Articles | Volume 30, issue 1
© Author(s) 2023. This work is distributed underthe Creative Commons Attribution 4.0 License.
Weather pattern dynamics over western Europe under climate change: predictability, information entropy and production
- Final revised paper (published on 09 Jan 2023)
- Preprint (discussion started on 16 Aug 2022)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
: Report abuse
RC1: 'Comment on egusphere-2022-778', Anonymous Referee #1, 09 Sep 2022
- AC1: 'Reply on RC1', Stéphane Vannitsem, 18 Nov 2022
RC2: 'Comment on egusphere-2022-778', Anonymous Referee #2, 28 Oct 2022
- AC2: 'Reply on RC2', Stéphane Vannitsem, 18 Nov 2022
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Stéphane Vannitsem on behalf of the Authors (18 Nov 2022) Author's response Author's tracked changes Manuscript
ED: Referee Nomination & Report Request started (21 Nov 2022) by Stefano Pierini
RR by Anonymous Referee #2 (11 Dec 2022)
ED: Publish as is (14 Dec 2022) by Stefano Pierini
AR by Stéphane Vannitsem on behalf of the Authors (15 Dec 2022)
Review on: Weather pattern dynamics over Western Europe under climate change: Predictability, Information Entropy and Production
Author: Stephane Vannitsem
Submitted to Nonlinear Processes in Geophysics, manuscript egusphere-2022-778
This study considers the atmosphere as a nonequilibrium steady state system and applies methods published by Gaspard (2004) to determine predictability and irreversibility by the information entropy in observational and simulated data. The author uses block entropies for the forward and the time reversed block entropy in time series describing the North Atlantic/West European weather. The time evolution is described as a coarse-grained sequence of visited boxes. The predictability is assessed by the forward entropy and the entropy production by the irreversibility due to time reversal asymmetry.
The data are Großwetterlagen in the Eastern North Atlantic/Western Europe sector, which had been extracted in observations and scenario simulations. The daily time series are reduced by clustering of the patterns to 3, 6 and 8 time series. The observational time period is 1850-2019, and the simulated data is for 1900-2100. As the numerical effort for the joint probabilities is enormous, the 30 patterns had to be drastically reduced to 3, 6 and 8. Furthermore, the block lengths had to be reduced to two, to calculate the entropy S2. Thus, the present study is at the border of computational feasibility.
The study is insightful and relevant, although somehow preliminary, mostly due to computation restrictions. The agreement with previous studies hints at a reproducible core of results. The author should try to respond to the concerns, and if possible, less costly analyses might be added.
I have several concerns, mostly on the use of Großwetterlagen and the nonstationarities in the data (mentioned in line 199).
Line 10: is?
Lines 149-154: the paragraph could be clearer, is n=7?
Figure 3 caption: length of words, n?
Figures 4,5: a legend would be useful.