On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments

Grudzien, Colin; Bocquet, Marc; Carrassi, Alberto

doi:https://doi.org/10.5194/gmd-13-1903-2020

Articles | Volume 13, issue 4

https://doi.org/10.5194/gmd-13-1903-2020

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

https://doi.org/10.5194/gmd-13-1903-2020

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

Articles | Volume 13, issue 4

Development and technical paper

|

16 Apr 2020

Development and technical paper |

| 16 Apr 2020

On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments

Colin Grudzien, Marc Bocquet, and Alberto Carrassi

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Final revised paper (published on 16 Apr 2020)
Preprint (discussion started on 24 Oct 2019)

Interactive discussion

Status: closed

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

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RC1: 'Review', Anonymous Referee #1, 03 Jan 2020
RC2: 'Review', Anonymous Referee #2, 09 Jan 2020
AC1: 'Authors' response', Colin Grudzien, 08 Feb 2020

Peer-review completion

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

AR by Colin Grudzien on behalf of the Authors (08 Feb 2020) Author's response Manuscript

ED: Publish subject to minor revisions (review by editor) (06 Mar 2020) by James Maddison

AR by Colin Grudzien on behalf of the Authors (07 Mar 2020) Author's response Manuscript

ED: Publish subject to technical corrections (09 Mar 2020) by James Maddison

Short summary

All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.