Determining emissions of environmentally important gases using data from aircraft and satellites with Dorian Abbot, Arlene Fiore, Colette Heald, Daniel Jacob, Dylan Jones, Jennifer Logan, Loretta Mickley, Bob Yantosca Harvard University Randall Martin, Kelly Chance, Thomas Kurosu Harvard-Smithsonian Glen Sachse NASA Langley; Don Blake UCI; David Streets Argonne National Laboratory; Henry Fuelberg, Chris Kiley FSU Paul Palmer

Global 3d chemistry transport model Top-down and bottom-up emission inventories GOME, MOPITT, SCIAMACHY TES, OMI

CMDL network for CO and CO 2 CO inverse modeling Product of incomplete combustion; main sink is OH Lifetime ~1-3 months Relative abundance of observations Previous studies found a discrepancy between Asian emission inventories and observations

RH + OH  …  CO 1000s km Direct & indirect emissions CMDL site Many 100s km 10s km Increasing model transport error Limitation of remote data for inverse model calculations

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

Forward model (GEOS-CHEM) Inverse model P3B, DC8 observations y Emissions x FF BB BF Modeling Overview x s = x a + (K T S y -1 K + S a -1 ) -1 K T S y -1 (y – Kx a ) S S = (K T S y -1 K + S a -1 ) -1 y = Kx a +  DACOM (Sachse)

TRACE-P CO Emissions Inventories Biomass burning: Variability from observed daily firecount data (AVHRR) (Heald/Logan) Anthropogenic emissions for Y2K1 (fuel consumption) (Streets)

Tagged model CO simulation for TRACE-P China Japan Southeast Asia Korea Rest of World [OH] from full-chemistry model (CH 3 CCl 3  = 6.3 years) Global 3D CTM 2x2.5 deg resolution

GEOS-CHEM CO [ppb] Lat [deg] Observation A priori A priori emissions have a large negative bias in the boundary layer

x s = x a + (K T S y -1 K + S a -1 ) -1 K T S y -1 (y – Kx a ) S S = (K T S y -1 K + S a -1 ) -1 x = state vector (emissions) y = observation vector (TRACE-P CO, ppb) Choice of state vector… - Aggregate anthropogenic emissions - Aggregate Korea/Japan Inverse Model (a.k.a. Weighted linear least-squares) Gain matrix

o Emission uncertainties for Asia S a : Anthropogenic (D. Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) Biomass burning: 50%; Chemistry (largely CH 4 ): 25% o Observation uncertainty S y : Measurement accuracy (1%) Representation (14ppb or 25%): Model errors… GEOS-CHEM 2x2.5  cell TRACE-P Error specification is crucial Estimated: 1 sigma value about mean observed 2x2.5 value

All latitudes (measured-model) /measured Altitude [km] Mean bias RRE Model error: (y*RRE) 2 ~38ppb (>70% of total observation error) MODEL ERROR

Korea + Japan Southeast Asia China (BB) China (anthropogenic) A priori A posteriori 1-sigma uncertainty Rest of World Our best estimate is insensitive to inverse model assumptions

GEOS-CHEM CO [ppb] Lat [deg] Observation A priori A posteriori A posteriori emissions improve agreement with observations

[10 18 molec cm -2 ] MOPITT shows low CO columns over Southeast Asia during TRACE-P GEOS-CHEMMOPITT MOPITT – GEOS-CHEM [10 18 molec cm -2 ] c/o Heald, Emmons, Gille Largest difference

Next steps with CO…  Multi-species inversion will bring additional information: -CH 3 CN will bring information about biomass burning -CO 2 used to disaggregate emissions from Korea and Japan (CO 2 /CO)

Direct & indirect emissions Can calculate emissions of anthropogenic halocarbon X given the X:CO slope and CO emissions Western Pacific CO, species with   CO, +many other species Asian continent Blake group: CH 3 CCl 3, CCl 4, Halons 1211, 1301, 2402, CFCs 11, 12, 113, 114, km Fresh emissions

Back-trajectories of top 5% of observed values indicate local sources Proxy for OH Only a strong local source

 CO:  CH 3 CCl 3 relationships  = value above “background”

Gg/yr CH 3 CCl 3 CCl 4 CFC-11 CFC-12  CH 3 CCl 3,CCl 4,CFCs 11 & 12): -represents >80% of East Asia ODP (70% of total global ODP) ODP Gg/yr (East Asia) -  East Asia ODP = 70% -  Global ODP = 20% Eastern Asia estimates Large global & regional implications  Methodology has the potential to monitor magnitude and trends of emissions of a wide range of environmentally important gases Previous work This work  0.9  1.4  2.3  3.0

PlatformmultipleERS-2TerraENVISATSpace station AuraTBD SensorTOMSGOMEMOPITTMODIS/ MISR SCIAMACHYMIPASSAGE-3TESOMIMLSCALIPSOOCO Launch O3O3 NN/LLL NL CONN/LL CO 2 N/LN NOL NO 2 NN/LN HNO 3 LL CH 4 N/LN HCHONN/LN SO 2 NN/LN BrONN/LN HCNL aerosolNNNLNN N = Nadir L = Limb Satellite data will become integral to the study of tropospheric chemistry in the next decade

Nadir-viewing SBUV instrument Pixel 320 x 40 km am cross-equator time Global coverage in 3 days Global Ozone Monitoring Experiment HCHO slant columns fitted: nm - fitting uncertainty 4 x molec cm- 2 HCHO JULY 1997 Isoprene Biomass Burning

Isoprene dominates HCHO production over US during summer Southern Oxidant Study 1995 North Atlantic Regional Experiment 1997 [ppb] Surface source (mostly isoprene+OH) Continental outflow Altitude [km] measurements GEOS-CHEM model Defined background CH 4 + OH

Using HCHO Columns to Map Isoprene Emissions isoprene HCHO hours OH hours Displacement/smearing length scale km h, OH E ISOP = ___________ k HCHO  HCHO Yield ISOP  HCHO

[10 16 molec cm -2 ] GEOS-CHEM GOME r 2 = 0.7 n = 756 Bias = 11% HCHO columns – July 1996 Model:Observed HCHO columns GEIA isoprene emissions [10 12 atoms C cm -2 s -1 ] BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U.S. IN SUMMER

[10 12 atom C cm -2 s -1 ] GOME isoprene emissions (July 1996) agree with surface measurements r 2 = 0.77 Bias -12% 50

GEOS-CHEMGOME GEOS-CHEM molecules cm -2 SEASONAL VARIABILITY IN GOME HCHO COLUMNS 02.5 r>0.75 bias~20% MAR APRAUG MAY JUN SEP JUL OCT

Interannual Variability ~30% molecules cm -2 °C GOME TT 95 INTERANNUAL VARIABILITY IN GOME HCHO COLUMNS ( ) August Monthly Means & Temperature Anomaly TT