1 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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2 Content of presentation FLEXPART transport model Statistical
analysis of observation data: Methane results for Zeppelin station
Inversion basics Applications to halocarbon emissions
FLEXINVERT
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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
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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
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Footprint emission sensitivity maps averaged for the four
seasons (upper panels) and normalized to annual mean Transport
climatology (2001-2012) DJFMAMJJASON
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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
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7 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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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
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9 Ashbaugh method Detrended and deseasonalized 2001-2012 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%
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10 Ashbaugh method local scale Detrended and deseasonalized
2001-2012 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%
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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
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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
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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
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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
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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
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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)
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First application to East Asia Emission sensitivity log(s m 3
kg -1 ) Variable grid resolution
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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
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Application to East Asia (2) SourceDatasetTotal (TgCH 4 y -1 )
anthropogenic - rice cultivation - waste - fuels - animal
agriculture EDGAR-4.2331 natural wetlandsLPJ DGVM model175 biomass
burningGFED-313 geologicalbased on Etiope et al. 200855
termitesSanderson et al. 199619 wild animalsOlson et al. 19975
soilsRidgewell et al. 1999-38 oceanLambert and Schmidt, 199317
TOTAL577 Prior emissions
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Results (1) China a priori:61.6 TgCH 4 /y China a
posteriori:59.6 TgCH 4 /y Annual mean fluxes for 2009
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