the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Dec 2023
ClimaMeter: Contextualising Extreme Weather in a Changing Climate
Abstract. Climate change is a global challenge with multiple far-reaching consequences, including the intensification and increased frequency of many extreme weather events. In response to this pressing issue, we present ClimaMeter, a platform designed to assess and contextualise extreme weather events relative to climate change. The platform offers near real-time insights into the dynamics of extreme events, serving as a resource for researchers, policymakers, and being a science dissemination tool for the general public. ClimaMeter currently analyses heatwaves, cold spells, heavy precipitation and windstorms. This paper elucidates the methodology, data sources, and analytical techniques on which ClimaMeter relies, providing a comprehensive overview of its scientific foundation. To illustrate Climameter, we provide four examples, the December 2022 North American Winter Storm, the August 2023 Guangdong – Hong Kong Flood, the late 2023 French Heatwave and the July 2023 windstorm Poly. They underscore the role of ClimaMeter in fostering a deeper understanding of the complex interactions between climate change and extreme weather, with the hope of ultimately contributing to informed decision-making and climate resilience.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-2643', Anonymous Referee #1, 30 Jan 2024
Please see my comments in the attached file.
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AC1: 'Reply on RC1', Davide Faranda, 13 Mar 2024
This paper documents the ClimaMeter online platform in detail, outlining the methodology used to carry out the analysis but also describing the graphical elements used to visualise the results, and the protocol used to write the reports. Examples of previous reports are included, with more detailed analysis provided in the supplementary material (although these are not discussed in the main text), along with templates for writing the reports. The paper is well written throughout, and the methodology and protocol are clearly defined. However, it is not entirely clear to me what the purpose of the paper is: is the intention merely to publish the protocol template to allow readers to produce their own ClimaMeter-style analyses, or to establish the scientific basis for the project? The level of detail sometimes suggests the former, but I think that the paper would benefit from offering more insight into the reasoning behind the modelling and reporting choices made, both to provide the scientific foundations for the work, and also as a guide to understanding and interpreting the official ClimaMeter output. Overall the ClimaMeter approach is a valuable addition to the D&A toolkit. I anticipate that the paper will be suitable for publication after relatively minor changes: no new analysis is required, but more discussion of the analyses presented and reflection on the rationale behind some of the modelling choices is needed.
We thank the Reviewer for taking the time to review our paper on the ClimaMeter online platform. We appreciate their positive feedback on the clarity of our methodology, protocol, and documentation of the graphical elements used in visualizing the results, as well as the suggestions for improvement. Regarding the purpose of the paper, our intention is twofold: firstly, to provide a comprehensive overview of the ClimaMeter platform, including its methodology and protocol, to enable readers to replicate our analyses. Secondly, we aim to provide a scientific basis for the project. To ensure that this second goal is achieved, we will follow the Reviewer’s suggestion of including a more detailed discussion of the methodological choices in the revised version of the manuscript. We provide further detail on this point in our replies to the Reviewer’s specific comments below.
My main criticism of this paper would be that some of the most interesting information has been relegated to figures in the appendices. The reports reproduced in section 5 are, in essence, publicly available elsewhere: rather than reproducing them here, it would be useful to see more detailed discussion of the elements of the additional plots, particularly the diagnostic plots showing analogue quality, predictability and persistence, and an explanation of how those plots should be interpreted. This should be done for at least one of the studies in detail, and preferably for all (although in this case I would just highlight the most interesting features). Similarly, there needs to be some discussion in the main text of how much the results might be altered if using the ERA5 dataset, rather than MSWX: and if the results are very different, is this because of the weather conditions on the analogue days, or because entirely different analogues are found? Are the results more stable for some hazards than others? An understanding of these potential limitations of the method is vital to understand when the method will be of most use, and when other methods may be more appropriate.
We recognize the importance of discussing key information within the main text rather than relegating it to appendices. Following the Reviewer’s suggestion, we have decided to limit the number of example reports provided in the main text to two, and include the additional plots that are currently in the Appendix for those. The other two examples will be moved to the Appendix. The additional plots included in the main text will be commented in detail, with a specific focus on the interpretation of the different evaluation metrics that we compute for the analogues. This will hopefully improve the clarity of our methodology, as well as provide a more robust scientific basis for the whole ClimaMeter protocol, in response to the Reviewer’s overarching comment. Additionally, we will expand the discussion of the differences between the MSWX and ERA5 data, and will evaluate whether to include the ERA5 figures that are currently in the Appendix in the main paper instead. We will also provide a summary of our qualitative evaluation of the differences between the two datasets for different hazard types. However, we see no easy way of including a robust quantitative evaluation of this in the paper, as we do not currently have enough ClimaMeter reports to account for seasonal and geographical dependence of such differences for multiple hazard types. We will mention this point as a limitation of our current approach, and something that should be investigated once we have processed a larger number of extreme events.
Specific points
40-44. Storyline-based (or reforecast-based) approaches to attribution do consider extreme weather events in terms of the weather system: while I appreciate the distinction between that and the ClimaMeter approach, the existence of such methods should at least be acknowledged here. Also suggest changing ‘classical’ to ‘probabilistic’ to highlight exactly what is meant by the term.
We agree with the Reviewer and will update this passage to cite work such as van Garderen (2020) and Leach et al. (2021), which are good examples of storyline-based and forecast-based attribution approaches (although we are aware that these are only two of many studies on these topics). Your suggestion to replace "classical" with "probabilistic" to clarify the intended meaning of the term is well-taken, and we will update the text accordingly.
van Garderen, L., Feser, F., and Shepherd, T. G.: A methodology for attributing the role of climate change in extreme events: a global spectrally nudged storyline, Nat. Hazards Earth Syst. Sci., 21, 171–186, https://doi.org/10.5194/nhess-21-171-2021, 2021.
Leach, N. J., Weisheimer, A., Allen, M. R., & Palmer, T. (2021). Forecast-based attribution of a winter heatwave within the limit of predictability. Proceedings of the National Academy of Sciences, (49), e2112087118.
73-76. A more thorough discussion of potential limitations of the method is needed. How sensitive is the analogue quality to the domain used, or to the choice of dataset? How sensitive are the results of the analysis to these factors? You could also add that, while there is a risk of underestimating the role of climate change due to the warming state of the climate during the reference period, the comparison is at least well defined and avoids extrapolation beyond the available data.
Thank you for your feedback. We agree on the need to expand on the method's potential limitations, including sensitivity to domain and dataset choices (for the latter, see also our reply to the Reviewer’s previous comment regarding the use of ERA5). We also agree that our approach is very conservative, in that our reference (past) period is not a preindustrial climate, but rather a mid-to-late 20th century climate. We plan to add a discussion of these limitations either at the end of Sect. 2, or in a new section coming just before the conclusions.
77. Data download and pre-processing’ doesn’t quite cover all of the steps in this section - perhaps ‘data pre-processing and analysis’?
We agree, and will update the subsection title as suggested.
87-88. If MSWX did provide mid-tropospheric fields, would it be preferable to use those? Has any testing been done to understand how much difference this would make to the results?
Thank you for raising these important points.Indeed, previous research from some of the authors of this study(Fery and Faranda, 2023; Jezequel et al., 2018) has emphasized the importance of geopotential height, especially for deep convective events and heatwaves. Moreover, Holmberg et al. (2023) showed that analogues selected on Z500 can yield regionally different results from those selected on SLP. However, the results in Faranda et al. (2022) have shown that surface fields are nonetheless reasonably effective for detecting analogues of extreme events. In conjunction with the new discussion of the limitations of our approach (see our reply to a previous comment), we will discuss the potential benefits of using mid-tropospheric fields as opposed to surface fields if the former were available from the MSWX dataset. We will also present an example from a past event where we analyse the analogues using Z500 rather than SLP.
Fery, L., & Faranda, D. (2023). Changes in synoptic circulations associated with documented derechos over France in the past 70 years. Weather and Climate Dynamics, accepted.
Holmberg, E., Messori, G., Caballero, R., & Faranda, D. (2023). The link between European warm-temperature extremes and atmospheric persistence. Earth System Dynamics, 14(4), 737-765.
Jézéquel, A., Yiou, P., and Radanovics, S.: Role of circulation in European heatwaves using flow analogues, Clim. Dynam., 50, 1145–1159, https://doi.org/10.1007/s00382-017-3667-0, 2018. a
95. The method actually used to find the analogues gets a bit lost here - I’d suggest moving ‘that is, the surface pressure fields minimizing the Euclidean distance to the event itself’ after ‘in terms of the event’s surface pressure pattern only’ so that the analogues are defined right away. Similarly, ‘once analogues are found… best 15 in each period’ seems to belong at the start of point 5.
In our revised manuscript, we will follow the Reviewer’s suggestion to clarify the approach used to identify the analogues. This is a key part of our methodology and needs to be easily reproducible by the readers.
140-145. First, please clarify in the text how ‘the dial points 95% to the right’ should be interpreted (for readers unfamiliar with the format it’s confusing to keep having to refer to the plot to check). Secondly, It should be highlighted that the relative sizes of the effects of climate change and natural variability are not actually estimated, which could be considered a limitation of the method when compared to the standard probabilistic approach.
We will clarify the interpretation of the dial in the revised text. Concerning the Reviewer’s second point, we will highlight in the new discussion of methodological limitations that we indeed do not estimate quantitatively the relative sizes of the effects of climate change and natural variability, contrary to other probabilistic approaches. This will hopefully provide readers with a comprehensive understanding of the method's capabilities and constraints.
Finally, I think the reasoning behind this dial needs to be explained in more detail. The argument here is that if the 15 analogues in the past are consistently in a different phase to the 15 analogues in the present, then this constitutes evidence that natural variability contributed significantly to the difference from past to present. This seems (partly) counter-intuitive to me, in that I would expect some circulation patterns to be more likely to appear in particular phases of ENSO in both periods and the phases of ENSO to be independent of the period chosen: so I wouldn’t expect the best analogues in the past to be systematically associated with a different phase of ENSO (I can see the argument more clearly in the case of lower-frequency modes such as the AMO, where each period may be dominated by a different phase). Can you elaborate on this? Or is it actually the case that ENSO is less often found to have a significant effect? This is perhaps something that could be discussed with reference to Figure 2 (I note that it’s very unusual for all three modes of variability to have a significant effect, but can only speculate as to why).
We understand this concern. In our revised manuscript, we will provide a more detailed explanation of the rationale behind the dial and its interpretation. As the Reviewer states, specific circulation patterns may appear more often in conjunction with specific phases of a given large-scale variability mode. If, in both periods, this is true (continuing the Reviewer’s example, if all analogues in both periods appear during El Nino), then we make the (simplified) assumption that ENSO did non contribute to the differences we see - i.e. no role of natural variability as associated with ENSO. If, however, analogues in the two periods occur during different ENSO phases, then this difference may affect the differences we see in the surface variables that we analyse. As the Reviewer correctly states, a priori one would not expect the best analogues in the past to be systematically associated with a different phase of ENSO than the analogues in the present, but empirically this occurs relatively often. We will count how often each mode of variability presents a significant change between past and present analogues in all the ClimaMeter reports we have produced to date, and will include a discussion of this statistic in the revised text. We will also include statistics on how often only one versus two or three of the modes of variability change.
148: Suggest rephrasing as ‘Q is simply the average Euclidean distance of each analogue from all other analogues’ or similar. Otherwise this almost seems to suggest that 15 more analogues are found for each of the analogues, and Q computed for those.
We will rephrase this passage as suggested, to clarify that Q is computed based on the distances between all analogues.
149-155. Which category is assigned if Q is below the 75th percentile in one period and between the 75th and 95th in the other? (it may be easier to rephrase these categories in terms of ‘below the 95th percentile’ to ensure an exhaustive definition, and to capture any cases where one period is above the 95th and the other is below the 75th?)
We agree that the current definition is ambiguous and will update this passage in the revised text. Specifically, the case described by the reviewer corresponds to the dial set at 65%.
170. Why are the significance maps only included for surface pressure changes but not the hazard variable? I would have thought (perhaps naively) that significant changes in the hazard would be of more interest.
We focus on the surface map to identify atmospheric circulation changes and then we use hazard variables to see the effects of these changes on precipitation, temperature, and wind speed. Furthermore, the surface pressure, being spatially smoother than hazard variables, is the most suitable field for a proper evaluation of Euclidean distances. Indeed also the hazard variables only show significant changes, there is an error in our text but not in the figures. This will be corrected in the next version
173. Please add a line explaining why the Cramer-von Mises test was used (presumably to compare the two distributions nonparametrically - is this a more stringent test than just comparing means, and therefore more likely to assign differences to indices of natural variability than to climate change?)
We will add this explanation. As the Reviewer correctly notes, we selected the two-sided Cramer-von Mises test to compare pairs of distributions in a non parametric way, rather than using a parametric test for the mean.
179-181. The choice to stick to a pre-specified report format may seem like an odd one to many scientists reading this paper, so I think it would be worth adding a line or two here explaining the benefits (and limitations) of doing so. It would be particularly interesting to hear of any feedback from the media on this - do journalists find it easier to digest this kind of complex information when it is presented in a format that they have become familiar with?
We thank the Reviewer for this interesting suggestion. We will add more discussion on this, also after having gathered input from the team that supports us with media contacts.
199. ‘As soon as possible’ can mean very different things, so I think it would be useful to highlight just how quickly this analysis can be produced - please add the typical/target timeframe for release.
Generally, we run analysis and produce the corresponding report within 2-3 days of the event. We will specify this in the revised text.
201. What kind of feedback prompts an update of the report?
Feedback prompting an update of the report typically includes suggestions or criticisms that we receive.. So far, examples include colleagues or journalists finding errors in the figures of analysis, requests for clarification, updated estimates of the damage or of the meteorological values. Overall, we take into account any feedback that helps improve the reports and try to incorporate them as much as possible. This discussion will be included in the updated version of the paper
205-223. I don’t think this description of the website structure adds anything to the paper, and would recommend removing it. However, a discussion of the details behind Figure 2 (perhaps just before the conclusions) could be informative - for example, discussing the relative frequency with which each mode of variability is found to be significant, and commenting on the fact that a high proportion of events studied have no close analogues: is this because the core team are choosing to study the more meteorologically unusual events, so we shouldn’t expect to find any close analogues? Or is there some factor that could be affecting the quality of the analogues identified, such as sensitivity to the domain size?
We understand the concern regarding the description of the website structure (sections 205-223), and will consider moving this to the Appendix in the revised version of the paper. Regarding Figure 2, we will compute and discuss some statistics on the different modes of variability as per our reply to one of the previous comments. We will further include a discussion of analogue quality in the new section focussing on methodological limitations. We indeed believe, as the Reviewer states, that the generally poor quality of the analogues is due to the fact that we are selecting extreme events, whose large-scale meteorological drivers are often as unusual as the event itself.
224-399. It’s not clear to me what the purpose is of simply reproducing these four studies here without any further discussion or analysis. For readers hoping to replicate the analysis for another event, it would be far more useful to choose one study to focus on, and to walk through the process of defining the event and interpreting the analysis. The more detailed figure for that study should be moved from the Appendix to the main text, and the elements that are not already discussed in the text commented on (in particular, the violinplots showing the predictability and persistence, and the plots of trends in the distribution of analogues). The differences and similarities between the MSWX and ERA5 results should also be addressed here. If the four separate case studies are retained in the main text, there needs to be some discussion in the conclusion of what they illustrate about the method.
As per our previous replies, we plan to move the extended analysis and ERA5 plots to the main text, and limit the examples to two, in order to be able to discuss them in greater detail.
231. Broken Wikipedia link/citation.
We will update accordingly.
282. Should be ‘Haikui’.
We will change accordingly.
402. Typo: ‘this critical issue’.
We will change accordingly.
401-404. This paragraph rather implies that no other methods or groups exist to communicate the changing risks of extreme events, which is simply not true (there are now several operational met services carrying out rapid attribution studies and communicating them to the media, as well as WWA). It would be more accurate to say that ClimaMeter is part of a continuing effort to contextualise extreme and hazardous events: to me, the innovation here is that, rather than using statistical methods that analyse time series of events that may arise from different processes, ClimaMeter considers changes in extreme weather arising from the same circulation patterns, which allows a more nuanced understanding of the spatial and multivariate changes associated with the weather type of interest. (This is particularly important for wind and precipitation, because unlike in univariate probabilistic attribution, the event definition doesn’t require averaging over the spatial domain and so doesn’t smooth out the local extremes)
It was not our intention to imply that ClimaMeter is the only initiative communicating on extreme weather events in a changing climate. We will revise this passage as suggested to acknowledge the fact that there are other complementary initiatives. We will also better highlight, as noted by the Reviewer, the novel contribution that ClimaMeter brings to the field in terms of the focus on circulation patterns rather than extreme value statistics.
410 Rather than referring to the four case studies, it would be more relevant to refer to the map in Figure 2.
We will modify accordingly.
411-412. I’m not sure what ‘These underscore the significance of contextualising extreme events, as a tool to understand the broader context within which they occur’ means here.
This paragraph is probably superfluous, and we plan to shorten it and merge it with the following paragraph in the revised version of the text.
490 This should be updated to 2001-2024 to be current at time of publication.
We will change accordingly.
Figures A1-A8. The values of ENSO, AMO and PDO associated with the event are missing from the plots.
Thank you for spotting this, we will add these values in.
Citation: https://doi.org/10.5194/egusphere-2023-2643-AC1
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AC1: 'Reply on RC1', Davide Faranda, 13 Mar 2024
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RC2: 'Comment on egusphere-2023-2643', Anonymous Referee #2, 09 Feb 2024
Please find my comments in the attached file.
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AC2: 'Reply on RC2', Davide Faranda, 13 Mar 2024
This manuscript presents a novel platform for assessing extreme events (“ClimaMeter”), which offers a useful database for past and future analyses. The authors present the protocol for ClimaMeter and explain its output and how the events are visualised. The presentation is clear and well-structured. In my opinion, the manuscript should be acceptable for publication after minor revisions.
General comments
In addition to reviewing the manuscript, I have read the report of Referee #1 and generally agree with their comments. In particular, I had the same confusion about the exact purpose of the paper. The discussion is very general, with explanations that could be reported on the ClimaMeter website, and does not have the specific details you would expect from a scientific paper. A more detailed description of (at least) one of the studies, as Referee #1 proposed, would certainly improve the scope of the paper.
We thank the Reviewer for dedicating their time to assess our paper and for the positive feedback. As detailed in our replies to Reviewer #1, we plan to limit ourselves to two examples in the main paper, but including the figures that are now in the Appendix to provide a description and discussion that goes beyond what is reported on the ClimaMeter website.
Specific comments
L53-55 – A bit more detail could be given on the event selection. Are there any plans on making this a more automatic process?
At the present there is no plan for an automated process, and we admit that the event selection is also to some extent steered by the time availability of the core group members. However, we are open for the future to use some automated event detection method (e.g., Latent Dirichlet Allocation as tested by some of the authors of this manuscript in a previous study by Fery et al. ) to select the events to be analysed. We will add more details on this in the revised text.
Fery, L., Dubrulle, B., Podvin, B., Pons, F., & Faranda, D. (2022). Learning a weather dictionary of atmospheric patterns using Latent Dirichlet Allocation. Geophysical Research Letters, 49(9), e2021GL096184.
L72 – A climate reconstruction still uses a combination of observations and models; how is this independent of model bias?
We agree that this is an imprecise wording. We will rephrase it to “minimises the influence of model bias”. Indeed, the whole idea of data assimilation is to limit the inherent biases of the model by “anchoring” it to observational data.
L81 – It would be useful to report the actual grid size of MSWX here.
We will add this information in the revised version. The grid size is 0.1°x0.1°.
L94 – It is unclear to me how these analogues are exactly defined. Do you ever consider similar events at other locations?
Analogues are defined as those surface pressure maps displaying closest Euclidean distances with respect to the event itself within the analyzed domain. We consider an area of interest around the event and do not look for similar patterns at other locations. We will specify this in the revised text. The latter choice is motivated by the fact that similar SLP patterns at different locations could have very different surface effects (in terms of temperature, wind and precipitation) even in a perfectly stationary climate, and this would thus bias our analysis.
L140-145 – The way the gauge is presented makes it seem that the conclusion of natural variability vs climate change is very certain, but it is based on only three indices. You mention some of the drawbacks of this earlier in the text, but the final presentation (i.e. the left-hand gauge) can lead to the interpretation that an event is (for example) completely outside of natural variability, while there may be other factors playing a role. A more detailed description of the reasoning behind these choices will be helpful.
In response to some of the comments from Reviewer #1 we plan on adding a new section to discuss more systematically the methodological limitations of our approach. We will raise the point mentioned by the Reviewer (who is absolutely correct in their interpretation) in this new section.
Technical corrections
L17 – “Hurrícane” → “Hurricane”
L113 – “different” → “difference”
L136 – “or wind” → “and wind” (as you show them all)
L137 – “”past” and ”present”” → “”present” and ”past”” (as you show ”present” − ”past”)
Fig. 1 – “”past” and ”present”” → “”present” and ”past”” (as you show ”present” − ”past”)
L163 – “or wind” → “and wind” (as you show them all)
L209 – “analyzed” → “analysed”
Thank you for spotting these. We will correct all these typos and technical errors in the revised text.
Citation: https://doi.org/10.5194/egusphere-2023-2643-AC2
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AC2: 'Reply on RC2', Davide Faranda, 13 Mar 2024
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