Articles | Volume 16, issue 16
Atmos. Chem. Phys., 16, 10399–10418, 2016
https://doi.org/10.5194/acp-16-10399-2016
Atmos. Chem. Phys., 16, 10399–10418, 2016
https://doi.org/10.5194/acp-16-10399-2016

Research article 18 Aug 2016

Research article | 18 Aug 2016

A biogenic CO2 flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO2 analyses and forecasts

Anna Agustí-Panareda et al.

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Andrews, A. E., Kofler, J. D., Trudeau, M. E., Williams, J. C., Neff, D. H., Masarie, K. A., Chao, D. Y., Kitzis, D. R., Novelli, P. C., Zhao, C. L., Dlugokencky, E. J., Lang, P. M., Crotwell, M. J., Fischer, M. L., Parker, M. J., Lee, J. T., Baumann, D. D., Desai, A. R., Stanier, C. O., De Wekker, S. F. J., Wolfe, D. E., Munger, J. W., and Tans, P. P.: CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmos. Meas. Tech., 7, 647–687, https://doi.org/10.5194/amt-7-647-2014, 2014.
Balzarolo, M., Boussetta, S., Balsamo, G., Beljaars, A., Maignan, F., Calvet, J.-C., Lafont, S., Barbu, A., Poulter, B., Chevallier, F., Szczypta, C., and Papale, D.: Evaluating the potential of large-scale simulations to predict carbon fluxes of terrestrial ecosystems over a European Eddy Covariance network, Biogeosciences, 11, 2661–2678, https://doi.org/10.5194/bg-11-2661-2014, 2014.
Boussetta, S., Balsamo, G., Beljaars, A., Agusti-Panareda, A., Calvet, J.-C., Jacobs, C., van den Hurk, B., Viterbo, P., Lafont, S., Dutra, E., Jarlan, L., Balzarolo, M., Papale, D., and van der Werf, G.: Natural carbon dioxide exchanges in the ECMWF Integrated Forecasting System: Implementation and offline validation, J. Geophys. Res.-Atmos., 118, 1–24, https://doi.org/10.1002/jgrd.50488, 2013.
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This paper presents a method to adjust the sinks and sources of CO2 associated with land ecosystems within a global atmospheric CO2 forecasting system in order to reduce the errors in the forecast. This is done by combining information on (1) retrospective fluxes estimated by a global flux inversion system, (2) land-use information, and (3) simulated fluxes from the model. Because the method is simple and flexible, it can easily run in real time as part of a forecasting system.
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