@article{31681d2599a342a083709e4ac1d3bb52,
title = "Guidelines for studying diverse types of compound weather and climate events",
abstract = "Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.",
keywords = "climate change, compound events, environmental risk, guidelines, multidisciplinary, typology",
author = "Emanuele Bevacqua and {De Michele}, Carlo and Colin Manning and Ana{\"i}s Couasnon and Ribeiro, {Andreia F.S.} and Ramos, {Alexandre M.} and Edoardo Vignotto and Ana Bastos and Suzana Blesi{\'c} and Fabrizio Durante and John Hillier and Oliveira, {S{\'e}rgio C.} and Pinto, {Joaquim G.} and Elisa Ragno and Pauline Rivoire and Kate Saunders and {van der Wiel}, Karin and Wenyan Wu and Tianyi Zhang and Jakob Zscheischler",
note = "Funding Information: The authors acknowledge the European COST Action DAMOCLES (CA17109). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101003469. E. Bevacqua acknowledges financial support from the DOCILE project (NERC grant: NE/P002099/1). J. Zscheischler acknowledges the Swiss National Science Foundation (Ambizione grant 179876) and the Helmholtz Initiative and Networking Fund (Young Investigator Group COMPOUNDX; grant agreement no. VH‐NG‐1537). A. Couasnon acknowledges the Netherlands Organisation for Scientific Research (NWO) (VIDI grant no. 016.161.324). A.M. Ramos acknowledges the Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia, Portugal (FCT) through the project WEx‐Atlantic (PTDC/CTA‐MET/29233/2017) and Scientific Employment Stimulus 2017 (CEECIND/00027/2017). C. De Michele acknowledges the Italian Ministry of University and Research (Ministero dell'Universit{\`a} e della Ricerca) for the support through the PRIN2017 RELAID project. E. Ragno acknowledges the European Union's Horizon 2020 research and innovation programme (Marie Sk{\l}odowska‐Curie grant agreement No 707404). J.G. Pinto thanks the AXA Research Fund for support ( https://axa-research.org/en/project/joaquim-pinto ). S.C. Oliveira was financed by the Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide—Landslide EarlyWarning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES‐AMB/30052/2017). Funding Information: The authors acknowledge the European COST Action DAMOCLES (CA17109). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101003469. E. Bevacqua acknowledges financial support from the DOCILE project (NERC grant: NE/P002099/1). J. Zscheischler acknowledges the Swiss National Science Foundation (Ambizione grant 179876) and the Helmholtz Initiative and Networking Fund (Young Investigator Group COMPOUNDX; grant agreement no. VH-NG-1537). A. Couasnon acknowledges the Netherlands Organisation for Scientific Research (NWO) (VIDI grant no. 016.161.324). A.M. Ramos acknowledges the Funda??o para a Ci?ncia e a Tecnologia, Portugal (FCT) through the project WEx-Atlantic (PTDC/CTA-MET/29233/2017) and Scientific Employment Stimulus 2017 (CEECIND/00027/2017). C. De Michele acknowledges the Italian Ministry of University and Research (Ministero dell'Universit? e della Ricerca) for the support through the PRIN2017 RELAID project. E. Ragno acknowledges the European Union's Horizon 2020 research and innovation programme (Marie Sk?odowska-Curie grant agreement No 707404). J.G. Pinto thanks the AXA Research Fund for support (https://axa-research.org/en/project/joaquim-pinto). S.C. Oliveira was financed by the Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide?Landslide EarlyWarning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017). Open access funding enabled and organized by Projekt DEAL. Publisher Copyright: {\textcopyright} 2021 The Authors.",
year = "2021",
month = nov,
doi = "10.1029/2021EF002340",
language = "English",
volume = "9",
journal = "Earth's Future",
issn = "2328-4277",
publisher = "John Wiley & Sons",
number = "11",
}