top-down approach to estimation of the regional carbon...

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Top-down approach to estimation of the regional carbon budget in West Siberia

Study of the regional carbon fluxes through inverse modeling of the Siberian atmospheric CO2 observation

S.Maksyutov, T. Machida, K. Shimoyama, N. Kadygrov (1), G. Inoue(1,2) , P. Patra (3) , M. Arshinov, O. Krasov, B. Belan (4) , N. Fedoseev (5),

(1) NIES, Tsukuba (2)now at Nagoya, Univ. (3) FRCGC, Yokohama, Japan, (4) IAO, Tomsk (5) PI, Yakutsk, Russia

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Contents:

• What do we know from bottom up data

• Atmospheric observations

• Inverse modeling of the Siberian surface CO2 fluxes

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Bottom up. Empirical modeling of the forest carbon stock inventory and dynamicsForest state account (FSA) based: provides observations of the wood stock and

annual change in forest area by category (felling, fire, etc),


FSA: regions and enterprises

FSA data for each unit: wood stock, area - by species, age class

Net Primary ProductionPinus sibirica

0100200300400500600700800

0 100 200 300Age, Year

IIIIIIIVVVa

Carbon, g/m

2/yr

Empirical dynamic model

NPP map

Region Average NBP, t C/ha yr in 1961-2003

Alt Kray 0.54 Rep Altai 0.89 Kemerovo 0.43 Kurgan 0.92 Novosib 0.76 Omsk 0.72 Sverdlovsk 0.49 Tomsk 0.35 Tjumen 0.47 Khanty- Mansi 0.25 Jamalo-Nenetsk 0.18 Cheljabinsk 0.76 Shvidenko et al 2006

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Surgut1993 -

Novosibirsk-1997 Yakutsk-1998


1. Observations: airborne air sampling and analysis

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Top-down approach to estimation of the regional carbon budget in West SiberiaOBSERVATIONS

Igrim(IGR)（63o12’N, 64o24’E）47m, 24m

Demyanskoe(DEM)（59o47’N, 70o52’E）63m, 45m

Parabel(PRB)（58o15’N, 82o24’E）67m, 35m

Berezorechka (BRZ)（56o09’N, 84o20’E）80m, 40m, 20m, 5m

Yakutsk(YAK)（62o50’N, 129o21’E）70m, 11m

Noyabrsk(NOY)（63o26’N, 76o46’E）43m, 21m

Before 2007From 2007

European proj.

NoyabrskIgrim

Beloretsk

Demyanskoe

Azovo

ParabelBerezorechka

Savuushka

ZotinoYakutsk

Igrim(IGR)（63o12’N, 64o24’E）47m, 24m

Demyanskoe(DEM)（59o47’N, 70o52’E）63m, 45m

Parabel(PRB)（58o15’N, 82o24’E）67m, 35m

Berezorechka (BRZ)（56o09’N, 84o20’E）80m, 40m, 20m, 5m

Yakutsk(YAK)（62o50’N, 129o21’E）70m, 11m

Noyabrsk(NOY)（63o26’N, 76o46’E）43m, 21m

NoyabrskIgrim

Vaganovo

Demyanskoe

Azovo

ParabelBerezorechka

Savvushka

ZotinoYakutsk

6

350375400425

350375400425

CO

2 [p

pm]

63m 45m

350375400425

350375400425

43m 21m

350375400425

350375400425

47m 24m

350375400425

350375400425

67m 35m

2002 2003 2004 2005 2006 2007350375400425

350375400425

BRZ

DEM

NOY

IGR

PRB

YEAR

70m 11m

YAK

350375400425

350375400425

80m 40m

CO2 observations (hourly data)


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Method: Inverse model of the atmospheric CO2 transport is applied to constrain surface CO2 fluxes by the observed patterns of the atmospheric CO2 (with seasonal cycles)

Components:

1. Forward models: terrestrial ecosystem flux model (hourly to seasonal scale): coupled to atmospheric transport model.

2. Inverse model of atmospheric transport, finding optimal corrections to the surface fluxes


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Gurney et al Nature 2002

Inverse modeling of regional CO2 fluxes with annual mean observations.

West ----- Longitude ------ East

Observations (North of 30N)

Higher CO2 over emitting regions: N.America, Europe, East Asia

Coarse resolution inverse model, with annual mean observation used to constrain annual mean fluxes.

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Known distributions

of CO2

sources/sinks:From

fossil fuel burning(annual, upper), ocean exchange

(monthly, middle),terrestrial ecosystem

(monthly, lower)

IndustrialB

ottom-up

CA

SA M

odel

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Asian flux estimates, improved by recent observations.

-30 0 30 60 90 120 150 180

0

30

60

90

Tokyo-Sydney

HaterumaSendai

Ohchi-Ishi

Yakutsk

Novosibirsk

Surgut la la

-2

-1.5

-1

-0.5

0

0.5

1

1.5

Bor

NA

mer

ica

Tem

p N

Am

eric

a

Bor

eal

Asi

a

Tem

pera

teA

sia

SE

Asi

a

Euro

pe

W P

acifi

cO

cn

Pg

C /

year

basic inversionour estimate

Changes in estimated annual mean fluxes (left) due to adding the observations ( right )

Maksyutov et al., Tellus, 2003

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Observation network optimization

Maksyutov et al., EOS, AGU Fall, 2002Patra & Maksyutov, GRL, 2002

Adding new observations reduces uncertainty of the flux estimates

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Observation system optimization

Estimated impact of JAL vertical profiles only

Tem

pAm

er.a

T.Eu

ras.b

T.Eu

ras.c

T.Eu

ras.d

SEAs

ia.a

SEAs

ia.b

Aust

ral.b

Aust

ral.c

Euro

pe.a

N.Pa

cific

W.P

acifi

c

S.Pa

cific

Tr.In

dian

jal-all 54 42 19 35 66 35 43 16 37 11 12 12 12beijing 1 2 11 22 1 0 0 0 0 2 0 0 0pusan 1 6 6 14 1 0 0 0 0 3 1 0 0seoul 1 2 8 17 1 0 0 0 0 3 0 0 0shanghai 1 38 3 6 2 2 0 0 0 2 1 0 0hongkong 1 2 0 2 3 20 0 0 0 1 1 0 0manila 0 1 0 1 1 4 0 0 0 0 0 0 0denpasar 0 0 0 0 9 1 1 11 0 0 1 0 3jakarta 0 0 0 0 22 1 0 4 0 0 3 0 4kuala 0 1 0 0 37 1 0 0 0 0 3 0 0singapore 0 0 0 0 60 2 0 0 0 0 3 0 0brisbane 0 0 0 0 0 0 13 1 0 0 1 5 0sydney 0 0 0 0 0 0 39 1 0 0 1 11 1london 0 0 0 0 0 0 0 0 2 1 0 0 0amsterdam 0 0 0 0 0 0 0 0 5 1 0 0 0paris 0 0 0 0 0 0 0 0 10 1 0 0 0milano 0 0 0 0 0 0 0 0 29 1 0 0 0rome 0 0 0 0 0 0 0 0 9 1 0 0 0losangeles 44 2 1 2 2 0 0 0 0 4 2 0 0honolulu 1 4 0 2 2 0 0 0 0 3 1 0 0lasvegas 42 2 1 2 1 0 0 0 0 4 1 0 0eastasia 2 41 17 34 4 30 0 0 0 5 1 0 1seasia 1 2 0 1 64 12 1 15 0 0 5 2 9europe 0 0 0 0 1 0 0 0 37 3 0 0 0usa 54 5 1 3 3 0 0 0 0 8 2 0 0austral 1 0 0 0 1 0 42 1 0 0 1 12 1

JAL continuous observations(CONTRAIL)5 aircrafts (747, 777)FAA certified equipment

Moderate resolution inversion42 land, 11 oceanSame as (Patra et al JGR 2005)-SOFIS paper

Percent flux uncertainty reduction

Maksyutov et al 2004, report to JAL project advisory board meeting

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Observation system optimization

0 1 2 3 4 5Precision (ppmv)

0.3

0.4

0.5

0.6

0.7

Flux

Unc

erta

inty

(G

t C/y

r pe

r re

gion

)

S2 (115 GV + 15 optimal station)

S1 (115 GV station)

SOFIS (S)

S + S1S + S2

Japanese space research program

SOFIS – sun occultationImpact on flux uncertainty

Patra et al JGR 2005

New approach – nadir obs., GOSAT

Maksyutov et al Arch ISPRS 2004

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Top down: atmospheric transport and inverse modeling

Region based inversion FRCGC Japanglobal 2.5deg transport, monthly fluxes

Planned: grid based inversion LSCE France,Global 3x4 deg, weekly fluxes

340

360

380

400

CO

2 [ppm

]

Berezorechka Aircraft (1km)

2002 2003 2004 2005

Monthly flux pulses from each region are used to fit monthly average observations weekly fluxes at each grid are constrained

by each separate afternoon average concentrations (night-time excluded), 4-D var asiimilation to be used

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without Siberian dataCASA model + inversion

with Siberian data

Use of the tower dataReduces uncertainty

Earlier drawdown of sink

Seasonal variation of CO2 Flux （South half of the WS） with 64 regioninversion and 1 year (2005) of tower data

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W. Siberian inverse model fluxes with 66 region inversion

Red - inverse model, green – CASA, blue – Sim-Cycle Inverse model 2004 – spin-up, 2005 - use actual data

West Siberia North

West Siberia South

Several sites No observations

Central Siberia North

Central Siberia South

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Summary

Inverse model study of seasonal flux variability suggests the observational data are useful for estimating the regional CO2 flux seasonality, provided there are enough observations, adjusting and validating large scale flux simulations with biogeochemical models at monthly and daily time scale. The annual mean flux estimation requires more accurate analysis and is relatively less robust.

Using the tower observations we confirmed that earlier flux drawdown simulated by Sim-Cycle model in northern West Siberia is fitting the observed CO2 better than with CASA model. Amplitude-wise both models agree with inversion within model uncertainty range during warm season.

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Acknowledgements

LeadershipG. Inoue (NIES), T. Nakazawa (Tohoku Univ)

Observations and data analysis conducted byT. Watai, A. Shinohara (NIES)

M. Arshinov, O. Krasnov, D. Davidov, A. Fofonov (IAO), N.Fedoseev (PI) N.Vinnichenko(CAO)

Inverse modeling designed byP. Rayner (LSCE) and Transcom collaborators

top-down approach to estimation of the regional carbon...

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