Environmental Studies Institute, Santa Clara University
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Lab 5: Climate Change and Air Pollution
Part A: Assessing change in global temperatures
Part B: How has Covid-19 affected patterns of air pollution?
Work in Teams of 2 or 3 students for Part A
Work Individually for Part B
What to turn in: A team lab report with all plots, tables, and brief answers to the questions in Part A, plus your
individual calculations and your answers to the questions in Part B.
The report is due Friday November 19 at 11:59pm. Please upload to Camino.
NOTE: This lab requires working in Excel Spreadsheets. We have an in-lab, in-person tutorial with Teri
Escobar, SCU’s Head of Technology Training, who will review the Excel skills you will need to complete the
lab (so everyone, regardless of Excel experience, will have the baseline training to complete the lab).
Please take notes during Teri’s tutorial, and follow along using the handout, but do not attempt to do the lab
during the tutorial. You will have ample time to work on the calculations and answer the questions by working
with your classmates for Part A (and individually for Part B).
Part A: What are global temperatures really doing? Long-term trends vs. short-term fluctuations
Introduction
We’ve discussed in lecture how average global temperatures have been increasing. Figuring out the trend of
global temperatures sounds simple enough, but is it really?
There are a few challenges: first, we do not have instrumental records that go as far back and are as evenly
distributed as we would like. Since the 19th Century, surface weather stations have recorded temperatures at
various locations around the world. However, only the land portions of the globe are sampled in this way and
the density of stations is greatest in the industrialized nations. Since about 1867, the number and distribution of
stations has been large enough to provide an adequate sample of global surface temperature variations from year
to year so it can be used for assessing global temperatures. This record provides the best documentation of
recent global climate change and is at the center of the debate over humankind’s potential to modify Earth’s
climate. Most recently, satellites, with their global coverage, have been recording a truer estimate of global
temperature (in the lower troposphere, not at the Earth’s surface) in recent years. However, satellite data exist
only since 1980 so are not included in this lab’s exercise.
A second issue (and there are many more) with global temperatures is that they are not going up in one nice
straight line – There is great variance (there are quite a few “ups and downs”) depending on the time scale
you’re considering – yearly, several years, decades, millennia…. These “ups and downs” are there because
climate is determined by so many different processes, such as how the Earth travels around the sun, the strength
of the sun’s radiation, plate tectonics, and many more.
Objectives
• Our main objective for this lab is to get a first-hand idea of how much the temperature has been
warming between 1880 and the present, and appreciate how that overall warming compares to annual
fluctuations (what temperatures are doing from year to year). Why is this important? Let me illustrate
with an example: let’s say the overall warming (1880 to 2020) is 1°C, but that temperature varied by
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0.9°C from year to year on average. That overall warming would not be very meaningful/impressive,
would it? On the other hand, if temperature varied on the order of 0.9°C from year to year and the
overall warming was 5°C, you probably would be more confident that the overall warming is
meaningful.
• Another objective of this lab is to figure out if we can see “events” like El Nino or volcanic eruptions in
the global temperature record. Are they having any effect at all on average global temperatures?
• The ultimate goal is to give you a sense of the difficulties encountered in trying to extrapolate recent
temperature trends into the future, and the need for models to make reasoned predictions of temperature
change.
• We will use air pollution data from different locales around the world to detect the effect due to the
pandemic lockdown on air quality.
• The final objective of this lab is to practice Excel skills you will use countless times in your studies and
likely in your future careers.
Lab Instructions
DOWNLOAD the Excel file with global temperatures provided on Camino and examine the contents. The data
are saved in columns, with the year, monthly temperatures (in degrees C), seasonal mean temperatures (e.g.,
DJF = December-January-February, which is the climatological definition of Northern Hemisphere winter), and
annual mean global temperature. Follow Teri Escobar’s tutorial on how to carry out the steps below in Excel.
First step: Convert the data into temperatures in °C. The values in these cells are all changes in temperature in
hundredth’s of a degree C relative to the average temperature between 1951-1980 (baseline). This baseline
temperature is 14.0 °C as stated at the bottom of the spreadsheet. So a value of 75 means the temperature is
14.75 °C. Using formulas and the “fill down” function, make new columns with temperatures for Annual,
northern hemisphere winter (DJF) and northern hemisphere summer (JJA). Use these to answer the following
questions.
Question 1: How much does temperature typically change from one year to the next, and can we predict
which direction temperature will be changing?
a) Just looking at your data (use the yearly data), what is your hypothesis regarding question 1?
b) Spend some time to inspect the data in consecutive years in your spreadsheet: is the direction of change
predictable? I.e., can you detect a warming or cooling trend by inspecting consecutive years in short-
term periods?
c) Based on a) and b), does it seem possible to predict whether the next year is going to be warmer than
this year (globally)?
Question 2: Can we predict global climate 6 months in advance?
Make a scatter plot of DJF (December through February) temperature vs. JJA (June through August)
temperature (do NOT plot either one vs. time but rather DJF versus JJA). Remember not to use the first line of
data (1880) because the Winter 1880 data is missing, so start with the year 1881). Add a linear trendline to the
data and display the R2 value under the legend. R2 is a statistic that will give some information about the
goodness of fit of a model. In regression, the R2 tells you how well the regression line approximates the real
data points. The value of R2 varies from 0 to 1.0. A value of 1.0 indicated that the regression line perfectly fits
the data.
a) Include the scatterplot in your write-up. Describe how the JJA and the DJF temperature are related.
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b) What is the R2 value and what does it tell you about the relationship between the JJA and DJF
temperatures?
c) Do especially warm winters (or warm temperatures during December – February) tend to be followed by
especially warm or cold summers (temperature during June- August)? (Of course, DJF is only winter in
the Northern Hemisphere; the issue we are exploring is generally whether global climate can be
predicted 6 months in advance, and we’re calling DJF “winter” because we are in the northern
hemisphere).
Question 3: Can we see the effect of major volcanic eruptions on global temperatures?
Following is a list of major volcanic eruptions since 1867 and the year that each occurred:
1883 Krakatau, 1890 Unidentified, 1902 Soufriere/Pelee, 1902 Santa Maria, 1912 Katmai, 1963 Agung, 1968
Fernandina Island, 1982 El Chichón, 1991 Pinatubo
For any three of the above eruptions, note the average annual global temperature for the year before the
eruption, the year in which the eruption occurred, the year after the eruption, two years after the eruption, and
three years after the eruption. (Making a table to summarize this info for the volcanic events of your choosing).
a) Include the table in your write up. What is the typical magnitude and sign (warming or cooling) of the
effect of volcanoes on global climate?
b) How long does it take for the climate to return to normal after a major eruption?
Question 4: Can we see the effect of El Nino on global temperatures?
Following is a list of El Niño years in recent times: 1951, 1953, 1957, 1963, 1965, 1969, 1973, 1977, 1983,
1987, 1991, 1997-1998, 2002-2003, 2006-2007, and 2014-2016. (For El Niño events starting near Christmas
time, the following year is listed, since that is when the peak temperature anomaly usually occurs.)
Choose three El Niño/Southern Oscillation (ENSO) events (not ones that occurred during the year of a major
volcanic eruption). Note the average annual global temperature for the year before the ENSO, for the ENSO
year, and for the year after the ENSO.
a) Can ENSO be detected in the global temperature record even though it is basically a regional/tropical
phenomenon?
b) By how much and in which direction (warming or cooling)?
Question 5: What is the overall temperature change since 1880?
a) Make a scatterplot of annual temperature vs. time for the years 1880-2019 and calculate its slope and
intercept. Include the scatterplot in your write-up. How would you describe the general appearance of
this curve? Describe the major features of the global temperature time series (does it generally go up or
down, where are highs and lows?)
b) How much warming has occurred over the entire time series? How does that compare to the year-to-year
variability you determined in 1c?
c) Now make a chart of annual temperature vs. time for only the years 1975-2019. How does the slope
compare to the 1880-2019 curve?
d) How does 2020 look so far?
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e) What are the 5 warmest years in the record? The 5 coldest? Have we come anywhere close to having
one of the coldest years in the record during your lifetime?
Part B: How has Covid-19 affected patterns of air pollution?
Introduction
The current pandemic gave us the unprecendented opportunity to examine the impacts of human behavior on
the environment. As this pandemic constituted the greatest short-term global modification to human behavior in
history, the goal of this section of the lab is to use these records to assess the maximum capability of humans to
modify our behavior and our environmental quality.
Objective: The specific objective of this section of the lab is to give you an understanding of the impact that the
Covid-19 lockdowns have had on air pollution and resulting human health, by quantifying the potential decrease
in asthma cases in children in 10 cities around the globe.
The impact of Covid-19 on air pollution
Air pollution is the deadliest environmental problem in the world, responsible for 7 million premature deaths
annually. It causes dramatic reductions in life expectancy, greater than war or disease!
We will be focusing on just one specific air pollutant, PM2.5 (particulate matter under 2.5 microns, or millionths
of a meter, in diameter). Its main source is from fossil fuel combustion.
In the “Air pollution” spreadsheet (Posted on Camino), 2019 and 2020 PM2.5 concentrations are given for 10
major cities. The units are micrograms per cubic meter. First, calculate the absolute change between 2020 and
2019. A decrease should be a negative number. An increase should be a positive number. Second, calculate the
percent change. Percent change is defined as:
Question 1: What is the average percent decrease between 2019 and 2020 air pollution values in these cities?
Next, we’ll quantify the human health benefits of this improvement in air quality. There are a multitude of ways
in which air quality impacts human health, each with a measurable relationship. Examples are: premature
deaths, excess ER visits, school absences, excess dollars cost to the health system. Here we’ll focus on just one
of the impacts of PM2.5 pollution: childhood asthma cases.
The relationship between PM2.5 concentration and the childhood asthma rate is expressed by the equation:
10(-β*ΔPC) – 1
Where β is the risk coefficient of 0.0025 (Sheppard et al., 1999) and ΔPC is the change in pollutant
concentration (this is your absolute change from above).
In order to translate this into asthma cases, first determine the current number of children with asthma with the
following rough approximations:
23% of the population are children. 6% of children have asthma.
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Use this approximation and the metro area population from the spreadsheet to calculate the number of children
currently with asthma in each city.
Next, multiply the change in asthma rate in your 10(-β*ΔPC) – 1 column by the population at risk (children
currently with asthma). This is the change in the number of asthma cases.
Copy and paste your calculations as a table in your Word document, and answer question 2 below.
Question 2: Which city has seen the greatest total decrease in childhood asthma cases? How many children
total in these 10 cities will not develop asthma as a result of these pollution reductions?
[Use the formula =sum(…)]
Please note: These results are for just one air pollutant (PM2.5) and just one impact (childhood asthma). The
total impact of these reductions in air pollution is far greater!
Question 3: Find and link to your lab report a news article (or a peer-reviewed publication) about the effect that
Covid-19 has had on air pollution around the world (feel free to choose any location, not necessarily one of the
10 cities in the table, though you are free to focus on one of those). Provide a proper APA citation format for
your article and brief summary (a couple of paragraphs) of it.
Your Part B assignment should be a Word document with the Excel table pasted within with your calculations
for questions 1 and 2, and your typed answers for question 3.
References:
Hansen, J., and S. Lebedeff. 1987. Global trends of measured surface air temperature, J. Geophys. Res. 92,
13,345-13,372.
Hansen, J., and S. Lebedeff. 1988. Global surface air temperatures: Update through 1987. Geophys. Res. Lett.
15, 323-326.
Hansen, J., and H. Wilson. 1993. Commentary on the significance of global temperature records. Climatic
Change 25, 185-191.
Hansen, J., H. Wilson, M. Sato, R. Ruedy, K. Shah, and E. Hansen. 1995. Satellite and surface temperature data
at odds? Climatic Change, 30(1): 103-117.
Hansen, J.R. Rueddy, M. Sato, K. Lo. 2002. Global warming continues. Science, 295, p.275.