A typology of compound weather and climate events

Zscheischler, Jakob; Martius, Olivia; Westra, Seth; Bevacqua, Emanuele; Raymond, Colin; Horton, Radley M.; Hurk, Bart van den; AghaKouchak, Amir; Jézéquel, Aglaé; Mahecha, Miguel D.; Maraun, Douglas; Ramos, Alexandre M.; Ridder, Nina; Thiery, Wim; Vignotto, Edoardo

doi:10.1038/s43017-020-0060-z

Cited by 806 publications

(494 citation statements)

References 160 publications

Supporting

Mentioning

484

Contrasting

Unclassified

Order By: Relevance

“…We note a selection of these from the climate literature in Table 2 under 'Statistical approaches' and 'Modelling approaches' , and refer interested readers to refs. 8,13 for a more complete description.…”

Section: Quantitative and Conceptual Methodsmentioning

confidence: 99%

“…As further elaborated in Box 1, 'connection' incorporates and builds on the physical-hazard-based framework of 'compound' weather and climate events [8][9][10][11][12] ; 'interacting' , 'cascading' or 'multi-risk' natural hazards [13][14][15][16][17][18] ; and systemic risks and complexity science 19 . Our discussion is closely informed by advances and assessments in these fields, but homes in on attributes unique to extreme weather and climate events as well as on the exacerbating role that anthropogenic actions can play with regards to both their severity and impacts.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Understanding and managing connected extreme events

et al. 2020

Self Cite

View full text Add to dashboard Cite

Extreme weather and climate events and their impacts can occur in complex combinations, an interaction shaped by physical drivers and societal forces. In these situations, governance, markets and other decision-making structures-together with population exposure and vulnerability-create nonphysical interconnections among events by linking their impacts, to positive or negative effect. Various anthropogenic actions can also directly affect the severity of events, further complicating these feedback loops. Such relationships are rarely characterized or considered in physical-sciences-based research contexts. Here, we present a multidisciplinary argument for the concept of connected extreme events, and we suggest vantage points and approaches for producing climate information useful in guiding decisions about them.

show abstract

Section: Quantitative and Conceptual Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Understanding and managing connected extreme events

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, we presented a method for identifying drivers of extreme impacts using crop failure as an example. Such approaches are highly sought after to identify compound drivers of large impacts (Zscheischler et al, 2020). Our method allows to investigate potential drivers at a global scale using a highly automated scheme based on Lasso regression.…”

Section: Predicting Bad Yield Yearsmentioning

confidence: 99%

“…Drivers in this context refer to climate and weather processes and phenomena. With respect to yields at the local scale, multiple drivers can compound an impact through a sequence of weather events (temporally compounding); one weather event may also change the vulnerability of the crop to a subsequent weather event (preconditioning); or multiple drivers may interact and impact crops at the same time (multivariate events) (Zscheischler et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

Vogel¹,

Rivoire²,

Deidda³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve understanding and forecasting. In this study we investigate whether key meteorological drivers of extreme impacts can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply the logistic Lasso regression to predict which weather conditions during the growing season lead to crop failure. We obtain good model performance in Central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields, that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects both between meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the Lasso regression model is a useful tool to automatically detect compound drivers of extreme impacts, and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.

show abstract

“…47-62: Although you mention this slightly (Hao et al (2018), 'compound context'), I think this paragraph could benefit from a better overview of the growing compound extremes literature, by referring to e.g. Leonard et al (2014), Zscheischler et al (2018) or Zscheischler et al (2020) L. 86: I think it would be better to cite the textbook by Nelsen (2006) instead of Schweizer (1991, as the first version of that textbook certainly invigorated the study and application of copulas, especially as you do not cite it anywhere else. Salvadori and De Michele (2010), I think it would be more relevant to cite e.g.…”

A typology of compound weather and climate events

Cited by 806 publications

References 160 publications

Understanding and managing connected extreme events

Understanding and managing connected extreme events

Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

Some comments and suggestions for Tootoonchi et al. (2020)

Contact Info

Product

Resources

About