Urban signals in high-resolution weather and climate simulations: role of urban land-surface characterisation
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ORIGINAL PAPER
Urban signals in high-resolution weather and climate simulations: role of urban land-surface characterisation Denise Hertwig1 · Sue Grimmond1 · Margaret A. Hendry2 · Beth Saunders1 · Zhengda Wang1,3 · Marine Jeoffrion1,4 · Pier Luigi Vidale1 · Patrick C. McGuire1,5,6 · Sylvia I. Bohnenstengel7 · Helen C. Ward8 Simone Kotthaus9
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Received: 27 November 2019 / Accepted: 8 June 2020 © The Author(s) 2020
Abstract Two urban schemes within the Joint UK Land Environment Simulator (JULES) are evaluated offline against multi-year flux observations in the densely built-up city centre of London and in suburban Swindon (UK): (i) the 1-tile slab model, used in climate simulations; (ii) the 2-tile canopy model MORUSES (Met Office–Reading Urban Surface Exchange Scheme), used for numerical weather prediction over the UK. Offline, both models perform better at the suburban site, where differences between the urban schemes are less pronounced due to larger vegetation fractions. At both sites, the outgoing short- and longwave radiation is more accurately represented than the turbulent heat fluxes. The seasonal variations of model skill are large in London, where the sensible heat flux in autumn and winter is strongly under-predicted if the large city centre magnitudes of anthropogenic heat emissions are not represented. The delayed timing of the sensible heat flux in the 1tile model in London results in large negative bias in the morning. The partitioning of the urban surface into canyon and roof in MORUSES improves this as the roof tile is modelled with a very low thermal inertia, but phase and amplitude of the grid box-averaged flux critically depend on accurate knowledge of the plan-area fractions of streets and buildings. Not representing non-urban land cover (e.g. vegetation, inland water) in London results in severely under-predicted latent heat fluxes. Control runs demonstrate that the skill of both models can be greatly improved by providing accurate land cover and morphology information and using representative anthropogenic heat emissions, which is essential if the model output is intended to inform integrated urban services. Keywords JULES · Land surface models · MORUSES · Surface-energy balance · Urban modelling
1 Introduction As urbanisation levels and urban populations continue to grow (United Nations 2018), there is an increasing need for urban climate services (Baklanov et al. 2018). Climate models need to represent future extremes correctly, such as the intensity and frequency of heat waves, droughts or
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00704-020-03294-1) contains supplementary material, which is available to authorized users. Denise Hertwig
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Extended author information available on the last page of the article.
floods. Urban areas can strongly exacerbate such events at the local scale due to their impact on the surface energy balance (SEB) and surface hydrology. Global climate models (GCM) have become increasi
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