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

Enhanced software and platform for the Town Energy Balance (TEB) model

The Town Energy Balance (TEB) model (Masson, 2000) is a physically based single layer Urban Canopy Model (UCM) to calculate the urban surface energy balance at neighborhood scale assuming a simplified canyon geometry. It includes several capabilities (Table 1) that have been extensively evaluated offline with flux observations (Lemonsu, Grimmond, & Masson, 2004; Leroyer, Mailhot, Bélair, Lemonsu, & Strachan, 2010; Masson, Grimmond, & Oke, 2002; Pigeon, Moscicki, Voogt, & Masson, 2008) and online coupled to atmospheric models such as ALARO (Gerard, Piriou, Brožková, Geleyn, & Banciu, 2009) in ALARO-TEB (Hamdi, Degrauwe, & Termonia, 2012), the Global Environmental Multiscale (GEM; Côté et al. (1998)) in GEM-TEB (Lemonsu, Belair, & Mailhot, 2009), Meso-NH (Lac et al., 2018; Lafore et al., 1998) in TEB-MesoNH (Lemonsu & Masson, 2002), the Regional Atmospheric Modeling System (RAMS; Pielke et al. (1992)) in RAMS-TEB (Freitas, Rozoff, Cotton, & Dias, 2007), the Advanced Regional Prediction System (ARPS; Xue et al., (2000)) in ARPS-TEB (Rozoff, Cotton, & Adegoke, 2003), and the Weather Research and Forecasting (WRF; Skamarock et al. (2019)) in WRF-TEB (Meyer et al., 2020). Here, we present an enhanced software and platform for the TEB model to help scientists and practitioners wishing to use the TEB model in their research as a standalone software application or as a library in their own software. This includes several features such as crossplatform support for Windows, Linux, and macOS using CMake (Kitware Inc., 2020), static and dynamic library generation for integration with other software/models, namelist-based configuration, integration with MinimalDX (Meyer & Raustad, 2019) and PsychroLib (Meyer & Thevenard, 2019) to improve the modelling of air conditioners (AC) and psychrometric calculations respectively, a thin interface used in the coupling with WRF-CMake (Riechert & Meyer, 2019), helper functions for Python for pre- and post-processing inputs and outputs files, and a tutorial in Jupyter Notebook to allow users to quickly become familiar with the general TEB modeling workflow. In the new platform we implement testing at every code commit through continuous integration (CI) and automate the generation of documentation. The project is developed as a free, open source, community-driven project on GitHub (https://github.com/teb-model/teb) to support existing and new model applications with enhanced functionality. We welcome contributions and encourage users to provide feedback, bug reports and feature requests, via GitHub’s issue system at https://github.com/teb-model/teb/issue

Influence of large offshore wind farms on North German climate

Wind farms impact the local meteorology by taking up kinetic energy from the wind field and by creating a large wake. The wake influences mean flow, turbulent fluxes and vertical mixing. In the present study, the influences of large offshore wind farms on the local summer climate are investigated by employing the mesoscale numerical model METRAS with and without wind farm scenarios. For this purpose, a parametrisation for wind turbines is implemented in METRAS. Simulations are done for a domain covering the northern part of Germany with focus on the urban summer climate of Hamburg. A statistical-dynamical downscaling is applied using a skill score to determine the required number of days to simulate the climate and the influence of large wind farms situated in the German Bight, about 100 km away from Hamburg.Depending on the weather situation, the impact of large offshore wind farms varies from nearly no influence up to cloud cover changes over land. The decrease in the wind speed is most pronounced in the local areas in and around the wind farms. Inside the wind farms, the sensible heat flux is reduced. This results in cooling of the climate summer mean for a large area in the northern part of Germany. Due to smaller momentum fluxes the latent heat flux is also reduced. Therefore, the specific humidity is lower but because of the cooling, the relative humidity has no clear signal. The changes in temperature and relative humidity are more wide spread than the decrease of wind speed. Hamburg is located in the margins of the influenced region. Even if the influences are small, the urban effects of Hamburg become more relevant than in the present and the off-shore wind farms slightly intensify the summer urban heat island

WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model

Urban land surface processes need to be represented to inform future urban‐climate and building‐energy projections. Here, the single layer urban canopy model Town Energy Balance (TEB) is coupled to the Weather Research and Forecasting (WRF) model to create WRF‐TEB. The coupling method is described generically, implemented into software, and the issue of scientific reproducibility is addressed by releasing all code and data with a Singularity image. The coupling is implemented modularly and verified by an integration test. Results show no detectable errors in the coupling. Separately, a meteorological evaluation is undertaken using observations from Toulouse, France. The latter evaluation, during an urban canopy layer heat island episode, shows reasonable ability to estimate turbulent heat flux densities and other meteorological quantities. We conclude that new model couplings should make use of integration tests as meteorological evaluations by themselves are insufficient, given that errors are difficult to attribute because of the interplay between observational errors and multiple parameterization schemes (e.g. radiation, microphysics, boundary layer)

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

Effects of urbanization on precipitation in Beijing

Since the 1980s, the industrialization and urbanization of the Beijing area has entered a period of high-speed growth. This paper asks the question: How have such great changes in urban land-use over the past decades impacted urban precipitation? In this study, we investigate and analyze the effects of urbanization on the summer precipitation in Beijing using numerical modeling approaches. Applying the numerical mesoscale atmospheric model METRAS, we determine the impact of surface cover on 13 heavy precipitation events. We implement five idealized land-use scenarios: Reference scenario, No-urban scenario, High-building scenario, Urban-expand scenario, and No-vegetation scenario. There is nearly no difference in the mean precipitation sum across all 13 simulated rain events and between the urban-scenarios and the rural-scenario. We find effects of urbanization on precipitation only in some single cases. We conclude urbanization does effect the local precipitation of Beijing; it reduces rainfall in the urban area and increases rainfall downwind of the city. In some cases, larger percentage of sealed area could give rise to the heavier precipitation or extreme rain events. And we conclude the urban pattern significantly impacts rainfall area and intensity. Increased urban size or density may speed up rain clouds while increased urban height may disrupt or bifurcate the clouds. Our results offer a new viewpoint and further the study of urban impacts on precipitation (UIP). The results are important for sustainable and harmonious development of the economy, society, and environment in Beijing as well as other cities with rapid urbanization