Probabilistic attribution of extreme precipitation to aerosol perturbations 
Climate change may impact societies in particular by extreme events; extreme precipitation andlightning from deep convection (thunderstorms) are a particular threat. Anthropogenic modification ofthe atmospheric composition may drive changes in these events. Different from greenhouse gases andglobal warming, aerosol particle emissions may directly impact deep convection. This project will (i)assess the impact of aerosols on deep convection as simulated by the ICON regional model withexisting large-domain large-eddy simulations, a multi-model ensemble, and ground-basedobservations as reference, and (ii) investigate in a probabilistic attribution framework (ensemblemodelling of factual and counterfactual conditions) the impact of aerosols on extreme rain andlightning. Possible work in a second phase will investigate these in a long-term and global framework.
Institution: University of Leipzig
Contact: Johannes Quaas

Investigation of an Ensemble of Regional Climate Models with Respect to Land-Atmosphere Feedback and Extreme Precipitation 
This project detects and analyses heavy precipitation events (HPE), both single convective events and intense precipitation episodes, with the potential to small- and large-scale flooding over Central Europe. It especially focuses on the analysis of related physical processes relevant for the formation and development of these HPEs, in particular the impact of land-atmosphere (L-A) feedback. The main objective of this project is to analyze the ability of the relatively new ReKliEs-De ensemble of regional climate simulations to reproduce intensities, frequencies, and durations of heavy precipitation events. For a historical climatological period, the classical evaluation strategy is extended to L-A metrics, which characterize the influence of local to regional processes as well as the role of soil, land cover, and the atmosphere leading to heavy precipitation. Furthermore, changes in the statistical and physical quantities of these events are investigated and related to modifications of the forcing mechanisms and L-A feedback for two different climate change scenarios. We will  investigate the influence of an improved representation of L-A feedback mechanisms and extreme precipitation events in a series of convection-permitting (CP) simulations on the kilometer scale where a better representation of orography, soil and land-cover heterogeneities, temporal and spatial evolutions of the surface layer and the planetary boundary layer (PBL), and thus, convergence zones and convection initiation can be expected. Our research will lead to a deeper understanding of intensive and extreme precipitation and thus also provide new insights into climate change.
Institutions: BTU Cottbus - Senftenberg, University Hohenheim
Contact: Klaus Keuler, Volker Wulfmeyer, Kirsten Warrach-Sagi

Linking Atlantic seasonal climate variability to heatwave occurrence over Europe
In the past two decades, Europe has suffered from major heatwaves: the extreme heat event that occurred in 2003 caused around 30.000 deaths, and in 2015, 2018 and 2019 many European cities were subject to record-breaking temperatures. Hence, being able to know in advance that a heatwave might occur would be of tremendous benefit for planning. However, is it possible to make informed statements about a potentially upcoming heatwave several months in advance? One potential candidate for memory in the climate system that might enable us to make statement about a potentially upcoming heatwave, is the Atlantic Ocean. The present project aims to tackle a potential influence of the North Atlantic ocean on European summer extremes in a systematic way. As a first step, we will analyze the Max Planck Institute for Meteorology Grand Ensemble (MPI-GE), consisting of 100 simulations from 1850 to 2005. We will compare the frequency and occurrence of heat extremes to re-analysis. As a second step, we will implement statistical methods to automatically detect past heatwave events, and analyze the Atlantic Ocean’s behavior during spring. Using state-of-art methods, such as Machine Learning (ML) and non-gaussian verification techniques, we will investigate which regions of the Atlantic Ocean play a major role in the occurrence of heatwaves over Europe. With such a robust identification and description of potential North Atlantic SST pre-cursors for European summer extremes, we will aim to improve seasonal heatwave predictions in a final step.
Institution: University Hamburg
Contact: Goratz Beobide Arsuaga, André Düsterhus, Johanna Baehr

Scales of Atmospheric spatio-temporal Variability and their role in determining the past and future frequency of Extreme events
The aim of SAVE is to determine the role of atmospheric characteristics, structures and processes in the generation, amplification or attenuation of extreme events by applying a multivariate approach. Especially with regard to the spatial resolution of climate simulations, it is to be expected that the climatological representation of extremes is suboptimal. Therefore, the project goals are: i) determination of scale-dependent atmospheric drivers for extreme events by examining the relationship between atmospheric conditions and processes and the occurrence of extremes on different spatio-temporal scales, ii) analysis of past changes in the occurrence of extremes with respect to changes in the identified scale-dependent drivers, and ii) transfer of the identified relationships of drivers and extremes to climate projections in order to enhance the prediction of future changes in the frequency and intensity of extreme events. Our analysis is based on a multi-scale approach using data sets with different spatial resolutions. This approach allows for the estimation of drivers of extremes on large (global) scales using global reanalysis, continental scales using regional reanalysis and local scales using convection-resolving reanalysis. Furthermore, the planned approach exploits the spatio-temporal drivers identified using reanalysis data (as approximate for the true state) to enhance (regional) climate predictions by applying or transferring the determined statistical relationships between drivers and extremes.
Institution: University Bonn, Deutscher Wetterdienst, Hans-Ertel-Zentrum für Wetterforschung
Contact: Sabrina Wahl

Space-time statistics of extreme precipitation
Precipitation extremes are a space-time phenomenon, but have been statistically treated in the past mainly from single point observations. Here, we aim at the statistical analyses of the dynamics of space-time extremes and at the development of new statistical approaches to describe it. As one main outcome, area-intensity-duration-frequency (AIDF) curves will be estimated for past, present and future times. In addition, questions regarding the temporal development of theses extremes, clustering phenomena, unusual events will be addressed. The research utilizes especially high space-time resolution precipitation data from weather radar in addition to the rain gauge and climate model re-analyses data.
Institution: University Hannover, University Stuttgart
Contact: Uwe Haberlandt , Jochen Seidel

Compact Description and Statistical Modeling for Non-Stationary Spatial Weather Extremes
Detection and attribution (D&A) of climate change requires a compact description of the spatio-temporal climate state. While, in high dimensional spaces, particularly on small scales, internal climate variability is typically too large for a climate change signal to be significant, a suitable reduction of degrees of freedom can improve the signal-to-noise ratio and, thus, increase the potential to detect less strong signals in this setting. Therefore, in our project, we analyze and develop methods for information compression of high-resolution spatial weather variables and integrate them into the D&A framework for extreme weather related to moist deep convection. So far, most of the available strategies aim at the description of the bulk of the distribution while our analysis focuses on the tail behavior. Thus, we compare different methods and assess their assets and drawbacks with respect to extreme weather. The methods investigated comprise data adaptive decomposition such as principal component analysis, filter approaches using wavelet decomposition, or dynamical decomposition based on reduced dynamical models. Furthermore, we consider various spatial extreme value models. In particular, we develop new statistical models describing the spatial dependence structure of single extreme events based on Pareto processes.
Institutions: University Bonn, University Stuttgart
Contact: Petra Friederichs, Marco Oesting

Identification and Statistics of Extreme Events (Present, Future and Trends) Using Markov Chains and Climate Networks
Standard statistical methods in climatology have certain weaknesses in detecting and analyzing present and future climate extremes. The goal of MarNet is therefore to reduce these deficiencies and complement conventional approaches by applying new and innovative methods, which are not commonly used in the context of extreme events, namely Climate Networks and Markov Chains. A Climate Network consists of nodes which are connected by edges. Nodes can either be observation sites or grid points of a climate model, and connections between such nodes are established if time series of meteorological data are there correlated. In this way, statistical similarities between nodes can be revealed, by which physical interrelations in the climate system can be analyzed. The method is therefore particularly suited to identify spatial (hot spots; changing locations) and temporal patterns in present and future extreme events. A Markov Chain is a time series of different states of a system (e.g. extreme or not-extreme) in which the probability of a system to change its state is incorporated in a transition matrix. By using different descriptors of a Markov Chain, certain characteristics of a system can be determined, such as the tendency of a system to stay in a given state, or to return to this state, and the predictability of a state transition. In this way, Markov Chains can be used to analyze the frequency, duration and regularity of present extreme events and describe their future change.
Institution: Karlsruhe Institute of Technology
Contact: Gerd Schäder, Markus Breil

Lagrangian Analysis and Modeling of Correlated Weather and Climate Extremes
Extreme weather and climate events are of great general societal interest since they cause significant economic damages and fatalities each year. The description and prediction of extremes is involved since these events can be correlated. European windstorms, for example, occur more often in bunches than if they were independent events (Franzke 2013, Blender et al. 2015). The observed clustering of extremes is caused by meteorological processes like Rossby waves and downstream development. Blender et al. (2015) have shown that the return times between severe windstorms are non-exponential and have to be modeled by a fractional Poisson process instead of the standard Poisson model for rare events. Extreme events are typically examined from a Eulerian perspective by determining the extreme value characteristics at particular stations or grid points. Here we propose a different and novel approach by taking a Lagrangian view on extremes to grasp their accumulated impact. The vortex tracking (Blender et al. 1997, Sienz et al. 2010) is combined with an analysis of the extreme value characteristics and the clustering properties. The return times between two extreme events are scientifically relevant and important for stakeholders and the insurance industry. We aim to understand and predict severe weather and climate events by combining the Lagrangian cyclone view with newly developed concepts for the serial clustering of extreme events. We will elucidate the driving mechanisms of the serial clustering of cyclones.
Institution: University Hamburg
Contact: Richard Blender, Christian Franzke, Alexia Karwat