A Glance at how Boulder, CO Weather has Changed over the Last 12 years
Climate change has been at the top of world issues in recent years sparking vast amounts of research to be conducted around the world regarding the issue. The effects climate change has on the environment is very drastic and can do as much as cause severe drought, intense storms, fires, melting of polar ice caps, rising sea levels and extreme temperatures.
To dive more into this topic I wanted to look at the shift in weather patterns, specifically temperature in Boulder, Colorado across the last 12 years. My initial goals of studying this data was to be able to recognize some patterns and trends that could give me better insight to what climate change looks like in my local area. As a side note I also thought of the data as useful in providing me better insight about what weather to expect in the different months across the year and if those stats were steady or shifting.
The dataset I was provided with was collected from January 1, 2010 to August 20, 2022. The dataset included data about date, average temperature, daily minimum and maximum temperature, dewpoint, visibility, wind information and precipitation. My initial concerns before diving into the data was what the proper interpretation of temperature shifts would look like. I did not come into this analysis to make a solid conclusion or statement based on the data provided but I need some prior research to better understand what I was looking at. After a little bit of research I found a useful bit of information by climate.gov that stated since the early 1980s the Earth has increased temperature by roughly 0.32 degrees Fahrenheit every decade. If the temperature were actually increasing I would expect to see a change similar to that in Boulder from 2010 to 2022 from my data.
To interpret the dataset I was provided I had to open the boulder_csv file using the pandas module in python. To make it easier to look at my data, I converted the date column from string format to date/time using pandas pd.to_datetime. This conversion made it generally easier for me to group my data by year and month, making my analysis much more simple.
To start my statistical analysis I looked at the data using pandas head() and tail() to better understand what I was working with. I then computed the descriptive statistics which provided both valuable and some confusing information. From the statistics it shows that the average temperature in Boulder over this time frame was 52 degrees Fahrenheit which surprised me initially. I thought this was bit cool considering the amount of mild days I had experienced during the years. The large range of temperatures Boulder experiences in a year puts this number into perspective a little better. The highest temperature recorded in Boulder during this time frame was 104 degrees Fahrenheit and the lowest temperature recorded was -13 degrees Fahrenheit.
The more important data I wanted to look at though was the yearly temperature shift. I figured the best way to do this was to group the data by year and take the average temperature of those years and look at all of them together. What I found was very inconclusive in my opinion. There was no clear pattern when I looked at how all the averages lined up next to each other. In both 2010 and 2011 the average temperatures were in the 52 degrees with a slight decrease in temperature from 2010 to 2011. The following year 2012, the average temperature shot up to 55 degrees on average. Then in 2013 the average temperature dropped all the way back to about 51.5 degrees which is the most extreme change out of all the years I analyzed. This temperature average stayed the same for 2014 then hovered between 53–53.5 degrees on average for every year after up to 2022 besides 2019 where the temperature averaged 50.5 degrees. If I were to only look at the temperatures from the years 2010 to 2022 I would say of course there has been a temperature increase in the last decade but when looking at all of it together there are many different shifts in temperature across the years with both decreasing and increasing possibilities. From this there are no significant conclusions I can make based off of these numbers alone.
When looking at the other side of my analysis consisting of the monthly average data there was more conclusive data that helped me understand how temperature shifted throughout the year itself. It could be stated that on average February was the coldest month of the year with an average of 33 degrees and July was the hottest month of the year with an average of 74 degrees. The most drastic shifts in temperature change during the year was between February in March with an average increase of over 10 degrees and between September and October with a average decrease of 14 degrees.
When looking back at my analysis it wasn't that complicated and I gained some interesting insights but none that I would say are 100% conclusive. To conduct a full proof analysis with maximum certainty I would have to collect more data and bring more variables into the analysis to conclude any temperature changes and what has influenced such.
