Sensitivity of inverse estimation of annual mean CO2 sources and sinks to ocean-only sites versus all-sites observational networks

Prabir K. Patra, Kevin Gurney, A. Scott Denning, Shamil Maksyutov, Takakiyo Nakazawa, David Baker, Philippe Bousquet, Lori Bruhwiler, Yu Han Chen, Philippe Ciais, Songmiao Fan, Inez Fung, Manuel Gloor, Martin Heimann, Kaz Higuchi, Jasmin John, Rachel M. Law, Takashi Maki, Bernard C. Pak, Philippe PeylinMichael Prather, Peter J. Rayner, Jorge Sarmiento, Shoichi Taguchi, Taro Takahashi, Chiu Wai Yuen

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Inverse estimation of carbon dioxide (CO2) sources and sinks uses atmospheric CO2 observations, mostly made near the Earth's surface. However, transport models used in such studies lack perfect representation of atmospheric dynamics and thus often fail to produce unbiased forward simulations. The error is generally larger for observations over the land than those over the remote/marine locations. The range of this error is estimated by using multiple transport models (16 are used here). We have estimated the remaining differences in CO2 fluxes due to the use of ocean-only versus all-sites (i.e., over ocean and land) observations of CO2 in a time-independent inverse modeling framework. The fluxes estimated using the ocean-only networks are more robust compared to those obtained using all-sites networks. This makes the global, hemispheric, and regional flux determination less dependent on the selection of transport model and observation network.

Original languageEnglish (US)
Article numberL05814
JournalGeophysical Research Letters
Volume33
Issue number5
DOIs
StatePublished - Mar 16 2006
Externally publishedYes

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sinks
oceans
sensitivity
ocean
atmospheric dynamics
Earth surface
carbon dioxide
modeling
simulation
land

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)

Cite this

Sensitivity of inverse estimation of annual mean CO2 sources and sinks to ocean-only sites versus all-sites observational networks. / Patra, Prabir K.; Gurney, Kevin; Denning, A. Scott; Maksyutov, Shamil; Nakazawa, Takakiyo; Baker, David; Bousquet, Philippe; Bruhwiler, Lori; Chen, Yu Han; Ciais, Philippe; Fan, Songmiao; Fung, Inez; Gloor, Manuel; Heimann, Martin; Higuchi, Kaz; John, Jasmin; Law, Rachel M.; Maki, Takashi; Pak, Bernard C.; Peylin, Philippe; Prather, Michael; Rayner, Peter J.; Sarmiento, Jorge; Taguchi, Shoichi; Takahashi, Taro; Yuen, Chiu Wai.

In: Geophysical Research Letters, Vol. 33, No. 5, L05814, 16.03.2006.

Research output: Contribution to journalArticle

Patra, PK, Gurney, K, Denning, AS, Maksyutov, S, Nakazawa, T, Baker, D, Bousquet, P, Bruhwiler, L, Chen, YH, Ciais, P, Fan, S, Fung, I, Gloor, M, Heimann, M, Higuchi, K, John, J, Law, RM, Maki, T, Pak, BC, Peylin, P, Prather, M, Rayner, PJ, Sarmiento, J, Taguchi, S, Takahashi, T & Yuen, CW 2006, 'Sensitivity of inverse estimation of annual mean CO2 sources and sinks to ocean-only sites versus all-sites observational networks', Geophysical Research Letters, vol. 33, no. 5, L05814. https://doi.org/10.1029/2005GL025403
Patra, Prabir K. ; Gurney, Kevin ; Denning, A. Scott ; Maksyutov, Shamil ; Nakazawa, Takakiyo ; Baker, David ; Bousquet, Philippe ; Bruhwiler, Lori ; Chen, Yu Han ; Ciais, Philippe ; Fan, Songmiao ; Fung, Inez ; Gloor, Manuel ; Heimann, Martin ; Higuchi, Kaz ; John, Jasmin ; Law, Rachel M. ; Maki, Takashi ; Pak, Bernard C. ; Peylin, Philippe ; Prather, Michael ; Rayner, Peter J. ; Sarmiento, Jorge ; Taguchi, Shoichi ; Takahashi, Taro ; Yuen, Chiu Wai. / Sensitivity of inverse estimation of annual mean CO2 sources and sinks to ocean-only sites versus all-sites observational networks. In: Geophysical Research Letters. 2006 ; Vol. 33, No. 5.
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AU - Bousquet, Philippe

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AU - Ciais, Philippe

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AU - Gloor, Manuel

AU - Heimann, Martin

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AU - John, Jasmin

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AU - Maki, Takashi

AU - Pak, Bernard C.

AU - Peylin, Philippe

AU - Prather, Michael

AU - Rayner, Peter J.

AU - Sarmiento, Jorge

AU - Taguchi, Shoichi

AU - Takahashi, Taro

AU - Yuen, Chiu Wai

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