page contains the hook only. It is intended to spark interest
in the topic and lead students to ask questions or make predictions.
weather effects every day of our lives. Barely a day goes by that
most people don't check out the day's weather forecast, or maybe
even the forecast for the next ten days. A phenomenon that meteorologist
have linked to weather patterns around the globe is known as El
Niño. We have heard both plausible and incredible accounts
of the effects of El Niño in the
Goddard Space Flight Center
is all the big fuss about El Niño? Here are some sites
to give you some background on El Niño:
Do you remember
whether the 1997-98 El Niño affected the weather where
you live? What should we expect when El
Niño strikes next?
information you gathered from the El Niño web sites, how
do you think El Niño should have affected the weather where
or not El Niño really did affect our weather is still being
debated. How would you determine whether El Niño affected
the weather in the ways you predicted?
might ask similar but different questions than those listed here.
Remember, the more students are guided to ask specific
questions, the less inquiry-oriented the activity.
The big question
is: Did the most recent 199798 El Niño affect
the climate of your students specific geographic region?
students investigate what El Niño is, perhaps by investigating
the above web sites, they should be ready to:
the climate parameters (e.g., temperature and precipitation)
that El Niño could reasonably be expected to influence.
- Make predictions
as to what effect the El Niño phenomenon might have had
on these parameters for a particular region (drier, wetter,
To test their
predictions, students will need to answer questions similar to:
the 1997-1998 El Niño significantly affect the amount
of rainfall (drier/wetter) our region received during that time
the 1997-1998 El Niño significantly affect the temperatures
(warmer/cooler) of our region during that time period?
students have asked questions related to the topic, they will
need to decide a number of things, including:
of data needed to answer the questions
tools for data manipulation
how data will be manipulated and presented
be obvious to your students that they are going to need rainfall
and/or temperature data to test their predictions. It might not
be obvious what format this data should be in. The example provided
here uses average monthly rainfall and temperatures, but students
might select something else similar.
project, it is important that students look for data sets that
include monthly temperature and precipitation averages over several
decades, because 30 years of data are typically used for identifying
a typical climatic year for a region.
task for this project is for students to define what "significant"
means. What does it mean to be significantly drier/wetter or warmer/cooler?
Depending on the age of the students this might be an opportunity
to apply a real-world application of standard deviation or quartiles.
The example shown will use both standard deviation and quartiles.
students, it is probably reasonable to define "significant"
as being "double/half the average rainfall" or "five
degrees above/below average."
of the data students use will be numerical. Graphing calculators
or spreadsheets are excellent tools for looking at and comparing
numerical data. The example shown will use a spreadsheet.
be comparing El Niño years to other years, perhaps to the
overall average. Charting data from the El Niño years and
comparing it to the other years will provide answers to most of
the students questions. Including in the chart a "range"
around an average year is beneficial. This can be done by adding
and subtracting from the average standard deviations, quartiles,
or whatever parameter students chose to be "significant."
An example is provided below.
is often a giant leap from defining the type(s) of data desired
and actually finding the data. Providing guidance to students
in finding the necessary data may be necessary.
data is easy to find on the Internet, as are overall averages
However, finding several decades worth of data listed as monthly
averages can be difficult.
are struggling to find the data they need you might point them
to Regional Climate
Centers (http://www.wrcc.dri.edu/rcc.html) to locate data
for the region they are interested in.
Plains Regional Climate Centers, for example, have excellent
data sets available, but even these can be very difficult to find
once you are in these sites.
data/information usually has to be manipulated before it can answer
any questions. Students might be unaware of how data can best
be manipulated, so teacher guidance may be appropriate.
raw temperature and rainfall data is likely not enough to answer
any questions. For example, the following is a segment of the
monthly temperature averages for San Diego, California, starting
in 1914 and ending in 2001. A graphical manipulation of this data
would help students make better inferences of this data.
Too much data to enter in a Spreadsheet by hand?
should know that spreadsheets and graphing calculators are the
some of the best tools for manipulating numerical data.
this data intro a spreadsheet by hand is cumbersome and will take
some time. Often it is possible to copy and paste the data from
the browser straight into a spreadsheet. This typically works
when both the web browser and the spreadsheet application are
both produced by the same company - such as Microsoft's Internet
Explorer and Excel.
the data is into the spreadsheet it can be manipulated to help
answer questions. In this example, averages and standard deviations
for each month were calculated. The Average + 1 Standard Deviation
(Ave + 1 SD) and the Average - 1 Standard Deviation (Ave
- 1 SD) were then calculated. Both average and standard deviation
are relatively easy formulas to find and use in Microsoft Excel.
- The formula
=AVERAGE(B3:B89) calculates the average for cells B3
- The formula
=STDEV(B3:B89) calculates the standard deviation for
cells B3 through B89.
the Average + 1 Standard Deviation (Ave + 1 SD) was calculated
by typing in cell B93, =B91+B92.
the Average - 1 Standard Deviation (Ave - 1 SD) was calculated
by typing in cell B93, =B91-B92.
68% of all months should fit between Ave + 1 SD and Ave - 1 SD.
If we compare the 1997-1998 years to Ave + 1 SD and Ave - 1 SD
in a chart, we should be able to see if any months were noticeably
precipitation, standard deviation causes problems in that Ave
- 1 SD will yield a negative number -- and it's impossible
to have negative rainfall! Instead of using standard deviation
with rainfall, it works best to use quartiles with rainfall.
Quartiles are relatively easy to calculate in Excel as well:
- The formula
=QUARTILE(B3:B89,1) calculates the lower quartile for
the cells B3 through B89.
- The formula
=QUARTILE(B3:B89,3) calculates the upper quartile for
the cells B3 through B89.
you and your students interpret quartiles, just keep in mind that
data points above the upper quartile are above the 75th percentile,
and data points below the lower quartile are below the 25th percentile.
result is meaningful unless communicated appropriately. Discussion
of findings should be supported. There may or may not be definitive
answers to the questions students raised.
this example, five months in 1997 were above Ave + 1 SD. Students
might conclude that El Niño caused the temperatures in
San Diego to be above normal.
this example, during the first seven months of 1998 (the heart
of El Niño), rainfall levels were either near, above, or
significantly above the 75th percentile. Students will likely
conclude that El Niño did significantly increase the amount
of rainfall received in San Diego.
often lead to new questions, starting the inquiry cycle over again.
might now be interested and ask their own questions about El Niño
or other weather phenomena, such as La Niña:
- How did
El Niño affect weather in other parts of the world?
- What is
La Niña and how does it affect weather different than