Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Parthipan, Raghul; Christensen, Hannah M.; Hosking, J. Scott; Wischik, Damon J.

doi:https://doi.org/10.5194/gmd-16-4501-2023

Articles | Volume 16, issue 15

https://doi.org/10.5194/gmd-16-4501-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-16-4501-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 15

Development and technical paper

|

10 Aug 2023

Development and technical paper |

| 10 Aug 2023

Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik

Download

Final revised paper (published on 10 Aug 2023)
Preprint (discussion started on 04 Oct 2022)

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2022-912', Anonymous Referee #1, 05 Nov 2022

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-912/egusphere-2022-912-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2022-912-RC1
RC2: 'Comment on egusphere-2022-912', Pavel Perezhogin, 04 Mar 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-912/egusphere-2022-912-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2022-912-RC2
AC1: 'Comment on egusphere-2022-912', Raghul Parthipan, 25 Apr 2023

We would like to thank both our reviewers for taking the time to provide such great in-depth comments and suggestions. We have made the appropriate changes and believe the manuscript is notably improved as a result. We have attached a supplement which includes a summary of the changes we have made, and notes to address specific comments from each reviewer.

Citation: https://doi.org/10.5194/egusphere-2022-912-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Raghul Parthipan on behalf of the Authors (25 Apr 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (04 May 2023) by Travis O'Brien

RR by Anonymous Referee #1 (22 May 2023)

ED: Publish subject to technical corrections (16 Jun 2023) by Travis O'Brien

AR by Raghul Parthipan on behalf of the Authors (20 Jun 2023) Manuscript

Short summary

How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.

Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Download

Interactive discussion

Peer review completion

Suggestions for revision or reasons for rejection