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Characterization of OCO-2 and ACOS-GOSAT biases and errors for CO2 flux estimates

Authors :
Coleen M. Roehl
Sébastien Roche
Junjie Liu
Rigel Kivi
Sébastien C. Biraud
Frank Hase
David Crisp
Dave Pollard
Isamu Morino
Pauli Heikkinen
Kimberly Strong
Markus Rettinger
Osamu Uchino
Manvendra K. Dubey
Paul O. Wennberg
Debra Wunch
David W. T. Griffith
Kathryn McKain
Yao Té
Martine De Mazière
Mahesh Kumar Sha
Christof Petri
Ralf Sussmann
S. C. Wofsy
Omaira Elena García Rodríguez
Eliezer Sepúlveda
Edward J. Dlugokencky
Voltaire A. Velazco
Gregory B. Osterman
Kei Shiomi
Laura T. Iraci
Justus Notholt
Susan S. Kulawik
Sean Crowell
Brendan Fisher
David Baker
Colm Sweeney
Nicholas M. Deutscher
Michael R. Gunson
Annmarie Eldering
Thorsten Warneke
Pascal Jeseck
Dietrich G. Feist
Matthäus Kiel
Christopher W. O'Dell
Publication Year :
2019
Publisher :
Copernicus GmbH, 2019.

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.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........6f1d30f39f0ba88a46f02aa1b2dfc6b8
Full Text :
https://doi.org/10.5194/amt-2019-257