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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/acp-2020-1094
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-1094
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Oct 2020

30 Oct 2020

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This preprint is currently under review for the journal ACP.

New approach to evaluate satellite derived XCO2 over oceans by integrating ship and aircraft observations

Astrid Müller1, Hiroshi Tanimoto1, Takafumi Sugita1, Toshinobu Machida1, Shin-ichiro Nakaoka1, Prabir K. Patra2, Joshua Laughner3, and David Crisp4 Astrid Müller et al.
  • 1National Institute for Environmental Studies, Tsukuba, Japan
  • 2Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
  • 3California Institute of Technology, Pasadena, CA, USA
  • 4Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, USA

Abstract. Satellite observations provide spatially-resolved global estimates of column-averaged mixing ratios of CO2 (XCO2) over the Earth's surface. The accuracy of these datasets can be validated against reliable standards in some areas, but other areas remain inaccessible. To date, limited reference data over oceans hinders successful uncertainty quantification or bias correction efforts, and precludes reliable conclusions about changes in the carbon cycle in some regions. Here, we propose a new approach to analyze and evaluate seasonal, interannual and latitudinal variations of XCO2 over oceans by integrating cargo-ship (SOOP, Ship Of Opportunity) and commercial aircraft (CONTRAIL, Comprehensive Observation Network for Trace gases by Airliner) observations with the aid of state-of-the art atmospheric chemistry-transport model calculations. The consistency of the in situ based column-averaged CO2 dataset (in situ XCO2) with satellite estimates was analyzed over the Western Pacific between 2014 and 2017, and its utility as reference dataset evaluated. Our results demonstrate that the new dataset accurately captures seasonal and interannual variations of CO2. Retrievals of XCO2 over the ocean from GOSAT (Greenhouse gases observing satellite: NIES v02.75, National Institute for Environmental Studies; ACOS v7.3, Atmospheric CO2 Observation from Space) and OCO-2 (Orbiting Carbon Observatory, v9r) observations show a negative bias of about 1 parts per million (ppm) in northern midlatitudes, which was attributed to measurement uncertainties of the satellite observations. The NIES retrieval had higher consistency with in situ XCO2 at midlatitudes as compared to the other retrievals. At low latitudes, it shows many fewer valid data and high scatter, such that ACOS and OCO-2 appear to provide a better representation of the carbon cycle. At different times, the seasonal cycles of all three retrievals show positive phase shifts of one month relative to the in situ data. The study indicates that even if the retrievals complement each other, remaining uncertainties limit the accurate interpretation of spatiotemporal changes in CO2 fluxes. A continuous long-term XCO2 dataset with wide latitudinal coverage based on the new approach has a great potential as a robust reference dataset for XCO2 and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.

Astrid Müller et al.

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Astrid Müller et al.

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Short summary
Over oceans, high uncertainties in satellite CO2 retrievals exist due to limited reference data. We combine commercial ship and aircraft observations, and, with the aid of model calculations, obtain column-averaged mixing ratios of CO2 (XCO2) data over the Pacific. This new dataset has a great potential as a robust reference for XCO2 measured from space and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.
Over oceans, high uncertainties in satellite CO2 retrievals exist due to limited reference data....
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