1 / 27

Arctic pollution: early results from the NASA/ARCTAS aircraft mission

Arctic pollution: early results from the NASA/ARCTAS aircraft mission. Daniel J. Jacob. with Jenny A. Fisher, Qiaoqiao Wang , Jingqiu Mao, Chris D. Holmes, Christopher Pickett-Heaps. and support from NASA and NSF. ARCTAS-A: April 2008. ARCTAS-B: June-July 2008.

tiara
Download Presentation

Arctic pollution: early results from the NASA/ARCTAS aircraft mission

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Arctic pollution: early results from the NASA/ARCTAS aircraft mission Daniel J. Jacob with Jenny A. Fisher, Qiaoqiao Wang , Jingqiu Mao, Chris D. Holmes, Christopher Pickett-Heaps and support from NASA and NSF ARCTAS-A: April 2008 ARCTAS-B: June-July 2008

  2. URGENT NEED TO BETTER UNDERSTANDARCTIC ATMOSPHERIC COMPOSITION AND CLIMATE THE ARCTIC IS A BEACON OF GLOBAL CHANGE • Rapid warming over past decades • Receptor of mid-latitudes pollution • Large and increasing influence from boreal forest fires POTENTIALLY LARGE RESPONSE • Melting of polar ice sheets and permafrost • Decrease of snow albedo from soot depostion • Efficient UV/Vis absorption by ozone, soot • Halogen radical chemistry UNIQUE OPPORTUNITY FOR NASA • Large NASA satellite fleet for atmospheric composition and radiation • Interagency and international collaboration through POLARCAT consortium of atmospheric composition field campaigns during International Polar Year (2007-2008) Sea Ice Extent, 10/16/07

  3. ARCTAS Field Campaign Strategy: Maximize the value of satellite data for improving models of atmospheric composition and climate Satellite instruments: CALIOP, GOME-2, OMI, TES, MLS, SCIAMACHY, MODIS, MISR, MOPITT, AIRS • Aerosol optical depth, properties • H2O, CO, methane, ozone, NO2, HCHO, SO2, BrO Calibration and Validation Retrieval development Correlative information Small scale structure and processes Aircraft: DC-8, P-3B, B200 • Comprehensive in situ chemical and aerosol • measurements • Active remote sensing of ozone, water vapor • and aerosol optical properties • Passive radiance measurements Model error evaluation Data assimilation Diagnostic studies Models • Source-receptor relationships for pollution • Inverse modeling for estimating emissions • Aerosol radiativeforcing, climate response • Detailed chemical processing

  4. ARCTAS Science Theme 1:Transport of mid-latitudes pollution to the Arctic European influence Seasonal sulfate build-up AIRS CO column, 5 Apr 2008 TOPSE 1-2 2-5 5-8 km May Feb Scheuer et al., 2003 J. Worden, JPL Juying Warner (UMBC), Jenny Fisher (Harvard) • Satellite capabilities: • CO (TES, AIRS, MOPITT) • ozone (TES, OMI-MLS) • aerosols (CALIOP, MODIS, MISR) • methane (TES, AIRS) • Aircraft added value: • detailed chemical composition • tracers of sources • vertical information • What are the transport pathways • for different pollutants? • What are the contributions from different source regions, what are the source-receptor relationships?

  5. b 2000 4500 1000 3000 0 Fire trend over past decade CALIOP view of fire plume MISR injection height ARCTAS Science Theme 2: Boreal forest fires Siberian fire, July 26, 2006 A. Soja, LaRC C. Trepte, LaRC R. Kahn, JPL • Satellite capabilities: • aerosols (CALIOP, MODIS, MISR, • OMI) • CO (TES, AIRS, MOPITT, MLS) • ozone (TES, OMI-MLS) • methane (TES) • Aircraft added value: • detailed chemical composition • aerosol properties • pyroconvective outflow • What are the chemical compositions & • evolution of the fire plumes? • What are their aerosol optical properties, how do these evolve? • What determines the injection heights? • What are the implications for regional and global atmospheric composition?

  6. ARCTIC Science Theme 3: Aerosol radiative forcing CALIOP clouds and smoke Arctic haze MISR true-color fire plume C. Trepte, LaRC R. Kahn, JPL • Satellite capabilities: • UV/Vis/IR reflectances (Cloudsat, • MODIS, MISR, OMI) • multi-angle sensing (MISR) • lidar (CALIOP) • Aircraft added value: • detailed in situ aerosol characterization • remote sensing of radiances, fluxes • BRDFs • What is the regional radiative forcing from Arctic haze, fire plumes? • How does this forcing evolve during plume aging? • What are the major sources of soot to the Arctic? • How does soot deposition affect ice albedo?

  7. Ozone, Hg depletion events OMI tropospheric BrO TES tropospheric ozone Sprovieri et al. [2005] K. Chance, Harvard/SAO J. Worden, JPL ARCTAS Science Theme 4: Chemical processes • Satellite capabilities: • Ozone (TES, OMI/MLS) • BrO (OMI, SCIAMACHY, GOME-2) • CO (TES, AIRS, MOPITT) • Aircraft added value: • detailed chemical characterization, constraints on photochemical models • validation of OMI tropospheric BrO • HOx measurement intercomparison • What controls HOx -NOx radical chemistry in the Arctic? • What drives halogen radical chemistry, what is its regional extent? • What are the regional implications for ozone, aerosols, mercury? • How does stratosphere-troposphere exchange affect tropospheric ozone in the Arctic?

  8. ARCTAS PLATFORMS DC-8 P-3B B-200 Chemistry and Aerosols Radiation and Aerosols Aerosol satellite validation 9 Instruments HSRL – CALIPSO RSP – GLORY 21 instruments Satellite Teams: CALIPSO, MODIS, TES, OMI, AIRS, MISR, MOPITT Model forecasts/analyses: GEOS-5, GOCART, GEOS-Chem, STEM, MOZART, LaRC Ground Teams:UAF, NATIVE, ARC-IONS • In-field collaborations with concurrent aircraft missions: • NOAA/ARCPAC, DOE/ISDAC (spring) • NSF/Pre-HIPPO, French/German GRACE (summer)

  9. SPRING DEPLOYMENT (1-21 April 2008) April 2008 Siberian fires, 2001-2009 (MODIS data, Aprils in red) MODIS fire counts, April 08

  10. Yellowknife Cold Lake Canadian fire climatology 1980-2004 SUMMER DEPLOYMENT (June 26-July 14, 2008) N. Saskatchewan fires MODIS fire counts, July 08

  11. GEOS-Chem Chemical Transport Model (CTM) Global 3-D model of atmospheric composition • Driven by NASA//GEOS-5 meteorological data with 1/2ox2/3o horizontal resolution, • degraded to 2ox2.5o in GEOS-Chem • 2008 anthropogenic emissions, daily fire emissions from FLAMBE inventory • Applied here to simulations of CO, aerosols, oxidants, mercury, methane

  12. (G. Diskin, NASA) CARBON MONOXIDE (CO) AS POLLUTION TRACER Source from incomplete combustion, lifetime of months Adjust model emissions to fit ARCTAS data; anthropogenic , fires  Model source attribution mean vertical profile • Dominance of anthropogenic sources reflects long CO lifetime • Asian anthropogenic source dominates background • European source also important near surface • Russian fires contribute to variability APRIL 2008 EMISSIONS storm track surface Fisher et al. [2009]

  13. OBSERVATION OF ARCTIC CO POLLUTIONBY THE AIRS SATELLITE INSTRUMENT Russian fire plume over SW Alaska at 3-5 km, well observed by AIRS European pollution plume over N Pole below 2 km, not detected by AIRS Observation in thermal IR DC-8 track in black elIl(T1) T1 absorbing gas Apr 9 Apr 16 blackbody radiation Il(T0) AIRS is sensitive to pollution above 2 km, not below To Surface Fisher et al. [2009]

  14. INTERANNUAL VARIABILITY OF ARCTIC CO POLLUTION OBSERVED BY AIRS 2003-2008 April mean Interannual variability of April mean • European sector most polluted, N American sector cleanest • Asian sector unusually clean in 2008 despite large Russian fires! Fisher et al. [2009]

  15. SPRING 2008 METEOROLOGICAL ANOMALY Correlation of AIRS CO over Alaska with ENSO AIRS CO column Mean April sea-level pressure 2003-2008 2008 Ocean Nino Index 2004 2005 2006 2007 2008 Year (April) • Weak Aleutian Low hinders transport of Asian pollution to Arctic, • appears related to La Nina conditions • Implies strong Asian pollution influence over Arctic under El Nino conditions Fisher et al. [2009]

  16. MEAN AEROSOL LATITUDE-ALTITUDE CURTAINS DURING ARCTAS sulfate ammonium nitrate sea salt SPRING 170W-135W 60N 90N > 7 km 2-7 km 0-2 km SUMMER • Larger aerosol loading in spring than in summer; • Little vertical gradient in aerosol optical depth in spring but large gradient in composition 60N 90N dust organic C black C J. Hair, NASA LaRC; J. Dibb, UNH; B. Anderson, NASA LaRC

  17. Model SOURCES OF SULFATE AND ACIDITY IN ARCTIC SPRING Observed [NH4+ ] vs. 2[SO42- ]+[NO3- ] • Model misses sulfate source below 2 km. Europe? Siberia? • Aerosol is generally acidic but ranges from concentrated H2SO4 near surface to near-neutral in fire plumes J. Fisher, Harvard; J. Dibb, UNH

  18. MEAN C AEROSOL PROFILES (spring) observed Model (fires/anthro) Organic Carbon (OC) Black Carbon (BC) no model scavenging below 258 K model scavenging at all T • OC aerosol is mainly from Russian fires, BC has both fire and anthropogenic (fuel) • contributions • Treatment of scavenging in cold clouds is a large source of model uncertainty Q. Wang, Harvard; B. Anderson, NASA LaRC

  19. C AEROSOL DISTRIBUTIONS IN THE ARCTIC (April) Mean tropospheric concentrations simulated by GEOS-Chem BC OC • Elevated values over Alaska are not representative of the Arctic • Fire enhancements are superimposed on anthropogenic background QiaoqiaoWang, Harvard

  20. O2 hn GENERAL MECHANISM FOR OXIDANT CHEMISTRY O3 STRATOSPHERE 8-18 km TROPOSPHERE hn NO2 NO H2O O3 hn, H2O H2O2 ROOH HO2/RO2 OH Deposition CO, VOC carbonyl CO from combustion Volatile Organic Compounds (VOCs) from biosphere, fuel, industry SURFACE NOx from fuel combustion, lightning, soils

  21. MODEL vs. OBSERVED VERTICAL PROFILES IN ARCTAS-A Sensitivity to HO2 uptake by aerosols (reaction probability g(HO2)) model w/HO2 uptake gas-only model obs Arctic spring particularly conducive to HO2 uptake by aerosols because of (1) cold T, (2) high aerosol, (3) slow photochemical cycling J. Mao, Harvard; P. Wennberg, Caltech; A. Weinheimer and A. Fried, NCAR; R. Cohen, UC Berkeley; G. Huey, GIT

  22. TWO POSSIBLE MECHANISMS FOR HO2 UPTAKE BY AEROSOL HO2 H2O2(g) HO2 HO2(aq) H2O2(aq) produces H2 O2 HSO4- OH HSO5- SO5- + H2O HSO3- terminal sink SO42- + 2H+ Explaining the ARCTAS observations requires HO2 uptake to be a terminal sink  non-conventional chemistry Jingqiu Mao, Harvard

  23. ANTHROPOGENIC PERTURBATION: fuel combustion waste incineration mining THE MERCURY CYCLE: MAJOR PROCESSES oxidation: OH, O3, Br? Hg(II) Hg(0) reduction: hν, aq? highly water-soluble volatilization evapo- transpiration volcanoes erosion deposition ATMOSPHERE SOIL/OCEAN oxidation particulate Hg Hg(II) Hg(0) biological uptake reduction uplift burial SEDIMENTS

  24. ATMOSPHERIC SOURCES OF Br RADICALS (BrOx≡ Br + BrO) OH, hv BrOx STRATOSPHERE halons (CBr3F,…) FREE TROPOSPHERE brominated hydrocarbons OH, hv BrOx CH2 Br2 CHBr3 CH3Br BOUNDARY LAYER hv sea salt aerosol BrOx BrOx plankton hv Human activities SEA ICE

  25. Elevated BrO columns seen from space were generally not in boundary layer, more likely in lowermost stratosphere (correlated with tropopause depressions) Mean Hg(0) profile observed from aircraft vs. simulated by GEOS-Chem ARCTAS-A OBSERVATIONS AS TEST OF Hg-Br CHEMISTRY model Hg(0) trop. only Thule Altitude, km Observed (R.W. Talbot, UNH) Hg(0) observations Imply more rapid stratospheric oxidation than simulated in GEOS-Chem; consistent with elevated BrO in lowermost stratosphere? Chris Holmes, Harvard

  26. Estimated to account for ~10% of global boreal wetland methane source Hudson Bay Lowlands, Canada

  27. METHANE VERTICAL PROFILES IN HUDSON BAY LOWLANDS May 5 Jun 23 Jul 1 Jul 4 Jul 5 Model Obs. pre-HIPPO pre-HIPPO ARCTAS ARCTAS ARCTAS Onset of source in late June? Model source of 5 Tg a-1 in Hudson Bay Lowlands (Jed Kaplan bottom-up inventory) matches observations but is much higher than previous estimates (0.5-2 Tg a-1) C. Pickett-Heaps, Harvard; G. Diskin, NASA LaRC; S. Wofsy, Harvard

More Related