Human-made climate change is increasingly impacting our living conditions, destroying infrastructure and threatening lives. The recent years and months have led to unprecedented events and record-breaking temperatures, with March being the tenth month in a row being the hottest so far (Copernicus). Not only rising temperatures but also altered precipitation patterns pose a challenge to the existing urban environments and thus to 2/3 of the human population (REF). Cities are especially affected since they consist of mainly sealed surfaces, which enhance climate change impacts such as increased heat or intensified precipitation events due to their different characteristics as natural areas (e.g. albedo, heat capacity, infiltration). Within KNOWING two climate impacts are investigated comprehensively – flooding (fluvial, pluvial) and its impact on infrastructure as well as heat and its impact on health. Therefore, two urban areas (Granollers, Spain and Tallinn, Estonia) are considered. To quantify possible interventions to adapt to current and future climate change impacts, two different models are applied: PALM-4U [1] and ICM-Infoworks. PALM-4U is an urban climate model used for quantifying the impact of greening on urban heat load. ICM-infoworks is used for assessing adaptation measures to lessen pluvial flooding. As both models rely on land use, the changes planned to adapt to heat (e.g. greening and unsealing) and those for reducing flooding (retention areas, unsealing) coincide. Within PALM-4U interventions such as increased tree cover, implementation of recreational parks, renaturalization of rivers and building-related measures (e.g. green roofs, retrofitting) are considered. All interventions leading to increased unsealing of areas and thus infiltration, also lessen the risk of flooding and can thus be implemented within ICM-Infoworks, quantifying the impact of the same interventions on flood risk. By assessing the impact of the same interventions within two models and concerning two different climate risks (heat and flooding) the double effectiveness is accounted for, thus allowing the holistic approach of adaptation to climate change.

In order to plan and implement appropriate adaptation measures to protect settlements, the economy and other assets from natural hazards, it is important to know the potential consequences of climate change. Accordingly, the Plan-B model region within the Rhine Valley of the west-Austrian state of Vorarlberg is supported by the KLAR! program of the Austria Climate and Energy Fund to assist with this endeavour. The region encompasses some 91,000 inhabitants, making it one of the most densely populated regions in Austria. The focus of this work was on a detailed climate modelling study of the Plan-B region using the microscale urban climate model MUKLIMO_3. An accurate representation within the model involved processing input data from the Copernicus high-resolution datasets such as the digital elevation model, CORINE land cover, imperviousness and tree cover density supplemented by locally available building, land cover and vegetation data. The availability of measurements from a dense network of weather stations during July 2022 allowed the model accuracy to be evaluated for two case studies, and showed good results in representing the spatial variations of the region at a resolution of 50 m. The modelling results from MUKLIMO_3 for the historical and current climate periods show hot-spots concentrated within the built-up urban areas. In such locations, annual averages of up to 70 summer days and 27 hot days are simulated for the period 1991-2020, consistent with the observations. Conversely, in the vicinity of water bodies such as Lake Constance, within parks or areas outside the urban fabric, annual averages of about 20-30 summer days and 2-8 hot days are more typical. From 1961 to 2020 the annual numbers of summer days, hot days and tropical nights are shown to increase across the entire domain reflecting the warming trend due to climate change. Climate model projection data from an ensemble of EURO-CORDEX models under the emissions scenarios RCP4.5 and RCP8.5 are input to show the expected heat load changes across the region for two future time periods. When compared with the 1991-2020 period, 2041-2070 can expect local increases of up to 20 summer days and 4 hot days for RCP4.5, and 28 summer days and 9 hot days for RCP8.5. A larger disparity in the results between the scenarios is shown for the period 2071-2100, with local increases of up to 56 summer days and 35 hot days for the extreme RCP8.5 scenario simulated relative to the 1991-2020 period. These results provide important information for decision makers and urban planners on where climate change adaptation measures to reduce heat load should be planned.

As the majority of the population live in cities, it is important to understand the urban climate and how it can change in the future. Accordingly, the ACRP-funded project LUCRETIA investigates how land use and land cover determine local climate characteristics within cities in Austria.Historical land use data has been obtained for Graz and Vienna for a number of years and used as input into the microscale urban climate model MUKLIMO_3 to simulate both cities in conditions representing a typical summer day. In conjunction with the cuboid method, climate indices such as the average number of summer and hot days per year have been calculated to establish how the heat load changes from one year to another. Differences in the heat load have been related to changes in the land use focusing on (i) the change that occurs in situ and (ii) the change that occurs in the neighbourhood.It is shown that land use categories can be ordered according to their heat load, with categories containing larger amounts of greenery generally having lower heat loads. With the land use categories sorted in such a way, it enables a relatively quick assessment to be made of the effect of replacing one land use category with another, without having to employ expensive modelling tools. Furthermore, it is shown that land-use changes not only affect the heat load of the changed area in situ, but also the neighbourhood around where the change was made. This demonstrates that land-use changes may have a broader spatial impact than initially anticipated. The results from this study can serve as guidance for city planners regarding future land use and land cover changes.

Die Auswirkungen des Klimawandels können in Städten durch den urbanen Hitzeinseleffekt und die hohe Bevölkerungsdichte besonders ausgeprägt sein. Deshalb sind Informationen über die thermischen und dynamischen Verhältnisse in der Stadt essentiell für eine zukunftsfähige Stadtplanung. Im Rahmen des Projektes LUCRETIA (gefördert durch das Austrian Climate Research Programme) werden innerstädtische Temperaturmuster in Wien, Österreich, mit Hilfe von Stadtklimamodellen (MUKLIMO_3, PALM-4U) und Daten privater Wetterstationen untersucht. Während die Dichte herkömmlicher Wetterstationsnetze in der Regel zu gering ist, um die Temperaturmuster in Städten zu erfassen und die Ergebnisse von Stadtklimamodellen zu bewerten, bieten private Wetterstationen ein dichtes Messnetz, insbesondere in Städten. In Wien stehen für unseren Untersuchungszeitraum im August 2018, nach der Qualitätskontrolle, mehr als 1000 private Wetterstationen der Firma Netatmo zur Verfügung. Erste Untersuchungen haben gezeigt, dass die Lufttemperaturmessungen der Netatmo Stationen gut mit den Messungen von konventionellen Stationen übereinstimmen. Die beobachteten Unterschiede werden auf die unterschiedlichen Standorte der Stationen und mikroskalige Effekte zurückgeführt. Im Rahmen des Projektes LUCRETIA werden Ergebnisse zweier unterschiedlicher Modelle (MUKLIMO_3, PALM-4U), mit verschiedener räumlicher Auflösung, für einen dreitägigen Zeitraum im August 2018 mit Hilfe der privaten Wetterstationen genauer untersucht. Ein Vergleich der Netatmo-Temperaturdaten mit den Ergebnissen des Stadtklimamodells MUKLIMO_3 für Wien, mit einer räumlichen Auflösung von 100m x 100m, zeigt für viele Stationen ein ähnliches Muster wie der Vergleich zwischen konventionellen Stationen und den Modellergebnissen: die Netatmo Stationen verzeichnen am Nachmittag eine schnellere Temperaturabnahme als die Simulation und die Temperatur in der Nacht wird vom Modell häufig überschätzt. Dennoch stimmen die räumlichen Muster in der Nacht, welche von den Messungen beziehungsweise den Simulationsergebnissen abgeleitet wurden, gut miteinander überein. Tagsüber sind die räumlichen Temperaturmuster unterschiedlich. Die Netatmo Daten weisen tagsüber, wegen mikroskaliger Effekte, sehr starke kleinräumige Unterschiede auf. Im nächsten Schritt werden die Modellergebnisse der räumlich höher aufgelösten Modelle (MUKLIMO_3 (20m), PALM4U (2m)) mit den Netatmo Daten verglichen und die Auswirkungen der verschiedenen Landnutzungsklassen auf die Temperatur in der Stadt untersucht.

Climate change impacts are amplified in cities due to the urban heat island effect and the high population density. Information about the intra-urban temperature patterns is therefore crucial to support resilient city planning. Within the ACRP funded project LUCRETIA, the intra-urban temperature patterns in Vienna, Austria, are investigated using urban climate models (MUKLIMO_3, PALM-4U) and data from citizen weather stations. While the density of conventional weather station networks is usually too low to capture the temperature patterns in cities and to assess urban climate model results, citizen weather stations provide a dense monitoring network, especially in cities. In Vienna, more than 1000 citizen weather stations from the company Netatmo are available for our study period in August 2018, after the quality control. First investigations showed, that air temperature measurements from citizen weather stations are in good agreement with measurements from conventional stations. The observed differences are attributed to the different locations of the stations and micro-scale effects. A preliminary comparison of citizen weather station data with urban climate model results from MUKLIMO_3 for Vienna revealed for some of the stations similar patterns as the comparison between conventional stations and model results: a reasonably good agreement during the day, after model initialization, and a temperature overestimation at night. Within LUCRETIA we are assessing in more detail the model results (MUKLIMO_3, PALM-4U) for a three day period in August 2018, thereby looking at the effect of the different land-use classes within the city. In addition, we will investigate whether similar spatial temperature patterns are identified when using urban climate models and data from citizen weather stations.

In recent years, the representation of climate information in a way to support decision making has been gaining momentum. Worldwide, these so-called climate services are emerging as an essential tool to connect the advances in climate science with the domains of climate change adaptation. The methodology developed within the CLARITY project (funded through European Union funding program Horizon 2020) is aimed at implementing a new generation of climate services specifically designed to assess adaptation measures at the city level under the effects of extreme weather events in the context of climate change. These effects are assessed based on observations as well as climate projections, and the subsequent derivation of climate indices to address changes in climate extremes. The dynamical-statistical downscaling of regional climate model results is used to obtain this information on fine spatial scales (100 m), hence providing urban scale projections and enabling climate sensitivity simulations of adaptation measures on the urban scale. The climate adaptation strategies encompass, among others, green roofs, increasing roof albedo, as well as changes in soil sealing. Here, the climate assessment methodology developed within CLARITY will be discussed in detail, and results for the city of Linz (Austria) presented. In addition, the usage of these methods and results within the CLARITY climate service as well as the connection to urban climate change resilience will be highlighted.