Retrieving XCO2 from GOSAT FTS over East Asia Using Simultaneous Aerosol Information from CAI

In East Asia, where aerosol concentrations are persistently high throughout the year, most satellite CO2 retrieval algorithms screen out many measurements during quality control in order to reduce retrieval errors. To reduce the retrieval errors associated with aerosols, we have modified YCAR (Yonsei Carbon Retrieval) algorithm to YCAR-CAI to retrieve XCO2 from GOSAT FTS measurements using aerosol retrievals from simultaneous Cloud and Aerosol Imager (CAI) measurements. The CAI aerosol algorithm provides aerosol type and optical depth information simultaneously for the same geometry and optical path as FTS. The YCAR-CAI XCO2 retrieval algorithm has been developed based on the optimal estimation method. The algorithm uses the VLIDORT V2.6 radiative transfer model to calculate radiances and Jacobian functions. The XCO2 results retrieved using the YCAR-CAI algorithm were evaluated by comparing them with ground-based TCCON measurements and current operational GOSAT XCO2 retrievals. The retrievals show a clear annual cycle, with an increasing trend of 2.02 to 2.39 ppm per year, which is higher than that measured at Mauna Loa, Hawaii. The YCAR-CAI results were validated against the Tsukuba and Saga TCCON sites and show an root mean square error of 2.25, a bias of −0.81 ppm, and a regression line closer to the linear identity function compared with other current algorithms. Even after post-screening, the YCAR-CAI algorithm provides a larger dataset of XCO2 compared with other retrieval algorithms by 21% to 67%, which could be substantially advantageous in validation and data analysis for the area of East Asia. Retrieval uncertainty indicates a 1.39 to 1.48 ppm at the TCCON sites. Using Carbon Tracker-Asia (CT-A) data, the sampling error was analyzed and was found to be between 0.32 and 0.36 ppm for each individual sounding.

[1]  Jhoon Kim,et al.  Aerosol Property Retrieval Algorithm over Northeast Asia from TANSO-CAI Measurements Onboard GOSAT , 2017, Remote. Sens..

[2]  S. Solomon The Physical Science Basis : Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[3]  Nicola Jones,et al.  Troubling milestone for CO 2 , 2013 .

[4]  Jianping Mao,et al.  Sensitivity studies for space-based measurement of atmospheric total column carbon dioxide by reflected sunlight. , 2004, Applied optics.

[5]  Mark Lawrence,et al.  Solar irradiance reduction to counteract radiative forcing from a quadrupling of CO2: climate responses simulated by four earth system models , 2012 .

[6]  Ulrich Platt,et al.  Iterative maximum a posteriori ( IMAP )-DOAS for retrieval of strongly absorbing trace gases : Model studies for CH 4 and CO 2 retrieval from near infrared spectra of SCIAMACHY onboard , 2005 .

[7]  David Crisp,et al.  Long-Term Vicarious Calibration of GOSAT Short-Wave Sensors: Techniques for Error Reduction and New Estimates of Radiometric Degradation Factors , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Qilong Min,et al.  A fast radiative transfer model for simulating high‐resolution absorption bands , 2005 .

[9]  Jean-Michel Hartmann,et al.  An improved O2 A band absorption model and its consequences for retrievals of photon paths and surface pressures , 2008 .

[10]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[11]  Justus Notholt,et al.  Calibration of column-averaged CH4 over European TCCON FTS sites with airborne in-situ measurements , 2012 .

[12]  J. Russell,et al.  Ground‐based observations of Arctic O3 loss during spring and summer 1997 , 1999 .

[13]  Yoshifumi Ota,et al.  CO2 retrieval algorithm for the thermal infrared spectra of the Greenhouse Gases Observing Satellite: Potential of retrieving CO2 vertical profile from high‐resolution FTS sensor , 2009 .

[14]  Hartmut Boesch,et al.  Orbiting Carbon Observatory: Inverse method and prospective error analysis , 2008 .

[15]  T. Wigley,et al.  The pre-industrial carbon dioxide level , 1983 .

[16]  Tatsuya Yokota,et al.  Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results , 2009 .

[17]  David Crisp,et al.  Measuring atmospheric carbon dioxide from space with the Orbiting Carbon Observatory-2 (OCO-2) , 2015, SPIE Optical Engineering + Applications.

[18]  Justus Notholt,et al.  Calibration of TCCON column-averaged CO2: the first aircraft campaign over European TCCON sites , 2011 .

[19]  Hajime Okamoto,et al.  Global three‐dimensional simulation of aerosol optical thickness distribution of various origins , 2000 .

[20]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[21]  John Robinson,et al.  Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON , 2015 .

[22]  G. Toon,et al.  Spaceborne measurements of atmospheric CO2 by high‐resolution NIR spectrometry of reflected sunlight: An introductory study , 2002 .

[23]  W. Briggs Statistical Methods in the Atmospheric Sciences , 2007 .

[24]  Pieter P. Tans,et al.  Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement record , 1995 .

[25]  Hartmut Boesch,et al.  Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing , 2016, Remote. Sens..

[26]  Ilse Aben,et al.  Evidence of systematic errors in SCIAMACHY-observed CO 2 due to aerosols , 2005 .

[27]  Ilse Aben,et al.  Retrievals of atmospheric CO2 from simulated space-borne measurements of backscattered near-infrared sunlight: accounting for aerosol effects. , 2009, Applied optics.

[28]  David G. Streets,et al.  U.S. NO2 trends (2005–2013): EPA Air Quality System (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI) , 2015 .

[29]  M. Buchwitz,et al.  Space‐based near‐infrared CO2 measurements: Testing the Orbiting Carbon Observatory retrieval algorithm and validation concept using SCIAMACHY observations over Park Falls, Wisconsin , 2006 .

[30]  Taro Takahashi,et al.  Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models , 2002, Nature.

[31]  K. Moffett,et al.  Remote Sens , 2015 .

[32]  Robert J. D. Spurr,et al.  VLIDORT: A linearized pseudo-spherical vector discrete ordinate radiative transfer code for forward model and retrieval studies in multilayer multiple scattering media , 2006 .

[33]  Teruyuki Nakajima,et al.  Detection of aerosol types over the East China Sea near Japan from four‐channel satellite data , 2002 .

[34]  Sergey Oshchepkov,et al.  Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space: Validation of PPDF-based CO_2 retrievals from GOSAT , 2012 .

[35]  James B. Abshire,et al.  Calibration of the Total Carbon Column Observing Network using aircraft profile data , 2010 .

[36]  Tomoyuki Urabe,et al.  The instrumentation and the BBM test results of thermal and near-infrared sensor for carbon observation (TANSO) on GOSAT , 2006, SPIE Optics + Photonics.

[37]  Brent N. Holben,et al.  Characteristics of aerosol types from AERONET sunphotometer measurements , 2010 .

[38]  Hartmut Boesch,et al.  Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite (GOSAT): Comparison with ground‐based TCCON observations and GEOS‐Chem model calculations , 2012 .

[39]  Rebecca Castano,et al.  The ACOS CO 2 retrieval algorithm – Part II: Global X CO 2 data characterization , 2012 .

[40]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[41]  Tatsuya Yokota,et al.  Retrieval algorithm for CO 2 and CH 4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite , 2010 .

[42]  Charles D. Keeling,et al.  The Concentration and Isotopic Abundances of Carbon Dioxide in the Atmosphere , 1960 .

[43]  Hartmut Boesch,et al.  Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission , 2011, Remote. Sens..

[44]  Rebecca Castano,et al.  The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations , 2011 .

[45]  Justus Notholt,et al.  The Total Carbon Column Observing Network , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[46]  Peter J. Rayner,et al.  Global observations of the carbon budget, 2, CO2 column from differential absorption of reflected sunlight in the 1.61 μm band of CO2 , 2002 .

[47]  Pieter P. Tans,et al.  Evidence for interannual variability of the carbon cycle from the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory Global Air Sampling Network , 1994 .

[48]  K. Strong,et al.  Consistent evaluation of GOSAT, SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON , 2015 .

[49]  J. Slusser,et al.  On Rayleigh Optical Depth Calculations , 1999 .

[50]  Masakatsu Nakajima,et al.  Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring. , 2009, Applied optics.

[51]  Sander Houweling,et al.  Evaluation of various observing systems for the global monitoring of CO2 surface fluxes , 2010 .

[52]  Toshihiko Takemura,et al.  Consistency of the aerosol type classification from satellite remote sensing during the Atmospheric Brown Cloud–East Asia Regional Experiment campaign , 2007 .

[53]  Hartmut Boesch,et al.  Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space: Validation of PPDF-based CO_2 retrievals from GOSAT , 2012 .

[54]  Piet Stammes,et al.  Aerosol influence on polarization and intensity in near-infrared O2 and CO2 absorption bands observed from space , 2009 .

[55]  B. Dawson,et al.  INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC) , 2008 .