Comparing interannual variability in three regional single-model initial-condition large ensembles  (SMILEs) over Europe

von Trentini, Fabian; Aalbers, Emma E.; Fischer, Erich M.; Ludwig, Ralf

doi:https://doi.org/10.5194/esd-11-1013-2020

Articles | Volume 11, issue 4

https://doi.org/10.5194/esd-11-1013-2020

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

Special issue:

Large Ensemble Climate Model Simulations: Exploring Natural...

https://doi.org/10.5194/esd-11-1013-2020

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

Articles | Volume 11, issue 4

Research article

|

18 Nov 2020

Research article |

| 18 Nov 2020

Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe

Fabian von Trentini, Emma E. Aalbers, Erich M. Fischer, and Ralf Ludwig

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Final revised paper (published on 18 Nov 2020)
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Preprint (discussion started on 20 Nov 2019)
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Interactive discussion

Status: closed

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

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RC1: 'Reviewer comments', Anonymous Referee #1, 08 Jan 2020
- AC1: 'Reply to reviewer #1', Fabian von Trentini, 30 Apr 2020
RC2: 'Reviewer comments', Anonymous Referee #2, 13 Feb 2020
- AC2: 'Reply to reviewer #2', Fabian von Trentini, 30 Apr 2020

Peer-review completion

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

ED: Reconsider after major revisions (16 May 2020) by Sebastian Milinski

AR by Fabian von Trentini on behalf of the Authors (26 Jun 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (17 Jul 2020) by Sebastian Milinski

RR by Anonymous Referee #3 (10 Aug 2020)

Suggestions for revision or reasons for rejection

Summary of the manuscript and previous review comments

This paper compares projections of seasonal temperature, precipitation, heatwaves and droughts over Europe on three SMILEs based on regional climate models. The paper has some really interesting results, such as the general agreement of the sign of the change, although not the magnitude across the three SMILEs used. The paper provides a significant contribution to the literature as possibly the first intercomparion of regional climate model SMILEs. The scientific results are generally sound and well founded, however the paper itself often lacks clarity and precision, making the job of the reader difficult at times. I recommend revisions to the text in the manuscript before publication.

This is my first review of the manuscript. I note that the following issue pointed out by the original reviewers is not completely resolved.

1. There are still some instances where the term internal variability is confusing and it is unclear which metric is being talked about and whether you refer to inter-annual variability or general variability.

Examples are:
lines 224/234

Comments on the manuscript:

Section 3.2 – can you do a statistical test on the means and spread to be able to say for certain whether the different models are truly different?

91-110 – should this explanation be in the introduction or methods?

164/174 – is this really all models? Or all ensemble members? This is an important distinction

184 why do we expect this to be normally distributed?

210 – you say ‘looks like’ can you test this?

In general I am confused about how you calculate some of the variability metrics, in some places you refer to standard deviations and other places variance. Can you clarify this?
Lines 91, 234,235,239,246 are places where I was unsure what metric you were using

253 – can you be more specific about the ‘period around this year’ statement?

Conclusions: These are mostly context and future work. Can you make some stronger conclusions from this manuscript. What did you find? Why is it important?

The following set of textual suggestions are meant as just suggestions. These are some examples of where the choice of wording is confusing or the grammer is incorrect. This is not an extensive list and I strongly suggest the authors revisit the text for clarity and to make sure all sentances are gramatically correct and make sense.

Textual suggestions (with line number first):

Abstract
10 Define IAV in its first instance
11 get rid of ‘often’
12 might be better to say something like: ‘represent a combination of external forcing and IAV’
14 powerful ‘tools’
14 ‘many’ instead of ‘multiple’
16 ‘summer and winter’
18 what do you mean by the two extreme indicators? How are they biased?
24 what do you mean ‘mostly follow’?
19 ‘some of the individual realisations show’
21 ‘the’ significance
23 dont need both ‘further’ and ‘also’

Introduction
28 remove the words ‘Next to’ replace with ‘In addition to’ and replace ‘another’ with ‘an’
29 ‘on’ different timescales
30 replace ‘like’ with ‘such as’
33 ‘utmost’
36 replace ‘especially’ with ‘particularly’
38 do you need tas/pr in the intro, I would move this information to the methods

Data
125 ‘between’ the three ensembles
130 ‘with’ different
131 ‘each initialisation using atmospheric perturbations’
134 replace inter-compared with ‘compared’
136 ‘because of its availability for Europe and because it has similar spatial resolution’

Section 3.1
142 You first words are ‘The indicators’ - the reader does not remember what this refers to

Section 3.2
174 replace ‘show’ with precipitation increase in all models – models don’t show a result they do something
175 similar standard deviation and range of what?

Section 3.3
180 sentence is unclear
186 should read ‘is concentrated inside’
192 – stable conditions is unclear
193 ‘this’ approach
205 ‘and’ maximum duration
206 – weird sentence – ‘even better’ is an odd choice of words
208 comma before the word but
209 ‘to represent’ IAV
210 ‘to represent’
211 ‘ are not expected’
217 do you mean ‘for’ the maximum duration?
221 ‘which is probably the most difficult’
223 what is a ‘outlier member’?
227 ‘with’ not after

Section 3.4
difference ‘in’
350 ‘unstable’
357 ‘only possible’

5 Discussion
Why is the discussion full of really short paragraphs? This reads as a list and could be made flow better and clearer.

361 – weird sentence – I would say the number of SMILEs available
369 – by biases do you mean different mean states?
379 ‘ Better agreement’ - remove the A
392 – what do you mean by ’in accordance’ ?
404-406 and 416-419 are difficult to understand
410 might be? Or is?
423 what does this tell us?
236 ‘challenges’

6 Conclusions
430 This sentence is hard to read
434/449 This was true, however there a now quite a few studies using multiple SMILEs
442 remove (dis)

Figure captions:
F4 ‘shading’ and replace ‘via’ with ‘using’
F5 ‘ during the period’
F5 – mention that individual lines are individual ensemble members
F7 – tell us that the reference period is 1980-2009
F8 – is this variance calcualted as square of the std?
F8 how do you define a significant difference?
F10 how do you pool IAV?

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ED: Publish subject to minor revisions (review by editor) (14 Aug 2020) by Sebastian Milinski

AR by Fabian von Trentini on behalf of the Authors (24 Aug 2020) Author's response Manuscript

ED: Publish subject to technical corrections (17 Sep 2020) by Sebastian Milinski

Dear authors,

Thank you for the revised manuscript. The major concerns by the reviewers have been adequately adressed.

There are a number of minor issues that should be clarified before the manuscript is published:

l.34: 'oscillations' implies periodic behaviour. 'variations' or 'variability' might be more accurate.
l.93: '[...] EM is a much better estimate of the forced response than detrending single members' suggestion: '[...] than a trend fitted to single members.' The fitted trend is an estimate of the forced response, not the residual after detrending.
l.104: '[...] only occur as a result of internal variability' Wouldn't it be more accurate to say that you want to rule out changes that occur due to insufficient sampling of internal variability? i.e. replace 'internal variability' with 'sampling uncertainty'?
l.112: 'inter-member spread [...] is analysed for each ensemble member' This doesn't make sense because inter-member spread can only be computed across members, not for an individual member. Please rephrase.
l.165: 'When models generally agree on the spread of members, the confidence in the models' ability to represent internal variability gets higher.' Models being similar to each other does not necessarily indicate that they are close to reality. Only a comparison with observations, as done in a later section, can show this.
l.220: 'only several' Do you mean 'several cases' or 'only a few cases'?
l.363: 'member size' -> 'ensemble size'
l.368: 'to detect the significance of changes[...]' Please mention the time period you are referring to. Changes by the end of the 21st century?
l.432: 'confident' -> 'robust'?
l.439: Be more specific. Which assumptions are challenged?
l.455: 'moderate agreement [...] good approximations' The formulations seems odd. Can you rephrase this?
l.468-473: Ending your conclusions with this general question without providing an answer based on your own results can distract from your actual results. Can you remove this paragraph or connect it to the findings of this study?

Best regards,
Sebastian Milinski

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AR by Fabian von Trentini on behalf of the Authors (01 Oct 2020) Manuscript

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Short summary

We compare the inter-annual variability of three single-model initial-condition large ensembles (SMILEs), downscaled with three regional climate models over Europe for seasonal temperature and precipitation, the number of heatwaves, and maximum length of dry periods. They all show good consistency with observational data. The magnitude of variability and the future development are similar in many cases. In general, variability increases for summer indicators and decreases for winter indicators.