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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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