1. Introduction
All regions of the United States have already experienced impacts of anthropogenic climate change, including warmer temperatures, changing precipitation patterns, and more frequent and intense extreme weather events (Melillo et al. 2014). While 60% of Americans think that global warming is affecting weather in the United States, only 33% report having ever personally experienced its effects (Leiserowitz et al. 2017). This reflects a tendency for climate change to be considered a distant threat—more likely to harm other places and people, or to happen in the future.
There is evidence that perceptions of personal experience with climate change can influence climate change beliefs, concerns, risk perceptions, and mitigation behaviors (e.g., Myers et al. 2013; Akerlof et al. 2013; Shao 2016). For example, a survey of U.K. and Australian residents in 2010 found that those who reported having experienced climate change impacts believed more in climate change, perceived climate risks to be greater, and engaged more in mitigation behaviors (Reser et al. 2012). In turn, it is also important to understand how individuals form these perceptions of local or personal climate change impacts. Previous studies suggest that perceived personal experience with the effects of climate change may reflect actual local long-term weather and climate trends (Hamilton and Keim 2009; Shao 2016), as well as recent conditions (Zaval et al. 2014; Joireman et al. 2010).
This study further explores how weather influences overall perceptions of climate change and extreme weather. The theoretical background draws on psychological processing theory to understand how personal experience provides new information about climate change and why this mechanism can be limited by psychological distance and motivated reasoning. A review of previous literature shows that individuals accurately perceive recent local weather conditions, but are less certain about long-term trends. However, few studies have compared which types of weather are most important to perceptions. To build on the previous literature, this research asks two questions: 1) Does recent weather influence perceptions of overall personal experience with climate change and extreme weather? and 2) Which types of recent weather most influence perceptions of overall personal experience with climate change and extreme weather? New York State (NYS) residents’ perceptions of their personal experiences with climate change and extreme weather are assessed in relation to two types of weather (temperature and precipitation) and two measurement methods (relative-to-normal conditions and nonrelative conditions) over two recent time periods (1 day and 1 week).
2. Literature review
a. Psychological processing
The psychological literature describes two main ways in which people process new information: analytical and experiential (Sloman 1996; Epstein 1994; Loewenstein et al. 2001; Tversky and Kahneman 1974). Analytical processing is characterized by logical conscious reasoning and abstract symbolic concepts. For example, analytical processing of climate change could include analyzing computer models of global climate patterns. Experiential processing is an unconscious mechanism that associates the present situation with previous knowledge and feelings. Experiential processing is more spontaneous, vivid, and affective than analytical processing (Loewenstein et al. 2001; Weber 2010; Epstein 1994). In terms of climate change, this type of processing might occur as personally experiencing increasingly frequent extreme storms or noticing changes in average temperatures. While people rely on a combination of both mechanisms, experiential processing is often more influential unless analytical information is presented concretely (Marx et al. 2007; Sloman 1996).
The complexity of climate change makes it an abstract concept that is described by statistical analyses of meteorological conditions that vary greatly over space and time, projections of possible future impacts, and estimates of invisible causes like greenhouse gas emissions (Weber and Stern 2011). As a result, analytical processing would seem to be the main way that people learn about climate change. However, studies on decision-making show that in complex and uncertain situations, individuals rely more on experiential rather than analytical information (Marx et al. 2007; Loewenstein et al. 2001; Lejarraga 2010). For example, in a study of monetary gambling behavior, individuals made more profitable decisions when they were allowed to test risky scenarios firsthand instead of being told outcome probabilities (Weber et al. 2004).
Experiential processing of climate change occurs when an individual has a personal experience with the climate. However, personal experience and climate change are concepts with different levels of psychological distance, which is measured in four aspects: spatial, temporal, social, and certainty (Liberman and Trope 2008; Trope and Liberman 2010). Personal experience is psychologically close, as it refers to something that happened to oneself at a specific location and time. Similarly, events that occur to close friends and family, or that are experienced secondhand through images or vivid descriptions, can also feel psychologically close. On the other hand, global or faraway weather, long-term climate states, and possible future changes are all psychologically distant. Research suggests that psychologically close information may be more influential: individuals tend to think about the current day even when asked about temperature trends over longer time periods (Druckman and Shafranek 2017; Druckman 2015).
Experiential processing can also be affected by existing abstract ideas, a process known as motivated reasoning (Kunda 1990). The belief that climate change is happening can influence how individuals perceive their personal experiences with climate change or weather (Myers et al. 2013; Reser et al. 2012; Shao 2016). For example, a longitudinal study of U.S. adults found that respondents who believed in climate change agreed more that they had personally experienced it, compared to those who did not believe that it was happening (Myers et al. 2013). Similarly, individuals who believed that climate change is happening interpreted a wider range of warmer-than-normal temperatures to be evidence of climate change, compared to those who did not believe in climate change (Broomell et al. 2017). It is also important to specify whether belief refers to anthropogenic climate change, as some individuals agree that weather is changing but think it is due to natural or other causes. Previous studies examining climate change perceptions have often measured both the belief that climate change is happening and what is causing it (e.g., Hamilton and Stampone 2013; Howe et al. 2015; Shao 2017).
Previous research has also highlighted the influence of geographic and sociodemographic factors on climate change views, including perceived personal experience with impacts. This may be due to individuals adhering to the views of groups with which they identify (Kahan 2012) or the way in which individual characteristics affect experiences (Fishbein and Ajzen 2010). The sociodemographic factor most commonly found to influence climate change views is political party or ideology, as responding to climate change has become a political and social issue (McCright 2011; Dunlap et al. 2016). Democrats and Liberals tend to report the highest perception of climate change impacts, with Republicans and Conservatives having the lowest (Hamilton and Keim 2009; Shao 2016; Goebbert et al. 2012; Deryugina 2013; McCright et al. 2014; Shao and Goidel 2016; Marquart-Pyatt et al. 2014). This effect increases when individual’s social circles are mostly consistent with their own ideology, as opposed to including many people with different partisan views (Hamilton et al. 2018). There is less consensus on whether other sociodemographic characteristics influence perceptions of weather and climate change, including age, education, gender, income, and race (Goebbert et al. 2012; Howe et al. 2013; Hamilton and Keim 2009; Shao 2016; Shao and Goidel 2016). Previous studies have also described regional patterns in perceptions of weather and climate change trends (Howe et al. 2013; Hamilton and Keim 2009).
b. Perceptions of personal experience with climate change
Few previous studies have examined the influence of weather on climate change perceptions, explicitly. Instead, most studies measured individuals’ perceptions of specific weather that the public may associate with climate change (e.g., warmer-than-usual temperatures). This section reviews studies of the general public in developed countries and focuses on perceptions of weather conditions and trends as opposed to perceptions of discrete or extreme weather events.
1) Perceptions of temperature
Previous research suggests that the general public is able to accurately assess temperatures on the current day (Li et al. 2011; Zaval et al. 2014) and the previous day (Zaval et al. 2014). Public perceptions of recent seasonal temperature averages also tend to reflect actual conditions (McCright et al. 2014; Howe and Leiserowitz 2013). However, there is mixed evidence for whether individuals’ perceptions of temperatures over a year or more are accurate. In the 2007–08 Gallup World Poll of 89 countries, individuals were more likely to report local warming in areas with increasing positive temperature anomalies (Howe et al. 2013). On the other hand, U.S. surveys from 2008 to 2011 largely found no relationship between perceptions of changing temperatures and actual trends over 3–19 years (Goebbert et al. 2012; Shao and Goidel 2016). The latter two studies (Goebbert et al. 2012; Shao and Goidel 2016) did not specify a time period in their survey questions, so the time period considered by individuals may not match the one measured by the authors. Additionally, both studies took place in the United States, where perceptions of climate change are heavily shaped by social factors, such as political party (Dunlap et al. 2016), which could decrease the influence of actual weather.
There is evidence that perceptions of long-term temperatures may instead be influenced by more recent conditions. Two U.S. studies found that respondents’ perceptions of warming over the past year or more were significantly correlated with temperatures at the time of the interview or during the previous day (Zaval et al. 2014; Joireman et al. 2010). Combined with accurate perceptions of short-term temperatures, this supports the theory that recent (i.e., psychologically close) weather is more influential.
2) Perceptions of precipitation
Precipitation experience is usually measured as extreme events (e.g., storms and hurricanes) or related impacts (e.g., flooding and drought) instead of direct measures (e.g., accumulation of rain and snow). This may be due to precipitation data being limited, both spatially and temporally, and there being high variability in precipitation patterns (see, e.g., Hamilton and Stampone 2013). As a result, there is little previous research regarding public perceptions of precipitation levels. In one U.S. study, greater precipitation amounts significantly increased the probability that respondents perceived the most recent winter or summer as having been wetter than normal instead of having the same or drier conditions (Howe and Leiserowitz 2013). This suggests that individuals may be able to accurately assess precipitation levels outside of extreme events, but the lack of previous evidence necessitates further study.
3) Perceptions of overall changes in weather
There is mixed evidence of how individuals’ perceptions of climate change or general weather may be related to specific types of weather. A U.S. study found that neither actual temperature nor precipitation levels were related to the probability that respondents thought that the weather had been stranger than normal “in the past few years” (Shao 2016). The lack of a significant relationship may be due to the unspecified time period in the question, while the authors measured actual weather as seasonal anomalies over the past 3 years (Shao 2016). A study administered at the same time, focused on residents of rural areas in nine U.S. states, found that longer trends in temperature (over 38 years) significantly predicted the percentage of respondents who perceived that climate change had affected their families and communities (Hamilton and Keim 2009). This latter study also examined seasonal temperature trends, finding that the influence on perceptions of climate impacts was strongest for winter but similar for other seasons (Hamilton and Keim 2009).
4) Quantifying weather conditions
Most studies described above examined individuals’ perceptions of changes in weather. These are relative measures that compare current to usual (“normal”) conditions and are expressed as trends or anomalies (deviations) from normal. When measuring perceptions of personal experience, the ideal time period over which normal climate conditions are considered should be short enough to have been experienced by many adults, but long enough to represent long-term trends. Studies generally have used anomalies compared to a 30-yr historical average (Shao 2016; McCright et al. 2014; Howe and Leiserowitz 2013; Goebbert et al. 2012), although one used a 15-yr average (Zaval et al. 2014). Others measured trends over 10–38 years (Shao and Goidel 2016; Hamilton and Keim 2009). Only one study was identified that used a nonrelative measure with no context of normal or previous weather: temperature at the time of the survey (Joireman et al. 2010).
The distinction between perceptions of relative and nonrelative weather may be important for understanding how individuals experientially process weather and climate change. Since experiential processing is weighted toward recent experiences, perceptions may be more influenced by current weather conditions, such as feeling hot or cold on a given day. Perceptions of nonrelative conditions do not require an individual to remember previous conditions in order to understand whether the current situation is, for example, warmer or cooler than usual. On the other hand, for individuals to conceptually link current or recent experiences to climate change, they would need to know if it is unusual or changed from normal, which would be a relative measure. However, no previous studies were identified that compared whether individuals’ perceptions of personal experience were more related to relative or nonrelative weather measures.
3. Research questions
This study evaluates the influence of recent weather on public perceptions of climate change and extreme weather. Multiple types of weather are included to explore which types of weather are influential. Since motivated reasoning may be a strong determiner of perceptions of personal experience, climate change belief and multiple sociodemographic factors are included in all models as a control.
a. Research question 1: Does recent weather influence perceptions of overall personal experience with climate change and extreme weather?
Previous studies have considered time periods of 1–2 days or much longer (months, seasons, or years), so this question considers two lengths of time: the current day and the previous week.
Hypothesis 1.1: Weather on the current day will predict perceptions of personal experience.
Hypothesis 1.2: The average weather for the preceding week (the current day plus the 6 days before) will predict perceptions of personal experience.
b. Research question 2: Which types of recent weather most influence perceptions of overall personal experience with climate change and extreme weather?
Previous studies have tended to focus on one type of weather (temperature or precipitation) and one type of measurement (relative or nonrelative measures). The goal of this question is to understand which aspects of weather are most relevant to experiential processing.
Hypothesis 2.1: Temperature conditions will more strongly predict perceptions of personal experience, compared to precipitation.
Hypothesis 2.2: Both relative measures (percentiles of normal conditions) and nonrelative measures (°F or inches of precipitation) of recent weather will predict perceptions of personal experience.
4. Methods
This research focuses on NYS, a state with diverse climate regions, industries, and population demographics (Rosenzweig et al. 2011). Statewide survey data were coupled with weather measures to investigate answers to our research questions. Study methodology and measures are described below.
a. Survey methodology
Perceptions of personal experience with climate change were measured as part of the 2014 Empire State Poll (ESP), a telephone survey of adult (18 years and older) residents of NYS, conducted from 18 January through 5 March 2014 (Cornell University Survey Research Institute 2014; Xian and Meyers 2014; Allred 2014). The sample of completed surveys was 800 respondents, split evenly between downstate (nine counties around New York City) and upstate (52 remaining counties) to ensure a thorough sampling of upstate residents. Respondents’ location was measured as their self-reported county of residence at the time of the survey, which was used to assign their NYS region (upstate or downstate). The ESP also measured individual sociodemographic characteristics. Respondents were from 61 of 62 NYS counties, and the seven respondents who did not report their county of residence were removed from analyses. Upstate/downstate population-based sample weights were applied to analyses so that results were representative of NYS residents.
The Survey Research Institute of Cornell University conducted the survey as a dual-frame random digit dial (landline and cellular) telephone survey in both English and Spanish. The sample, acquired from Marketing Systems Group, excluded known business telephone numbers, disconnected numbers, and nonhouseholds. Every listed telephone in NYS had an equal chance of being included in the survey. For each telephone number selected, the adult household member who was a resident of NYS and had the most recent birthday was selected to complete the survey, ensuring random selection of members within each household. The cooperation rate (66%) and response rate (21%) are comparable to those obtained by other surveys of NYS residents, including the 2009–11 American Community Survey by the U.S. Census (Xian and Meyers 2014). A descriptive summary of ESP responses and variable coding are presented in Table 1.
Descriptive results from the 2014 ESP, written as a percentage (number) of respondents (n = 793).
b. Survey measures
Respondents’ perceptions of personal experience were measured as their level of agreement (on a 5-point scale: strongly disagree = 1, neutral = 3, strongly agree = 5) with the statement, “I have personally experienced the effects of extreme weather or climate change.” This statement did not specify a time period and therefore represents overall perceptions rather than perceptions of specific weather events or trends. This measure combines experiences with both climate change and extreme weather. As a result, respondents may be describing their experience with extreme weather events, climate change, or both.
Respondents’ belief in anthropogenic climate change was measured with a combination of two questions. The first assessed belief in the reality and current timing of climate change: “Do you believe climate change is happening?” Response options were “yes” (n = 645), “no” (n = 118), or “don’t know” (n = 30). The second assessed perceived causes of climate change. Respondents were asked to identify the cause of climate change, “assuming climate change is happening.” Response options were “caused mostly by human activity” (n = 454), “caused mostly by natural changes in the environment” (n = 201), “other” (n = 87), “none of the above because climate change isn’t happening” (n = 50), or “don’t know” (n = 1). A composite measure was created to split respondents into three categories:
Belief that climate change is not happening or not sure whether it is happening. These respondents answered the first question as “no” or “don’t know” and the second as “none of the above because climate change isn’t happening.”
Belief that climate change is happening and caused by natural or other causes. These respondents answered the first question as “yes” and the second as “caused mostly by natural changes in the environment” or “other.”
Belief that climate change is happening and caused by humans. These respondents answered the first question as “yes” and the second as “caused mostly by human activity.”
Respondents’ sociodemographic characteristics were also measured in the ESP. Respondents reported their political affiliation on a 7-point response scale from strong Democrat to strong Republican. Six respondents were excluded from analyses because they did not know their political party (n = 2) or refused to answer (n = 4). Respondents’ gender (male or female) was recorded based on the interviewer’s assessment, with no missing values. Respondents’ age was calculated based on their self-reported year of birth subtracted from the year of the survey (one missing value) and centered for analyses. Respondents’ education level was recorded as the highest level of academic achievement completed on a 7-point scale, with no missing values. To avoid low sample sizes, responses were combined into three groups: 1) high school graduate or lower [no schooling or grades 1–8 (n = 11); 2) high school incomplete (n = 43) and high school graduate (n = 157)]; and 3) college complete or incomplete or other training [technical, trade, or vocational school (n = 24), some college or 2-yr Associate degree (n = 210), or 4-yr college degree (n = 187)] and postgraduate training or professional school after college (n = 161).
c. Recent weather
Recent weather was measured as maximum and minimum temperatures and total precipitation in each respondent’s county on the day of the survey and the average of the week preceding the survey (the day of the survey plus the 6 days before that). Daily weather data for the time periods of interest were obtained from the Northeast Regional Climate Center (NRCC) as a high-resolution (5 km × 5 km) grid (DeGaetano and Belcher 2007a), based on original data from the NOAA Regional Climate Center Applied Climate Information System [methodology described by DeGaetano and Belcher (2007b)]. Weather was quantified in two ways: nonrelative measures (degrees Fahrenheit or inches of precipitation) and relative measures (percentile of the climate normal, a statistical distribution of each weather measure over a historical time period). For relative measures, a 30-yr climate normal is generally used (Rosenzweig et al. 2011), so a 1981–2010 normal was used for temperature. Precipitation data at the desired resolution were only available from 2002 on, so a 2002–10 precipitation normal was used.
To match weather data to ESP respondents’ locations, weather measures were aggregated at the county level. Some NYS counties are large enough that weather measures at times differed greatly across the county, but respondents’ location within counties was not known. For measures to represent the average weather experienced by a county resident, county averages were weighted by census tract population. Gridded weather data were linked to the county (NYS GIS Clearinghouse 2016) and 2010 census tract (U.S. Census Bureau 2010) polygons within which they were located using Excel and QGIS, an open-source GIS software. Average weather measures for each census tract were calculated based on the average of the internal grid points or, for census tracts smaller than the grid size, the value of the grid point nearest to the census tract center. Population-weighted census tract weather measures were calculated as the average weather measure for that census tract multiplied by the census tract’s proportion of the county population. Final population-weighted county weather measures were computed as the sum of all population-weighted census tract weather measures within each county.
d. Data analysis
Population-weighted county weather measures were matched to each ESP respondent using their self-reported county and survey date [dataset processed by Fownes and Allred (2018)]. Statistical analyses were performed in R to test the relationship between ESP respondents’ perceptions of personal experience, recent weather, and climate change belief.
To answer research questions 1 and 2, multiple linear regressions assessed the influence of recent weather and climate change belief on respondents’ perceptions of personal experience (the dependent variable). A control model included climate change belief, NYS region, and sociodemographic characteristics as predictor variables. All other regression models included all control variables and a weather measure. Weather measures were centered so that regression intercepts represent predicted perceptions of personal experience for ESP respondents who experienced the mean values of each weather measure. For all predictors included in multiple regression models, variance inflation factors showed no evidence of multicollinearity, with values of 2.14 or less.
5. Results
a. Descriptive results
ESP respondents generally believed that climate change is happening and that they had experienced climate change or extreme weather (Table 1). The majority (69%) of respondents agreed or strongly agreed that they had personally experienced the effects of extreme weather or climate change, while only 18% disagreed or strongly disagreed, and 13% were neutral. A larger proportion of respondents (81%) believed that climate change is happening. In total, 52% of respondents believed that climate change is happening and due to human causes, with 29% believing in climate change due to natural or other causes; 19% of respondents reported that climate change was not happening or that they were unsure.
Respondents were evenly split between the two NYS regions, as was the intent of the survey. When responses were adjusted using population-based survey weights, downstate representation increased to 64%. More respondents were Democrats and strong Democrats (38%) than Republicans or strong Republicans (19%). A large proportion of respondents identified as Independent (43%), with some of those reporting that they were close to Democrat (15%) or close to Republican (7%). Respondents’ ages ranged from 19 to 94 years, with a mean age of 49 and a median age of 50. There were similar numbers of male (49%) and female (51%) respondents. Almost all respondents had completed high school or higher education, with only 1% not completing grade 8 and 5% having partially completed high school. Half of respondents started or completed college or completed additional training (53%), while fewer went no further than high school (27%) or attended postgraduate school or training (20%).
During the 3-month study period, from late winter through early spring, respondents experienced a wide range of temperatures and precipitation (Table 2). Daily maximum and minimum temperatures ranged from below to above freezing, and relative measures show that these were within the range of normal temperatures for that time of year. Daily nonrelative precipitation levels were generally low. A few days had precipitation greater than 1 in., which is a general cutoff for extreme precipitation for NYS (Rosenzweig et al. 2011). Average temperatures and precipitation levels for the week preceding the survey were similar to daily values.
Descriptive results of weather measures for all ESP respondents on the day of and the week preceding the survey (n = 793). Data for each respondent are population-weighted county averages.
b. Motivated reasoning
Climate change belief was a significant predictor of perceptions of personal experience (model 1 in Tables 3 and 4). The intercept for the control model predicts the perceptions for an individual who does not believe in climate change, lives downstate, identifies as a strong Democrat, is male, is of mean age (49 years old), and has completed high school or less schooling. This respondent is predicted to slightly agree with the statement that they had personally experienced the effects of extreme weather or climate change (a score of 3.5 on the 5-point scale). Believing in anthropogenic climate change increased respondents’ perceptions of personal experience by about 0.8, while belief in climate change due to natural or other causes increased it by a lesser amount (0.6).
Regression models predicting perceptions based on climate change belief, sociodemographic factors, and local weather measures (day of the survey). Data presented as unstandardized B coefficients (standard errors) and standardized β coefficients. NMax is nonrelative maximum temperature, RMax is relative maximum temperature, NMin is nonrelative minimum temperature, RMin is relative minimum temperature, NP is nonrelative precipitation, and RP is relative precipitation. Note that *p < 0.05 and **p < 0.001.
Regression models predicting perceptions based on climate change belief, sociodemographic factors, and local weather measures (1 week prior to survey). Data presented as unstandardized B coefficients (standard errors) and standardized β coefficients. NMax is nonrelative maximum temperature, RMax is relative maximum temperature, NMin is nonrelative minimum temperature, RMin is relative minimum temperature, NP is nonrelative precipitation, and RP is relative precipitation. Note that *p < 0.05 and **p < 0.001.
Of the sociodemographic characteristics added to the model, political party most strongly predicted perceptions of personal experience. As the party scale moves from strong Democrat to strong Republican, perceptions of personal experience are predicted to decrease by 0.7. Gender was also a significant predictor, with perceptions increasing by 0.2 for females compared to males. Age and education did not significantly influence perceptions of personal experience. In multiple models, the NYS region significantly influenced perceptions, with those upstate agreeing less (−0.2) than those downstate that they had personally experienced the effects of extreme weather or climate change. Altogether, climate change belief, region, and sociodemographic characteristics explain 15.7% of the variation in perceptions of personal experience.
c. Research question 1: Does recent weather influence perceptions of overall experience with climate change?
Hypothesis 1.1 was slightly supported, as only one of the weather measures significantly influenced perceptions of personal experience (model 5 in Table 3). Relative minimum temperature on the day of the survey was positively related to perceptions; as temperatures increased, respondents agreed more that they had experienced the effects of extreme weather or climate change. No other weather measures were significant predictors of personal experience. The nonrelative minimum temperature on the day of the survey had borderline significance (p = 0.09), further suggesting that minimum temperatures may be meaningful to respondents’ perceptions.
Weather had a lesser influence on perceptions of personal experience compared to climate change belief. The standardized beta coefficient for relative minimum temperature on the day of the survey was smaller than those for climate change beliefs. Furthermore, the explained variance in this regression (model 5) increased very little (up to 0.5%), compared to the control model. There was a wide range of both nonrelative maximum and minimum temperatures among respondents for each response about perceptions of personal experience, supporting the statistical result that local temperatures were only a small influence on perceptions.
Hypothesis 1.2 was not supported, as none of the weather measures over the preceding week were significant predictors of perceptions (Table 4).
d. Research question 2: Which types of recent weather most influence perceptions of overall personal experience with climate change?
Hypothesis 2.1 was partially supported, as minimum temperature was a significant predictor of respondents’ perceptions of personal experience, but precipitation was not. While there was wide variation in both minimum and maximum temperatures, precipitation amounts were generally very low, with a few extreme events. As a result, there may not have been enough statistical power to detect a relationship between precipitation levels and perceptions of personal experience. Hypothesis 2.2 was partially supported, as only a relative weather measure was a significant predictor of perceptions of personal experience.
6. Discussion and future research
a. Experiential processing of weather and climate change
Results support previous findings that recent weather may influence the public’s perceptions of personal experience with overall weather or climate change (Joireman et al. 2010; Zaval et al. 2014). Although the influence of recent weather was much smaller than that of climate change belief and political party, a single day affecting overall climate or extreme weather perceptions at all demonstrates how experiential processing of climate may be weighted toward recent events. Only a weather measure on the day of survey administration was significant, suggesting that the recency bias of experiential processing applies to very recent time periods for the types of measures included in this study. The influence of short-term weather on individuals’ perceptions of long-term trends may be partly explained by findings that the general public often confuses weather and climate (Reynolds et al. 2010). Furthermore, it indicates that experiential processing of psychologically distant phenomena, such as long-term global climate change, is disproportionately affected by psychologically close weather that may be associated with these phenomena.
Temperatures may be particularly salient in this study because the ESP was administered during winter and early spring months. Other studies have found that the general public has reported experiencing warmer winters in recent years and associates such warming with climate change (Hamilton and Keim 2009; Akerlof et al. 2013; Howe and Leiserowitz 2013). Both maximum and minimum temperatures were used because those are generally associated with different times of day and therefore may be experienced under different circumstances. Minimum temperatures may have been more influential than maximum temperatures because they might be associated in respondents’ minds with the extremity of winter. Future studies should consider surveying respondents across multiple seasons (see, e.g., Howe and Leiserowitz 2013; McCright et al. 2014).
While precipitation was not a significant predictor of perceptions of personal experience, the range of precipitation levels during the study period was small, with the majority of respondents experiencing no precipitation, which limited the statistical detection of a relationship. Of the two identified studies that assessed precipitation as a predictor of self-reported personal experience (Shao 2016; Howe and Leiserowitz 2013), only one found a significant relationship (Shao 2016), while the other was limited by data availability and variability in precipitation patterns (Hamilton and Stampone 2013). This variability could also make it harder for individuals to think about long-term trends, or inhibit them from seeing precipitation conditions as associated with climate change. Instead of more common precipitation patterns, multiple studies have found that public perceptions of precipitation and water availability are influenced by extreme events such as storms, flooding, and drought (Akerlof et al. 2013; Goebbert et al. 2012; Shao and Goidel 2016; Howe et al. 2014; Switzer and Vedlitz 2017).
Our results suggest that individuals experience weather based on comparison to normal past conditions (relative measures), instead of on their instantaneous feelings (nonrelative measures). Previous findings suggest that individuals can accurately perceive relative temperature and precipitation conditions over periods of up to 10 years (see section 2). However, the two studies that examined how weather influences overall weather or climate change perceptions used much longer time periods of weather than the 1 day and 1 week used here: average seasonal anomalies over 3 years (Shao 2016) and 38-yr trends (Hamilton and Keim 2009). Therefore, the results of the current study suggest that individuals may also consider short-term conditions to be indicative of long-term trends. To more robustly assess the difference between relative and nonrelative weather measures, authors could employ a survey sampling strategy that allows them to account for spatial differences between the two measures. Furthermore, spatial patterns in either measure may be correlated with other variables (for a further discussion, see section 6c).
It should be acknowledged that it is challenging for researchers to estimate actual weather experienced by respondents. An individual’s experience with weather depends on their exact location. As a result, average meteorological measurements over an area (such as a whole county) do not necessarily capture the exact weather exposure of any specific individual. In the study area of NYS, some counties are large enough that weather can vary greatly from one end of the county to another, so a county average might misrepresent the weather experienced in the most populous region. To address this limitation, population-weighted weather averages were used to best estimate the average weather that the most individuals were exposed to, a strategy also employed by Howe et al. (2013). Future studies that are able to identify the location of their respondents at a higher resolution (e.g., zip code) would not need to weight their weather measures. Additionally, meteorological conditions do not necessarily translate into impacts felt by individuals. For example, heavy rains may not result in flooding, or dry hot days may not feel as extreme as humid hot days. Similarly, exposure to extreme weather is affected by individuals’ access to a climate-controlled environment, such as a car or office building. Future studies might consider certain thresholds of weather that would impact individuals, such as high temperatures above a locally relevant heat index, unseasonal frosts or thaws, or heavy rains that cause erosion or flooding.
The survey question used in this study to assess perceptions of personal experience combines both climate change and extreme weather. As a result, respondents may have answered with just climate change or extreme weather in mind. However, there is evidence that some (but not all) members of the general public respond similarly to questions about these topics: a 2015 national survey found that respondents’ perceptions of personal experience with climate change were significantly correlated to perceptions of personal experience with extreme weather (Pearson r = 0.26, n = 998, p < 0.001; S. Allred 2015, unpublished data). It is recommended that future studies separate these questions, which would allow research to determine whether the general public differentiates between climate change and extreme weather events, which are often discussed by the public as impacts of climate change (Whitmarsh 2008; Leiserowitz 2006; Borick and Rabe 2010; Akerlof et al. 2013; Leiserowitz et al. 2012).
b. Motivated reasoning
Our results support previous findings that personal experience is interpreted through motivated reasoning. As a result, individuals who believe strongly that climate change is not happening may not notice unusual local weather or not interpret it as a signal of change in the climate system. For example, recent extreme cold fronts in the northeast United States have been scientifically attributed to a loss of Arctic sea ice resulting from climate change (Zhang et al. 2016), but these frigid temperatures are often interpreted by climate change deniers as evidence against global “warming” (McGrath 2014). The opposite effect can also occur, with those who believe in climate change interpreting any extreme weather event as evidence of its reality. Motivated reasoning is weaker and experiential processing is stronger in groups with weaker beliefs about climate change, such as individuals in the United States who do not identify with a political party (Hamilton and Stampone 2013; Egan and Mullin 2012). The possibility for an interaction between the effects of motivated reasoning (such as those stemming from climate change belief) and actual weather on perceptions of personal experience indicates that future studies should include both variables concurrently in analyses.
Our results also suggest that NYS residents may be a unique population with regard to the strength of their climate knowledge. More respondents believed that climate change was happening (81%), compared to 64% of respondents in a national survey also conducted in early 2014 (Leiserowitz et al. 2017). Additionally, a higher percentage of respondents reported having personally experienced the effects of extreme weather or climate change (69%), compared to 34% of respondents nationally (Leiserowitz et al. 2017). The influence of climate change belief on perceptions of personal experience through motivated reasoning suggests that the large proportion of respondents who believe in climate change may be partly responsible for the many who reported personal experience with climate changes’ effects.
Sociodemographic and regional factors were also found to influence perceptions of personal experience. These factors have previously been found to shape views on climate change (see section 2) and therefore provide further support for the importance of motivated reasoning in the general public’s psychological processing of extreme weather. After climate change belief, political party was the most influential predictor of personal experience and was significant in all multiple regression models, confirming previous research (Hamilton and Keim 2009; Shao 2016; Goebbert et al. 2012; Deryugina 2013; McCright et al. 2014; Shao and Goidel 2016; Hamilton et al. 2018; Marquart-Pyatt et al. 2014). However, there is room for further study on how party can influence climate change views. For example, Hamilton and Stampone (2013) found a significant interaction between political party and weather on the influence of climate change perceptions: weather influenced Independents’ perceptions much more than Democrats’ and Republicans’ perceptions, indicating that Independents’ views on climate change may be more flexible. Age and education were not significant predictors in the multiple regression models. This is not surprising, given that previous studies differed in respect to whether or not these characteristics influence climate change views (Goebbert et al. 2012; Howe et al. 2013; Hamilton and Keim 2009; Shao 2016; Shao and Goidel 2016). NYS region was a significant predictor across most models, suggesting that climate change perceptions are influenced by regional factors not covered by the other sociodemographic measures included here.
c. Other considerations
In addition to the predictors included here, other factors may shape the public’s perceptions of personal experiences with weather or climate change. The literature on social–psychological formation of climate change perceptions suggests that additional influence may come from media attention and communication by thought leaders such as politicians or scientists (Spence et al. 2011; Whitmarsh 2008; Weber and Stern 2011; Whitmarsh 2009). Furthermore, trust in the information source influences how the message may be interpreted, such as how partisan media coverage of climate change influences Democrats and Republicans differently (Carmichael et al. 2017).
Climate attitudes and beliefs may also be spatially dependent. For example, the two strongest predictors of climate change perceptions in our study (belief in climate change and political party) differed by NYS region: respondents who lived downstate were more likely to believe in climate change and identify as Democrat. This geographic pattern is similar to those found in other studies (e.g., Howe et al. 2015). Downstate New York generally has warmer temperatures than upstate New York (Rosenzweig et al. 2011), but in our multivariate analyses, the potential effect of weather measures was dwarfed by the predictive power of political party and climate beliefs. The spatial nature of these predictor variables underscores the importance of considering geographic orientation variables, such as our inclusion of NYS region. Future studies with sufficient statistical power could further examine these potential dependencies through multilevel analyses that include respondents’ specific location (e.g., Cutler 2015) or through explicit assessment of spatial dependencies.
7. Conclusions
Although some authors have suggested that perceptions of local climate impacts may prompt mitigation and adaptation actions (Lorenzoni and Pidgeon 2006; Spence et al. 2012; Weber 2006), this is not always the case. Psychologically close information such as personal experience will be linked to other psychologically close objects, including emotions (Brügger et al. 2015). For example, a survey found that individuals’ perceptions of climate change risk were related to their fear levels when they had read about local impacts of climate change (Brügger et al. 2016). In contrast, individuals who read about global (psychologically distant) climate impacts seemed to base their risk perceptions on their belief that climate change was happening (a psychologically distant construal; Brügger et al. 2016). The link between climate change perceptions and action is further complicated by a variety of factors, including individuals’ attachment to global versus local places and defensiveness triggered by feelings of low capacity to effect change on a global issue (Brügger et al. 2015).
However, evidence that experiential processing influences climate change views is important to climate change communicators and educators, as the public’s personal experiences with climate change could vacillate as daily, yearly, and decadal weather patterns pass through. This has implications for how individuals feel concern about or take action on climate change: winter chill might reduce the public’s worry, while summer heat could lend to a feeling of urgency. Planners of events related to climate change (such as surveys, political campaigns, or commercial promotions) should consider how recent weather could affect individuals’ responses. Communication strategies could highlight recent weather that follows climate change trends or could discuss the difference between short-term weather and climate change.
Acknowledgments
This material is based upon work that was funded by Federal Capacity (Hatch) Funds from the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Atkinson Center for a Sustainable Future. We would also like to acknowledge support of the Cornell Institute for Climate Smart Solutions and the Cornell Survey Research Institute. We also thank Drew Margolin, Art DeGaetano, and Jessica Spaccio for their assistance and advice. This research was previously published as a master’s thesis.
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