Klimaprojektionen für das Modellgebiet Lüneburger Heide

Für das Modellgebiet der Lüneburger Heide werden zur Mitte des 21. Jahrhunderts für alle Jahreszeiten höhere Mitteltemperaturen projiziert. Zum Ende des 21. Jahrhunderts sind noch größere Temperaturzunahmen zu erwarten. Im Winter steigen die Temperaturen jeweils am stärksten, im Frühjahr am geringsten. Dabei nehmen im Winter die niedrigen Tagesmitteltemperaturen stärker zu als die höheren und Eis- und Frosttage treten deutlich seltener auf. Im Sommer können Tage mit extremen Temperaturen wie Hitzetage und Tropentage bzw. -nächte deutlich häufiger auftreten. Im Jahr nimmt die Anzahl der Tage mit Temperaturen höher als 5° C deutlich zu, was eine wichtige physiologische Schwelle für das Wachstum von Pflanzen ist. Im Verlauf des Jahrhunderts unterscheiden sich die für das B1 Szenario simulierten Temperaturen immer deutlicher von den Ergebnissen für die A1B und A2 Szenarien. Das bedeutet, wenn es gelingt, die Treibhausgasemissionen zu vermindern, deutlich geringere Klimaänderungen zu erwarten sind. Die projizierten Niederschläge nehmen 2036-2065 in allen Jahreszeiten für alle Szenarien leicht zu, mit Ausnahme abnehmender Niederschläge für das A1B Szenario im Sommer. Insgesamt sind die Veränderungen im Sommer sehr gering und zeigen keinen klaren Trend. Zum Ende des 21. Jahrhunderts dagegen zeigen die meisten Simulationen im Sommer eine Niederschlagsabnahme mit den stärksten Änderungen im A1B Szenario. In Winter und Herbst verstärkt sich die Niederschlagszunahme, sodass eine Umverteilung der Niederschläge im Jahresverlauf stattfindet mit insgesamt im Jahresmittel leicht steigenden Werten. Zudem zeigt sich im Sommer trotz abnehmender Niederschläge eine Zunahme der Intensität von starken Niederschlägen

Impact of Air-to-Air Heat Pumps on Energy and Climate in a Mid-Latitude City

Exploring the potential effects of transitioning entirely to air-to-air heat pumps (AAHPs), we use an integrated weather and heat pump model to understand their performance across several building and weather conditions in Toulouse, France. In central Toulouse, where electric and gas heating are similarly adopted, a shift to AAHPs cuts annual electric consumption. Yet, during colder periods, a drop in their efficiency can cause a spike in electricity use. In regions predominantly relying on non-electric heaters, such as gas boilers, introducing AAHPs is expected to increase electricity demand as the heating system transitions to all-electric, though to a lesser extent and with much greater efficiency than traditional systems such as electric resistive heaters. In a separate analysis to evaluate the impact of AAHPs on local climate conditions, we find that AAHPs have a small influence of about 0.5 {\deg}C on the outdoor air temperature. This change is thus unlikely to meaningfully alter AAHPs' performance through feedback.Comment: Submitted manuscrip

Urban Climate, Human behavior & Energy consumption: from LCZ mapping to simulation and urban planning (the MapUCE project)

International audienceThe MApUCE project aims to integrate in urban policies and most relevant legal documents quantitative data from urban microclimate, climate and energy.The primary objective of this project is to obtain climate and energy quantitative data from numerical simulations, focusing on urban microclimate and building energy consumption in the residential and service sectors, which represents in France 41% of the final energy consumption. Both aspects are coupled as building energy consumption is highly meteorologically dependent (e.g. domestic heating, air-conditioning) and heat waste impact the Urban Heat Island. We propose to develop, using national databases, a generic and automated method for generating Local Climate Zones (LCZ) for all cities in France, including the urban architectural, geographical and sociological parameters necessary for energy and microclimate simulations.As will be presented, previous projects on adaptation of cities to climate change have shown that human behavior is a very potent level to address energy consumption reduction, as much as urban forms or architectural technologies. Therefore, in order to further refine the coupled urban climate and energy consumption calculations, we will develop within TEB (and its Building Energy Module) a model of energy consumer behavior.The second objective of the project is to propose a methodology to integrate quantitative data in urban policies. Lawyers analyze the potential levers in legal and planning documents. A few “best cases” are also studied, in order to evaluate their performances. Finally, based on urban planning agencies requirements, we will define vectors to include quantified energy-climate data to legal urban planning documents. These vectors have to be understandable by urban planners and contain the relevant information.To meet these challenges, the project is organized around strongly interdisciplinary partners in the following fields: law, urban climate, building energetics, architecture, sociology, geography and meteorology, as well as the national federation of urban planning agencies.In terms of results, the cross-analysis of input urban parameters and urban micro-climate-energy simulated data will be available on-line as standardized maps for each of the studied cities. The urban parameter production tool as well as the models will be available as open-source. LCZ and associated urban (and social!) indicators may be integrated within the WUDAPT database

Changes of western European heat wave characteristics projected by the CMIP5 ensemble

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Projizierte Veränderungen des regionalen Klimas im Raum Hamburg

Die globalen Klimaänderungen wirken sich regional auf Hamburg aus. Bis Mitte des 21. Jahrhunderts muss sich die Metropole auf steigende Temperaturen einstellen, das bedeutet ca. 1 K bis 3 K höhere Temperaturen im Winter und ca. 1 K bis 1,5 K im Sommer. Der Temperaturanstieg hängt bis zur Mitte des 21. Jahrhunderts nur geringfügig davon ab, wie hoch die Menge an Treibhausgasen ist, die global ausgestoßen wird. Ab Mitte des 21. Jahrhunderts wird ein deutlicher Unterschied zwischen den Szenarien mit vergleichsweise hohen Treibhausgasemissionen (A1B und A2) und dem Szenario mit vergleichsweise niedrigen Treibhausgasemissionen (B1) erkennbar. Je mehr Treibhausgase ausgestoßen werden, desto höher ist der zu erwartende Temperaturanstieg. Dementsprechend steigt die Anzahl von Sommer- und Hitzetagen künftig an. Dies wird sehr wahrscheinlich tagsüber zu einer erhöhten Hitzebelastung für die Hamburger Bevölkerung führen. Auch die Anzahl der Tropennächte wird steigen, bleibt aber absolut betrachtet mit ein bis vier Tagen pro Jahr auch in Zukunft gering. Des Weiteren muss sich Hamburg in Zukunft auf zunehmende Niederschlagsmengen einstellen. Einzige Ausnahme ist der Sommer, für den zeigen gegen Ende des 21. Jahrhunderts die meisten Simulationen eine Abnahme der Niederschlagsmengen. Einher geht dies mit einer Zunahme der Häufigkeit von Starkniederschlagen. In allen anderen Jahreszeiten nimmt sowohl die Niederschlagsmenge als auch die Häufigkeit von Starkniederschlagen zu. Beim Niederschlag ist – im Gegensatz zur Temperatur – der Einfluss der global ausgestoßenen Treibhausgasmenge deutlich geringer, der Einfluss der natürlichen Variabilität des Klimas jedoch deutlich höher

Towards decarbonisation targets by changing setpoint temperature to avoid building overcooling and implementing district cooling in (sub)tropical high-density cities – A case study of Hong Kong

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Statistical-dynamical downscaling of the urban heat island in Hamburg, Germany

Regional climate models provide climate projections on a horizontal resolution in the order of 10 km. This is too coarse to sufficiently simulate urban climate related phenomena such as the urban heat island (UHI). Therefore, regional climate projections need to be downscaled. A statistical-dynamical method for the UHI was developed and applied to provide urban climate results at a high resolution with little computational costs. For the downscaling, weather situations relevant for the UHI are determined. This is done by combining objective weather pattern classification based on a k-means cluster analysis of ERA-40 reanalysis data and a regression-based statistical model of the observed UHI of Hamburg. The resulting days for each weather pattern are simulated with the mesoscale meteorological model METRAS at 1 km horizontal resolution. To obtain the average UHI for a climate period, the mesoscale model results are statistically recombined weighted by the frequency of the corresponding weather patterns. This is done for present-day climate (1971–2000) using reanalysis data to yield the current climate UHI. For the future climate periods 2036–2065 and 2070–2099 the results of regional climate projections are employed. Results are presented for Hamburg (Germany). The present day UHI pattern is well reproduced compared to temperature data based on floristic mapping data. The magnitude of the early night-time UHI is underestimated when compared to observed minimum temperature differences. The future UHI pattern does only slightly change towards the end of the 21st century based on A1B scenario results of the RCMs REMO and CLM. However, for CLM the number of days with high UHI intensities significantly increases mainly due to a decrease in near-surface relative humidity

A statistical–dynamical downscaling methodology for the urban heat island applied to the EURO-CORDEX ensemble

International audienceAbstract Regional Climate Models (RCMs) are the primary climate information available to public stakeholders and city-planners to support local adaptation policies. However, with resolution in the order of ten kilometres, RCMs do not explicitly represent cities and their influence on local climate (e.g. Urban Heat Island; UHI). Downscaling methods are required to bridge the gap between RCMs and city scale. A statistical–dynamical downscaling methodology is developed to quantify the UHI of the city of Paris (France), based on a Local Weather Types (LWTs) classification combined with short-term high-resolution (1-km) urban climate simulations. The daily near-surface temperature amplitude, specific humidity, precipitation, wind speed and direction simulated by the RCMs are used for the LWTs attribution. The LWTs time series is associated to randomly selected days simulated with the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance to calculate the UHI corresponding to the successive LWTs. The downscaling methodology is applied to the EURO-CORDEX ensemble driven by the ERA-Interim reanalysis, and evaluated for the 2000–2008 period against station observations and a 2.5-km reanalysis. The short-term dynamical simulations slightly underestimate and overestimate near-surface minimum and maximum air temperature respectively, but capture the UHI intensity with biases in the order of a tenth of a degree. RCMs show significant differences in the variables used for the LWTs attribution, but the seasonal LWT frequencies are captured. Consequently, the reconstructed temperature fields maintain the small biases of the Meso-NH simulations and the statistical–dynamical downscaling greatly improves the UHI compared to the raw data of RCMs