in prep for JGR

10-model mean response of MDA8 ozone to 20% reductions of foreign emissions: MAM average
Ensemble mean base case MDA8 O3 Ensemble mean MDA8 O3 decrease from -20% EA + EU + SA anthrop. emissions ppb Ensemble mean MDA8 O3 decrease from -20% NA anthrop. emissions ppb Variability in response to foreign emissions? e.g., clean vs. polluted conditions? (Models regridded to common 2°x2.5° grid) ppb

Positive bias in ensemble during summer
MDA8 O3 response to 20% emissions reductions: 3 foreign regions vs. North America region Response to foreign emissions greatest in spring For most regions O3 response (to -20% foreign emissions ) is ~0.5 ppbv in spring Positive bias in ensemble during summer Response to domestic emissions greatest in summer at high end of O3 distribution For all regions, response to domestic emissions greater than to foreign emissions (~2-10x; varies with season) Reidmiller, AGU, 2008; in prep for JGR

What is the contribution of hemispheric transport to metrics relevant to attainment of O3 air quality standards? How important is interannual variability? What is causing model spread? Can we tie to specific processes and use observational constraints to reduce uncertainty? Can we scale O3 responses to other combinations and magnitudes of emission changes? How will climate change affect hemispheric transport of air pollution?

Image from http://www. cpc. noaa. gov/products/precip/CWlink/pna/nao
MOZART2 simulations GMI simulations GMI simulations MOZART runs

Inter-model spread generally larger than that due to interannual variability in meteorology
Multi-model mean (2001) Individual HTAP models (2001) GMI model 2001 (B. Duncan) GMI model 2006 MOZART2 model 2001 MOZART2 model individual years, 2002 through 2006

A measure of uncertainty in SR relationships (sfc o3): Model spread can be > factor of 2
Multi-model mean Models do not always rank in consistent way! Identify processes responsible for this spread (emissions/transport/chemistry) Evaluate which models most accurately represent those processes To generate “more useful” information: Comparison with surface obs shows no relationship btw “base-case bias” and source-receptor relationship (e.g., NA->EU)

Individual models sometimes rank consistently in terms of source region influence on multiple receptors Individual model Possible explanations: -- Emissions (not major player: similar across models, except EU NMVOC) -- Export / transport / mixing processes -- Chemistry

Surface O3 decrease over EU
Idealized tracer experiments (CO tagged by region; all models use same emissions) suggest some role for vertical mixing in source region EXAMPLE for NA  EU surface ozone Springtime (MAM) r2 = 0.56 Surface O3 decrease over EU from -20% NA emis. (SR1-SR6NA; ppb) Individual model CORRELATE WITH PAN! Index = measure of exchange btw pbl & free trop Note: found little correlation with NAEU surface ozone and NA NOx emissions (in general NOx emissions don’t differ by much across the models, so model spread seems to be more driven by chemistry/transport) Ratio of NA CO tracer burden at 3-8km to 0-2km over NA Less vertical mixing More vertical mixing Index adapted from Martin Schultz

Models likely differ in export of O3 + precursors, downwind chemistry, and transport to receptor region PAN NOx O3 O3 NOy N2O5 O3 other organic nitrates HNO3 Fires Land biosphere Human activity Ocean Ocean Continent 1 NOy partitioning (e.g., PAN vs. HNO3) influences O3 formation potential far from source region

PAN transport can lead to highly efficient O3 production downwind
Hudman et al., JGR, 2004 Emmerson & Evans, ACPD, 2008: “significant variations in the calculated concentration of PAN between the schemes” [model chemical mechanisms] Does surface O3 response to emission changes in foreign regions correlate with: -- O3 response over source region? Yes EU (r2= ); NO in other regions (r2<0.2) -- Changes in PAN over upwind and/or receptor region?

Separation of “lowest sensitivity” models and higher
Change in PAN over upwind region (or receptor) correlates with surface ozone change over receptor region NAEU O3 vs. N. Atlantic PAN: r2 = 0.36 EANA O3 vs. N. Pacific PAN: r2 = 0.36 Surface o3 decrease over receptor region (ppb) Individual model Individual model Change in PAN burden (trop column) over upwind region in spring (Tg) Separation of “lowest sensitivity” models and higher

But, simulated changes in PAN correlate with “base-case” regional PAN
What observations would help discriminate among models (and reduce uncertainties in O3 response to foreign emission changes)? Simulated O3 response not correlated with total ozone or ozone bias w.r.t. obs. Over N. Pacific (MAM): r2 = 0.65 Over N. Atlantic (MAM): r2 = 0.87 Total column PAN burden in base case simulation (Tg) Decrease in PAN burden from -20% EA emissions from -20% NA emissions Individual model But, simulated changes in PAN correlate with “base-case” regional PAN  Potential for PAN measurements to help reduce uncertainty? 2001 Observations: TRACE-P (Asian outflow), PHOBEA (inflow to N. America)

What causes the 1 ppb spread across models in surface ozone response to -20% global [CH4]?
Full range of 18 models surface O3 decrease Annual mean (ppb ) EU NA E Asia S Asia Surface O3 decrease over EU vs. NA (ppb) Strong correlations for all regions: EA vs. EU r2 = 0.75 SA vs. EA r2 = 0.83 NA vs. SA r2 = 0.86 Some models more responsive to [CH4] changes r2 = 0.88 Individual model

Surface O3 decrease (ppb)
Differences in lifetime against tropospheric OH are a strong contributor to model spread in O3 response to CH4 Surface O3 decrease (ppb) over NA from -20% [CH4] Total atmospheric methane lifetime Multi-model mean CH4 lifetime against tropospheric OH: 10.2 ± 1.7 Agrees with obs-derived (methyl chloroform) estimate : [Prinn et al., 2005] Normalizing by CH4 lifetime reduces inter-model range from 1 to 0.5 ppb (similar in other receptor regions)

HTAP Event Simulations: Moving towards process-based evaluation
I. Bey, M. Evans, K. Law, R. Park, E. Real, S. Turquety 1. chemical signatures of air masses, 2. chemical evolution in background vs. polluted plume ensembles, 3. export efficiencies, 4. injection heights on biomass burning plumes Preliminary results from the French model MOCAGE, courtesy of N. Bousserez and J.-L- Attié, Laboratoire d’aérologie Toulouse, France observations model “clean” lower trop. biomass burning influenced air masses middle-upper troposphere “polluted” lower trop. from Isabelle Bey’s presentation at DC June 2008 HTAP meeting

O3 response to -20% vs. -100% EU emissions: NOx vs. NMVOC in spring
Intercontinental O3 response to changes in NMVOC emissions scales linearly; not so for NOx (except summer) O3 response to -20% vs. -100% EU emissions: NOx vs. NMVOC in spring (GMI model) NOx: Non-linearity often ≥ 2 (except in summer) NMVOC: Linear Change of scale (5x gives same height bars) Receptor regions Receptor regions Relative benefit of decreasing NOx vs. NMVOC emissions increases with the magnitude of emission reductions Wu et al., submitted to GRL

What is the contribution of hemispheric transport to metrics relevant to attainment of O3 air quality standards? How important is interannual variability? What is causing model spread? Can we tie to specific processes and use observational constraints to reduce uncertainty? Can we scale O3 responses to other combinations and magnitudes of emission changes? How will climate change affect hemispheric transport of air pollution? 1/16/09 update on future climate HTAP simulations: “The RCP process is expected to publish scenarios in the February/March timeframe. Given the central role that these scenarios are likely to play in the science and policy processes over the next several years, we believe that it is worth waiting to make some final decisions until this work is completed.” – Terry Keating (US EPA) and Andre Zuber (EC), co-chairs of TF HTAP

Conclusions: Hemispheric Transport of O3
More info at TF HTAP Interim Report, Fiore et al., JGR, in press Benchmark for future: Robust estimates + key areas of uncertainty “Import Sensitivities” (D O3 from anthrop. emis. in the 3 foreign vs. domestic regions): during month of max response to foreign emis; during month of max response to domestic emissions Comparable O3 decrease from reducing equivalent % of CH4 and NOx+NMVOC+CO over foreign regions ( ppb for 20% reductions) Variability of O3 response within large HTAP regions; U.S. example -- multi-model mean captures much of day-to-day variability Inter-model differences >> year-to-year variability Different model responses seem partially due to vertical mixing, PAN -- potential for PAN measurements to help constrain O3 response? Non-linearity in O3 response to foreign NOx emissions impacts relative benefit of NOx vs. NMVOC emission controls

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