arXiv subjects

Sebastian Buschow

Publications and source records attributed to Sebastian Buschow.

5 recordsLinked to original sources

arXiv2026

On the role of moderator and mediator variables in conditional event attribution of heatwaves over Europe

This study examines how a risk-based attribution analysis of European heat wave events under climate change varies when explanatory variables are included in the analysis. Instead of relying solely on temperature statistics, the analysis is conducted while conditioning on large-scale circulation or pre-existing climate anomalies. Using the hot European summers of 2006 and 2007 as examples, we observe several systematic effects. Controlling for the presence of atmospheric blocking explains much of the natural temperature variability and greatly improves the separation of present and pre-industrial distributions in both years. In 2006, the predominant strong blocking raised the expected temperatures, so that, by comparison, the observed anomaly no longer appeared unusually hot, even in a pre-industrial climate. More complex effects occur when the conditions are part of the causal chain of climate change, for example, acting as moderators or mediators. By conditioning on such variables, we may remove part of the climate change signal and thus fundamentally alter the outcome of the attribution study. Using a simplified model we demonstrate why attribution studies with different kinds of conditions can reach different, seemingly contradictory conclusions.

physics.ao-ph↗

arXiv2026

Non-stationary time series attribution for heatwaves over Europe

The increasing occurrence of extreme weather events since the beginning of the 21st century has led to the development of new methods to attribute extreme events to anthropogenic climate change. The way in which the extreme event is defined has a major influence on the attribution result. A frequently overlooked aspect concerns the temporal dependence of extremes. This study presents an approach for attributing complete time series during extreme events to anthropogenic forcing. The approach is based on a non-stationary Markov process using bivariate extreme value theory to model the temporal dependence of the time series. We calculate the likelihood ratio of an observational time series from ERA5 given the distributions as estimated from CMIP6 simulations with historical natural-only and natural and anthropogenic forcing scenarios. The spatial fields are condensed by the extremal pattern index (EPI) as a compact description of spatial extremes. In addition, the study examines the extent to which attribution statements about the occurrence of extreme heat events change when the effect of the mean warming is eliminated. The resulting attribution statement provides very strong evidence for the scenario with anthropogenic drivers over Europe, especially since the beginning of the 21st century. For central and southern Europe, the influence of anthropogenic greenhouse gas emissions on heatwaves could already have been proven in the 1960s using today's knowledge. There is no reliable signal apart from a general shift in the temperature distribution, neither in terms of the temporal dependence of extreme heat days nor in terms of the shape of the extreme value distribution.

physics.ao-ph↗

arXiv2024

Reconciling risk-based and storyline attribution with Bayes theorem

The question to what extent climate change is responsible for extreme weather events has been at the forefront of public and scholarly discussion for years. Proponents of the "risk-based" approach to attribution attempt to give an unconditional answer based on the probability of some class of events in a world with and without human influences. As an alternative, so-called "storyline" studies investigate the impact of a warmer world on a single, specific weather event. This can be seen as a conditional attribution statement. In this study, we connect conditional to unconditional attribution using Bayes theorem: in essence, the conditional statement is composed of two unconditional statements, one based on all available data (event and conditions) and one based on the conditions alone. We explore the effects of the conditioning in a simple statistical toy model and a real-world attribution of European summer temperatures conditional on blocking. The resulting attribution statement is generally strengthened if the conditions are not affected by climate change. Conversely, if part of the trend is contained in the conditions, a weaker attribution statement may result.

physics.ao-ph↗

arXiv2023

Explaining heatwaves with machine learning

Heatwaves are known to arise from the interplay between large-scale climate variability, synoptic weather patterns and regional to local scale surface processes. While recent research has made important progress for each individual contributing factor, ways to properly incorporate multiple or all of them in a unified analysis are still lacking. In this study, we consider a wide range of possible predictor variables from the ERA5 reanalysis, and ask, how much information on heatwave occurrence in Europe can be learned from each of them. To simplify the problem, we first adapt the recently developed logistic principal component analysis to the task of compressing large binary heatwave fields to a small number of interpretable principal components. The relationships between heatwaves and various climate variables can then be learned by a neural network. Starting from the simple notion that the importance of a variable is given by its impact on the performance of our statistical model, we arrive naturally at the definition of Shapley values. Classic results of game theory show that this is the only fair way of distributing the overall success of a model among its inputs. With this approach, we find a non-linear model that explains 70% of reduced heatwave variability, 27% of which are due to upper level geopotential while top level soil moisture contributes 15% of the overall score. In addition, Shapley interaction values enable us to quantify overlapping information and positive synergies between all pairs of predictors.

physics.ao-ph↗

arXiv2018

Local dimension and recurrent circulation patterns in long-term climate simulations

With the recent advent of a sound mathematical theory for extreme events in dynamical systems, new ways of analyzing a system's inherent properties have become available: Studying only the probabilities of extremely close Poincar\'{e} recurrences, we can infer the underlying attractor's local dimensionality -- a quantity which is closely linked to the predictability of individual configurations, as well as the information gained from observing them. This study examines possible ways of estimating local and global attractor dimensions, identifies potential pitfalls and discusses conceivable applications. The Portable University Model of the Atmosphere (PUMA) serves a test subject of intermediate complexity between simple mathematical toys and truly realistic atmospheric data-sets. It is demonstrated that the introduction of a simple, analytical estimator can streamline the procedure and allows for additional tests of the agreement between theoretical expectation and observed data. We furthermore show how the newly gained knowledge about local dimensions can complement classical techniques like principal component analysis and may assist in separating meaningful patterns from mathematical artifacts.

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