1. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Investigation of global budgets of reactive trace gases by synergistic use of model simulations and satellite observations Maria Kanakidou Environmental Chemical Processes Laboratory Department of Chemistry University of Crete, Heraklion, Greece mariak@chemistry.uoc.gr

2. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Altitude of NO x emissions. Image: AT2-ELS Need to observe & model a chemically complex atmosphere To understand changes in the chemical composition of the atmosphere and related AQ & climate impacts Why? Challenge complexity

3. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Ravishankara Science, 1997  improving knowledge, modeling & observing capabilities continuous interactions

4. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Highlights on synergistic use of satellite observations and 3-d CTMs Trace gases –NO 2 –HCHO –CHOCHO

5. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Model Synergistic use of models and observations Meteorology Emissions Transformation/ Chemistry output Observations - Satellite - others comparison transport feedback Input Various parameters Satellite maps

6. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr To consider when comparing satellite observations to model results Over-pass time (diurnal profiles) Spatial resolution /lifetime of compounds Errors & uncertainties in retrievals Errors & uncertainties in modeling  effort to reduce differences Synergistic use of observations and models Forward modeling: emissions  concent. Inverse modeling: emissions  concent.

7. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Why Inverse emission modeling? To verify and improve available estimates of atmospheric pollutants emissions To improve performance of atmospheric models, especially in diagnostic studies To verify control strategies for atmospheric emissions To develop a general observation-based methodology for estimating parameters of the atmosphere that cannot be observed directly Alternative to bottom-up construction of emission inventories Modified from Beekmann GEIA summer school 2007

8. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Ground based networks  high temporal frequency  small instrumental error  poor spatial coverage  representativity problems Satellite observations  low temporal frequency  larger instrumental error  poor vertical resolution  large spatial coverage  defined horizontal representativity WDGCC (World Data Centre for Greenhouse Gases) Modified from Beekmann GEIA summer school 2007 Observations + Intensive campaigns + aircraft data

9. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Top-down emission estimates Adjoint model – –addresses non linearity –handles large number of control parameters. example for short-lived species: Combined inversion for CO and NOx Muller and Stavrakou, ACP, 2005 bringing the model predictions as close as possible to a set of observations, by varying a set of control parameters. –CO, NOx anthropogenic emissions –CO/VOC, NOx natural emissions –CO, NOx dep. velocity –CO, NOx vegetation fires emission factors –Forest fires, savana fires burnt biomass

10. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr The inversion is constrained by: NOAA/CMDL CO mixing ratios Ground-based FTIR CO vertical column abundances GOME tropospheric NO 2 columns (Univ. Bremen) Simultaneous optimization of the total annual CO, BVOC & NOx emissions over large regions (39 flux parameters) chemical feedbacks via the adjoint constant seasonality of the sources Error matrices assumed diagonal Müller and Stavrakou, ACP, 2005 Big-region inversion of the 1997 CO, BVOC and NOx emissions – combined inversion

11. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr a : prior fluxes B : CO&NOx optimized using CMDL,CO columns and GOME B1/B2 : halved/doubled errors on the control variables C : as B, with a constraint on the methane lifetime (9.6 yrs) Optimization results for 1997 NOx fluxes Müller and Stavrakou, ACP, 2005

12. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Top-down emission estimates Linearisation of relationship between emissions and trace constituent columns Concentration = factor x Emissions Use of bottom-up emissions as a priori Examples: HCHO/ isoprene: Palmer et al., JGR, 2003 NO 2 global : Martin et al., JGR, 2003 NO 2 over Europe: Konovalov et al., ACP, 2006

13. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr aldehydes proxy for VOC emissions C5H8C5H8C5H8C5H8 C 10 H 16 C2H2C2H2C2H2C2H2 C2H4C2H4C2H4C2H4 C3H6C3H6C3H6C3H6 NO NO 2 RO 2 OH O3O3 NO 3 Strong chemical coupling C 15 H 24 transport Fast conversion

14. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Using HCHO to constraint VOC emissions (isoprene) To simplify: HCHO is mainly controlled by CH 4 levels (background) and isoprene emissions Palmer et al., JGR, 2003 red data points: HCHO columns from sources other than isoprene NOx dependent HCHO yields Linearise emissions and Concentration Column= α x Emissions Emissions =f(HCHO column)

15. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Palmer et al., JGR, 2003 Emissions =f(HCHO column)

16. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Guenther et al., MEGAN model ACP, 2006 Missing HCHO from AVOC Terpenes & sesquiterpenes Need for an additional VOC tracer?

17. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Formaldehyde and Glyoxal from space Wittrock et al., GRL, 33, L16804 doi:10.1029/2006GL026310, 2006 CHOCHO/ HCHO

18. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr 6º/3 o 4º/2 o 31 hybrid layers, up to 10 hPa  Meteorology input from ECMWF re- analysis project data-archive: 6 hourly data of geopotential height, temperature, specific humidity and horizontal winds. (http://www.ecmwf.int/data/era.html)http://www.ecmwf.int/data/era.html  Emissions from POET Granier et al (2005)  The model considers the sulphur and ammonia chemistry and the oxidation of C 1 -C 5 Volatile Organic Compounds (VOC) including isoprene, organic acids & highly simplified terpenes and aromatic chemistry.  On-line gas-phase chemistry & secondary aerosol formation & primary carbonaceous particles. primary carbonaceous particles.  Heterogeneous reactions on particles  Glyoxal formation from isoprene, ethene, propene, acetylene, BTX  Glyoxal destruction by OH, photolysis, NO 3, deposition (dry + wet) TM4 :global 3-d CTM

19. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr TM4 : 3-d global chemistry transport model results Only photochemical source of glyoxal Myriokefalitakis, Vrekoussis, Tsigaridis, Wittrock, Richter, Bruhl, Volkamer, Burrows, Kanakidou, submitted to ACPD, 2007

20. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr What can we learn from comparisons between models and satellite observations ? TM4/SCIA =0.62, r =0.99 Only above continents TM4/SCIA =0.43, r =0.95 All points Significant scatter over the oceans may indicate the existence of primary or secondary tropical sources of CHOCHO over the oceans that are neglected or underestimated by the model Myriokefalitakis et al., ACPD submitted

21. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr What can we learn from comparisons between models and satellite observations ? TM4_2x3/SCIA =0.62, r =0.99 TM4_4x6/SCIA =0.60, r =0.95 TM4 underestimates the SCIAMACHY observations in 2005 anthropogenic contribution to the photochemical formation of glyoxal improves comparison Above continents TM4_4x6no_anth/SCIA =0.53, r =0.95 Myriokefalitakis et al., ACPD submitted

22. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Using TM4 to analyse the annual signal Myriokefalitakis et al., ACPD submitted All photochemical sources biogenics anthropogenic Biomass burning

23. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Seasonal patterns observations/ TM4 simulations and source contribution biogenic anthropogenic All sources SCIAMACHY Myriokefalitakis et al., ACPD submitted

24. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Glyoxal global budget Chemical Production: 56 Tg/y (70% BVOC, 17% C 2 H 2, 11% aromatics, 2% C 2 H 4, C 3 H 6 ) Destruction by OH: 12 Tg/y Photodissociation: 36 Tg/y Dry deposition: 3 Tg/y Wet deposition: 5 Tg/y Lifetime : 3 h (shorter if anthropogenic contribution is neglected) Burden : 19 Gg Myriokefalitakis et al., ACPD submitted

25. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr observations from space could provide a powerful tool to evaluate VOC emissions Myriokefalitakis et al., ACPD submitted No anthropogenic CHOCHO/HCHO_satellite above the 30 most populated areas= 0.037±0.014 Above biogenic sources = 0.050±0.025 synergistic use of glyoxal and formaldehyde All sources Anthropogenic contribution

26. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Perspectives use of the CHOCHO/HCHO ratio to evaluate anthropogenic and biogenic VOC emissions How important can be primary BB emissions of glyoxal?  implications Understand and analyse the oceanic signal of glyoxal. Analyse interannual patterns/emissions Brain thinking Distinguish primary from secondary emissions

27. Remote Sensing & Emission Inventories: Best of two worlds Maria Kanakidou ECPL Univ of Crete mariak@chemistry.uoc.gr Thank you for your attention Myriokefalitakis, Vrekoussis, Tsigaridis,Wittrock, Richter, Bruhl, Volkamer, Burrows, Beekmann