Storyline approach to extreme event characterization

Climate Dynamics, 2020

A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for nearsurface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes.

Advances in Statistical Climatology, Meteorology and Oceanography, 2017

The goal of the attribution of individual events is to estimate whether and to what extent the probability of an extreme climate event evolves when external conditions (e.g., due to anthropogenic forcings) change. Many types of climate extremes are linked to the variability of the large-scale atmospheric circulation. It is hence essential to decipher the roles of atmospheric variability and increasing mean temperature in the change of probabilities of extremes. It is also crucial to define a background state (or counterfactual) to which recent observations are compared. In this paper we present a statistical framework to determine the dynamical (linked to the atmospheric circulation) and thermodynamical (linked to slow forcings) contributions to the probability of extreme climate events. We illustrate this methodology on a record precipitation event that hit southern United Kingdom in January 2014. We compare possibilities for the creation of two states (or <q>worlds</q>...

Quaternary International, 2018

Extreme weather and climate events, although rare at any particular location, can lead to different amount of loss to exposed human and natural systems, even to disasters. In this paper, the most authoritative definitions of "extreme events" given by the World Meteorological Organization and Intergovernmental Panel on Climate Change have been considered, as well as the underlying basic concepts, i.e., selected intensity levels, selected percentiles, multiples of the standard deviation, return period, distribution tails, imprint left, cause-effect relationships, natural disasters. Definitions and criteria have been tested with real world case studies using long instrumental records (300 years of daily temperature and 200 years of daily precipitation in Bologna, Italy) and proxy series (1000 years of Venice lagoon frozen over and 300 years of Po River outflow). The analysis reveals that each definition leads to particular consequences, e.g., in the peak over threshold theory, if the threshold is expressed in absolute terms, the number of extreme events may change with the climate period as tested with the Venice case study; as opposed, the relative definition based on percentiles or standard deviation will keep unchanged this frequency. Again, considering extreme events those external to the 10 th or 90 th percentile of the distribution may lead to a return period too short, e.g., 10 days for daily records, while a 10 year return period would require 1 st or 99 th percentiles, as tested with the daily temperature and precipitation. In addition, the distribution of a series may substantially change shape passing from daily to monthly and yearly averages as tested with the series taken as examples. The specific case of proxies is also considered analysing their uncertainties and categorization. The lagoon frozen over as a consequence of exceptionally severe winters constitutes an example of extremes based on an absolute threshold, as well as cause-effect relationship, and the return period was highly affected by the change of climate periods. The example of the overflow of the Po River suggests that the occurrence of extremes, and their intensity, may be altered by other factors that concur to the final result.

Earth's Future, 2021

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, deeplandslides 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. Plain Language Summary Many societal and environmental impacts from events such as droughts and storms arise from a combination of weather and climate factors referred to as a compound event. Considering the complex nature of these high-impact events is crucial for an accurate assessment of climate-related risk, for example to develop adaptation and emergency preparedness strategies. However, compound event research has emerged only recently, therefore our ability to analyze these events is still BEVACQUA ET AL.

Weather and Climate Extremes, 2016

We present an overview of practices and challenges related to the detection and attribution of observed changes in climate extremes. Detection is the identification of a statistically significant change in the extreme values of a climate variable over some period of time. Issues in detection discussed include data quality, coverage, and completeness. Attribution takes that detection of a change and uses climate model simulations to evaluate whether a cause can be assigned to that change. Additionally, we discuss a newer field of attribution, event attribution, where individual extreme events are analyzed for the express purpose of assigning some measure of whether that event was directly influenced by anthropogenic forcing of the climate system.

Nonlinear Processes in Geophysics, 2011

Frontiers in Water

Nonlinear Processes in Geophysics, 2011

We review work on extreme events, their causes and consequences, by a group of European and American researchers involved in a three-year project on these topics. The review covers theoretical aspects of time series analysis and of extreme value theory, as well as of the deterministic modeling of extreme events, via continuous and discrete dynamic models. The applications include climatic, seismic and socio-economic events, along with their prediction.

2016

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