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Delayed Mode Monitoring of Greenhouse Gases

Delayed mode monthly mean fields
Delayed mode monthly mean fields Monthly mean pressure level fields for CH4 and CO2 from the delayed-mode production stream. The delayed-mode stream is running about 5 months behind real-time to make maximum use of satellite and in-situ observations that are currently not provided in real-time. The output of the delayed-mode monitoring is used in the delayed-mode flux inversions.

Delayed mode monthly mean total columns
Delayed mode monthly mean total columns Monthly mean total column fields for CH4 and CO2 from the delayed-mode production stream. The delayed-mode stream is running about 5 months behind real-time to make maximum use of satellite and in-situ observations that are currently not provided in real-time. The output of the delayed-mode monitoring is used in the delayed-mode flux inversions.

Delayed mode methane flux inversions
Delayed mode methane flux inversions

These monthly mean plots illustrate the flux inversion from the MACC delayed-mode analysis of CH4 concentrations, based on the TM5-4DVAR inverse modeling system [Bergamaschi et al., 2009]. While the MACC delayed-mode analysis assimilates SCIAMACHY CH4 retrievals into the IFS model, we use in addition also high accuracy surface measurements from the NOAA global cooperative air sampling network in the inversion. The latter constrain significantly the surface mixing ratios in remote regions (ocean) and allow deriving corrections for potential small latitudinal or seasonal biases of the satellite data. 3D fields of CH4 mixing ratios from the TM5-4DVAR inversion are available upon request.

While the results shown here present our first best effort, using NRT delayed-mode observations with some preliminary quality control, the inversion set-up is still being improved and therefore results should not be taken as final.

Acknowledgments

We thank Ed Dlugokencky for provision of surface measurements from the NOAA Earth System Research Laboratory (ESRL) global cooperative air sampling network.

References

Bergamaschi, P., C. Frankenberg, J. F. Meirink, M. Krol, M. G. Villani, S. Houweling, F. Dentener, E. J. Dlugokencky, J. B. Miller, L. V. Gatti, A. Engel, and I. Levin, Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals, J. Geophys. Res., 114, doi:10.1029/2009JD012287, 2009.


CH4 columns from SCIAMACHY, IASI and TANSO retrievals
CH4 columns from SCIAMACHY, IASI and TANSO retrievals

These monthly mean maps (1x1 degrees) show the column-averaged methane mixing ratios retrieved from three different instruments. Data used to produce these plots come from:

  • SCIAMACHY onboard ENVISAT using the IMAP algorithm version 5.5 [Frankenberg et al. 2005, 2008a, 2008b, 2011]. The IMAP algorithm uses the proxy method to account for lightpath modifications. To this end, CH4 is retrieved simultaneously with carbon dioxide (CO2) and their ratio is multiplied with modelled CO2 fields from CarbonTracker [Peters et al. 2007].
  • TANSO-FTS onboard GOSAT using a similar algorithm as for SCIAMACHY. The algorithm uses the proxy method to account for lightpath modifications. To this end, CH4 is retrieved simultaneously with carbon dioxide (CO2) and their ratio is multiplied with modelled CO2 fields from CarbonTracker [Peters et al. 2007].
  • IASI onboard Metop-A using of a non-linear inference scheme based on neural networks. The algorithm leads to the retrieval of a mid-to-upper tropospheric integrated content representative of the 5-15 km range in the tropics, night and day, for clear-sky only [Crevoisier et al. 2009, 2013].

References

Frankenberg, C., J. F. Meirink, M. van Weele, U. Platt, and T. Wagner (2005), Assessing Methane Emissions from Global Space-Borne Observations, Science, 308 (5724), 1010-1014, doi:10.1126/science.1106644.

Frankenberg, C., P. Bergamaschi, A. Butz, S. Houweling, J. Meirink, J. Notholt, A. Petersen, H. Schrijver, T. Warneke, and I. Aben (2008a), Tropical methane emissions: A revised view from SCIAMACHY onboard ENVISAT, Geophys. Res. Lett., 35 (5), L15811, doi:10.1029/2008GL034300.

Frankenberg, C., T. Warneke, A. Butz, I. Aben, F. Hase, P. Spietz, and L. R. Brown (2008b), Pressure broadening in the 2v3 band of methane and its implication on atmospheric retrievals, Atmospheric Chemistry and Physics, 8 (17), 5061-5075, doi:10.5194/acp-8-5061-2008.

Frankenberg, C., I. Aben, P. Bergamaschi, E. J. Dlugokencky, R. van Hees, S. Houweling, P. van der Meer, R. Snel, and P. Tol (2011), Global column-averaged methane mixing ratios from 2003 to 2009 as derived from  SCIAMACHY: Trends and variability, J. Geophys. Res., 116, D04302, doi:10.1029/2010JD014849.

Peters, W. et al. (2007), An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, PNAS, 104 (48),18925-18930, doi:10.1073/pnas.0708986104.

C. Crevoisier, D. Nobileau, A. M. Fiore, R. Armante, A. Chedin, and N. A. Scott (2009), Tropospheric methane in the tropics - first year from IASI hyperspectral infrared observations, Atmos. Chem. Phys., 9, 6337-6350, doi:10.5194/acpd-9-6855-2009

Crevoisier C., Nobileau D., Armante R., Crepeau L., Machida T., Sawa Y., Matsueda H., Schuck T., Thonat T., Pernin J., Scott N.A., and Chedin A., The 2007-2011 evolution of tropical methane in the mid-troposphere as seen from space by MetOp-A/IASI, Atmos. Chem. Phys., 13, 4279-4289 doi:10.5194/acp-13-4279-2013, 2013.


Delayed mode carbon dioxide flux inversions
Delayed mode carbon dioxide flux inversions

This data describes the CO2 surface fluxes over more than three decades, from 1979 to 2013, at resolution 3.75° x 1.9° (longitude-latitude) and 3-hourly, based on 131 CO2 mole fraction station records from three large databases:

  • the NOAA Earth System Research Laboratory archive (NOAA CCGG),
  • the World Data Centre for Greenhouse Gases archive (WDCGG),
  • the Réseau Atmosphérique de Mesure des Composés à Effet de Serre database (RAMCES).

The four databases include both in situ measurements made by automated quasi-continuous analysers and irregular air samples collected in flasks and later analyzed in central facilities.

The flux inversion builds on a variational Bayesian inversion system, like the 4D-Var data assimilation system used in MACC-II, which allows the fluxes to be estimated at relatively high resolution over the globe. It uses a single 35-year inversion window, therefore enforcing the physical and statistical consistency of the inverted fluxes. Fluxes and mole fractions are linked in the system by a global atmospheric transport model. A series of flux inventories, flux climatologies and flux error models regularizes the solution to the flux inference problem. The uncertainty of the inverted fluxes is quantified from the Bayesian theory by a robust Monte Carlo method.

The inversion results can be downloaded in the form of NetCDF files from: http://apps.ecmwf.int/datasets/data/macc_ghg_inversions/

References

Chevallier, F., M. Fisher, P. Peylin, S. Serrar, P. Bousquet, F.-M. Bréon, A. Chédin, et P. Ciais (2005), Inferring CO2 sources and sinks from satellite observations: method and application to TOVS data. J. Geophys. Res., 110, D24309, doi:10.1029/2005JD006390.

Chevallier, F., F.-M. Bréon, and P. J. Rayner (2007), The contribution of the Orbiting Carbon Observatory to the estimation of CO2 sources and sinks: theoretical study in a variational data assimilation framework. J. Geophys. Res., 112, D09307, doi:10.1029/2006JD007375.

Chevallier, F., et al. (2010), CO2 surface fluxes at grid point scale estimated from a global 21-year reanalysis of atmospheric measurements. J. Geophys. Res., 115, D21307, doi:10.1029/2010JD013887


Delayed mode nitrous oxide flux inversions
Delayed mode nitrous oxide flux inversions

This data describes the N2O surface fluxes over 12 years, from 1998 to 2009, at 3.75° x 2.5° (longitude-latitude) and monthly resolution, based on air mole fraction records from 70 N2O sites plus ship-based and ocean mooring records from:

These databases include both in situ measurements made by automated quasi-continuous analysers and irregular air samples collected in flasks and later analyzed in central facilities.

The flux inversion is based on a variational Bayesian inversion system, like the 4D-Var data assimilation system used in MACC-II, which allows the fluxes to be estimated at relatively high resolution over the globe. Fluxes and mole fractions are linked in the system by a global atmospheric transport model, which accounts for the loss of N2O in the stratosphere via photolysis and reactions with metastable oxygen atoms O( 1D ). A series of flux model simulations, flux inventories, and flux error models regularizes the solution to the flux inference problem. The uncertainty of the inverted fluxes is quantified from the Bayesian theory by a robust Monte Carlo method.

The inversion results can be downloaded in the form of NetCDF files from: http://www-lscedods.cea.fr/invsat/PYVAR11_MACC/N2O/

References

Thompson, R., P. Bousquet, F. Chevallier, P. Rayner, P. Ciais, 2011: Impact of the atmospheric sink and vertical mixing on nitrous oxide fluxes estimated using inversion methods. J. Geophys. Res., 116, D17307 (http://onlinelibrary.wiley.com/doi/10.1029/2011JD015815/abstract)


SCIAMACHY CH4 columns
SCIAMACHY CH4 columns

These monthly mean maps (2x2 degrees) show the column-averaged methane mixing ratios retrieved from SCIAMACHY onboard ENVISAT using the IMAP algorithm version 6.0 [Frankenberg et al. 2005, 2008a, 2008b, 2011]. The IMAP algorithm uses the proxy method to account for lightpath modifications. To this end, CH4 is retrieved simultaneously with carbon dioxide (CO2) and their ratio is multiplied with modelled CO2 fields from CarbonTracker [Peters et al. 2007].

References

Frankenberg, C., J. F. Meirink, M. van Weele, U. Platt, and T. Wagner (2005), Assessing Methane Emissions from Global Space-Borne Observations, Science, 308 (5724), 1010-1014, doi:10.1126/science.1106644.

Frankenberg, C., P. Bergamaschi, A. Butz, S. Houweling, J. Meirink, J. Notholt, A. Petersen, H. Schrijver, T. Warneke, and I. Aben (2008a), Tropical methane emissions: A revised view from SCIAMACHY onboard ENVISAT, Geophys. Res. Lett., 35 (5), L15811, doi:10.1029/2008GL034300.

Frankenberg, C., T. Warneke, A. Butz, I. Aben, F. Hase, P. Spietz, and L. R. Brown (2008b), Pressure broadening in the 2v3 band of methane and its implication on atmospheric retrievals, Atmospheric Chemistry and Physics, 8 (17), 5061-5075, doi:10.5194/acp-8-5061-2008.

Frankenberg, C., I. Aben, P. Bergamaschi, E. J. Dlugokencky, R. van Hees, S. Houweling, P. van der Meer, R. Snel, and P. Tol (2011), Global column-averaged methane mixing ratios from 2003 to 2009 as derived from  SCIAMACHY: Trends and variability, J. Geophys. Res., 116, D04302, doi:10.1029/2010JD014849.

Peters, W. et al. (2007), An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, PNAS, 104 (48),18925-18930, doi:10.1073/pnas.0708986104.