Surface and atmospheric driven variability of the single‐layer urban canopy model under clear‐sky conditions over London

Tsiringakis, A. ORCID: https://orcid.org/0000-0002-6922-5086, Holtslag, A. A. M., Grimmond, S. ORCID: https://orcid.org/0000-0002-3166-9415 and Steeneveld, G. J. ORCID: https://orcid.org/0000-0002-5922-8179 (2020) Surface and atmospheric driven variability of the single‐layer urban canopy model under clear‐sky conditions over London. Journal of Geophysical Research: Atmospheres, 125 (14). e2019JD032167. ISSN 2169-8996 doi: 10.1029/2019JD032167

Abstract/Summary

Urban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single‐layer urban canopy model when fully interactive with a 1‐D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54‐h period with clear‐sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/91912
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Geophysical Union
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