Comparison of CH4 inversions based on 15 months of GOSAT and SCIAMACHY observations

Over the past decade the development of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) retrievals has increased the interest in the use of satellite measurements for studying the global sources and sinks of methane. Meanwhile, measurements are becoming available from the more advanced Greenhouse Gases Observing Satellite (GOSAT). The aim of this study is to investigate the application of GOSAT retrievals to inverse modeling, for which we make use of the TM5-4DVAR inverse modeling framework. Inverse modeling calculations are performed using data from two different retrieval approaches: a full physics and a lightpath proxy ratio method. The performance of these inversions is analyzed in comparison with inversions using SCIAMACHY retrievals and measurements from the National Oceanic and Atmospheric Administration-Earth System Research Laboratory flask-sampling network. In addition, we compare the inversion results against independent surface, aircraft, and total-column measurements. Inversions with GOSAT data show good agreement with surface measurements, whereas for SCIAMACHY a similar performance can only be achieved after significant bias corrections. Some inconsistencies between surface and total-column methane remain in the Southern Hemisphere. However, comparisons with measurements from the Total Column Carbon Observing Network in situ Fourier transform spectrometer network indicate that those may be caused by systematic model errors rather than by shortcomings in the GOSAT retrievals. The global patterns of methane emissions derived from SCIAMACHY (with bias correction) and GOSAT retrievals are in remarkable agreement and allow an increased resolution of tropical emissions. The satellite inversions increase tropical methane emission by 30 to 60 TgCH4/yr compared to initial a priori estimates, partly counterbalanced by reductions in emissions at midlatitudes to high latitudes. Key Points GOSAT and SCIAMACHY retrievals lead to comparable emission patterns GOSAT retrievals are found much less affected by biases than SCIAMACHY Combining satellite and in-situ observations point to remaining inconsistencies ©2013. American Geophysical Union. All Rights Reserved.

[1]  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 .

[2]  Hermann W. Bange,et al.  MEMENTO: a proposal to develop a database of marine nitrous oxide and methane measurements , 2009 .

[3]  F. Keppler,et al.  Methane emissions from terrestrial plants under aerobic conditions , 2006, Nature.

[4]  I. Vigano.  Aerobic methane production from organic matter , 2005 .

[5]  J. Randerson,et al.  An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker , 2007, Proceedings of the National Academy of Sciences.

[6]  J. Hansen,et al.  Global warming in the twenty-first century: an alternative scenario. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[7]  R. Francey,et al.  High Precision Long-Term Monitoring of Radiatively Active and Related Trace Gases at Surface Sites and from Aircraft in the Southern Hemisphere Atmosphere , 1999 .

[8]  Akihiko Kuze,et al.  Toward accurate CO_2 and CH_4 observations from GOSAT , 2011 .

[9]  C. Lanczos.  An iteration method for the solution of the eigenvalue problem of linear differential and integral operators , 1950 .

[10]  L. Gatti,et al.  Methane flux, vertical gradient and mixing ratio measurements in a tropical forest , 2011 .

[11]  K. Kvenvolden,et al.  Gaia's breath—global methane exhalations , 2005 .

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

[13]  J. B. Miller,et al.  Contribution of anthropogenic and natural sources to atmospheric methane variability , 2006, Nature.

[14]  Sander Houweling,et al.  Trends and inter-annual variability of methane emissions derived from 1979-1993 global CTM simulations , 2002 .

[15]  I. Semiletov,et al.  Extensive Methane Venting to the Atmosphere from Sediments of the East Siberian Arctic Shelf , 2010, Science.

[16]  David J. Lary,et al.  Lagrangian four‐dimensional variational data assimilation of chemical species , 1995 .

[17]  Christopher D. Barnet,et al.  Mid-upper tropospheric methane in the high Northern Hemisphere: Spaceborne observations by AIRS, aircraft measurements, and model simulations , 2010 .

[18]  Michael B. McElroy,et al.  Three-dimensional climatological distribution of tropospheric OH: Update and evaluation , 2000 .

[19]  Shamil Maksyutov,et al.  TransCom model simulations of CH4 and related species: linking transport, surface flux and chemical loss with CH4 variability in the troposphere and lower stratosphere , 2011 .

[20]  L. H. öglund-Isaksson.  Global anthropogenic methane emissions 2005 – 2030 : technical mitigation potentials and costs , 2022 .

[21]  G. Etiope,et al.  Reappraisal of the fossil methane budget and related emission from geologic sources , 2008 .

[22]  G. Etiope,et al.  A new estimate of global methane flux from onshore and shallow submarine mud volcanoes to the atmosphere , 2004 .

[23]  J. F. Meirink,et al.  Optimizing global CO emission estimates using a four-dimensional variational data assimilation system and surface network observations , 2011 .

[24]  P. M. Lang,et al.  Atmospheric methane levels off: Temporary pause or a new steady‐state? , 2003 .

[25]  A. Schapendonk,et al.  No evidence for substantial aerobic methane emission by terrestrial plants: a 13C-labelling approach. , 2007, The New phytologist.

[26]  Vivienne H. Payne,et al.  Validation of TES methane with HIPPO aircraft observations: implications for inverse modeling of methane sources , 2011 .

[27]  David Rind,et al.  Chemistry of the Global Troposphere' Fluorocarbons as Tracers of Air Motion , 2007 .

[28]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[29]  Peter Bergamaschi,et al.  Tropical methane emissions: A revised view from SCIAMACHY onboard ENVISAT , 2008 .

[30]  J. Lelieveld,et al.  Methane formation in aerobic environments , 2009 .

[31]  James M. Russell,et al.  Seasonal Cycles and QBO Variations in Stratospheric CH4 and H2O Observed in UARS HALOE Data , 1998 .

[32]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory , 2007 .

[33]  Hartmut Boesch,et al.  Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations , 2011 .

[34]  Peter Bergamaschi,et al.  Atmospheric Chemistry and Physics Atmospheric Methane and Carbon Dioxide from Sciamachy Satellite Data: Initial Comparison with Chemistry and Transport Models , 2022 .

[35]  John A. Pyle,et al.  Modeling trace gas budgets in the troposphere: 2. CH4 and CO , 1993 .

[36]  L. Höglund-Isaksson.  Global anthropogenic methane emissions 2005-2030: technical mitigation potentials and costs , 2012 .

[37]  Peter Bergamaschi,et al.  Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite - Part 2: Methane , 2008 .

[38]  S. Houweling,et al.  Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2 , 2013 .

[39]  Derek M. Cunnold,et al.  Renewed growth of atmospheric methane , 2008 .

[40]  Thomas Kaminski,et al.  Inverse modeling of methane sources and sinks using the adjoint of a global transport model , 1999 .

[41]  Christopher D. Barnet,et al.  Characterization and validation of methane products from the Atmospheric Infrared Sounder (AIRS) , 2008 .

[42]  Philippe Bousquet,et al.  Constraining global methane emissions and uptake by ecosystems , 2011 .

[43]  Philippe Ciais,et al.  Source attribution of the changes in atmospheric methane for 2006–2008 , 2010 .

[44]  Akihiko Kuze,et al.  Toward accurate CO2 and CH4 observations from GOSAT , 2011 .

[45]  Peter Bergamaschi,et al.  Global column-averaged methane mixing ratios from 2003 to 2009 as derived from SCIAMACHY: Trends and variability , 2011 .

[46]  J. F. Meirink,et al.  Inverse Modeling of Global and Regional CH4 Emissions Using SCIAMACHY Satellite Retrievals , 2009 .

[47]  Velders Gjm,et al.  Description of the RIVM 2-dimensional stratosphere model , 1995 .

[48]  Henk Eskes,et al.  Methane Emissions from Sciamachy Observations Sensitivity Analysis of Methane Emissions Derived from Sciamachy Observations through Inverse Modelling Acpd Methane Emissions from Sciamachy Observations , 2022 .

[49]  Alain Chedin,et al.  Tropospheric methane in the tropics – first year from IASI hyperspectral infrared observations , 2009 .

[50]  Kaarle Kupiainen,et al.  Simultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food Security , 2012, Science.

[51]  Giuseppe Etiope,et al.  Geologic emissions of methane to the atmosphere. , 2002, Chemosphere.

[52]  J. F. Meirink,et al.  Assessing Methane Emissions from Global Space-Borne Observations , 2005, Science.

[53]  R. Weiss,et al.  Estimating regional methane surface fluxes: the relative importance of surface and GOSAT mole fraction measurements , 2012 .

[54]  Michael B. McElroy,et al.  Constraints on meridional transport in the stratosphere imposed by the mean age of air in the lower stratosphere , 2001 .

[55]  Ilse Aben,et al.  CH4 retrievals from space‐based solar backscatter measurements: Performance evaluation against simulated aerosol and cirrus loaded scenes , 2010 .

[56]  P. Crill,et al.  Emission of methane from plants , 2009, Proceedings of the Royal Society B: Biological Sciences.

[57]  P. Jöckel,et al.  Small Interannual Variability of Global Atmospheric Hydroxyl , 2011, Science.

[58]  I. Aben,et al.  Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations , 2012 .

[59]  Ilse Aben,et al.  Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: Performance comparison of proxy and physics retrieval algorithms , 2012 .

[60]  R. Errico.  What is an adjoint model , 1997 .

[61]  Inez Y. Fung,et al.  Methane emission from natural wetlands: Global distribution, area, and environmental characteristics of sources , 1987 .

[62]  R. Dickinson,et al.  Couplings between changes in the climate system and biogeochemistry , 2007 .

[63]  Philippe Ciais,et al.  Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations , 2010 .

[64]  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 .

[65]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. Ii: Numerical Results , 2007 .

[66]  Michael B. McElroy,et al.  A 3‐D model analysis of the slowdown and interannual variability in the methane growth rate from 1988 to 1997 , 2004 .

[67]  Lieven Clarisse,et al.  Characterization of methane retrievals from the IASI space-borne sounder , 2009 .

[68]  Peter Bergamaschi,et al.  Four‐dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations , 2008 .

[69]  Paul J. Crutzen,et al.  An inverse modeling approach to investigate the global atmospheric methane cycle , 1997 .

[70]  Peter Bergamaschi,et al.  Inverse modeling of European CH 4 emissions: sensitivity to the observational network , 2009 .

[71]  S. Wofsy,et al.  HIAPER Pole-to-Pole Observations (HIPPO): fine-grained, global-scale measurements of climatically important atmospheric gases and aerosols , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[72]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .