References

ACPD

Atmospheric Chemistry and Physics Discussions

ACPD

1680-7375

Copernicus GmbH

Göttingen, Germany

10.5194/acpd-8-19917-2008

Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter

Feng

¹ Palmer

P. I.

¹ Bösch

² Dance

School of Geosciences, University of Edinburgh, King's Buildings, Edinburgh EH9 3JN, UK

Department of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UK

Department of Mathematics and Department of Meteorology, University of Reading, Reading RG6 6BB, UK

28 11 2008

8 6 19917 19955

This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This article is available from http://www.atmos-chem-phys-discuss.net/8/19917/2008/acpd-8-19917-2008.html

The full text article is available as a PDF file from http://www.atmos-chem-phys-discuss.net/8/19917/2008/acpd-8-19917-2008.pdf

We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2</sub) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the prior error covariance to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day lag window, recognising that XCO2</sub measurements include surface flux information from prior time windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2</sub includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths <0.3; and b) scene-dependent averaging kernels that relate the CO2 profiles to XCO2</sub, accounting for differences between nadir and glint measurements, and the associated scene-dependent observation errors. We show that OCO XCO2</sub measurements significantly reduce the uncertainties of surface CO2 flux estimates. Glint measurements are generally better at constraining ocean CO2 flux estimates. Nadir XCO2</sub measurements over the terrestrial tropics are sparse throughout the year because of either clouds or smoke. Glint measurements provide the most effective constraint for estimating tropical terrestrial CO2 fluxes by accurately sampling fresh continental outflow over neighbouring oceans. We also present results from sensitivity experiments that investigate how flux estimates change with 1) bias and unbiased errors, 2) alternative duty cycles, 3) measurement density and correlations, 4) the spatial resolution of estimated flux estimates, and 5) reducing the length of the lag window and the size of the ensemble.

References 1

Baker, D F., Doney, S C., and Schimel, D S.: Variational data assimilation for atmospheric CO2, Tellus Ser. B, 58, 359–365, 2006.

Barkley, M P., Monks, P S., Frieß, U., Mittermeier, R L., Fast, H., Körner, S., and Heimann, M.: Comparisons between SCIAMACHY atmospheric CO2 retrieved using (FSI) WFM-DOAS to ground based FTIR data and the TM3 chemistry transport model, Atmos. Chem. Phys., 6, 4483–4498, 2006.

Barkley, M. P., Monks, P. S., Hewitt, A. J., Machida, T., Desai, A., Vinnichenko, N., Nakazawa, T., Yu Arshinov, M., Fedoseev, N., and Watai, T.: Assessing the near surface sensitivity of SCIAMACHY atmospheric CO2 retrieved using (FSI) WFM-DOAS, Atmos. Chem. Phys., 7, 3597–3619, 2007.

Bishop, C H., Etherton, B J., and Majumdar, S J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, 2001.

Bösch, H., Baker, D., Connor, B., O'Brien, D., Crisp, D., and Miller, C.: Global Characterization of X$_\rm CO_2$ Retrievals from OCO Observations, in preparation, 2008.

Bousquet, P., Peylin, P., Ciais, P., Quere, C L., Friedlingstein, P., and Tans, P P.: Regional changes in carbon dioxide fluxes of land and oceans since 1980, Science, 290, 1342–1346, 2000.

Bovensmann, H., Burrows, J., Buchwitz, M., Frerick, J., Noël, S., Rozanov, V., Chance, K. V., and Goede, A.: SCIAMACHY: Mission objectives and measurement modes, J. Atmos. Sci., 56, 127–150, 1999.

Bruhwiler, L. M. P., Michalak, A. M., Peters, W., Baker, D. F., and Tans, P.: An improved Kalman Smoother for atmospheric inversions, Atmos. Chem. Phys., 5, 2691–2702, 2005.

Chevallier, F.: Impact of correlated observation errors on inverted CO2 surface fluxes from OCO measurements, Geophys. Res. Lett., 34, L24804, \doi10.1029/2007GL030463, 2007b.

Chevallier, F., Bréon, F.-M., and Rayner, P J.: 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, \doi10.1029/2006JD007375, 2007a.

Chevallier, F. M F., Peylin, P., Bousquet, S. S P., Bréon, F.-M., Chédin, A., and Ciais, P.: Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data, J. Geophys. Res., 110, D24309, \doi10.1029/2005JD006390, 2005.

Connor, B J., Boesch, H., Toon, G., Sen, B., Miller, C., and Crisp, D.: Orbiting Carbon Observatory: Inverse method and prospective error analysis, J. Geophys. Res., 113, D05305, \doi10.1029/2006JD008336, 2008.

Crisp, D., Atlas, R. M., Breon, F. -M., et al.: The Orbiting Carbon Observatory (OCO) Mission, Adv. Space. Res., 34(4), 700–709, 2004.

Ehrendorfer, M.: A review of issues in ensemble-based Kalman filtering, Meteorol. Z., 16, 795–818, 2007.

Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99(C5), 10 143–10 162, 1994.

Evensen, G.: The Ensemble Kalman Filter: Theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003.

GLOBALVIEW-CO2: Cooperative Atmospheric Data Project Carbon Dioxide, CD-ROM, NOAA GMD, Boulder, Colorado, USA (also available via anonymous FTP to ftp.cmdl.noaa.gov, path:/ccg/co2/GLOBALVIEW, 2006.

Gurney, K. R., Law, R. L., Denning, A. S., et~al.: Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models, Nature, 415, 626–630, 2002.

Houtekamer, P L. and Mitchell, H L.: Data assimilation using an ensemble Kalman filter technique, Mon. Weather Rev., 126, 796–811, 1998.

Houweling, S., Kaminski, T., Dentener, F., Lelieveld, J., and Heimann, M.: Inverse modeling of methane sources and sinks using the adjoint of a global transport model, J. Geophys. Res., 104, 26 137–26 160, 1999.

Houweling, S., Hartmann, W., Aben, I., Schrijver, H., Skidmore, J., Roelofs, G.-J., and Breon, F.-M.: Evidence of systematic errors in SCIAMACHY-observed CO2 due to aerosols, Atmos. Chem. Phys., 5, 3003–3013, 2005.

Law, R M., Chen, Y H., and Gurney, K R.: TransCom-3 CO2 inversion intercomparison: 2. Sensitivity of annual mean results to data choices, Tellus, Ser. B, 55, 580–595, 2003.

Livings, D M.: Aspects of the Ensemble Kalman Filter, mSc thesis, Department of Mathematics, University of Reading, UK, 34–37, 2005.

Livings, D M., Dance, S L., and Nichols, N K.: Unbiased ensemble square root filters, Physica D., 237/8, 1021–1028, 2008.

Lorenc, A C.: The potential of the ensemble Kalman filter for NWP–A comparison with 4D-Var, Q. J. Roy. Meteorol. Soc., 129, 3183–3203, 2003.

Maksyutov, S., Kadygrov, N., Nakatsuka, Y., Patra, P K., Nakazawa, T., Yokota, T., and Inoue, G.: Projected impact of the GOSAT observations on regional CO2 flux estimations as a function of total retrieval error, Journal of Remote Sensing Society of Japan, in press, 2008.

Marland, G., Boden, T A., and Andres, R J.: Global, Regional, And National CO2 Emissions, in Trends: A Compendium of Data on Global Change, Tech. Rep. 2007, 7346, Carbon Dioxide Information Analysis Center Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tenn., USA, 2007.

Miller, C. E., Crisp, D., DeCola, P. L., et~al.: Precision requirements for space-based X$_\rm CO_2$ data, J. Geophys. Res, D10314, \doi10.1029/2006JD007659, 2007.

Palmer, P I., Suntharalingam, P., Jones, D. B A., Jacob, D J., Streets, D G., Fu, Q., Vay, S A., and Sachse, G W.: Using CO2:CO correlations to improve inverse analyses of carbon fluxes, J. Geophys. Res., 111, D12318, \doi10.1029/2005JD006697, 2006.

Palmer, P. I., Barkley, M. P., and Monks, P. S.: Interpreting the variability of space-borne CO2 column-averaged volume mixing ratios over North America using a chemistry transport model, Atmos. Chem. Phys., 8, 5855–5868, 2008.

Patra, P K., Maksyutov, S., Sasano, Y., Nakajima, H., Inoue, G., and Nakazawa, T.: An evaluation of CO2 observations with Solar Occultation FTS for Inclined-Orbit Satellite sensor for surface source inversion, J. Geophys. Res., 108(D24), 4759, \doi10.1029/2003JD003661, 2003.

Peters, W., Miller, J B., Whitaker, J., Denning, A S., Hirsch, A., Krol, M C., Zupanski, D., Bruhwiler, L., and Tans, P P.: An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations, J. Geophys. Res., 110, D24304, \doi10.1029/2005JD006157, 2005.

Peylin, P., Baker, D., Sarmiento, J., Ciais, P., and Bousquet, P.: Influence of transport uncertainty on annual mean and seasonal inversions of atmospheric CO2 data, J. Geophys. Res., 107(D19), 4385, \doi10.1029/2001JD000857, 2002.

Potter, C S., Randerson, J T., Field, C B., Matson, P A., Vitousek, P M., Mooney, H A., and Klooster, S A.: Terrestrial ecosystem production: A process model based on global satellite and surface data, Global Biogeochem. Cy., 7(4), 811–842, 1993.

Randerson, J T., Thompson, M V., Conway, T J., Fung, I Y., and Field, C B.: The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide, Global Biogeochem. Cy., 11(4), 535–560, 1997.

Rayner, P J., Law, R M., O'Brien, D M., Butler, T M., and Dilley, A C.: Global observations of the carbon budget: 3. Initial assessment of the impact of satellite orbit, scan geometry, and cloud on measuring CO2 from space, J. Geophys. Res., 107(D21), 4557, \doi10.1029/2001JD000618, 2002.

Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys., 3, 1919–1964, 2003.

Schneising, O., Buchwitz, M., Burrows, J. P., Bovensmann, H., Reuter, M., Notholt, J., Macatangay, R., and Warneke, T.: Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite – Part 1: Carbon dioxide, Atmos. Chem. Phys., 8, 3827–3853, 2008.

Stewart, L M., Dance, S., and Nichols, N.: Information content and correlated observation errors, Int. J. Numer. Meth. Fl., 56, 1521–1527, 2008.

Suntharalingam, P., Randerson, J. T., Krakauer, N., Logan, J. A., and Jacob, D. J.: The influence of reduced carbon emissions and oxidation on the distribution of atmospheric CO2: implications for inversion analysis, Global Biogeochem. Cy., 19, GB4003, \doi10.1029/2005GB002466, 2005.

Takahashi, T., Wanninkhof, R T., Feely, R A., Weiss, R F., Chapman, D W., Bates, N R., Olafsson, J., Sabine, C L., and Sutherland, C S.: Net sea-air CO2 flux over the global oceans, proceedings of the 2nd international symposium CO2 in the oceans: CGER 1037, National Institute for Environmental Studies, Tsukuba, Japan, 915 pp., 1999.

Takahashi, T., Sutherland, S. C., Sweeney, C., et~al.: Global sea-air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects, Deep Sea Res., Part II, 49, 1601–1622, 2002.

Tiwari, Y K., Gloor, M., Engelen, R J., Chevallier, F., Rödenbeck, C., Körner, S., Peylin, P., Braswell, B H., and Heimann, M.: Comparing CO2 retrieved from Atmospheric Infrared Sounder with model predictions: Implications for constraining surface fluxes and lower-to-upper troposphere transport, J. Geophys. Res., 111, D17106, \doi10.1029/2005JD006681, 2006.

van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., and Arellano Jr., A. F.: Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, 2006.

Wang, X., Bishop, C H., and Julier, S J.: Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble, Mon. Weather Rev., 132, 1590–1605, 2004.

Yevich, R. and Logan, J A.: An assessment of biofuel use and burning of agricultural waste in the developing world, Global Biogeochem. Cy., 17, 1095, \doi10.1029/2002GB001952, 2003.

Zupansi, M.: Maximum likelihood Ensemble Filter: Theoretical Aspects, Mon. Weather Rev., 133, 1710–1726, 2005.