Analyzing Historical Climatology Records in Introductory Geoscience
Elizabeth A. Clark* and Mary E. Savina, Department of Geology, Carleton College
*Current affiliation: Department of Civil and Environmental Engineering, University of Washington; Email: eclark@hydro.washington.edu
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We archive these presentations in a common course folder for students
to refer to when building hypotheses about the role of climate change
in the Dust Bowl (Fig. 1).
This exercise has been used in the context of a two-course “dyad” called
“Agriculture and the American West” that combined American literature
with geology. One theme that carried through the course was the
question: “What caused the Dust Bowl?” Students were challenged to
consider physical and societal factors: drought, soil types, land quality,
capitalism, migrant farmers, and the Depression.
This question lent itself to the insertion of an information literacy portion
of the project, as well as creating an opportunity for the development of
writing and critical thinking skills.
To evaluate these factors, students consulted first-hand/contemporary
accounts of the Dust Bowl from government documents, newspapers
and periodicals. They also considered the depictions of the Dust Bowl in
fiction, music, art, film, and photography. Information from the Excel
exercise presented here was incorporated as evidence related to the
role of climate/drought in the Dust Bowl.
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5-year moving average
Mean annual
The NDP-041 data set, used in this exercise, is distributed by the Carbon Dioxide
Information Analysis Center based in Oak Ridge National Laboratories, Tennessee.
The data is available in two forms:
1) As a download from the CDIAC ftp site at ftp://cdiac.esd.ornl.gov/pub/ndp041
2) On CD-ROM (contact the CDIAC directly, see http://cdiac.esd.ornl.gov/ for
information).
further
Because the data files containing the NDP-041 are so large, users should open them in
Microsoft Word (or similar) and then select and paste the data sets from specific stations
into Excel. The “text to columns” command prepares the data for analysis. We have
found it most convenient to replace the -999 (no data) code with blank cells. We complete
the data formatting process to this point to allow students to focus on statistical analysis
and graph interpretation, rather than preparation. Advanced students work directly with
the original files.
To extend the time period of analysis, students download additional data from the IRI
(International Research Institute)/LDEO (Lamont-Doherty Earth Observatory) Climate
Data Library at http://ingrid.ldgo.columbia.edu/ . Stations can be located in the
“Introduction to Climate Data: Station List” using the station ID number or station name as
it appears in the NDP-041 data set. When saving the columnar data, users should
change file type to “All files (*.*). This file contains the same data as the NDP-041 data
set with some additional data. The dates are formatted as months since January 1960.
Some months will be missing, so be check to ensure data formatting when adding this
data to the NDP-041 list.
Data Availability
International Research Institute for Climate Prediction, 2003, IRI/LDEO Climate data library,
http://ingrid.ldgo.columbia.edu/ (October 2003).
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1) Has the earth been warming?
2) If so, is the warming rate steady over time?
3) Is warming uniform over the globe?
Students work in small groups (3-4) analyzing data from a wide distribution of climate stations, including those
located at high latitudes. We attempt to assign stations that have long, complete records (Fig. 2).
To assess the issue of global warming using the available data sets, students use Microsoft Excel to calculate and
plot the annual mean temperatures for individual stations. They also plot the 5-year moving average of
temperature to clarify the relationships between long-term and short-term warming trends (Fig. 3).
Exercise #2: The Dust Bowl
In this exercise, students examine temperature and precipitation data from North America to answer the
following:
1) What happened climatologically during the Dust Bowl years?
Students use Excel to calculate and plot mean annual precipitation, 5-year and 10-year moving averages of
precipitation (Fig. 4), and summer precipitation (Fig. 5). Analysis of these graphs requires students to
consider the temporal variability of data analysis. Instead of assuming that mean annual precipitation or
mean annual temperature can account for all noted climate instabilities, students note the possibility that the
frequency of high intensity rainfall, which would not be absorbed, changed during the Dust Bowl years.
Students also plot the 5-year moving averages of precipitation and temperature in order to examine the
combined effects of these factors in creating drought conditions (Fig. 5).
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Figure 2. Map showing approximate
locations of climate stations selected
for Exercise #1.
Mean Annual Precipitation at Toronto
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To address these questions, students work in the same teams, analyzing monthly and annual precipitation and
temperature data from 1890 to 1950 at stations across North America.
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Exercise #1: Global Warming
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Figure 3. A) Average annual and 5-year moving average temperature anomalies, based on average temperature between 1951-1980, at Jakutsk; B) Mean annual
temperature and 5-year moving average temperature at Jakutsk; C) Mean annual temperature and 5-year moving average temperature at Toronto; D) Average
annual and 5-year moving average temperature anomalies, based on average temperature between 1951-1980, at Toronto. Anomalies based on mean temperature
between 1951 and 1980 (a relatively stable climatological period). The records for Jakutsk extend from 1829 to 1993 and for Toronto from 1841 to 1993. These
stations have fairly complete records. Jakutsk shows a general “warming” trend, while Toronto exhibits slightly more variability. Spurious peaks in the Jakutsk record
are due to the averaging of years for which some monthly data is missing. Their effect, in this case, suggests that students should remove incomplete years from their
plot records.
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5-year Moving Average of Precipitation and
Temperature at Toronto
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2) Were heat and dryness confined to the classic “Dust Bowl” area of the High Plains or were the climate
changes broader in scope?
Supplemental Data
Boden, T. and Nelson, T., 1993, CDIAC’s numeric data package collection: Selected data sets relevant to studies of
greenhouse gases and climate.
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In this exercise, students examine temperature data from a global distribution of sites to evaluate the following
Monthly temperature data (in tenths of degrees Celsius) and monthly precipitation data
(in tenths of mm) are located in <temp.data.Z> and <precip.data.Z>, respectively.
These files contain temperature data for 7533 stations and precipitation data for 6039
stations worldwide for the period spanning1693 to 1990. See <temp.statinv> and
<precip.statinv> for corresponding station inventories, including station number, name,
location, dates of record, and percent data missing.
References
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The NDP-041 Data Set
Record lengths are variable. Longer,
more complete records are available for
much of western Europe. Data availability
for areas outside of western Europe has
improved in the last 300 years.
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Year
Precipitation (mm)
Integrating Graphical Analysis and Historical
Documentation
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Formatting Data
Figure 1. Examples of PowerPoint slides used to present findings. In
this format graphs and conclusions can easily be archived for quick
reference.
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Annual Temperature Anomaly at Toronto
Precipitation (mm)
2) Building students’ confidence in their public speaking abilities.
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(D)
Mean Temperature at Toronto
1990
Year
5-year moving average precipitation
5-year moving average temperature
Total Summer Precipitation in
Toronto
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5-year moving average summer precipitation
Total summer precipitation
Figure 5. 5-year moving average of
precipitation and of temperature at
Toronto (far left), and total summer
(June + July + August) precipitation in
Toronto (near left). Precipitation data
span the years from 1840 to 1988 and
temperature from 1841 to 1993. Note
on the far left a period of high
temperatures
and
moderate
precipitation in the 1950s, lower
temperatures and higher precipitation
in
the
1970s,
and
climbing
temperatures accompanied by lower
precipitation in the 1930s.
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Year
5-year moving average
Mean annual
Precipitation Anomaly (mm)
1) Acquainting the entire class with spatial and temporal climate
variability on a global-scale;
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Precipitation (mm)
After students have analyzed the data, they present their findings to the
class. Two objectives met in this part of the exercise include:
(C)
MeanTemperature at Jakutsk
Temperature Anomaly (oC)
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The effectiveness of this exercise is evaluated during PowerPoint presentations of each group’s findings and when
reading students’ final papers.
Presentation of Results
Temperature (oC)
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Temperature (oC)
The exercise introduces students to the geographic and temporal limitations of data availability and the ways that these
limitations affect our analyses. It also helps students to develop confidence in their ability to construct and interpret
graphs. Students are given the opportunity to re-evaluate the significance of their findings when constructing original
hypotheses for the cause of the Dust Bowl in a final paper.
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(oC)
In response to the difficulties of introducing statistical data analysis into a broad-based introductory geology course, we
have developed an exercise that exposes students to the types of climate data available and to the potential
applications of these data. This exercise can be used as a stand-alone piece or incorporated in later coursework. For
information on how this piece has been integrated into the course, see the section titled “Integrating Graphical Analysis
and Historical Documentation.”
(B)
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Temperature Anomaly
Objectives
Annual Temperature Anomaly at Jakutsk
Temperature (oC)
(A)
Annual Precipitation Anomaly at Toronto
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Figure 4. Mean annual precipitation and 5-year
moving average precipitation in Toronto (top), and
average annual and 5-year moving average
precipitation anomaly, based on average
temperature between 1951 and 1980 (bottom).
Graphs generally show higher precipitation from
1845-1865 and an abrupt precipitation decline
from 1928-1941.