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

2. Rise in Tropospheric Ozone over the 20 th Century Observations at mountain sites in Europe [Marenco et al., 1994] Concentrations of O 3 have increased dramatically due to human activity

3. Tropospheric O 3 is an important climate forcing agent

4. Boundary layer (0-2km) NOx, RH, CO Continent 1 Continent 2 Ocean Free troposphere (Greenhouse gas) NO HO 2 OH NO 2 O3O3 hv Direct intercontinental transport of pollutants Global background O 3 O3O3 O3O3 Impact of human activity on background O 3 RH+OH  HCHO + products

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

6. Nadir-viewing SBUV instrument Pixel 320 x 40 km 2 10.30 am cross-equator time (globe in 3 days) O 3, NO 2, BrO, OClO, SO 2, HCHO, H 2 O, cloud Global Ozone Monitoring Experiment HCHO slant columns fitted: 337-356nm HCHO JULY 1997 Isoprene Biomass Burning

7. 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

8. HCHO columns – July 1996 r 2 = 0.7 n = 756 Bias = 11% Model:Observed HCHO columns [10 16 molec cm -2 ] GEOS-CHEM HCHO GOME HCHO [10 12 atoms C cm -2 s -1 ] GEIA isoprene emissions (7.1 Tg C) BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U.S. IN SUMMER

9. Using HCHO Columns to Map Isoprene Emissions isoprene HCHO hours OH hours Displacement/smearing length scale 10-100 km h, OH E ISOP = ___________ k HCHO  HCHO Yield ISOP  HCHO

10. Isoprene emissions (July 1996) [10 12 atom C cm -2 s -1 ] 50 (5.7 Tg C) 7.1 Tg C GEIA GOME

11. GOME isoprene emissions (July 1996) agree with surface measurements r 2 = 0.77 Bias -12% ppb 0 12 GOME r 2 = 0.53 Bias -3% GEIA

12. 10 16 molecules cm -2 °C 0 2.5 -2 2 GOME TT 95 INTERANNUAL VARIABILITY IN GOME HCHO COLUMNS (1995-2001) August Monthly Means & Temperature Anomaly TT 97 98 01 00 99 96 10 16 molecules cm -2 0 2.5 Abbot et al, 2003

13. CO inverse modeling Product of incomplete combustion; main sink is OH Lifetime ~1-3 months Relative abundance of observations Big discrepancy between Asian emission inventories and observations CMDL network for CO and CO 2 TRACE-P (Transport And Chemical Evolution over the Pacific) data can improve level of disaggregation of continental emissions

14. Observation vector y State vector (Emissions x) Modeling Overview x s = x a + (K T S y -1 K + S a -1 ) -1 K T S y -1 (y – Kx a ) y = Kx a +  Inverse model x = Annual emissions from Asia (Tg C/yr) y = TRACE-P CO (ppb) Forward model (GEOS-CHEM)

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

16. 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 s = retrieved state vector (the CO sources) X a = a priori estimate of the CO sources S a = error covariance of the a priori K = forward model operator S y = error covariance of observations = instrument error + model error + representativeness error Inverse Model (a.k.a. Weighted linear least-squares) Gain matrix Choice of x… - Aggregate anthropogenic emissions (colocated sources) - Aggregate Korea/Japan (coarse model grid resolution)

17. GEOS-CHEM Error specification is crucial S a Anthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) Biomass burning: 50% Chemistry (~CH 4 ): 25% S y Measurement accuracy (2%) Representation (14ppb or 25%) GEOS-CHEM 2x2.5  cell TRACE-P All latitudes (measured-model) /measured Altitude [km] Mean bias RRE Model error (y*RRE) 2 ~38ppb (>70% of total observation error)

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

19. [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 Large differences over NW Indian & SE Asia

20. Observed CO 2 :CO correlations are consistent with Chinese biospheric emissions of CO 2 40% too high Offshore China Over Japan Slope (> 840 mb) = 22 R 2 = 0.45 Slope (> 840 mb) = 51 R 2 = 0.76 Japan China Suntharalingam et al, 2003 Problem : Modeled Chinese CO 2 :CO slopes are 50% too large CO 2 /CO 50% CO increase from inverse model not enough Reconciliation with observations: decrease a CO 2 source with high CO 2 :CO biosphere

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

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

23. 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  State estimation (e.g., large-scale t-dep. source inversions)

24. Spare slides

25. GEOS-CHEM global 3D model: 101 Driven by DAO GEOS met data 2x2.5 o resolution/26 vertical levels O 3 -NO x -VOC chemistry GEIA isoprene emissions Aerosol scattering: AOD:O 3

26. 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

27. 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: CH 3 CCl 3, CCl 4, Halon 1211, CFCs 11, 12 (Blake, UCI)

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

29. 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) -103.1 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

30. PlatformmultipleERS-2TerraENVISATSpace station AuraTBD SensorTOMSGOMEMOPITTMODIS/ MISR SCIAMACHYMIPASSAGE-3TESOMIMLSCALIPSOOCO Launch197919951999 2002 2004 2005 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

31. [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

32. Launched March 2002 GOME + IR channels (CO, CH 4, CO 2 ) Nadir and limb viewing capabilities X-Y pixel resolution ~26x15 km (nadir) SCIAMACHY/Envisat instrument Initial comparisons look promising (8/23/02) C/o A. Maurellis Eastern Europe through Africa CO

33. vertical column = slant column /AMF satellite d  HCHO Earth Surface HCHO mixing ratio C(  )  lnI B /  Scattering weightsShape factor w(  ) = - 1/AMF G  lnI B /  Sigma coordinate (  ) S(  ) = C(  )  air /  HCHO AMF = AMF G  w(  ) S(  ) d  1 1 0 GEOS-CHEM

34. GOME GEOS-CHEM 10 16 molecules cm -2 SEASONAL VARIABILITY IN GOME HCHO COLUMNS (’97) 02.5 r>0.75 bias~20% MAR APRAUG MAY JUN SEP JUL OCT

35. GEOS-CHEM Isoprene “volcano” [10 16 molec cm -2 ] July 7 1996 July 20 1996 mm c/o Y-N. Lee, Brookhaven National Lab. Missouri Illinois Kansas [ppb] Aircraft data @ 350 m during July 1999 OZARKS SOS 1999 GOME Surface temperature [K] Slant column HCHO [10 16 mol cm -2 ] Temperature dependence of isoprene emission

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