1 Applications of inverse modeling for understanding of emissions and analysis of observations Rona Thompson, Andreas Stohl, Ignacio Pisso, Cathrine Lund Myhre, et al.

2 Content of presentation FLEXPART transport model Statistical analysis of observation data: Methane results for Zeppelin station Inversion basics Applications to halocarbon emissions FLEXINVERT

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

Can be run both forward (from sources) or backward (from measurement stations) in time, whatever is more efficient Here: Backward in time for 20 days Model output: 4-dimensional emission sensitivity field (3 space dimensions plus days backward in time) Mixing ratio = emission sensitivity field x emission flux field column_t/Zeppelin_ polar_column_t_1.html Model set-up

Footprint emission sensitivity maps averaged for the four seasons (upper panels) and normalized to annual mean Transport climatology ( ) DJFMAMJJASON

6 Cluster analysis Cluster analysis of trajectory output (Dorling et al., 1992) Cluster analysis can be used to stratify measurement data according to transport pathway Disadvantage: no good control on the ”shape” of the clusters, no clear separation of sources, no quantitative information on emissions

7 Cluster analysis ( ) 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 ”Ashbaugh method” Ashbaugh, 1983; Ashbaugh et al., 1985 Define a grid Associate M measurements with trajectories and calculate total gridded residence time S T from individual gridded residence times where i, j are grid indices. Then, select subset with L=M/10 highest 10% measured concentrations To identify source/sink areas, calculate If concentration not associated with transport: R P (i,j) = 0.1 everywhere Where there is a source: R P (i,j) > 0.1

9 ”Ashbaugh method” Detrended and deseasonalized CH 4 data Emission sensitivity S p Emission sensitivity normalized by emission sensitivity for all data R p log(s m -3 kg -1 ) Highest 10%Lowest 10%

10 ”Ashbaugh method” – local scale Detrended and deseasonalized methane data Emission sensitivity S p Emission sensitivity normalized by emission sensitivity for all data R p log(s m -3 kg -1 ) Highest 10%Lowest 10%

11 The inverse modeling problem Needs a large set of atmospheric concentration measurements, ideally from many stations and/or campaigns Want to use these data to determine the emissions of the studied substance Substance can be subject to removal processes (e.g., aerosols) or considered (almost) passive on short time-scales (e.g., CH 4 ) To use inverse modelling, the underlying atmospheric transport model must be able to account for these processes, i.e., it must be possible to establish quantitative source-receptor relationships Systematic errors in the model would (likely) cause bias in retrieved emissions

Aim: Determination of the emission sources from air concentration measurements M... M x N matrix of emission sensitivities from transport model calculations …often called source-receptor relationship x... Emission vector (N emission values) y... Observation vector (M observations) Difficulty: poorly constrained problem; large spurious emissions can easily result to satisfy even single measurement data points as there is no penalty to unrealistic emissions Solution: Tikhonov regularization: ||x|| 2 is small Bayesian inversion basics

Slight reformulation if a priori information is available y o... Observation vector (M observations) x a... A priori emission vector (N emission values) Tikhonov regularization: ||x-x a || 2 is small We are seeking a solution that has both minimal deviation from the a priori, and also minimizes the model error (difference model minus observation) Bayesian inversion basics

1 2 Minimization of the cost function 1. Term: minimizes squared errors (model – observation) 2. Term: Regularization term  x,  o... Uncertainties in the a priori emissions and the observations diag(a) … diagonal matrix with elements of a in the diagonal The uncertainties of the emissions and of the „observations“ (actual mismatch between model and observations) give appropriate weights to the two terms Bayesian inversion basics

Halocarbon emissions in China 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

New development by Rona Thompson: FLEXINVERT Description planned for Geosci. Mod. Dev. Planned as an open-source development Partly builds on Stohl et al. (2009) algorithm Algorithm specifically developed for long-lived greenhouse gases Allows coupling of 20-day FLEXPART backward runs with global model output Modular, so can be adjusted to different requirements (CH 4, CO 2, N 2 O, SF 6, etc.) Allows flexible time resolution of the emissions (e.g., monthly) Facilitates error correlations of the prior emissions (spatially and temporally) Calculates posterior flux error covariances (i.e., errors in emissions)

First application to East Asia Emission sensitivity log(s m 3 kg -1 ) Variable grid resolution

Application to East Asia (1) Atmospheric observations in nested domain InstituteTypeNo. sites CAMSin-situ (CRDS)4 NIESin-situ (GC-FID)2 NOAAflask (GC-FID)4 JMAin-situ (NDIR)3 KMAin-situ (GC-FID)1 NIERin-situ (GC-FID)1 TOTAL15

Application to East Asia (2) SourceDatasetTotal (TgCH 4 y -1 ) anthropogenic - rice cultivation - waste - fuels - animal agriculture EDGAR natural wetlandsLPJ DGVM model175 biomass burningGFED-313 geologicalbased on Etiope et al termitesSanderson et al wild animalsOlson et al soilsRidgewell et al oceanLambert and Schmidt, TOTAL577 Prior emissions

Results (1) China a priori:61.6 TgCH 4 /y China a posteriori:59.6 TgCH 4 /y Annual mean fluxes for 2009

Results (2) OBS PRIOR POST BKGND

Conclusions In MOCA, we will use inverse modeling as a tool to analyze CH 4 data using station network (Zeppelin, Pallas, etc.) using campaign data Algorithm (almost) ready but will need further development/testing Will also utilize other means of analyzing data