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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 14 Jan 2020

Submitted as: model evaluation paper | 14 Jan 2020

Review status
This preprint is currently under review for the journal GMD.

Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1

Joe McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Margarita Choulga, Andrew Dawson, Richard Engelen, Zak Kiping, and Simon Lang Joe McNorton et al.
  • European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK

Abstract. Atmospheric flux inversions use observations of atmospheric CO2 to provide anthropogenic and biogenic CO2 flux estimates at a range of spatiotemporal scales. Inversions require prior flux, forward model and observation errors to estimate posterior fluxes and uncertainties. We use a numerical weather prediction model to diagnose the global forward model error associated with uncertainties in the initial meteorological state, physical parameterisations and in-model biogenic response to meteorological uncertainty. We then compare the error with the atmospheric response to uncertainty in the prior anthropogenic emissions. Although transport errors are variable, average total column CO2 (XCO2) transport errors over anthropogenic emission hotspots (0.1–0.8 ppm) are comparable to, and often exceed prior monthly anthropogenic flux uncertainties project onto the same space (0.1–1.4 ppm). Average near-surface transport error at 3 sites (Paris, Caltech and Tsukuba) range from 1.7–7.2 ppm. The global average XCO2 transport error standard deviation plateaus at ~0.1 ppm after 2–3 days, after which atmospheric mixing significantly dampens the concentration gradients. Error correlations are found to be highly flow-dependent, with XCO2 spatiotemporal correlation length scales ranging from 0 km to 700 km and 0 to 260 minutes. Globally, the average model error caused by the biogenic response to atmospheric meteorological uncertainties is small (< 0.01 ppm); however, this increases over high flux regions and is seasonally dependent (e.g Amazon January/July: 0.24 ± 0.18 ppm/0.13 ± 0.07 ppm). In general, flux hotspots are well correlated with model transport errors. Our model error estimates, combined with the atmospheric response to anthropogenic flux uncertainty, are validated against 3 TCCON XCO2 sites. Results indicate our model and flux uncertainty accounts for 21–65 % of the total uncertainty. The remaining uncertainty originates from additional sources, such as observation, numerical and representation errors, and structural errors in the biogenic model. An underrepresentation of transport and flux uncertainties could also contribute to the remaining uncertainty. Our quantification of CO2 transport error can be used to help derive accurate posterior fluxes and error reductions in future inversion systems. The model uncertainty diagnosed here can be used in varying degrees of complexity and with different modelling techniques by the inversion community.

Joe McNorton et al.

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Joe McNorton et al.

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Latest update: 20 Feb 2020
Publications Copernicus
Short summary
To infer carbon emissions from observations using atmospheric models, a detailed knowledge of uncertainty is required. The uncertainties associated with models are often estimated because they are difficult to attribute. Here we use a state-of-the-art weather model to assess the impact of uncertainty in the wind fields on atmospheric concentrations of carbon dioxide. These results can be used to help quantify the uncertainty in estimated carbon emissions from atmospheric observations.
To infer carbon emissions from observations using atmospheric models, a detailed knowledge of...