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Data availability
The CO2 mole fraction data from the Chinese sites used in this study are available at https://doi.org/10.17632/w3bwmr6rfg.1 on http://data.mendeley.com.
Code availability
We acknowledge the Python Software Foundation: Python Language Reference, version 3.7.7; available at http://www.python.org. We also acknowledge Matplotlib (v3.1.3, https://doi.org/10.5281/zenodo.3984190). The community-led GEOS-Chem model of atmospheric chemistry and transport is maintained centrally by Harvard University (http://acmg.seas.harvard.edu/geos/) and is available on request. The ensemble Kalman filter code is publicly available as PyOSSE (https://www.nceo.ac.uk/data-tools/atmospheric-tools/).
References
Wang, J. et al. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature 586, 720–723 (2020).
Schuh, A. E. et al. On the role of atmospheric model transport uncertainty in estimating the Chinese land carbon sink. Nature https://doi.org/10.1038/s41586-021-04258-9 (2022).
Schuh, A. E. et al. Quantifying the impact of atmospheric transport uncertainty on CO2 surface flux estimates. Glob. Biogeochem. Cycles 33, 484–500 (2019).
Acknowledgements
We thank the OCO-2 Model Intercomparison Project for sharing their individual model data to help us to prepare this response.
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P.I.P. and L.F. prepared this response. P.I.P. led the writing of the paper, with contributions from L.F., Y.L., S.F., H.B., C.W.O., X.T., D.Y., L.L. and C.X.
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Wang, J., Feng, L., Palmer, P.I. et al. Reply to: On the role of atmospheric model transport uncertainty in estimating the Chinese land carbon sink. Nature 603, E15–E16 (2022). https://doi.org/10.1038/s41586-021-04259-8
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DOI: https://doi.org/10.1038/s41586-021-04259-8
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