Bias corrections of GOSAT SWIR XCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and XCH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; with TCCON data and their evaluation using aircraft measurement data

Mayuri Inoue ,Voltaire A Velazco,Martine De Mazière,Tomoaki Tanaka,Yukio Fukuyama,Coleen M Roehl,Tatsuya Yokota,Takahiro Nakatsuru,Justus Notholt,Thomas Blumenstock,James Barney,Thorsten Warneke,Dietrich G Feist,Erica A Barrow,Pieter P Tans,Patrick W Hillyard,Isamu Morino,Markus Rettinger,Debra Wunch,Kazuhiro Tsuboi,Hidekazu Matsueda,Yukio Yoshida,Toshinobu Machida,John Robinson,Sabrina G Arnold,Ralf Sussmann,Kei Shiomi,Frank Hase,Yousuke Sawa,James R Podolske,Nicholas M Deutscher,Rigel Kivi,Osamu Uchino,Colm Sweeney,David Griffith ,J V Pittman ,A E Andrews ,S Kawakami ,L T Iraci ,E A Kort ,Manvendra K Dubey ,V Sherlock ,E Kyrö ,Sébastien Biraud ,Mat­thias Schneider ,P O Wennberg

doi:10.5194/amt-9-3491-2016

Abstract

Abstract. We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

Highlights

Atmospheric carbon dioxide (CO2) and methane (CH4) are crucially important anthropogenic greenhouse gases that contribute to global warming and future climate change
Following Wunch et al (2011b), Cogan et al (2012) performed bias correction of gases Observing SATellite (GOSAT) XCO2 data retrieved from the University of Leicester full physics (UoL-FP) retrieval algorithm using pseudo observations based on GEOS-Chem model calculations
Note that the regression coefficients are calculated from GOSAT data retrieved within ±5◦ boxes centered at 20 Total Carbon Column Observing Network (TCCON) sites, that are located in Gain-H regions even though we aim to correct GOSAT XCO2 and XCH4 data from around the world, including Gain-H and Gain-M regions

Summary

Introduction

Atmospheric carbon dioxide (CO2) and methane (CH4) are crucially important anthropogenic greenhouse gases that contribute to global warming and future climate change. Wunch et al (2011b) have attempted to correct spatially and temporally varying biases in the Atmospheric CO2 Observations from Space retrievals of the GOSAT (ACOS-GOSAT; O’Dell et al, 2012; Crisp et al, 2012) data obtained over land using an empirical linear regression model with which they correlated variabilities in XCO2 retrievals with surface albedo, the difference between the retrieved and a priori surface pressure, airmass, and the oxygen A-band spectral radiance. They used the GOSAT data in the Southern Hemisphere as the reference values for the linear regression and evaluated the bias correction against the Total Carbon Column Observing Network (TCCON) data from the Northern Hemisphere.

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