Articles | Volume 12, issue 4
Research article
 | Highlight paper
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2021-4', John Dunne, 19 Mar 2021
  • RC2: 'Comment on esd-2021-4', Anonymous Referee #2, 19 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (23 Jun 2021) by Ning Zeng
AR by User deleted account on behalf of the Authors (05 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (02 Oct 2021) by Ning Zeng
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