We wish to acknowledge the support of Sandia National Laboratories, and the Center for Risk and Crisis Management at the University of Oklahoma, both of which provided support for the collection of the data utilized in this study. We also thank Kuhika Gupta, Mark James, Joe Ripberger, and Geoboo Song for their helpful advice on this project.
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For example, Rush Limbaugh’s lectures (as can be seen in many YouTube clips) on what he refers to as the climate change “hoax” and “fraud” often prominently feature unexpectedly cold weather events.
The characterization of these views of nature as “myths” underscores the point that the perspective of each cultural type represents a partial vision of reality. The concordant myth is generalized among those who share a cultural type because that vision is consistent with the biases of that cultural type.
The comparisons of the respondent characteristics with Census data for the appropriate years showed the samples to provide very close approximations of gender, age, and regional distributions of the U.S. population. See the supplemental material for a detailed comparison of Census and sample characteristics.
SSI’s SurveySpot members are recruited exclusively using permission-based techniques. Unsolicited e-mail is not employed. The membership of SurveySpot is continuously changing, but at the time of our samples it consisted of approximately two million households with about five million household members. Only one member in each household can participate in the SurveySpot panel. SSI maintains a subpanel of approximately 400 000 members whose demographics are roughly proportioned to national census characteristics. Our sample was randomly drawn from the 400 000 census balanced subpanel. The cooperation rate for the 2011 survey, based on the proportion of respondents who initially accessed the survey that completed the survey, was 55.93%.
Phone surveys were implemented in 2008 (n = 608), 2010 (n = 529), and 2011 (n = 593), although only the 2008 telephone survey included the full list of questions employed in this analysis. The phone surveys in 2010 and 2011 included only a subset of the survey questions, and were designed to permit us to track the consistency of the Internet and telephone survey results on key questionnaire items over time. See the supplemental material for more information on the surveys.
We included dummy variables representing the survey mode in all of our models to determine whether the estimates based on the telephone data differed significantly from those based on the online data. In no cases were the results statistically significant. These model results can be obtained from the authors upon request.
Because the questionnaire included items on climate change as well as weather perceptions, the 2011 version of the survey used an experimental design to test for possible “priming” effects of question order. The results of the experiment found no substantively or statistically significant priming effects. Details of the experimental results are included in the supplemental materials.
The zip code tabulation area (ZCTA) file was used to link zip codes with the latitude/longitude location of each respondent. The NCEP–NCAR reanalysis data are stored on a 2.5° latitude × 2.5° longitude grid and the PDSI data are stored by climate division. The ground validation used for each respondent corresponds to the closest great arc distance calculated between the reanalysis grid point/center point of the climate division to the respondents’ latitude/longitude location.
While a more precise question wording would have been preferable, the terminology generally matches the 3-yr to longer-term comparisons that are implicit in the reanalysis and PDSI data.
We recognize that the PSDI is an imperfect indicator of local flood conditions, inasmuch as the measure was developed as a drought indicator. However, in periods of long-term above normal moisture (identified by positive values of departures in the 3-yr average from the 30-yr average PDSI), flooding is more likely. Therefore, we use negative values of the PSDI departure as a proxy for times and regions with more frequent drought conditions, and positive values as a surrogate to identify times and regions in which flooding would have been more frequent.
While we could derive competing conjectures about the directions of these effects, we use them here chiefly as control variables. We would have preferred to have been able to include a measure of the number of years the respondent had lived within their resident zip code in order to account for differences in experience and familiarity with local weather patterns. Unfortunately the survey questionnaires did not include such a measure.
A correlation matrix of all of the independent variables is included in the supplementary materials.
We also controlled for the year in which the survey was administered by inserting separate dummy variables for each year. There was no substantive effect on any of the variables of interest, so the year variables were omitted from the final model.
We employ the BIC rather than the Akaike information criterion (AIC) because the former accounts for variation in sample size. Our models differ modestly in sample size because of item-missing data.
To simplify the analysis, these results display the predicted probability of a logit model where perceptions of temperature increases are equal to 1 and stayed the same and decreased are equal to 0.