Articles | Volume 12, issue 4
https://doi.org/10.5194/esd-12-1139-2021
https://doi.org/10.5194/esd-12-1139-2021
Research article
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15 Nov 2021
Research article | Highlight paper |  | 15 Nov 2021

Trivial improvements in predictive skill due to direct reconstruction of the global carbon cycle

Aaron Spring, István Dunkl, Hongmei Li, Victor Brovkin, and Tatiana Ilyina

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Cited articles

Balmaseda, M. A., Dee, D., Vidard, A., and Anderson, D. L. T.: A Multivariate Treatment of Bias for Sequential Data Assimilation: Application to the Tropical Oceans, Q. J. Roy. Meteor. Soc., 133, 167–179, https://doi.org/10/czgj3m, 2007. a, b, c
Brady, R., Spring, A., Huang, A., Banihirwe, A., and Bell, R.: pangeo-data/climpred: Release v2.1.4 (2.1.4), Zenodo, https://doi.org/10.5281/zenodo.5347774, 2021. a
Brady, R. X. and Spring, A.: Climpred: Verification of Weather and Climate Forecasts, Journal of Open Source Software, 6, 2781, https://doi.org/10/gh9646, 2021. a
Brune, S. and Baehr, J.: Preserving the Coupled Atmosphere–Ocean Feedback in Initializations of Decadal Climate Predictions, WIREs Clim. Change, 11, e637, https://doi.org/10/ghtnt8, 2020. a
Dunkl, I., Spring, A., Friedlingstein, P., and Brovkin, V.: Process-based analysis of terrestrial carbon flux predictability, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-38, in review, 2021. a, b
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Numerical carbon cycle prediction models usually do not start from observed carbon states due to sparse observations. Instead, only physical climate is reconstructed, assuming that the carbon cycle follows indirectly. Here, we test in an idealized framework how well this indirect and direct reconstruction with perfect observations works. We find that indirect reconstruction works quite well and that improvements from the direct method are limited, strengthening the current indirect use.
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