the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatial structures of emerging hot & dry compound events over Europe from 1950 to 2023
Abstract. Compound events (CE), characterized by the combination of climate phenomena that are not necessarily extreme individually, can result in severe impacts when they occur concurrently or sequentially. Understanding past and potential future changes in their occurrence is thus crucial. The present study investigates historical changes in the probability of hot and dry compound events over Europe and North Africa, using ERA5 reanalyses spanning the 1950–2023 period. Two key questions are addressed: (1) Where and when did the probability of these events emerge from natural variability, and what is the spatial extent of this emergence? This is explored through the analysis of “time” and “periods” of emergence, noted ToE and PoE, defined as the year from which and the moments during which changes in compound event probabilities exceed natural variability. The new concept of PoE allows for more in-depth signal analysis. (2) What drives the emergence? More specifically, what are the relative contributions of changes in marginal distributions versus in the dependence structure to the change of compound events probability? The signal is modelled with bivariate copula, allowing for the decomposition of these contributions. A focus on the dependence component is explored to quantify its effect on the signal’s emergence. The results reveal clear spatial patterns in terms of emergence and contributions. Five areas are studied in greater depth, selected for their contrasted signal behaviors. In some regions, the frequency of hot and dry events increased, mainly due to a change in the marginals. However, other regions see a decrease of CE probabilities, mainly driven by a change in the drought index. Although the dependence component is rarely the main contributor to PoE, it remains necessary to detect signal’s emergence. Without considering the dependence component, the date of ToE and the duration of PoE can be overestimated as well as underestimated (even more than 20 years) depending on the area. These findings provide new insights into the drivers of CE probability changes and open avenues for advancing attribution studies, ultimately improving assessments of risks associated with past and future climate change.
- Preprint
(6301 KB) - Metadata XML
-
Supplement
(8406 KB) - BibTeX
- EndNote
Status: open (until 23 Apr 2025)
-
RC1: 'Comment on egusphere-2025-461', Anonymous Referee #1, 24 Mar 2025
reply
Summary
The objective of this article is to propose a novel methodology for quantifying the emergence of statistically significant compound events and then apply this approach to extreme temperature and drought conditions across Europe estimated using ERA5 reanalysis. The authors define a Period of Emergence to quantify whether the probability that the dependence between compound events is temporarily statistically significant (Period of Emergence) or permanently statistically significant (Time of Emergence; an existing method within time series analysis). They then compare these probabilities under two assumptions: that the dependence structure between compound hazards does not change over time, and that this dependence structure does change over time. Finally, they quantify the relative contribution of each component (the drought index, the temperature index, and their dependence structure) to the Time of Emergence and Period of Emergence over time. They compare findings across five regions in Europe. They make an R package available for researchers to conduct similar analyses on any compound event pair. They explore the impact of two signals on the Time of Emergence and Period of Emergence for compound event probability. One, in black, is the probability of compound event if we take the dependence structure between temperature and drought to be constant over time. The other, in blue, is the probability of compound event if we take the dependence structure between temperature and drought to change over time. When either signal temporarily exceeds a confidence interval of noise, call it a period of emergence. When either signal permanently exceeds a confidence interval of noise, call it a time of emergence. Note that the signal is weakened under the assumption of constant dependence structure and is strengthened under the assumption of changing dependence structure.
Overall, this manuscript is well-written and provides a compelling argument for the inclusion of this novel methodology into compound event research. They find that a statistically significant compound event signal emerges earlier in time than previously recognized when 1) the dependence structure is allowed to change over time and 2) a period of emergence is quantified. They quantify how the contribution to these compound events has changed over time, allowing for regional and location-specific applications to compound events. This research contributes to the field of compound and multi-hazard research because it expands our definition of compound events using a novel statistical approach that can be readily applied to any event pair. The manuscript follows a logical structure and identifies potential approaches for future research that would enable more than two hazards to be simultaneously analyzed for their time-varying dependence. There are few clarifying, structural, and editorial changes needed prior to accepting this manuscript. Comments and suggestions are provided below and organized into major and minor.
Major comments
- Generally lacking helpful descriptions of each figure, see minor comments for suggestions.
- Section 5.1 – I’m missing a takeaway from this section. What do we now understand if we consider period of emergence and / or changing dependence structure between compound events. Simply that some areas are more at risk of compound hazard than previously expected? That more compound events can be attributed to a dependence structure or when a period of emergence is considered? How does this affect researchers and exposed communities? How can researchers, insurance providers, community planners, etc. use the results of this study to improve outcomes from future compound events? Can we link this kind of analysis with research exploring the drivers of compound events?
- Section 5.2 – how would your analysis change if you considered aggregating your indices at different temporal resolutions? Ie. daily, weekly, monthly max values? Min values? What is the sensitivity of your analysis to your chosen variables and chosen resolutions? Would the application of this method to climate model simulations affect the confidence interval of noise?
Minor comments
Line
- 55 needs a citation for ‘most damaging impacts’.
- 60-61 explain why covariance matrix is not appropriate for CE.
- Paragraph starting on 48 is very difficult to follow, consider rewriting from line 55 to the end, potentially break up into a few paragraphs, remove excessive detail and streamline.
- I missed the explanation as to why a gaussian assumption of the dependence between variables is not appropriate for compound extremes.
- 254 componen is missing a t; perhaps remove ‘even’ before ‘negative’, or find another way to highlight this otherwise counterintuitive statement.
- 271 Perhaps outline this argument as a counterfactual. When I first read this section, I thought your argument was that it is safe to assume that dependence can be kept constant. But I see that you are setting up the argument that this assumption is invalidated through this modeling framework and that you indeed have similar findings to Wang et al., 2021 in the prior sentence.
- 289 please elaborate on the line ‘not considering the dependence evolution would advance the signal emergence’. Does this suggest that signal emergence occurs earlier in the record under the assumption of constant dependence? I think rewording this sentence could provide needed clarity.
- 415 please include something like (78% of the grid points observe a time of emergence prior to the end of the temporal record) for clarity.
- 420 for my own interest, do any points experience a period of emergence, but then return to within the noise confidence interval? If so, where do those locations occur?
- Table 2. Please elaborate on this description. Include units, describe each of the four contributions, etc.
- Figure 1. It would be helpful to visualize the signal prior to smoothing. Is that possible to include in a figure or even in the supplement?
- Figure 2. Explain what the difference between the red and black arrows is. Is it appropriate to compare regions of different spatial scales, covering different proportions of land and ocean area?
- Figure 3. Are you able to show with different colors / hatch marks the four cases where white color is? It’s currently difficult to decipher what white means across the region. It would also be helpful to give a short summary of your interpretation of this figure.
- Figure 4. It would also be helpful to give a short summary of your interpretation of this figure.
- Figure 9. It would also be helpful to give a short summary of your interpretation of this figure.
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
121 | 35 | 3 | 159 | 11 | 4 | 4 |
- HTML: 121
- PDF: 35
- XML: 3
- Total: 159
- Supplement: 11
- BibTeX: 4
- EndNote: 4
Viewed (geographical distribution)
Country | # | Views | % |
---|---|---|---|
United States of America | 1 | 53 | 32 |
France | 2 | 22 | 13 |
China | 3 | 15 | 9 |
United Kingdom | 4 | 14 | 8 |
Germany | 5 | 10 | 6 |
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
- 53