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ADDRESSING QUANTITATIVE REASONING AND ANALYTICAL WRITING SKILLS IMPROVEMENT USING
GLOBAL AND LOCAL DATA SETS IN AN INTRODUCTORY GLOBAL CLIMATE CHANGE COURSE
NIEMITZ, JEFFREY W., Department of Geology, Dickinson College, P.O. Box 1773, Carlisle, PA 17013, [email protected]
EXERCISE I: UNDERSTANDING WEATHER AND CLIMATE
ABSTRACT # 63108
The data collected is typical for most newspapers i.e. max. , min., and average temp for the day, the normal max., min. and average temps for that day, the extreme temps
for the day, and the precipitation for the data and record rainfall for the day. They are informed they will need to do this for the first class of the semester.
During the first class we talk about the difference between weather and climate. They are then asked to find the climate data for the same week of days from any other year in
the climate record for their hometown or nearby city. This requires them to search the Web for historical climate data for their town.
OBJECTIVES: 1) To start collecting and analyzing weather data; 2) to begin searching the Web for the required climate data; 3) to learn to graphically present all data
subsets using EXCEL; 4) to begin to understand basic statistics such as maximum, minimum and averages, and the concept of standard deviation; 5) the difficulty of
predicting weather even 24 hours in advance; and 6) the difference between weather and climate in terms of time and meteorological variability.
EXAMPLE:
WEATHER DATA FOR HARRISBURG, PA (JANUARY 15-22, 2000)
Day Max Temp Min Temp Mean
Norm X Last yr hi Last yr lo Record hi Record lo pred hi
15-Jan
33
18
26
28
25
13
67
-3
16-Jan
54
27
41
28
32
17
62
-4
42
17-Jan
24
12
18
28
40
15
65
-6
26
18-Jan
19
7
13
28
52
25
66
-6
25
19-Jan
39
17
28
28
44
32
66
14
32
20-Jan
31
26
29
28
47
31
68
-16
32
21-Jan
19
12
16
28
42
29
64
-22
25
22-Jan
23
7
15
28
39
27
64
-9
26
24
13
18
22
16
0
14
MDTppt Norm ppt
0.97
1.26
0.97
1.35
0.97
1.44
0.97
1.53
0.97
1.62
0.98
1.71
1.16
1.8
1.16
1.89
Precipitation
Max-Min-Predicted Temperatures
60
2
0.4
1951
1959
Norm
Max Temp
50
0.3
1.4
pred lo
40
30
20
30
20
1893
1982
-10
1994
0.6
0.4
10
1994
B
C
0
15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan
Plot B shows the standard deviation
of temperatures for one week
Including the extreme range. Note
that several days of extreme lows
and highs were set in one year
Plot C shows the differences between
predicted and actual high and low
temperatures. Students note the
difficulty in even short-term forecasts
Harrisburg Max T -Dec. 1999, 2000
Mean Temperatures
Plot D (precipitation) shows that in
the long-term record it has rained
every day and in the year-to-date
record Harrisburg was behind and in
fact in a prolonged drought
Harrisburg Avg. T - Dec. 1999, 2000
60
avg 1999
min 2000
max 2000
min 1999
40
avg 2000
50
avg norm
min norm
max norm
35
0.05
0
0
15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan
50
max 1999
0.1
Right Scale
D
Harrisburg Min T - Dec. 1999, 2000
80
70
PPT
0.2
-30
15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan
40
0.15
Ppt to date
1936
1994
-20
45
0.2
Norm ppt
1964
Plot A shows a typical data set for
one week in January. Students would
recognize that the max., min., average,
and range of temperatures varies even
over a short time period
1
0.8
0
0
15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan
0.25
1.2
10
A
Left Scale
pred hi
Record hi
Record lo
1994
10
0.35
1.6
Min Temp
Daily
Norm
40
T (oF)
1.8
1967
50
Daily
30
1951
60
Min Temp
40
1990
T (oF)
50
1990
1990
Inches
Max Temp
1937
Inches
70
25
50
Temp (F)
30
Temp (F)
60
Temp (F)
T (oF)
Over the last two decades the sciences at Dickinson College have reformed
their curricula from a traditionally separated lecture and laboratory to an
integrated active learning experience where by inductive reasoning students
learn fundamental scientific principles and concepts. In Geology, we use
topics of broad geologic interest (e.g., History of Life, Plate Tectonics,
Oceanography) as a context for giving students practice in honing basic life
skills specifically writing and quantitative reasoning. Topical courses allow
significant depth in the content and thus lend themselves to using large
datasets as vehicles for teaching fundamental principles and quantitative
reasoning. The following discussion uses as an example our Global Climate
Change introductory course. While traditional in its content (meteorology,
climatology, paleoclimatology), the difference between weather and climate,
the interactions between regional climate phenomena via teleconnections,
and the evidence for and substantiation of long term climate change can be
inferred using large datasets available for the most part on the World Wide
Web. We have found that the introduction and statistical manipulation of
large datasets needs to be progressive in nature. Starting with simple
exercises using EXCEL as a tool give the students confidence when more
complex datasets and statistical analyses are introduced. In addition we
found that initially most students were unfamiliar with the basic functions
of EXCEL. As time goes on we see less and less need for remedial
spreadsheet instruction and can “raise the bar” with regard to the
complexity of the datasets and exercise objectives. Moreover, we are
finding that the students are readily translating the EXCEL and data
analysis skills to other classes as we track those who take introductory
classes and continue on to other electives or courses in the major. Here we
present three exercises which require the acquisition and analysis of
different datasets and increase in complexity over time. Each exercise
requires a certain amount of dataset extraction, manipulation, and analysis
as substantiation of inferences made in a 2-3 page fully formatted
analytical paper.
PPT
0
0
0
0
0
0.01
0.18
0
80
60
20
INTRODUCTION
pred lo
Means-Extreme Temperature
Max-Min-Mean Temperatures
T(oF)
Many undergraduate students cannot adequately interpret large, complex
datasets even when presented in graphical form. The need to improve our
student’s quantitative reasoning and analytical writing skills has lead to the
development of a series of integrated exercises in our introductory global
climate change course. Global climate datasets are excellent resources for
helping students improve their quantitative reasoning skills and understand
of temporal and spatial interactive global processes. In an effort to provide
formative assessment for student progress in both these critical skills, labs
start with simple data extraction from newspapers and hand graphing and
culminate in large and complex database analyses using Excel with
computer graphing skills and basic statistics integrated into short written
assignments. In advance of the first exercise, students gather a week’s
worth of data from their hometown newspapers. Then the students find
their state climatologist’s website and download the same data from the
year before. They graph these data for both time periods, compare them,
and turn their data and reasoned interpretations into a two-page paper.
The following week a few students’ examples are highlighted to show the
range of weather and climate change. By analyzing student results
anonymously all learn the kinds of misinterpretations that can result and
the depth of analysis that can be done even with a small dataset. Dataset
size and complexity increases in subsequent labs using climate phenomena
such as ENSO, monsoon intensity, and drought to explore the relationships
between global climate change and local manifestations of those changes
over time. Datasets come from the websites including NCDC climate,
USGS stream gauge, and LTRR tree ring records. Besides learning the
basic functions of Excel, students’ data analyses include regression and
basic spectral analysis. Improved quantitative and written skills do
translate to other courses and, hopefully, the quantitative literacy all
citizens need in the 21st century.
ASSIGNMENT: Collect one week of local weather data (predicted and actual temperature and precipitation) from your hometown newspaper.
30
20
40
30
40
20
Daily
Norm
Last yr
15
E
10
15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan
G
F
H
10
0
20
1
8
15
Dates in December
Plot E shows the students the variability
of daily mean temperatures from one
year to the next compared to the longterm mean
20
10
30
22
29
1
8
15
Dates in December
22
29
1
8
15
22
Dates in December
29
When comparing the months of December for 1999 and 2000, the maximum (F), minimum (G), and average (H) temperatures for 1999 are significa
EXERCISE
III: ENSO, CLIMATE
CHANGE, AND
STREAM FLOW IN THE WESTERN U.S.
EXERCISE II: ANALYSIS OF EFFECTS OF LATITUDE, ATTITUDE, AND CONTINENTALITY USING HISTORICAL
TEMPERATURE
AND PRECIPITATION
RECORDS
Determine
whether
Southern
Oscillation
in the
has America.
an effect on temperature, precipitation and st
ASSIGNMENT: Using the National Climate Data Center’s Global Historical Climatology Network find historical temperature andASSIGNMENT:
precipitation data for
two cities,
one inthe
North
America
and one on
anysouthwest
continent Pacific
but North
OBJECTIVES: 1) to determine if regional climate phenomena have global control of weather historically 2) to use EXCEL to do regres
Your two cities should be at least 40o latitude apart. One city should be on a coastline, the other at least 500 km from the coast.
OBJECTIVES: 1) A geography lesson in finding appropriate cities; 2) Finding good historical records in a dataset with 7000+ stations with records back to 1800; 3) manipulating, graphing, and analyzing a large dataset in EXCEL; 4) synthesizing the data to determine
EXAMPLES: SNOHOMISH, WA and TOMBSTONE, AZ
EXAMPLE:
Students are given a list of weather stations for which there are associated USGS gauging station in the states of Arizona, California, Oreg
Locale they choose. The students already know how to get to the NCDC site, now they must find a site with good ENSO data and explore
DATA SHEET
City #1 PORT TOWNSEND, WASHINGTON, USA
Longitude:122.75o W
Latitude: 48.1o N
Elevation (meters): 0 m
Distance from coast (km): 0 km
# of years of record: Temp: 86 PPT: 86
Other geographically interesting facts:
In rain shadow of Olympic Mts.
On the Straits of Juan de Fuca
City #2 BOULIA, AUSTRALIA
Longitude: 139.9o E
Latitude: 22.9o S
Elevation (meters): 146 m
Distance from coast (km): 780 KM
# of years of record: Temp: 86 PPT: 86
Other geographically interesting facts:
Middle of Great Australian Desert
Version 1
Precipitation Data:
Rainfall Data
Through
1990
Version 2
Temperature Data:
Regularly Updated
Max/Min
Temperature Data*
Southern Oscillation Index (SOI)
http://www.cgd.ucar.edu/cas/catalog/climind/soi.html
Water Resources of the United States
http://water.usgs.gov./
http://www.ncdc.noaa.gov/oa/climate/ghcn/ghcn.SELECT.html
Snohomish Q vs. PPT
Tombstone Q vs PPT
16000
1200
800
600
Rain-Discharge Relationship
12000
10000
8000
6000
2000
0
0
10
12
0
14
5
10
1
2
0
0
0
10
1961 Boul Temp
40
-12
YEARS
SOI INDEX
19
82
19
77
19
72
19
67
19
47
19
42
19
62
30
F
2
25
20
0
15
-2
10
-4
-6
5
-8
0
YEARS
PRECIPITATION (in.)
4
82
4
1915 Boul Temp
50
-10
6
19
2
20
-8
60
SNOHOMISH PPT VS SOI
SOI
Ppt Snohomish
12 per. Mov. Avg. (SOI)
12 per. Mov. Avg. (Ppt Snohomish)
77
6
70
YEARS
19
3
-6
-10
72
AVERAGE YRS
8
80
0
19
EXTREME YRS
-4
-8
2000
67
10
90
-6
4000
19
-2
6000
62
100
14
-4
19
0
8000
47
110
-2
42
2
10000
19
120
C
0
19
37
19
37
19
42
19
47
19
52
19
57
19
62
19
67
19
72
19
77
19
82
16
12
5
4
130
Two examples of association of SOI with
Precipitation, temperature, and stream discharge
show relatively weak correlation For Tombstone,
AZ and a relatively strong correlation for
Snohomish, WA. Plot A shows good correlation of
rainfall with discharge because rains tend to be
monsoonal with significant runoff potential.
However, in Plot B discharge and SOI do not show
consistent correlation. Monsoonal rains are
correlated to El Nino and La Nina events. Plot C
shows small temperature range variations.
Abnormally low high and low temps for a year
show inconsistent correlation with La Nina events
(note circled years). Rain-Discharge relations in
Snohomish (plot D) is attributable to runoff and
snowmelt depending on time of year. Both
discharge and rainfall versus SOI show strong
correlations with abnormally high rain and
discharge being correlated with La Nina events
and low rain and discharge correlated with El
Nino events. 12-point moving averages help in
delineating trends.
2
12000
37
1974 Boul Ppt
Temp Tombstone
SOI
4
14000
19
1914 Boul Ppt
140
18
JA
N
FE
B
M
A
R
A
PR
M
A
Y
JU
N
JU
L
A
U
G
SE
P
O
C
T
N
O
V
D
EC
1950 Pt Town Temp
1942 Pt Town Ppt
6
4
TOMBSTONE TEMP VS SOI
SOI INDEX
AVERAGE YR
1944 Pt Town Temp
0
19
37
19
42
19
47
19
52
19
57
19
62
19
67
19
72
19
77
19
82
RIGHT SCALE
7
Inches
T (oF)
1874 Pt Town Ppt
JA
N
FE
B
M
AR
AP
R
M
AY
JU
N
JU
L
AU
G
SE
P
O
C
T
N
O
V
D
EC
30
200
YEARS
20
8
80
40
400
-10
TEMP (oF)
9
90
50
-4
-8
Inches
10
100
EXTREME YR
600
Average and Extreme Precipitation
Average and Extreme Temp. Years
60
-2
-6
Plots above come from the CLIMVIS website. Students would work in EXCEL to average the max and min
temperatures and with precipitation graph the monthly values for the period of record. This would be
discussed in the paper in terms of relative temperature range and amount of precipitation at each site. Extreme
and average years for Temp and Ppt for each city would also be compared (see plots A and B below) graphically.
70
800
E
16000
DISCHARGE (CFS)
SOI INDEX
2
6
18000
1000
DISCHARGE (CFS)
B
20000
TOMBSTONE
SOI
Q Snohomish
SOI
12 per. Mov. Avg. (Q Snohomish)
12 per. Mov. Avg. (SOI)
19
1200
SOI INDEX
6
0
25
SNOHOMISH Q VS SOI
TOMBSTONE Q VS SOI
4
20
PRECIPITATION (in.)
PRECIPITATION (in.)
Q Tombstone
15
19
57
8
57
6
19
4
19
52
2
Rain-Discharge Relationship
4000
200
0
D
52
400
Snow Melt-Discharge Relationship
19
DISCHARGE (CFS)
SNOHOMISH
DISCHARGE (CFS)
A
1000
14000
DISCUSSION AND CONCLUSIONS:
At Dickinson College we have used large datasets obtained from the World
Wide Web for a variety of disciplines including climate change, environmental
geology, oceanography, geochemistry, geomorphology, and others. Large data sets
can be generated from long-term studies of various geologic processes for example,
stream chemistry and changes in meanders of small streams. I have also used tree
ring and ice core databases to do quantitative reasoning in my climate course.
With each analysis a paper is required which asks the students to formulate
the problem, the methodologies used to obtain and synthesize the data, and a
discussion of the data analysis. In this way the students receive practice in
analytical writing. By the third or fourth exercise the students have become quite
good at manipulating data in EXCEL and better at synthesizing data. They learn
as we see in the SOI vs. climate of the Western US exercise that positive results will
not always be achieved and frequently the data are not easy to analyze. However,
there is always an explanation for the data which may require a change in ones
hypothesis.
One other benefit we have found is that the statistical analysis skills have
translated to other courses minimizing the time spent reteaching basic EXCEL
techniques.
There are some pitfalls to these kinds of exercises:
•In the teaching of EXCEL for use as a statistical analysis tool, instructions must be
very clear and detailed otherwise the students will be lost before encountering the
data from which we desire them to learn about process. A primer in EXCEL will
help those who are totally unfamiliar with EXCEL and be a good refresher for
those who are familiar with EXCEL.
•Add new statistical techniques gradually and re-use the familiar techniques. Some
redundancy is good.
•In crease the sophistication of the databases from week to week to challenge
students.
•Make sure databases used do not contain complexities for searching which would
confuse introductory students. If they do and are still useful, the instructor may
need to tailor the database searching as was done in Exercise III. This requires a
lot of pre-lab trial and error experimentation on the part of the instructor.
•The expected outcomes of exercises can be obtained more rapidly if part of the
database search is done for the students ahead of time. This is particularly useful if
you are using data from a dataset searched in previous labs and re-searching would
be unnecessarily time consuming.