Name: __________________________
Date: _____________________
Section______
TA: Lisha Roubert
Lab #3: Temperature Trends in Madison, WI
Objective: The objective of this exercise is to introduce you to the analysis of temperature time series.
You will calculate statistical parameters (mean, median, extreme values, range, standard deviation) which
are often used to study climate data. You will also determine the effect that the seasonal cycle has on
temperature data.
1) Calculating statistical parameters -Download the 1998_2000_Jan_MSN dataset from the
course website. There is one dataset in Sheet1 and another dataset in Sheet2. The fourth column
of both dataset contains the daily mean temperature of all the days in January in year 1998
(Sheet1) and 2000 (Sheet2) for Madison Dane County airport. Do the following with the data for
both years:
a. Use Excel to find the mean temperature of January. Which year has a larger mean
temperature? Do any extreme values in the dataset influence the mean temperature? Given
that a strong El Nino event happened in 1997/1998 and 2000 was not a strong El Nino year,
how might have the El Nino event caused changes to the mean temperature of January in
Madison in 1998?
b. Find the median temperature of January. Do any extreme values in the dataset influence the
median temperature?
c. Find the monthly temperature range. Which year had the larger range? If you were to guess a
temperature range for a coastal city, but with the same elevation and latitude as Madison,
would the temperature range be larger or smaller than Madison’s temperature range? Why?
d. Find the standard deviation of the temperature in January.
e. This website shows the climatology data from 1971 to 2000 of Madison Dane County airport.
http://mrcc.sws.uiuc.edu/climate_midwest/historical/temp/wi/474961_tsum.html
Compare the single year monthly mean temperature with the 30 year climatological mean
temperature in January. How far away from normal is it? Is it warmer or colder than normal?
2) Removing seasonal cycles from data- As explained in class, seasonal cycles are removed from
data because variations in temperature caused by the seasons in certain regions can sometimes
make it very hard to study other phenomena that have an effect on temperatures. In this exercise
you will determine if removing the seasonal cycle from the given datasets makes a difference.
a) Download the dataset monavg_temp_MSN. Sheet 1 of this dataset contains monthly average
temperatures for Madison WI from 1984-2009. Use this data to make a scatter plot of Monthly
Mean Temperatures vs. year. Describe the plot results you obtained.
b) Now go to Sheet2 of the monavg_temp_MSN dataset. This dataset is the same dataset you used
in part 2a), but with removed seasonal cycle. Use this data to make a scatter plot of Monthly
Mean Temperatures vs. year. Is this plot similar or different to the one you made in part 1a)?
Why? Explain.
c) If you did part 1a) and part 1b) with Monthly Mean Temperatures for Jamaica, which is located in
the Caribbean, would you expect both your plots to look very similar or different? Why? Explain.
(Think about what you’ve learned in class today.)
Extra thought question: (Bonus Question)
This map is a representation of average global albedo. The albedo is a measure of surface
reflectivity of the sun’s radiation. The value of albedo ranges from 0 = non-reflective to 1=100%
reflective surface. As you can see from the map, albedo increases as you move towards the poles
and decreases towards the equator. The poles have the highest albedo while the tropics have the
lowest albedo.
a) Based on the map, what do you think causes the albedo to be higher in the poles and lower
in the Tropics?