Theoretical comparison of subgrid turbulence in atmospheric and oceanic quasi-geostrophic models

Kitsios, Vassili; Frederiksen, Jorgen S.; Zidikheri, Meelis J.

doi:https://doi.org/10.5194/npg-23-95-2016

Articles | Volume 23, issue 2

https://doi.org/10.5194/npg-23-95-2016

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

https://doi.org/10.5194/npg-23-95-2016

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

Articles | Volume 23, issue 2

Research article

|

14 Apr 2016

Research article |

| 14 Apr 2016

Theoretical comparison of subgrid turbulence in atmospheric and oceanic quasi-geostrophic models

Vassili Kitsios, Jorgen S. Frederiksen, and Meelis J. Zidikheri

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Final revised paper (published on 14 Apr 2016)
Preprint (discussion started on 10 Dec 2015)

Interactive discussion

Status: closed

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

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- Supplement

RC C611: 'New material?', Anonymous Referee #1, 05 Jan 2016
- AC C704: 'Response to reviewer 1', Vassili Kitsios, 16 Mar 2016
RC C616: 'turbulence theory applied to QG models', Anonymous Referee #2, 08 Jan 2016
- AC C709: 'Response to reviewer 2', Vassili Kitsios, 16 Mar 2016
RC C629: 'Review of ''Theoretical comparison of subgrid turbulence in the atmosphere and ocean'' by Kitsios et al.', Anonymous Referee #3, 12 Jan 2016
- AC C715: 'Response to reviewer 3', Vassili Kitsios, 16 Mar 2016

Peer-review completion

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

AR by Vassili Kitsios on behalf of the Authors (16 Mar 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (16 Mar 2016) by Harindra Joseph Fernando

RR by Anonymous Referee #1 (18 Mar 2016)

Suggestions for revision or reasons for rejection

\noindent{\bf General comments}

My impression of the paper is that the benchmark simulations in section 3 are
exactly the same as those from the authors' papers in J. Atmos. Sci. (2012) and
Ocean Modelling (2013). The diagnostics applied to these simulations (as described
in section 4) are exactly the same. The results in section 5 seem to be the same,
at least qualitatively. The scaling laws in section 6 combine the two data sets
to estimate the parameters in a "unified" scaling law; the exponents are new,
but not significantly different from the previous papers. The LES in
section 7 based on the new scaling laws clearly don't appear in previous works.
But the results in figure 5 are very similar to the equivalent figures in the
2012 and 2013 papers; this suggests that the small changes to the scaling laws
proposed in this paper have a small impact on the accuracy of the LES.

The revised manuscript includes some minor changes stating what is new here: a
new scaling law that applies to both large- and small-deformation radius QG. But
it contains very little direct comparison with the results of their previous studies.
Are the new scaling laws meant to replace those from the 2012 and 2013 papers?
If so, are the new scaling laws clearly superior to the previous ones?

I would like to see a more precise statement of what is new here as compared to
the previous papers, as well as a discussion comparing the results of sections 6
(new scaling laws) and 7 (new LES) with the previous scaling laws and their LESs.

\noindent{\bf Specific comments}

$\alpha^j$ and $\kappa^j$ in equation (1) are not constants. If they were, then
they would not depend on wavenumber $n$ in equation (3) and on lines 160--162 and
168--169.

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RR by Anonymous Referee #2 (21 Mar 2016)

RR by Anonymous Referee #3 (28 Mar 2016)

ED: Publish as is (29 Mar 2016) by Harindra Joseph Fernando

AR by Vassili Kitsios on behalf of the Authors (01 Apr 2016)

Short summary

To numerically simulate the atmosphere and ocean, the large eddies are resolved on a grid, and the effect the small unresolved eddies have on the large ones is modelled. Improper modelling leads to resolution-dependent results. We solve this long-standing problem by calculating the model coefficients from high-resolution simulations, and characterise the coefficients with a set of scaling laws. Low-resolution simulations adopting these laws reproduce the statistics of the high-resolution cases.