Inferring climate variability from nonlinear proxies: application to palaeo-ENSO studies

Emile-Geay, J.; Tingley, M.

doi:https://doi.org/10.5194/cp-12-31-2016

Articles | Volume 12, issue 1

https://doi.org/10.5194/cp-12-31-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/cp-12-31-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 12, issue 1

Research article

|

15 Jan 2016

Research article |

| 15 Jan 2016

Inferring climate variability from nonlinear proxies: application to palaeo-ENSO studies

J. Emile-Geay and M. Tingley

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Final revised paper (published on 15 Jan 2016)
Preprint (discussion started on 08 Jul 2015)

Interactive discussion

Status: closed

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

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- Supplement

RC C1209: 'Review', Anonymous Referee #1, 30 Jul 2015
- AC C2440: 'Response to Referee #1', Julien Emile-Geay, 23 Nov 2015
RC C2072: 'Inferring climate variability from nonlinear proxies: application to paleo-ENSO studies', Anonymous Referee #2, 21 Oct 2015
- AC C2445: 'Reply to Referee #2', Julien Emile-Geay, 23 Nov 2015
EC C2075: 'Editor's comment', Volker Rath, 21 Oct 2015

Peer-review completion

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

ED: Publish subject to minor revisions (review by Editor) (29 Nov 2015) by Volker Rath

AR by Julien Emile-Geay on behalf of the Authors (30 Nov 2015) Author's response Manuscript

ED: Publish as is (30 Nov 2015) by Volker Rath

Short summary

Ignoring nonlinearity in palaeoclimate records (e.g. continental run-off proxies) runs the risk of severely overstating changes in climate variability. Even with the correct model and parameters, some information is irretrievably lost by such proxies. However, we find that a simple empirical transform can do much to improve the situation, and makes them amenable to classical analyses. Doing so on two palaeo-ENSO records markedly changes some of the quantitative inferences made from such records.