How large are temporal representativeness errors in paleoclimatology?

Amrhein, Daniel E.

doi:https://doi.org/10.5194/cp-16-325-2020

Articles | Volume 16, issue 1

https://doi.org/10.5194/cp-16-325-2020

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

Special issue:

Paleoclimate data synthesis and analysis of associated uncertainty...

https://doi.org/10.5194/cp-16-325-2020

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

Articles | Volume 16, issue 1

Research article

|

17 Feb 2020

Research article |

| 17 Feb 2020

How large are temporal representativeness errors in paleoclimatology?

Daniel E. Amrhein

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Final revised paper (published on 17 Feb 2020)
Preprint (discussion started on 11 Feb 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 “How large are temporal representativeness errors in paleoclimatology” by Daniel E. Amrhein', Anonymous Referee #1, 23 Apr 2019
- AC2: 'Response to R1', Dan Amrhein, 18 Jun 2019
RC2: 'Review of Amrhein', Anonymous Referee #2, 30 Apr 2019
- AC1: 'Reply to R2', Dan Amrhein, 18 Jun 2019

Peer-review completion

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

ED: Reconsider after major revisions (12 Jul 2019) by Liping Zhou

AR by Dan Amrhein on behalf of the Authors (31 Aug 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (19 Nov 2019) by Denis-Didier Rousseau

RR by Anonymous Referee #3 (25 Nov 2019)

ED: Publish as is (25 Nov 2019) by Denis-Didier Rousseau

AR by Dan Amrhein on behalf of the Authors (26 Nov 2019)

Short summary

Usefully combining models and data to learn about past climates relies in part on translating observed paleoclimate quantities into the discrete time and space of climate models. This work addresses errors that can arise in translation when time offsets, smoothing, or averaging is different in data and models. These errors depend on sampling timescales, which are important to report in proxy metadata, and can be large relative to the climate signals of interest.