Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach

MacLeod, Dave; Cloke, Hannah; Pappenberger, Florian; Weisheimer, Antje

doi:https://doi.org/10.5194/hess-20-2737-2016

Articles | Volume 20, issue 7

https://doi.org/10.5194/hess-20-2737-2016

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

https://doi.org/10.5194/hess-20-2737-2016

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

Articles | Volume 20, issue 7

Research article

|

12 Jul 2016

Research article |

| 12 Jul 2016

Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach

Dave MacLeod, Hannah Cloke, Florian Pappenberger, and Antje Weisheimer

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Final revised paper (published on 12 Jul 2016)
Supplement to the final revised paper
Preprint (discussion started on 17 Feb 2016)
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

RC1: 'Review of 'Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach' by MacLeod et al.', Rene Orth, 10 Mar 2016
- AC1: 'Response to RC1', Dave MacLeod, 05 Apr 2016
RC2: 'Review of 'Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach' by MacLeod et al.', Jan Danhelka, 12 Mar 2016
- AC2: 'Response to RC2', Dave MacLeod, 05 Apr 2016

Peer-review completion

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

ED: Publish subject to technical corrections (06 Jun 2016) by Luis Samaniego

AR by David MacLeod on behalf of the Authors (06 Jun 2016) Manuscript

Short summary

Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between the land surface and the atmosphere. Estimates have been made of the length of soil moisture memory; however, we show here how estimates of memory show large variation with uncertain model parameters. Explicit representation of model uncertainty may then improve the realism of simulations and seasonal climate forecasts.