Evaluating the impact of built environment characteristics on urban boundary layer dynamics using an advanced stochastic approach

Abstract

Abstract. Urban land-atmosphere interactions can be captured by numerical modeling framework with coupled land surface and atmospheric processes, while the model performance depends largely on accurate input parameters. In this study, we use an advanced stochastic approach to quantify parameter uncertainty and model sensitivity of a coupled numerical framework for urban land-atmosphere interactions. It is found that the development of urban boundary layer is highly sensitive to surface characteristics of built terrains. Changes of both urban land use and geometry impose significant impact on the overlying urban boundary layer dynamics through modification on bottom boundary conditions, i.e., by altering surface energy partitioning and surface aerodynamic resistance, respectively. Hydrothermal properties of conventional and green roofs have different impacts on atmospheric dynamics due to different surface energy partitioning mechanisms. Urban geometry (represented by the canyon aspect ratio), however, has a significant nonlinear impact on boundary layer structure and temperature. Besides, managing rooftop roughness provides an alternative option to change the boundary layer thermal state through modification of the vertical turbulent transport. The sensitivity analysis deepens our insight into the fundamental physics of urban land-atmosphere interactions and provides useful guidance for urban planning under challenges of changing climate and continuous global urbanization.

Biospheric and anthropogenic contributors to atmospheric CO2 variability in a residential neighborhood of Phoenix, Arizona

“…The evolution of CO 2 concentration in the UBL is modeled by a single column atmospheric model (SCM) based on a modified K ‐theory [ Noh et al , ; Hong et al , ; Song and Wang , , , , ]. The governing turbulence diffusion equations in the SCM for a generic scalar atmospheric variable c in the UBL can be expressed by [ Song and Wang , ] where t is the evolution time; z is the elevation above the land surface; w is the vertical wind speed; c could be temperature, humidity, or CO 2 concentration; and is the vertical kinematic flux of heat (if c is temperature), moisture (if c is humidity), and carbon dioxide (if c is CO 2 concentration) correspondingly.…”

“…The surface flux at the lower boundary of the UBL is provided by EC measurements, while the upper boundary (entrainment) is prescribed as with β c as the empirical fraction, specifically β c = 0.1–0.3 for temperature [ Noh et al , ], β c = −1.0–0 for humidity [ de Arellano et al , ], and β c = −0.2–3.0 for CO 2 concentration [ de Arellano et al , ]. Specifically for Phoenix site, we used 0.2 as the entrainment rate of heat flux and 0.95 as the entrainment rate of moisture flux and CO 2 flux based on previous studies [ Song and Wang , , , , ]. Detailed parameterizations for UBL height, eddy diffusivity, velocity scale, and CO 2 flux within the UBL for both daytime and nighttime are described in Table [ Deardorff , ; Yu , ; Hong et al , ; Hong , ; Ouwersloot and Vilà‐Guerau de Arellano , ].…”

“…The evolution of CO 2 concentration in the UBL is modeled by a single column atmospheric model (SCM) based on a modified K-theory [Noh et al, 2003;Hong et al, 2006;Song and Wang, 2015a, 2015b, 2016a, 2016b. The governing turbulence diffusion equations in the SCM for a generic scalar atmospheric variable c in the UBL can be expressed by [Song and Wang, 2015a] Journal of Geophysical Research: Atmospheres 10.1002/2016JD026267…”

On‐ and off‐line evaluation of the single‐layer urban canopy model in London summertime conditions

“…The majority of the studies use a 3D set‐up, which limits the ability to link the model response to changes in surface parameters and investigate feedback mechanisms in detail. To circumvent this drawback, the SCM set‐up can be used (e.g., Song and Wang, ; ; Nemunaitis‐Berry et al . ), which allows for a detailed investigation of the model response to changes in surface parameters, while at the same time having a much lower computational cost than the 3D set‐up.…”

Thermodynamic characterisation of urban nocturnal cooling

“…The presence of excessively elevated temperature in city cores, as compared to their rural surroundings, is the main driver of many adverse urban environmental problems, ranging from thermal discomfort and human health (Kovats and Hajat, 2008), to building energy efficiency (Akbari and Konopacki, 2005; Wang et al, 2016), to air quality degradation (Taha, 1997; Sarrat et al, 2006). In addition, field observations as well as model simulations have shown that the UHI effect is often more prominent during nighttime (Chow et al, 2012; Song and Wang 2016a, b). This is especially true for densely built cities with downtown areas full of skyscrapers and narrow and deep street canyons (e.g.…”