the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Return levels of extreme European windstorms, their dependency on the NAO, and potential future risks
Matthew D. K. Priestley
David B. Stephenson
Adam A. Scaife
Daniel Bannister
Christopher J. T. Allen
David Wilkie
Abstract. Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼60 years of comprehensive reanalysis data that is dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to choice of historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints from the WISC project. Estimates of the 10-year and 200-year return levels are provided. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and setting the tail location parameter, with a less detectable influence on rarer extremes and the tail scale parameter. The optimal length of historical data required to make an accurate return level estimation is quantified using both observed and simulated timeseries of the historical NAO. For estimating 200-year return levels, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels and so there is more variability in estimates. Using recent estimates of plausible future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high return period losses across a range of hazards without the additional complexities of a full catastrophe model.
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Matthew D. K. Priestley et al.
Status: open (until 10 Apr 2023)
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RC1: 'Comment on nhess-2023-22', Anonymous Referee #1, 24 Mar 2023
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The manuscript presents an interesting exploration of European windstorm extremes, how these can be statistically modelled, and their dependence on NAO. The manuscript poses several useful and relevant questions, motivated by identified knowledge gaps. I think overall the manuscript is well structured and provides some useful insights into the answers to the questions posed. I appreciate the discussions of the limitations of the analysis but think in some places the methods could be better justified to strengthen the credibility of the conclusions made. I would thus recommend the manuscript for publication after moderate/major revisions. Below are some suggestions for improvement which I hope the authors may consider in preparing a final version.
MAIN POINTS
- Sections 3.1 and 3.2 state that the scale parameter is ‘independent of large scale patterns such as the NAO’ and there is an ‘undetectable signal in the α parameters’. It seems that these statements are made based purely on a visual inspection of Figure 4. My concern is that this these are possibly strong statements without justification based on further exploration. Could it be possible that there is an NAO signal in some regions and not in others (e.g. over some parts of Northern Europe where α1 is ~-3)? Could the authors provide a test of significance for this relationship, at least for the three most studied locations (Bergen, London, Madrid)? This would give greater credibility to this assumption and the results that follow, which are based on this assumption (e.g., the conclusion that the relative importance of NAO decreases with increasing return period in all locations).
- Please provide some additional justification/explanation in Section 3.3:
- Please could you add some additional justification of why you use a 5-year validation period. This seems like a very short window/small sample for estimating a 200-year return period.
- Are your results in Figure 7 conditional on the fact that you calculate the MSE based on a baseline 5-year period? Would this change if you used e.g. a 10-year window, changing your conclusions?
- The increase in variability in the MSE in Madrid with catalogue length is not mentioned. Do you have any thoughts on this you could add? E.g., Is this due to the difference in NAO phase between the two periods being compared (e.g., similar to the results in section 3.4)?
- Would the conclusion that ‘historical catalogues longer than 5-15 years do not yield improvements in the return level estimation’ be made if a different validation period length were used, or is this statement relative to the 5-year based estimate? Please could you clarify? I.e., Is the conclusion of this section that only 5-15 years of footprints are needed to reliably estimate the 200-year event? I’m not sure I am convinced of this from this analysis.
- Please provide more clarity in Section 3.5:
- The exploration of a future NAO state of +1.5 is interesting, but please can you make it clearer that this is a theoretical demonstration rather than a value that has been derived from climate projections. E.g., in the conclusion (line 283) you state ‘under future NAO states…’, which implies over-confidence in this evolution to a +1.5 state.
- I am not totally clear on the approach taken in this section and which parts of Figure 10 relate to which method. There is the discussion of ‘simulated events’ and ‘theoretical return levels’. Am I right in thinking that the same approach as in Section 3.4 is used to simulate events with varying NAO state and these make up the box plots, and then the approach in Appendix B is used to calculate the theoretical return levels shown by the red crosses? My confusion comes from lines 251-253 where you reference Appendix B when talking about the event simulation. Please could you clarify this and provide more detail in these lines.
- In the text you describe simulating events with reference NAO 0 and +1.5, but the plots in Figure 10 also include a box plot for reference NAO -1.5, this is slightly confusing. It is also a bit confusing using sigma again in this part of the method (because you use sigma to represent the tail scale parameter earlier in the manuscript). Could this be changed to another symbol?
OTHER MINOR POINTS
- Abstract – The abstract jumps to describing results related to the location and scale parameters but does not mention prior to this that an EVA model is used. Could this be mentioned briefly to set the scene e.g. in line 4?
- Line 64 – ‘..for a 72-hour period of each ...’ typo?
- Lines 75-77, could you include references for the ‘spatial Gaussian filter’ method, and the validation results?
- Line 83 – do you take the NAO index for the middle day of the 72-hour footprint window, or an average over the 72 hours? Could you add this detail here?
- Line 85 – My understanding is that the statistical model is applied to locations separately. Please could you add this to the beginning of Section 2.3 for clarity (i.e., ‘for any given location…’)?
- Lines 90-91 – please could you include clarity on why you introduce a separation between footprints with and without strong winds. Is this to justify the model being suitable for WISC footprints which only include strong winds?
- Line 98 – I appreciate that you discuss the limitations of assuming a shape parameter of zero, but maybe this could be referenced on this line too to reassure the reader you have considered this to be an assumption. The models in Fig 2 look good though, supporting this assumption – did you find a similar good fit in other locations?
- Line 110 – for completeness it would be appreciated if the combining and rearranging of equations 1-5 to produce equation 6 could be included in the appendix.
- Figure 2 – How were the 95% confidence intervals calculated (e.g. bootstrapping)? Please could you add this detail to the caption? The same is true for a few the other figures.
- Line 175 – The manuscript notes that the 200-year return levels are higher when using the NAO covariate, and this seems to be the case for both NAO=0.5 and -0.5. Do you have a hypothesised explanation for this? Is this linked to your conclusion at the end of Section 3.5 (line 261)?
- I appreciated the additional plots in the supplementary material. E.g. fig S2 was useful for demonstrating the relative contributions of the components of the model for different return periods.
- Line 197 – ‘5- year validation period to quantify of..’ typo?
- Line 220 – it is noted that the simulated gusts exceed the WISC dataset considerably. Could you add a statement as to whether this is to be expected, and if so, why? Is it due to the much longer simulation period?
- Conclusion – where appropriate please caveat the conclusions made in the bullet points based on your answers to my questions/comments above
- Line 304 – there as been some recent exploration of wind-gusts in the UKCP convection permitting model projection that it might be good to reference here, e.g., https://link.springer.com/article/10.1007/s00382-021-06011-4#Sec13
Citation: https://doi.org/10.5194/nhess-2023-22-RC1
Matthew D. K. Priestley et al.
Matthew D. K. Priestley et al.
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