Open-Source Ansatz zur Abschätzung Sozioökonomischer Klimafolgen für Deutschland am Beispiel Extremer Hitze

Vorhersage und ProjektionDie Zunahme von Wetter- und Klimaextremen durch den voranschreitenden Klimawandel ist zunehmend mit gesellschaftlichen Beeinträchtigungen und ökonomischen Kosten verbunden. Eine umfassende Quantifizierung und nutzerspezifische Kommunikation dieser sozioökonomischen Klimafolgen an politische und privatwirtschaftliche Entscheider ist für die Vermeidung möglicher Folgen oder eine adäquate Anpassung unerlässlich. Eine Abschätzung sozioökonomischer Klimafolgen erfordert (i) Daten zur klimatischen Gefährdung, (ii) Informationen zur räumlichen Exposition sozioökonomischer Größen, (iii) Annahmen zur ihrer Sensitivität, als auch (iv) eine Maschinerie, um diese Größen gekoppelt auszuwerten. Hierfür wird in diesem Vortrag die open-source python Plattform CLIMADA [1,2] vorgestellt und zur sozioökonomischen Folgenabschätzung durch Wetter- und Klimaextreme auf Deutschland angewendet. Am Beispiel von extremer Hitze wird demonstriert, wie projizierte klimatische Trends mit unterschiedlichen Szenarien für den demographischen Wandel auf sub-nationaler Skala wechselwirken und so die möglichen Auswirkungen (z.B. durch hitzebedingte Übersterblichkeit [3]) verstärkt werden könnten. Die Anwendung von CLIMADA ist nicht nur auf Klimaprojektionen beschränkt, sondern erlaubt eine räumlich aufgelöste und nahtlose Bereitstellung von sozioökonomischen Risiken und ökonomischen Schäden durch Wetter- und Klimaextreme von der Wettervorhersage bis zum Ende des Jahrhunderts

Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ∘ C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region

Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 $$^{\circ }$$C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region

Eps8 Regulates Axonal Filopodia in Hippocampal Neurons in Response to Brain-Derived Neurotrophic Factor (BDNF)

A novel signaling cascade controlling actin polymerization in response to extracellular signals regulates filopodia formation and likely also neuronal synapse formation

The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG

In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weather situation-based regionalization method (in German: WETTerlagen-basierte REGionalisierungsmethode, WETTREG), is described. It belongs to the class of multi-site parametric models that aim at the representation of the spatial dependence among weather variables with conditioning on exogenous atmospheric predictors. The development of the WETTREG WG was motivated by (i) the requirement of climate impact modelers to obtain input data sets that are consistent and can be produced in a relatively economic way and (ii) the well-sustained hypothesis that large scale atmospheric features are well reproduced by climate models and can be used as a link to regional climate. The WG operates at daily temporal resolution. The conditioning factor is the temporal development of the frequency distribution of circulation patterns. Following a brief description of the strategy of classifying circulation patterns that have a strong link to regional climate, the bulk of this paper is devoted to a description of the WG itself. This includes aspects, such as the utilized building blocks, seasonality or the methodology with which a signature of climate change is imprinted onto the generated time series. Further attention is given to particularities of the WG’s conditioning processes, as well as to extremes, areal representativity and the interface of WGs and user requirements

Klimawandel und Wetterlagen

Die Veröffentlichung dokumentiert die Ergebnisse eines Forschungsprojektes, in dem untersucht wurde, welchen Einfluss großräumige Strömungsmuster in der Atmosphäre (Großwetterlagen) auf das Auftreten von Wetterextremen in Sachsen hatten und zukünftig haben können. Dafür wurde ein Verfahren zur Klassifikation durch multiple Regression entwickelt und angewendet. Belastbare Zunahmen von Wetterextremen sind z. B. bei heißen und niederschlagsarmen warmen Tagen sowie etwas weniger sicher bei Schwüle und Starkwind im Winterhalbjahr zu erwarten. Die Veröffentlichung richtet sich an Klimaforscher, -modellentwickler und -modellanwender

Downscaling and Evaluation of Seasonal Climate Data for the European Power Sector

Within the Clim2Power project, two case studies focus on seasonal variations of the hydropower production in the river basins of the Danube (Germany/Austria) and the Douro (Portugal). To deliver spatially highly resolved climate data as an input for the hydrological models, the forecasts of the German Climate Forecast System (GCFS2.0) need to be downscaled. The statistical-empirical method EPISODES is used in this approach. It is adapted to the seasonal data, which consists of ensemble hindcasts and forecasts. Beside this, the two case study regions need specific configurations of the statistical model, providing appropriate predictors for the meteorological variables. This paper describes the technical details of the adaptation of the EPISODES method for the needs of Clim2Power. We analyse the hindcast skill of the downscaled hindcasts of all four seasons for the two variables near-surface (2 m) temperature and precipitation, and conclude that on the average the skill is conserved compared to the global model. This means that the seasonal information is available at a higher spatial resolution without losing skill. Furthermore, the output of the statistical downscaling is nearly bias-free, which is, beside the higher spatial resolution, an added value for the climate service