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Developments in regional inverse modellingR. L. Thompson and A. StohlNILU, Norsk Institutt for Luftforskning, Norway
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Outline• Introduction to FLEXINVERT• Case study: methane fluxes in the high latitudes
– data selection criteria– transport uncertainties– optimized CH4 fluxes
• Developments for FLEXINVERT-CO2
– planned inversion framework
• Summary & Conclusions
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Overview of FLEXINVERT
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Lagrangian model,FLEXPARTinputdatafrome.g.ECMWF
fluxsensitivities
H
initialcond.sensitivities
Hini
priorfluxesx0
initialmixingratio fields
yini
observedmixing ratiofromaircraft,
ships,groundsites
y
Optimizefluxesargminx [(x – x0)TB-1(x – x0)+(Hx – y)TR-1(Hx – y)]
optimizedmixing ratio
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Definition of forward modelMixing ratios are modeled according to:
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hi, t , ′tout xi, ′t
out
hi, t , ′′tini yi, ′′t
ini hi, t , ′tnest xi, ′t
nest
yi, t
nested domain
global domain
ymod = Hnestxnest +Houtxout +Hiniyini
Thompson and Stohl, GMD, 2014
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Background mixing ratiosTo determine the background contribution (Hiniyini):Couple to global 3D mixing ratio fields in time domain
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hi,nini = ∂y
∂yi,n=ni, j ,nJ j
ni,j,n no. particles in grid-cellJj total no. particles in trajectoryyi,n mixing ratio from global model
Thompson and Stohl, GMD, 2014
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Background mixing ratiosMonthly 2D fields from bivariate interpolation of NOAA flask data, plus model estimate of stratospheric mixing ratio (yini)
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180°W 120°W 60°W 0° 60°E 120°E 180°E90°S
60°S
30°S
0°
30°N
60°N
90°N
1700
1750
1800
1850
1900
1950
2000
180°W 120°W 60°W 0° 60°E 120°E 180°E90°S
60°S
30°S
0°
30°N
60°N
90°N
0
20
40
60
80
100
a)
b)
yini
Hini
background mixing ratio:ybg = Hiniyini + Houtxout
1800
1900
2000
2100
2200
2300
CH
4 (pp
b)
IGR
01 02 03 04 05 06 07 08 09 10 11 12
OBSPRIORBKGND
Thompson and Stohl, GMD, 2014
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FLEXINVERT spatial grid
10
12
14
16
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50°N58°N
66°N72°N
80°N
Optimize grid based on flux sensitivities and, optionally, prior fluxes by aggregating grid cells until meet threshold
e.g. flux sensitivities, units log(s m3 kg-1) e.g. optimized inversion grid
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Methane in the high northern latitudes
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Observations
ZEP
TIK
TERZOT
PAL
CHL
BAL
CBA
LLBETL
MHD
FSD
ESP
CDL
KRSIGR
NOYDEM
AZV
VGN
YAKCHM
flaskin situ
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Networks:• JR-STATION (7 sites)• EC (7 sites)• NOAA (3 sites)
Stations:• Pallas, FMI• Zeppelin, NILU• Mace Head, AGAGE• Teriberka, MGO• Zotto, MPI-BGC
Total of 17 in-situ & 5 flask-sampling sites used to constrain fluxes
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Data selection criteriaProblem modeling PBL in winter at continental sites:filter data using observed temp. gradient and wind speed criteria
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2009.00 2009.02 2009.04 2009.06 2009.08
2000
2200
2400
2600
CH4 (
ppb)
OBSECMWFFNLPBL_TEST
2009.00 2009.02 2009.04 2009.06 2009.08−35−30−25−20−15−10−5
Tem
pera
ture
(°C) upper
lower
2009.00 2009.02 2009.04 2009.06 2009.080
5
10
15
20
Win
dspe
ed (m
/s)
2009.50 2009.52 2009.54 2009.56 2009.58
2000
2200
2400
2600
CH
4 (pp
b)
OBSECMWFFNLPBL_TEST
2009.50 2009.52 2009.54 2009.56 2009.5805
1015202530
Tem
pera
ture
(°C
) upperlower
2009.50 2009.52 2009.54 2009.56 2009.580
5
10
15
20
Win
dspe
ed (m
/s)
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Transport uncertainties
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Estimate transport uncertainties using proxy of difference between simulations with ECMWF EI versus NCEP FNL
0
10
20
30
40
unce
rtain
ty (p
pb)
winter
0
10
20
30
40
unce
rtain
ty (p
pb)
summer
Errors calculated 3-hourly for 1 year – use daily mean errors each month for all inversion years
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Prior flux estimatesSource category Dataset Total (Tg y-1)
Natural Wetlands LPX-Bern 202
Termites Sanderson et al. 1996 19
Wild animals Houweling et al. 1999 5
Ocean Lambert et al. 1993 17
Soil uptake LPX-Bern -49
Biomass Burning GFED-3.1 13
Anthropogenic Fuel and Industry EDGAR-4.2FT2010 150
Enteric fermentation EDGAR-4.2FT2010 101
Waste EDGAR-4.2FT2010 61
Rice cultivation LPX-Bern 36
Global total 556
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Modeled mixing ratios
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ZEP
TIK
TERZOT
PAL
CHL
BAL
CBA
LLBETL
MHD
FSD
ESP
CDL
KRSIGR
NOYDEM
AZV
VGN
YAKCHM
flaskin situ
1800
1900
2000
2100
2200
2300
CH
4 (pp
b)
FSD
1800
1900
2000
2100
2200
2300
CH
4 (pp
b)
LLB
1800
1900
2000
2100
2200
2300
CH
4 (pp
b)
ETL
01 02 03 04 05 06 07 08 09 10 11 12
1800
1900
2000
2100
2200
2300
CH
4 (pp
b)
IGR
1800
1900
2000
2100
2200
2300
CH
4 (pp
b)
KRS
1800
1900
2000
2100
2200
2300
CH
4 (pp
b)
PAL
01 02 03 04 05 06 07 08 09 10 11 12
OBSPRIORPOSTBKGND
Comparison of observed, prior and posterior mixing ratios at selected sites for 2009
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Optimized fluxes
0.00
0.05
0.10
0.15
0.20
DJF MAM JJA SON
−0.10
−0.05
0.00
0.05
0.10
gCH
4 m-2 d
ay-1
gCH
4 m-2 d
ay-1
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Seasonal mean: posterior and difference (posterior – prior)
Thompson et al., in prep., 2016
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Seasonal flux variability
2 4 6 8 10 120
20406080
100120
Tg C
H 4 y−
1
North Eurasia
2 4 6 8 10 120
1020304050
Tg C
H 4 y−
1
WSL
2 4 6 8 10 120
5
10
15
20
Tg C
H 4 y−
1
HBL
2 4 6 8 10 120
5
10
15
20Tg
CH 4
y−1
Alberta
2 4 6 8 10 120
1020304050
Tg C
H 4 y−
1
North America
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Thompson et al., in prep., 2016
case 1: prior wetlands LPX-Berncase 2: prior wetlands LPJ-DGVMsolid lines: posteriordashed lines: priorgrey-shading: uncertainty
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Inter-annual variability
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2006 2008 2010 2012 2014−20
−10
0
10
20
Tg C
H 4 y−
1
North Eurasia
2006 2008 2010 2012 2014−20
−10
0
10
20
Tg C
H 4 y−
1
WSL2006 2008 2010 2012 2014
−10
−5
0
5
10
Tg C
H 4 y−
1
North America
2006 2008 2010 2012 2014−4
−2
0
2
4
Tg C
H 4 y−
1
HBL
2006 2008 2010 2012 2014−4
−2
0
2
4Tg
CH 4
y−1
Alberta
Thompson et al., in prep., 2016
case 1: prior wetlands LPX-Berncase 2: prior wetlands LPJ-DGVMsolid lines: posteriordashed lines: priorshading: uncertainty
p-value < 0.01 p-value < 0.01
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Comparison to other estimates
N. America HBL N. Eurasia WSL
Prior this study 9.5 ± 5.1 2.9 ± 2.0 44.4 ± 12.5 11.0 ± 5.0
Posterior this study 16.6 ± 0.9 2.7 ± 0.14 55.2 ± 2.1 19.9 ± 0.4
Bergamaschi et al. 2013 12.2 3.6 30.4 11.6
Bruhwiler et al. 2014 8.1 2.7 49.7 18.4
Berchet et al. 2015 5 – 28
Miller et al. 2014 21.3 ± 1.6 2.4 ± 0.32
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Shown for 2005 – 2010 (overlapping period). Units TgCH4 y-1.
Thompson et al., in prep., 2016
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Developments for CO2 inversions
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FLEXINVERT-CO2
Statistical model of fluxes:optimize land biosphere fluxes, fixed ocean and fossil fuel fluxes
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Land Bio. flux
Fossil fuel flux
Ocean flux
Statistical flux model
FLEXPART emission sensitivities
Modelled CO2concentrations
Observed CO2concentrations
Model versus observation comparison
Forward run
Optimization
parametersFixed component
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Statistical modelStatistical model based on Rödenbeck et al. 2005:
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f (x, y, t) = f fix, i (x, y, t)+αi fsh, i (x, y, t)mt=1
Nt
∑ gmt, itime (t)gms, i
space(x, y)pmt, ms, ims=1
Ns
∑"
#$
%
&'
i=1
N
∑
fixedfluxes(ocean,ff.)
optimizedfluxes(landbiosphere)
spatio-temporaldecomposition
parameters
2J(p) = (p − pb )TB−1(p − pb )+ (H f(p)− y)
TR−1(H f(p)− y)
Minimize cost function J(p) using gradient method:
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Summary & ConclusionsMethane in the high northern latitudes• Total flux north 50°N of 81 TgCH4 y-1 or ~15% of global total• Anthropogenic emissions in Alberta significantly
underestimated by inventories, e.g. EDGAR-v4.2 • Wetlands emissions in HBL comparable to LPX-Bern and
other inversion estimates• Anthropogenic emissions in WSL likely underestimated in
EDGAR-v4.2
Developments for CO2 inversions• initial design in place end of 2016• first inversions of CO2 planned in 2017
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AcknowledgementsObservations:
E. Dlugokencky, M. Sasakawa, T. Machida, D. Worthy, T. Aalto, J. Lavric, C. Lund Myhre
Miscellaneous:R. Spahni, G. van der Werf, P. Bergamaschi
Financial support: Nordforsk funded project: eSTICCResearch Council of Norway funded projects:ICOS-Norway, SLICFONIA and EVA
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