A consequence of urban heat islands in summer is an increase in the use of air conditioning in urbanized areas, which while cooling the insides of buildings, releases waste heat to the atmosphere. A coupled model consisting of a meso-scale meteorological model (MESO-NH) and an urban energy balance model (TEB) has been used to simulate and quantify the potential impacts on street temperature of four air conditioning scenarios at the scale of Paris. The first case consists of simulating the current types of systems in the city and was based on inventories of dry and evaporative cooling towers and free cooling systems with the river Seine. The other three scenarios were chosen to test the impacts of likely trends in air conditioning equipment in the city: one for which all evaporative and free cooling systems were replaced by dry systems, and the other two designed on a future doubling of the overall air conditioning power but with different technologies. The comparison between the scenarios with heat releases in the street and the baseline case without air conditioning showed a systematic increase in the street air temperature, and this increase was greater at nighttime than day time. It is counter-intuitive because the heat releases are higher during the day. This is due to the shallower atmospheric boundary layer during the night. The increase in temperature was 0.5°C in the situation with current heat releases, 1°C with current releases converted to only sensible heat, and 2°C for the future doubling of air conditioning waste heat released to air. These results demonstrated to what extent the use of air conditioning could enhance street air temperatures at the scale of a city like Paris, and the importance of a spatialized approach for a reasoned planning for future deployment of air conditioning in the city.

International audienceSocieties have to both reduce their greenhouse gas emissions and undertake adaptation measures to limit the negative impacts of global warming on the population, the economy and the environment. Examining how best to adapt cities is especially challenging as urban areas will evolve as the climate changes. Thus, examining adaptation strategies for cities requires a strong interdisciplinary approach involving urban planners, architects, meteorologists, building engineers, economists, and social scientists. Here we introduce a systemic modelling approach to the problem.Our four-step methodology consists of: first, defining interdisciplinary scenarios; second, simulating the long-term evolution of cities on the basis of socio-economic and land-use models; third, calculating impacts with physical models (such as TEB), and; finally, calculating the indicators that quantify the effect of different adaptation policies. In the examples presented here, urban planning strategies are shown to have unexpected influence on city expansion in the long term. Moreover, the Urban Heat Island should be taken into account in operational estimations of building energy demands. Citizens’ practices seem to be an efficient lever for reducing energy consumption in buildings.Interdisciplinary systemic modelling appears well suited to the evaluation of several adaptation strategies for a very broad range of topics

City-descriptive input data for urban climate models: Model requirements, data sources and challenges Abstract 1) Introduction 1.1 Brief overview of urban atmospheric modelling 1.2 Scale issues: mesoscale and microscale 1.3 Coverage issues: from city-scale to global modelling 1.4 Fit for purpose 2) Land use and land cover classes 2.1 Description of the parameters and their relevance 2.2 Methodologies to gather land cover data 2.2.1. Remote sensing methods 2.2.2. From vector topographical databases and land registries 2.2.3. Data fusion 3) Morphological parameters 3.1 Description of the parameters and their relevance 3.2 Links between morphological parameters 3.3 Methodologies to gather morphological parameters 3.3.1 Data from remote sensing 3.3.2 GIS treatment of 2.5D cadaster vector data of individual buildings 3.3.4 Crowdsourcing or deep learning methods 4) Architectural parameters 4.1 Description of the parameters and their relevance 4.2 Developing comprehensive architectural databases 4.3 Methodologies to gather architectural information 4.3.1 Identification of representative archetypes 4.3.2 Remote sensing and image processing 4.3.3 Crowdsourcing 5) Socioeconomic data and building use 5.1 Description of the parameters and their relevance 5.2 Methodologies to gather uses, socioeconomic and anthropogenic heat parameters 5.2.1 From inventories 5.2.2 Crowdsourcing 6) Urban vegetation 6.1 Description of the parameters and their relevance 6.2 Methodologies to collect vegetation parameters at mesoscale 28 6.3 Methodologies to collect vegetation parameters at microscale 29 7) Discussion 30 7.1 Licensing issues 30 7.2 Cataloguing issues 31 7.3 Data quality 7.4 Open data 31 7.5 Research challenges for the next decade 32 7.6 From data of various origins to Urban Climate Services 33 8 Conclusions 33 Appendix 1: Overview of several global land cover data sets with an urban description 34 Acknowledgements 36 References 36