Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1595-2022
https://doi.org/10.5194/gmd-15-1595-2022
Methods for assessment of models
 | Highlight paper
 | 
23 Feb 2022
Methods for assessment of models | Highlight paper |  | 23 Feb 2022

Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

Christopher Holder, Anand Gnanadesikan, and Marie Aude-Pradal

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-167', Anonymous Referee #1, 08 Oct 2021
  • CEC1: 'Comment on gmd-2021-167', Juan Antonio Añel, 12 Oct 2021
    • AC2: 'Reply on CEC1', Christopher Holder, 21 Dec 2021
  • RC2: 'Comment on gmd-2021-167', Anonymous Referee #2, 15 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Christopher Holder on behalf of the Authors (21 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (07 Jan 2022) by Richard Mills
AR by Christopher Holder on behalf of the Authors (14 Jan 2022)  Author's response    Manuscript
Download
Short summary
It can be challenging to understand why Earth system models (ESMs) produce specific results because one can arrive at the same result simply by changing the values of the parameters. In our paper, we demonstrate that it is possible to use machine learning to figure out how and why particular components of an ESM (such as biology or ocean circulations) affect the output. This work could be applied to observations to improve the accuracy of the formulations used in ESMs.