A multi-diagnostic approach to cloud evaluation

Williams, Keith D.; Bodas-Salcedo, Alejandro

doi:https://doi.org/10.5194/gmd-10-2547-2017

Articles | Volume 10, issue 7

https://doi.org/10.5194/gmd-10-2547-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-10-2547-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 7

Model evaluation paper

|

06 Jul 2017

Model evaluation paper |

| 06 Jul 2017

A multi-diagnostic approach to cloud evaluation

Keith D. Williams and Alejandro Bodas-Salcedo

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Final revised paper (published on 06 Jul 2017)
Supplement to the final revised paper
Preprint (discussion started on 03 Jan 2017)
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: 'A review of A Multi-Diagnostic Approach to Cloud Evaluation', Anonymous Referee #1, 05 Feb 2017
- AC1: 'Response to referee 1', Keith Williams, 30 Mar 2017
RC2: 'Review of "A multi-diagnostic approach to cloud evaluation"', Anonymous Referee #2, 07 Mar 2017
- AC2: 'Response to referee 2', Keith Williams, 30 Mar 2017

Peer-review completion

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

AR by Keith Williams on behalf of the Authors (31 Mar 2017) Author's response Manuscript

ED: Publish subject to minor revisions (Editor review) (24 Apr 2017) by Klaus Gierens

Dear authors,

I acknowledge your thorough revision to the manuscript. The paper is now almost ready for acceptance. A couple of minor issues remain, though, and these can and should be easily fixed.

1) Page 5, new text: unfortunately, during revision of the text, some new notions have entered the text without explanation. These are:
a) "the convection diagnosis parcel",
b) "the (Zhang and Klein, 2013) data",
c) "inhomogeneously forced".
Probably these notions are clear to a reader of Walters et al 2017. But, in particular as this paper is still in preparation, the reader should get a chance to understand your text without looking into that reference.

2) Page 10, several places: Here you introduce a "scattering ratio". I believe this is what usually is called "backscattering ratio". If so, please use the standard terminology. Otherwise, "scattering ratio" must be defined.

3) Page 16, ll. 374/375: the averaging effect of what?

4) Page 17, l. 380: I don't understand what this means: "by lack of averaging smaller than 0.05". Please rewrite.

5) Figure 2: Please check whether the 1d and 2d histograms are all normalised to unity. For instance, it seems that the integrals under the curves in 2a, bottom right, are different; it seems also that the 2d integral in 2c, top left, is much larger than in the other histograms in that panel.

6) Figures 7 and 9: please add "statistically" before "significant and provide the significance level selected (0.05 or 0.01 or even smaller?). The word "statistically" is important here, since this notion should be clearly distinguished from "physically significant". Physical significance means that there is a physical effect, while statistical significance is just a hint for you that it is worthwhile to look further into the simulation results.

Grammar and spelling errors:
(just listed by page and line number, not specified explicitly)

P. 1, l. 21.
P. 5, l. 102, l. 112.
P. 9, l. 188, l. 191, ll. 193-194.
P. 12, l. 259.
P. 15, l. 344, l. 352.
P. 16, l. 362.
P. 17, l. 380.
P. 21, l. 480: at least in Latin "maxima" is plural, but I'm not sure how it is in English (I guess it should be "maximum" twice).
P. 21, l. 484.
P. 24, l. 543

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AR by Keith Williams on behalf of the Authors (26 Apr 2017) Author's response Manuscript

ED: Publish as is (27 Apr 2017) by Klaus Gierens

AR by Keith Williams on behalf of the Authors (28 Apr 2017)

Short summary

The simulation of cloud is problematic for general circulation models. As clouds come in differing types, areal coverage, altitude and reflectivity, it is possible for a model to appear to perform well against a particular observational dataset through a compensation of errors. Here we evaluate a model's cloud simulation against a range of observational datasets, globally and across weather–climate timescales, in order to provide a comprehensive assessment.