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Hook | Questions | Procedures | Data Investigation | Analysis | Findings | New Questions |
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The student page contains the hook only. It is intended to spark interest in the topic and lead students to ask questions or make predictions. The 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
So...what 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? From the information you gathered from the El Niño web sites, how do you think El Niño should have affected the weather where you live? Whether 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? Students 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 1997–98 El Niño affect the climate of your students’ specific geographic region? After your students investigate what El Niño is, perhaps by investigating the above web sites, they should be ready to:
To test their predictions, students will need to answer questions similar to:
After students have asked questions related to the topic, they will need to decide a number of things, including:
Type(s) of Data It should 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. For this 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. Defining "Significant" Another important 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. With younger students, it is probably reasonable to define "significant" as being "double/half the average rainfall" or "five degrees above/below average." Investigation Tool(s) The bulk 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. Manipulating Data Student should 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. There 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. Current weather data is easy to find on the Internet, as are overall averages (WorldClimate.com or Weather.com). However, finding several decades worth of data listed as monthly averages can be difficult. If students 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. Other Regions
The Western and High 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.
Raw 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. Looking at 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.
Your students should know that spreadsheets and graphing calculators are the some of the best tools for manipulating numerical data. Entering 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. Once 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.
Approximately 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 abnormal.
For 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:
When 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.
No 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. In 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.
In 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.
Answers often lead to new questions, starting the inquiry cycle over again. Students might now be interested and ask their own questions about El Niño or other weather phenomena, such as La Niña:
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Activity created by Philip
Molebash
Adapted from an activity created by Randy
Bell. Margaret Niess and Lynn Bell
