TROPOSPHERIC OZONE AS A CLIMATE GAS AND SATELLITE

TROPOSPHERIC OZONE AS A CLIMATE GAS, AND SATELLITE OBSERVATIONS OF ITS PRECURSORS Daniel J. Jacob

CHEMISTRY, AEROSOLS, AND CLIMATE: TROPOSPHERIC UNIFIED SIMULATION (CACTUS) A NASA Interdisciplinary Science (IDS) investigation: Harvard (Jacob, P. I. ), Caltech (Seinfeld), NASA/GISS (Rind), UC Irvine (Prather) Demissions D land use D climate forcing Atmospheric chemistry CACTUS model GISS GCM Aerosol microphysics “…Kyoto also failed to address two major pollutants that have an impact on warming: black soot and tropospheric ozone. Both are proven health hazards. Reducing both would not only address climate change, but also dramatically improve people's health. ” (George W. Bush, June 11 2001 Rose Garden speech) D climate D chemistry

GLOBAL BUDGET OF TROPOSPHERIC OZONE Global sources and sinks, Tg O 3 yr-1 (GEOS-CHEM model) O 2 hn O 3 STRATOSPHERE 8 -18 km Chem prod in troposphere 4900 Chem loss in troposphere 4200 Transport from stratosphere 500 Deposition 1200 TROPOSPHERE O 3 hn, H 2 O NO 2 OH Deposition hn NO HO 2 CO, CH 4, VOC H 2 O 2

GLOBAL DISTRIBUTION OF TROPOSPHERIC OZONE Lifetime is ~ weeks Climatology of observed ozone at 400 h. Pa in July from ozonesondes and MOZAIC aircraft (circles) and corresponding GEOSCHEM model results for 1997 (contours). GEOS-CHEM tropospheric ozone columns for July 1997 Li et al. [2001]

ANTHROPOGENIC INCREASE IN TROPOSPHERIC OZONE DRIVEN BY NOx AND METHANE EMISSIONS PRESENT-DAY EMISSIONS NOx (Tg N yr-1) LIGHTNING 5. 8 SOILS 5. 1 STRATOSPHERE 0. 2 METHANE (Tg C yr-1) WETLANDS 180 BIOMASS BURNING 20 ANIMALS 90 LANDFILLS 50 GAS 60 BIOMASS FOSSIL FUEL BURNING AIRCRAFT 23. 1 5. 2 BIOFUEL 0. 5 2. 2 RICE TERMITES 85 25 COAL 40

RISE IN TROPOSPHERIC OZONE OVER 20 th CENTURY Preindustrial ozone models } Observations at mountain sites in Europe [Marenco et al. , 1994]

RADIATIVE FORCING FROM TROPOSPHERIC OZONE Annual mean values (0. 49 W m-2 globally) (9. 6 mm) Global radiative forcing DF (W m-2) Mickley et al. [1999] 0. 46 IPCC [2001] range 0. 3 -0. 5 From 19 th century obs [Mickley et al. , 2001] 0. 80 But how good is radiative forcing as an indicator of climate change, when this forcing is so heterogeneous?

GISS GCM ANALYSIS OF CLIMATE RESPONSE TO TROPOSPHERIC OZONE CHANGE OVER 20 th CENTURY GCM equilibrium simulation for present-day climate with present vs. preindustrial tropospheric ozone; includes “Q-flux” ocean Also sensitivity simulations with DO 3 = 18 ppb, DCO 2 = 25 ppm, giving same DF equilibrium climate present-day ozone Preindustrial ozone DF = 0. 49 W m-2 DT = 0. 3 o. C L. J. Mickley, Harvard

INHOMOGENEITY OF CLIMATE RESPONSE TO OZONE CHANGE OVER 20 th CENTURY • Greater warming in northern hemisphere • Strong cooling in stratosphere: Stratospheric ozone Tropospheric ozone 9. 6 mm L. J. Mickley, Harvard Surface

LOWER STRATOSPHERIC COOLING FROM TROPOSPHERIC OZONE IS STRONGEST IN ARCTIC WINTER particularly sensitive region for recovery of ozone layer! GCM temperature change in lower stratosphere in DJF (o. C) from increasing tropospheric ozone over 20 th century L. J. Mickley, Harvard

CLIMATE RESPONSE EXPERIMENTS WITH IDENTICAL GLOBAL RADIATIVE FORCINGS (0. 49 W m-2) FROM: 1. 2. 3. D tropospheric ozone D uniform tropospheric ozone (18 ppv) D carbon dioxide (25 ppmv) • CO 2 is a more effective warming agent at surface • In lower stratosphere, CO 2 causes warming while tropospheric ozone causes cooling L. J. Mickley, Harvard

WHY IS CO 2 MORE EFFECTIVE THAN OZONE FOR SURFACE WARMING AT SAME RADIATIVE FORCING? Correlation of forcing with 500 h. Pa humidity in tropics (25 N-25 S) Ozone CO 2 DFCO 2 – DFO 3 Overlap of CO 2 and H 2 O bands causes CO 2 forcing to shift poleward where ice feedback enhances warming L. J. Mickley, Harvard

GCM SURFACE WARMING PATTERNS (o. C) FROM INCREASING TROPOSPHERIC OZONE OVER 20 th CENTURY – JJA SURFACE Tropospheric ozone Equivalent uniform CO 2 Difference (white = insignificant or high altitude) Largest warmings downwind of ozone source regions – ozone there is more effective than CO 2 L. J. Mickley, Harvard

USING SATELLITE MEASUREMENTS OF NO 2 AND HCHO COLUMNS (SOLAR BACKSCATTER) TO MAP NOx AND VOC EMISSIONS GOME satellite instrument Tropospheric NO 2 column ~ ENOx Tropospheric HCHO column ~ EVOC ~ 2 km BOUNDARY LAYER hn (420 nm) NO NO 2 O 3, RO 2 1 day Emission hn (340 nm) HCHO OH CO hours VOC HNO 3 Deposition NITROGEN OXIDES (NOx) Emission VOLATILE ORGANIC CARBON (VOC)

IN SCATTERING ATMOSPHERE, THE AIR MASS FACTOR (AMF) OF A SOLAR BACKSCATTER MEASUREMENT DEPENDS ON VERTICAL DISTRIBUTION OF THE GAS Illustrative retrieval of HCHO column at 340 nm geometric AMFG = 2. 08 actual AMF = 0. 71 Palmer et al. [2001] what GOME sees Instrument sensitivity w(s) (“scattering weight”) Vertical shape factor S(s) (normalized mixing ratio)

USE GLOBAL 3 -D MODEL DRIVEN BY ASSIMILATED METEOROLOGICAL DATA TO PROVIDE AMFs FOR EVERY SATELLITE VIEWING SCENE GEOS-CHEM GLOBAL 3 -D MODEL OF TROPOSPHERIC CHEMISTRY: provides S(s) LIDORT RAD. TRANSFER MODEL: provides w(s) SATELLITE DATA spectral fit SLANT COLUMN ADVANTAGES OF 3 -D MODEL APPROACH FOR COMPUTING AMFs AMF VERTICAL COLUMN • Best information applied to each scene • Consistency in comparing model and observed columns • Apply with any 3 -D model (recalculate AMFs using tabulated scattering weights)

HCHO COLUMNS FROM GOME OVER U. S. : July 1996 means Palmer et al. [2001] BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U. S. IN SUMMER GOME slant GOME vertical GEOS-CHEM vertical GEIA isoprene emissions R = 0. 83 Bias 14% Precision: 4 x 1015 cm-2 Difference

MAPPING OF ISOPRENE EMISSIONS FOR JULY 1996 BY SCALING OF GOME FORMALDEHYDE COLUMNS [Palmer et al. , 2002] GOME COMPARE TO… GEIA (IGAC inventory) BEIS 2 (official EPA inventory)

SEASONAL VARIABILITY OF HCHO COLUMNS (9/96 -8/97) - proxy for isoprene emissions Dorian Abbott (Harvard) GOME GEOS-CHEM MAR JUL APR AUG MAY SEP JUN OCT

GOME Tropospheric NO 2 r = 0. 75 bias=5% GEOS-CHEM Tropospheric NO 2 with a priori emissions (scaled GEIA) DJF 96 -97 MAM 1997 JJA 1997 R. V. Martin SON 1996 1015 molecules cm-2

OPTIMIZED NOx EMISSION INVENTORY FROM GOME R. V. Martin, Harvard A POSTERIORI A PRIORI

TRACE-P AIRCRAFT MISSION (March-April 2001): top-down constraints on Asian emissions Jacob et al. [2002] CTMs Chemical forecasts CO fossil and biofuel bottom-up emissions (D. R. Streets, ANL) CO aircraft observations (G. W. Sachse) MOPITT CO Observations (IR emission) Daily CO biomass burning emissions from AVHRR (C. L. Heald, Harvard)

MOPITT VALIDATION PROFILES DURING TRACE-P aircraft Aircraft w/ MOPITT av kernels MOPITT Averaging kernels

MEAN MOPITT CO COLUMN DATA DURING TRACE-P (Mar-Apr 2001) MOPITT GEOSCHEM model w/av kernels C. L. Heald, Harvard Difference

TROPOSPHERIC EMISSION SPECTROMETER (TES) Std. products Nadir Limb Temperature X X Surface temp. X Land surf. emissivity X O 3 X X CO X X H 2 O X X CH 4 X X NO X HNO 3 X • FTIR spectrometer (3. 3 - 15. 4 mm) to be launched on Aura satellite in 2004 (P. I. Reinhard Beer, JPL) • Field of view: 0. 5 x 8 km 2

TES NADIR RETRIEVAL OF CO About 3 pieces of information on vertical profile: ~ 1 more than MOPITT Averaging kernels CO retrieval “True” profile (GEOS-CHEM) a priori retrieved D. B. Jones, Harvard

WILLTES NADIR CO OBSERVATIONS IMPROVE OUR ABILITY TO QUANTIFY REGIONAL CO EMISSIONS? Assume that we know the true sources of CO 1 day GEOS-CHEM CO data (March 10, 2001) along TES orbit at 500 h. Pa From these sources of CO. Use GEOS-CHEM model to simulate “true” CO concentration field Sample model along TES orbit tracks, apply TES averaging kernels and noise to simulate retrieval of this CO field Make a priori (deliberately wrong) estimate of CO sources by applying errors to the “true” source Apply optimal estimation inverse method Obtain a posteriori sources and errors; How successful are we at finding the true source and reducing the error?

CONSTRUCTING THE MODEL ERROR COVARIANCE MATRIX USING TRACE-P OBSERVATIONS AND FORECASTS • • • Assume that difference between successive GEOS-CHEM CO forecasts during TRACE-P (to+48 h and to + 24 h) describes the covariant error structure (“NMC method”) Assume that mean bias in GEOS-CHEM model simulation of CO in TRACE-P is due to emission errors, and that residual relative error (RRE) is due to transport; use RRE to scale covariant error structure. Add representativeness error of 5% (small) based on subgrid variablity in TRACE-P aircraft data Forecast error (blue) and scaled model GEOS-CHEM error, transport error (red) TRACE-P simulation CO TRACE-P data D. B. Jones and P. I. Palmer, Harvard

MODEL TRANSPORT ERRORS (DIAGONALS OF ERROR COVARIANCE MATRIX) RELATIVE ERROR 850 h. Pa, March-April 2001 D. B. Jones, Harvard

REGIONAL CO SOURCES TO BE RETRIEVED IN TES SIMULATION EXERCISE EUFF NAFF ASFF AFBB CHEM SABB RWFF “True” Emissions (Tg/yr) NAFF: 121. 3 EUFF: 131. 1 ASFF: 258. 3 ASBB: 96. 0 AFBB: 193. 9 ASBB SABB: 96. 5 RWBB: 98. 0 RWFF: 149. 8 CHEM: 1125. 0 Total = 2270 Tg/yr D. B. Jones, Harvard RWBB • FF = Fossil Fuel + Biofuel • All sources include contributions from oxidation of VOCs; assume 50% error • OH is specified • Use a “tagged CO” method to estimate contribution from each source (Jacobian matrix) • Use linear inverse method to solve for annually-averaged emissions

INVERSION RESULTS (5 days of TES data, Mar 10 -15 2001) a priori a posteriori true • With Gaussian unbiased error statistics, TES is extremely powerful for constraining global sources of CO (because it provides lots of data!) • Still very powerful when using only data above 500 h. Pa • Realization of this potential rests on quantification of error statistics, particularly for the model transport error – a very difficult problem! D. B. Jones, Harvard