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
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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Interactive discussion
- RC1: 'Review of amt-2019-257', Anonymous Referee #2, 02 Dec 2019
- 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
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Cited
9 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
- Impact of a Regional U.S. Drought on Land and Atmospheric Carbon E. Lee et al. 10.1029/2019JG005599
- 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
- Multisensor and Multimodel Monitoring and Investigation of a Wintertime Air Pollution Event Ahead of a Cold Front Over Eastern China X. Hu et al. 10.1029/2020JD033538
- A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021) F. Jiang et al. 10.5194/essd-14-3013-2022