Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling

Myrvoll-Nilsen, Eirik; Sørbye, Sigrunn Holbek; Fredriksen, Hege-Beate; Rue, Håvard; Rypdal, Martin

doi:https://doi.org/10.5194/esd-11-329-2020

Articles | Volume 11, issue 2

https://doi.org/10.5194/esd-11-329-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-11-329-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 2

Research article

| Highlight paper

|

08 Apr 2020

Research article | Highlight paper |

| 08 Apr 2020

Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling

Eirik Myrvoll-Nilsen, Sigrunn Holbek Sørbye, Hege-Beate Fredriksen, Håvard Rue, and Martin Rypdal

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Final revised paper (published on 08 Apr 2020)
Preprint (discussion started on 18 Nov 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling', Anonymous Referee #1, 19 Dec 2019
- AC1: 'Response to reviewer #1', Eirik Myrvoll-Nilsen, 30 Dec 2019
RC2: 'Review', Anonymous Referee #2, 04 Jan 2020
- AC2: 'Response to reviewer #2', Eirik Myrvoll-Nilsen, 31 Jan 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (review by editor) (01 Feb 2020) by Michel Crucifix

AR by Eirik Myrvoll-Nilsen on behalf of the Authors (17 Feb 2020) Author's response Manuscript

ED: Publish subject to technical corrections (01 Mar 2020) by Michel Crucifix

AR by Eirik Myrvoll-Nilsen on behalf of the Authors (09 Mar 2020) Manuscript

Short summary

This paper presents efficient Bayesian methods for linear response models of global mean surface temperature that take into account long-range dependence. We apply the methods to the instrumental temperature record and historical model runs in the CMIP5 ensemble to provide estimates of the transient climate response and temperature projections under the Representative Concentration Pathways.