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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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**Applications of inverse modeling for understanding of**emissions and analysis of observations • RonaThompson, Andreas Stohl, IgnacioPisso, Cathrine Lund Myhre, et al.**Contentofpresentation**• FLEXPART transport model • Statisticalanalysisofobservation data: Methaneresultsfor Zeppelin station • Inversionbasics • Applications to halocarbonemissions • FLEXINVERT**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**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**Transport climatology (2001-2012)**• Footprintemissionsensitivitymapsaveraged for thefourseasons (upper panels) and normalized to annualmean DJF MAM JJA SON**Clusteranalysis**• Clusteranalysisoftrajectory output (Dorling et al., 1992) • Clusteranalysiscan be used to stratifymeasurement data according to transport pathway • Disadvantage: nogoodcontrolonthe ”shape” oftheclusters, noclearseparationofsources, noquantitativeinformationonemissions**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**”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**”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)**”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)**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**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**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)**Minimizationofthecostfunction**1. Term:minimizessquarederrors (model – observation)2. Term:Regularizationtermx, o ... Uncertainties in the a priori emissionsandtheobservations diag(a) … diagonal matrixwithelementsof a in thediagonal The uncertaintiesoftheemissionsandofthe „observations“ (actualmismatchbetween model andobservations) giveappropriateweightstothetwoterms Bayesianinversionbasics 2 1**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**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)**First application to East Asia**Emission sensitivity log(s m3 kg-1) Variable grid resolution**Application to East Asia (1)**Atmospheric observations in nested domain**Application to East Asia (2)**Prior emissions**Results (1)**Annual mean fluxes for 2009**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**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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