Developments in regional inverse modelling

Developments in regional inverse modellingR. L. Thompson and A. StohlNILU, Norsk Institutt for Luftforskning, Norway

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

20 June 2016 ICOS Workshop Lund 2

Overview of FLEXINVERT


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

Definition of forward modelMixing ratios are modeled according to:


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

Background mixing ratiosTo determine the background contribution (Hiniyini):Couple to global 3D mixing ratio fields in time domain


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


Background mixing ratiosMonthly 2D fields from bivariate interpolation of NOAA flask data, plus model estimate of stratospheric mixing ratio (yini)


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


FLEXINVERT spatial grid

10

12

14

16


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

Methane in the high northern latitudes

8

Observations

ZEP

TIK

TERZOT

PAL

CHL

BAL

CBA

LLBETL

MHD

FSD

ESP

CDL

KRSIGR

NOYDEM

AZV

VGN

YAKCHM

flaskin situ


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

Data selection criteriaProblem modeling PBL in winter at continental sites:filter data using observed temp. gradient and wind speed criteria


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)

Transport uncertainties


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

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

Modeled mixing ratios


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

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


Seasonal mean: posterior and difference (posterior – prior)

Thompson et al., in prep., 2016

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



case 1: prior wetlands LPX-Berncase 2: prior wetlands LPJ-DGVMsolid lines: posteriordashed lines: priorgrey-shading: uncertainty

Inter-annual variability


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


case 1: prior wetlands LPX-Berncase 2: prior wetlands LPJ-DGVMsolid lines: posteriordashed lines: priorshading: uncertainty

p-value < 0.01 p-value < 0.01

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


Shown for 2005 – 2010 (overlapping period). Units TgCH4 y-1.


Developments for CO2 inversions

18

FLEXINVERT-CO2

Statistical model of fluxes:optimize land biosphere fluxes, fixed ocean and fossil fuel fluxes


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

Statistical modelStatistical model based on Rödenbeck et al. 2005:


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:

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


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


Developments in regional inverse modelling

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