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A method is presented which applies bias adjustments to climate indicators that are based on fixed thresholds, e.g., the number of hot days with the maximum temperature exceeding 30 ∘C or the number of days with heavy precipitation in exceedance of 20 mm rainfall. The bias adjustment first identifies the percentile of the required threshold value in reference climate data. Then it computes the value of this percentile for the individual historical climate model simulations – here an ensembles of EURO-CORDEX model runs, including dynamical and statistical models. Finally, the climate indicator is re-calculated for each model. The method is applied to climate projections as well, giving further insight into the projected development of the ensemble for extreme conditions. It is assessed that communication to the public and decision makers is improved by expressing these changes in extremes based on absolute values.

WEREX ist die auf den Freistaat Sachsen adaptierte Version des statistischen Regionalisierungsverfahrens WETTREG zur Erzeugung von regionalen Klimaprojektionen bis zum Ende des 21. Jahrhunderts. Mit diesem Vorhaben erfolgte eine Weiterentwicklung zur Version WEREX-VI. Das Ensemble umfasst die Szenarienfamilien RCP (2.6 | 4.5 | 8.5) und SRES (A1B) und besteht aus stationsbezogenen Zeitreihen mit Tageswerten für Klimaelemente im Zeitraum 1951 bis 2100 für das Gebiet des Freistaates Sachsen, einschließlich Randstreifen (inklusive Polen, Tschechien). Die Veröffentlichung dokumentiert das WEREX-VI-Ensemble und richtet sich an Klima- und Klimafolgenforscher. Redaktionsschluss: 03.02.2020

Warum sollten wir eine globale Erwärmung von mehr als 2 Grad vermeiden? Hochauflösenden Klimasimulationen für Mitteleuropa zeigen und liefern der Politikberatung erstmals umfassenden Ergebnisse, was es für Deutschland bedeuten würde, wenn sich eine globale Erwärmung um mehr als 2 Grad nicht mehr vermeiden ließe. Sie liefern somit die Grundlage für Abschätzungen der Klimafolgen in verschiedenen Bereichen der Gesellschaft zur Anpassung an den Klimawandel. Dem sogenannten »Weiter-wie-bisher« Szenario steht ein »Klimaschutz« Szenario gegenüber welches zeigt, was sich vermeiden ließe, wenn jetzt die Weichen für eine nachhaltige Energieversorgung und andere Klimaschutzmaßnahmen entsprechend des Pariser Abkommens von 2015 gestellt werden. Extreme Witterung, wie sie in der jüngsten Vergangenheit auftrat, könnte spätestens in der zweiten Hälfte des 21. Jahrhunderts zur Normalität gehören. Darüber hinaus werden die globalen Folgen durch das Abschmelzen von über Jahrtausenden gewachsenen Gletschern und Eisschilden den Meeresspiegel um bis zu mehr als einen Meter ansteigen lassen und somit Küstenregionen vor enorme Herausforderungen stellen. Why should we avoid global warming of more than 2 degrees? For the first time, high-resolution climate simulations for Central Europe show, and provide policy advice on consequences for Germany, should a global warming by more than 2 degrees no longer be avoided. They provide the basis for estimating the climate impacts and thus for adaptation to climate change for various sectors of society. The so-called »as-usual« scenario is juxtaposed with a »climate protection« scenario which shows what could be avoided if the course is laid in for sustainable energy supply and other climate protection measures according to the Paris Agreement of 2015. Extreme weather conditions, as they occurred in the recent past, could become normal in the second half of the 21st century at the latest. In addition, the global consequences of melting glaciers and ice shelfs, which have accumulated over thousands of years, will cause sea levels to rise by up to more than a meter, posing enormous challenges to coastal regions

National Road Administrations (NRAs) are confronted by a number of challenges in their efforts to maintain the resilience of their road network in the face of increasing climate change impacts and reduce the carbon emissions associated with the maintenance and operation of their network. This paper describes the development of tools and guidance designed to help NRAs address two of these challenges; making the business case for climate change adaptation and embedding consideration of climate change in their operations and procurement processes. The work is being carried out as part of the DeTECToR (Decision-support Tools for Embedding Climate Change Thinking on Roads) project, commissioned through the Conference of European Directors of Roads (CEDR) Transnational Research Programme. The paper presents the work undertaken in the first half of the project, focusing on the approach to developing the tools and describing the next steps which includes working with selected NRAs to trial the tools and guidance in a series of pilot studies.

Communication between providers and users of climate model simulation results still needs to be improved. In the German regional climate modeling project ReKliEs-De a midterm user workshop was conducted to allow the intended users of the project results to assess the preliminary results and to streamline the final project results to their needs. The user feedback highlighted, in particular, the still considerable gap between climate research output and user-tailored input for climate impact research. Two major requests from the user community addressed the selection of sub-ensembles and some condensed, easy to understand information on the strengths and weaknesses of the climate models involved in the project.

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.

This 60 page booklet comprises the results of the German climate research project "ReKliEs-De - Regionale Klimaprojektionen Ensemble für Deutschland". Publisher of the paper version is "Hessisches Landesamt für Naturschutz, Umwelt und Geologie". The paper is in German language.

This 100 page booklet is an how-to for users of the results from the German climate research project "ReKliEs-De - Regionale Klimaprojektionen Ensemble für Deutschland". The publisher of the printed version is "Hessisches Landesamt für Natur, Umwelt und Geologie". The paper is in German language.

When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (ESD). In this context the question is addressed: Is an empirical statistical downscaling method capable of yielding results that are comparable to those by dynamical RCMs? Based on the presented ESD method, the comparison of RCM and ESD results show a high amount of agreement. In addition the empirical statistical downscaling can be applied directly to a GCM or a GCM-RCM cascade. The paper aims at comparing the consequences of employing various CGM-RCM-ESD combinations on the derived future changes of temperature and precipitation. This adds to the insight on the scale dependency of downscaling strategies. Results for one GCM with several scenario runs driving several RCMs with and without subsequent empirical statistical downscaling are presented. It is shown that there are only small differences between using the GCM results directly or as a GCM-RCM-ESD cascade.

A system to derive tracks of barometric minima is presented. It is deliberately using coarse input data in space (order of 2°×2°) and time (6-hourly to daily) as well as information from just one geopotential level. It is argued that the results are, for one robust in the sense of an assumption of the IMILAST Project that the use of as simple as possible metrics should be strived for and for two tailored to the input from reanalyses and GCMs. The methodology presented is a necessary first step towards an automated storm track recognition scheme which will be employed in a second paper to study the future development of atmospheric dynamics in a changing climate. The process towards obtaining storm tracks is two-fold. In its first step cyclone centers are being identified. The performance of this step requires the existence of closed isolines, i.e., a topology in which a grid-point is surrounded by neighbours which all exhibit higher geopotential. The usage of this topology requirement as well as the constraint of coarse data may lead, though, to limitations in identifying centers in geopotential fields with shallow gradients that may occur in the summer months; moreover, some centers may potentially be missed in case of a configuration in which a small scale storm is located at the perimeter of a deep and very large low (a kind of "dent in a crater wall"). The second step of the process strings the identified cyclone centers together in a meaningful way to form tracks. By way of several examples the capability to identify known storm tracks is shown.

When studying concentrations of particulate matter with a size of 10 µm or below (PM10), measured locally, it becomes evident that two main portions need to be quantified: The concentration produced by sources in the vicinity of the station and the long range transports. The traditional approaches include analyses of the components of PM10, comparisons upwind and downwind of a station, investigation of trajectories and complex chemical transport modelling. The development of an independent strategy which makes use of statistical methods, including regression and correlation analysis is a reasonable alternative. This method, presented here, does not apply the concept of PM10 sources, but, rather, analyzes the relations between times series of PM10 measurements and atmospheric properties. It is applied to identify the shares of the local portion and the large-scale background plus a stochastic portion that cannot be attributed to either of the two. Using regression analysis, a set of objectively chosen meteorological parameters is used to reconstruct the local PM10 measurement series, defining the local portion. This weather-dependent part of the series is then removed and the residuum, which contains the large-scale PM10 background and a stochastic portion is analyzed further with correlations. Results are shown for a three-year set of data which includes well over 250 PM10 stations across Germany. The data is analyzed according to different stratifications, such as the PM10 load and the wind direction as well as for the data set as a whole. In a further development of the method, a study of PM10 transports across several border sections is shown.

Für das Regionalisierungsverfahren WEREX, dessen Ergebnisse der Abschätzung einer zukünftig möglichen Klimaentwicklung in Sachsen dienen, wurden Optimierungsmöglichkeiten erarbeitet und überprüft. Im Ergebnis konnten die Vorteile der Verfahren WEREX und KlaMuKo, einem statistischen Ansatz zur verbesserten Darstellung von Extremen, zur Neuentwicklung WMSax2.0 zusammengeführt werden. Dieses Konzept wurde anhand einer Sensitivitätsuntersuchung als aussichtsreich bestätigt und bildet die fachlich-technische Grundlage zur Erstellung einer neuen Klimaprojektion für den Freistaat Sachsen. Die Veröffentlichung richtet sich an regionale Akteure, Planungsbüros, Bildungseinrichtungen und Unternehmen.

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.

The regional atmospheric climate model HIRHAM has been applied to the Arctic. Simulations for the whole year 1990 and for an ensemble of winter months (January of 1985-1995) have been performed. The comparison of the simulations with observational data analyses shows that the general spatial patterns are in good agreement with the data, in both the vertical structure and the annual cycle. For an additional validation of the model results, a multivariate classification of large-scale circulation patterns has been applied to the January ensemble model simulations.

A 2-step downscaling method for general circulation model (GCM) outputs is presented. The investigation is based on daily upper air geopotential and humidity fields (Atlantic-European sector) and surface observations (52 German chmate stations) from 1966 to 1993. In the first step, significant circulation patterns are identified using cluster analysis which takes advantage of Information exclusively from upper air fields. This leads to composite charts which can be readily interpreted synoptically, but which only explain a portion of the variance of the local weather elements. In the second step, a conditional (weather pattern-dependent) stepwise screening regression analysis is performed for each weather element and 6 German climate regions A principal finding is that the modelled (downscaled) local c h a t e is in good agreement with observations, particularly for the temperature regime, due to the fact that a major part of the variance is explained after the 2 steps. Including 700 hPa humidity slightly improves the explained variance. An application to a CCM control run is added. It shows that the method is capable of reconstructing interannual variability of local weather elements.