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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.

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Content of presentationFLEXPART transport model

Statistical analysis of observation data: Methane results for Zeppelin station

Inversion basics

Applications to halocarbon emissions

FLEXINVERT

Lagrangian particle dispersion modelTurbulence and convection parameterizationsDry and wet depositionData 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

http://zardoz.nilu.no/~andreas/STATIONS/ZEPPELIN/Zeppelin_201001/ECMWF/polar_column_t/Zeppelin_201001.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 (2001-2012)

DJF MAM JJA SON

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Cluster analysisCluster 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

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Cluster analysis (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

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”Ashbaugh method”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 identify source/sink areas, calculate

If concentration not associated with transport: RP(i,j) = 0.1 everywhere

Where there is a source: RP(i,j) > 0.1

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”Ashbaugh method”Detrended and deseasonalized 2001-2012 CH4 data

Emission sensitivity

Sp

Emission sensitivity normalized by emission

sensitivity for all data

Rp

log(s m-3 kg-1)Highest 10% Lowest 10%

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”Ashbaugh method” – local scaleDetrended and deseasonalized 2001-2012 methane data

Emission sensitivity

Sp

Emission sensitivity normalized by emission

sensitivity for all data

Rp

log(s m-3 kg-1)Highest 10% Lowest 10%

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The inverse modeling problemNeeds 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., CH4)

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 relationshipx ... 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

yo ... Observation vector (M observations)xa ... A priori emission vector (N emission values)

Tikhonov regularization: ||x-xa||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

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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 observationsdiag(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-23a 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: FLEXINVERTDescription 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 (CH4, CO2, N2O, SF6, 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 AsiaEmission sensitivity log(s m3 kg-1)

Variable grid resolution

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

Institute Type No. sites

CAMS in-situ (CRDS) 4

NIES in-situ (GC-FID) 2

NOAA flask (GC-FID) 4

JMA in-situ (NDIR) 3

KMA in-situ (GC-FID) 1

NIER in-situ (GC-FID) 1

TOTAL 15

Application to East Asia (2)

Source Dataset Total (TgCH4 y-1)

anthropogenic - rice cultivation - waste - fuels - animal agriculture

EDGAR-4.2 331

natural wetlands LPJ DGVM model 175

biomass burning GFED-3 13

geological based on Etiope et al. 2008 55

termites Sanderson et al. 1996 19

wild animals Olson et al. 1997 5

soils Ridgewell et al. 1999 -38

ocean Lambert and Schmidt, 1993 17

TOTAL 577

Prior emissions

Results (1)

China a priori: 61.6 TgCH4/y

China a posteriori: 59.6 TgCH4/y

Annual mean fluxes for 2009

Results (2)

OBSPRIORPOSTBKGND

0.270.53

0.450.57

0.330.57

0.370.49

0.380.50

0.120.26

0.400.71

0.640.79

0.520.72

0.410.69

0.290.35

0.270.71

ConclusionsIn MOCA, we will use inverse modeling as a tool to analyze CH4 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