One mechanism for climate change is the collected impact of changes in land cover or land use. Such changes are especially significant in urban areas where much of the world’s population lives. Satellite observations provide a basis for characterizing the physical modifications that result from urbanization. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the National Aeronautics and Space Administration (NASA) Terra satellite measures surface spectral albedos, thermal emissivities, and radiative temperatures. A better understanding of these measurements should improve our knowledge of the climate impact of urbanization as well as our ability to specify the parameters needed by climate models to compute the impacts of urbanization. For this purpose, it is useful to contrast urban areas with neighboring nonurban surfaces with regard to their radiative surface temperatures, emissivities, and albedos. Among these properties, surface temperatures have been most extensively studied previously in the context of the “urban heat island” (UHI). Nevertheless, except for a few detailed studies, the UHI has mostly been characterized in terms of surface air temperatures. To provide a global analysis, the zonal average of these properties are presented here measured over urban areas versus neighboring nonurban areas. Furthermore, individual cities are examined to illustrate the variations of these variables with land cover under different climate conditions [e.g., in Beijing, New York, and Phoenix (a desert city of the United States)]. Satellite-measured skin temperatures are related to the surface air temperatures but do not necessarily have the same seasonal and diurnal variations, since they are more coupled to surface energy exchange processes and less to the overlying atmospheric column. Consequently, the UHI effects from skin temperature are shown to be pronounced at both daytime and nighttime, rather than at night as previously suggested from surface air temperature measurements. In addition, urban areas are characterized by albedos much lower than those of croplands and deciduous forests in summer but similar to those of forests in winter. Thus, urban surfaces can be distinguished from nonurban surfaces through use of a proposed index formed by multiplying skin temperature by albedo.

Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply constant, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, and 0.96 for bare soil as in the National Center for Atmospheric Research (NCAR) Community Land Model version 2 (CLM2). This is the so-called constant-emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the constant emissivity induces errors in modeling the surface energy budget, especially over large arid and semiarid areas where ɛ is far smaller than unity. One feasible solution to this problem is to apply the satellite-based broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities (ɛλ) in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity (ɛ) required by land surface models. The observed emissivity data show strong seasonality and land-cover dependence. Specifically, emissivity depends on surface-cover type, soil moisture content, soil organic composition, vegetation density, and structure. For example, broadband ɛ is usually around 0.96–0.98 for densely vegetated areas [(leaf area index) LAI > 2], but it can be lower than 0.90 for bare soils (e.g., desert). To examine the impact of variable surface broadband emissivity, sensitivity studies were conducted using offline CLM2 and coupled NCAR Community Atmosphere Models, CAM2–CLM2. These sensitivity studies illustrate that large impacts of surface ɛ occur over deserts, with changes up to 1°–2°C in ground temperature, surface skin temperature, and 2-m surface air temperature, as well as evident changes in sensible and latent heat fluxes.

This paper reports on two types of comparisons that were conducted. First, 10-yr modeled skin temperatures were compared with observations to evaluate model simulations of this quantity. The simulations were conducted with the NCAR CCM2 coupled with the Biosphere–Atmosphere Transfer Scheme (BATS). The observations were obtained from TIROS-N/HIRS-2 and the First ISLSCP Field Experiment in situ measurements. Second, modeled skin temperatures were compared with surface-air temperatures to illustrate the differences between them at various spatial and temporal resolutions. This is the first such study of skin temperature in a GCM. When compared with the observations, it is evident that the CCM2–BATS can successfully reproduce many features of skin temperature, including its global-scale pattern, seasonal and diurnal variations, and the effects of the land surface type. However, modeled skin temperature seems to be underestimated in high latitudes in January and overestimated in low- and midlatitudes,... Abstract This paper reports on two types of comparisons that were conducted. First, 10-yr modeled skin temperatures were compared with observations to evaluate model simulations of this quantity. The simulations were conducted with the NCAR CCM2 coupled with the Biosphere–Atmosphere Transfer Scheme (BATS). The observations were obtained from TIROS-N/HIRS-2 and the First ISLSCP Field Experiment in situ measurements. Second, modeled skin temperatures were compared with surface-air temperatures to illustrate the differences between them at various spatial and temporal resolutions. This is the first such study of skin temperature in a GCM. When compared with the observations, it is evident that the CCM2–BATS can successfully reproduce many features of skin temperature, including its global-scale pattern, seasonal and diurnal variations, and the effects of the land surface type. However, modeled skin temperature seems to be underestimated in high latitudes in January and overestimated in low- and midlatitudes,...