This paper investigates the impact of residential density on vehicle usage and fuel consumption.The empirical model accounts for both residential self-selection effects and non-random missing data problems. While most previous studies focus on a specific region, this paper analyzes national level data from the 2001 National Household Travel Survey. Comparing two households that are equal in all respects except residential density, the household residing in an area that is 1000 housing units per square mile denser (roughly 50% of the sample average) will drive 1341 (6.9%) less miles per year and will consume 65 (7.0%) fewer gallons of fuel than the household in the less dense area. The joint effect of the contextual density measure (density in the context of its surrounding area) and residential density is quantitatively larger than the sole effect of residential density. A simulation moving a household from suburban to urban area reduces household annual mileage by 18%.

This study examines the vertically resolved cloud measurements from the cloud-aerosol lidar with orthogonal polarization instrument on Aqua satellite from June 2006 through May 2007 to estimate the extent to which the mixed cloud-phase composition can vary according to the ambient temperature, an important concern for the uncertainty in calculating cloud radiative effects. At −20°C, the global average fraction of supercooled clouds in the total cloud population is found to be about 50% in the data period. Between −10 and −40°C, the fraction is smaller at lower temperatures. However, there are appreciable regional and temporal deviations from the global mean (> AE 20%) at the isotherm. In the analysis with coincident dust aerosol data from the same instrument, it appears that the variation in the supercooled cloud fraction is negatively correlated with the frequencies of dust aerosols at the −20°C isotherm. This result suggests a possibility that dust particles lifted to the cold cloud layer effectively glaciate supercooled clouds. Observations of radiative flux from the clouds and earth's radiant energy system instrument aboard Terra satellite, as well as radiative transfer model simulations, show that the 20% variation in the supercooled cloud fraction is quantitatively important in cloud radiative effects, especially in shortwave, which are 10 − 20 W m −2 for regions of mixed-phase clouds affected by dust. In particular, our results demonstrate that dust, by glaciating supercooled water, can decrease albedo, thus compensating for the increase in albedo due to the dust aerosols themselves. This has important implications for the determination of climate sensitivity.aerosol-cloud interaction | ice nucleation | mixed cloud phase | super cooled water | cloud radiative effect C old clouds that consist of mixed-phase particles, are ubiquitous in the Earth's upper and middle troposphere. Changes in the liquid/ice-phase composition in such clouds may significantly affect the radiative balance of the earth atmosphere system because the cloud radiative properties for both the shortwave (SW) and longwave (LW) such as cloud optical depth, single-scattering albedo, and emissivity vary according to the phase of cloud particles (1-3). It is therefore of fundamental importance to examine the variation in the cloud-phase composition for accurate calculations of cloud radiative effects.In fact, the cloud-phase composition in mixed-phase clouds is complicatedly affected by several factors other than temperature; e.g., ice nuclei (IN) aerosols. Most of climate models, however, have calculated the cloud-phase composition with limited sophistication, simply as a function of grid-mean temperature (4). For example, it has been typically assumed that clouds are composed entirely of ice and liquid particles below −40°C and above 0°C, respectively, of a mixture of ice and liquid phases between −40 and 0°C. The fraction of supercooled liquid particles within mixed-phase clouds is represented as a linear function of temperatures in genera...

[1] Weekly cycles of the concentration of anthropogenic aerosols have been observed in many regions around the world. The phase and the magnitude of these cycles, however, vary greatly depending on region and season. In the present study the authors investigated important features of the weekly cycles of aerosol concentration and the covariations in meteorological conditions in major urban regions over east China, one of the most polluted areas in the world, in summertime during the period 2001-2005/2006. The PM10 (aerosol particulate matters of diameter < 10 mm) concentrations at 29 monitoring stations show significant weekly cycles with the largest values around midweek and smallest values in weekend. Accompanying the PM10 cycle, the meteorological variables also show notable and consistent weekly cycles. The wind speed in the lower troposphere is relatively small in the early part of the week and increases after about Wednesday. At the same time, the air temperature anomalies in low levels are positive and then become negative in the later part of the week. The authors hypothesize that the changes in the atmospheric circulation may be triggered by the accumulation of PM10 through diabatic heating of lower troposphere. During the early part of a week the anthropogenic aerosols are gradually accumulated in the lower troposphere. Around midweek, the accumulated aerosols could induce radiative heating, likely destabilizing the middle to lower troposphere and generating anomalously vertical air motion and thus resulting in stronger winds. The resulting circulation could promote ventilation to reduce aerosol concentrations in the boundary layer during the later part of the week. Corresponding to this cycle in anthropogenic aerosols the frequency of precipitation, particularly the light rain events, tends to be suppressed around midweek days through indirect aerosol effects. This is consistent with the observed anthropogenic weather cycles, i.e., more (less) solar radiation near surface, higher (lower) maximum temperature, larger (smaller) diurnal temperature range, and fewer (more) precipitation events in midweek days (weekend).

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966-83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs' sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpack to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated.It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1 m of soil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snowcover fraction as a function of snow depth is observed at the catchment but not in any of the models.