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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-366
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-366
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Sep 2020

29 Sep 2020

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This preprint is currently under review for the journal AMT.

Analysis of 3D Cloud Effects in OCO-2 XCO2 Retrievals

Steven T. Massie1, Heather Cronk2, Aronne Merrelli3, K. Sebastian Schmidt1, Hong Chen1, and David Baker4 Steven T. Massie et al.
  • 1Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Colorado, 80303, USA
  • 2Colorado State University, Fort Collins, Colorado, 80523, USA
  • 3Space Science and Engineering Center, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
  • 4Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, 80523, USA

Abstract. The presence of 3D cloud radiative effects in OCO-2 retrievals is demonstrated from an analysis of 2014–2019 OCO-2 XCO2raw retrievals, bias corrected XCO2bc data, ground based Total Carbon Column Observation Network (TCCON) XCO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud and radiance fields. Averaged over the year, 40 % and 75 % of OCO-2 Quality Flag QF = 0 (best quality) and QF = 1 (lesser quality) retrievals are within 4 km of clouds. 3D radiative transfer calculations indicate that 3D cloud radiative perturbations at this cloud distance, for an isolated low altitude cloud, are larger in absolute value than those due to a 1 ppm increase in CO2. OCO-2 measurements are therefore susceptible to 3D cloud effects. Four 3D cloud metrics, based upon MODIS radiance and cloud fields and stand-alone OCO-2 measurements, relate XCO2bc-TCCON averages to 3D cloud effects. This analysis indicates that the operational bias correction has a non-zero residual 3D cloud bias for both QF = 0 and QF = 1 data. XCO2bc −TCCON averages at small cloud distances differ from those at large cloud distances by −0.4 and −2.2 ppm for the QF = 0 and QF = 1 data over the ocean. Mitigation of 3D cloud biases by a Table look-up technique, that utilizes nearest cloud distance (Distkm) and spatial radiance heterogeneity (CSNoiseRatio) 3D metrics, reduces QF = 1 ocean and land XCO2bc −TCCON averages from −1 ppm to near ± 0.2 ppm. The ocean QF = 1 XCO2bc-TCCON averages can be reduced to the 0.5 ppm level if 60 % (70 %) of the QF = 1 data points are utilized, by applying Distkm (CSNoiseRatio) metrics in a data screening process. Over land the QF = 1 XCO2bc–TCCON averages are reduced to the 0.5 (0.8) ppm level if 65 (63) % of the data points are utilized by applying Diastkm (CSNoiseRatio) data screening. The addition of more terms to the linear regression equations used in the current bias correction processing, without data screening, however, did not introduce an appreciable improvement in the standard deviations of the XCO2bc-TCCON statistics.

Steven T. Massie et al.

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Steven T. Massie et al.

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Latest update: 28 Oct 2020
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Short summary
The OCO-2 science team is working to retrieve CO2 measurements that can be used by the carbon cycle community to calculate regional sources and sinks of CO2. The retrieved data, however, is in need of improvements in accuracy. This paper discusses several ways in which 3D cloud metrics (such as the distance of a measurement to the nearest cloud) can be used to account for cloud effects in the OCO-2 CO2 data files.
The OCO-2 science team is working to retrieve CO2 measurements that can be used by the carbon...
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