Jakob Runge

Jakob Runge
German Aerospace Center (DLR) | DLR · Institute of Data Science

PhD

About

73
Publications
26,361
Reads
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2,774
Citations
Introduction
Jakob Runge heads the Causal Inference group at the German Aerospace Center’s Institute of Data Science in Jena since 2017 and is chair of Climate Informatics at TU Berlin since 2021. Jakob studied physics at Humboldt University Berlin and obtained his PhD at the Potsdam Institute for Climate Impact Research in 2014. In 2020 he won an ERC Starting Grant with his interdisciplinary project CausalEarth.
Additional affiliations
October 2017 - present
German Aerospace Center (DLR)
Position
  • Group Leader
Description
  • I am leading the group on climate Informatics that combines innovative data science methods from different fields (machine learning, causal inference, nonlinear dynamics) for climate research.
February 2016 - April 2018
Imperial College London
Position
  • Research Associate (James S. McDonnell Fellow)
September 2014 - January 2016
Potsdam Institute for Climate Impact Research
Position
  • PostDoc Position
Education
January 2011 - August 2014
Humboldt-Universität zu Berlin
Field of study
  • Physics
October 2004 - July 2010
Humboldt-Universität zu Berlin
Field of study
  • Physics

Publications

Publications (73)
Preprint
Full-text available
Complex dynamical systems are prevalent in many scientific disciplines. In the analysis of such systems two aspects are of particular interest: 1) the temporal patterns along which they evolve and 2) the underlying causal mechanisms. Time-series representations like discrete Fourier and wavelet transforms have been widely applied in order to obtain...
Article
Full-text available
Ecosystems are projected to face extreme high temperatures more frequently in the near future. Various biotic coping strategies exist to prevent heat stress. Controlled experiments have recently provided evidence for continued transpiration in woody plants during high air temperatures, even when photosynthesis is inhibited. Such a decoupling of pho...
Article
Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of...
Chapter
The need to understand and predict weather and climate has always been a concern of humanity. A main current challenge derives from the Earth's complex nonlinear dynamics, high dimensionality of observational as well as model datasets, and the property that the relevant processes often are emergent dynamical phenomena that are challenging to extrac...
Article
Full-text available
Describing ecosystem carbon fluxes is essential for deepening the understanding of the Earth system. However, partitioning net ecosystem exchange (NEE), i.e. the sum of ecosystem respiration (Reco) and gross primary production (GPP), into these summands is ill-posed since there can be infinitely many mathematically-valid solutions. We propose a nov...
Conference Paper
Skin cancer is the most common form of cancer, and melanoma is the leading cause of cancer related deaths. To improve the chances of survival, early detection of melanoma is crucial. Automated systems for classifying skin lesions can assist with initial analysis. However, if we expect people to entrust their well-being to an automatic classificatio...
Conference Paper
Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery at 20 m resolution, matching...
Article
Full-text available
Understanding the dependencies of the terrestrial carbon and water cycle with meteorological conditions is a prerequisite to anticipate their behaviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of variables across temporal and spatial scales. Additionally these interactions might di...
Preprint
Full-text available
Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery at 20m resolution, matching t...
Conference Paper
Full-text available
The quest to understand cause and effect relationships is at the basis of the scientific enterprise. In cases where the classical approach of controlled experimentation is not feasible, methods from the modern framework of causal discovery provide an alternative way to learn about cause and effect from observational, i.e., non-experimental data. Re...
Preprint
Full-text available
Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a direct consequence of a bias in the training dataset, frequently caused by the co-occurrence of relevant features and irrelevant ones. To mitigate this issue, we require...
Article
Full-text available
Understanding the dependencies of the terrestrial carbon and water cycle is a prerequisite to anticipate their be- haviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of vari- ables, time- and space scales. Additionally the interactions might differ among vegetation types or climatic...
Chapter
Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a direct consequence of a bias in the training set, frequently caused by the co-occurrence of relevant features and irrelevant ones. To mitigate this issue, we require lea...
Preprint
Full-text available
Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variables and Earth's surface at such...
Conference Paper
We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders. We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of con...
Chapter
Deep neural networks are tremendously successful in many applications, but end-to-end trained networks often result in hard to understand black-box classifiers or predictors. In this work, we present a novel method to identify whether a specific feature is relevant to a classifier’s decision or not. This relevance is determined at the level of the...
Article
Full-text available
Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this di...
Preprint
Full-text available
Understanding the dependencies of the terrestrial carbon and water cycle is a prerequisite to anticipate their behaviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of variables, time- and space scales. Additionally the interactions might differ among vegetation types or climatic regi...
Article
Full-text available
Tropical convective activity represents a source of predictability for mid-latitude weather in the Northern Hemisphere. In winter, the El Niño–Southern Oscillation (ENSO) is the dominant source of predictability in the tropics and extratropics, but its role in summer is much less pronounced and the exact teleconnection pathways are not well underst...
Preprint
We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders. We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of con...
Preprint
Full-text available
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scale...
Preprint
Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this diffic...
Conference Paper
Full-text available
Scientific inquiry seeks to understand natural phenomena by understanding their underlying processes, i.e., by identifying cause and effect. In addition to mere scientific curiosity, an understanding of cause and effect relationships is necessary to predict the effect of changing dynamical regimes and for the attribution of extreme events to potent...
Preprint
Full-text available
Abstract. Tropical convective activity represents a source of predictability for mid-latitude weather in the Northern Hemisphere. In winter, the El Niño–Southern Oscillation (ENSO) is the dominant source of predictability in the tropics and extra-tropics, but its role in summer is much less pronounced and the exact teleconnection pathways are not w...
Article
Full-text available
Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanaly...
Preprint
Full-text available
We consider causal discovery from time series using conditional independence (CI) based network learning algorithms such as the PC algorithm. The PC algorithm is divided into a skeleton phase where adjacencies are determined based on efficiently selected CI tests and subsequent phases where links are oriented utilizing the Markov and Faithfulness a...
Article
Full-text available
The dynamics of biochemical processes in terrestrial ecosystems are tightly coupled to local meteorological conditions. Understanding these interactions is an essential prerequisite for predicting, e.g. the response of the terrestrial carbon cycle to climate change. However, many empirical studies in this field rely on correlative approaches and on...
Conference Paper
Full-text available
State-of-the-art machine learning methods,especially deep neural networks, have reached impressiveresults in many prediction and classification tasks. Risingcomplexity and automatic feature selection make theresulting learned models hard to interpret and turns theminto black boxes. Advances into feature visualization havemitigated this problem but...
Conference Paper
Full-text available
We propose a spatiotemporal model system to evaluate methods of causal discovery. The use of causal discovery to improve our understanding of the spatiotemporal complex system Earth has become widespread in recent years (Runge et al., Nature Comm. 2019). A widespread application example are the complex teleconnections among major climate modes of v...
Conference Paper
The paper introduces a novel conditional in- dependence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case. Existing CI-based methods such as the PC algorithm and also common methods from other frameworks suf- fer from low recall and partially inflat...
Conference Paper
Full-text available
Understanding the complex interdependencies of processes in our climate system has become one of the most critical challenges for society with our main current tools being cli- mate modeling and observational data analysis, in particular observational causal discovery. Causal discovery is still in its infancy in Earth sciences and a major issue is...
Article
Full-text available
p>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...
Article
Full-text available
Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample...
Chapter
Full-text available
Causality analysis represents one of the most important tasks when examining dynamical systems such as ecological time series. We propose to mitigate the problem of inferring nonlinear cause-effect dependencies in the presence of a hidden confounder by using deep learning with domain knowledge integration. Moreover, we suggest a time series anomaly...
Article
Full-text available
Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to dedu...
Article
Skillful forecasts of the Indian summer monsoon rainfall (ISMR) at long lead times (4–5 months in advance) pose great challenges due to strong internal variability of the monsoon system and nonstationarity of climatic drivers. Here, we use an advanced causal discovery algorithm coupled with a response-guided detection step to detect low-frequency,...
Article
Full-text available
https://academic.oup.com/nsr/article/6/4/616/5369430?rss=1
Article
Full-text available
Atmospheric dynamics: Polar stratospheric influence on mid-latitude cold extremes Cold spells in the Northern Hemisphere mid-latitudes are influenced by the stratospheric polar vortex in two different ways. Marlene Kretschmer from the Potsdam Institute for Climate Impact Research, Germany, and collaborators use cluster analysis to show there are tw...
Article
Full-text available
The dynamical relationship between magnetic storms and magnetospheric substorms presents one of the most controversial problems of contemporary geospace research. Here, we tackle this issue by applying a causal inference approach to two corresponding indices in conjunction with several relevant solar wind variables. We demonstrate that the vertical...
Article
Full-text available
Causal network reconstruction from time series is an emerging topic in many fields of science. Beyond inferring directionality between two time series, the goal of causal network reconstruction or causal discovery is to distinguish direct from indirect dependencies and common drivers among multiple time series. Here, the problem of inferring causal...
Article
Full-text available
Questions of causation are foundational across science and often relate further to problems of control, policy decisions, and forecasts. In nonlinear dynamics and complex systems science, causation inference and information flow are closely related concepts, whereby “information” or knowledge of certain states can be thought of as coupling influenc...
Conference Paper
Full-text available
It is a constant challenge to better under-stand the underlying dynamics and forces driving theEarth system. Advances in the field of deep learning allowfor unprecedented results, but use of these methods inEarth system science is still very limited. We present aframework that makes use of a convolutional variationalautoencoder as a learnable kerne...
Article
Full-text available
Conditional independence testing is a fundamental problem underlying causal discovery and a particularly challenging task in the presence of nonlinear and high-dimensional dependencies. Here a fully non-parametric test for continuous data based on conditional mutual information combined with a local permutation scheme is presented. Through a neares...
Article
Variability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead-time because they poorly capture low-frequency pr...
Article
Full-text available
Detecting causal associations in time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system or the human brain. Interactions in high-dimensional dynamical systems often involve time-delays, nonlinearity, and strong autocorrelations. These present major challenges for causal discovery technique...
Article
Full-text available
In recent years, the Northern Hemisphere midlatitudes have suffered from severe winters like the extreme 2012/13 winter in the eastern United States. These cold spells were linked to a meandering upper-tropospheric jet stream pattern and a negative Arctic Oscillation index (AO). However, the nature of the drivers behind these circulation patterns r...
Article
Full-text available
Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as the Eartha € s climate to volcanic eruptions, extreme events or geoengineering. Here a data-driven approach is introduced based on a dimension reduction, causal reconstruction, and novel networ...
Article
Full-text available
Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent work has focused on causal definitions of information transfer excluding effects of common drivers and indirect influences. While the former clearly constitutes a spurious...
Article
Full-text available
We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the langu...
Article
Full-text available
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Ther...
Article
Full-text available
Lagged cross-correlation and regression analysis are commonly used to gain insights into interaction mechanisms between climatological processes, in particular to assess time delays and to quantify the strength of a mechanism. Exemplified on temperature anomalies in Europe and the tropical Pacific and Atlantic, the authors study lagged correlation...
Thesis
Der technologische Fortschritt hat in jüngster Zeit zu einer großen Zahl von Zeitreihenmessdaten über komplexe dynamische Systeme wie das Klimasystem, das Gehirn oder das globale ökonomische System geführt. Beispielsweise treten im Klimasystem Prozesse wie El Nino-Southern Oscillation (ENSO) mit dem indischen Monsun auf komplexe Art und Weise durch...
Conference Paper
Full-text available
The article introduces an information-theoretic approach to detect and quantify causal couplings in the complex cardiovascular system. In a first step a causal algorithm detects the coupling delays and in a second step the causal strength of each coupling mechanism is quantified using the recently introduced momentary information transfer. As a pre...
Article
Full-text available
Earth's climate exhibits internal modes of variability on various timescales. Here we investigate multi-decadal variability of the Atlantic meridional overturning circulation (AMOC), Northern Hemisphere sea-ice extent and global mean temperature (GMT) in an ensemble of CMIP5 models under control conditions. We report an inter-annual GMT variability...
Article
Full-text available
Lagged cross-correlation and regression analysis are commonly used to gain insights into interaction mechanisms between climatological processes, in particular to assess time delays and to quantify the strength of a mechanism. Exemplified on temperature anomalies in Europe and the tropical Pacific and Atlantic, the authors study lagged correlation...
Article
Full-text available
Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. In this work, we apply the recently proposed climate network approach for characterizing the evolving correlation structure of the Earth's climate system based on reanalysis...
Article
Full-text available
This review provides a summary of methods originated in (non-equilibrium) statistical mechanics and information theory, which have recently found successful applications to quantitatively studying complexity in various components of the complex system Earth. Specifically, we discuss two classes of methods: (i) entropies of different kinds (e.g., on...
Article
Full-text available
The dependencies of the lagged (Pearson) correlation function on the coefficients of multivariate autoregressive models are interpreted in the framework of time series graphs. Time series graphs are related to the concept of Granger causality and encode the conditional independence structure of a multivariate process. The authors show that the comp...
Article
Full-text available
Earth's climate exhibits internal modes of variability on various time scales. Here we investigate multi-decadal variability of the Atlantic meridional overturning circulation (AMOC) in the control runs of an ensemble of CMIP5 models. By decomposing global-mean-temperature (GMT) variance into contributions of the AMOC and Northern Hemisphere sea-ic...