The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal

Gobiet, A.; Suklitsch, M.; Heinrich, G.

doi:https://doi.org/10.5194/hess-19-4055-2015

Articles | Volume 19, issue 10

https://doi.org/10.5194/hess-19-4055-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-19-4055-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 19, issue 10

Research article

|

06 Oct 2015

Research article |

| 06 Oct 2015

The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal

A. Gobiet, M. Suklitsch, and G. Heinrich

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Final revised paper (published on 06 Oct 2015)
Supplement to the final revised paper
Preprint (discussion started on 16 Jun 2015)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC C2589: 'minor revisions', Anonymous Referee #1, 15 Jul 2015
- AC C2844: 'Response to Referee #1', Andreas Gobiet, 26 Jul 2015
RC C2592: 'Comments on The effect of empirical-statistical correction of intensity-dependent model errors on the climate change signal” by A. Gobiet et al.', Anonymous Referee #2, 15 Jul 2015
- AC C2997: 'Response to Referee #2', Andreas Gobiet, 05 Aug 2015

Peer-review completion

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

ED: Publish subject to minor revisions (Editor review) (26 Aug 2015) by Ludovic Oudin

AR by Andreas Gobiet on behalf of the Authors (04 Sep 2015) Author's response Manuscript

ED: Publish as is (10 Sep 2015) by Ludovic Oudin

AR by Andreas Gobiet on behalf of the Authors (11 Sep 2015)

Short summary

The effect of empirical-statistical bias correction methods, like quantile mapping (QM), on the simulated climate change signals (CCS) is currently strongly discussed and is often regarded as deficiency of bias correction methods. We demonstrate that, quite the contrary, QM can lead to an improved CCS and also has the potential to serve as an empirical constraint on model uncertainty in climate projections.