Prabir K. Patra

Acknowledgments: S. Maksyutov, K. Gurney and TransCom-3 modellers

TransCom Meeting, Paris; 13-16 June 2005

Sensitivity CO2 sources and sinks to ocean versus land-dominated

observational networks.

Yet another sensitivity study!

Plan of the talk

– Why network sensitivity (using IAVs in flux anomalies)

– Experimental setup (based on T3-L1 & L2)– Some results (may be useful for synthesis)– Conclusions

64-Regions Inverse Model(using 15 years of interannually varying NCEP/NCAR winds)

Patra et al., Global Biogeochem. Cycles., revised, 2005a

]/)(/)[([1 21 0

121

12S

M

MDpredicted

N

N CSSCDDT

Inv. Setup Chi222 reg 2.1564 reg 1.1164+IAV 0.99

CS = cs1 + cs2…

Flux anomaly (6-month running averages) and initial conditions

Flux anomaly = TD

I Flux – avg. sea. cyc

Comparison of land flux anomalies

Comparison of ocean flux anomalySource: C

. Lequere

Sensitivity to networks

and inversion

methods(!)

Thanks to:Philippe BousquetChristian Rodenbeck

Validation…

Validation…

Conclusions: IAV in fluxes (and fluxes indirectly) is controlled mainly by network selection

Assumption: Biases in flux estimation are linked mainly to transport model errors

Inverse model framework and present day network (70% real data for the period 1999-2001)

Land Fluxes – Netw

ork and m

odel Dependency

Ocean Fluxes –

Netw

ork Dependency

Signal gradients at optimal stations - tropical

Signal gradients within regions – high/midlats

Global &

hemispheric S

caleFluxes – N

etwork D

ependency

Land and Ocean Fluxes (70% real) – ocean versus all networks

Land Seasonal C

ycle

Ocean S

easonal Cycle

Conclusions1. The IAV is controlled mainly by observational

network selection, less on techniques

2. Biases in fluxes estimation are linked to transport model errors

3. For synthesis of CO2 sources and sinks, we need to revisit the estimations

• Different networks• Separate time period for inversion

4. Finally, any suggestions are welcome

Do not reject the land stations, but be careful …