Utah State University
From the SelectedWorks of Peter D Howe
2019
Hot dry days increase perceived experience with global warming Peter D Howe
Available at: https://works.bepress.com/peter_howe/61/ 1 Hot dry days increase perceived experience with global
2 warming
1 1 2 1 3 Jennifer R. Marlon⇤ ,XinranWang, Matto Mildenberger ,ParrishBergquist, 3 4 5 6 4 Sharmistha Swain , Katharine Hayhoe ,PeterD.Howe, Edward Maibach ,and 1 5 Anthony Leiserowitz
1 6 School of Forestry and Environmental Studies, Yale University, 195 Prospect
7 Street, New Haven, CT 06511 3 8 Climate Center, Texas Tech University, Lubbock TX 79409-1015 2 9 Department of Political Science, Texas Tech University, 113 Holden Hall,
10 Boston and Akron Streets, Lubbock, TX 79409-1015 5 11 Department of Political Science, University of California Santa Barbara, Santa
12 Barbara, CA 93106-9420 4 13 Department of Environment and Society, Utah State University, 5215 Old Main
14 Hill, Logan, UT 84322-5215 6 15 Center for Climate Change Communication, George Mason University, Fairfax,
16 VA, 22030
17 August 7, 2019
⇤Corresponding author: [email protected]; 203-436-2598
1
Electronic copy available at: https://ssrn.com/abstract=3453287 18 Abstract
19 Public perceptions of climate change in the United States are deeply rooted in cultural
20 values and political identities. Yet, as the public experiences extreme weather and other
21 climate change-related impacts, their perceptions of the issue may shift. Here, we explore
22 whether, when, and where local climate trends have already influenced opinions about global
23 warming in the United States. Using a large national survey dataset (n=13,607), we com-
24 pare Americans’ climate views with corresponding trends in seven different high-resolution
25 climate indicators for the period 2008 to 2015. We find that increases in hot dry day expo-
26 sure significantly increases individuals’ perceptions that they have personally experienced
27 global warming. We do not find robust evidence that other precipitation and temperature
28 anomalies have had a similar effect. We also use multilevel modeling to explore county-level
29 patterns of perceived experiences with climate change. Whereas the individual-level analy-
30 sis describes the causal relationship between a changing climate and individuals’ perceived
31 experience, the multilevel model depicts county-level changes in perceived experience re-
32 sulting from particular climate trends. Overall, we find that exposure to extreme weather,
33 specifically hot dry days, has a modest influence on perceived experience, independent of
34 the political and socio-demographic factors that dominate U.S. climate opinions today.
35 1 Introduction
36 Public concern about climate change has been rising in recent years, a trend that some polling
37 suggests is linked to extreme weather exposure (Ballew et al., 2019; Pew Research Center, 2019;
38 Gallup, 2019). The percentage of Americans who believe that global warming will harm them
39 personally has risen faster than other climate opinions, such as beliefs about climate change’s
40 causes or support for climate policies (Ballew et al., 2019). Moreover, survey respondents also
41 say that experiencing or learning about climate change impacts leads them to worry more about
42 climate risks (EPIC, 2019; Deeg et al., 2019).
43 Prior social science research examining the presumed link between weather and climate
44 change opinions, however, has found mixed results. Values, cultural identities, and politics
45 tend to dominate direct experience as drivers of climate beliefs (Egan and Mullin, 2017; Mc-
46 Cright and Dunlap, 2011; Weber and Stern, 2011; Marquart-Pyatt et al., 2014; Mildenberger
47 and Leiserowitz, 2017). Correspondingly, descriptive analysis finds strong partisan polarization
48 about self-reported experience of global warming (Figure 1). Even at local scales, climate change
49 appears socially constructed and interpreted through ideological lenses, rather than driven by
50 individuals’ objective experiences of changes in weather and climate.
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Electronic copy available at: https://ssrn.com/abstract=3453287 51 Nonetheless, which kinds of direct experiences of a changing climate, if any, can overcome the
52 partisan divide remains an open question. Of the dozens of papers that assess the relationship
53 between a changing climate and public climate concern in the U.S. and abroad, many papers
54 show inconsistent or null results (Egan and Mullin, 2017; McCright and Dunlap, 2011; Weber
55 and Stern, 2011; Howe and Leiserowitz, 2013; Zaval et al., 2014; Weber and Stern, 2011; Li et al.,
56 2011; Zaval et al., 2014; Shao and Goidel, 2016; Marquart-Pyatt et al., 2014; Shao et al., 2014;
57 Mildenberger and Leiserowitz, 2017). While some studies demonstrate a link between weather
58 experiences and public opinion about climate change, effect sizes are small, generally stem from
59 very recent weather (i.e., over recent days or weeks), and/or are often short-lived (Konisky et al.,
60 2016; Egan and Mullin; Palm et al., 2017; Scruggs and Benegal, 2012; Kaufmann et al., 2017;
61 Brooks et al., 2014; Bergquist and Warshaw, 2019).
62 To date, efforts to study the relationship between objective climate indicators and subjec-
63 tive individual experiences have been constrained by the absence of comprehensive spatially and
64 temporally disaggregated climate and opinion indicators. Some studies have advanced the liter-
65 ature by using national panels to assess how the relationships between impacts and perceptions
66 change over time (Carmichael and Brulle, 2017; Brulle et al., 2012; Donner and McDaniels, 2013;
67 Scruggs and Benegal, 2012). However, these studies often assume that climate change is experi-
68 enced uniformly across the country. Similarly, time series studies that examine the relationship
69 between regional climate changes and individuals’ opinion implicitly assume that individuals
70 who live hundreds or even thousands of miles away from each other experience climate change
71 in the same way (Marquart-Pyatt et al., 2014; Zahran et al., 2006).
72 Other panel and cross-sectional work explicitly examines links between individual opinion
73 and local climate indicators (Konisky et al., 2016; Egan and Mullin; Brooks et al., 2014; Palm
74 et al., 2017; Scruggs and Benegal, 2012; Brody et al.; Goebbert et al.; Hamilton and Keim,
75 2009; Hamilton and Stampone; Deryugina, 2013; Howe and Leiserowitz, 2013; Mildenberger and
76 Leiserowitz, 2017; Howe, 2018). These papers match respondents more precisely with the climate
77 extremes they actually experience. However, their results are not generalizable outside the short
78 time frames covered by each study. Many of these studies also examine only a limited set of
79 climate indicators.
80 Finally, one recent study matches state-level climate indicators with aggregated estimates
81 of climate concern over time (Bergquist and Warshaw, 2019). This produces highly robust and
82 externally valid results because it precisely matches treatment and responses, and because it
83 examines a long time frame. Still, state-level treatments incorporate substantial uncertainty,
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Electronic copy available at: https://ssrn.com/abstract=3453287 Figure 1: Percentage of Republicans and Democrats who say they have personally experienced the effects of global warming, by congressional district, 2008-2018. Data from Mildenberger et al. (2017). For Democrats, estimates range from 50.7% in PA-5 to 72.4% in CA-13. For Republicans, estimates range from 14.8% in KY-1 to 48% in NY-15.
84 particularly for climate indicators whose occurrence varies substantially within states.
85 Another limitation of prior work is the focus on simple climate change indicators like mean,
86 minimum, or maximum temperature, which only capture a single dimension of multidimen-
87 sional changes. While many studies have examined integrated metrics like the National Oceanic
88 and Atmospheric Administration’s (NOAA) Climate Extremes Index, and the Palmer Drought
89 Severity Index (PDSI), these metrics have been devised for climate and weather science and do
90 not necessarily reflect the way people perceive changes in weather and climate. For example,
91 recognizing this, NOAA has recently constructed a heat index, which is a combination of both
92 temperature and humidity. This metric better reflects how human beings experience and per-
93 ceive a heat wave. The individual measure of temperature or moisture alone do not adequately
94 capture that lived and perceived experience. One study explored experience in an open-ended
95 fashion (using content analysis) and found three of the four most common ways that people say
96 they have personally experienced global warming were evident in the climatic record from their
97 community; this research examined only one county, however (Akerlof et al., 2013).
98 In this paper we address these challenges to measurement and causal identification by merg-
99 ing a unique georeferenced large-sample multi-wave survey dataset with high-resolution spatial
100 data on climate trends. We operationalize exposure to climate change with new precision by
101 matching respondents to the local climate indicators they experience based on their geocoded ad-
102 dresses. Anomalies and/or trends in seven climate indicators are constructed to simultaneously
103 assess responses to a variety of events and trends (described in detail below). Two tempera-
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Electronic copy available at: https://ssrn.com/abstract=3453287 104 ture indicators and three precipitation indicators are constructed to facilitate comparison with
105 previous research, which largely relies on similar simple measures. Two hybrid indicators are
106 also constructed based on the hypothesis that humans perceive weather and climate changes
107 holistically.
108 We also advance research in this area with a strong focus on causal identification. Previous
109 literature has often used cross-sectional models without assessing spatially confounding variables.
110 Weather patterns are often assumed to be exogenous (i.e., unrelated to demographics, attitudes,
111 or other variables of interest in a model), but this may not be the case, especially at state
112 or regional scales. Thus, scholars should, at a minimum, include geographic fixed effects in
113 their models to ensure valid comparability between individuals who experience a temperature
114 anomaly and those who do not (Deryugina, 2013; Egan and Mullin; Bergquist and Warshaw,
115 2019). We thus use a more sophisticated analytical approach to investigate the potential causal
116 relationship between experienced weather and perception, including a sensitivity analysis to
117 account for potential unobserved confounding variables.
118 2 Methods and Data
119 We analyze Americans’ climate views using a large national survey dataset (n=13,607) from
120 2008-2015 coupled with high-resolution monthly climate data. First, we spatially and tempo-
121 rally match this climate data to each respondent by selecting the climate indicators for the 12
122 months preceding the date each respondent was surveyed, in the county where each respon-
123 dent lives. Second, we identify individual-level drivers of climate opinion by assessing which
124 local climate conditions influence perceived personal experience with global warming. We use
125 alinearprobabilitymodelincludingcontrolsforindividual-levelsocialandpoliticaldetermi-
126 nants of belief in climate change and geographic-level fixed effects that account for unobserved
127 confounders. This allows us to causally identify the climate indicator(s) that alter perceived
128 experience of global warming. We demonstrate the robustness of our results by conducting a
129 sensitivity analysis for potential unobserved confounders.
130 Third, we model the spatiotemporal signature of these climate indicators on aggregate public
131 beliefs by incorporating the variable(s) identified in step one into a multilevel model with post-
132 stratification (MRP). The model describes the spatial distribution of perceived experiences with
133 global warming. We use it to assess the degree to which we can observe the spatial signature of
134 the climate indicator(s) in the geographic distribution of opinion. Whereas the individual-level
135 analysis identifies the causal relationship between a changing climate and individual climate con-
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Electronic copy available at: https://ssrn.com/abstract=3453287 136 cern, the multilevel model describes how climate events are associated with aggregated climate
137 concern at particular times and places.
138 2.1 Measuring perceived experience with climate change
139 Our public opinion data come from 12 waves of the nationally representative Climate Change
140 in the American Mind (CCAM) survey (Supplement A.1), conducted between 2008 and early
141 2015 for the Yale Program on Climate Change Communication (YPCCC) and George Mason
142 University (Mason) Center for Climate Change Communication (combined n = 13,607) (Ballew
143 et al., 2018). The surveys were conducted using IPSOS’ (formerly GfK) KnowledgePanel, an
144 online panel of members drawn from the US population using probability sampling methods.
145 Potential panel members were recruited using random digit dial and address-based sampling
146 techniques to cover essentially all (non-institutional) residencies. Those who chose to join the
147 panel but did not have Internet access were loaned computers and provided Internet access.
148 Respondents were weighted, post survey, on the basis of key demographics (age, gender, race,
149 education, income), to match US Census Bureau norms for US adults. We geolocate respondents
150 on the basis of their ZIP+4 codes or through jittered geocoded addresses (150 m radius) provided
151 by the survey contractors.
152 Our analysis focuses on respondents’ perceived experience with climate change. To measure
153 this concept, respondents were asked: “How strongly do you agree with the following statement:
154 I have personally experienced the effects of global warming?” The ordinal response scale was:
155 Strongly agree (4), Somewhat agree (3), Somewhat disagree (2), Strongly disagree (1). Data for
156 this question have been collected in 12 survey waves since 2008. This question exhibits both
157 temporal (Figure 2) and spatial (Figure 3) variation.
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Electronic copy available at: https://ssrn.com/abstract=3453287 Figure 2: Percentage of Americans who say they have personally experienced the effects of global warming, 2008-2015. Data are from the Climate Change in the American Mind (CCAM) surveys (Ballew et al., 2018) conducted by the Yale Program on Climate Change Communication and George Mason Center for Climate Change Communication.
I have personally experienced the effects of global warming
Difference from US average, 2015 (30%)
−15 to −12 −12 to −9 −9 to −6 −6 to −3 −3 to 0 0 to 3 3 to 6 6 to 9 9 to 12 12 to 23%
Figure 3: Projected percentage of residents in each county who say they have personally expe- rienced global warming in 2015 (difference from the national average of 30%). Perceived global warming experience is higher than average in more liberal counties, which generally tend to be urban and coastal, and to have higher-than-average non-white populations. Figure employs data estimated and validated by Howe et al. (2015) and Mildenberger et al. (2016).
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Electronic copy available at: https://ssrn.com/abstract=3453287 158 Our models also include individual-level controls for gender (1=M, 2=F); education (1=Less
159 than high school, 4=Bachelors or higher), age (1=18-24 years, 5=65 years and older), race/ethnicity
160 (White, non-Hispanic; Black, non-Hispanic; Hispanic; Other, non-Hispanic), income (1=Less
161 than $25,000, 6=$100,000 or more), political party (Democrat, Republican, Independent, No
162 party/Not interested), and political ideology (1=Very conservative, 5=Very liberal). In some
163 models, we also use a continuous measure of partisanship prepared by Catalist, a national voter
164 file vendor. This score was appended to the dataset at the individual level and estimates the
165 likelihood, on a scale from 0 to 100, of each respondent identifying as a Democrat rather than a
166 Republican, with 100 being the most likely to identify as a Democrat. To maintain participant
167 anonymity, Catalist and GfK matched the voter file data to respondents before the research
168 team received the survey data.
169 2.2 Measuring exposure to extreme weather and climate change
170 We measure changes in climate and weather using seven different indicators, including three
171 based on precipitation alone, two based on temperature alone, and two hybrid indicators that
172 combine temperature and precipitation conditions. The three precipitation indicators are the
173 number of dry days, defined as days with less than 0.01 inches of precipitation in 24 hours; the
174 number of heavy precipitation events, defined as the ratio of cumulative three-day precipitation
175 over the national 90th percentile to cumulative three-day precipitation; and the number of very
176 heavy precipitation events, defined the same way except using the 99th percentile of the national
177 distribution. The two temperature indicators are the number of hot days, defined as the number
178 of days per year with maximum temperature over 90 F; and seasonal maximum temperature,
179 defined as the average maximum temperature over the three months preceding the survey date.
180 Finally, the two hybrid indicators are snow days, defined as a day where maximum (not average)
181 temperature is below 2 C, the minimum temperature is below 0 C, and precipitation is above
182 0.1 inches; and hot dry days, defined as days where maximum temperature exceeds 90 and
183 precipitation is less than 0.01 inches in 24 hours. These last two definitions do not perfectly
184 encapsulate a universal definition of hot dry conditions nor do they differentiate between days
185 with freezing rain, sleet, snow, or rain that occurs in near-freezing conditions and/or transitions
186 to snow, as often occurs. However, at the scale of our analysis they provide a useful proxy for
187 such conditions.
188 We calculate trend variables for all seven climate indicators; these reflect long-term changes
189 in temperature and precipitation. We also calculate anomaly variables for four of the seven
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Electronic copy available at: https://ssrn.com/abstract=3453287 190 climate indicators. The anomalies reflect events such as heat waves, relatively snowless winters,
191 or heavy rain events. We thus use a total of eleven unique climate trend and anomaly variables,
192 summarized in Tables 1 and 2. The indicators are generated from high-resolution spatial data
193 in which daily precipitation and maximum and minimum temperature observations have been
194 gridded to a uniform spatial resolution of 1/16 (6km)(Livnehetal.,2013).Weidentifythegrid
195 cell closest to each respondent’s location and the twelve-month period immediately preceding
196 the date on which the respondent was surveyed. We then calculate each climate variable for
197 each respondent.
198 We use daily values to calculate annual and/or seasonal anomalies. Specifically, anomalies
199 reflect the deviation in the number of days measured by each indicator during the twelve months
200 prior to the survey date, relative to a 1971-2000 base period. For example, if the survey was
201 conducted in Sept 2012, then data from Sept 2011 to Aug 2012 is used to represent the most
202 recently experienced conditions for survey respondents. The anomaly is calculated as the differ-
203 ence between the Sept 2011-Aug 2012 value compared to the average value for the entire base
204 period of 1971-2000.
205 Trends are calculated as the long-term change in the number of days since 1980 to the date
206 of the survey. We calculate this using linear regressions of each indicator on time. For example,
207 if the survey was conducted in Sept 2012, we use data from August 1980 to August 2012 for each
208 grid cell. The trend variables are the unstandardized coefficients from simple linear regressions
209 using time as the independent variable and each climate indicator as the dependent variable.
210 The climate variables are scaled with respect to the time period for which they are measured
211 (e.g., one year for the hot dry day anomalies, three months for the seasonal maximum temper-
212 ature anomalies, etc.). This allows comparability over time and across variables. Each climate
213 variable is standardized to have a mean of 0 and a standard deviation of 1 within the matched
214 survey wave. The two precipitation variables are also log-transformed to reduce skewness.
215 2.3 The individual-level effect of exposure on perceived experience
216 At the individual level, we use linear probability models (LPM) to test the relative strength of
1 217 different weather and climate variables on perceived personal experience with global warming.
218 Similar to prior work (Egan and Mullin; Deryugina, 2013; Bergquist and Warshaw, 2019; Konisky
219 et al., 2016), our causal identification strategy rests on the exogenous assignment of weather
1We view our individual-level models as estimating the conditional expectation function, so estimation of the marginal effect of treatment on an outcome can be undertaken with either linear probability or limited dependent variable models (Angrist and Pischke, 2008, 94-99). We use LPM models to maximize transparency and interpretability.
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Electronic copy available at: https://ssrn.com/abstract=3453287 Climate Variable Definition Seasonal Tmax Anomaly C of Tmax in most recent season relative to 1971-2000 base Seasonal Tmax Slope Change in C of Tmax in most recent season relative to 1971-2000 base Hotdays Anomaly Prior year’s # days with Tmax > 90 F Hotdays Slope Change in # days with Tmax > 90 F Drydays Anomaly Prior year’s # days with daily Pr < 0.01” in 24 hrs Drydays Slope Change in # days with daily Pr < 0.01” in 24 hrs Hot & Dry Days Anomaly Prior year’s # days/yr with Tmax > 90 FandPr<0.01”in24hrs Hot & Dry Days Slope Change in # days/yr with Tmax > 90 FandPr<0.01”in24hrs Snow Days Slope Change in # days/yr with precipitation > 0.1 inches and Tmax < 2 C Heavy Precip Ratio of 3-day extreme pr/3-day total pr, 90th Percentile Very Heavy Precip Ratio of 3-day extreme pr/3-day total pr, 99th Percentile
Table 1: Climate indicators tested as potential predictors of personal experience with global warming.
Variables Unit Mean St.Dev. Min Max Median Climate Seasonal Tmax Anomaly C 0.268 1.338 5.734 6.833 0.122 Seasonal Tmax Slope Trend 0.008 0.026 0.174 0.145 0.009 Hotdays Anomaly Day 1.631 14.933 107.284 79.571 0.033 Hotdays Slope Trend 0.014 0.341 2.901 1.693 0.005 Drydays Anomaly Day 17.436 6.049 0.000 31.000 17.000 Drydays Slope Trend 0.017 0.532 2.867 2.373 0.039 Hot & Dry Days Anomaly Day 0.495 11.483 96.588 71.554 1.125 Hot & Dry Days Slope Trend 0.003 0.255 1.815 1.698 0.013 Snow Days Slope Trend 0.044 0.160 2.131 1.005 0.000 Heavy Precip Ratio Ratio 0.238 0.129 0.000 0.939 0.207 VeryHeavyPrecipRatio Ratio 0.030 0.039 0.000 0.470 0.017 Political Catalist partisan score Score 51.100 36.354 0.060 99.820 54.300
Table 2: Summary statistics for climate and political variables used for individual-level analysis.
220 experiences to members of the public. Conceptually, this means that, conditional on geography,
221 people do not choose to live in areas that are experiencing the effects of climate change because
222 of their political identities or other factors that also influence their perceptions of climate change.
223 Under the conditional ignorability assumption, we argue that treatment is arbitrarily assigned
224 once we include fixed effects for every region, state, and county. We also include a variety of
225 controls in our models, including demographics, party affiliation, and political ideology.
226 We use a series of balance checks to assess the conditional ignorability assumption for each of
227 our climate variables. Our balance checks (see Supplement) assess whether any individual-level
228 variables predict exposure to extreme weather and climate change. We regress the treatment
229 (climate) variables on the same demographic covariates used in our main models and on the state,
230 county, and regional fixed effects. These models allow us to assess whether, conditional on these
231 covariates and within each geographic unit, treatment assignment is independent of potential
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Electronic copy available at: https://ssrn.com/abstract=3453287 232 outcomes. A significant relationship between any of these covariates and our treatment variables
233 indicates that treatment assignment is unlikely to be as-if random.
234 We probe the conditional ignorability assumption for all trend and anomaly variables. We
235 also report the results of sensitivity analyses that explore the vulnerability of our conclusions to
236 additional unobserved confounders that may shape individual exposure to extreme weather and
237 climate indicators.
238 2.4 The spatial signature of exposure to extreme weather and climate
239 change
240 We extend our individual-level analysis to examine the spatial signature of climate change on
241 county-level models of perceived experience. Building on the individual-level model, we use
242 multilevel regression and post-stratification (MRP) to describe how climate variables are asso-
243 ciated with county-level beliefs about personal experience with climate change. MRP models
244 are a commonly-used method for estimating the spatial distribution of public opinion and in-
245 volve two steps (for more detailed treatments, see Park et al., 2006; Lax and Phillips, 2009;
246 Warshaw and Rodden, 2012; Buttice and Highton, 2013). The model employed here is based
247 on a well-validated model detailed in (Howe et al., 2015; Mildenberger et al., 2016). Our online
248 supplementary materials provide additional information about MRP methods and our models.
249 We add to our MRP model the hotdrydays climate variable, which has a causal effect on
2 250 perceptions in our individual-level analysis. We assess how the addition of this climate variable
251 affects the predictive accuracy of our multilevel model and examine specific climate events that
252 are associated with increased perceived experience with climate change. Whereas the individual-
253 level analysis allows us to examine the causal influence of different types of events (e.g., droughts,
254 heavy rains, etc.), the county-level analysis allows us to examine the predictive signature of
255 climate events at specific times and places.
256 3 Results
257 We begin by estimating the causal effect of exposure to weather shifts on individual perceptions
258 of global warming experience. A causal interpretation of our effects depends on a conditional 2When we incorporate this climate variable into the MRP model, we spatially weight the climate values by population densities (CIESIN, 2017) to account for the differential distribution of population in some (especially large) counties. Weighting is accomplished by multiplying the climate variables by a 2010 population density grid, which is first standardized and log transformed onto a 0-1 scale. Spatial weighting allows our estimates to reflect the climate conditions experienced by the population of each county, even when that distribution is uneven across space. This method captures climate changes that are more truly "local" than does simply averaging from many grid cells across the geographic area of a county.
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Electronic copy available at: https://ssrn.com/abstract=3453287 259 ignorability assumption: once we condition on geography, exposure to climate extremes is as-if-
260 randomly assigned. Some physically-driven phenomena coincide with well-known sociopolitical
261 patterns. For example, urban areas tend to be warmer than their neighboring suburbs, and
262 urban residents tend to be more liberal than those in nearby suburbs. Thus, we first use balance
263 tests to probe the assumption that climate variables are exogenous to social and political factors
3 264 that also drive an individual’s perception of global warming experience.
265 We find balance between treated and untreated units for our temperature and precipita-
266 tion anomaly variables, but we observe some imbalance for the various slope (trend) variables
267 (Supplemental Table A.2). Thus, consistent with our priors, the anomaly variables are very
268 likely exogenous whereas the trend variables may be subject to confounding due to spurious
269 relationships between regional climate trends and sociopolitical patterns. Overall, these tests
270 point to a causal relationship between climate anomalies and public perceptions. For complete-
271 ness, we test the relationship between perceptions and both climate trends and anomalies (Table
272 3). Based on poor support for the conditional ignorability assumption for the trend variables,
273 the results for these are best interpreted as indicating systematic association rather than direct
274 causal relationships.
275 Model results (Table 3) identify the relationships between perceived experience with global
276 warming and local climate variables. Model 1 includes only climate variables (Column 1 in
277 Table 3). Only the hot dry days anomaly has a statistically significant relationship with perceived
278 experience. A one standard deviation unit increase in hot dry days anomaly leads to a significant
279 0.0434 unit of increase in perceived experience (on a 1 to 4 scale). To examine the robustness of
280 these effects to the inclusion of other possible drivers of perceived experience, we add controls
281 for respondents’ race, age, sex, level of education, party identification, ideology, and household
282 income. The results, shown in Table 3, Column 2, remain largely unchanged. Using a limited
283 dependent variable model provides similar results (see Supplement Table A.3).
284 How substantively important is the effect of hot dry days on Americans’ perceived personal
285 experience with global warming? In Table 3, Column 3, we show the results from a model that
286 only uses this specific anomaly, demographic covariates, and a continuous measure of partisanship
287 for each respondent (see Section 2). The magnitude of the effect of climate is quite similar in
288 this model, and the continuous measure of partisanship helps us compare the effect size of
289 hot dry days with that of partisanship (widely perceived to be the primary driver of climate
290 concern (Egan and Mullin, 2017)). We standardize both variables for the analysis and find that
3Spatial variations in the climate variables tested in the individual-level analysis are presented in Supplement Figure A-2
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Electronic copy available at: https://ssrn.com/abstract=3453287 291 a1standarddeviation(11.5days)changeinexposuretohotdrydayanomalieshasthesame
1 292 impact on climate perceptions as a 8 standard deviation shift in partisanship, or 4.7 percentage
293 points – a modestly-sized effect.
Table 3: The effect of climate variables on perceived personal experience with global warming between 2008-2015. coefficients are provided for each variable and model. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Standard errors clustered at the state level.
Dependent variable Personally experienced global warming Model 1 Model 2 Model 3 Climate variables Seasonal Tmax anomaly 0.0194. 0.0165. Seasonal Tmax slope 0.00179 0.000686 Hot days anomaly 0.00678 0.00340 Hot days slope 0.0349 0.0212 Dry days anomaly 0.00457 0.00462 Dry days slope 0.0118 0.00333 Hot dry days anomaly 0.0434⇤ 0.0470⇤⇤ 0.0368⇤⇤⇤ Hot dry days slope 0.0183 0.0340 Snow days slope 0.0125 0.00317 Extreme ppt (90th) ratio 0.00882 0.00918 Extreme ppt (99th) ratio 0.000832 0.00264 Sociodemographics Age 0.00864 0.0216⇤⇤ Sex (female) 0.00720 0.0105 Education 0.0383⇤⇤⇤ 0.0599⇤⇤⇤ Household Income 0.0182⇤⇤⇤ 0.0204⇤⇤⇤ Hispanic 0.0868⇤⇤ 0.0695⇤ Black, Non-Hispanic 0.000454 0.0413 Other, Non-Hispanic 0.0208 0.00491 Republican 0.235⇤⇤⇤ Democrat 0.204⇤⇤⇤ No Party 0.0382 Ideology 0.161⇤⇤⇤ Catalist partisanship 0.282⇤⇤⇤
Intercept 1.934 1.884 2.065 Region FE YY Y State FE YY Y County FE YY Y Observations 13,607 13,607 12,082 R2 0.1594 0.267 0.249 Adjusted R2 0.0205 0.143 0.113
Note: ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01
294 We also examine whether the effect of hot dry days depends on an individual’s political ori-
295 entation (Figure 4). The differences between partisan group point estimates are not statistically
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Electronic copy available at: https://ssrn.com/abstract=3453287 Personally experienced GW
Democrat ● Liberal ●
Independent ● Moderate ●
Republican ● Conservative ●
0 0.05 0.1 0 0.05 0.1 Hot dry days anomaly Hot dry days anomaly
Figure 4: Marginal effects of party affiliation (left) and ideology (right) on the relationship between hot dry days and perceived experience of global warming from 2008-2015. All estimated effects are positive, and partisan group differences are not statistically different from one another. In addition, the confidence intervals overlap zero for Democrats, Independents, and Moderates, and thus are not statistically significantly different from zero for these groups.
296 significant either between Democrats and Republicans or between liberals and conservatives.
297 Thus it appears that both sets of partisans respond similarly to hot dry days.
298 Despite the plausibility of our causal identification strategy, one might remain concerned that
299 an unobserved confounder exists, even after geographic conditioning and controlling for diverse
300 sociopolitical variables. We use a bounding approach (see Supplemental Methods), to assess
301 the effect implied by biases at varying levels of confounding (Supplement Figure A-1). Given
302 that ideology is well-established as one of the primary drivers of climate change perceptions,
303 we assume a confounder exists with the same ability as ideology to explain residual variance in
304 the relationship between hot dry day anomalies and climate perceptions. We then estimate the
305 hypothesized effects of this unobserved confounder at different levels (while accounting for other
306 covariates). The results show that even if an unobserved confounder as influential as ideology
307 exists, our effect estimate remains robust. In fact, we would need a confounder more than five
308 times as predictive of residual variance (in both hot dry days and the outcome) as ideology to
309 compromise our identified causal effect. This analysis imposes a high standard on potentially
310 problematic factors.
311 At the county level, our multilevel model and poststratification (MRP) analysis reveals the
312 spatiotemporal associations between hot dry days and perceived experience (Figure 5). County-
14
Electronic copy available at: https://ssrn.com/abstract=3453287 313 level estimates of perceived experience are shown as differences from the national average both
314 without hot dry days in the model (Figure 5, first row) and with hot dry days in the model
4 315 (Figure 5, second row). Given the subtlety of the differences in the two models, we then
316 subtract the absolute values predicted from the model without a climate variable from the model
317 that includes hot dry days to produce model prediction differences (Figure 5, third row). Here
318 the model shows where hot dry days has a clear impact on county-level perceived experience.
319 Places and years where increased hot dry days are associated with higher rates of perceived
320 experience with global warming are shown in red, while darker gray counties reflect areas and
321 times where fewer hot dry days locally are associated with reduced perceived experiences of
322 global warming. Regional patterns are evident in almost every year, but the impacts appear
323 strongest in California in 2008, 2010, and 2014, in Texas in 2010 and 2011, and in the Midwest
324 in 2012. Hot dry day anomalies are particularly pronounced during the drought that initially
325 affected Texas and later extended north into the central Midwest; this event was widespread,
326 severe, and sustained, with substantial physical, social, and economic impacts. The modeled
327 effects of hot dry days on perceived experience mirror the spatial distribution of hot dry days in
328 each year (Figure 5, fourth row).
Original YCOM Model
34% 31% 35% 38% 33% 30% Personally experienced global warming (difference from US average)
-15 −12 −9 −6 −3 0 3 6 9 12 15% YCOM model + hot dry day anomalies
35% 31% 35% 38% 33% 30%
Model prediction difference −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6%
Hot dry day anomalies −24 −20 −16 −12 −8 −4 0 4 8 12 16 20 24
2008 2010 2011 2012 2013 2014
Figure 5: Spatiotemporal signature of the hot dry day anomaly, September 2012 through March 2015. Top row (A): perceived experience with climate change at the county level for years 2009- 2014 using the Yale Climate Opinion Maps model (Howe et al., 2015). Second row (B): estimates of experience with climate change using a model adding hot dry day anomalies at the county level. Third row: differences between (A) and (B). Bottom row: hot dry day anomalies by year.
4In the SI we show how inclusion of each of the potential climate predictors affects predictive mean standard error referenced against an independent (external) validation dataset.
15
Electronic copy available at: https://ssrn.com/abstract=3453287 329 4 Discussion
330 The high spatial resolution of our large individual-level dataset and our focus on causal inference
331 offers new insights into the relationship between objective climate trends and subjective expe-
332 riences of global warming. Of the eleven climate variables investigated, only an increase in hot
333 dry days significantly increases individuals’ perceptions that they have personally experienced
334 the effects of global warming. A one standard deviation increase in exposure to hot dry day
335 anomalies modestly increases the probability that an individual thinks that they have experi-
336 enced climate change. These results stand in contrast to previous studies that found null or
337 negligible effects of climate extremes on public experience, belief, and risk perceptions of global
338 warming (Marquart-Pyatt et al., 2014; Marlon et al., 2018; Mildenberger and Leiserowitz, 2017;
339 Palm et al., 2017), and align more closely with studies finding consistent though modest effects
340 (Howe et al., 2013; Bergquist and Warshaw, 2019; Egan and Mullin; Kaufmann et al., 2017; Shao
341 et al., 2014; Bohr, 2017). Robustness checks and a sensitivity analysis support the interpreta-
342 tion of this finding as causal. By contrast, we find no evidence that simple precipitation and
343 temperature anomalies or trends have had a measurable impact on perceived experience with
344 global warming.
345 Hot dry days may surpass other indicators in terms of its effects on perception for several
346 reasons. First, its effects occur at much broader scales than those of flooding or snow, which
347 therefore impact a larger population. In addition, hot dry days are associated with extreme heat
348 and drought, but also with wildfires, and thus can have far-reaching effects on diverse sectors,
349 such as agriculture, recreation, and tourism. In addition, extreme heat and drought can persist
350 for months or longer, thereby affecting not only individuals and communities, but also generating
351 economic losses nationally that are twice as high as flood events (NOAA, 2019).
352 Our results also reinforce the role of political orientation and demographic factors as im-
353 portant influences on perceived global warming experience. Consistent with prior research,
354 Democratic party affiliation and liberal ideology are substantially stronger than hot dry days
355 in predicting perceived experience with global warming (Howe and Leiserowitz, 2013; McCright
356 et al., 2014; Howe, 2018). However, although we did not find different effects of hot dry days on
357 perceived experience between partisan groups, the effect estimates were highest for Independents,
358 consistent with research showing that people whose views of global warming are least driven by
359 partisan thinking show evidence of experiential learning via weather and climate (Myers et al.).
360 White non-Hispanic individuals and those with higher incomes tend to report less experience
361 with the effects of global warming, after adjusting for party affiliation and ideology. Our results
16
Electronic copy available at: https://ssrn.com/abstract=3453287 362 suggest that the association between education, income, and race with experience is widespread
363 across the US and robust. Nonetheless, even after controlling for these factors, we do find that
364 Americans update their perceptions of global warming in response to exposure to hot dry days.
365 Our analysis also offers a methodological template for future research on this topic. Sev-
366 eral prior studies have demonstrated an association between climate changes and individuals’
367 perceptions, but only one previous study has empirically probed the conditional ignorability
368 assumption (Egan and Mullin). Most studies assume that weather events are exogenous by na-
369 ture, even though some climatic phenomena such as the urban heat island effect are correlated
370 with political and social characteristics that also predict climate beliefs. We use balance tests
371 to assess the exogeneity of our climate variables and demographic predictors, and sensitivity
372 analyses to assess the robustness of our results to potential confounders. Using the balance tests
373 and sensitivity analyses for support, we reach a robust conclusion about the causal influence
374 of hot dry days on Americans’ perceived experience with global warming. Future work should
375 also carefully consider how political orientations and demographic characteristics of a popula-
376 tion might coincide with climate trends, to minimize the risk of making causal claims based on
377 spurious correlations.
378 This analysis also provides a stronger foundation upon which future work investigating the
379 relationships between experience and extreme weather can build. Our analysis spans eight years
380 and precisely matches individuals to the weather anomalies they experience. The high spatial
381 resolution of our climate data and long time series of climate public opinion data enable us to
382 precisely determine which climate events the public interprets as indicators of a changing climate.
383 Specifically, multilevel modeling of the data allows the spatial visualization of climate changes.
384 Coupled with the causal modeling, we can now see where and when the public perceived specific
385 climate changes as “global warming.” Additionally, we gain analytical leverage from using an
386 integrated temperature and moisture climate indicator that appears to better reflect changes that
387 people actually perceive and recall. The significance of the hotdrydays metric suggests a need
388 to develop better measures of climate change using integrated and cumulative metrics. Had we
389 tested only recent temperature anomalies, cumulative hot days, and/or dry days, we would have
390 found null results. Future research should explore integrated measures of environmental change
391 that are designed to match human perception (e.g., the "feels-like temperature metric used
392 in many weather apps) rather than ready-made climatological indices designed for agriculture
393 (e.g., the PDSI). Moreover, as extreme weather worsens, the associations people make to global
394 warming and climate change may evolve, and different metrics may become more important
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Electronic copy available at: https://ssrn.com/abstract=3453287 395 in different regions simultaneously. A better understanding of how effects accumulate is also
396 necessary.
397 We also find that the public responds more readily to climate anomalies than to trends.
398 Future work might incorporate other dimensions such as the magnitude and recency of events to
399 produce climate change indicators that are more closely linked to perceived personal experience
400 with global warming. The duration and accumulation of the effects we find also warrant more
401 attention. Prior work shows that the public response to weather events is short-lived (Egan and
402 Mullin; Konisky et al., 2016; Kaufmann et al., 2017; Palm et al., 2017; Scruggs and Benegal,
403 2012) or degrades if a warming trend is not sustained (Bergquist and Warshaw, 2019). We do
404 not know, though, how the duration of the effect might change as climate events become more
405 frequent or more severe. Will they translate into support for political action? Economic losses
406 from climate change and natural hazards are on the rise (Hsiang et al., 2017). Whether the
407 increasing impacts of climate change will durably influence political incentives to reduce the
408 climate threat remains an open empirical question.
409 Finally, our data, which span from 2008-2015, omit several recent extreme weather seasons,
410 which future research should incorporate. For example, we do not examine the effect of extreme
411 weather events such as the powerful North Atlantic hurricane season of 2017, the U.S. western
412 wildfires from 2016 to 2018, or the Midwest flooding in 2019. Further developing our knowledge
413 about how climate change manifests locally in different parts of the country, which of these
414 manifestations are most salient to the public, and whether and when such changes are considered
415 by the public as evidence of global warming, will enable communicators, educators, and others
416 to better explain how climate change affects us all, and what can be done to mitigate and adapt
417 to its ongoing impacts.
418 5 Conclusions
419 Our understanding of how the climate system is changing has far outpaced our understand-
420 ing of how the public perceives and interprets these changes. Yet, personal experience with
421 environmental changes may be a critical motivating factor in protecting ourselves and society
422 from future damages. What makes people think they have experienced global warming? While
423 politics remain the primary driver of global warming risk perceptions in the U.S., we find a
424 modest causal effect of actual climatic changes on perceived experience with global warming. In
425 particular, we find a clear and robust influence of an integrated measure of climate change on
426 perceived personal experience: hot dry days. No other variables, whether shorter-term temper-
18
Electronic copy available at: https://ssrn.com/abstract=3453287 427 ature anomalies or trends, recent heavy precipitation, snow day trends, dry days, or hot days
428 alone (anomalies or trends) appear to significantly influence public perceptions.
429 Our analysis suggests that designing climate indicators that align more closely with human
430 perceptions of environmental change and stress can yield new insights into public engagement
431 with global warming. It also points to some limits in public understanding of the climate
432 threat. The combination of extreme heat and humidity can be even more deadly than dry heat,
433 particularly in urban areas and in places unaccustomed to extreme heat (Howe et al., 2019). Yet,
434 our research suggests that it is dry heat that the public links with global warming. Experimental
435 testing is needed to identify specific messages that might support a stronger understanding of
436 the causal relationships between climate change and its impacts among vulnerable publics.
437 As attribution science advances, new opportunities are emerging to help the public directly
438 contextualize their lived experiences within a scientific framework. This framework can increas-
439 ingly quantify the extent to which human-induced climate change is exacerbating or increasing
440 the probability of summer heat and prolonged dry conditions. From the first formal individual
441 event analysis of the European heatwave of 2003, which showed that human-induced warming
442 had at least doubled the risk of such an event occurring (Stott et al., 2004), to the analysis of
443 the June 2019 European heat wave which showed it was made at least five times more likely
444 and 4 C hotter as a result of a changing climate (van Oldenborgh et al., 2019), scientists are
445 now able to provide the public with clear statements about the relationship between long-term
446 climate trends and individual weather events. Our study emphasizes the importance of doing
447 so. Experiences with certain types of climate impacts have already increased people’s awareness
448 of and perception that they have experienced the impacts of climate change. Communicating
449 these links and helping people appropriately interpret their direct experience as an impact of
450 climate change may greatly support public engagement with the issue.
19
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Electronic copy available at: https://ssrn.com/abstract=3453287 611 A Electronic Supplemental Material: Methods Appendix
612 A.1 Survey Data
613 Table A.1 gives details on the time, mode, and sample size of public opinion surveys used to
614 conduct this analysis.
Survey Data Time (and mode) Sample October 2008 (online) 2164 January 2010 (online) 1001 June 2010 (online) 1024 May 2011 (online) 1010 November 2011 (online) 1000 April 2012 (online) 1008 September 2012 (online) 1061 April 2013 (online) 1045 December 2013 (online) 830 April 2014 (online) 1384 October 2014 (online) 1275 March 2015 (online) 1263
Table A.1: Dataset time, mode and sample sizes for public opinion surveys used in this analysis
615 A.2 Balance Tests on Treatment Assignment
616 Apriori,weareskepticalthattrendvariablesarefullyexogenoustopoliticalidentities,evenafter
617 conditioning on local geographies. This is because, within local geographies and by definition,
618 trends vary less over a set time period than anomalies. Trends represent long-term shifts in
619 climate indicators, whereas anomalies represent the deviation from a base period over a set
620 time interval. We therefore expect the spatial pattern of climate trends to vary less over our
621 eight years of surveys than the spatial pattern of climate anomalies. As such, clusters of like-
622 minded individuals living in particular counties may experience more similar climate trends
623 than anomalies. In turn, unobserved confounding variables that drive perceptions of global
624 warming experiences are more likely to be correlated with these between-county differences than
625 temperature anomalies. Conversely, climate anomaly variables are more credibly assigned in a
626 conditionally-as-if-random way.
627 Table A.2 shows the results of our balance tests. We regress treatment status (e.g. climate
628 variables) on diverse sociopolitical variables, conditional on geography. If the conditional ignor-
629 ability assumption holds, none of these covariates should be significantly predictive of treatment
630 assignment. As we suspected, some covariates are predictive of trend variables, even after con-
A-1
Electronic copy available at: https://ssrn.com/abstract=3453287 631 ditioning on geography. However, we do not find evidence that these covariates predict anomaly
variables.
Table A.2: Results balance check tests. Coefficients are from regression of covariates onto the eleven treatment variables. Significant effects suggest the presence of confounding variables and likely violation of the conditional ignorability assumption.
Dependent variable Tmax Tmax HD HD DD DD HDD HDD Snowday Precip Precip anomaly slope anomaly slope anomaly slope anomaly slope slope 90% 99%
Ideology 0.010 0.003 0.004 0.012⇤ 0.004 0.013⇤⇤ 0.003 0.003 0.004 0.028⇤⇤ 0.012 (0.010) (0.008) (0.010) (0.006) (0.008) (0.006) (0.009) (0.007) (0.006) (0.011) (0.011)
Republican 0.008 0.033 0.009 0.025 0.007 0.018 0.035 0.026 0.007 0.062⇤ 0.025 (0.031) (0.024) (0.030) (0.019) (0.023) (0.018) (0.029) (0.020) (0.019) (0.033) (0.033)
Democrat 0.030 0.040⇤ 0.008 0.0003 0.008 0.003 0.018 0.012 0.020 0.029 0.024 (0.031) (0.024) (0.030) (0.019) (0.023) (0.017) (0.029) (0.020) (0.019) (0.033) (0.033)
No Party 0.020 0.044 0.007 0.030 0.048⇤ 0.021 0.002 0.029 0.002 0.020 0.033 (0.038) (0.029) (0.037) (0.023) (0.028) (0.021) (0.035) (0.025) (0.024) (0.040) (0.041)
Hispanic 0.004 0.070⇤⇤⇤ 0.027 0.081⇤⇤⇤ 0.015 0.046⇤⇤ 0.010 0.088⇤⇤⇤ 0.027 0.037 0.078⇤⇤ (0.033) (0.025) (0.032) (0.020) (0.024) (0.019) (0.030) (0.021) (0.020) (0.035) (0.035)
Black, 0.020 0.041 0.046 0.043⇤⇤ 0.010 0.049⇤⇤ 0.023 0.056⇤⇤ 0.006 0.046 0.042 Non-Hispanic (0.035) (0.027) (0.033) (0.021) (0.026) (0.019) (0.032) (0.022) (0.021) (0.037) (0.037)
Other, 0.031 0.065⇤⇤ 0.033 0.055⇤⇤ 0.006 0.008 0.009 0.029 0.039⇤ 0.023 0.014 Non-Hispanic (0.038) (0.029) (0.036) (0.023) (0.028) (0.021) (0.035) (0.024) (0.023) (0.040) (0.040)
Income 0.004 0.006 0.002 0.001 0.001 0.004 0.004 0.002 0.001 0.001 0.003 (0.005) (0.004) (0.005) (0.003) (0.004) (0.003) (0.005) (0.003) (0.003) (0.006) (0.006)
Age 0.009 0.005 0.003 0.005 0.003 0.008⇤ 0.001 0.002 0.008⇤ 0.016⇤⇤ 0.019⇤⇤ (0.008) (0.006) (0.007) (0.005) (0.006) (0.004) (0.007) (0.005) (0.005) (0.008) (0.008)
Constant 0.088 0.599⇤⇤ 0.879⇤⇤ 1.206⇤⇤⇤ 0.374 0.282 0.671⇤⇤ 0.166 0.087 0.074 0.042 (0.358) (0.276) (0.345) (0.219) (0.266) (0.201) (0.330) (0.231) (0.220) (0.378) (0.380)
Region FE Y Y Y Y Y Y Y Y Y Y Y State FE Y Y Y Y Y Y Y Y Y Y Y County FE Y Y Y Y Y Y Y Y Y Y Y Observations 13,866 13,866 13,869 13,869 13,888 13,869 13,869 13,869 13,867 13,867 13,867 Adjusted R2 0.113 0.474 0.181 0.669 0.512 0.720 0.246 0.629 0.668 0.013 0.001
Note: ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01
632
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Electronic copy available at: https://ssrn.com/abstract=3453287 633 A.3 Robustness Tests of Model Results
634 Table A.3 re-estimates the paper’s core specifications using a limited dependent variable ap-
635 proach, rather than linear probability models. The results are unchanged. Sensitivity analysis
636 of the model to potentially unobserved confounding variables (described in Figure A-1) also
637 demonstrates model specification robustness.
Table A.3: Robustness check using limited dependent variable model. The results are based on logit estimates where the dependent variable is equal to 1 if the respondent said they have personally experienced global warming, and 0 if they have not.
Binary dependent variable Personally experienced global warming Model 1 Model 2 Model 3 Climate variables Seasonal Tmax anomaly 0.00622 0.00261 Seasonal Tmax slope 0.0296 0.0305 Hot days anomaly 0.00581 0.0163 Hot days slope 0.0540 0.0625 Dry days anomaly 0.00328 0.00387 Dry days slope 0.0485 0.0340 Hot dry days anomaly 0.0775⇤ 0.101⇤⇤ 0.0604⇤⇤ Hot dry days slope 0.0845 0.0830 Snow days slope 0.00723 0.0262 Extreme ppt (90th) ratio 0.00413 0.00237 Extreme ppt (99th) ratio 0.00257 0.00533 Sociodemographics Age 0.0171 0.0171 Sex (female) 0.0272 0.0272 Education 0.101⇤⇤⇤ 0.101⇤⇤⇤ Household Income 0.0389⇤⇤ 0.0393⇤⇤ Hispanic 0.128. 0.0852 Black, Non-Hispanic 0.0373 0.151⇤ Other, Non-Hispanic 0.0808 0.0236 Republican 0.485⇤⇤⇤ Democrat 0.424⇤⇤⇤ No Party 0.0833 Ideology 0.326⇤⇤⇤ Catalist partisanship 0.569⇤⇤⇤
Intercept 1.261 1.455 1.535 Observations 13,906 13,516 12,332
Note: ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01
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Electronic copy available at: https://ssrn.com/abstract=3453287 0.4 0.3 confounder(s) with the outcome confounder(s) 0.2 5 ideology_c (0.05) of unobserved 0.1 2 R
Unadjusted (0.049) 0.0
0.0 0.1 0.2 0.3 0.4
2
Hypothetical partial Hypothetical partial R of unobserved confounder(s) with the treatment
Figure A-1: Sensitivity analysis for the estimated effect of hot dry days on perceptions of per- sonal exposure to climate change, using a model with covariates and geographic fixed effects as described in the text. The horizontal axis specifies a hypothesized strength of association be- tween confounding covariates and the treatment (hot dry days anomaly at the individual level), in terms of the partial variance in the climate indicator explained by the confounding covariate after accounting for other covariates. The vertical axis hypothesizes how strongly confounding is related to the outcome, perception of personal exposure, again in terms of partial variance explained. The adjusted effect implied by each level of hypothesized confounding is shown by the contours. The conventional estimate assumes zero confounding, and is shown in the bottom left corner (“Unadjusted”). The red diamond shows the effect on our estimate if a hypothetical unobserved confounding covariate existed that had five times the predictive power as ideology, the strongest observed predictor that we can think of. Even a confounding covariate this strong would not attenuate the existence of a causal relationship between hot dry day anomalies and perceptions of climate change experience, which would still be significant (to the bottom left of the dashed red line).
638 A.4 Additional Details on MRP modelling
639 Multilevel regression and poststratification (MRP) is increasingly being used to model subna-
640 tional attitudes and political preferences from national polls (for more detailed treatments, see
641 Park et al., 2006; Lax and Phillips, 2009; Warshaw and Rodden, 2012; Buttice and Highton,
642 2013). An MRP analysis involves two stages. First, individual survey responses are modeled
643 as a function of demographics, location, and geographic covariates (the "multilevel regression
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Electronic copy available at: https://ssrn.com/abstract=3453287 644 model"). In this way, unique geographic variability from local residents (while controlling for
645 their demographic characteristics) can be captured and used to estimate opinions for nearby
646 places. The second step is poststratification, where the fitted estimates for each demographic-
647 geographic respondent type are weighted by their actual (census-based) population percentages
648 for a given area. Percentages of respondents with a particular preference can then be estimated
649 for every state, county, or other geographic unit.
650 In our multi-level regression model, we use individual-level demographics and climate vari-
651 ables, group-level geographic characteristics and grouped random effects. Race, gender, educa-
652 tional attainment, and an interaction term of race by gender by education are treated as random
653 effects. Individuals are also grouped geographically according to their state and county to eval-
654 uate the random intercepts. County-level geographic characteristics are used as fixed effects
655 predictors to improve model fit. The hotdrydays anomalies variable is used as both fixed and
656 random effects: the fixed effect identifies the predictive power of this climate variable whereas
657 the random effect (varying slope) allows for regional variations in the effects of hot dry days.
For each individual i,themodelisspecifiedas:
1 race education gender wave race.education.gender county Pr(yi = 1) = logit ( 0 + ↵j[i] + ↵k[i] + ↵l[i] + ↵m[i] + ↵j[i],k[i],l[i] + ↵c[i] )
658 where
↵race N(0, 2 ),forj=1,...,4 j ⇠ race
↵education N(0, 2 ),fork=1,...,4 k ⇠ education
↵gender N(0, 2 ),forl=1, 2 l ⇠ gender
↵wave N(0, 2 ),form=1,...,12 m ⇠ wave
↵race.education.gender N(0, 2 ),forj=1,...,4; k =1,...,4; l =1, 2 j,k,l ⇠ race.education.gender
659 Each variable is indexed over individual i and over response categories j, k, l, m, and c for
660 race, education, gender, wave, and county-level geography variable, respectively. The county
661 variable is further modeled as:
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Electronic copy available at: https://ssrn.com/abstract=3453287 ↵county N(↵region + ↵state + drive drive + samesex samesex + carbon carbon + pres pres c ⇠ r[c] s[c] · c · c · c · c + hotdrydays hotdrydays , 2 ),forc=1,...,1889 · i county
662 where
↵region N( hotdryday hotdryday , 2 ),forr=1,...,9 r ⇠ · i region
↵state N(0, 2 ),fors=1,...,49 s ⇠ state
663 County-level covariates include the percentage of individuals who drive alone in a given
664 county, the percentage of same-sex households in a given county, the level of point source carbon
665 dioxide emissions in a given county, and the 2012 Democratic Presidential vote share in a given
666 county. These covariates have shown to be predictive of climate beliefs and behaviors in other
667 studies (Howe et al., 2015; Mildenberger et al., 2016).
668 Other than the geographic covariates, hotdrydays anomalies is retained in the model due
669 to its strong and consistent significance identified in the individual-level analyses as well as
670 in results from preliminary analyses (not shown) exploring other dependent variables, such as
671 global warming beliefs, risk perception and policy support. Hotdrydays is calculated as the
672 difference in the number of days with maximum temperature over 90 Fandprecipitationless
673 than 0.01 inches in 24 hours relative to a 1971-2000 base period. Examples of the climate data
674 are visualized in Figure ??.
675 For post-stratification, we use 2012 5-year American Community Survey data cross-tabulated
676 by education attainment, gender, and race/ethnicity across all counties. Hotdrydays,matched
677 to the survey wave, is weighted by population and aggregated at the county level before merging
678 with the census cross-tabs. Our fitted model is then used to estimate the average opinion of each
679 demographic-geographic individual type, for each of the survey waves. Specifically, the random
680 slope of hotdrydays captures the different effects in each region in response to local changes. For
681 instance, the model specifically estimates the average response of a White male with a Bachelor’s
682 degree or higher living in the West-South Central region in April 2013.
683 We validate our MRP results for individuals who report personally experiencing global warm-
684 ing using two methods: 1) an internal cross-validation technique and 2) an external validation
685 (i.e., by comparison with an independent dataset collected via phone by SRBI in 2013).
686 For our internal cross-validation, we compare our MRP estimates to estimates derived from
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Electronic copy available at: https://ssrn.com/abstract=3453287 687 disaggregation (the sample averages for each geographical subunit) (Pacheco, 2011). Using
688 repeated simulations, subsamples of varying sizes were randomly selected from a large-population
689 state and used to simulate the samples of smaller provinces or districts A-3. At the state level,
690 this procedure operates as follows:
691 We draw 99 random samples of size n from the state with the greatest number of respon- •
692 dents (California). We add this sampled data to all non-Californian data in our dataset. In
693 effect, we are simulating California as if it were a small state in our dataset with a limited
694 number of direct observations. What we want to do is compare model performance given
695 this limited amount of revealed data about Californians with the actual mean Californian
696 support observed across our entire California dataset. We call this dataset that combines
697 non-Californian data with a Californian random sample of size n the “training set”.
698 We run an MRP model using this training dataset, and use this model to predict support •
699 for a given opinion among Californian Republicans. We compare the predicted opinion
700 from the training set with the observed mean of the random sample of Californians of size
701 n. We calculate the mean absolute error of the MRP prediction against the simple sample
702 average.
703 We repeat steps 1 and 2 for the 25th, 50th, 75th and 90th percentile of state sample sizes •
704 (n = 7, 20, 63, 133). We also track the mean absolute error as compared to the average of
705 sampled Californians.
706 We repeat steps 1 through 3 for Florida, and for two sample questions. •
707 For the disaggregation method, we estimate the opinion levels for each state using only
708 the data from respondents in the state. Estimates using the disaggregation method are very
709 unreliable for states with few respondents. For states with large numbers of respondents, such
710 as California and Florida, the disaggregated estimates are closer to the MRP estimates. The
711 strength of the MRP approach at low state-level sample sizes is evidenced by its smaller error as
712 compared with disaggregation, while for larger sample sizes the MRP estimates quickly converge
713 to the disaggregation estimates (see Fig A-3). In some cases of states with very large sample
714 sizes, disaggregation can produce smaller estimates than MRP because of the tendency for MRP
715 estimates to regress towards mean values. Because extremely few states have such large numbers,
716 however, MRP overwhelmingly produces better estimates than disaggregation when downscaling
717 for the entire US.
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Electronic copy available at: https://ssrn.com/abstract=3453287 Hot dry day anomalies −24 −20 −16 −12 −8 −4 0 4 8 12 16 20 24
Dry day trends −1.25 −1 −0.75 −0.5 −0.25 0 0.25 0.5 0.75 1 1.25
Seasonal tmax anomalies −5 −4 −3 −2 −1 0 1 2 3 4 5
Snow day trends −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6
Extreme/total precipitation −5 −4 −3.6 −3.2 −2.8 −2.4 −2 −1.6 −1.2 −0.6
2008 2010 2011 2012 2013 2014
Figure A-2: Climate variables.
718 To further validate our results, we compared our MRP estimates with results from four
719 state-level surveys conducted using phone surveys through SRBI A-3 A-4.
720 We use external validation to compare the predictive accuracy of different models each con-
721 structed with only one of the climate variables considered. We compare these results with a
722 base model that includes no climate variables. Results are calculated using mean county-level
723 estimates for four states and two cities. The validation data come from six representative tele-
724 phone surveys (conducted by SRBI) for CA, CO, OH, and TX and two cities (San Francisco,
725 Columbus). These surveys used the same item wording as the online panel surveys, and they
726 were administered concurrently with the 2013 nationally representative YPCCC/GMU survey.
727 Figure A-5 compares the differences between the base predictive model without any climate
728 variables with results from the different models that each include only one of the weighted climate
729 variables. We find that the hotdrydays variable improves estimates of perceived experience with
730 global warming as compared to the base model for Texas and Ohio (including Columbus), but
731 other climate indicators – specifically Tmax in California and heavy precipitation or snowdays
732 outperform the hotdrydays indicator in some cases. These results highlight the diverse expression
733 of climate change in different parts of the country and suggest that while the severe drought
734 had the largest discernable impact on public experience with global warming between 2008-2015,
735 these results could change over time. In addition, these results only reflect validation for a single
736 year (2013).
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Electronic copy available at: https://ssrn.com/abstract=3453287 Figure A-3: Comparison between MRP and Disaggregation methods using the YCOM+Climate model for four states with the largest populations.
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Electronic copy available at: https://ssrn.com/abstract=3453287 Figure A-4: Differences between SRBI 2013 state-level values for California, Colorado, Ohio, Texas, and the national average, compared with estimated values based on the YCOM+Climate and the original YCOM model (Howe et al., 2015) without hotdrydays. Prediction differences for both models are also shown. MRP estimates are also from 2013.
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Electronic copy available at: https://ssrn.com/abstract=3453287
CA TX 3
0 ●
−3 ● ● ● ● ● ● ● ● ● ● ● −6 ● ● n = 800 n = 800 −9 CO OH 3 ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● −3
−6 n = 798 n = 800 −9 San Francisco Columbus
external validation dataset validation external 3 − ● ● ● 0 ● ● ● ●
−3 ● ● −6 ● ● ● ● n = 700 n = 700 −9 ● Model estimates Mean Absolute Error Base TmaxDryday ● Hotdryday Precip90Precip99Snowday 3 ● ● ● ● ● ● 0
−3
−6
−9
Base TmaxDryday Hotdryday Precip90Precip99Snowday
Figure A-5: Validation against independent state and city-level survey data in 2013. ’Mean Absolute Error’ is the average across all 6 other cases.
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Electronic copy available at: https://ssrn.com/abstract=3453287