The Seasonality of Belief: How Temperatures in the U.S. Affect Public Interest in Global Climate Change S. J. RALSTON EAS 4480

Goals and Motivation Wanted to investigate how the temperature outside affected people's belief in global warming Since we can't directly measure belief, chose to measure interest instead Motivated primarily by comments made during "Snowpocalpyse" this past winter e.g. "Look at all this snow! So much for Global Warming."

Methods Obtained regional search data from the U.S. from Google Trends In the process of obtaining U.S. temperature data from the National Climatic Data Center, a department of NOAA They don't want to give me the data and haven't explained why yet If no temperature data can be obtained, will simply compare search data to season, or a temperature proxy

Methods, cont. Search data is provided on a weekly basis, but temperature data (if/when it arrives) is on a monthly basis i.e. The search volume is measured once per week, while the temperature is measured once per month Therefore, interpolated the temperature data for once-per-week sampling frequency Examined search terms "global warming" and "climate change" versus temperature using Welch's Method

Methods, cont. Looking at periodogram, phase lag, and coherence of the data Until real temperature data comes in, using a sine function oscillating between +70 and +30 degrees Fahrenheit with a period of 6 months. Values of +70/+30 were chosen based on national averages from https://www.ncdc.noaa.gov/sotc/national/ Search data covers a range of 120 months (from Jan. 2004 - Dec. 2013), so using matching temp. data

Search Volume: A Caveat From Google Trends: "Numbers represent search interest relative to the highest point on the chart. If at most 10% of searches for the given region and time frame were for "pizza," we'd consider this 100. This doesn't convey absolute search volume." In other words, we know only how much interest there was in "pizza" relative to the time there was the most interest in pizza.

Google Trends: Relatively From www.google.com/trends

Google Trends: For Reference... From www.google.com/trends

Preliminary Results Although temperature data has not yet been acquired, an early look at periodicity—coupled with a false temperature vector—has been informative Seems already to have a six-month periodicity The question (which we will answer with the real temperature data) is whether high or low temperatures are correlating with the changes in search volume, and if we can improve upon the coherence observed

Preliminary Results, cont. X: weeks, Y: temp and search volume X: 1/weeks, Y: cross-PSD estimate

Preliminary Results, cont. X: 1/weeks, Y: Coherence Estimate X: 1/weeks, Y: PSD Estimate by FFT

Preliminary Results, cont. Phase lag For "global warming," up to +0.5 weeks For "climate change," up to +0.5 weeks also Significant periodicity observed at 6 months (27 weeks, to be exact), but this is likely due to overbearing influence of regular sinusoidal temperature proxy FFT analysis of search terms alone yields a lot of noise, vague periodicity around 6 month, 1 year marks

Conclusions More robust analysis is needed Specifically, real temperature data, if we want to get anything useful Preliminary results indicate a possible correlation between seasonal temperature changes and public interest in global climate change Pizza is way more interesting to the general public than global climate change

THANK YOU! Questions?