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Applications of inverse modeling for understanding of emissions and analysis of observations

Applications of inverse modeling for understanding of emissions and analysis of observations. Rona Thompson , Andreas Stohl , Ignacio Pisso , Cathrine Lund Myhre, et al. Content of presentation. FLEXPART transport model

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Applications of inverse modeling for understanding of emissions and analysis of observations

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  1. Applications of inverse modeling for understanding of emissions and analysis of observations • RonaThompson, Andreas Stohl, IgnacioPisso, Cathrine Lund Myhre, et al.

  2. Contentofpresentation • FLEXPART transport model • Statisticalanalysisofobservation data: Methaneresultsfor Zeppelin station • Inversionbasics • Applications to halocarbonemissions • FLEXINVERT

  3. The FLEXPART model Model descriptions in Atmospheric Environment, Boundary Layer Meteorology, Atmospheric Chemistry and Physics • Lagrangian particle dispersion model • Turbulence and convection parameterizations • Dry and wet deposition • Data input from ECMWF, GFS, MM5, WRF,… Used at probably >100 institutes from several dozen countries

  4. Modelset-up • Can be run both forward (from sources) or backward (from measurementstations) in time, whatever is more efficient • Here: Backward in time for 20 days • Model output: 4-dimensional emissionsensitivityfield (3 spacedimensionsplusdaysbackward in time) • Mixing ratio = emissionsensitivityfield x emissionfluxfield • http://zardoz.nilu.no/~andreas/STATIONS/ZEPPELIN/Zeppelin_201001/ECMWF/polar_column_t/Zeppelin_201001.polar_column_t_1.html

  5. Transport climatology (2001-2012) • Footprintemissionsensitivitymapsaveraged for thefourseasons (upper panels) and normalized to annualmean DJF MAM JJA SON

  6. Clusteranalysis • Clusteranalysisoftrajectory output (Dorling et al., 1992) • Clusteranalysiscan be used to stratifymeasurement data according to transport pathway • Disadvantage: nogoodcontrolonthe ”shape” oftheclusters, noclearseparationofsources, noquantitativeinformationonemissions

  7. Clusteranalysis (2001-2012) • Siberia and Central Asia = SCA, Western Arctic Ocean = WAO, Arctic Ocean = AO, Canada and Greenland = CGA, North Atlantic Ocean = NAO, East Asia and North Pacific = EA, Europe and North America = ENA, Siberia Northeast Asia = SNEA

  8. ”Ashbaughmethod” • Ashbaugh, 1983; Ashbaugh et al., 1985 • Define a grid • Associate M measurements with trajectories and calculate total gridded residence time ST from individual gridded residence times • where i, j are grid indices. Then, select subset with L=M/10 highest 10% measured concentrations • To identifysource/sink areas, calculate • Ifconcentration not associatedwith transport: RP(i,j) = 0.1 everywhere • Wherethere is a source: RP(i,j) > 0.1

  9. ”Ashbaughmethod” Detrended and deseasonalized 2001-2012 CH4 data Emission sensitivity Sp Emission sensitivity normalized by emission sensitivity for all data Rp Highest 10% Lowest 10% log(s m-3 kg-1)

  10. ”Ashbaughmethod” – localscale Detrended and deseasonalized 2001-2012 methane data Emission sensitivity Sp Emission sensitivity normalized by emission sensitivity for all data Rp Highest 10% Lowest 10% log(s m-3 kg-1)

  11. The inverse modeling problem • Needs a large setofatmosphericconcentrationmeasurements, ideally from manystations and/or campaigns • Want to usethese data to determinetheemissionsofthestudiedsubstance • Substancecan be subject to removalprocesses (e.g., aerosols) or considered (almost) passive onshorttime-scales (e.g., CH4) • To use inverse modelling, theunderlyingatmospheric transport model must be able to account for theseprocesses, i.e., it must be possible to establishquantitativesource-receptorrelationships • Systematicerrors in themodelwould(likely) cause bias in retrievedemissions

  12. Bayesianinversionbasics Aim: Determination oftheemissionsourcesfromairconcentrationmeasurements M... M x Nmatrixofemissionsensitivitiesfromtransport model calculations … oftencalledsource-receptor relationshipx ... Emission vector (Nemissionvalues)y ... Observation vector (Mobservations) Difficulty:poorlyconstrainedproblem; large spuriousemissionscaneasilyresulttosatisfyevensinglemeasurementdatapointsasthereisnopenaltytounrealisticemissionsSolution:Tikhonovregularization: ||x||2issmall

  13. Bayesianinversionbasics Slightreformulationif a priori informationisavailable yo ... Observation vector (Mobservations)xa ... A priori emissionvector (Nemissionvalues) Tikhonovregularization: ||x-xa||2issmall Weareseeking a solutionthathasboth minimal deviationfromthe a priori, and also minimizesthe model error (difference model minus observation)

  14. Minimizationofthecostfunction 1. Term:minimizessquarederrors (model – observation)2. Term:Regularizationtermx, o ... Uncertainties in the a priori emissionsandtheobservations diag(a) … diagonal matrixwithelementsof a in thediagonal The uncertaintiesoftheemissionsandofthe „observations“ (actualmismatchbetween model andobservations) giveappropriateweightstothetwoterms Bayesianinversionbasics 2 1

  15. Example: HFC-23 a by-product of HCFC-22 production Black dots: 3 measurement stations Top panel: emission distribution available a priori Bottom panel: inversion result Asterisks: known locations of HCFC-22 factories Halocarbon emissions in China

  16. New development by Rona Thompson: FLEXINVERT • Descriptionplanned for Geosci. Mod. Dev. • Planned as an open-sourcedevelopment • PartlybuildsonStohl et al. (2009) algorithm • Algorithmspecificallydeveloped for long-livedgreenhouse gases • Allowscouplingof 20-day FLEXPART backward runs with global model output • Modular, so can be adjusted to differentrequirements (CH4, CO2, N2O, SF6, etc.) • Allowsflexible time resolutionoftheemissions (e.g., monthly) • Facilitateserrorcorrelationsofthe prior emissions (spatially and temporally) • Calculatesposteriorfluxerrorcovariances (i.e., errors in emissions)

  17. First application to East Asia Emission sensitivity log(s m3 kg-1) Variable grid resolution

  18. Application to East Asia (1) Atmospheric observations in nested domain

  19. Application to East Asia (2) Prior emissions

  20. Results (1) Annual mean fluxes for 2009

  21. Results (2) 0.27 0.53 0.40 0.71 0.45 0.57 0.64 0.79 0.33 0.57 0.52 0.72 0.37 0.49 0.41 0.69 0.38 0.50 0.29 0.35 OBS PRIOR POST BKGND 0.12 0.26 0.27 0.71

  22. Conclusions In MOCA, wewilluse inverse modeling as a tool to analyze CH4 data usingstationnetwork (Zeppelin, Pallas, etc.) usingcampaign data Algorithm (almost) readybutwillneedfurtherdevelopment/testing Willalsoutilizeothermeansofanalyzing data

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