Reconstructing paleoclimate fields using online data assimilation with a linear inverse model

Perkins, Walter A.; Hakim, Gregory J.

doi:https://doi.org/10.5194/cp-13-421-2017

Articles | Volume 13, issue 5

https://doi.org/10.5194/cp-13-421-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/cp-13-421-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 13, issue 5

Research article

|

08 May 2017

Research article |

| 08 May 2017

Reconstructing paleoclimate fields using online data assimilation with a linear inverse model

Walter A. Perkins and Gregory J. Hakim

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Final revised paper (published on 08 May 2017)
Supplement to the final revised paper
Preprint (discussion started on 06 Dec 2016)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review on Reconstructing past climate by using proxy data and a linear climate model Walter A. Perkins and Gregory J. Hakim', Anonymous Referee #1, 12 Jan 2017
- AC1: 'Response to Referee #1', Walter Perkins, 01 Mar 2017
RC2: 'Review of “Reconstructing past climate by using proxy data and a linear climate model”, by Perkins and Hakim', Anonymous Referee #2, 30 Jan 2017
- AC2: 'Response to Referee #2', Walter Perkins, 02 Mar 2017

Peer-review completion

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

ED: Publish subject to minor revisions (review by Editor) (02 Mar 2017) by Joel Guiot

AR by Walter Perkins on behalf of the Authors (19 Mar 2017) Author's response Manuscript

ED: Publish as is (23 Mar 2017) by Joel Guiot

AR by Walter Perkins on behalf of the Authors (31 Mar 2017)

Short summary

We examine the skill of a novel data assimilation approach to paleoclimate reconstruction that uses linear climate model forecasts. Many reconstruction studies forego the use of forecasts from climate models due to their high computational expense and relatively low skill. We show that the use of simpler linear models can improve reconstruction skill for both global mean temperature and spatial fields. Improvements displayed seem to be related to dynamical constraints from the forecasts.