Jakob Zscheischler

Helmholtz Young Investigator Group leader at Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany

SNF Ambizione Fellow at Climate and Environmental Physics, Physics Institute, University of Bern, Switzerland

Member of the Oeschger Centre for Climate Change Research

Chair of European COST Action DAMOCLES (Understanding and modeling compound climate and weather events)

Key words:
Compound Events, Climate Extremes, Land-Atmosphere Dynamics, Machine Learning


Climate and Environmental Physics
Physics Institute, University of Bern
Sidlerstrasse 5
CH-3012 Bern
Tel.: +41 31 631 42 75
Email: zscheischler-at-climate.unibe.ch
          Oeschger Centre for Climate Change Research (OCCR)
University of Bern
Falkenplatz 16
CH-3012 Bern

ORCID iD iconorcid.org/0000-0001-6045-1629


About me

I'm an Earth system scientist with a background in mathematics, biogeochemistry and climate science. My research focus are compound weather and climate events. Compound events refer to the combination of climatic drivers that contribute to environmental and societal risk. This blog post provides some background information and motivation. I'm the Chair of the European COST Action DAMOCLES (Understanding and modeling compound climate and weather events, CA17109), which brings together climate scientists, engineers, social scientists, impact modellers and decision-makers and coordinates national research projects on compound events. In a recent review we propose four differenty types of compound events, with the goal to aid in compound event analysis.

In my Ambizione project at University of Bern, we develop new metrics to evaulate physical models with respect to compound events. Elisabeth Tschumi works in my group as a PhD student and studies the effects of different drought-heat signatures on carbon dynamics. Since September 2019 I'm co-supervising the PhD prject of Natacha Legrix on compound events in the ocean, together with Thomas Frölicher. Furthermore, I supervise Aris Marcolongo in an SNF funded project on Machine learning for detecting compound climate drivers of extreme impacts.

I have recently started a new group at the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany funded through a Helmholtz Young Investigator Grant (1.8M€). In my new group we will use machine learning to identify compounding meteorological drivers of extreme impacts such as floods, vegetation mortality and crop failure.

Currently I'm managing guest editor for two Special Issues, one Inter-journal Special Issue on Understanding compound weather and climate events and related impacts in the EGU journals BG, ESD, HESS, NHESS and one Special Issue on Compound Weather and Climate Events in Weather and Climate Extremes.

Elements of a compound weather and climate event. Compound events consist of multiple climate drivers and/or multiple hazards (illustrated by the green and blue boxes, respectively) that potentially cause an impact (red box). Modulators (for example, the El Niño–Southern Oscillation) influence the frequency, magnitude and location of the drivers and, thus, possibly change hazard frequency and intensity. Climate change can affect all elements contributing to a compound event, that is, modulators, drivers and hazards. Arrows refer to a direct causal link between the different elements (figure from Zscheischler et al. (2020): A typology of compound weather and climate events, Nature Reviews Earth & Environment).


18-29 January 2021 DAMOCLES Training School on Dynamical Modeling of Compound Events, Budapest, Hungary (postponed due to COVID19)
12-15 January 2021 Workshop on Compound Events at University of Bern
1 October 2020 Start as a Helmholtz Young Investigator Group leader at Helmholtz Centre for Environmental Research - UFZ, in Leipzig, Germany
16 September 2020 Seminar at Weather and Climate Risk Group, ETH Zurich
25-27 November 2019 Annual MC and WG meeting of DAMOCLES, Tallinn, Estonia
09-11 October 2019 Session Chair, “The challenge of responding to compound events” at Herrenhausen Conference, Herrenhausen Palace, Hannover, Germany
23 Sep - 4 Oct 2019 Organizer and lecturer, Training School on Statistical Modeling of Compound Events, Lake Como, Italy
13 June 2019 Talk at the Workshop on Causality and Extremes, EPFL, Lausanne, Switzerland
10-12 June 2019 Panel speaker at the Fourth Northern European Conference on Emergency and Disaster Studies (NEEDS), Uppsala, Sweden
29-31 May 2019 Opening keynote at the Workshop on Correlated Extremes, Columbia University, New York City, USA. All talks were recorded and are available at the workshop webpage.
24-26 April 2019 Co-organizer of the Workshop on physical modeling supporting a “storyline approach”, Oslo, Norway
07-12 April 2019 Co-convener at EGU session Understanding and modelling compound climate and weather events and their impacts, Vienna, Austria

Recent research

Paper in Weather and Climate Extremes: Multivariate quantile mapping methods for ensemble post-processing

Statistical post-processing is an indispensable tool for providing accurate weather forecasts and early warnings for weather extremes. Most statistical post-processing is univariate, with dependencies introduced via use of an empirical copula. Here we compare a re-shuffled standard ensemble model output statistics (EMOS) approach with two multivariate bias adjustment approaches that have not been used before in a post-processing context: 1) the multivariate bias correction with N-dimensional probability density function transform (MBCn) and 2) multivariate ranks that are defined with optimal assignment (OA). These methods have the advantage that they are able to explicitly capture the dependence structure that is present in the observations. Our results demonstrate that the spatial and inter-variable dependence structure is more realistic in MBCn and OA compared to ECC or the Schaake Shuffle. This highlight the importance of considering the dependence between variables and locations in the statistical post-processing of weather forecasts.

Paper in Earth System Dynamics: Identifying drivers of extreme impacts: an application to simulated crop yields

Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. We investigate whether key meteorological drivers of extreme impacts can be identified using LASSO, a method that allows for automated variable selection and is able to handle collinearity between variables. We investigate crop failure using annual wheat yield as simulated by a crop model driven by 1600 years of daily weather data from a global climate model. Nearly everywhere the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events.

EGU highlight paper: Evaluating the dependence structure of compound precipitation and wind speed extremes

Climate models are used to assess future climate risk, but it is largely unknown how well models represents compound extremes. Here, we introduce a new metric that measures whether the tails of bivariate distributions show a similar dependence structure across different datasets. We analyse compound precipitation and wind extremes in reanalysis data and different high-resolution simulations for central Europe. Overall, boundary conditions in the weather model WRF appear to be the key factor in explaining differences in the dependence behaviour between strong wind and heavy precipitation between simulations. In comparison, external forcings (RCP8.5) are of second order. Our approach offers new methodological tools to evaluate climate model simulations with respect to compound extremes.

Paper in Nature Communications: Global hotspots for the occurrence of compound events

We provide the first analysis of multiple multivariate compound events potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate compound events including many socio-economically important regions such as North America, Russia and western Europe. Our results provide initial guidance to assess the regional risk of compound events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate compound events.

Paper in Biogeosciences: Risk of crop failure due to compound dry and hot extremes estimated with nested copulas

The interaction between co-occurring drought and hot conditions is often particularly damaging to crop's health and may cause crop failure. We model the trivariate dependence between spring maximum temperature and spring precipitation and wheat and barley yields over two province regions in Spain with nested copulas. Based on the full trivariate joint distribution, we (i) estimate the impact of compound hot and dry conditions on wheat and barley loss and (ii) estimate the additional impact due to compound hazards compared to individual hazards. We find that crop loss increases when drought or heat stress is aggravated to form compound dry and hot conditions and that an increase in the severity of compound conditions leads to larger damage. Our results highlight the additional value of a trivariate approach for estimating the compounding effects of dry and hot extremes on crop failure risk. The approach can effectively contribute to design management options and guide the decision-making process in agricultural practices.

Paper in Science: High-impact marine heatwaves attributable to human-induced global warming

Marine heatwaves (MHWs)—periods of extremely high ocean temperatures in specific regions—have occurred in all of Earth’s ocean basins over the past two decades, with severe negative impacts on marine organisms and ecosystems. However, for most individual MHWs, it is unclear to what extent they have been altered by human-induced climate change. We show that the occurrence probabilities of the duration, intensity, and cumulative intensity of most documented, large, and impactful MHWs have increased more than 20-fold as a result of anthropogenic climate change. MHWs that occurred only once every hundreds to thousands of years in the preindustrial climate are projected to become decadal to centennial events under 1.5°C warming conditions and annual to decadal events under 3°C warming conditions. Thus, ambitious climate targets are indispensable to reduce the risks of substantial MHW impacts.

Paper in Weather and Climate Extremes: The record-breaking compound hot and dry 2018 growing season in Germany

Record breaking hot temperatures were observed in many places around the world in 2018, causing heat-related deaths, crop failure, wildfires and infrastructural damages. In Germany, extremely hot temperatures were accompanied by extremely low precipitation, compounding the impacts. Here we show that since measurements started in 1881, Germany has never experienced as hot and dry conditions during March to November as in 2018. We analyse the rarity of the event and illustrate that estimates of return periods for such compound extreme events are extremely high but very uncertain and strongly depend on the way they are estimated. Statistical projections of the bivariate temperature-precipitation distribution suggests that a growing season such as 2018 will become less likely at warmer global mean temperatures due to slight increases in precipitation. In contrast, climate models project an increasing likelihood of a 2018-like event and much larger uncertainties both for temperature and precipitation at different warming levels.

Review on compound events in Nature Reviews Earth & Environment: A typology of compound events

Although many climate-related disasters are caused by compound events, the understanding, analysis, quantification and prediction of such events is still in its infancy. We propose a typology of compound events and suggest analytical and modelling approaches to aid in their investigation. The four classes are: preconditioned, where a weather-driven or climate-driven precondition aggravates the impacts of a hazard; multivariate, where multiple drivers and/or hazards lead to an impact; temporally compounding, where a succession of hazards leads to an impact; and spatially compounding, where hazards in multiple connected locations cause an aggregated impact.

Perspective in Nature Climate Change: Understanding and managing connected extreme events

Impacts of extreme weather and climate events are shaped by physical drivers and societal forces. Governance, markets and other decision-making structures create nonphysical interconnections among events by linking their impacts, to positive or negative effect. Such relationships are rarely characterized or considered in physical-sciences-based research contexts. Here, we present a multidisciplinary argument for the concept of connected extreme events, and we suggest vantage points and approaches for producing climate information useful in guiding decisions about them. This Perspective discusses the concept and challenge of connected extreme events, exploring research approaches and decision-making strategies.

Paper in Climatic Change: Countrywide climate features during recorded climate-related disasters

Climate-related disasters cause substantial disruptions to human societies. The International Disaster Database (EM-DAT) records climate-related disasters on a country basis. Although disasters are classified into different meteorological categories, they are usually not linked to observed climate anomalies. We link past disasters with actual climate anomalies and find that disasters classified as droughts and heat waves are associated with significant annual countrywide anomalies in both temperature and precipitation. Our results suggest that extreme weather disasters in developed countries are typically associated with larger climate anomalies compared to developing countries, which can be explained by different levels of vulnerability.

Paper in Nature Communications: Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond.

Paper in Earth's Future: Concurrent 2018 hot extremes across Northern Hemisphere due to human‐induced climate change

Extremely high temperatures pose an immediate threat to humans and ecosystems. The 2018 spring‐to‐summer season was characterized by concurrent heatwaves in many areas in the northern mid-latitudes. We show that it is virtually certain that the 2018 North‐Hemispheric concurrent heat events would not have occurred without human‐induced climate change. Coverage: CarbonBrief, ETH News

Paper in Nature Geoscience: Drought impacts on terrestrial primary production underestimated by satellite monitoring

We identify a common bias in different state-of-the-art satellite-based estimates of terrestrial photosynthesis, and found that this bias is closely related to droughts and their apparent impact on the functioning of vegetation. In other words, common satellite-based vegetation monitoring methods underestimate the impact of droughts. A press relase put together by first author Beni Stocker is available here.

Paper in Earth System Dynamics: The effect of univariate bias adjustment on multivariate hazard estimates

Most climate models have biases in basic climatic variables throughout the world. Adjusting these biases is necessary for modeling climate impacts. Yet widely used bias adjustment methods do not adjust the dependence structure between variables. Here we demonstrate that those methods do not work well for multivariate impacts. We illustrate this problem using fire risk and heat stress as impact indicators. Using an approach that adjusts not only biases in the individual climate variables but also biases in the correlation between them can resolve these problems.

Paper in Nature: Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage

Land ecosystems absorb a large amount of anthropogenic carbon dioxide emissions. Year-to-year variations in the atmospheric CO2 growth rate are mostly due to fluctuating carbon uptake by land ecosystems. A better understanding of these fluctuations will help to make better predictions of future carbon uptake. We use recent observations of terrestrial water storage changes derived from satellite gravimetry to show that the CO2 growth rate is strongly sensitive to observed changes in terrestrial water storage.

Perspective paper in Nature Climate Change: Future climate risk from compound events

Floods, wildfires, heatwaves and droughts often result from a combination of interacting physical processes across multiple spatial and temporal scales. We introduce the concept of ‘compound events’, referring to the combination of processes (climate drivers and hazards) that lead to a significant impact. Improving our understanding of high-impact events requires a better understanding of compound events and needs to bring together climate scientists, engineers, social scientists, impact modellers and decision-makers.


Full list of publications available at ORCID iD iconorcid.org/0000-0001-6045-1629 and

Curriculum Vitae

Since 10/2020 Helmholtz Young Investigator Group leader at Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
Since 11/2018 Ambizione Fellow at the Climate and Environmental Physics, Physics Institute, University of Bern.
Member of the Oeschger Center for Climate Change Research
2/2015 - 10/2018 Postdoc at the Institute for Atmospheric and Climate Science at ETH Zurich
6/2014 - 12/2014 Postdoc at Max Planck Insitute for Biogeochemistry, Jena, Germany
2013 Visiting Scientist at Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
10/2010 - 6/2014 PhD student at Max Planck Insitute for Biogeochemistry (Jena, Germany) and Max Planck Institute for Intelligent Systems (Tübingen, Germany)
2010 Diploma in Mathematics