Characterization of OCO-2 and ACOS-GOSAT biases and errors for CO2 flux estimates
- 1BAER Institute, 625 2nd Street, Suite 209, Petaluma, CA, USA
- 2GeoCarb Mission, University of Oklahoma, Norman, OK, USA
- 3Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
- 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- 5National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Boulder, CO, USA
- 6University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA
- 7Lawrence Berkeley National Laboratory, Earth Science Division, Berkeley, CA, USA
- 8Harvard University, Cambridge, MA, USA
- 9Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
- 10Department of Physics, University of Toronto, Toronto, Canada
- 11Centre for Atmospheric Chemistry, School of Earth, Atmosphere and Life Sciences, University of Wollongong, Wollongong, NSW, Australia
- 12University of Bremen, Bremen, Germany
- 13Oscar M. Lopez Center for Climate Change Adaptation and Disaster Risk Mgmt. Foundation Inc., Manila, Philippines
- 14Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
- 15Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
- 16Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), Garmisch-Partenkirchen, Germany
- 17National Institute of Water and Atmospheric Research, Lauder, New Zealand
- 18National Institute for Environmental Studies (NIES), Tsukuba, Japan
- 19Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
- 20Lehrstuhl für Physik der Atmosphäre, Ludwig-Maximilians-Universität München, Munich, Germany
- 21Deutsches Zentrum für Luft und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
- 22Max Planck Institute for Biogeochemistry, Jena, Germany
- 23Finnish Meteorological Institute, Sodankylä, Finland
- 24NASA Ames Research Center, Moffett Field, CA, USA
- 25EORC Earth Observation Research Center, JAXA Japan Aerospace Exploration Agency
- 26Los Alamos National Laboratory, Los Alamos, NM, USA
- 27Izaña Atmospheric Research Center, Meteorological State Agency of Spain (AEMet), Tenerife, Spain
- 28LERMA-IPSL, Sorbonne Université, CNRS, Observatoire de Paris, Université PSL, Paris, France
Abstract. We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.
Susan S. Kulawik et al.
Susan S. Kulawik et al.
Susan S. Kulawik et al.
Viewed (geographical distribution)
5 citations as recorded by crossref.
- New approach to evaluate satellite-derived XCO<sub>2</sub> over oceans by integrating ship and aircraft observations A. Müller et al. 10.5194/acp-21-8255-2021
- Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020) J. Liu et al. 10.5194/essd-13-299-2021
- Validation of OCO-2 error analysis using simulated retrievals S. Kulawik et al. 10.5194/amt-12-5317-2019
- Data reduction for inverse modeling: an adaptive approach v1.0 X. Liu et al. 10.5194/gmd-14-4683-2021
- Evaluation of earth system model and atmospheric inversion using total column CO2 observations from GOSAT and OCO-2 P. Patra et al. 10.1186/s40645-021-00420-z