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

Abstract. The anthropogenic heat flux can be an important part of the urban surface energy balance. Some of it is due to energy consumption inside buildings, which depends on building use and human behaviour, both of which are very heterogeneous in most urban areas. Urban canopy parametrisations (UCP), such as the Town Energy Balance (TEB), parametrise the effect of the buildings on the urban surface energy balance. They contain a simple building energy model. However, the variety of building use and human behaviour at grid point scale has not yet been represented in state of the art UCPs. In this study, we describe how we enhance the Town Energy Balance in order to take fractional building use and human behaviour into account. We describe how we parametrise different behaviours and initialise the model for applications in France. We evaluate the spatio-temporal variability of the simulated building energy consumption for the city of Toulouse. We show that a more detailed description of building use and human behaviour enhances the simulation results. The model developments lay the groundwork for simulations of coupled urban climate and building energy consumption which are relevant for both the urban climate and the climate change mitigation and adaptation communities.

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