the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Nov 2024
Intensity and dynamics of extreme cold spells of the 21st century in France from CMIP6 data
Abstract. Cold extremes significantly impact society, causing excess mortality, strain on healthcare systems, and increased demand on the energy system. With global warming, these extremes are expected to decrease, as observed in various indicators. This study simulates extreme cold spells of 15 days using a Stochastic Weather Generator (SWG) based on circulation analogues and importance sampling, adapted for CMIP6 data. Our results show that the most extreme cold spells decrease in intensity with global warming, making 20th-century-like events (e.g. 1985 in France) nearly impossible by the end of the 21st century. However, some events of similar intensity may still occur in the near future. Such events are associated with patterns of atmospheric dynamics that convey cold air from high latitudes into Europe. Those atmospheric circulation patterns show a consistent high-pressure system over Iceland and a strong low-pressure system over southwestern Europe in ERA5 and CMIP6 models. We show that nudging the SWG towards this type of pattern triggers extreme cold spells, even in a warmer world. We also evaluate the ability of CMIP6 models to represent such an atmospheric pattern. This study highlights the importance of understanding cold spell dynamics and the relevance of rare events algorithms and large ensemble models to simulate low-probability, high-impact events, offering insights into the future evolution of cold extremes.
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RC1: 'Comment on egusphere-2024-3473', Anonymous Referee #1, 12 Dec 2024
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This paper investigates the evolution of 15-day cold extremes over France in 11 CMIP6 models, using a Stochastic Weather Generator (SWG) to increase the sample size and thereby allow conditioning on very extreme events. The authors argue that this provides something like a statistical equivalent of ensemble boosting, which is a method applied within a physical model. The methodology used here was introduced in a companion paper (Cadiou and Yiou, https://doi.org/10.5194/egusphere-2024-612) where it was applied to reanalysis data. However, I cannot ascertain the current status of that paper, which is unfortunate. I have to admit that I do not have a good feel for the meaning of the statistics being produced by this methodology, but since the same methodology is applied across different epochs, I understand that the differences between epochs are meaningful (i.e. we are perhaps more interested in precision than in accuracy here). The particular choice of region and cold-extreme length were motivated by implications for energy system demand, and the choice of CMIP6 models by the ready availability of the output data. That might all seem a bit arbitrary, but the subject of cold extremes has been rather neglected until recently (except for a reduction in cold extremes being a classical indicator of climate change), and I can’t imagine that the results would change with the inclusion of more CMIP6 models. Thus I believe that this study will be of interest to the climate science community.
I personally find the most interesting result to concern the connection between the WCC circulation pattern (which is not one of the canonical North Atlantic circulation patterns) and 15-day cold extremes over France. If I understand correctly, the fact that conditioning on temperature or on WCC appears to yield pretty much the same cold extremes makes the WCC a unique predictor of those cold extremes (i.e. both necessary and sufficient). Moreover, the WCC pattern appears to be reasonably well simulated in the CMIP6 models, and to not exhibit a robust response to climate change up to the warming levels investigated. These findings support the value of storyline approaches for this phenomenon, and the use of CMIP6 models, both of which are useful things for researchers to know.
My only criticism of the study is that it does seem a bit perverse to throw away ensemble members and then try to increase the sample size statistically from the single remaining member! This data set would seem to provide a perfect opportunity for validation of the SWG, to see whether it can reconstruct the missing variability, since for some models there are presumably reasonably large ensembles available.
My other comments are all minor:
Line 10: You should say what the outcome was of this analysis of the CMIP6 models.
Line 57: Blackport and Screen (2020) is a curious citation to support this statement since it argues precisely the opposite.
Lines 122-125: Was the K-S test applied to the raw model data or the bias-adjusted model data? The latter would be more informative, I think.
Lines 255-256: Is this after bias adjustment? That is what the caption to Figure 2 suggests. If so, that seems to be quite an interesting finding.
Figure 3: Can you clarify how the whiskers are constructed? The caption says the lower whiskers are constructed symmetrically to the upper whiskers, but they don’t look symmetric in the figure. Also, in a few cases (notably IPSL for the middle epoch of SSP3-7.0) there would appear to be a very large number of outliers in the cold tail, which seems highly relevant for this study; what do you make of those?
Lines 305-306: This is an interesting point, since it is quite common to look at the correlation between circulation indices and seasonal mean temperatures across the entire distribution. Can you say a bit more (rather than just a caveat, which is not so informative) about the extent to which the correlations in the tail that you are analysing are related, or not, to correlations across the entire distribution? That would be useful information for the reader, because it would guide any subsequent studies.
Lines 364-366: This statement is based on Figure 8, but I am having trouble reconciling Figure 8 with Figure 6. In Figure 6, I can hardly see any difference between the four panels for the third epoch, which is where one should presumably see the sensitivity to scenario.
Figure 7: It looks by eye as if the difference between the coldest events (those conditioned on either tas or WCC) and the rest increases as climate warms, which would seem to go against the general expectation of the coldest extremes over western Europe warming faster than the overall distribution (because of the reduced temperature difference with the Arctic). Can you comment on this? More generally, it would be useful to place this analysis within the context of the wider literature on projected changes in temperature extremes.
Lines 654-656: This submitted version of Sippel et al. should be deleted since the published version is provided by the very next entry in the reference list.
Citation: https://doi.org/10.5194/egusphere-2024-3473-RC1
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