NOCES meeting Plymouth, 2005 June 13-15 Top-down v.s. bottom-up estimates of air-sea CO 2 fluxes :...

NOCES meetingPlymouth, 2005 June 13-15

Top-down v.s. bottom-up estimates of air-sea CO2 fluxes : No winner so far …

P. Bousquet, A. Idelkadi, C. Carouge, P. Peylin, Z. Lachkar,Laboratoire des Sciences du Climat et de l’Environnement (France)

and NOCES WP1 modelers

With the contributions of P. J. Rayner (LSCE), C. Rodenbeck (MPI), G. McKinley (MIT)

Forward modeling : Impact of spatio-temporal air-sea fluxes of CO2 and O2 (WP1) on atmospheric concentrationsPenetration of marin air within Eurasian continent

Inverse modeling :Interannual air-sea flux estimates with uncertainties at a regional scale on a monthly basisComparison of inversion estimates with fluxes from WP1

Time schedule : Year 1 : - LMDZt transport model with interannual meteorology DONE Yr1Year 2 : - forward modeling using air-sea fluxes from WP1 DONE

- Comparison with atmospheric observations (AEROCARB) DONE- Set up of inversion procedure DONE Yr2

Year 3 : - Inversions using air-sea fluxes from WP1 NOT DONE- Comparison with WP1 estimates and other estimates DONE

WP2 : Atmospheric modeling of interannual variability

CO2 Atmospheric network

Spatial discretisation of atmospheric inversion

LMDZt, a GCM with offline, retro-transport and zooming capabilities

• 96x72 cells : • 3.75°x2.5°---> 0.5°x0.5° (Europe)

• 19/38 hybrids vertival layers

• Direct transport nudged •on ECMWF analysis

• Back-transport capability

Jour 2 Jour 4

Forward and inverse simulations

• Forward modeling :

• Impact of IAV of ocean CO2 flux at atm. stations around Europefor different ocean models

• Impact of IAV of transport of ocean CO2 fluxes at atm. Stations around Europe

• Inverse modeling :

• Impact of different spatio-temporal ocean flux priors on inversion results

• Comparison between inverse results to ocean models outputs at basin scale on a monthly basis

Atmospheric Inversions

Atmosphericconcentrations

Surface fluxes

360

365

370

375

380

2001 2001.2 2001.4 2001.6 2001.8 2002

dates

CO2

(ppm)

Yearly observationsMBL & coastal sites

Monthly observationsMBL & coastal sites

Synoptic observationsAll sites (continental)

368

369

370

371

372

373

374

2001 2001.2 2001.4 2001.6 2001.8 2002

dates

CO2

(ppm)

350

360

370

380

390

400

2001 2001.2 2001.4 2001.6 2001.8 2002

dates

CO2

(ppm)

Transport model- Coarse global models

- Global high resolution models,zoomable, nested or regional models

Monthly flux estimates Large regions

Monthly flux estimatesModel resolution or Small regions

Yearly flux estimates Large regions

Spatial structure of modelled air-sea CO2 fluxes

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COCO-NEMURO ORCA2 (MPI) ORCA2 (IPSL)

Takahashi Climatology MPI model MIT model

QuickTime™

et undécom

presseur TIFF (LZW)


gC/m2/yr -90 90

Temporal variations of air-sea CO2 fluxes (1)






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Top-down inversions v.s. bottom-up ocean models (1)





Top-down inversions v.s. bottom-up ocean models (1’)

Mc Kinley et al., 2004







sont requis pour visionner cette image.Peylin et al., 2005

Top-down inversions - TRANSCOM 3 experiment (1)



NOCES - 13-15 June 2005, Plymouth, UK 2

In this study we analyze the variations of DIC, TA, Temperature and salinity observed along the same track, between Iceland and the Newfoundland over the period 1993-2003.

Ocean measurements

Cruise track : 1993-2003

The analysis is focus on the

open ocean region, 53°N-62°N/45°W-20°W

Iceland

Corbiere, pers. com

And observations ? (1)

290310330350370390

1992199419961998200020022004Year

fCO2 (µatm)CO2 air MH (µatm)CO2 océan (µatm)CO2 air GV (µatm)

Figure 6 : Evolution de la fugacité dans l’eau de surf ace de l’atlantique Nord (zone 53-

60°N) déduite des mesures de DIC et TA du programme Suratlante. Sont indiquées également les fugacité dans l’air, déduites d e la base GlobalView (2003, vert) ou de s mesures de CO2 monitorées à Mace Hhead ( M.Ramonet, cf rapport FlamenCO2 groupe 2). La s aisonalité de fCO2 océanique est bien marquée (proche de l’équilibre en hiver boréal, sous-saturée au printemps). On note une tendance à l’accroissement du CO2 océanique, lié principalement à un réchauffement d’environ 2°C sur la décennie.

Mace-HeadOcéanGlobalview

North Atlantic pCO2 measurements

Metzl et al., pers. Comm.


-20

-15

-10

-5

0

5

10

15

20

1993 1995 1997 1999 2001 2003

CO2 flux (mmol/m2/d)

SURATLANT CO2 flux

Inversion CO2 monthly flux

The sink is lower than observed and the model doesn’t capture the seasonal variation and the extreme event in 2003


Corbiere, pers. com

-10-9-8-7-6-5-4-3-2-101

1993 1994 1995 1996 1997 1998Year

CO2 flux (mmol/m2/d)

Ocean model is close to the observations for the period 1993-1997and he does capture the seasonal variability

ORCA-PISCES Flux

SURATLANT Flux



Corbiere, pers. com




Peylin et al., 2005


Gruber et al., 2002






Seasonal Sea-to-Air CO2 fluxes in the Southern Ocean

-12

0

12

0 3 6 9 12 15 18 21 24

Month (2 years rep.)

Flux (mmol/m2/d)

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

Flux (PgC/yr)

OISO

Clim

ORCA-P

AIMwinter source

summer sink

Metzl et al., Liège 2-6 May 2005

Atmospheric forward modelling









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MHD

SCH

HUN

ORL

Contribution of air-sea fluxes to CO2 IAV at atmospheric stations (LMDZ model)

0.0 0.2 0.4

Conclusions (temporary..)

• IAV of air-sea CO2 fluxes in ocean models mostly stays within ± 0.1 GtC/yr at basin scale except in the equatorial pacific (± 0.3 GtC/yr)---> counteracting effects occur at least in the North Atlantic: DIC/SST, convection/export, DIC/Alkalinity, other ??

• IAV of air-sea CO2 fluxes in inversions is larger, up to ± 0.5GtC/yr at basin scale. IAV of Pacific ocean and austral ocean only slightly dominate other ocean regions.---> Effects of the inversion algorithm & set-up (prior error structure, aggregation error,..) and of the choice and the number of atmospheric stations used (critical when including continental sites).

• Agreement in equatorial pacific and in the fact that pacific dominates IAV.

Climate and weather

fields

Atmospheric Transport model

Atmosphericmeasurements

Towards a carbon cycle data assimilation system

optimizedFluxes

Emissionsinventories

Ecologicalmeasurements

Forest/soil inventories

Eddy Flux towers

Remote sensing of

Atm. conc

Data assimilation

link

optimizedmodel

parameters

Model of land surface fluxes

Remote sensing of Vegetation properties

NOCES meeting Plymouth, 2005 June 13-15 Top-down v.s. bottom-up estimates of air-sea CO 2 fluxes :...

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Transcript of NOCES meeting Plymouth, 2005 June 13-15 Top-down v.s. bottom-up estimates of air-sea CO 2 fluxes :...