Towards a more integrated approach to tropospheric chemistry Paul Palmer Division of Engineering and Applied Sciences, Harvard University Acknowledgements: Dorian Abbot, Kelly Chance, Colette Heald, Daniel Jacob, Dylan Jones, Loretta Mickley, Parvadha Suntharalingham, Glen Sachse (NASA LaRC)

Rise in Tropospheric Ozone over the 20th Century Concentrations of O3 have increased dramatically due to human activity Observations at mountain sites in Europe [Marenco et al., 1994]

Tropospheric O3 is an important climate forcing agent

Impact of human activity on background O3 hv O3 (Greenhouse gas) NO2 NO Global background O3 Free troposphere OH HO2 Boundary layer (0-2km) RH+OH HCHO + products Direct intercontinental transport of pollutants O3 O3 NOx, RH, CO Continent 1 Ocean Continent 2

Constructing a self-consistent representation of the atmosphere Global 3d chemistry transport model (GEOS-CHEM) GOME, MOPITT, SCIAMACHY TES, OMI

Global Ozone Monitoring Experiment Nadir-viewing SBUV instrument Pixel 320 x 40 km2 10.30 am cross-equator time (globe in 3 days) O3, NO2, BrO, OClO, SO2, HCHO, H2O, cloud HCHO slant columns fitted: 337-356nm Isoprene Biomass Burning HCHO JULY 1997

Isoprene dominates HCHO production over US during summer North Atlantic Regional Experiment 1997 Southern Oxidant Study 1995 measurements GEOS-CHEM model Altitude [km] Altitude [km] Defined background CH4 + OH [ppb] Continental outflow Surface source (mostly isoprene+OH)

r2 = 0.7 n = 756 Bias = 11% Model:Observed HCHO columns HCHO columns – July 1996 BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U.S. IN SUMMER [1012 atoms C cm-2 s-1] GEIA isoprene emissions (7.1 Tg C) r2 = 0.7 n = 756 Bias = 11% Model:Observed HCHO columns [1016molec cm-2] GEOS-CHEM HCHO GOME HCHO

Using HCHO Columns to Map Isoprene Emissions kHCHO HCHO EISOP = _______________ kISOPYieldISOPHCHO Displacement/smearing length scale 10-100 km hours hours HCHO OH h, OH isoprene

Isoprene emissions (July 1996) [1012 atom C cm-2 s-1] 5 (5.7 Tg C) Isoprene emissions (July 1996) GOME 7.1 Tg C GEIA

GOME isoprene emissions (July 1996) agree with surface measurements ppb 12 r2 = 0.53 Bias -3% GEIA r2 = 0.77 Bias -12% GOME

INTERANNUAL VARIABILITY IN August Monthly Means & Temperature Anomaly GOME HCHO COLUMNS (1995-2001) August Monthly Means & Temperature Anomaly GOME T GOME T 2.5 2 95 99 96 1016 molecules cm-2 °C 00 -2 97 01 98 2.5 1016 molecules cm-2 Abbot et al, 2003

CMDL network for CO and CO2 CO inverse modeling Product of incomplete combustion; main sink is OH Lifetime ~1-3 months Relative abundance of observations CMDL network for CO and CO2 Big discrepancy between Asian emission inventories and observations TRACE-P (Transport And Chemical Evolution over the Pacific) data can improve level of disaggregation of continental emissions

Modeling Overview y = Kxa +  Forward model (GEOS-CHEM) Inverse model State vector (Emissions x) Forward model (GEOS-CHEM) Observation vector y Inverse model xs = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa) x = Annual emissions from Asia (Tg C/yr) y = TRACE-P CO (ppb)

A priori Observation Global 3D CTM 2x2.5 deg resolution Biomass burning AVHRR (Heald/Logan) Fuel consumption (Streets) Observation A priori CO [ppb] Lat [deg] A priori emissions have a large negative bias in the boundary layer China Japan Southeast Asia Rest of World Global 3D CTM 2x2.5 deg resolution [OH] from full-chemistry model (CH3CCl3  = 6.3 years) Korea x = emissions from individual countries and individual processes (BB, BF, FF)

Inverse Model (a.k.a. Weighted linear least-squares) xs = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa) SS = (KTSy-1K + Sa-1)-1 Xs = retrieved state vector (the CO sources) Xa = a priori estimate of the CO sources Sa = error covariance of the a priori K = forward model operator Sy = error covariance of observations = instrument error + model error + representativeness error Gain matrix Choice of x… Aggregate anthropogenic emissions (colocated sources) Aggregate Korea/Japan (coarse model grid resolution)

Error specification is crucial GEOS-CHEM Error specification is crucial Sa Anthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) Biomass burning: 50% Chemistry (~CH4): 25% Sy Measurement accuracy (2%) Representation (14ppb or 25%) GEOS-CHEM 2x2.5 cell TRACE-P All latitudes (measured-model) /measured Altitude [km] Model error (y*RRE)2 ~38ppb (>70% of total observation error) Mean bias RRE

Best estimate is insensitive to inverse model assumptions 1-sigma uncertainty A priori A posteriori A posteriori emissions improve agreement with observations CO [ppb] Lat [deg] China (BB) China (BB) Southeast Asia Rest of World Korea + Japan China (anthropogenic) Observation A priori A posteriori

MOPITT shows low CO columns over Southeast Asia during TRACE-P GEOS-CHEM [1018 molec cm-2] MOPITT – GEOS-CHEM Large differences over NW Indian & SE Asia [1018 molec cm-2] c/o Heald, Emmons, Gille

Problem: Modeled Chinese CO2:CO slopes are 50% too large Observed CO2:CO correlations are consistent with Chinese biospheric emissions of CO2 40% too high Offshore China Over Japan Slope (> 840 mb) = 22 R2 = 0.45 Slope (> 840 mb) = 51 R2 = 0.76 Japan China Problem: Modeled Chinese CO2:CO slopes are 50% too large CO2/CO 50% CO increase from inverse model not enough Reconciliation with observations: decrease a CO2 source with high CO2:CO biosphere Suntharalingam et al, 2003

Future satellite missions The “A Train” 1:38 PM 1:30 PM 1:15 PM Aura Cloudsat CALIPSO Aqua OCO PARASOL OCO - CO2 column OMI - Cloud heights OMI & HIRLDS – Aerosols MLS& TES - H2O & temp profiles MLS & HIRDLS – Cirrus clouds CALPSO- Aerosol and cloud heights Cloudsat - cloud droplets PARASOL - aerosol and cloud polarization OCO - CO2 MODIS/ CERES IR Properties of Clouds AIRS Temperature and H2O Sounding Due for launch in 2004 IR, high res. Fourier spectrometer (3.3 - 15.4 mm) Has 2 viewing modes: nadir and limb Spatial resolution of nadir view = 8x5 km2 C/o M. Schoeberl

Concluding remarks Satellite observations are starting to revolutionize our understanding of chemistry in the lower atmosphere Proper validation of these data with in situ measurements is critical for their interpretation – need to integrate Correlations between multiple species provide untapped source of information on source inversions Future will be fully-coupled chemical data assimilation: Optimized, comprehensive 4-d view of the atmosphere

Spare slides

GEOS-CHEM global 3D model: 101 Driven by DAO GEOS met data 2x2.5o resolution/26 vertical levels O3-NOx-VOC chemistry GEIA isoprene emissions Aerosol scattering: AOD:O3

Main transport processes: TRACE-P data can improve level of disaggregation of continental emissions Feb – April 2001 Main transport processes: DEEP CONVECTION OROGRAPHIC LIFTING FRONTAL LIFTING warm air cold air cold front

Only a strong local source Back-trajectories of top 5% of observed values indicate local sources (removed from analysis) Proxy for OH Only a strong local source Selected halocarbons measured during TRACE-P: CH3CCl3, CCl4, Halon 1211, CFCs 11, 12 (Blake, UCI)

Potential of TES nadir observations of CO: An Observing System Simulation Experiment New Concept: test science objectives of satellite instruments before launch Objective: Determine whether nadir observations of CO from TES have enough information to reduce uncertainties in estimates of continental sources of CO Inverse model with realistic errors After 8 days of observations (operating half time) Jones et al, 2003

 CH3CCl3 : CO relationships  = value above latitudinal “background”

Large global & regional implications Eastern Asia estimates CH3CCl3,CCl4,CFCs 11 & 12): represents >80% of East Asia ODP (70% of total global ODP) 103.1 ODP Gg/yr (East Asia)  East Asia ODP = 70%  Global ODP = 20% Previous work 3.0 This work 2.3 Gg/yr 1.4 0.9 CCl4 CH3CCl3 CFC-11 CFC-12 Methodology has the potential to monitor magnitude and trends of emissions of a wide range of environmentally important gases

Satellite data will become integral to the study of tropospheric chemistry in the next decade Platform multiple ERS-2 Terra ENVISAT Space station Aura TBD Sensor TOMS GOME MOPITT MODIS/MISR SCIAMACHY MIPAS SAGE-3 TES OMI MLS CALIPSO OCO Launch 1979 1995 1999 2002 2004 2005 O3 N N/L L CO CO2 NO NO2 HNO3 CH4 HCHO SO2 BrO HCN aerosol N = Nadir L = Limb

MOPITT shows low CO columns over Southeast Asia during TRACE-P GEOS-CHEM [1018 molec cm-2] MOPITT – GEOS-CHEM Largest difference c/o Heald, Emmons, Gille [1018 molec cm-2]

SCIAMACHY/Envisat instrument Launched March 2002 GOME + IR channels (CO, CH4, CO2) Nadir and limb viewing capabilities X-Y pixel resolution ~26x15 km (nadir) CO Initial comparisons look promising (8/23/02) Eastern Europe through Africa C/o A. Maurellis

vertical column = slant column /AMF GEOS-CHEM satellite lnIB/ Sigma coordinate () dHCHO 1 Earth Surface HCHO mixing ratio C() Scattering weights Shape factor S() = C() air/HCHO w() = - 1/AMFG lnIB/ 1 AMF = AMFG  w() S() d

SEASONAL VARIABILITY IN GOME HCHO COLUMNS (’97) GEOS-CHEM GOME GEOS-CHEM MAR JUL APR AUG MAY SEP r>0.75 bias~20% JUN OCT 1016 molecules cm-2 2.5

Isoprene “volcano” GOME GEOS-CHEM SOS 1999 [1016 molec cm-2] Surface temperature [K] Slant column HCHO [1016 mol cm-2] Temperature dependence of isoprene emission c/o Y-N. Lee, Brookhaven National Lab. Missouri Illinois Kansas [ppb] Aircraft data @ 350 m during July 1999 OZARKS SOS 1999 July 7 1996 July 20 1996 [1016 molec cm-2] mm

CO, CO2, halocarbons, BC, + many others… Correlations between different species provide additional constraints to inverse problems, e.g. EX = (X:CO) ECO 2 km Fresh emissions Direct & indirect emissions CO, CO2, halocarbons, BC, + many others… Asian continent Western Pacific

Concluding remarks revolutionize validation interpretation Correlation assimilation: