A Common Framework for Approaches to Extreme Event Attribution

Shepherd, Theodore G.

doi:10.1007/s40641-016-0033-y

Cited by 202 publications

(178 citation statements)

References 39 publications

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“…The attribution of extreme climate events (to climate change) requires decomposing an extreme event into its dynamical (linked to the atmospheric circulation) and thermodynamical (linked to temperature) components [35]. The dynamical signal is generally difficult to estimate [36]. We have not determined the cause of the winter circulation trends, which could be tied to internal variability [33], but this observed emerging trend helps constraining the dynamical component in the event attribution process.…”

Section: Resultsmentioning

confidence: 99%

Recent Trends in the Recurrence of North Atlantic Atmospheric Circulation Patterns

et al. 2018

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A few types of extreme climate events in the North Atlantic region, such as heatwaves, cold spells, or high cumulated precipitation, are connected to the recurrence of atmospheric circulation patterns. Understanding those extreme events requires assessing longterm trends of the atmospheric circulation. This paper presents a set of diagnostics of the intra-and interannual recurrence of atmospheric patterns. Those diagnostics are devised to detect trends in the stability of the circulation and the return period of atmospheric patterns. We detect significant emerging trends in the winter circulation, pointing towards a potential increased predictability. No such signal seems to emerge in the summer. We find that the winter trends in the dominating atmospheric patterns and their recurrences do not depend of the patterns themselves.

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Section: Resultsmentioning

confidence: 99%

Recent Trends in the Recurrence of North Atlantic Atmospheric Circulation Patterns

et al. 2018

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“…the increase in probability of landslides as a result of a wildfire). This challenge of attribution is currently in the forefront of the climate change community, where attempts are made to determine the existence of causal relationships between anthropogenic climate change and specific extreme events (Stott et al, 2013;Shepherd, 2016).…”

Section: Classifying Interaction Typesmentioning

confidence: 99%

Hazard interactions and interaction networks (cascades) within multi-hazard methodologies

2016

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Abstract. This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability between successive hazards, and (iii) prioritise resource allocation for mitigation and disaster risk reduction.

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“…The computation of the occurrence probability p 1 therefore requires one to specify a class of events, a topic that has been particularly discussed in the attribution community recently (Trenberth et al 2015;Hannart et al 2016;Otto et al 2016;Shepherd 2016;Harrington 2017). The traditional approach (used, e.g., for the computation of return periods in climate monitoring) is to define the class as all events equally or more intense than the observed one.…”

Section: The Four Steps Of Event Definitionmentioning

confidence: 99%

“…In this case, p 1 corresponds to the tail of the distribution (Pr{X ≥ x 0 }), which fits into the mathematical framework of extreme value theory (Coles 2001) and is highly relevant for most climate and impacts applications. Alternatively, it has been recently proposed to define the event as accurately as possible and to identify its causal chain of contributors in a deterministic way (Shepherd 2016). Scrutinizing an event in such a "storyline" perspective is indeed helpful for both climate monitoring and physical understanding (Hoerling et al 2013).…”

Section: The Four Steps Of Event Definitionmentioning

confidence: 99%

Defining Single Extreme Weather Events in a Climate Perspective

Cattiaux

Ribes

2018

Bulletin of the American Meteorological Society

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Weather extremes are the showcase of climate variability. Given their societal and environmental impacts, they are of great public interest. The prevention of natural hazards, the monitoring of single events, and, more recently, their attribution to anthropogenic climate change constitute key challenges for both weather services and scientific communities. Before a single event can be scrutinized, it must be properly defined; in particular, its spatiotemporal characteristics must be chosen. So far, this definition is made with some degree of arbitrariness, yet it might affect conclusions when explaining an extreme weather event from a climate perspective. Here, we propose a generic road map for defining single events as objectively as possible. In particular, as extreme events are inherently characterized by a small probability of occurrence, we suggest selecting the space–time characteristics that minimize this probability. In this way, we are able to automatically identify the spatiotemporal scale at which the event has been the most extreme. According to our methodology, the European heat wave of summer 2003 would be defined as a 2-week event over France and Spain and the Boulder, Colorado, intense rainfall of September 2013 a 5-day local event. Importantly, we show that in both cases, maximizing the rarity of the event does not maximize (or minimize) its fraction of attributable risk to anthropogenic climate change.

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A Common Framework for Approaches to Extreme Event Attribution

Cited by 202 publications

References 39 publications

Recent Trends in the Recurrence of North Atlantic Atmospheric Circulation Patterns

Recent Trends in the Recurrence of North Atlantic Atmospheric Circulation Patterns

Hazard interactions and interaction networks (cascades) within multi-hazard methodologies

Defining Single Extreme Weather Events in a Climate Perspective

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