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https://doi.org/10.5194/egusphere-2025-4683
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-4683
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Nov 2025
| 12 Nov 2025
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Invited perspectives: Towards usable compound event research
Abstract. Supporting stakeholders with science-based decision-making to mitigate and adapt to climate change impacts is a central mandate of the climate research community. In particular, mapping out scenario-dependent climate risk landscapes is one of the most pressing challenges. Increasingly, communities and regions are experiencing high-impact climate and weather extremes that arise from a complex interplay of processes and events acting across various spatial and temporal scales. To account for these emerging trends, there is a growing recognition that both climate impact and early warning research needs to incorporate risks from compound events to better inform climate adaptation and mitigation efforts. This demand for more fine-grained and applicable knowledge gives rise to new data and modeling needs, and can increase uncertainties. Consequently, new methodological approaches and effective communication strategies are required for making research usable outside scientific communities. In this perspective, we reflect on this usability challenge by discussing impact data products, early warning and modeling capabilities, and communication tools, urging climate impact scientists to increasingly incorporate usability considerations in their research to meet the pressing demand for usable compound event research.
How to cite. Kornuber, K., Bevacqua, E., Madruga de Brito, M., Jäger, W. S., Rivoire, P., Rogers, C. D. W., Banfi, F., Batibeniz, F., Carruthers, J., de Michele, C., de Angeli, S., Deidda, C., de Ruiter, M. C., Fink, A. H., Goulart, H. M. D., Küpfer, K., Ludwig, P., Maraun, D., Messori, G., Nath, S., O’Loughlin, F., Pinto, J. G., Poschlod, B., Ramos, A. M., Raymond, C., Ribeiro, A. F. S., Singh, D., Gutierrez, L. S., Ward, P. J., and White, C. J.: Invited perspectives: Towards usable compound event research, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-4683, 2025.
Competing interests: Some authors are members of the editorial board of the journal NHESS.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.Download & links
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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on egusphere-2025-4683', Anonymous Referee #1, 30 Nov 2025 reply
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RC2: 'Comment on egusphere-2025-4683', David N. Bresch, 02 Dec 2025
reply
The perspective is very well written. It provides a comprehensive overview, followed by a structured account of the way forward.
I do not see a need for revision, only two truly minor suggestions:
Iine 196ff, you might consider to cite also: Themessl, M., Enigl, K., Reisenhofer, S., Köberl, J., Kortschak, D., Reichel, S., Ostermann, M., Kienberger, S., Tiede, D., Bresch, D. N., Röösli, T., Lehner, D., Schubert, C., Pichler, A., Leitner M., and Balas, M., 2022: Collection, Standardization and Attribution of Robust Disaster Event Information – A Demonstrator of a National Event-Based Loss and Damage Database in Austria. Geosciences, 12/8, 283. https://www.mdpi.com/2076-3263/12/8/283
And on a more general level, the following two papers outlines a conceptual approach that could easily be extended to multiple hazard and compound perspectives: Kam, P. M., Ciccone, F., Kropf, C. M., Riedel, L., Fairless , C., and Bresch D. N., 2024: Impact-based forecasting of tropical cyclone-related human displacement to support anticipatory action. Nature Communications, 15:8795 . https://doi.org/10.1038/s41467-024-53200-w; and Stalhandske, Z., Steinmann, C.B., Meiler, S., Sauer, I., Vogt, T., Bresch, D. N., and Kropf, C. M., 2024: Global multi-hazard risk assessment in a changing climate. Sci. Rep. 14, 5875. https://doi.org/10.1038/s41598-024-55775-2
Citation: https://doi.org/10.5194/egusphere-2025-4683-RC2 -
RC3: 'Comment on egusphere-2025-4683', Anonymous Referee #3, 12 Dec 2025
reply
Kornhuber and many colleagues have submitted an interesting manuscript on the current state of compound event research and align some ways forward. It is well written, and suitable to NHESS I believe. Below my comments on the ideas and text. In general I believe the manuscript will form a welcome addition to the literature, but I do suggest some changes to the text are made before final publication. These should, in my opinion, include rephrasing or extending of some of the ideas presented.
Comments on the ideas presented
- The way to go from climate research to decision making is highly complex. Though highly important, any claim of science being ‘usable’, therefore needs to be at least somewhat build on evidence. The manuscript in its current form I believe addresses many relevant points to advance the science (e.g. improved impact databases) and improve the quality of statements to society (e.g. call for better uncertainty information), but I wonder whether it would truly lead to more usable compound event research. Have any societal users of compound event research, e.g. policy makers, city planners, first responders, insurance companies, etc., been consulted on what they need and/or what they are currently missing? If so, I strongly advise to add this perspective to the manuscript. If not, then I believe the manuscript should be read and presented in a slightly different way, as a statement from the academic community to the same community, on how ‘we’ should aim to do better on these aspects, and maybe also that ‘we’ should reach out to societal partners and have conversations on what is known, available, and unknown, and importantly, what is missing from their point of view.
- Line 753 - “We call for impact data from a variety of sectors, …, to be made readily.” I of course fully agree with such a call, but can’t see it leading to anything concrete. If the data exists, what are the current barriers and how can they be breached, and if data doesn’t exist, how can we start the systematic collection. For this manuscript to be ‘useful’ the advise should be a bit more concrete.
- Line 828 - “New accountability standards for hazard and climate risk estimates from the private sector could help in securing reliability and trust is such products.” Who is to set and check such standards? What does the private sector think of such standards? From an academic point of view I understand this idea and wish, but I wonder if it is workable. Do users currently not trust the risk estimates from the private sector? I fear they might actually have too much trust, and would not be able to check adequately whether the supplier of climate information adheres to (any) standards. If you are not an expert, how would you know to question the uncertainty information or assumptions?
Comments on the text
- I fully realise there is a massive amount of literature on the topic of compound events, as such it is impossible to refer to all relevant studies. However, I find the literature cited to be too focussed on the papers by the authors of the submitted manuscript. For example, the below sentence holds 11 references, of which (upon quick inspection) it seems 10 are effectively self-citations. The next example scores 3/3, where better examples exist that already use impact indicators for renewable energy system risks, even for compound and co-occurrence problems. Please cite a wider range of the available relevant literature.
- Line 78 - “Sectors at risk include infrastructure and urban resilience (Hemmati et al. 2022), agriculture (Kornhuber et al. 2023; Lesk et al. 2022), water and ecosystem management (Lian et al. 2025), and biodiversity conservation, public health (Raymond, Matthews, et al. 2020; Rogers et al. 2021), energy systems (Lesk and Kornhuber 2022), and particularly globally interconnected networks such as food systems (Kornhuber et al. 2020; Lesk et al. 2021), transport, trade and supply chains, and the insurance and financial sectors (Dolk et al. 2023; Singh et al. 2023).”
- Line 688 - “For example, for the renewable energy sector, an impact indicator could use climate model simulations to identify future extreme, widespread heatwaves and wintertime cold spells (Singh et al. 2024; Mattu et al. 2025), (which increase electricity demand), that co-occur with very low wind conditions (which decrease energy production) (Meng et al. 2025).”
- This is probably my own lack of knowledge, perspective or misunderstanding, but I assumed ‘impact data’ would hold information on (societal) impacts. Reading your list of challenges, I am then surprised (line 148) that a problem is that events are not described in their full complexity, particularly the interactions between drivers. Are we talking about ‘impact data’ in that case, or are we talking about ‘hazard/vulnerability/exposure interactions leading to risk and then events data’. I suggest you define what you mean by ‘impact data’ early in section 1, maybe in the paragraph starting at line 141?
- Line 463 - Another source of those datasets are simulations done with numerical weather prediction (NWP) models, especially those for seasonal forecasts. Many applications of UNSEEN rely on smartly mining the hindcast archive of seasonal forecasts.
- Line 562 - Are the inconsistencies between bias-corrected-output and drivers really larger (i.e. amplified) after multi-variate bias correction than after univariate bias correction?
- Line 572 - Even the most data rich regions, e.g. North America or Western Europe, have blind spots for some variables. Soil moisture is a very unconstrained model variable, and highly relevant for compound drought-heat events.
- Line 635 - you note high-impact and low-impact storylines here, which essentially capture model uncertainty. I think it might be better to reserve the word ‘impact’ for (societal) consequences in your manuscript, and use a different term here. Using the actual terms for the source of uncertainty, here model uncertainty, in my opinion helps stakeholders/users to better understand why the uncertainty exists and hence design policy in a way that deals with the uncertainty in the correct way. As such, maybe high-sensitivity and low-sensitivity storylines might be a better term? Consider aligning the terms in Figure 3.
- Line 655 - PGW and also spectral nudging simulations (Feser et al), both take an observed event and transpose it to a different climatic condition. Besides the downside you mention in the next paragraph (no unprecedented event types), I would like to somewhere see added that you also can’t investigate event-specific changes in dynamics, e.g. longer lasting blocked patterns.
Minor
- Line 93 - Is it Sobel & Cohen or Coen & Sobel? Are Coen and Cohen two people, if not, why was the order of names reversed relative to the authorship of the paper?
- Line 173 - Consider adding a reference to “Drought is a continuum” to this line at your example of drought, I believe the paper matches quite well with your example.
- Figure 2 - good figure. However, the colouring choices make that the most important/novel aspects are least prominent. Is there a way to slightly tone down the colours in the middle row, whilst making more prominent your identified challenges and potential solutions (e.g. black font)?
- Line 433 - “Climate models are essential tools for exploring compound events under different climatic conditions on timescales of decades to centuries.” I find the last part of this sentence slightly confusing, ‘under different climatic conditions’ seems strong enough, ‘on timescales of decades to centuries’ is then confusing. I wonder whether the here and now, or the past are not relevant, I believe they are.
- Section 3 - maybe consider adding a subcaption when you start discussing bias adjustments?
References
- Feser, Frauke, and Theodore G. Shepherd. "The concept of spectrally nudged storylines for extreme event attribution." Communications Earth & Environment 6.1 (2025): 677.
- Van Loon, Anne F., et al. "Drought as a continuum–memory effects in interlinked hydrological, ecological, and social systems." Natural Hazards and Earth System Sciences 24.9 (2024): 3173-3205.
Citation: https://doi.org/10.5194/egusphere-2025-4683-RC3
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Kai Kornuber
CORRESPONDING AUTHOR
International Institute for Applied Systems Analysis, Laxenburg, Austria
Lamont Doherty Earth Observatory, Columbia University, New York City, USA
Emanuele Bevacqua
Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Mariana Madruga de Brito
Department of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
Wiebke S. Jäger
Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Pauline Rivoire
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Cassandra D. W. Rogers
Bureau of Meteorology, Melbourne, Australia
Fabiola Banfi
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
Fulden Batibeniz
Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research (OCCR), University of Bern, Bern, Switzerland
Institute for Atmospheric and Climate Science, ETH, Zurich, Switzerland
James Carruthers
Newcastle University, Newcastle upon Tyne, United Kingdom
Carlo de Michele
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
Silvia de Angeli
Université de Lorraine, CNRS, LIEC, F-54000 Nancy, France
Université de Lorraine, LOTERR, F-57000 Metz, France
Cristina Deidda
Department of Water and Climate, Vrije Universiteit Brussel, Brussels, Belgium
Marleen C. de Ruiter
Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Andreas H. Fink
Institute of Meteorology and Climate Research Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Henrique M. D. Goulart
Deltares, Delft, The Netherlands
Katharina Küpfer
Institute of Meteorology and Climate Research Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Patrick Ludwig
Institute of Meteorology and Climate Research Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Douglas Maraun
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
Gabriele Messori
Dept. of Earth Sciences, Uppsala University, Uppsala, Sweden
Swedish Centre for Impacts of Climate Extremes (climes), Uppsala, Sweden
Dept. of Meteorology, Stockholm University, Stockholm, Sweden
Shruti Nath
Department of Physics, University of Oxford, UK
Fiachra O’Loughlin
UCD Dooge Centre for Water Resources Research, University College Dublin, Ireland
Joaquim G. Pinto
Institute of Meteorology and Climate Research Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Benjamin Poschlod
Research Unit Sustainability and Climate Risk, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
Alexandre M. Ramos
Institute of Meteorology and Climate Research Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Colin Raymond
University of California, Los Angeles, Los Angeles, CA, USA
Andreia F. S. Ribeiro
Department of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research- UFZ, Leipzig, Germany
Deepti Singh
School of the Environment, Washington State University, Vancouver, WA, USA
Laura Suarez Gutierrez
Laboratoire des Sciences du Climat et de l’Environnement (LSCE), Paris, France
Institute for Atmospheric and Climate Science, ETH, Zurich, Switzerland
Philip J. Ward
Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Deltares, Delft, The Netherlands
Christopher J. White
University of Strathclyde, Glasgow, United Kingdom
Short summary
Impacts from extreme weather events are becoming increasingly severe under global warming, in particular when events occur simultaneously or successively. While these complex event combinations are often difficult to analyse as impact data, early warning schemes or modelling frameworks might not be fit for purpose. In this perspective we reflect on the usability of compound event research to bridge the gap between academic research and real-world applications, by formulating a set of guidelines.
Impacts from extreme weather events are becoming increasingly severe under global warming, in...
This perspective aims to advance the visibility of research on compound (extreme) events, and assess its usability along four dimensions: impact data, prediction and early warning, modelling and projection, and addressing uncertainty. The paper has a very long author list, and possibly suffers from that as it certainly has the feeling of being written by a committee. On the other hand, it is a state-of-the-art summary and there is not much in there to disagree with. Perhaps ironically, the ways forward (the italic text in section 5) are all quite idealistic so I’m not convinced they are themselves “usable”. So I think the paper is perhaps skirting the really hard questions. Nevertheless, for someone new to the field, this will be a useful reference.
Minor comments:
Line 93: I am confused by “Sobel & Cohen (Coen and Sobel 2022)”. It should just be “Coen and Sobel (2022)”.
Line 96: I don’t think that Sobel (2021) argues against blue-sky fundamental research, only that we have long had enough knowledge to justify mitigation, so if one is interested in usability, it is better to focus on adaptation than on the drivers of climate change.
Lines 99-101: That seems over-stated. You seem to be suggesting that we cannot act without perfect information. There is so much uncertainty in the human dimension that even rather uncertain climate information can be useful. It all depends on the decision context.
Lines 462-468: This text concerning CMIP-class models seems extremely glib when it comes to compound extremes, especially for anything involving heavy precipitation (many papers by Kendon, Fowler, Prein, etc.). And don’t most CMIP-class models still struggle to simulate persistent flow anomalies?
Lines 602-603: Why do you say “non-deterministic”? After all, the title of Lorenz (1963) is “Deterministic nonperiodic flow”. Chaos can be deterministic, and the Navier-Stokes equations are deterministic. (Not that it matters: I would suggest simply deleting “non-deterministic” since it is beside the point.)
Line 615: Why deterministic? Wouldn’t a probabilistic prediction be acceptable, if it was available?