Study on the transport and inverse modeling of CO2

Yosuke Niwa

Ryoichi Imasu, Masaki SatohCenter for Climate System Research (CCSR),

The University of Tokyo

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Study on the transport and inverse modeling of CO2

Yosuke Niwa

Ryoichi Imasu, Masaki SatohCenter for Climate System Research (CCSR),

The University of Tokyo

1

Overview

1. Uncertainty of CO2 fluxes

2. CO2 flux estimation methods

3. Inverse modeling

4. Flux estimation

5. Comparison with other study

6. Summary

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Uncertain Surface CO2 Fluxes

Biomass Burning

Bush Fires in Southern Mozambiquefrom NASA

Tierras Bajas Deforestation, Boliviafrom NASA

Deforestation

global CO2 growth rate

Global CO2 concentration is determined almost by CO2 flux at the Earth surface.Our understanding of the surface CO2 flux is insufficient.

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from WDCGG site

Surface CO2 Flux Estimation Bottom-Up Approach

• Direct measurement at flux towers or above oceans• Biosphere model

precisevery few measurement sites.hard to cover globe

Top-Down Approach• Inversion modeling :

derive flux information from atmospheric observation datarelatively more measurement sitesEasy to cover globeEstimates of CO2 fluxes from several studies show considerabledisagreement.

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Inverse Modeling

Surface CO2 flux Observation Data

a priori data

a posteriori data

1. Forward Simulation

2. Inversion

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Atmospheric Tracer Transport Model

Bayesian Statistics

Inversion StudiesBousquet et al., 2000 : 19 regions, 1980-1998 Rodenbeck et al 2003 : 8deg. X 10deg., 1982-2001Patra et al., 2005, 2006: 64 regionsBaker et al, 2006: 22 regions, 1991-2000, TransCom experiment (13 models)

Estimated fluxes are quantitatively very different by inversion set ups, especially due to transport models

GOSAT(JAXA)

OCO(NASA)

Expanding Measurement Network

commercial air-line( JAL Foundation)

spatial coverage broaden by air-craft and satellite measurements

WDCGG surface measurement net work

more frequent measurementmonthly → hourly

A highly sophisticated transport model is needed to use many kinds of data

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Tracer Transport ModelNonhydrostatic Icosahedral Atmospheric Model (NICAM)• Next Generation GCM

• Consistent With Continuity (CWC)– Tracer transport is completely consistent with air density change

Both mass conservation and Lagrangian conservation are achieved.

Good property for simulation of long-lived tracers

Horizontal Resolution glevel-05, (dx~240km)

Vertical Layer z*, 40 layer (~60km)

Advection Horizontal:

Upwind-biased scheme (Miura, 2007)

Vertical:

2nd centered difference with limiter (Thuburn, 1996)

Cumulus Convection Arakawa-Schubert (1974)

Boundary Layer Improved version of Mellor-Yamada2

(Nakanishi-Niino, 2004)

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Purpose of our study is…

• to know how much our inversed fluxes are different from other studies and understand the reason of its difference.

comparing with TransCom3 models

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Inverse model

)]s(s)C(s)s(sd)(MsC(d)d)Ms 000 11[(2

1 :functioncost TTJ

)()(])()([ : estimated 1111000 MsddCMsCMdCMss TT

111 ])()([)( :flux estimated of covarianceerror

0sCMdCMsC T

i

jiijj VTsfTc )]([)]([

)( fTcd jjj

observationmodeled concentration

factor scaling :iregion each in flux basis :

operator transport:flux background :

pointn observatio : estimated isflux in which region :

sVTfji

* of covarianceerror :(*)factor scaling : factor scaling estimated :

modelrt by transpo madefunction response :

CssM

0 priori a

seek s which minimize J

(Baker, 2001)

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Inversion Setup

• Fluxes to be estimated22 regions (land 11+ocean 11) for 1991-2000

• Background fluxes– Biospheric flux: NEP flux from CASA model – Fossil fuel emission: CDIAC– Air-sea exchange: Takahashi et al., 1999

• a priori estimate and uncertainty – The same as Baker et al., 2006

• Observations– GLOBALVIEW-2006, 78 sites

Observation site used and 22 regions

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Estimated Interannual Variability of CO2 Fluxes

Global

Northern

Tropical

Southern

landocean

• Global interannual variability is simulated consistent with other 13 models.

• During ‘97~’98 El Nino, the amplitude of flux vaiability in tropical area is smaller, while in southern area larger.

• No difference in Ocean flux viability

bold line : estimated flux2 thin lines : estimated errorbackground : estimated flux of TransCom

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Long Time Mean Flux Estimation

Relatively large sinks and sources can be seen in some areas.e.g. Boreal N America, Temp. S America, Tropical Asia, Southern Ocean

Land Flux Ocean Flux

blue: this study, green: TransCom models

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Aggregated Long Term Mean Flux

• Stronger source in Tropical lands and oceans• Stronger sinks in Southern areas, especially in Southern Lands

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Why we got strong source in tropical and strong sink in south?

Simulatedinter-hemispheric difference (IHD)

Simulated annual mean surface zonal CO2 from background flux data

Simulated IHD by NICAM is smaller than other models.

black : TransCom modelsred : NICAM

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Why Strong Source in Tropical and Strong Sink in South?Tropical Land Tropical Ocean

Southern Land Southern Oceanred : this studyblack : TransCom3

IHD

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Est

imat

ed F

lux

• In southern area:

– Observed CO2 concentration in southern area is lower than simulated one and smaller IHD needs stronger sinks .

– Relatively many observation data at ocean area constrain ocean fluxes, while land fluxes are not constrained.

• Tropical area:

– Strong upward transport dilutes flux information at the surface (most measurement sites are located at the surface)

• More observation data are needed to constrain fluxes at those areas

Why Strong Source in Tropical and Strong Sink in South?

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Summary

• Our understanding of the surface CO2 flux is insufficient.

• Inversion method is one method for estimating surface CO2 fluxes.

• Estimated temporal and spatial flux variability by using NICAM are generally similar to those by other models.

• Larger flux variability in southern lands and smaller flux variability in tropical lands during 97/98 ENSO.

• Strong source in tropical and strong sink in southern region.

• Strong sink in southern oceans is related to small IHD simulated by NICAM.

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Comparison with Bottom-up Approaches

This Study Baker et al, 2006 McGuire et al., 2001

Northern Land -2.8±0.3 -2.6±0.3 -1.3 ~ -0.3

Tropical Land 3.1±0.8 1.9±0.7 -0.2 ~ 0.5

Southern Land -2.2±0.7 -1.4±0.6 0.0 ~ 0.2

This Study Baker et al, 2006 Takahashi et al., 2002

Northern Ocean -0.8±0.2 -1.1±0.2 -1.1

Tropical Ocean 1.1±0.3 0.8±0.3 0.9

Southern Ocean -1.1±0.3 -0.8±0.3 -1.5

This Study Baker et al, 2006 IPCC, AR4

Global Biosphere -1.9±0.6 -2.1±0.5 1.0±0.6

Global Ocean -0.8±0.5 -1.1±0.5 2.2±0.4

Bottom-up approachesThere are still much differences…

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