1 Hot dry days increase perceived experience with global

2 warming

∗1 1 2 1 3 Jennifer R. Marlon , Xinran Wang , Matto Mildenberger , Parrish Bergquist , 3 4 5 6 4 Sharmistha Swain , Katharine Hayhoe , Peter D. 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 March 15, 2021

∗Corresponding author: [email protected]; 203-436-2598

1 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 perceived experiences

23 of global warming in the United States. Using a large national survey dataset (n=13,607),

24 we compare Americans’ experiences of climate with corresponding trends in seven different

25 high-resolution climate indicators for the period 2008 to 2015. We find that increases in

26 hot dry day exposure significantly increases individuals’ perceptions that they have person-

27 ally experienced global warming. We do not find robust evidence that other precipitation

28 and temperature anomalies have had a similar effect. We also use multilevel modeling to

29 explore county-level patterns of perceived experiences with climate change. Whereas the

30 individual-level analysis describes a likely causal relationship between a changing climate

31 and individuals’ perceived experience, the multilevel model depicts county-level changes in

32 perceived experience resulting from particular climate trends. Overall, we find that expo-

33 sure to hot dry days, has a modest influence on perceived experience, independent of the

34 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 increasing exposure to a changing climate (Ballew et al., 2019; Pew Research

38 Center, 2019; Gallup, 2019). The percentage of Americans who believe that global warming

39 will harm them personally has risen faster than other climate opinions, such as beliefs about

40 climate change’s causes or support for climate policies (Ballew et al., 2019). Moreover, survey

41 respondents also say that experiencing or learning about climate change impacts leads them

42 to worry more about climate risks (EPIC, 2019; Deeg et al., 2019). Here we focus on how

43 Americans’ perceived experiences with global warming across the country compare with local

44 measurements of a broad suite of climate changes. We focus on perceived experience rather

45 than on attitudes about concern, seriousness, or policy preferences because direct experience

46 influences are precognitive processes and thus precede the development of beliefs and attitudes

47 about global warming (Weber, 2006). Further developing our understanding of how physical

48 environmental changes influence perceived experience is thus fundamental to our understanding

49 of global warming beliefs and attitudes.

50 Prior social science research examining how weather and climate change experience influences

2 51 climate risk perceptions and other opinions, however, has found mixed results. Values, cultural

52 identities, and politics tend to dominate over direct experience as drivers of climate beliefs, con-

53 cerns, and policy preferences (Egan and Mullin, 2017; McCright and Dunlap, 2011; Weber and

54 Stern, 2011; Marquart-Pyatt et al., 2014; Mildenberger and Leiserowitz, 2017). Correspondingly,

55 descriptive analysis finds strong partisan polarization about self-reported experience of global

56 warming (Figure 1). Even at local scales, one’s experience of climate change appears socially con-

57 structed and interpreted through ideological lenses, rather than driven by individuals’ objective

58 experiences of changes in weather and climate.

59 Nonetheless, which kinds of direct experiences of a changing climate, if any, can overcome the

60 partisan divide remains an open question. Of the dozens of papers that assess the relationship

61 between a changing climate and public climate concern in the U.S. and abroad, many papers

62 show inconsistent or null results (Egan and Mullin, 2017; McCright and Dunlap, 2011; Weber

63 and Stern, 2011; Howe and Leiserowitz, 2013; Zaval et al., 2014; Weber and Stern, 2011; Li et al.,

64 2011; Zaval et al., 2014; Shao and Goidel, 2016; Marquart-Pyatt et al., 2014; Shao et al., 2014;

65 Mildenberger and Leiserowitz, 2017). While some studies demonstrate a link between weather

66 experiences and public opinion about climate change, effect sizes are small, generally stem from

67 very recent weather (i.e., over recent days or weeks), and/or are often short-lived (Konisky et al.,

68 2016; Egan and Mullin, 2012; Palm et al., 2017; Scruggs and Benegal, 2012; Kaufmann et al.,

69 2017; Brooks et al., 2014; Bergquist and Warshaw, 2019). Given that the impacts of a changing

70 climate on temperature and rainfall patterns, seasonal shifts, drought and other extreme weather

71 events are increasingly evident, a better understanding of how Americans are experiencing these

72 changes is warranted.

73 To date, efforts to study the relationship between objective climate indicators (i.e., param-

74 eters that reflect a particular dimension of a changing climate, such as temperature) and sub-

75 jective individual experiences have been constrained by the absence of comprehensive spatially

76 and temporally disaggregated climate and opinion data. Some studies have advanced the liter-

77 ature by using national panels to assess how the relationships between impacts and perceptions

78 change over time (Carmichael and Brulle, 2017; Brulle et al., 2012; Donner and McDaniels, 2013;

79 Scruggs and Benegal, 2012). However, these studies often assume that climate change is experi-

80 enced uniformly across the country. Similarly, time series studies that examine the relationship

81 between regional climate changes and individuals’ opinion implicitly assume that individuals

82 who live hundreds or even thousands of miles away from each other experience climate change

83 in the same way (Marquart-Pyatt et al., 2014; Zahran et al., 2006).

3 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 Other panel and cross-sectional work explicitly examines links between individual opinion

85 and local climate indicators (Konisky et al., 2016; Egan and Mullin, 2012; Brooks et al., 2014;

86 Palm et al., 2017; Scruggs and Benegal, 2012; Brody et al., 2008; Goebbert et al., 2012; Hamil-

87 ton and Keim, 2009; Hamilton and Stampone, 2013; Deryugina, 2013; Howe and Leiserowitz,

88 2013; Mildenberger and Leiserowitz, 2017; Howe, 2018). These papers match respondents more

89 precisely with the climate extremes they actually experience. However, their results are not

90 generalizable outside the short time frames covered by each study. Many of these studies also

91 examine only a limited set of climate indicators.

92 Finally, one recent study matches state-level climate indicators with aggregated estimates

93 of climate concern over time (Bergquist and Warshaw, 2019). This produces highly robust and

94 externally valid results because it precisely matches treatment and responses, and because it

95 examines a long time frame. Still, state-level treatments incorporate substantial uncertainty,

96 particularly for climate indicators whose occurrence varies substantially within states.

97 Another limitation of prior work is the focus on simple climate change indicators like mean,

98 minimum, or maximum temperature, which only capture a single dimension of multidimensional

99 changes. While many studies have examined integrated metrics like the National Oceanic and

100 Atmospheric Administration’s (NOAA) Climate Extremes Index, and the Palmer Drought Sever-

101 ity Index (PDSI), these metrics have been devised for climate and weather science and do not

102 necessarily reflect the way people perceive changes in weather and climate. Recognizing this,

103 NOAA calculates a heat index, which is a combination of both temperature and humidity, that

4 104 better reflects how human beings experience and perceive a heat wave. The individual measure

105 of temperature or moisture alone do not adequately capture that lived and perceived experience.

106 One study explored experience in an open-ended fashion (using content analysis) and found

107 three of the four most common ways that people say they have personally experienced global

108 warming were evident in the climatic record from their community; this research examined only

109 one county, however (Akerlof et al., 2013).

110 In this paper we address these challenges to measurement and causal identification by merg-

111 ing a unique georeferenced large-sample multi-wave survey dataset with high-resolution spatial

112 data on climate trends. We operationalize exposure to climate change with new precision by

113 matching respondents to the local climate indicators they experience based on their geocoded ad-

114 dresses. Anomalies and/or trends in seven climate indicators are constructed to simultaneously

115 assess responses to a variety of events and trends (described in detail below). Two tempera-

116 ture indicators and three precipitation indicators are constructed to facilitate comparison with

117 previous research, which largely relies on similar simple measures. Two hybrid indicators are

118 also constructed based on the hypothesis that humans perceive weather and climate changes

119 holistically.

120 We also advance research in this area with a strong focus on causal identification. Previous

121 literature has often used cross-sectional models without assessing spatially confounding vari-

122 ables. Weather patterns are often assumed to be exogenous (i.e., unrelated to demographics,

123 attitudes, or other variables of interest in a model), but this may not be the case, especially at

124 state or regional scales. Thus, scholars should, at a minimum, include geographic fixed effects

125 in their models to ensure valid comparability between individuals who experience a temperature

126 anomaly and those who do not (Deryugina, 2013; Egan and Mullin, 2012; Bergquist and War-

127 shaw, 2019). We thus use a more sophisticated analytical approach to investigate the potential

128 causal relationship between experienced weather and perception, including a sensitivity analysis

129 to account for potential unobserved confounding variables.

130 2 Methods and Data

131 We analyze Americans’ climate views using a large national survey dataset (n=13,607) from

132 2008-2015 coupled with high-resolution monthly climate data. First, we spatially and tempo-

133 rally match this climate data to each respondent by selecting the climate indicators for the 12

134 months preceding the date each respondent was surveyed, in the county where each respon-

135 dent lives. Second, we identify individual-level drivers of climate opinion by assessing which

5 136 local climate conditions influence perceived personal experience with global warming. We use

137 a linear probability model including controls for individual-level social and political determi-

138 nants of belief in climate change and geographic-level fixed effects that account for unobserved

139 confounders. This allows us to causally identify the climate indicator(s) that alter perceived

140 experience of global warming. We demonstrate the robustness of our results by conducting a

141 sensitivity analysis for potential unobserved confounders.

142 Third, we model the spatiotemporal signature of these climate indicators on aggregate public

143 beliefs by incorporating the variable(s) identified in step one into a multilevel model with post-

144 stratification (MRP). The model describes the spatial distribution of perceived experiences with

145 global warming. We use it to assess the degree to which we can observe the spatial signature of

146 the climate indicator(s) in the geographic distribution of opinion. Whereas the individual-level

147 analysis identifies the causal relationship between a changing climate and individual climate con-

148 cern, the multilevel model describes how climate events are associated with aggregated climate

149 concern at particular times and places.

150 2.1 Measuring perceived experience with climate change

151 Our public opinion data come from 12 waves of the nationally representative Climate Change

152 in the American Mind (CCAM) survey (Supplement A.1), conducted between 2008 and early

153 2015 for the Yale Program on Climate Change Communication (YPCCC) and George Mason

154 University (Mason) Center for Climate Change Communication (combined n = 13,607) (Ballew

155 et al., 2018). The surveys were conducted using IPSOS’ (formerly GfK) KnowledgePanel, an

156 online panel of members drawn from the US population using probability sampling methods.

157 Potential panel members were recruited using random digit dial and address-based sampling

158 techniques to cover essentially all (non-institutional) residencies. Those who chose to join the

159 panel but did not have Internet access were loaned computers and provided Internet access.

160 Respondents were weighted, post survey, on the basis of key demographics (age, gender, race,

161 education, income), to match US Census Bureau norms for US adults. We geolocate respondents

162 on the basis of their ZIP+4 codes or through jittered geocoded addresses (150 m radius) provided

163 by the survey contractors.

164 Our analysis focuses on respondents’ perceived experience with climate change. To measure

165 this concept, respondents were asked: “How strongly do you agree with the following statement:

166 I have personally experienced the effects of global warming?” The ordinal response scale was:

167 Strongly agree (4), Somewhat agree (3), Somewhat disagree (2), Strongly disagree (1). Data for

6 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.

168 this question have been collected in 12 survey waves since 2008. This question exhibits both

169 temporal (Figure 2) and spatial (Figure 3) variation.

7 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).

170 Our models also include individual-level controls for gender (1=M, 2=F); education (1=Less

171 than high school, 4=Bachelors or higher), age (1=18-24 years, 5=65 years and older), race/ethnicity

172 (White, non-Hispanic; Black, non-Hispanic; Hispanic; Other, non-Hispanic), income (1=Less

173 than $25,000, 6=$100,000 or more), political party (Democrat, Republican, Independent, No

174 party/Not interested), and political ideology (1=Very conservative, 5=Very liberal). In some

175 models, we also use a continuous measure of partisanship prepared by Catalist, a national voter

176 file vendor. This score was appended to the dataset at the individual level and estimates the

177 likelihood, on a scale from 0 to 100, of each respondent identifying as a Democrat rather than a

178 Republican, with 100 being the most likely to identify as a Democrat. To maintain participant

179 anonymity, Catalist and GfK matched the voter file data to respondents before the research

180 team received the survey data.

8 181 2.2 Measuring exposure to extreme weather and climate change

182 We measure changes in climate and extreme weather events using seven different indicators,

183 including three based on precipitation alone, two based on temperature alone, and two hybrid

184 indicators that combine temperature and precipitation conditions. The three precipitation indi-

185 cators are the number of dry days, defined as days with less than 0.01 inches of precipitation in

186 24 hours; the number of heavy precipitation events, defined as the ratio of cumulative three-day

187 precipitation over the national 90th percentile to cumulative three-day precipitation; and the

188 number of very heavy precipitation events, defined the same way except using the 99th percentile

189 of the national distribution. These metrics of heavy and very heavy precipitation are designed to

190 capture flooding, while recognizing that many areas can experience heavy precipitation without

191 flooding as well. The two temperature indicators are the number of hot days, defined as the

◦ 192 number of days per year with maximum temperature over 90 F; and seasonal maximum temper-

193 ature, defined as the average maximum temperature over the three months preceding the survey

194 date. Finally, the two hybrid indicators are snow days, defined as a day where maximum (not

◦ ◦ 195 average) temperature is below 2 C, the minimum temperature is below 0 C, and precipitation is

◦ 196 above 0.1 inches; and hot dry days, defined as days where maximum temperature exceeds 90 F;

197 and precipitation is less than 0.01 inches in 24 hours. These last two definitions do not perfectly

198 encapsulate a universal definition of hot dry conditions nor do they differentiate between days

199 with freezing rain, sleet, snow, or rain that occurs in near-freezing conditions and/or transitions

200 to snow, as often occurs. However, at the scale of our analysis they provide a useful proxy for

201 such conditions.

202 We calculate trend variables for all seven climate indicators; these reflect long-term changes

203 in temperature and precipitation. We also calculate anomaly variables for four of the seven

204 climate indicators. The anomalies reflect events such as extreme heat events (i.e., heat waves),

205 relatively snowless winters, or heavy rain events, including those that produce flooding. We thus

206 use a total of eleven unique climate trend and anomaly variables, summarized in Tables 1 and

207 2. The indicators are generated from high-resolution spatial data in which daily precipitation

208 and maximum and minimum temperature observations have been gridded to a uniform spatial

◦ 209 resolution of 1/16 ( 6km) (Livneh et al., 2013). We identify the grid cell closest to each

210 respondent’s location and the twelve-month period immediately preceding the date on which the

211 respondent was surveyed. We then calculate each climate variable for each respondent.

212 We use daily values to calculate annual and/or seasonal anomalies. Specifically, anomalies

213 reflect the deviation in the number of days measured by each indicator during the twelve months

9 214 prior to the survey date, relative to a 1971-2000 base period. For example, if the survey was

215 conducted in Sept 2012, then data from Sept 2011 to Aug 2012 is used to represent the most

216 recently experienced conditions for survey respondents. The anomaly is calculated as the differ-

217 ence between the Sept 2011-Aug 2012 value compared to the average value for the entire base

218 period of 1971-2000. We chose a thirty-year base as it is the traditional climatological baseline

219 period used by NOAA relative to which trends are measured. We selected 1971-2000 as opposed

220 to a later time period (e.g. 1980-2010) because later periods would have overlapped with our

221 survey data collection, and we did not want to assess climate changes that occurred after re-

222 spondents took the survey. Given the increasing rate of warming since 1980, we expect that

223 a more recent base period would produce smaller effects since the differences from the warmer

224 base period would be smaller than from an older, cooler base period (Hansen et al., 2012).

225 Trends are calculated as the long-term change in the number of days since 1980 to the date

226 of the survey. We calculate this using linear regressions of each indicator on time. For example,

227 if the survey was conducted in Sept 2012, we use data from August 1980 to August 2012 for each

228 grid cell. The trend variables are the unstandardized coefficients from simple linear regressions

229 using time as the independent variable and each climate indicator as the dependent variable.

230 The climate variables are scaled with respect to the time period for which they are measured

231 (e.g., one year for the hot dry day anomalies, three months for the seasonal maximum temper-

232 ature anomalies, etc.). This allows comparability over time and across variables. Each climate

233 variable is standardized to have a mean of 0 and a standard deviation of 1 within the matched

234 survey wave. The two precipitation variables are also log-transformed to reduce skewness.

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◦F and Pr < 0.01” in 24 hrs Hot & Dry Days Slope Change in # days/yr with Tmax > 90◦F and Pr < 0.01” in 24 hrs 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.

10 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 Very Heavy Precip Ratio 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.

235 2.3 The individual-level effect of exposure on perceived experience

236 At the individual level, we use linear probability models (LPM) to test the relative strength

237 of different weather and climate variables on perceived personal experience with global warm-

238 ing. We view our individual-level models as estimating the conditional expectation function,

239 so estimation of the marginal effect of treatment on an outcome can be undertaken with either

240 linear probability or limited dependent variable models (Angrist and Pischke, 2008, 94-99). We

241 use LPM models to maximize transparency and interpretability. Similar to prior work (Egan

242 and Mullin, 2012; Deryugina, 2013; Bergquist and Warshaw, 2019; Konisky et al., 2016) that

243 seeks to make causal claims from observational data, our causal identification strategy rests on

244 the exogenous assignment of weather experiences to members of the public. Conceptually, this

245 means that, conditional on geography, people do not choose to live in areas that are experi-

246 encing the effects of climate change because of their political identities or other factors that

247 also influence their perceptions of climate change. Under the the assumption that there are

248 no unobserved confounding covariates (i.e., with the "conditional ignorability" assumption), we

249 argue that treatment is arbitrarily assigned once we include fixed effects for every region, state,

250 and county. We also include a variety of controls in our models, including demographics, party

251 affiliation, and political ideology.

252 To assess the ignorability assumption for each of our climate variables, we use a series of

253 balance checks and then conduct a sensitivity analysis (Cinelli and Hazlett, 2020). Our balance

254 checks (see Supplement) assess whether any individual-level variables predict exposure to ex-

255 treme weather and climate change. We regress the treatment (climate) variables on the same

11 256 demographic covariates used in our main models and on the state, county, and regional fixed

257 effects. These models allow us to assess whether, conditional on these covariates and within each

258 geographic unit, treatment assignment is independent of potential outcomes. A significant rela-

259 tionship between any of these covariates and our treatment variables indicates that treatment

260 assignment is unlikely to be as-if random. We probe the conditional ignorability assumption

261 for all trend and anomaly variables. We also report the results of sensitivity analyses that ex-

262 plore the vulnerability of our conclusions to additional unobserved confounders that may shape

263 individual exposure to extreme weather and climate indicators.

264 2.4 The spatial signature of exposure to extreme weather and climate

265 change

266 We extend our individual-level analysis to examine the spatial signature of climate change on

267 county-level models of perceived experience. Building on the individual-level model, we use

268 multilevel regression and post-stratification (MRP) to describe how climate variables are asso-

269 ciated with county-level beliefs about personal experience with climate change. MRP models

270 are a commonly-used method for estimating the spatial distribution of public opinion and in-

271 volve two steps (for more detailed treatments, see Park et al., 2006; Lax and Phillips, 2009;

272 Warshaw and Rodden, 2012; Buttice and Highton, 2013). The model employed here is based

273 on a well-validated model detailed in (Howe et al., 2015; Mildenberger et al., 2016). Our online

274 supplementary materials provide additional information about MRP methods and our models.

275 We add to our MRP model the hotdrydays climate variable, which the individual-level anal-

276 ysis suggests is influencing perceived experience of global warming. When we incorporate this

277 climate variable into the MRP model, we spatially weight the climate values by population den-

278 sities (CIESIN, 2017) to account for the differential distribution of population in some (especially

279 large) counties. Weighting is accomplished by multiplying the climate variables by a 2010 pop-

280 ulation density grid, which is first standardized and log transformed onto a 0-1 scale. Spatial

281 weighting allows our estimates to reflect the climate conditions experienced by the population of

282 each county, even when that distribution is uneven across space. This method captures climate

283 changes that are more truly "local" than does simply averaging from many grid cells across the

284 geographic area of a county. We assess how the addition of this climate variable affects the

285 predictive accuracy of our multilevel model and examine specific climate events that are asso-

286 ciated with increased perceived experience with climate change. Whereas the individual-level

287 analysis allows us to examine the influence of different types of events (e.g., droughts, heavy

12 288 rains, etc.), the county-level analysis allows us to examine the predictive signature of climate

289 events at specific times and places.

290 3 Results

291 We begin by estimating the causal effect of exposure to weather shifts on individual perceptions

292 of global warming experience. A causal interpretation of our effects depends on a conditional

293 ignorability assumption: once we condition on geography, exposure to climate extremes is as-if-

294 randomly assigned. Some physically-driven phenomena coincide with well-known sociopolitical

295 patterns. For example, urban areas tend to be warmer than their neighboring suburbs, and

296 urban residents tend to be more liberal than those in nearby suburbs. Thus, we first use balance

297 tests to probe the assumption that climate variables are exogenous to social and political factors

298 that also drive an individual’s perception of global warming experience. Spatial variations in the

299 climate variables tested in the individual-level analysis are presented in Supplement Figure A-1

300 We find balance between treated and untreated units for our temperature and precipita-

301 tion anomaly variables, but we observe some imbalance for the various slope (trend) variables

302 (Supplemental Table A.2). Thus, consistent with our priors, the anomaly variables are very

303 likely exogenous whereas the trend variables may be subject to confounding due to spurious

304 relationships between regional climate trends and sociopolitical patterns. Overall, these tests

305 point to a causal relationship between climate anomalies and public perceptions. For complete-

306 ness, we test the relationship between perceptions and both climate trends and anomalies (Table

307 3). Based on poor support for the conditional ignorability assumption for the trend variables,

308 the results for these are best interpreted as indicating systematic association rather than direct

309 causal relationships.

310 Model results (Table 3) identify the relationships between perceived experience with global

311 warming and local climate variables. Model 1 includes only climate variables (Column 1 in

312 Table 3). Only the hot dry days anomaly has a statistically significant relationship with perceived

313 experience. A one standard deviation unit increase in hot dry days anomaly leads to a significant

314 0.0434 unit of increase in perceived experience (on a 1 to 4 scale). To examine the robustness of

315 these effects to the inclusion of other possible drivers of perceived experience, we add controls

316 for respondents’ race, age, sex, level of education, party identification, ideology, and household

317 income. The results, shown in Table 3, Column 2, remain largely unchanged. Using a limited

318 dependent variable model provides similar results (see Supplement Table A.3).

319 How substantively important is the effect of hot dry days on Americans’ perceived personal

13 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.

320 experience with global warming? In Table 3, Column 3, we show the results from a model that

321 only uses this specific anomaly, demographic covariates, and a continuous measure of partisanship

322 for each respondent (see Section 2). The magnitude of the effect of climate is quite similar in

323 this model, and the continuous measure of partisanship helps us compare the effect size of

324 hot dry days with that of partisanship (widely perceived to be the primary driver of climate

325 concern (Egan and Mullin, 2017)). We standardize both variables for the analysis and find that

326 a 1 standard deviation (11.5 days) change in exposure to hot dry day anomalies has the same

1 327 impact on climate perceptions as a 8 standard deviation shift in partisanship, or 4.7 percentage

328 points – a modestly-sized effect.

329 We also examine whether the effect of hot dry days depends on an individual’s political ori-

330 entation (Figure 4). The differences between partisan group point estimates are not statistically

331 significant either between Democrats and Republicans or between liberals and conservatives.

332 Thus it appears that both sets of partisans respond similarly to hot dry days.

333 Despite the plausibility of our causal identification strategy, one might remain concerned that

334 an unobserved confounder exists, even after geographic conditioning and controlling for diverse

335 sociopolitical variables. We use a bounding approach (c.f. Cinelli and Hazlett, 2020 to assess the

336 effect implied by biases at varying levels of confounding (Figure 5). Given that ideology is well-

14 Table 3: The effect of climate variables on perceived personal experience with global warming between 2008-2015. β 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 −0.016 −0.014 Seasonal Tmax slope −0.002 −0.004 Hot days anomaly 0.009 0.006 Hot days slope 0.041 0.027 Dry days anomaly 0.005 0.004 Dry days slope −0.005 0.003 Hot dry days anomaly 0.043∗ 0.046∗ 0.039∗∗ Hot dry days slope −0.045 −0.038 Snow days slope −0.009 0.006 Extreme ppt (90th) ratio −0.009 −0.009 Extreme ppt (99th) ratio 0.0002 −0.002

Sociodemographics Age −0.008 −0.022∗∗∗ Sex (female) 0.007 0.011 Education 0.040∗∗ 0.062∗∗ Household Income −0.017∗∗ −0.020∗∗ Hispanic 0.092∗∗ 0.074∗ Black, Non-Hispanic 0.003 −0.035 Other, Non-Hispanic 0.014 0.001 Republican −0.239∗∗∗ Democrat 0.194∗∗∗ No Party 0.044 Ideology 0.160∗∗∗ Catalist partisanship 0.277∗∗∗

Intercept 1.925 1.863 2.058 Survey Wave FE YYY Region FE YYY State FE YYY County FE YYY Observations 13,607 13,345 12,082 R2 0.171 0.276 0.258 Adjusted R2 0.033 0.153 0.123 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

15 337 established as one of the primary drivers of climate change perceptions, we assume a confounder

338 exists with the same ability as ideology to explain residual variance in the relationship between

339 hot dry day anomalies and climate perceptions. We then estimate the hypothesized effects of this

340 unobserved confounder at different levels (while accounting for other covariates). The results

341 show that even if an unobserved confounder as influential as ideology exists, our effect estimate

342 remains robust. In fact, we would need a confounder more than five times as predictive of residual

343 variance (in both hot dry days and the outcome) as ideology to overturn our identified causal

344 effect. This analysis imposes a high standard on potentially problematic factors and underscores

345 the strong likelihood that the relationship we have identified is unlikely to be vulnerable to

346 omitted variable bias.

347 At the county level, our multilevel model and poststratification (MRP) analysis reveals the

348 spatiotemporal associations between hot dry days and perceived experience (Figure 6). County-

349 level estimates of perceived experience are shown as differences from the national average both

350 without hot dry days in the model (Figure 6, first row) and with hot dry days in the model (Figure

351 6, second row). In the SI we show how inclusion of each of the potential climate predictors affects

352 predictive mean standard error referenced against an independent (external) validation dataset.

353 Given the subtlety of the differences in the two models, we then subtract the absolute values

354 predicted from the model without a climate variable from the model that includes hot dry days

355 to produce model prediction differences (Figure 6, third row). Here the model shows where hot

356 dry days are clearly associated with county-level perceived experience. Places and years where

357 increased hot dry days are associated with higher rates of perceived experience with global

358 warming are shown in red, while darker gray counties reflect areas and times where fewer hot

359 dry days locally are associated with reduced perceived experiences of global warming. Regional

360 patterns are evident in almost every year, but the impacts appear strongest in California in

361 2008, 2010, and 2014, in Texas in 2010 and 2011, and in the Midwest in 2012. Hot dry day

362 anomalies are particularly pronounced during the drought that initially affected Texas and later

363 extended north into the central Midwest; this event was widespread, severe, and sustained,

364 with substantial physical, social, and economic impacts. The modeled effects of hot dry days

365 on perceived experience mirror the spatial distribution of hot dry days in each year (Figure 6,

366 fourth row).

16 0.5

5x ca_party (0.034) 0.4

0.3 3x ca_party (0.036) 0.2 of confounder(s) with the outcome of confounder(s) 2 R 1x ca_party (0.038) 0.1 Partial

Unadjusted (0.039) 0.0

0.0 0.1 0.2 0.3 0.4

Partial R2 of confounder(s) with the treatment

Figure 5: Sensitivity analysis for the estimated effect of hot dry days on perceptions of personal exposure to climate change, using a model with covariates and geographic fixed effects as de- scribed in the text. The horizontal axis specifies a hypothesized strength of association between 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 hot dry days estimate assumes zero confounding, and is shown in the bottom left corner (“Unadjusted”). The red diamonds show the effect on our estimate of hot dry days if hypothetical unobserved confounding covariates existed that had equivalent, or three times, or five times the predictive power as party affiliation, 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 expe- rience. That is, the red diamonds are still to the bottom left of the dashed red line, indicating that the relationship remains significant even with these hypothetical unobserved confounding covariates.

367 4 Discussion

368 The high spatial resolution of our large individual-level dataset and our focus on causal inference

369 offers new insights into the relationship between objective climate trends and subjective expe-

370 riences of global warming. Of the eleven climate variables investigated, only an increase in hot

371 dry days significantly increases individuals’ perceptions that they have personally experienced

17 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 6: 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.

372 the effects of global warming. A one standard deviation increase in exposure to hot dry day

373 anomalies modestly increases the probability that an individual thinks that they have experi-

374 enced climate change. These results stand in contrast to previous studies that found null or

375 negligible effects of climate extremes on public experience, belief, and risk perceptions of global

376 warming (Marquart-Pyatt et al., 2014; Marlon et al., 2018; Mildenberger and Leiserowitz, 2017;

377 Palm et al., 2017), and align more closely with studies finding consistent though modest effects

378 (Howe et al., 2013; Bergquist and Warshaw, 2019; Egan and Mullin, 2012; Kaufmann et al.,

379 2017; Shao et al., 2014; Bohr, 2017). Robustness checks and a sensitivity analysis support the

380 interpretation that excess heat is driving individual experience of global warming. By contrast,

381 we find no evidence that simple precipitation and temperature anomalies or trends have had a

382 measurable impact on perceived experience with global warming.

383 Hot dry days may surpass other indicators in terms of their effects on perception for several

384 reasons. First, the effects occur at much broader scales than those of flooding or snow, and

385 therefore impact a larger population. In addition, hot dry days are associated with extreme heat

386 and drought, but also with wildfires, and thus can have far-reaching effects on diverse sectors,

387 such as agriculture, energy, water supply, recreation, and tourism. In addition, extreme heat

388 and drought can persist for weeks, months, or even longer, thereby affecting not only individuals

18 389 and communities, but also generating economic losses nationally that are twice as high as flood

390 events (NOAA, 2019).

391 Most respondents in our study may not have considered that an increase in hot dry days is

392 causing them to believe that they are experiencing global warming. For communication pur-

393 poses, making such a link explicit, however, might facilitate understanding that global warming

394 is happening by some audiences. Our research thus has potential implications for climate com-

395 munication and may help to generate hypotheses for message testing.

396 Our results also reinforce the role of political orientation and demographic factors as im-

397 portant influences on perceived global warming experience. Consistent with prior research,

398 Democratic party affiliation and liberal ideology are substantially stronger than hot dry days

399 in predicting perceived experience with global warming (Howe and Leiserowitz, 2013; McCright

400 et al., 2014; Howe, 2018). However, although we did not find different effects of hot dry days on

401 perceived experience between partisan groups, the effect estimates were highest for Independents,

402 consistent with research showing that people whose views of global warming are least driven by

403 partisan thinking show evidence of experiential learning via weather and climate (Myers et al.,

404 2013). White non-Hispanic individuals and those with higher incomes tend to report less ex-

405 perience with the effects of global warming, after adjusting for party affiliation and ideology.

406 Our results suggest that the association between education, income, and race with experience

407 is widespread and robust. Nonetheless, even after controlling for these factors, we do find that

408 Americans update their perceptions of global warming in response to exposure to hot dry days.

409 Our analysis also offers a methodological template for future research on this topic. Sev-

410 eral prior studies have demonstrated an association between climate changes and individuals’

411 perceptions, but only one previous study has empirically probed the conditional ignorability as-

412 sumption (Egan and Mullin, 2012). Most studies assume that weather events are exogenous by

413 nature, even though some climatic phenomena such as the urban heat island effect are correlated

414 with political and social characteristics that also predict climate beliefs. We use balance tests to

415 assess the exogeneity of our climate variables and demographic predictors, and sensitivity analy-

416 ses to assess the robustness of our results to potential confounders. Using the balance tests and

417 sensitivity analyses for support, we argue for a causal influence of hot dry days on Americans’

418 perceived experience with global warming. Future work should also carefully consider how po-

419 litical orientations and demographic characteristics of a population might coincide with climate

420 trends in order to minimize the risk of making causal claims based on spurious correlations.

421 This analysis also adds to the foundation for investigating the relationships between expe-

19 422 rience and extreme weather. Our analysis spans eight years and precisely matches individuals

423 to the weather anomalies they experience. The high spatial resolution of our climate data and

424 long time series of climate public opinion data enable us to precisely determine which climate

425 events the public interprets as indicators of a changing climate. Multilevel modeling of the data

426 allows the spatial visualization of climate changes. Coupled with the modeling, we can now see

427 where and when the public perceived specific climate changes as “global warming.” Additionally,

428 we gain analytical leverage from using an integrated temperature and moisture climate indicator

429 that appears to better reflect changes that people actually perceive and recall. The significance

430 of the hotdrydays metric suggests that measures of climate change using integrated and cumu-

431 lative metrics might better reflect how individuals actually experience a changing climate. Had

432 we tested only recent temperature anomalies, cumulative hot days, and/or dry days, we would

433 have found null results. Future research could explore integrated measures of environmental

434 change that are designed to more closely match human perception (e.g., the "feels-like tempera-

435 ture metric used in many weather apps, or shifts in the growing season) rather than ready-made

436 climatological indices (e.g., the PDSI). Moreover, as extreme weather in particular worsens, the

437 associations people make to global warming and climate change may evolve, and different met-

438 rics may become more important in different regions simultaneously. A better understanding of

439 how effects accumulate is also necessary.

440 We also find that including hot dry day anomalies in models of county-level perceived experi-

441 ence with global warming substantively changes aggregate county-level estimates in some places

442 that have experienced large positive or negative deviations from normal in recent hot dry days.

443 However, these results at the county level should be interpreted with caution since extensive

444 county-level data are unavailable to validate these projections. Such validation, alongside addi-

445 tional efforts to understand the downstream effects of hot dry day experience on other climate

446 beliefs, attitudes, and policy preferences, should be undertaken before such climate data are

447 included in small-area estimation models of these other variables.

448 We also find that the public responds more readily to climate anomalies and extremes than

449 to trends. Future work might incorporate other dimensions such as the magnitude and recency

450 of events to produce climate change indicators that are more closely linked to perceived personal

451 experience with global warming. Some new research in this area is already emerging, with farmers

452 in Africa and Asia, for example (Foguesatto et al., 2020). The duration and accumulation of

453 the effects we find also warrant more attention. Prior work shows that the public response to

454 weather events is short-lived (Egan and Mullin, 2012; Konisky et al., 2016; Kaufmann et al.,

20 455 2017; Palm et al., 2017; Scruggs and Benegal, 2012) or degrades if a warming trend is not

456 sustained (Bergquist and Warshaw, 2019). We do not know, though, how the duration of the

457 effect might change as climate events become more frequent or more severe. Will they translate

458 into support for political action? Economic losses from climate change and natural hazards are

459 on the rise (Hsiang et al., 2017), and understanding the relationships between risk perceptions

460 and adaptation responses will be particularly vital in the most vulnerable places (Uddin et al.,

461 2017; Chingala et al., 2017; Etana et al., 2020). Finally, whether the increasing impacts of

462 climate change will durably influence political incentives to reduce the climate threat remains

463 an open empirical question.

464 Our data, which span from 2008-2015, omit several recent extreme weather seasons, which

465 future research could incorporate. For example, we do not examine the effect of recent powerful

466 North Atlantic hurricane seasons, the U.S. western wildfires after 2016, or recent Midwest flood-

467 ing. Further developing our knowledge about how climate change manifests locally in different

468 parts of the country, which of these manifestations are most salient to the public, and whether

469 and when such changes are considered by the public as evidence of global warming, will enable

470 communicators, educators, and others to better explain how climate change affects us all, and

471 what can be done to mitigate and adapt to its ongoing impacts.

472 5 Conclusions

473 Our understanding of how the climate system is changing has far outpaced our understand-

474 ing of how the public perceives and interprets these changes. Yet, personal experience with

475 environmental changes may be a critical motivating factor in protecting ourselves and society

476 from future damages. What makes people think they have experienced global warming? While

477 politics remain the primary driver of global warming risk perceptions in the U.S., we find a

478 modest causal effect of actual climatic changes on perceived experience with global warming. In

479 particular, we find a clear and robust influence of an integrated measure of climate change on

480 perceived personal experience: hot dry days. No other variables, whether shorter-term temper-

481 ature anomalies or trends, recent heavy precipitation, snow day trends, dry days, or hot days

482 alone (anomalies or trends) appear to significantly influence public perceptions.

483 Our analysis suggests that designing climate indicators that align more closely with human

484 perceptions of environmental change and stress can yield new insights into public engagement

485 with global warming. It also points to some limits in public understanding of the climate

486 threat. The combination of extreme heat and humidity can be even more deadly than dry heat,

21 487 particularly in urban areas and in places unaccustomed to extreme heat (Howe et al., 2019). Yet,

488 our research suggests that it is dry heat that the public links with global warming. Experimental

489 testing is needed to identify specific messages that might support a stronger understanding of

490 the causal relationships between climate change and its impacts among vulnerable publics.

491 As attribution science advances, new opportunities are emerging to help the public directly

492 contextualize their lived experiences within a scientific framework. This framework can increas-

493 ingly quantify the extent to which human-induced climate change is exacerbating or increasing

494 the probability of summer heat and prolonged dry conditions. From the first formal individual

495 event analysis of the European heatwave of 2003, which showed that human-induced warming

496 had at least doubled the risk of such an event occurring (Stott et al., 2004), to the analysis of

497 the June 2019 European heat wave which showed it was made at least five times more likely

◦ 498 and 4 C hotter as a result of a changing climate (van Oldenborgh et al., 2019), scientists are

499 now able to provide the public with clear statements about the relationship between long-term

500 climate trends and individual weather events. Our study emphasizes the importance of doing

501 so. Experiences with certain types of climate impacts have already increased people’s awareness

502 of and perception that they have experienced the impacts of climate change. Communicating

503 these links and helping people appropriately interpret their direct experience as an impact of

504 climate change may greatly support public engagement with the issue.

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29 682 A Electronic Supplemental Material: Methods Appendix

683 A.1 Survey Data

684 Table A.1 gives details on the time, mode, and sample size of public opinion surveys used to

685 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

686 A.2 Balance Tests on Treatment Assignment

687 A priori, we are skeptical that trend variables are fully exogenous to political identities, even after

688 conditioning on local geographies. This is because, within local geographies and by definition,

689 trends vary less over a set time period than anomalies. Trends represent long-term shifts in

690 climate indicators, whereas anomalies represent the deviation from a base period over a set

691 time interval. We therefore expect the spatial pattern of climate trends to vary less over our

692 eight years of surveys than the spatial pattern of climate anomalies. As such, clusters of like-

693 minded individuals living in particular counties may experience more similar climate trends

694 than anomalies. In turn, unobserved confounding variables that drive perceptions of global

695 warming experiences are more likely to be correlated with these between-county differences than

696 temperature anomalies. Conversely, climate anomaly variables are more credibly assigned in a

697 conditionally-as-if-random way.

698 Table A.2 shows the results of our balance tests. We regress treatment status (e.g. climate

699 variables) on diverse sociopolitical variables, conditional on geography. If the conditional ignor-

700 ability assumption holds, none of these covariates should be significantly predictive of treatment

701 assignment. As we suspected, some covariates are predictive of trend variables, even after con-

A-1 702 ditioning on geography. However, we do not find evidence that these covariates predict anomaly

703 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

A-2 704 A.3 Robustness Tests of Model Results

705 Table A.3 re-estimates the paper’s core specifications using a limited dependent variable ap-

706 proach, rather than linear probability models. The results are unchanged.

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

707 A.4 Additional Details on MRP modelling

708 Multilevel regression and poststratification (MRP) is increasingly being used to model subna-

709 tional attitudes and political preferences from national polls (for more detailed treatments, see

710 Park et al., 2006; Lax and Phillips, 2009; Warshaw and Rodden, 2012; Buttice and Highton,

A-3 711 2013). An MRP analysis involves two stages. First, individual survey responses are modeled

712 as a function of demographics, location, and geographic covariates (the "multilevel regression

713 model"). In this way, unique geographic variability from local residents (while controlling for

714 their demographic characteristics) can be captured and used to estimate opinions for nearby

715 places. The second step is poststratification, where the fitted estimates for each demographic-

716 geographic respondent type are weighted by their actual (census-based) population percentages

717 for a given area. Percentages of respondents with a particular preference can then be estimated

718 for every state, county, or other geographic unit.

719 In our multi-level regression model, we use individual-level demographics and climate vari-

720 ables, group-level geographic characteristics and grouped random effects. Race, gender, educa-

721 tional attainment, and an interaction term of race by gender by education are treated as random

722 effects. Individuals are also grouped geographically according to their state and county to eval-

723 uate the random intercepts. County-level geographic characteristics are used as fixed effects

724 predictors to improve model fit. The hotdrydays anomalies variable is used as both fixed and

725 random effects: the fixed effect identifies the predictive power of this climate variable whereas

726 the random effect (varying slope) allows for regional variations in the effects of hot dry days.

For each individual i, the model is specified as:

−1 race education gender wave race.education.gender county P r(yi = 1) = logit (γ0 + αj[i] + αk[i] + αl[i] + αm[i] + αj[i],k[i],l[i] + αc[i] )

727 where

race 2 αj ∼ N(0, σrace), for j = 1, ..., 4

education 2 αk ∼ N(0, σeducation), for k = 1, ..., 4

gender 2 αl ∼ N(0, σgender), for l = 1, 2

wave 2 αm ∼ N(0, σwave), for m = 1, ..., 12

race.education.gender 2 αj,k,l ∼ N(0, σrace.education.gender), for j = 1, ..., 4; k = 1, ..., 4; l = 1, 2

728 Each variable is indexed over individual i and over response categories j, k, l, m, and c for

729 race, education, gender, wave, and county-level geography variable, respectively. The county

730 variable is further modeled as:

A-4 county region state drive samesex carbon pres αc ∼ N(αr[c] + αs[c] + γ · drivec + γ · samesexc + γ · carbonc + γ · presc

hotdrydays 2 +γ · hotdrydaysi, σcounty), for c = 1, ..., 1889

731 where

region hotdryday 2 αr ∼ N(γ · hotdrydayi, σregion), for r = 1, ..., 9

state 2 αs ∼ N(0, σstate), for s = 1, ..., 49

732 County-level covariates include the percentage of individuals who drive alone in a given

733 county, the percentage of same-sex households in a given county, the level of point source carbon

734 dioxide emissions in a given county, and the 2012 Democratic Presidential vote share in a given

735 county. These covariates have shown to be predictive of climate beliefs and behaviors in other

736 studies (Howe et al., 2015; Mildenberger et al., 2016).

737 Other than the geographic covariates, hotdrydays anomalies is retained in the model due

738 to its strong and consistent significance identified in the individual-level analyses as well as

739 in results from preliminary analyses (not shown) exploring other dependent variables, such as

740 global warming beliefs, risk perception and policy support. Hotdrydays is calculated as the

◦ 741 difference in the number of days with maximum temperature over 90 F and precipitation less

742 than 0.01 inches in 24 hours relative to a 1971-2000 base period. Examples of the climate data

743 are visualized in Figure A-1.

744 For post-stratification, we use 2012 5-year American Community Survey data cross-tabulated

745 by education attainment, gender, and race/ethnicity across all counties. Hotdrydays, matched

746 to the survey wave, is weighted by population and aggregated at the county level before merging

747 with the census cross-tabs. Our fitted model is then used to estimate the average opinion of each

748 demographic-geographic individual type, for each of the survey waves. Specifically, the random

749 slope of hotdrydays captures the different effects in each region in response to local changes. For

750 instance, the model specifically estimates the average response of a White male with a Bachelor’s

751 degree or higher living in the West-South Central region in April 2013.

752 We validate our MRP results for individuals who report personally experiencing global warm-

753 ing using two methods: 1) an internal cross-validation technique and 2) an external validation

754 (i.e., by comparison with an independent dataset collected via phone by SRBI in 2013).

755 For our internal cross-validation, we compare our MRP estimates to estimates derived from

A-5 756 disaggregation (the sample averages for each geographical subunit) (Pacheco, 2011). Using

757 repeated simulations, subsamples of varying sizes were randomly selected from a large-population

758 state and used to simulate the samples of smaller provinces or districts A-2. At the state level,

759 this procedure operates as follows:

760 • We draw 99 random samples of size n from the state with the greatest number of respon-

761 dents (California). We add this sampled data to all non-Californian data in our dataset. In

762 effect, we are simulating California as if it were a small state in our dataset with a limited

763 number of direct observations. What we want to do is compare model performance given

764 this limited amount of revealed data about Californians with the actual mean Californian

765 support observed across our entire California dataset. We call this dataset that combines

766 non-Californian data with a Californian random sample of size n the “training set”.

767 • We run an MRP model using this training dataset, and use this model to predict support

768 for a given opinion among Californian Republicans. We compare the predicted opinion

769 from the training set with the observed mean of the random sample of Californians of size

770 n. We calculate the mean absolute error of the MRP prediction against the simple sample

771 average.

772 • We repeat steps 1 and 2 for the 25th, 50th, 75th and 90th percentile of state sample sizes

773 (n = 7, 20, 63, 133). We also track the mean absolute error as compared to the average of

774 sampled Californians.

775 • We repeat steps 1 through 3 for Florida, and for two sample questions.

776 For the disaggregation method, we estimate the opinion levels for each state using only

777 the data from respondents in the state. Estimates using the disaggregation method are very

778 unreliable for states with few respondents. For states with large numbers of respondents, such

779 as California and Florida, the disaggregated estimates are closer to the MRP estimates. The

780 strength of the MRP approach at low state-level sample sizes is evidenced by its smaller error as

781 compared with disaggregation, while for larger sample sizes the MRP estimates quickly converge

782 to the disaggregation estimates (see Fig A-2). In some cases of states with very large sample

783 sizes, disaggregation can produce smaller estimates than MRP because of the tendency for MRP

784 estimates to regress towards mean values. Because extremely few states have such large numbers,

785 however, MRP overwhelmingly produces better estimates than disaggregation when downscaling

786 for the entire US.

A-6 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

easonal tmax anomal S ies −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-1: Climate variables.

787 To further validate our results, we compared our MRP estimates with results from four

788 state-level surveys conducted using phone surveys through SRBI A-2 A-3.

789 We use external validation to compare the predictive accuracy of different models each con-

790 structed with only one of the climate variables considered. We compare these results with a

791 base model that includes no climate variables. Results are calculated using mean county-level

792 estimates for four states and two cities. The validation data come from six representative tele-

793 phone surveys (conducted by SRBI) for CA, CO, OH, and TX and two cities (San Francisco,

794 Columbus). These surveys used the same item wording as the online panel surveys, and they

795 were administered concurrently with the 2013 nationally representative YPCCC/GMU survey.

796 Figure A-4 compares the differences between the base predictive model without any climate

797 variables with results from the different models that each include only one of the weighted climate

798 variables. We find that the hotdrydays variable improves estimates of perceived experience with

799 global warming as compared to the base model for Texas and Ohio (including Columbus), but

800 other climate indicators – specifically Tmax in California and heavy precipitation or snowdays

801 outperform the hotdrydays indicator in some cases. These results highlight the diverse expression

802 of climate change in different parts of the country and suggest that while the severe drought

803 had the largest discernable impact on public experience with global warming between 2008-2015,

804 these results could change over time. In addition, these results only reflect validation for a single

805 year (2013).

A-7 Figure A-2: Comparison between MRP and Disaggregation methods using the YCOM+Climate model for four states with the largest populations.

A-8 Figure A-3: 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.

A-9

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 3 ● ● ● ● ● 0 ● ●

−3 ● ● −6 ● ● ● ● n = 700 n = 700 −9 ● Model estimates − external validation dataset validation Model estimates − external Mean Absolute Error Base Tmax DrydayPrecip90Precip99Snowday ● ● ● ● Hotdryday 3 ● ● ● 0

−3

−6

−9

Base TmaxDryday Hotdryday Precip90Precip99Snowday

Figure A-4: Validation against independent state and city-level survey data in 2013. ’Mean Absolute Error’ is the average across all 6 other cases.

A-10