Articles | Volume 12, issue 4
Earth Syst. Dynam., 12, 1139–1167, 2021
Earth Syst. Dynam., 12, 1139–1167, 2021

Research article 15 Nov 2021

Research article | 15 Nov 2021

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

Aaron Spring et al.

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Process-based analysis of terrestrial carbon flux predictability
István Dunkl, Aaron Spring, Pierre Friedlingstein, and Victor Brovkin
Earth Syst. Dynam. Discuss.,,, 2021
Revised manuscript accepted for ESD
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Related subject area

Earth system change: climate prediction
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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,, 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,, 2021. a
Brady, R. X. and Spring, A.: Climpred: Verification of Weather and Climate Forecasts, Journal of Open Source Software, 6, 2781,, 2021. a
Brune, S. and Baehr, J.: Preserving the Coupled Atmosphere–Ocean Feedback in Initializations of Decadal Climate Predictions, WIREs Clim. Change, 11, e637,, 2020. a
Dunkl, I., Spring, A., Friedlingstein, P., and Brovkin, V.: Process-based analysis of terrestrial carbon flux predictability, Earth Syst. Dynam. Discuss. [preprint],, in review, 2021. a, b
Short summary
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.
Final-revised paper