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Identifying Chemistry-Climate-Air Quality Connections To Inform Public Policy

Identifying Chemistry-Climate-Air Quality Connections To Inform Public Policy . Arlene M. Fiore. Acknowledgments . Jasmin John, Meiyun Lin, Vaishali Naik , Larry Horowitz, D.J. Rasmussen, Alex Turner, GAMDT (GFDL); Oliver Wild (U Lancaster): Mike Bauer (CU/GISS) .

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Identifying Chemistry-Climate-Air Quality Connections To Inform Public Policy

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  1. Identifying Chemistry-Climate-Air Quality Connections To Inform Public Policy Arlene M. Fiore Acknowledgments. Jasmin John, Meiyun Lin, VaishaliNaik, Larry Horowitz, D.J. Rasmussen, Alex Turner, GAMDT (GFDL); Oliver Wild (U Lancaster): Mike Bauer (CU/GISS) AAAS Meeting, Vancouver February 19, 2012

  2. 0.7 1.4 1.9 Addressing air quality and climate via methane emission controls: A viable option? (industrialized nations) 10% of anth. emissions 20% of anth. emissions 0 20 40 60 80 100 120 Methane reduction potential (Mton CH4 yr-1) IEA [2003] for 5 industrial sectors ~25% of global anthrop. emissions at cost-savings / low-cost >1 ppb decrease in global surface ozone West & Fiore, ES&T, 2005; Fiore et al., GRL, 2002

  3. Benefits of ~25% decrease in global anthrop. CH4 emissions CLIMATE OZONE AIR QUALITY Global mean avoided warming in 2050 (°C) [WMO/UNEP, 2011] • Range over • 18 models • ~ 1 ppb, robust across models (factor of 2 range) • [Fiore et al., JGR, 2009; TF HTAP, 2007, 2010; Wild et al., ACPD, 2012] • 7700-400,000 annual avoided cardiopulmonary • premature mortalities in the N. Hemisphere • uncertainty in concentration-response relationship only • [Casper Anenberg et al., ES&T, 2009]

  4. Atmospheric CH4 and surface O3 over the next century? Representative Concentration Pathways (RCPs) Tool: GFDL CM3 chemistry- climate model METHANE RCP8.5 RCP6.0 RCP4.5 RCP2.6 • ~2°x2°; 48 levels • Atm-ocean-sea ice-land GCM • Fully coupled chemistry in • troposphere+stratosphere • Aerosol – warm cloud interactions ppb c/o V. Naik NOx Emissions RCP4.5* WMGG RCP8.5 Tg N yr-1 c/o J. John RCP4.5 Donner et al., J. Climate, 2011; Golazet al., J. Climate, 2011; Naik et al., in prep, Horowitz et al., in prep

  5. Multiple feedbacks complicate projections of atmospheric CH4 and O3 abundances tCH4 Stratospheric O3 = Troposphere NOx k CH4 O3 + hν OH CH3+H2O O1D + H2O + T T NOx, CO, NMVOC, CH4 Anthropogenic sources Biospheric sources

  6. Negative feedback of warming climate on methane lifetime; anthrop. emission trajectory can amplify or counteract TROPOSPHERIC CH4 LIFETIME IN GFDL CM3 CHEMISTRY-CLIMATE MODEL RCP 8.5: tCH4: +4%: Doubling CH4offsets influence of warmer T (+4.5K) RCP4.5* WMGG only: tCH4: -5% Rising T(+1.4K), OH (LNOx, H2O) Years RCP4.5:tCH4: -13% More warming (+2.3K; aerosol), CO, CH4 decrease Percentage changes are (2081-2100) – (2006-2025) J. John et al., in prep

  7. How will O3 air quality evolve over North America? RCP emissions: lower? Warmer climate: higher? NO CLIMATE CHANGE O3 change estimated from sensitivities derived from TF HTAP model ensemble • Dramatic rise in CH4 in RCP8.5 opposes NOx-driven decreases Annual mean N. Amer. Surface O3 changes (ppb) [Wild et al., revised for ACP] July mean obs from U.S. EPA CASTNet site Penn State, PA 41N, 78W, 378m Observed O3-T correlation implies that changes in climate will influence air quality TEMP (C; 10am-5pm avg) MDA8 O3 (ppb)

  8. Over NE USA, stagnation episodes are a major driver of observed surface O3-T correlation: Future evolution? Leibensperger et al. [2008]: strong anticorrelation in summer between number of migratory cyclones over Southern Canada/NE U.S. and number of stagnation events and associated NE US high-O3 events Number of storms in region each summer (JJA) in RCP8.5, GFDL CM3 model • Robust across models? • [e.g., Lang and Waugh, 2011] • Can we evaluate modeled relationships btw air quality and climate? Cylones diagnosed from 6-hourly SLP with MCMS software from Mike Bauer, (Columbia U/GISS) A. Turner et al., in prep

  9. How well does a global chemistry-climate model simulate regional O3-temperature relationships? CASTNet sites, NORTHEAST USA “Climatological” O3-T relationships: Monthly means of daily max T and monthly means of MDA8 O3 AM3: 1981-2000 OBS: 1988-2009 r2=0.41, m=3.9 July Monthly avg. MDA8 O3 r2=0.28, m=3.7 Slopes (ppb O3 K-1) July Monthly avg. daily max T • Model captures observed O3-T relationship in NE USA in July, despite high O3 bias Month  Broadly represents seasonal cycle Rasmussen et al., Atmos. Environ., 2012

  10. What is the combined impact of climate + emission on surface O3 over North America? EMISSIONS CHANGE ONLY EMISSIONS + CLIMATE CHANGE 5 0 -5 -10 GFDL CM3 RCP8.5 RCP4.5 ens. mean Individual members Annual mean N. American surface O3 change (ppb) [Wild et al., revised for ACP] Why does O3 increase in GFDL CM3 RCP8.5? Higher CH4 sensitivity? Increased strat O3 influence?[e.g., Butchart et al., 2006; Hegglin & Shepherd, 2009; Kawase et al., 2011; Li et al., 2008; Shindell et al. 2006; Zeng et al., 2010]  How well do models represent strat-to-trop O3 transport?

  11. Western NA: Particularly active region for STE in present day,a good test case for model evaluation Upper level dynamics associated with a deep stratospheric ozone intrusion (21:00UTC May 27, 2010) AM3“nudged high-res” (~50km2 ) simulations Satellite observations 250 hPa potential vorticity AIRS total column ozone DU 250 hPa jet (color) 350 hPageopotential height (contour) GOES-West water vapor Decreasing humidity   AM3 resolves features consistently with satellite perspective M. Lin et al., in prep.

  12. Identifying chemistry-climate-air quality connections to inform public policy… some final thoughts • Cooling influence on climate (by lowering both methane and O3) • Decrease baseline surface O3 (robust across models) •  Complex chemistry-climate feedbacks along future trajectories • Methane controls as “win-win” for climate and O3 air quality • Analysis of long-term chemical and meteorological obsmay reveal key connections between climate and air pollution • Crucial for testing models used to project future changes •  Need to maintain long-term observational networks • Climate-driven influences on air quality •  Need better process understanding at regional scale; • new opportunities with chemistry-climate models •  Potential for shifts in relative importance of locally produced vs. transported O3

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