the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Characterization of OCO-2 and ACOS-GOSAT biases and errors for CO2 flux estimates
Susan S. Kulawik
Sean Crowell
David Baker
Junjie Liu
Kathryn McKain
Colm Sweeney
Sebastien C. Biraud
Steve Wofsy
Christopher W. O'Dell
Paul O. Wennberg
Debra Wunch
Coleen M. Roehl
Nicholas M. Deutscher
Matthäus Kiel
David W. T. Griffith
Voltaire A. Velazco
Justus Notholt
Thorsten Warneke
Christof Petri
Martine De Mazière
Mahesh K. Sha
Ralf Sussmann
Markus Rettinger
Dave F. Pollard
Isamu Morino
Osamu Uchino
Frank Hase
Dietrich G. Feist
Sébastien Roche
Kimberly Strong
Rigel Kivi
Laura Iraci
Kei Shiomi
Manvendra K. Dubey
Eliezer Sepulveda
Omaira Elena Garcia Rodriguez
Pascal Jeseck
Pauli Heikkinen
Edward J. Dlugokencky
Michael R. Gunson
Annmarie Eldering
David Crisp
Brendan Fisher
Gregory B. Osterman
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.
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Susan S. Kulawik et al.
Interactive discussion


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RC1: 'Review of amt-2019-257', Anonymous Referee #2, 02 Dec 2019
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RC2: 'Referee Comment', Anonymous Referee #1, 22 Jan 2020
Interactive discussion


-
RC1: 'Review of amt-2019-257', Anonymous Referee #2, 02 Dec 2019
-
RC2: 'Referee Comment', Anonymous Referee #1, 22 Jan 2020
Susan S. Kulawik et al.
Susan S. Kulawik et al.
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Cited
6 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
- A new exponentially decaying error correlation model for assimilating OCO-2 column-average CO<sub>2</sub> data using a length scale computed from airborne lidar measurements D. Baker et al. 10.5194/gmd-15-649-2022
- 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