Articles | Volume 15, issue 5
Model description paper
08 Mar 2022
Model description paper |  | 08 Mar 2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-83', Joe Melton, 28 May 2021
    • AC1: 'Reply on RC1', Qing Zhu, 11 Aug 2021
  • RC2: 'Comment on gmd-2021-83', Matthias Forkel, 22 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Qing Zhu on behalf of the Authors (07 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (20 Sep 2021) by Gerd A. Folberth
RR by Joe Melton (03 Oct 2021)
RR by Matthias Forkel (15 Oct 2021)
RR by D. I. Kelley (21 Jan 2022)
ED: Publish subject to minor revisions (review by editor) (23 Jan 2022) by Gerd A. Folberth
AR by Qing Zhu on behalf of the Authors (02 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (03 Feb 2022) by Gerd A. Folberth
Short summary
Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.