• Energy Research

Abstract 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.

Abstract 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.

This study investigated impacts of urban forms on the future thermal environment over Beijing, the capital city of China. Beijing is experiencing remarkable urban expansion and is planned to undergo the transformation of urban forms from single-centric (compact-city) to poly-centric city (dispersed-city). Impacts of urban forms on the future thermal environment were compared and evaluated by conducting numerical experiments based on a regional atmospheric model coupled with a single-layer urban canopy model as well as future climate forcing output from a global climate model. Results show that a dispersed city is efficient in reducing mean urban heat island intensity, but produces larger thermal loading and deeper thermal feedback at the regional scale compared to a compact city. Thermal comfort over downtown areas is reduced in compact-city scenario under future climate conditions. Future climate contributes almost 80% of the additional thermal loading over urban areas, with the remaining 20% contributed by urbanization (for both the compact-city and dispersed-city scenarios). The thermal contrast between the two urban forms is dominated by the expected future climate change. This study leads to two complementary conclusions: (i) for developing assessments related to current climate comfort, urban form of the city is important; (ii) for assessing future climate change impacts, the areal coverage of the city and urbanization extent emerges to be more important than the details related to how the urbanization will evolve.

This study investigated impacts of urban forms on the future thermal environment over Beijing, the capital city of China. Beijing is experiencing remarkable urban expansion and is planned to undergo the transformation of urban forms from single-centric (compact-city) to poly-centric city (dispersed-city). Impacts of urban forms on the future thermal environment were compared and evaluated by conducting numerical experiments based on a regional atmospheric model coupled with a single-layer urban canopy model as well as future climate forcing output from a global climate model. Results show that a dispersed city is efficient in reducing mean urban heat island intensity, but produces larger thermal loading and deeper thermal feedback at the regional scale compared to a compact city. Thermal comfort over downtown areas is reduced in compact-city scenario under future climate conditions. Future climate contributes almost 80% of the additional thermal loading over urban areas, with the remaining 20% contributed by urbanization (for both the compact-city and dispersed-city scenarios). The thermal contrast between the two urban forms is dominated by the expected future climate change. This study leads to two complementary conclusions: (i) for developing assessments related to current climate comfort, urban form of the city is important; (ii) for assessing future climate change impacts, the areal coverage of the city and urbanization extent emerges to be more important than the details related to how the urbanization will evolve.

Abstract As surface exchange processes are highly non-linear and heterogeneous in space and time, it is important to know the appropriate scale for the reasonable prediction of these exchange processes. For example, the explicit representation of surface variability has been vital in predicting mesoscale weather events such as late-afternoon thunderstorms initiated by latent heat exchanges in mid-latitude regions of the continental United States. This study was undertaken to examine the effects of different spatial scales of input data on modeled fluxes, so as to better understand the resolution needed for accurate modeling. A statistical procedure was followed to select two cells from the Southern Great Plains 1997 hydrology experiment region, each 20 km×20 km, representing the most homogeneous and the most heterogeneous surface conditions (based on soil and vegetation) within the study region. The NOAH-OSU (Oregon State University) Land Surface Model (LSM) was employed to estimate surface energy fluxes. Three scales of study (200 m, 2 and 20 km) were considered in order to investigate the impacts of the aggregation of input data, especially soil and vegetation inputs, on the model output. Model results of net radiation and latent, sensible and ground heat fluxes were compared for the three scales. For the heterogeneous area, the model output at the 20-km resolution showed some differences when compared with the 200-m and 2-km resolutions. This was more pronounced in latent heat (12% decrease), sensible heat (22% increase), and ground heat flux (44% increase) estimation than in net radiation. The scaling effects were much less for the relatively homogeneous land area with 5% increase in sensible heat and 4% decrease in ground heat flux estimation. All of the model outputs for the 2- and 20-km resolutions were in close agreement. The results suggested that, for this study region, soils and vegetation input resolution of about 2 km should be chosen for realistic modeling of surface exchange processes. This resolution was sufficient to capture the effects of sub-grid scale heterogeneity, while avoiding the data and computational difficulties associated with higher spatial resolutions.

Abstract As surface exchange processes are highly non-linear and heterogeneous in space and time, it is important to know the appropriate scale for the reasonable prediction of these exchange processes. For example, the explicit representation of surface variability has been vital in predicting mesoscale weather events such as late-afternoon thunderstorms initiated by latent heat exchanges in mid-latitude regions of the continental United States. This study was undertaken to examine the effects of different spatial scales of input data on modeled fluxes, so as to better understand the resolution needed for accurate modeling. A statistical procedure was followed to select two cells from the Southern Great Plains 1997 hydrology experiment region, each 20 km×20 km, representing the most homogeneous and the most heterogeneous surface conditions (based on soil and vegetation) within the study region. The NOAH-OSU (Oregon State University) Land Surface Model (LSM) was employed to estimate surface energy fluxes. Three scales of study (200 m, 2 and 20 km) were considered in order to investigate the impacts of the aggregation of input data, especially soil and vegetation inputs, on the model output. Model results of net radiation and latent, sensible and ground heat fluxes were compared for the three scales. For the heterogeneous area, the model output at the 20-km resolution showed some differences when compared with the 200-m and 2-km resolutions. This was more pronounced in latent heat (12% decrease), sensible heat (22% increase), and ground heat flux (44% increase) estimation than in net radiation. The scaling effects were much less for the relatively homogeneous land area with 5% increase in sensible heat and 4% decrease in ground heat flux estimation. All of the model outputs for the 2- and 20-km resolutions were in close agreement. The results suggested that, for this study region, soils and vegetation input resolution of about 2 km should be chosen for realistic modeling of surface exchange processes. This resolution was sufficient to capture the effects of sub-grid scale heterogeneity, while avoiding the data and computational difficulties associated with higher spatial resolutions.