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Risk Analysis DOI: 10.1111/risa.12571 Seeing is Believing? An Examination of Perceptions of Local Weather Conditions and Climate Change Among Residents in the U.S. Gulf Coast Wanyun Shao1,∗ and Kirby Goidel2 What role do objective weather conditions play in coastal residents’ perceptions of local climate shifts and how do these perceptions affect attitudes toward climate change? While scholars have increasingly investigated the role of weather and climate conditions on climaterelated attitudes and behaviors, they typically assume that residents accurately perceive shifts in local climate patterns. We directly test this assumption using the largest and most comprehensive survey of Gulf Coast residents conducted to date supplemented with monthly temperature data from the U.S. Historical Climatology Network and extreme weather events data from National Climatic Data Center. We find objective conditions have limited explanatory power in determining perceptions of local climate patterns. Only the 15- and 19-year hurricane trends and decadal summer temperature trend have some effects on perceptions of these weather conditions, while the decadal trend of total number of extreme weather events and 15- and 19-year winter temperature trends are correlated with belief in climate change. Partisan affiliation, in contrast, plays a powerful role affecting individual perceptions of changing patterns of air temperatures, flooding, droughts, and hurricanes, as well as belief in the existence of climate change and concern for future consequences. At least when it comes to changing local conditions, “seeing is not believing.” Political orientations rather than local conditions drive perceptions of local weather conditions and these perceptions— rather than objectively measured weather conditions—influence climate-related attitudes. KEY WORDS: Belief in climate change; concern for future climate change effects; perceptions of weather conditions; U.S. Gulf Coast 1. INTRODUCTION existed was fairly limited. Perhaps nowhere in the public opinion literature is Lippmann’s distinction between reality and perception more apropos than in public opinion toward climate change. Despite an overwhelming scientific consensus on the existence of climate change,(2,3) a substantial proportion of the American public remains unconvinced that climate change is real(4–8) or that it is particularly pressing or salient as a priority.(9,10) Scholars from various disciplines have explained variations in American public opinion toward climate change as a function of sociodemographic attributes, political predispositions, media exposure, and geographic factors such as local weather and Walter Lippmann(1) famously distinguished the objective world from “the pictures in our head,” a fictitious world built around stereotypes and images and designed to make sense of the complexity and nuance of the external world. For Lippmann, the capacity of the public to see the world as it really 1 Department of Sociology, Anthropology, and Geography at Auburn University, Montgomery, AL, USA. 2 Public Policy Research Institute and the Department of Communication at Texas A&M University TX, USA. ∗ Address correspondence to Wanyun Shao, Department of Sociology, Anthropology, and Geography at Auburn University, Montgomery, AL, USA; [email protected]. 1 C 2016 Society for Risk Analysis 0272-4332/16/0100-0001$22.00/1 2 climate,3 and macroeconomic conditions.(11–25) Most studies are conducted at the national level with survey questions focused on individual-level perceptions of the cause, occurrence, and impacts of climate change. While these studies have added greatly to our understanding of public attitudes toward climate change in the United States, they typically do not explain how variations in local conditions and, specifically, how changes in local climate patterns translate directly or indirectly into climate-related attitudes and behaviors. Here we ask two-related questions: Do local residents recognize changes in local climate patterns, and, if so, do these perceptions influence beliefs about the existence and consequences of climate change? We improve on existing literature by including objective measures of actual weather conditions and individual perceptions of changing conditions. Where actual weather conditions have been included in explanatory models, it is assumed that these shifts are correctly recognized by local residents and that perceptions of weather conditions are not a function of more general political orientations. Alternatively, where perceptions of weather conditions are included in these models, it is assumed that perception corresponds neatly to reality. If a survey respondent believes, for example, that air temperatures are increasing, researchers assume this reflects local context. In the current study, we explicitly test this assumption by modeling perceptions of changing weather conditions as a function of objective weather conditions over 10-, 15-, and 19-year time frames.4 In addition, we directly compare the effect of local weather conditions to preexisting political orientations (i.e., partisan affiliation), allowing insight into whether perceptions are the result of an unfiltered “reality” or a reality filtered by partisan affiliation. The results have important implications for local efforts at building community resilience and 3 Weather and climate are technically different. Weather refers to conditions of the local atmosphere over a short period of time, while climate means the cumulative behavior of atmosphere over relatively long periods of time (http://www.nasa.gov/ mission_pages/noaa-n/climate/climate_weather.html). The key to the distinction is the length of time. Laymen do not particularly differentiate these two concepts in the daily life. Oftentimes, they mean “climate” when they use the term “weather.” The term “weather” was used in the survey. It actually meant “climate.” Therefore, we use “local weather conditions” to describe the long-term climatic trends in local communities in this article. 4 The data of extreme weather events are extracted from National Weather Service storm data, which did not encompass a complete list of storm events prior to 1993. Shao and Goidel mitigating against current and future environmental risks. First, while international and national efforts have stalled, local officials and planners are working to mitigate against—and adapt to—local climate impacts.(26) Second, to effectively address local climate impacts, local planners and officials need to engage local residents to build support for resiliencebuilding policies and activities.(27) Prior research indicates that public support and engagement play a key role in creating effective natural hazards policies and plans.(28,29) Coastal communities provide an ideal setting for studying the effect of local conditions on perceptions of environmental risks. First, coastal communities have experienced significant population increases over time, thus exposing more individuals and properties to climate-related risks. In the United States, more than 1.2 million people move to the coast each year and more than 50% of the population is located in a coastal area.(3) Second, thanks to climate change, the number and severity of environmental risks in these areas are greater than just a decade ago.(30) In the current study, we focus our attention on the Gulf Coast region, which encompasses coastal areas within five states: Texas, Louisiana, Mississippi, Alabama, and Florida (see Fig. 1). These Gulf Coast communities are notably diverse in terms of weather patterns, population demographics, and the economic base of the community. Texas, for example, experiences far more drought relative to the other coastal states. We first examine what factors affect Gulf Coast’s residents’ perceptions of specific and general weather conditions, paying particular attention to the role of objective conditions relative to partisan affiliation. Because it is possible that the perception of weather is independent of real weather, we then investigate how one’s perceptions of weather relative to objective weather conditions affect general perceptions of climate change. The results help us understand how “reality” influences perceptions of changing weather conditions and how those perceptions influence climate-related attitudes. The results also inform local policymakers and officials as they work to engage local populations in resilience building, mitigation, and adaptationrelated activities and policies. Policymakers often assume that the key to engaging publics in mitigation and adaptation efforts is raising awareness, but this is predicated on residents sharing an understanding of the existence and risks associated with a changing climate. Local Weather Conditions and Climate Change 3 Fig. 1. Study area. 2. LITERATURE REVIEW In this article, we attempt to explain variations in perceptions of local weather conditions and climate change in the context of the U.S. Gulf Coast. In this section, we provide a brief literature review on theories that account for the nature and origins of risk perceptions from various disciplines. We also examine previous empirical studies that investigated the link between geographic factors and perceptions of weather and climate change. Public opinion scholar John Zaller(31) proposes that “every opinion is a marriage of information and predisposition: information to form a mental picture of the given issue, and predisposition to motivate some conclusion about it.” The information environment, of which the mass media constitutes a great portion, does not provide clear pictures of complicated issues like climate change. By framing news stories as science controversies and falsely balancing sources, the news media helps create the image of an unsettled scientific debate despite an overwhelming scientific consensus.(32,33) To further complicate matters, different media outlets self-select materials that conform to their political ideologies and/or editorial agendas.(34) The second component in Zaller’s model is predisposition. Predispositions serve as filters for processing new information, most often aligning new information with previously held beliefs. This cognitive phenomenon is categorized by psychologists as confirmation bias, which refers to the tendency to seek or interpret evidence “in ways that are partial to existing beliefs, expectations, or a hypothesis in hand.”(35) In the U.S. political context, Republicans and conservatives are more hesitant than Democrats and liberals to accept that climate change is occurring, that it is caused by human behavior, or that it will have catastrophic impacts.(19) Conservative political elites in the United States have successfully framed climate change as a political—rather than a scientific— issue.(36) The public forms their opinions towards political issues based on cues from politicians, government officials, journalists, and activists.(31) As a result, becoming more informed about climate change does not necessarily narrow the gap between experts, in this case the scientists, and the general public.(37) Disparities between experts’ risk assessment and public risk perceptions have been widely explored and well documented.(28,38–42) In terms of psychological processes, two different mechanisms operate when people process uncertain information: (1) experiential processing activates paleocortical brain structures and (2) analytic processing activates the neocortex.(42) The paleocortical structures are evolutionarily older than the neocortex,(42) suggesting our ancestors pervasively used experiential processes to reach decisions related to their survivals in the wilderness. Experts and professionals, thanks to their education and training, are more likely to submit to analytic processing when making professional judgments,(28) whereas, laypersons are more subject to affect and visceral reactions evoked by past experience.(41) Empirical studies have confirmed that one’s belief in climate change is significantly related to past experience with the weather.(21,43–46) A recent and 4 Shao and Goidel Table I. Sampling Strategy for Gulf Coast Climate Change Survey State Florida Alabama Mississippi Louisiana Texas Total Counties and Parishes Florida Panhandle: Wakulla, Franklin, Gulf, Bay, Walton, Okaloosa, Santa Rosa, Escambia Florida Peninsula: Taylor, Dixie, Levy, Citrus, Hernando, Pasco, Pinellas, Hillsborough, Manatee, Sarasota, Charlotte, Lee, Collier, Monroe, Jefferson Baldwin, Mobile Harrison, Hancock, Jackson Louisiana Southeast: Plaquemines, Tangipahoa, St. Tammany, Orleans, St. Bernard, Jefferson, LaFourche, Terrebonne Louisiana Southwest: Cameron, Vermillion, Iberia, St. Mary Texas Northeast: Jefferson, Chambers, Galveston, Harris, Brazonia, Matagorda, Jackson Texas Southeast: Calhoun, Aransas, Refugio, San Praticio, Nueces, Kleberg, Kennedy, Willacy Gulf Coast region severe cold winter, for example, increases skepticism about climate change(46) while increased flooding raises concerns about climate-related impacts.(44) In prior research, past experience is generally measured by using self-reported observations without incorporating actual weather conditions or incorporating real weather conditions without including individual perceptions. Yet, self-reports of past experience with weather and climate can be unreliable as recollections are subject to various heuristics and biases such as affect,(47) confirmation bias,(35) and representativeness, availability, and anchoring.(48) These biases can distort the reality in one’s mind by leading individuals to see weather conditions as confirming their existing worldview (confirmation) and to place greater weight on recent extreme weather events (availability). Experimental studies, for example, have found that belief in climate change is related to less relevant but more accessible information such as outdoor temperature,(49) daily temperature change,(24) and current temperatures,(23) all of which are contrary to the embodiment of climate change— a long-term climatic pattern. Alternatively, including actual weather conditions without capturing perceptions assumes changes in actual weather conditions are accurately recognized and incorporated into individual perceptions. This raises an interesting theoretical question: What happens when reality meets perception in the shape of changing local weather conditions? Empirical studies have found some evidence to support the link between one’s geographic context and environmental views.(13,15,16,22,25,50–52) The theoretical argument is that our perceptions and behaviors are empowered or restricted by local context. Rural communities with growing populations, for example, are often more skeptical of rapid Sample Size 418 421 619 620 510 411 420 437 3,856 development.(50) Economic conditions are similarly found to influence aggregate concern for climate change,(51) though county-level unemployment rates do not consistently affect an individual’s perceptions of global warming.(22) Peacock and Brody(52) find that one’s location is the most important factor determining perceptions of hurricane wind risk. Although perceptions of air quality are not directly related to actual air quality as described by readings of air monitoring stations, they are influenced by one’s neighborhood setting (urban vs. rural).(53) The location represented by driving distance from creeks in two watersheds in Texas was a significant factor in explaining one’s views on the level of water pollution in the creeks.(54) Scholars have also found that short-term weather fluctuations,(13,16) longer-run weather departures,(55,56) and long-term temperature trends(15,22) exert significant impacts on individual perceptions of climate change. Moreover, perceived weather and climate are significantly correlated with the actual conditions.(56,57) Despite these findings, studies also point out the limited explanatory power of geographic factors compared to attitudinal variables. For instance, the variation of risk perceptions of climate change is more accounted for by attitudinal and socioeconomic variables than physical vulnerability to climate change.(58) Additionally, all these studies create different measures to represent the geographic condition, for instance, the short-term weather fluctuations are measured based on different time spans in various studies.(13,16,23,24) This inconsistency in measurements across studies makes it difficult to directly compare results. Our overall goals are: (1) to compare various recent weather changes measured in a consistent way with perceptions of these changes; and (2) Local Weather Conditions and Climate Change 5 Table II. Percentage of Each Response to Survey Questions Concerning the Air Temperature, Numbers of Hurricanes, Droughts, Amount of Flooding, General Weather, and Climate Change Based on the Selected Data Including Only Long-Term (10, 15, and 19 years) Residents Specific Weather Events Decrease (%) /Pretty much the same /Yes /Not at all Same (%) /Somewhat different /No /Somewhat Increase (%) /Very different /Extremely 4.41 4.36 4.32 47.41 47.25 47.37 46.34 46.46 46.32 22.88 22.49 22.22 53.32 53.30 53.31 21.62 22.32 22.52 5.09 5.19 5.23 39.20 39.14 38.90 54.03 53.95 54.10 17.03 17.68 17.00 56.56 55.72 56.00 24.02 24.70 24.78 42.76 42.51 42.07 36.81 36.61 36.58 19.82 20.50 21.02 Air temperature (decrease, same, and increase) Over 10-year residence Over 15-year residence Over 19-year residence Number of hurricanes (decrease, same, and increase) Over 10-year residence Over 15-year residence Over 19-year residence Number of droughts (decrease, same, and increase) Over 10-year residence Over 15-year residence Over 19-year residence Amount of flooding (decrease, same, and increase) Over 10-year residence Over 15-year residence Over 19-year residence General weather (different) Over 10-year residence Over 15-year residence Over 19-year residence Belief in climate change (yes/no) Over 10-year residence Over 15-year residence Over 19-year residence 73.52 73.85 74.09 22.16 21.89 21.59 Concern for future climate change effects (not concerned at all: extremely concerned) Over 10-year residence Over 15-year residence Over 19-year residence 20.26 20.11 19.91 to examine how perceived and measured weather conditions altogether affect perceptions of climate change. Specifically, drawing from the literature, we test four hypotheses: H1: Partisans’ predispositions have significant effects on both perceptions of specific weather conditions and climate change. H2: Actual weather conditions have significant effects on perceived weather conditions and perceptions of climate change. H3: Perceived weather conditions have more explanatory power than actual weather conditions in determining perceptions of climate change. 29.86 30.22 30.22 12.42 12.44 12.68 H4: The information environment represented by various media outlets has significant effects on both perceptions of weather conditions and climate change. 3. MATERIAL AND METHODS To test these hypotheses, we combine survey data with aggregate environmental data including storm data and temperature data. The 2012 Gulf Coast Climate Change Survey was designed to shed some light on how coastal residents perceive local climate shifts, their willingness to take personal actions to adapt to the effects of climate change, and their support of local climate-related policies. Because 6 Shao and Goidel Table III. Ordered Logit Estimates for the Model of Perceptions About Air Temperature, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, and Objective Measures of Seasonal Air Temperature Trends Model (10-Year) Variable Model (15-Year) b Z b –0.007 0.121 0.005 –0.005 –0.142 0.066 –0.203 –2.30* 1.38 0.20 –0.13 –0.86 0.28 –3.23*** –0.006 0.105 0.005 0.009 –0.057 0.152 –0.174 –0.057 0.170 –0.125 0.059 –0.60 3.83*** –2.93** 1.33 0.086 –0.007 0.023 –0.076 2.00* –0.20 0.40 –1.14 Z Model (19-Year) b Z –1.68* 1.26 0.21 0.24 –0.34 0.59 –2.32* –0.008 0.116 –0.002 –0.004 –0.069 0.034 –0.136 –1.91 1.28 –0.06 –0.10 –0.38 0.12 –1.66* –0.062 0.164 –0.107 0.050 –0.61 3.21*** –2.20* 1.27 –0.052 0.158 –0.102 0.053 –0.49 2.70** –2.00* 1.29 0.037 0.080 –0.182 0.268 0.37 1.14 –1.94 1.23 0.130 0.114 –0.128 0.094 1.47 0.66 –1.41 0.56 Demographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] Objective environmental conditions Summer temperature [+] Fall temperature [+/–] Winter temperature [+] Spring temperature [+/–] N Pseudo R2 Wald χ 2 Prob (χ 2 ) 2,079 0.016 73.61 0.0000 1,845 0.014 30.72 0.0096 1,689 0.013 49.78 0.0000 *prob< 0.05 (one-tail test), **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). attitudes may be strongly shaped by local context, the survey was designed to provide an adequate sample across Gulf Coast states (Florida, Alabama, Mississippi, Louisiana, and Texas) and regionally within Florida, Louisiana, and Texas. To this end, the survey utilized stratified random sampling, drawing independent samples across and within states as outlined in Table I. Data were collected by telephone from January 3 through April 4, 2011.5 The final survey data were weighted to account for differences in the probability of selection based on the state and region where the respondent resides and to approximate most recent population estimates from the U.S. Census. Responses rate in this survey is 17.6%, meaning that 17.6% of calls to eligible respondents resulted in a completed interview.6 5 Due to the difficulty and expenses of targeting cell phone numbers in the U.S. Gulf Coast, the current survey did not include a cell phone sample as part of the data-collection endeavor. This is a significant limitation of this survey given the growing trend of only cell phone usage among the general population. 6 Response rates for the survey were calculated using the American Association of Public Opinion Research response rate calculator (http://www.aapor.org/AAPORKentico/Education- This survey provides a battery of questions about perceptions of specific local weather events, and general risk perceptions of climate change effects as well as information about respondent demographic characteristics and political predispositions. Because the items on the survey did not ask respondents to refer to a particular time period over which these weather and climate phenomena have changed, it is necessary to conduct a series of sensitivity analyses by including measures that represent long-term trends over different time spans, namely, 10, 15, and 19 years. The storm data provided by National Weather Service (NWS) were incomplete Resources/For-Researchers/Poll-Survey-FAQ/Response-RatesAn-Overview.aspx). The response rate in the current survey is 17.6%, meaning that 17.6% of calls to eligible respondents resulted in a completed interview. This response rate reflects current response rate estimates from national polling organizations. The Pew Center for Research, for example, reported that response rates had declined from 36% in 1997 to 9% in 2012 (see http://www.people-press.org/2012/05/15/assessing-therepresentativeness-of-public-opinion-surveys/). At least within the context of election surveys, declining response rates have not translated into response bias once data are appropriately weighted. Local Weather Conditions and Climate Change 7 Fig. 2. Spatial pattern of 10-year summer temperature trend across the Gulf Coast. prior to 1993, only including three types of extreme weather events: Hail, Tornado, and TSTM Wind. In order to be consistent across various time spans in the present analysis, we only trace back to 1993 when more complete storm data began to be available. Thus, the temperature trends are also constructed based on these time frames. Correspondingly, we include observations from respondents who have resided in their local communities for at least or more than 10, 15, and 19 years in these three separate analyses. As a result, 3,265, 2,912, and 2,660 observations out of the original 3,856 remain in the 10-, 15-, and 19-year analyses, respectively. The environmental data come from two sources. The extreme weather events data come from NWS at National Climatic Data Center (NCDC).(59) The temperature data come from the U.S. Historical Climatology Network.(60) The survey data provide geographic identification codes including state and county FIPS codes for each respondent. With the geographic information, we merge the environmental data with the survey data at the county level. 3.1. Dependent Variables The first category of dependent variables is perceptions of weather conditions. The first four variables in this category are related to perceptions of specific weather phenomenon. These variables include perceptions of air temperature, number of hurricanes, number of droughts, and amount of flooding. The survey provides three responses to the questions “Would you say air temperature/ 8 Shao and Goidel Table IV. Ordered Logit Estimates for Models of Perceptions of the Number of Major Hurricanes, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, and Objective Measures of Weather Conditions Model (10–Year) Variable Model (15–Year) Model (19–Year) b Z b Z b Z –0.009 –0.050 0.029 0.019 0.099 –0.619 –0.193 –2.07* –0.61 0.97 0.53 0.51 –2.26* –2.75** –0.010 –0.014 0.015 0.013 0.049 –0.728 –0.176 –2.41* –0.17 0.46 0.32 0.23 –2.38* –2.46** –0.009 0.004 0.010 0.036 0.025 –0.820 –0.166 –2.12* 0.05 0.31 0.88 0.11 –2.73** –2.21* 1.99* 0.61 2.80** –0.37 0.120 0.026 0.091 –0.013 1.60 0.43 2.59** –0.27 0.159 0.053 0.087 –0.016 2.14* 0.78 2.84** –0.31 –0.34 15.468 Demographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] 0.133 0.034 0.089 –0.017 Objective environmental conditions Hurricane number [+] N Pseudo R2 Wald χ 2 Prob (χ 2 ) –1.938 2073 0.010 42.87 0.0000 3.85*** 1848 0.013 85.20 0.0000 3.51*** 40.580 1692 0.015 69.42 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). number of hurricanes/number of droughts/amount of flooding have changed?” We recode “decreased” as –1, “about the same” 0, and “increased” 1. “Don’t know” responses are recoded as missing. The fifth variable is perceptions of general weather. This variable is derived from responses to the question “Is the weather very different?” We recode “pretty much the same”—0, “somewhat different”—1, and “very different”—2. The second category of dependent variables is perceptions of climate change. The first variable in this category is belief in climate change, which is based on responses to the question: “Do you think climate change is happening?” This variable is coded 1 for those who responded “yes,” 0 for those who responded “no.” “Don’t know” responses are recoded as missing. The second dependent variable is concern for future climate change effects. This variable is derived from responses to the question “How concerned are you that your community will be affected by future climate change?” We measure this variable on a scale from 0 (“not concerned at all”) to 4 (“extremely concerned”). “Don’t know” is coded as missing. Table II shows the percentage of each category for all dependent variables. 3.2. Independent Variables Demographic characteristics (control variables). We measure respondents’ age in years, ranging from 18 to 94 years. We measure Education on a scale from 1 (less than 9th grade) to 8 (doctorate or postdoc degree). Income is measured on a scale ranging from 1 (under $10,000) to 8 ($100,000 or more). We create two binary variables to represent race and ethnicity: black (“black” = 1; “other” = 0) and Hispanic (“Hispanic” = 1; “other” = 0). The code for sex is 1 (female) and 0 (male). The literature to date has provided an unclear picture of the role of demographic characteristics on environmental views. The most consistent demographic factor is gender. Females are more likely than males to perceive environmental risks and thus express higher level of concern.(11,13,15,20) Only a few studies have identified significant effects of race, with racial minorities being more concerned about climate change.(11,17,22) Similarly, only a few studies have found that older generations are less likely than younger people to believe in climate change or express concern about its future consequences.(16,22) The effects of education and income are typically mixed, with the effects dependent on the nature of the survey questions. Education and income are found to be positively Local Weather Conditions and Climate Change 9 Table V. Ordered Logit Estimates for Models of Perceptions of the Number of Droughts, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, and Objective Measures of Weather Conditions Model (10–Year) Variable Model (15–Year) Model (19–Year) b Z b Z b Z 0.006 0.217 0.010 0.011 –0.598 0.747 –0.269 2.08* 2.38* 0.46 0.35 –5.78*** 3.15** –3.91*** 0.003 0.212 0.020 0.003 –0.617 0.719 –0.258 1.08 2.12* 0.89 0.09 –4.88*** 3.06*** –3.67*** 0.001 0.169 0.020 0.004 –0.671 0.802 –0.264 0.44 1.70* 0.84 0.13 –4.86*** 3.38*** –3.78*** 1.45 2.08* –0.88 1.67 0.097 0.142 –0.094 0.069 1.51 2.38* –1.56 1.55 0.083 0.156 0.095 0.077 1.32 –0.054 –0.09 –0.189 Demographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] 0.085 0.118 –0.050 0.078 1.21 2.50* –1.66 1.74 Objective environmental conditions Drought number [+] N Pseudo R2 Wald χ 2 Prob (χ 2) 0.192 2,087 0.019 92.96 0.0000 1,855 0.021 81.81 0.0000 –0.19 1,679 0.023 74.23 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). related to belief in climate change(19) and climate policy support,(17) but negatively correlated with concern for this issue.(19) Increasing education and decreasing income lead to perceptions of greater risk of global warming.(22) Predisposition, represented by party identification, is measured on a three-point scale from 0 (Democrat) to 2 (Republican). Previous studies have consistently demonstrated that Republicans are less likely than Democrats to believe in the occurrence, human induction, and adverse impacts of climate change.(12,17,19,22) It is possible Republicans are also less likely to perceive adverse changes in local weather conditions because of the implication of climate change. Attentiveness to information about climate change. The first variable we include is a subjective measure of how informed one believes one is about climate change. This variable is measured on a scale from 0 (“not informed at all”) to 3 (“very well informed”). This survey also asked respondents a series of questions related to what specific sources they go to for climate change information. These questions asked respondents whether or not they go to information sources including: local newspaper, national newspaper, local television news, national television news, cable television news, news radio programs, talk radio programs, online news sources, and social networking sites. We recode the response “yes” as 1 and “no” as 0. A factor analyses across all three selected data sets revealed three factors with eigenvalues above 1. These factors are retained and included in the analysis. Attentiveness to local newspaper, national newspaper, local TV news, national TV news, and cable TV news are dominantly loaded on the first factor (traditional news media). Attentiveness to news radio programs and talk radio programs are loaded on the second factor (radio), whereas the third factor includes attentiveness to online news sources and social networking sites with high loadings (Internet). Because of the mixed views expressed by various media outlets, we do not speculate any specific direction of the effects of this group of variables on the dependent variables. Perception of weather conditions. The survey also asked Gulf Coast residents their impressions about the number of major hurricanes, air temperature change, number of droughts, and amount of flooding. We recode each of these items as –1 for “decreased,” 0 for “about the same,” and 1 for “increased.” We assume that perceptions of specific 10 Shao and Goidel Table VI. Ordered Logit Estimates for Models of Perceptions of the Amount of Flooding, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, and Objective Measures of Weather Conditions Model (10–Year) Variable Model (15–Year) Model (19–Year) b Z b Z b Z –0.001 0.183 0.006 0.111 0.218 –0.669 –0.118 0.26 1.86* 0.27 3.24*** 1.17 –2.55* –2.02* –0.002 0.172 0.015 0.098 0.299 –0.763 –0.128 –0.73 1.63 0.70 2.80** 1.54 –2.90** –2.07* –0.003 0.218 0.005 0.106 0.309 –0.731 –0.093 –0.91 2.19* 0.25 2.96** 1.56 –3.10** –1.39 –0.28 2.41* 1.39 0.94 –0.032 0.123 0.063 0.018 –0.55 2.52* 1.29 0.51 –0.025 0.121 0.059 0.031 –0.46 2.53* 1.14 0.89 0.87 0.258 0.76 –0.688 Demographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] –0.014 0.121 0.064 0.032 Objective environmental conditions Flooding amount [+] N Pseudo R2 Wald χ 2 Prob (χ 2 ) 0.265 2,072 0.014 54.05 0.0000 1,844 0.015 78.31 0.0000 –1.18 1,692 0.017 85.57 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). weather types are positively related to perceptions of general weather and climate change. Environmental variables. One of our main goals in this article is to examine how the objective measures of weather affect perceptions of weather and perceptions of climate change. We create two types of variables including trends of specific and total extreme weather events, and trends of seasonal air temperature over the past 10, 15, and 19 years. The data of extreme weather events come from the National Oceanic and Atmospheric Administration (NOAA). The data record “the occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce,” and they also include weather phenomena that “generate media attention” and “other significant meteorological events such as maximum or minimum temperatures or precipitation.”(59) The data have 48 types of extreme weather events. Specifically, the extreme weather event trends are derived from the annual number of extreme weather events in each county. Additionally, we derive the annual numbers of three specific extreme weather events— hurricanes, droughts, and flood—from the same data. In addition, we create four seasonal temperature trends using data from the United States Historical Climatology Network (USHCN). These trends are measured for four seasons defined by climatologists – winter (December, January, and February), spring (March, April, May), summer (June, July, August), and fall (September, October, November) – and are based on the mean seasonal temperatures for each year. The temperature values are given in 10ths of degrees Fahrenheit. Shao et al.(22) find solid evidence that increasing summer temperatures are positively related to individuals’ perceptions of the impacts and severity of global warming. Hamilton and Keim(15) find warming winters can lead people to perceive the effects of climate change, most conspicuously in snow country where warming winters are salient, whereas residents in the Mississippi Delta are more likely to think of floods, droughts, or hurricanes as an “effect of climate change.” We create the specific multidimensional climate indicators to test the relationships between perceptions and specific weather and climate phenomena. To calculate all the trends, we regress the annual number of extreme weather events by each county and the average seasonal temperature by each weather station on year, respectively. We then use Local Weather Conditions and Climate Change 11 Table VII. Ordered Logit Estimates for Models of Perceptions About General Weather, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, Perceptions of Extreme Weather, and Actual Weather and Climate Model (10–Year) Variable Model (15–Year) Model (19–Year) b Z b Z b Z 0.000 0.365 –0.102 –0.120 0.630 0.021 –0.221 –0.07 3.97*** –4.46*** –4.63*** 3.85*** 0.12 –3.04*** 0.002 0.355 –0.100 –0.117 0.617 0.022 –0.234 0.68 3.55*** –3.92*** –4.16*** 3.57*** 0.11 –2.99** 0.002 0.360 –0.106 –0.106 0.675 0.101 –0.210 0.60 3.30*** –4.19*** –3.54*** 3.75*** 0.61 –2.97** 0.126 0.103 –0.014 0.158 1.34 2.22* –0.28 2.75** 0.065 0.073 0.000 0.184 0.64 1.75 0.01 3.06** 0.072 0.075 –0.020 0.189 0.65 1.77 –0.40 2.96** 0.074 1.325 0.462 0.189 1.18 10.45*** 5.49*** 2.54** 0.075 1.381 0.502 0.149 1.32 9.66*** 5.49*** 1.94* 0.094 1.346 0.483 0.151 Sociodemographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] Perceptions of specific extreme weather Hurricane number [+] Air temperature [+] Drought number [+] Flooding amount [+] 1.48 9.05*** 4.48*** 2.14* Objective environmental conditions Extreme weather events [+] Summer temperature [+] Fall temperature [+/–] Winter temperature [+] Spring temperature [+/–] N Pseudo R2 Wald χ 2 Prob (χ 2 ) 0.029 0.067 –0.004 –0.024 –0.038 0.73 1.14 –0.15 –0.40 –0.45 –0.064 0.056 –0.069 0.105 0.084 1,978 0.138 936.29 0.0000 –1.71 0.43 –0.85 1.03 0.48 1,771 0.1407 1029.68 0.0000 –0.095 0.206 0.038 0.197 –0.112 –1.60 1.29 0.21 1.26 –0.82 1,624 0.139 809.12 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). the unstandardized bivariate regression coefficients to represent the 10-, 15-, and 19-year trends. Our hypothesis for this group of variables is that climate measures are positively correlated with the dependent variables, including perceptions of both weather conditions and climate change. It should be noted here that we conduct regression diagnostics for multicollinearity among the independent variables for each model presented in this article. In no case are the variance inflation factors (VIFs) sufficiently large to indicate that this is a matter of concern. 3.3. Methods Because we include contextual variables in our models, one significant issue arises in statistical tests. The error terms within one context (county or an area in the proximity of a weather station) are no longer independent of each other. Two dominant approaches are applied to address this issue: multilevel modeling(61) and clustered standard errors.(62) Our primary interest in the current research is with individual-level estimates rather than varying coefficients of clusters (counties). Furthermore, clustered standard errors and multilevel modeling are equally adequate for the precision estimates of group-level effects when the number of clusters is plentiful (i.e., above 20).(63) Thus, we present ordered logit models with clustered standard errors rather than multilevel models.7 With few exceptions, the results from the multilevel models reflect the results presented here. None of the differences affect 7 The results from these analyses are available upon request from the lead author. 12 Shao and Goidel Table VIII. Logit Estimates for Models of Belief in Climate Change, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, Perceptions of General and Specific Weather Phenomena, and Actual Weather and Climate Model (10-Year) Variable Model (15-Year) Model (19-Year) b Z b Z b Z –0.006 0.277 –0.049 0.071 0.736 –0.049 –0.772 –1.07 2.55** –1.26 1.43 2.58** –0.18 –8.10*** –0.006 0.315 –0.035 0.088 0.663 0.119 –0.761 –0.92 2.58** –0.83 1.71 2.34** 0.37 –7.10*** –0.007 0.344 –0.028 0.050 0.597 0.184 –0.808 –0.86 2.62** –0.61 0.94 1.85* 0.60 –7.34*** –0.251 0.303 –0.138 0.046 –2.1 4.39*** –2.61** 0.7 0* 0.332 –0.144 0 –0.260 4.24*** –2.26* 0.056 –1.98* 0.341 –0.118 0.83 –0.260 4.28*** –1.73 0.058 0.982 0.061 0.801 0.325 0.187 8.70*** 0.46 6.54*** 3.14*** 1.82* 1.027 0.069 0.846 0.322 0.102 9.48*** 0.49 6.04*** 2.95** 0.84 0.988 0.119 0.824 0.315 0.072 8.52*** 0.75 5.56*** 2.94** 0.65 Demographic attributes Age [–] Gender [+] Income [+] Education [+] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] Perceptions of weather General weather [+] Hurricane number [+] Air temperature [+] Drought number [+] Flooding amount [+] Objective environmental conditions Extreme weather events [+] Summer temperature [+] Fall temperature [+/–] Winter temperature [+/–] Spring temperature [+/–] N Pseudo R2 Wald χ 2 Prob (χ 2 ) 2.02* 0.15 0.74 0.57 –1.52 0.133 0.011 0.031 0.059 –0.196 0.112 –0.062 0.348 –0.545 0.196 1,910 0.269 647.29 0.0000 1.63 –0.35 1.91 –2.68** 0.59 1,708 0.278 1020.77 0.0000 0.155 0.124 0.242 –0.718 0.086 1.09 0.57 0.49 –2.05* 0.26 1,565 0.275 676.55 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). our substantive conclusions. Perceptions of local weather conditions are driven primarily by partisan affiliation, and perception of weather conditions— rather than actual weather conditions—affects belief in climate change and concern about future consequences. 4. RESULTS 4.1. Models of Individuals’ Perceptions of Weather In this section, we examine individual perceptions of weather and climate, namely, air temperature, hurricanes, droughts, flooding, and general weather. We begin with perceptions of air temperature; specifically we consider which seasonal air temperatures on perceptions of temperature change. Results in Table III demonstrate that this model specification does not account for much variance explained across three time spans. Nevertheless, the decadal summer trend is positively correlated with perceptions of air temperature. The Gulf Coast has witnessed a uniform increase in summer air temperatures from 2002 to 2011 (for the spatial pattern, please refer to Fig. 2). This result conforms to previous findings by Shao et al.(22) In addition, older people, Republicans, and those who turn to radio for information about climate change fail to perceive an increase in air temperature. Individuals who turn to newspaper and TV, in contrast, tend to think the air temperature is increasing. Overall, perceptions of air temperatures are more closely related to news sources and partisan affiliation than to actual weather conditions. Local Weather Conditions and Climate Change 13 Fig. 3. Spatial pattern of 10-year trend of extreme weather events across the Gulf Coast. In our next set of models, we consider perceptions of extreme weather phenomenon (see Tables IV–VI). Similar to the models for air temperatures and as reflected in the pseudo R2 , the models only weakly explain the variance in perceptions of extreme weather events. These results are consistent across models. Objective measures of drought and flooding are also not statistically related to perceptions of droughts or floods. We do find some evidence that objective weather conditions matter. While the 10-year trend for hurricanes fails to achieve traditional statistical significance, the 15-year and 19-year trends are positively related to perceptions that hurricanes are increasing. This may be due to the fact that the number of hurricane landfalls in the Gulf from 2002 to 2011 is too few to form any meaningful trends for the public to detect. The most consistent predictor in specific weather perceptions across all models is party identification, indicating that political orientation plays a dominant role in interpretation of weather-related conditions. Republicans are less likely than Democrats to perceive increasing air temperatures, hurricanes, droughts, and flooding. Tables III–VI demonstrate that various information sources have different consequences for recognition of extreme weather events. Newspaper and TV are most closely related to perceptions that air temperatures, flooding, droughts, and hurricanes are increasing. Moving from specific conditions to general weather perceptions, we first estimate ordered logit models without perceptions of specific weather events (Appendix Table A1). We then add this cluster of variables to the models (Table VII). The comparison between models with and without perceived climate suggests strong explanatory power of perceptions. First, the pseudo R2 increases substantially from 5% to around 14%. The cluster of perceptions of specific weather stands out as a 14 Shao and Goidel Fig. 4. Spatial pattern of 15-year winter temperature trend across the Gulf Coast region. dominant factor affecting perceptions of general weather. Three out of the four variables have positive effects. Second, while we find significant effects of two objective weather measures (summer temperature trends and annual extreme weather event trend), these effects are mediated by perceptions of specific weather conditions (Table VII). These results are consistent with previous findings that perceptions of specific weather phenomena have more explanatory power than objective weather conditions in predicting perceptions of general weather.(54,58,64,65) The results in Table VII also reveal that females, African Americans, and those who go to the Internet, newspaper, and TV for the climate information perceive greater changes in local weather conditions. Republicans and individuals with higher levels of income and education, in contrast, perceive the weather as pretty much the same. 4.2. Models of Perceptions of Climate Change In this section, we focus on models that explain individuals’ perceptions of climate change, focusing specifically on belief that climate change is happening and concern about the future consequences of climate change. We start with logit models of belief in climate change, first without and then with the group of variables relating to perceptions of weather (see Appendix Table A2 and Table VIII). As with the previous analyses, the results reflect the overall importance of perception relative to objective conditions. Adding the perceptions of weather conditions to the models increases the pseudo R2 by approximately 10% across the three time periods. In the cluster of demographic attributes, gender, race, and party identification conform to the literature that females, African Americans, and Democrats are more likely to express belief in climate change. Local Weather Conditions and Climate Change 15 Fig. 5. Spatial pattern of 19-year winter temperature trend across the Gulf Coast. Among information sources about climate change, exposure to newspaper and television is associated with increased belief in climate change while exposure to radio is associated with disbelief, likely reflecting the conservative bent of most talk radio programing. While this finding might seem counterintuitive, it fits well within the existing literature on how motivated reasoning influences climate-related attitudes.(66) As Table VIII reveals, perceived weather trumps objective weather in explaining variations of belief in climate change. This result conforms to previous studies.(64,65) Specifically, four out of five variables of perceptions of weather are significantly positive factors across three time frames. Specifically, people who perceive different weather, rising air temperatures, and increasing number of droughts and flooding amount, are inclined to believe climate change is happening. In comparison, the 10-year trend of extreme weather events is found to be significantly related to belief in climate change. Consistent with the hypothesis, the Gulf Coast residents who have experienced more extreme weather events over the past 10 years are more likely to believe climate change is happening. The increasing number of extreme weather events has been concentrated in the west portion of this region (see Fig. 3). The results suggest individuals tend to use the increasing number of extreme weather events as evidence for the occurrence of climate change. Winter temperature trends in the last 15 and 19 years are found to be negatively related to belief in climate change. Hamilton and Keim(15) identified regional variation concerning effects of warming winters on perception of climate change, and found that Mississippi Delta residents are less likely than their counterparts in snow country to correlate increasing winter temperature with climate change. The spatial patterns of 15- and 19-year winter temperature trends in the Gulf Coast (see Figs. 4 and 5) reveal that winter temperatures have been decreasing over these time spans. Overall, these results reinforce the notion that the public tends to relate abnormity in weather patterns to the existence of climate change. Finally, we consider how perception of local weather conditions affects concern about the future consequences of climate change. The comparison between models without (Appendix Table A3) and with (Table IX) the perception variables demonstrates the explanatory power of perceptions across three time periods. Individual perceptions of both general weather and specific weather phenomena 16 Shao and Goidel Table IX. Ordered Logit Estimates for Models of Concern for Future Climate Change Effects, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources About Climate Change, Belief in Climate Change, Perceptions of General and Specific Weather Phenomena, and Actual Weather and Climate Model (10-Year) Variable Model (15-Year) Model (19-Year) b Z b Z b Z –0.011 0.500 0.008 –0.102 0.145 –0.188 –0.391 –4.57*** 6.30*** 0.42 –3.81*** 0.89 –1.24 –5.80*** –0.012 0.507 –0.005 –0.101 0.170 –0.182 –0.362 –4.15*** 6.06*** –0.28 –3.57*** 0.97 –1.08 –5.16*** –0.013 0.527 –0.002 –0.103 0.219 –0.225 –0.332 –3.92*** 5.91*** –0.10 –3.49*** 1.23 –1.29 –4.36*** 0.124 0.151 0.093 0.066 0.889 1.60 2.96*** 2.68*** 1.40 7.09*** 0.132 0.157 0.090 0.071 0.952 1.52 2.86** 2.36* 1.65 7.89*** 0.121 0.165 0.091 0.072 0.992 1.34 2.82** 2.23* 1.46 8.46*** 0.477 0.231 0.403 0.396 0.262 8.09*** 3.92*** 5.10*** 6.15*** 3.92*** 0.489 0.179 0.430 0.403 0.280 7.42*** 2.64** 5.93*** 5.69*** 4.11*** 0.536 0.146 0.383 0.420 0.328 8.07*** 1.97* 5.32*** 5.47*** 4.78*** Demographic attributes (control variables) Age [–] Gender [+] Income [–] Education [–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] Belief in climate change [+] Perceptions of weather General weather [+] Hurricane number [+] Air temperature [+] Drought number [+] Flooding amount [+] Objective environmental conditions Extreme weather events [+] Summer temperature [+] Fall temperature [+/–] Winter temperature [+/–] Spring temperature [+/–] N Pseudo R2 Wald χ 2 Prob (χ 2 ) 0.048 –0.007 –0.018 0.116 0.009 0.90 –0.14 –0.54 1.73 0.11 1898 0.114 1795.96 0.0000 and beliefs in climate change directly affect concern for future climate change effects. The objective weather measures, in contrast, have limited and indirect effects. Extreme weather events and fall temperature are significant in the 10-year model without the perception variables (Appendix Table A3), but are nonsignificant when controlling for perceptions. The pseudo R2 also improves by approximately 6% across three time frames when perceptions of specific and general weather patterns are added to the models. Additionally, age, gender, education, and party identification are significant with younger, female, less educated, and Democratic residents expressing greater concern about the future consequences of climate change. The effect of education is somewhat surprising. McCright and Dunlap(19) contend that 0.021 0.127 –0.031 0.062 –0.067 0.52 1.24 –0.33 0.46 –0.30 1698 0.118 1842.90 0.0000 0.002 0.040 0.287 0.247 –0.222 0.02 0.28 1.31 1.22 –1.31 1555 0.124 1775.90 0.0000 individuals with higher level of education tend to have more confidence in human beings’ capacity to reverse the possible catastrophic impacts caused by climate change. 5. CONCLUSION In this study, we directly test to see if perceptions of changing weather conditions mirror actual weather conditions or whether they are more reflective of general political orientation. In doing so, we build on previous research finding that more general ideological beliefs and more specific existing attitudes toward global warming influence perceptions of local weather conditions.(56,67) Specifically, we find that perceptions of local weather conditions are influenced more by partisan affiliation than by objectively Local Weather Conditions and Climate Change measured conditions. Compared with Republicans, Democrats not only are more likely to perceive changes in local weather patterns, they also perceive an increase in the number and severity of hurricanes, droughts, and flooding. They are also more likely to perceive warming air temperature. Consistent with national-level findings, Gulf Coast Democrats are more likely to believe in climate change and express greater concern for its future effects. Objective environmental conditions, in contrast, are not consistently related to perceptions of local weather conditions or attitudes toward climate change. In Lippmann’s terms, our perceptions of local weather conditions are driven more by the “pictures in our head” than our external reality. For objective weather conditions to influence belief in climate change or concern about its future effects, they must first be recognized by local residents. Having said that, we do find some evidence that objective weather patterns can have effects on perceptions. For example, 10-year air temperature increases in summer affect perceptions that air temperatures are increasing and longer-term increases in the number of hurricanes (15- and 19-year trends) affect perceptions that the number of hurricanes is increasing. Nevertheless, individuals are more likely to attune to the combination of extreme weather events when perceiving climate change. One possible explanation is that the combination of severe weather events occurs more frequently than any specific weather event, and leaves a deeper and lasting impression on one’s mind. Individuals therefore can easily retreat to the memory with the general weather phenomenon of the recent past. Winter temperature trends of the last 15 and 19 years, however, are found to be negatively related to belief in climate change, net of the effects of individual-level factors. Winter temperatures have been universally decreasing across the Gulf Coast region in the last 15 and 19 years. Individuals may detect the abnormities in local climates and attribute them to the evidence of climate change occurrence. Additionally, the objective trends of hurricane numbers of the recent 15 and 19 years are positively related to individuals’ perceptions of this phenomenon. The discrepancy between the relative short-term period (10 years) and longerterm (15 and 19 years) might be due to the nature of rarity in hurricane landfalls. The period 2002–2011 witnessed only a few hurricane landfalls, and therefore made it hard to form any detectable trend for residents of the Gulf Coast to recall. Once we extend the time frame to 15 and 19 years, there is some evidence 17 as shown in this study that the public somehow has accurate recollections of hurricane landfalls. We have found a moderate amount of evidence to support our hypothesis that the objective climate context exerts significant impacts on perceptions of weather conditions and climate change. Furthermore, the information environment consisting of a variety of sources serves as an alternative to the geographic environment. Our findings show that people rely on different information sources to form their perceptions of specific weather phenomenon more than the actual environment. Nevertheless, the results of media exposure are mixed. Exposure to newspaper and TV is found to be positively correlated with most of these dependent variables, suggesting that newspaper and TV outlets generally tend to provide information to support the existence of climate change and raise concern for this issue. Individuals who rely on radio for information about climate change, however, are more likely to perceive an increasing trend of hurricanes and express more concern for future effects of climate change. Meanwhile, they are also less likely to perceive warming air temperatures. The Internet is found to have positive effects on some of the dependent variables. Specifically, individuals who go to the Internet for information about climate change tend to believe that flooding is increasing and the general weather is changing, and they also express higher level of concern for climate change. When it comes to engaging local communities in mitigation or adaptation-related behaviors or planning, the lesson from the study seems to be clear. It is important to do more than raising awareness. Instead, policymakers must take on the more burdensome task of overcoming partisan resistance. Democrats and Republicans differ not only in terms of their attitudes toward climate change but see a very different reality in terms of local weather. Where Democrats perceive rising air temperatures and an increasing number of hurricanes, droughts, and floods, Republicans are less likely to see changing conditions. As a result, they are also less likely to believe climate change is occurring or to express concern over its future consequences. Overall, then, we would conclude by turning the title on its head: seeing is not believing, believing is seeing. In concluding, there are several major limitations to this study. First, the survey items are vague in terms of the temporal and geographic context. We attempt to address this vagueness issue by 18 Shao and Goidel conducting sensitivity analyses. A more ideal solution would be more precise wording in future surveys clearly specifying the geographic scope and time frame for changes in local weather patterns. Second, this study aims to explore factors that determine perceptions of specific weather conditions, namely, air temperature, hurricanes, droughts, and flooding. The model specifications for these specific weather conditions leave much of the variance in individual perceptions unexplained. This research agenda is far from being complete. Experimental studies might further examine how individuals’ perceptions of specific weather events are shaped. Third, set in the Gulf Coast region, we do not examine how coastal residents perceive other coastal natural hazards such as coastal erosion, sea-level rise, hurricane strength, and water temperature. Future studies need to be conducted to examine the relationships between objective measures and perceptions of these natural hazards. Fourth, policymakers/planners are faced with an immense task of building resilient coastal communities. We do not examine how one’s objective environment impacts local policy support and citizens’ environmental behaviors. More studies are needed to fill this gap. ACKNOWLEDGMENTS We would like to thank LaDonn Swann, Tracy Sempier, and Melissa Schneider for their support in designing and implementing the 2012 Gulf Coast Climate Change Survey and Trey Marchbanks and Guy Whitten for their comments and suggestions on the final article. The survey research included in the analysis was supported by the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration’s Gulf of Mexico Coastal Storms Program under NOAA Award NA10OAR4170078, Texas Sea Grant, Louisiana Sea Grant, Florida Sea Grant, and Mississippi-Alabama Sea Grant Consortium. The views expressed herein do not necessarily reflect the views of any of these organizations. Neither the organizations nor the individuals named above bear any responsibility for any remaining errors. Table A1. Ordered Logit Estimates for Models of Perceptions About General Weather, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, and Actual Weather and Climate Model (10-Year) Variable Model (15-Year) Model (19-Year) b Z b Z b Z –0.002 0.378 –0.082 –0.100 0.451 0.087 –0.299 –0.57 4.34*** –3.57*** 2.69** 0.42 –4.69*** 0.000 0.372 –3.49*** –0.103 0.437 0.114 –0.306 0.10 3.95*** –0.076 –3.28*** 2.39** 0.57 –4.41*** –0.001 0.382 –2.98** –0.097 0.472 0.141 –0.280 –0.23 3.90*** –0.082 –3.09** 2.47** 0.67 –4.19*** 0.099 0.168 –0.046 0.171 1.25 4.34*** –1.03 3.33*** 0.047 0.146 –0.036 0.188 0.51 3.89*** –0.76 3.41*** 0.061 0.144 –0.049 0.196 0.64 3.59*** –1.09 3.17*** 0.061 0.122 –0.022 –0.025 –0.059 1.42 2.11* –0.96 –0.44 –0.70 –0.077 0.126 –0.008 0.063 0.090 –1.83 1.06 –0.11 0.66 0.51 –0.131 0.271 0.145 –0.098 0.176 –2.08* 1.99* 0.9 –0.65 1.19 Sociodemographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] Objective environmental conditions Extreme weather events [+] Summer temperature [+] Fall temperature [+/–] Winter temperature [+] Spring temperature [+/–] N Pseudo R2 Wald χ 2 Prob (χ 2 ) 2,091 0.052 217.85 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). 1,867 0.050 160.68 0.0000 1,710 0.051 149.55 0.0000 Local Weather Conditions and Climate Change 19 Table A2. Logit Estimates for Models of Belief in Climate Change, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources, and Actual Weather and Climate Model (10-Year) Variable Model (15-Year) Model (19-Year) b Z b Z b Z –0.008 0.433 –0.086 0.033 0.756 –0.069 –0.880 –1.54 4.23*** –2.55* 0.73 2.63** –0.26 –10.44*** ––0.008 0.428 ––0.075 0.047 0.692 0.127 ––0.845 –1.22 3.72*** –2.05* 1.04 2.42** 0.41 –9.05*** –0.009 0.448 –0.073 0.017 0.614 0.137 –0.877 –1.20 3.62*** –1.85 0.35 1.92* 0.45 –9.11*** –0.279 0.360 –0.191 0.137 –2.44* 6.32*** –3.73*** 2.41* –0.310 0.395 –0.198 0.152 –2.36* 6.51*** –3.45*** 2.51* –0.292 0.396 –0.184 0.152 –2.02* 6.28*** –2.97** 2.56** 0.122 0.053 0.001 0.017 –0.160 2.07* 0.80 0.02 0.18 –1.33 0.081 0.004 0.284 –0.478 0.216 1.28 0.03 1.90 –2.68** 0.78 0.099 0.230 0.201 –0.559 0.059 0.79 1.29 0.51 –1.84 0.23 Demographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] Objective environmental conditions Extreme weather events [+] Summer temperature [+] Fall temperature [+/–] Winter temperature [+/–] Spring temperature [+/–] N Pseudo R2 Wald χ 2 Prob (χ 2 ) 2,024 0.158 577.34 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). 1,802 0.159 516.77 0.0000 1,650 0.161 369.71 0.0000 20 Shao and Goidel Table A3. Ordered Logit Estimates for Models of Concern for Future Climate Change Effects, as a Function of Sociodemographic Characteristics, Attentiveness to Information Sources About Climate Change, and Actual Weather and Climate Model (10-Year) Variable Model (15-Year) Model (19-Year) b Z b Z b Z –0.010 0.562 –0.018 –0.066 0.175 –0.164 –0.540 –4.38*** 8.03*** –0.90 –2.58** 1.03 –0.81 –10.13*** –0.012 0.560 –0.030 –0.069 0.196 –0.128 –0.513 –4.21*** 7.91*** –1.48 –2.46* 1.07 –0.54 –8.44*** –0.013 0.601 –0.032 –0.070 0.223 –0.224 –0.481 –4.32*** 8.20*** –1.56 –2.5* 1.19 –0.95 –7.69*** 0.089 0.227 0.052 0.126 1.24 4.38*** 1.39 2.78** 0.081 0.238 0.050 0.129 1.04 4.25*** 1.25 3.22*** 0.075 0.250 0.047 0.140 0.92 4.29*** 1.20 3.09** 0.133 1.04 –0.068 0.122 –0.012 2.32** 0.025 0.182 0.026 –0.005 0.036 Demographic attributes Age [–] Gender [+] Income [+/–] Education [+/–] Race: African American [+] Race: Hispanic [+] Party identification [–] Attentiveness to information about climate change Informedness [+/–] Newspaper + TV [+/–] Radio [+/–] Internet [+/–] Objective environmental conditions Extreme weather events [+] Summer temperature [+] 0.061 Fall temperature [+/–] Winter temperature [+/–] Spring temperature [+/–] N Pseudo R2 Wald χ 2 Prob (χ 2 ) –2.03* 1.71 2,087 0.050 491.18 0.0000 0.48 1.38 0.26 –0.04 0.15 1,861 0.049 750.94 0.0000 –0.026 0.168 0.282 0.146 –0.051 –0.32 1.10 1.22 0.77 –0.30 1,703 0.052 561.15 0.0000 *prob< 0.05 (one-tail test); **prob< 0.01(one-tail test); ***prob< 0.001(one-tail test). 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