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

Prabir K. Patra, Kevin R. 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 Louis Sarmiento, Shoichi Taguchi, Taro Takahashi, Chiu Wai Yuen

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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
Issue number5
StatePublished - Mar 16 2006

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Earth and Planetary Sciences(all)


Dive into the research topics of 'Sensitivity of inverse estimation of annual mean CO<sub>2</sub> sources and sinks to ocean-only sites versus all-sites observational networks'. Together they form a unique fingerprint.

Cite this