An Urban Parameterization for a Global Climate Model. Part I: Formulation and Evaluation for Two Cities

Abstract

Urbanization, the expansion of built-up areas, is an important yet less-studied aspect of land use/land cover change in climate science. To date, most global climate models used to evaluate effects of land use/land cover change on climate do not include an urban parameterization. Here, the authors describe the formulation and evaluation of a parameterization of urban areas that is incorporated into the Community Land Model, the land surface component of the Community Climate System Model. The model is designed to be simple enough to be compatible with structural and computational constraints of a land surface model coupled to a global climate model yet complex enough to explore physically based processes known to be important in determining urban climatology. The city representation is based upon the "urban canyon" concept, which consists of roofs, sunlit and shaded walls, and canyon floor. The canyon floor is divided into pervious (e.g., residential lawns, parks) and impervious (e.g., roads, parking lots, sidewalks) fractions. Trapping of longwave radiation by canyon surfaces and solar radiation absorption and reflection is determined by accounting for multiple reflections. Separate energy balances and surface temperatures are determined for each canyon facet. A one-dimensional heat conduction equation is solved numerically for a 10-layer column to determine conduction fluxes into and out of canyon surfaces. Model performance is evaluated against measured fluxes and temperatures from two urban sites. Results indicate the model does a reasonable job of simulating the energy balance of cities.

An Urban Parameterization for a Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations

“…The energy is calculated by using the internal building temperature as a bottom boundary condition in the solution of the heat conduction equation (Oleson et al 2004). The total waste heat is generated by roof and sunlit and shaded walls as represented in our urban canyon model:…”

“…A separate simulation using a rural surface consisting of grassland was used to estimate the heat island intensity. Heat island intensity is computed from the urban-minusrural air temperature difference, where urban air temperature is the urban canopy layer (UCL) air temperature (Part I) and the rural air temperature is the 2-m air temperature diagnostic in CLM3 (Oleson et al 2004).…”

“…As a consequence of these changes, urban climates can differ significantly from surrounding natural ecosystems, often resulting in urban heat islands (e.g., Landsberg 1981). The simulation of urban climate impacts requires two major components: 1) the representation of the physical processes controlling energy and water fluxes and 2) the characterization of urban morphology and urban materials with respect to aerodynamic, radiative, and heat transfer properties (e.g., Terjung and O'Rourke 1980;Arnfield 2000;Masson 2000;Grimmond and Oke 2002;Martilli et al 2002;Best 2005;Oleson et al 2008, hereinafter Part I). We have developed an urban parameterization to simulate urban systems on a global scale (Part I) under a wide variety of climate and surface conditions.…”

“…We have developed an urban parameterization to simulate urban systems on a global scale (Part I) under a wide variety of climate and surface conditions. The urban model is integrated with the Community Land Model, version 3 (CLM3; Oleson et al 2004;Dickinson et al 2006).…”

Detecting change in urban areas at continental scales with MODIS data

“…Although these maps are static depictions of urban areas largely dependent on the input data sources (e.g., remote sensing, nighttime lights, census data), they have shown the potential for largearea maps of urban extent/expansion for a large number of applications, including: assessment of arable land (Tan et al, 2005;Avellan et al, 2012), water quality/availability (McDonald et al, 2011), natural resources (Lambin &,Meyfroidt, 2011), habitat loss (Radeloff et al, 2005) and biodiversity (Guneralp et al, 2013); air pollution monitoring and associated impacts to human health (Grimm et al, 2008;Cassiani et al, 2013); and regionalglobal modeling of climate (Oleson et al, 2008), hydrological (McGrane et al, 2014), and biogeochemical cycles (Nordbo et al, 2012;Zhao et al, 2013). At the same time, these maps have proven vital for investigating socio-economic issues such as population distribution (Jones et al, 2013), spatial patterns of disease risk (Tatem et al, 2007;Wilhelmi et al, 2013), poverty (Elvidge et al, 2009), and economic growth (Chen & Nordhaus, 2011), and for planning and policy in developing-country cities that lack this information (Scott et al, 2013;Deuskar et al, 2014).…”