Predicting an El Nino

What is an El Nino?

An El Niño is a large-scale ocean-atmosphere climate phenomenon in the tropical Pacific.  It is not a storm, it is not long term climate change, rather it is a periodic shift in normal weather conditions of the central pacific.  El Nino represents the warm phase of the periodic change in sea-surface temperature in the Eastern Tropical Pacific.  The cold phase is referred as La Nina.  El Nino’s have historically reached their peaks around Christmas time.    The name “El Niño,” is Spanish for ‘The Child’, referring to the birth of Christ at Christmas.

Task

In this task you will be part of a small team attempting to predict if the coming fall will bring us into an El Nino cycle, a La Nina cycle, or what oceanographers have jokingly referred to as a La Nada cycle, where conditions are normal. Your team will be responsible for collecting data, organizing it in an appropriate graphic form, and analyzing it for the purpose of making El Nino predictions.  After making your prediction, you will summarize how you reached your conclusion and then share your findings with the scientific community.   Successful prediction of an El Nino could save millions of dollars and perhaps hundreds of lives.

Section 1: Background

1. Describe normal ocean conditions in the Eastern Tropical Pacific (ETP). What direction does the wind blow? What is the sea surface temperature (SST?) Is it rainy or dry?
2. What are these conditions like in the ETP during La Niña years?
3. What are the benefits of successful predicting an El Nino Cycle?
4. What is the TAO network, and what is its purpose?
5. The TAO network has developed some problems that may affect our abilities to make a prediction, what are they?

Resources: Use the links below to answer these questions before you go any further.

• NOAA’s El Nino theme page
• El Nino Impacts on the ocean
• Benefits of successful El Nino predictions.
• El Nino’s impact on sea surface height and temperature

Section 2: Collect Data

It’s time for you to play the scientist and start to gather some sea surface temperature (SST) data. We will attempt to make use of the buoy located at 110 degrees West and 0 degrees North.  The following link will show you conditions right now: current data for this buoy.    If you look at the top picture you will see actual temperatures. If you look at the bottom picture you will see anomalies (differences from the norm.) Are temperatures right now above or below normal?If the anomalies are positive it means that it is warmer than normal. If they are negative it means they are colder than normal.

One day’s temperatures does not tell us much.  We need more data.  To get data for recent weeks follow the instructions below.

1. Click on the data link.
2. Click on the tab at the top for “data delivery” and you may have to select the “non-java version.”
3. Under averaging select daily.
4. Under data type select sea surface temperature.
5. In the buoys check 0º N 110º W
6. On the bottom select dates to give you the last 30 days
7. Click “deliver.” When requested, list ERHS as the organization, and classroom as the intended use.
8. When your data is delivered, select the file than ends in .ascii.  You will use this data in the analysis section.

Section 3: Analysis

You will now create a graph that shows the current temperature anomalies.  One way to predict the onset of an El Nino’s is by measuring how much warmer or colder the SST’s are from the normal mean temperature (temperature anomalies.) Here’s how:

1. Open a Google spread sheet.  In cell A1 type “Date.”  In the rest of the column A starting with cell A2, type in the dates for each of the 30 days.
2. In cell B1 type in Temperature.  In the rest of the column B starting with cell B2, type in the temperatures for each of the last 30 days.
3. In the cell C1 type in “anomaly.”  The average temperature for the month of August and September at this location is 23.0.  In each of the rest of column C, starting with cell C2 type in “=SUM (B2 -23.0)”.  Cell C3 will be the same except use cell B3 instead of B2.  See the picture below. 
   This creates a formula to calculate the temperature anomaly for each day. Copy cell C2 and paste that formula into the rest of the cells in column C.  It will automatically adjust the formula for you.
4. You should now be able to create a graph comparing the temperatures and the anomalies for the last month.  First highlight all of your data and dates.  Go to the insert menu and select chart.   Decide if you want bars, columns, or lines.  Go into the customize menu; give the graph a title, then label both the vertical and horizontal axis.  Save the graph.

Section 4: Conclusions

Your team will now write a short lab report.  Please write the entire report in 3rd person, past tense. Click here to see a sample lab report.

• ____/10 Points.  In your introduction summarize everything you learned in the background section.   Explain what an El Nino is, what it does the the weather of the Eastern Tropical Pacific, how it differs from La Nina, how we might predict it, and why prediction is important.
• ____/5 Points. For your hypothesis, write a statement indicating whether we are in an El Nino Year a La Nina year or somewhere in between.
• ____/5 Points. In your methods, explain what the TAO network is, the kind of data you gathered from it, and the dates the data cover.
• ____/10 Points. In your results, copy graph you made, and paste it into your lab report.
• ____/10 Points. For your conclusion, first do a little research.  What other factors (besides above normal SST) would indicate the onset of an El Nino or a La Nina? Can any of those factors be seen in the buoy data?  Go back and look at what else buoy data can tell you. Next, how do the temperature anomalies you graphed compare to the anomalies in the historical data records.  It may help you to know that the strongest El Nino years were 1982-83, 1997-98.  Weaker El Nino’s were recorded in 1986-87 and 2002-03, and 2010. Finally, read and summarize the experts latest prediction on the likelihood of an El Nino/ La Nina in the next few months.   Compare your prediction to the experts.

Credits:

The idea for this assignment came from: El Nino or El No No.  I have modified it significantly, but I owe them many thanks for ideas and direction.