David J. Sailor

Public Perception of Climate Change. Voluntary Mitigation and Barriers to Behavior Change

Mitigating global climate change requires not only government action but also cooperation from consumers. Population-based, cross-sectional surveys were conducted among 1202 respondents in Portland OR and Houston TX between June and September 2007 regarding awareness, concern, and behavior change related to climate change. The data were subjected to both quantitative and qualitative analyses. Awareness about climate change is virtually universal (98% in Portland and 92% in Houston) with the vast majority reporting some level of concern (90% in Portland and 82% in Houston). A multivariate analysis revealed significant predictors of behavior change: individuals with heightened concern about climate change (p<0.001); respondents with higher level of education (p= 0.03); younger compared with older individuals (p<0.001); and Portlanders more likely to change behavior compared with Houstonians (p<0.001). Of those who changed behavior, 43% reported having reduced their energy usage at home, 39% had reduced gasoline consumption, and 26% engaged in other behaviors, largely recycling. Qualitative data indicate a number of cognitive, behavioral, and structural obstacles to voluntary mitigation. Although consumers are interested in global climate change-mitigation strategies and willing to act accordingly, considerable impediments remain. Government policy must eliminate economic, structural, and social barriers to change and advance accessible and economical alternatives. Individual-level mitigation can be a policy option under favorable contextual conditions, as these results indicate, but must be accompanied by mitigation efforts from industry, commerce, and government.

Sensitivity of electricity and natural gas consumption to climate in the U.S.A.—Methodology and results for eight states

A methodology has been developed for assessing the sensitivity of electricity and natural gas consumption to climate at regional scales. The approach involves a multiple-regression analysis of historical energy and climate data, and has been applied to eight of the most energy-intensive states, representing 42% of the total annual energy consumption in the United States. Statistical models were developed using two sets of independent variables—primitive variables such as temperature, relative humidity, and wind speed, and derived variables including cooling degree days, heating degree days, and enthalpy latent days. The advantages and disadvantages of both modeling approaches are discussed in this paper, along with sample results for a combined analysis of residential and commercial consumption in eight states.

Air conditioning market saturation and long-term response of residential cooling energy demand to climate change

Existing state-level models relating climate parameters to residential electricity consumption indicate a nominal sensitivity of 2–4% for each degree Celsius increase in ambient temperatures. Long-term climate change will also impact electricity consumption through corresponding increases in the market saturation of air conditioning. In this paper we use air conditioning market saturation data for 39 US cities to develop a generalized functional relationship between market saturation and cooling degree days. The slope of this saturation curve is particularly high for cities that currently have low to moderate saturation. As a result, the total response of per capita electricity consumption to long-term warming may be much higher than previously thought. A detailed analysis of 12 cities in four states shows that for some cities changes in market saturation may be two to three times more important than the role of weather sensitivity of current loads. While actual behavioral response to climate change will be more complicated than that captured in our model of market saturation, this approach provides a new perspective on the sensitivity of space conditioning electricity consumption in the US to climate change.

Relating residential and commercial sector electricity loads to climate—evaluating state level sensitivities and vulnerabilities

A methodology for relating climate parameters to electricity consumption at regional scales has been applied to eight states resulting in predictive models of per capita residential and commercial electricity consumption. In isolating residential and commercial consumption these models allow for detailed analyses of urban electricity demand and its vulnerabilities to climate change at regional scales. Model sensitivities to climate perturbations and specific climate change scenarios have been investigated providing first-order estimates of how electricity demand may respond to climatic changes. The results indicate a wide range of electricity demand impacts, with one state experiencing decreased loads associated with climate warming, but the others experiencing a significant increase in annual per capita residential and commercial electricity consumption. The results indicate significantly different sensitivities for neighboring states, suggesting the inability to generalize results. In the long run the non-climatic factors responsible for these differences must be incorporated into the model structure, and assessments of changes in market saturation and related factors need to be included to make it amenable to long range forecasting.

Vulnerability of wind power resources to climate change in the continental United States

Renewable energy resources will play a key role in meeting the worlds energy demand over the coming decades. Unfortunately, these resources are all susceptible to variations in climate, and hence vulnerable to climate change. Recent findings in the atmospheric science literature suggest that the impacts of greenhouse gas induced warming are likely to significantly alter climate patterns in the future. In this paper we investigate the potential impacts of climate change on wind speeds and hence on wind power, across the continental US. General Circulation Model output from the Canadian Climate Center and the Hadley Center were used to provide a range of possible variations in seasonal mean wind magnitude. These projections were used to investigate the vulnerability of current and potential wind power generation regions. The models were generally consistent in predicting that the US will see reduced wind speeds of 1.0 to 3.2% in the next 50 years, and 1.4 to 4.5% over the next 100 years. In both cases the Canadian model predicted larger decreases in wind speeds. At regional scales the two models showed some similarities in early years of simulations (e.g. 2050), but diverged significantly in their predictions for 2100. Hence, there is still a great deal of uncertainty regarding how wind fields will change in the future. Nevertheless, the two models investigated here are used as possible scenarios for use in investigating regional wind power vulnerabilities, and point to the need to consider climate variability and long term climate change in citing wind power facilities.

Public perception and behavior change in relationship to hot weather and air pollution

BACKGROUND Changes in climate systems are increasing heat wave frequency and air stagnation, both conditions associated with exacerbating poor air quality and of considerable public health concern. OBJECTIVES Heat and air pollution advisory systems are in place in many cities for early detection and response to reduce health consequences, or severity of adverse conditions. Whereas the ability to forecast heat waves and/or air pollution episodes has become increasingly sophisticated and accurate, little is known about the effectiveness of advisories in altering public behavior. METHODS Air quality and meteorological conditions were measured during advisory and control days in Portland, OR and Houston, TX in 2005 and 2006 and 1962 subjects were interviewed by telephone about their perception and response to these conditions. RESULTS Elevated ambient temperatures were accurately recognized regardless of air conditioning use; in Portland, respondents resorted to active cooling behavior (AC, fan, etc.), while in Houston no such change was observed. More heat-related symptoms were reported in Portland compared to Houston, probably due to low air conditioning use in the northwest. One-third of study participants were aware of air quality advisories but only approximately 10-15% claimed to have changed activities during such an episode. Not the advisory, however, drove their behavior change, but rather the perception of poor air quality, which was not related to PM(2.5) or ozone measurements. CONCLUSIONS Messages are not reaching the public during potentially hazardous weather and air quality conditions. Climatic forecasts are increasingly predictive but public agencies fail to mount an appropriate outreach response.

Modeling the diurnal variability of effective albedo for cities

Abstract As applied to urban domains, traditional representations of surface albedo do not adequately account for the complex radiative exchange within the urban canopy. In this paper, we present a simple radiation model that takes into account the diurnal variation of short-wave radiation, including the effects of surface shading and radiation exchange among surfaces within the urban canopy. The model has been validated using prior observational studies and used to calculate the time-dependent effective short-wave reflectivity of hypothetical and case study urbanized grid cells, as well as the daily energy-weighted average of these parameters. Monte Carlo style simulations for four distinct urban land use categories indicate that the nadir-view albedo (NVA) typically underestimates daily solar radiative loads by 11–22%, depending upon the land use. We have also found that fairly detailed land-use classification schemes introduce large uncertainties in NVA. In fact, the uncertainty in albedo values for any urban land use category is comparable to the albedo variability across the various urban land use classifications. Furthermore, the magnitude of the diurnal variability of effective albedo for cities is large enough that neglecting it could adversely impact the ability to resolve the energy balance and circulation patterns associated with the urban heat island.

National Urban Database and Access Portal Tool

Based on the need for advanced treatments of high-resolution urban morphological features (e.g., buildings and trees) in meteorological, dispersion, air quality, and human-exposure modeling systems for future urban applications, a new project was launched called the National Urban Database and Access Portal Tool (NUDAPT). NUDAPT is sponsored by the U.S. Environmental Protection Agency (U.S. EPA) and involves collaborations and contributions from many groups, including federal and state agencies, and from private and academic institutions here and in other countries. It is designed to produce and provide gridded fields of urban canopy parameters for various new and advanced descriptions of model physics to improve urban simulations, given the availability of new high-resolution data of buildings, vegetation, and land use. Additional information, including gridded anthropogenic heating (AH) and population data, is incorporated to further improve urban simulations and to encourage and facilitate decision sup...

Exploring the building energy impacts of green roof design decisions – a modeling study of buildings in four distinct climates

This study explores the complex and interacting physical mechanisms that lead to building energy use implications of green roof design decisions. The EnergyPlus building energy simulation program, complete with an integrated green roof simulation module, was used to analyze the effects of roof surface design on building energy consumption. Simulations were conducted for both black and white membrane control roofs and nine variations of green roofs. The investigation included a total of eight buildings – new office and new multi-family lodging buildings, each in four cities representing diverse climatic conditions: Houston, Texas; New York City, New York; Phoenix, Arizona; and Portland, Oregon. Building energy performance of green roofs was generally found to improve with increasing soil depth and vegetative density. Heating (natural gas) energy savings were greatest for the lodging buildings in the colder climates. Cooling energy (electricity) savings varied for the different building types and cities. In all cases, a baseline green roof resulted in a heating energy cost savings compared to the conventional black membrane roof. In six of the eight buildings, the white roof resulted in lower annual energy cost than the baseline green roof. However, a high vegetative cover green roof was found to outperform the white roof in six of the eight buildings.

A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change

A methodology is presented for downscaling General Circulation Model (GCM) output to predict surface wind speeds at scales of interest in the wind power industry under expected future climatic conditions. The approach involves a combination of Neural Network tools and traditional weather forecasting techniques. A Neural Network transfer function is developed to relate local wind speed observations to large scale GCM predictions of atmospheric properties under current climatic conditions. By assuming the invariability of this transfer function under conditions of doubled atmospheric carbon dioxide, the resulting transfer function is then applied to GCM output for a transient run of the National Center for Atmospheric Research coupled ocean-atmosphere GCM. This methodology is applied to three test sites in regions relevant to the wind power industry—one in Texas and two in California. Changes in daily mean wind speeds at each location are presented and discussed with respect to potential implications for wind power generation.