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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.

Due to the combination of rapid global urbanization and climate change, urban climate issues are becoming relatively more important and are gaining interest. Compared to rural areas, the temperature in cities is higher (the urban heat island effect) due to the modifications in the surface radiation and energy balances. This study hypothesizes that the urban heat island can be mitigated by introducing open surface water in urban design. In order to test this, we use the WRF mesoscale meteorological model in which an idealized circular city is designed. Herein, the surface water cover, its size, spatial configuration, and temperature are varied. Model results indicate that the cooling effect of water bodies depends nonlinearly on the fractional water cover, size, and distribution of individual lakes within the city with respect to wind direction. Relatively large lakes show a high temperature effect close to their edges and in downwind areas. Several smaller lakes equally distributed within the urban area have a smaller temperature effect, but influence a larger area of the city. Evaporation from open water bodies may lower the temperature, but on the other hand also increases the humidity, which dampens the positive effect on thermal comfort. In addition, when the water is warmer than the air temperature (during autumn or night), the water body has an adverse effect on thermal comfort. In those cases, the water body eventually limits the cooling and thermal comfort in the surrounding city, and thus diverges from the original intention of the intervention.

International audience; Urban land surface schemes have been developed to model the distinct features of the urban surface andthe associated energy exchange processes. These models have been developed for a range of purposes and make differentassumptions related to the inclusion and representation of the relevant processes. Here, the first results of Phase 2 from an international comparison project to evaluate 32 urban land surface schemes are presented. This is the first large-scale systematic evaluation of these models. In four stages, participants were given increasingly detailed information about an urban site for which urban fluxes were directly observed. At each stage, each group returned their models’ calculated surface energy balance fluxes. Wide variations are evident in the performance of the models for individual fluxes. No individual model performs best for all fluxes. Providing additional information about the surface generally results in betterperformance. However, there is clear evidence that poor choice of parameter values can cause a large drop in performance for models that otherwise perform well. As many models do not perform well across all fluxes, there is need for caution in their application, and users should be aware of the implications for applications and decision making.

A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated. To date, no comparison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land-surface Parameterization Schemes. Here, the methods and first results from an extensive international comparison of 33 models are presented. The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas. The degree of complexity included in the models is outlined and impacts on model performance are discussed. During the comparison there have been significant developments in the models with resulting improvements in performance (root-mean-square error falling by up to two-thirds). Evaluation is based on a dataset containing net all-wave radiation, sensible heat, and latent heat flux observations for an industrial area in Vancouver, British Columbia, Canada. The aim of the comparison is twofold: to identify those modeling approaches that minimize the errors in the simulated fluxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations. There is evidence that some classes of models perform better for individual fluxes but no model performs best or worst for all fluxes. In general, the simpler models perform as well as the more complex models based on all statistical measures. Generally the schemes have best overall capability to model net all-wave radiation and least capability to model latent heat flux. © 2010 American Meteorological Society.

The height (h) of the stable boundary layer (SBL) is of major importance to understand the relevant processes that govern the SBL development. The SBL depth is the layer in which turbulence transport takes place, and thus governs the vertical structure of the lower atmosphere. Furthermore, release of pollutants below h during periods of weak mixing results in very high pollutant concentrations. For meteorological preprocessors for air quality models, h is the most critical quantity to estimate. This study evaluates the performance of two so-called multi-limit equations for h, and presents an alternative formulation (based on dimensional analysis) that depends on surface friction velocity, surface buoyancy flux, Coriolis parameter and the stratification above the boundary layer. The formulations are tested against four observational data sets (with varying roughness, latitude and land use) and Large Eddy Simulation from the first GEWEX Atmospheric Boundary Layer Study (GABLS). The multi-limit formulations show a significant negative bias for shallow SBLs. We find that the required parameters in the multi-limit Equations cannot be calibrated in a robust manner. The main advantages of the new formulation lies in a robust calibration, a significant reduced bias for small SBL depth and reduction of scatter compared over the whole range of observations to the two multi-limit Equations.