Joel Zeder

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Zeder

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Joel

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0000-0002-6796-9137

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Publications 1 - 8 of 8

The Effect of a Short Observational Record on the Statistics of Temperature Extremes
Zeder, Joel; Sippel, Sebastian; Pasche, Olivier C.; et al. (2023)
Geophysical Research Letters
In June 2021, the Pacific Northwest experienced a heatwave that broke all previous records. Estimated return levels based on observations up to the year before the event suggested that reaching such high temperatures is not possible in today's climate. We here assess the suitability of the prevalent statistical approach by analyzing extreme temperature events in climate model large ensemble and synthetic extreme value data. We demonstrate that the method is subject to biases, as high return levels are generally underestimated and, correspondingly, the return period of low-likelihood heatwave events is overestimated, if the underlying extreme value distribution is derived from a short historical record. These biases have even increased in recent decades due to the emergence of a pronounced climate change signal. Furthermore, if the analysis is triggered by an extreme event, the implicit selection bias affects the likelihood assessment depending on whether the event is included in the modeling.
Observed extreme precipitation trends and scaling in Central Europe
Zeder, Joel; Fischer, Erich (2020)
Weather and Climate Extremes
In this publication we aim to relate observed changes in Central European extreme precipitation to the respective large-scale thermodynamic state of the atmosphere. Maxima of long-term (1901–2013) daily precipitation records from a densely sampled Central European station network, spanning Austria, Switzerland, Germany and the Netherlands, are scaled with Northern Hemispheric and regional temperature anomalies. Scaling coefficients are estimated at station level and aggregated to infer a robust regional extreme precipitation – temperature relationship. Across Central Europe, an overall intensification and a positive scaling signal with Northern Hemispheric temperature is detected in annual, summer, and winter single-day to monthly maximum precipitation. Generally, the estimates are consistent also when only considering data after 1950, and the scaling of annual maxima is also significant for all individual countries but Austria. However, scaling magnitudes are found to vary considerably between seasons and subregions. Also, scaling with regional temperature is non-significant, except for winter extreme precipitation.
Decadal to centennial extreme precipitation disaster gaps — Long-term variability and implications for extreme value modelling
Zeder, Joel; Fischer, Erich M. (2024)
Weather and Climate Extremes
A material consequence of climate change is the intensification of extreme precipitation in most regions across the globe. The respective trend signal is already detectable at global to regional scales, but long-term variability still dominates local observational records, which are the basis for extreme precipitation risk assessment. Whether the frequency of extreme events is purely random or subject to a low-frequency internal variability forcing is therefore highly relevant for modelling the expected number of extreme events in a typical observational record. Based on millennial climate simulations, we show that long-term variability is largely random, with no clear indication of low-frequency decadal to multidecadal variability. Nevertheless, extreme precipitation events occur highly irregularly, with potential clustering (11% probability of five or more 100-year events in 250 years) or long disaster gaps with no events (8% probability for no 100-year events in 250 years). Even for decadal precipitation records, a complete absence of any tail events is not unlikely, as, for example, in typical 70-year observational or reanalysis data, the probability is almost 50%. This generally causes return levels – a key metric for infrastructure codes or insurance pricing – to be underestimated. We also evaluate the potential of employing information across neighbouring locations, which substantially improves the estimation of return levels by increasing the robustness against potential adverse effects of long-term internal variability. The irregular occurrence of events makes it challenging to estimate return periods for planning and for extreme event attribution.
Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change
Zeder, Joel; Fischer, Erich (2023)
Advances in Statistical Climatology, Meteorology and Oceanography
Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer "what if"questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost 2 °C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least 2.9 °C accounts for 60 %-80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.
Increasing extreme precipitation variability plays a key role in future record-shattering event probability
de Vries, Iris; Sippel, Sebastian; Zeder, Joel; et al. (2024)
Communications Earth & Environment
Climate events that break records by large margins are a threat to society and ecosystems. Climate change is expected to increase the probability of such events, but quantifying these probabilities is challenging due to natural variability and limited data availability, especially for observations and very rare extremes. Here we estimate the probability of precipitation events that shatter records by a margin of at least one pre-industrial standard deviation. Using large ensemble climate simulations and extreme value theory, we determine empirical and analytical record shattering probabilities and find they are in high agreement. We show that, particularly in high emission scenarios, models project much higher record-shattering precipitation probabilities in a changing relative to a stationary climate by the end of the century for almost all the global land, with the strongest increases in vulnerable regions in the tropics. We demonstrate that increasing variability is an essential driver of near-term increases in record-shattering precipitation probability, and present a framework that quantifies the influence of combined trends in mean and variability on record-shattering behaviour in extreme precipitation. Probability estimates of record-shattering precipitation events in a warming world are crucial to inform risk assessment and adaptation policies.
Climate Scenarios for Switzerland CH2018 – Approach and Implications
Fischer, Andreas M.; Strassmann, Kuno M.; Croci Maspoli, Mischa; et al. (2022)
Climate Services
To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland's climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland.
Storylines for unprecedented heatwaves based on ensemble boosting
Fischer, Erich; Fischer, Erich; Beyerle, Urs; et al. (2023)
Nature Communications
Recent temperature extremes have shattered previously observed records, reaching intensities that were inconceivable before the events. Could the possibility of an event with such unprecedented intensity as the 2021 Pacific Northwest heatwave have been foreseen, based on climate model information available before the event? Could the scientific community have quantified its potential intensity based on the current generation of climate models? Here, we demonstrate how an ensemble boosting approach can be used to generate physically plausible storylines of a heatwave hotter than observed in the Pacific Northwest. We also show that heatwaves of much greater intensities than ever observed are possible in other locations like the Greater Chicago and Paris regions. In order to establish confidence in storylines of ‘black swan’-type events, different lines of evidence need to be combined along with process understanding to make this information robust and actionable for stakeholders.
Quantifying the intensity and frequency of very rare climate extremes in a changing climate
Zeder, Joel (2023)
In recent years, the perception of climate change has been significantly influenced by the occurrence of extreme events that not only shattered previous records, but that were considered highly unlikely or even impossible. Notable examples include the Pacific Northwest heatwave and the exceptional precipitation event in western Europe in 2021. These extreme events often have disastrous consequences for society and the environment, thus underscoring the crucial importance of understanding their dependence on global warming. The most recent assessment report by the Intergovernmental Panel on Climate Change (IPCC) asserts that the intensification of heat and precipitation extremes due to climate change is virtually certain and already detectable in observational data. Nevertheless, assessing changes in the intensity and frequency of very rare events, which occur once in a millennium under stationary conditions, poses significant challenges. This is primarily due to the scarcity of these events and, therefore, of the respective data. Since the available observational time series are limited in both spatial coverage and duration, statistical estimation of such rare events requires extrapolation beyond the observed range, leading to substantial uncertainties. Furthermore, the inherent stochastic variability within the climate system further complicates the estimation of high return levels and periods. Nonetheless, such estimations are routinely conducted based on short observational time series, e.g. for critical infrastructure design or public health planning. This dissertation focuses on the quantification of very rare extreme events, utilising millennial simulations of global climate. The analysis employs methods from extreme value statistics that are specifically designed for studying such infrequent events. After an introductory overview in Chapter 1, Chapter 2 evaluates the estimation of high return levels and return periods based on short observational records in the context of heat extremes. Thereby, a systematic underestimation of 100-year return levels is revealed. Consequently, the return period of rare events is systematically overestimated. This deviation is most pronounced in current decades subject to a high warming rate. This systematic underestimation of high return levels can be attributed to a small-sample bias of the estimation method, which is quantified by analysing synthetically generated extreme value data. Chapter 3 explores the potential of further enhancing the heatwave extreme value model by incorporating not only global mean temperature as an indicator of climate change, but also physical process variables as additional predictors. These process variables include the regional atmospheric circulation field as well as local soil moisture conditions. The study demonstrates that this model, whose parameters are estimated from climate model data, better captures the variability of heat extremes compared to simpler statistical models. By pre-processing the predictors, the statistical model can further be applied to observed extreme events in reanalysis data. As a result, it is shown that approximately 50 % of the exceptional intensity of the Pacific Northwest heatwave can directly be attributed to climate change. A significant influence of the dominant high-pressure system is also identified. Additionally, the model enables the investigation of hypothetical “what if” scenarios, such as the intensity of the heatwave under pre-industrial conditions. In Chapter 4, the long-term variability of precipitation events is analysed. Time series of simulated extreme precipitation reveal the occurrence of several events with return periods of 100 years or more within a few decades, as well as extended periods without any such events. Based on the statistical distribution of the waiting time between two extreme events, it is demonstrated that these events occur randomly and independently of each other. The stochasticity accounts for both the observed random clustering and the prolonged absence of extreme precipitation events. The implications of these random clusters for extreme value modelling and the potential benefits of pooling data across neighbouring locations are also investigated. Chapter 5 summarises the previous chapters and outlines shared conclusions that emerged throughout the thesis. Overall, the method of extreme value statistics serves its purpose, despite the associated uncertainties inherent in the estimations. But minor systematic biases associated with certain modelling choices can cause return levels to be substantially underestimated. Consequently, extreme value modelling strategies addressing these biases and improving the quantification of uncertainties are presented at the end of this chapter, including future research avenues in climate extreme value modelling.

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