Plenary Lectures

Information on the plenary lectures at EVA 2021

Watch the Plenary Lecture recordings

Determining causes for the changing probability of weather and climate extremes

Gabriele Clarissa Hegerl, University of Edinburgh

As climate changes, the frequency and intensity of weather extremes is changing as well. Recent damaging extreme weather and climate events raise questions about the link between climate change and extreme events. Studies of past and recent extreme events highlight the unique factors that contribute to each event as well as common contributing factors, and how a changing climate changes their intensity and frequency. This talk gives examples of interpretation of observed extreme events and model projections, using methods from comparison of tendencies between physically based climate models and observations, use of very large, targeted ensembles of simulations, to analysis of analogue observed situations. It will be discussed how these methods can be combined to shed light despite data uncertainty and sparse sampling.

Climatic extremes: current statistical challenges

Daniel Cooley, Colorado State University

Extreme value analysis has a long history of describing the intensity of extreme weather events. Much of this historical work was done under an assumption of a stationary climate. A changing climate introduces challenges for the extremes statistics community. A fundamental question is the projection of future extremes. For example, one might need to estimate the magnitude of a 1-in-100 annual exceedance probability event for a future time, perhaps in 50 or 100 years. Climate projections are typically done via numerical climate models, but any projection estimate would possess not only statistical uncertainty from the data, but also uncertainty from the climate projection itself. Additionally, working with climate model output can introduce a calibration (or downscaling) issue to convert output from the model's spatial resolution to the posed question’s resolution, which often is a point corresponding to a monitoring station. Additionally, there are statistical challenges associated with detection and event attribution of climate change to extreme events. Detection is the process of statistically showing that climate has changed, which is more difficult for extremes than for mean changes. Event attribution is the process of assigning an amount of risk of an observed extreme event to the changed climate.

This talk aims to introduce and explore these ideas, as well as to illustrate possible statistical approaches from our own work. In particular, we will look at calibration of future river flow extremes; we will look at an approach of performing climate change detection via a principal component decomposition of extreme precipitation data, and we will look at performing event attribution of recent wildfire weather conditions for the Western US.

Speakers:

Gabi Hegerl, University of Edinburgh

Gabi Hegerl has a Ph. D in applied mathematics (numerical fluid dynamics) who focuses on identifying the drivers and mechanisms of observed climate change. Gabi published some of the first studies determining that recent warming is statistically different from climate variability, and pioneered a method that distinguishes between possible causes for climate change, such as greenhouse gas increases or changes in the sun. Gabi’s recent work has shown that human influences have changed global precipitation patterns and has made contributions to determining the causes of changing characteristics of extreme weather events. Gabi is co-leading the World Climate Research program’s grand challenge on extreme events, and of the lighthouse activity of safe landing spaces. She co-authored the US National Academies’ report on extreme event attribution. Gabi has had key roles in scientific assessments of climate change (IPCC), and is a fellow of the Royal Society, the American Geophysical Union, the American Meteorological Society, the Leopoldina and of the Royal Society of Edinburgh.

Dan Cooley, Colorado State University

Dan Cooley is a Professor in the Department of Statistics at Colorado State University. His research in extreme value analysis largely focuses on describing and modeling tail dependence and much of his research is motivated by quantifying risk associated with extreme weather events. He has been chair of the American Statistical Association's Committee on Climate Change Policy and was designated a Professor Laureate of CSU's College of Natural Sciences.

This article was published on 30 Sep, 2021

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