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Studying Snow and Ice Changes | 
Image courtesy MY NASA DATA
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Purpose:
To examine how snow and ice cover have changed on the Earth from 1994 to 2004, and to practice using some of the data analysis tools available at My NASA Data. |
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Grade Level: 9 to 12 |
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Estimated Time for Completing Activity: 1 - 2 periods
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Learning Outcomes:
- Understand how calculations can be performed on data expressed as maps, such as averaging or subtracting.
- Observe changes in snow and ice over a 10-year period and draw conclusions about trends indicated by the observations.
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National Standards:
- Math: Data Analysis and Probability
- Science Content: A Science as Inquiry
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AP Environmental Science Topics
- Atmospheric circulation
- Climate shifts
- Greenhouse effect
- Greenhouse gases
- Impacts and consequences of global warming
- Reducing climate change
- Weather and climate
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Virginia Standards of Learning:
- ES.3: The student will investigate and understand how to read and interpret maps, globes, models, charts, and imagery.
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Tools
- My NASA Data website
- Excel software
- Image display software (such as PowerPoint)
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Vocabulary: |
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Lesson Links:
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Background:
Scientists have recently been reporting that the average temperature of the Earth has been rising, usually referred to as 'global warming'. What is the evidence for this change? One piece of evidence could come from patterns of snow (on land) and ice (either sea ice floating on the oceans or glaciers on land) on the Earth - if the snow and ice are disappearing, it would indicate rising average temperatures. This exercise is meant to examine the pattern of snow and ice on the Earth to determine what trends are visible.
It is also worth noting that 'global warming' is a shorthand for 'global climate change'. This is because although a significant effect of climate change can be temperature increase, other changes may occur as well. Therefore, when we look at the snow and ice patterns on the Earth, we not only look for increases or decreases in the snow and ice cover, but we also look for changes in its distribution, which could be another indication of climate change. |
Procedure:
We will create maps that show the average of a year's worth of snow and ice data for the entire Earth, by using MY NASA DATA's 'Define Variable' procedure. This will be done for 2 years, 1994 and 2004. Then we will use the 'Compare Two' capability to find the difference between the 2 average maps (i.e., subtract the 1994 value from the 2004 value in all corresponding locations on the map), to see how the averages have changed in all locations between the 2 years.
1. Note that for this exercise, the LAS will do some calculations and retain them on the server, but only for the same connection session, or about 180 minutes. You need to start and end all of the server steps below in one session. Analysis can be done at a later time.
2. Go to mynasadata.larc.nasa.gov, and get to the Live Access Server Advanced Edition. For the dataset, choose Cryosphere, then (the only one) Monthly Snow and Ice Amount. Note this data is percent coverage of snow and ice, with values between 0 and 100. So, to get long-term trends we need to find averages, not sums.
3. Calculate the average snow and ice cover for 1994:
a. Click on Define Variable at left.
b. Be sure the 'Select analysis type' is 'Average'. These maps consist of a grid of squares covering the Earth with a value for each square in the map. Finding the average means the server will take the requested maps (12 months in this case) and add up the values in the same square on each of the maps, then divide to get the average for that square. Then it will choose a new square and do the same for all the maps for that square. The resulting map will have its squares filled with all the average values.
c. Give this calculation a name: though you can't see it all, a long name (the dataset name) has already been inserted in the 'Name for this variable' space, and '_1' appended on the end for the first calculation. Examine this name by clicking in the Name space and using the right-arrow key to scroll through it. Since the '_1' isn't very descriptive, change the ending to something like '_avg94'.
d. On the 'Apply to these axes' line, check off the box for T (time).
e. Set the dates to find the average from 15 Jan 1994 to 15 Jan 1995, giving us an entire year that is mostly in 1994.
f. Click Next (at right) to set the variable's definition. Then click Next again (now on the Constraints page) to see the map of the average. For later comparison purposes, save the .gif image, giving it a useful descriptive name.
g. So we can also examine the data, close the .gif image, and thus return to the Set Constraints page. This time in the 'Select output' space, choose 'Table of values (text)', and click Next. When you have the table, copy it all and paste it into Excel - you will then need to use Text to Columns to format the data. Check to be sure the data has imported correctly, and save the Excel file (but don't close it, as we will add to it).
4. Calculate the average snow and ice cover for 2004:
a. To start on a new calculation, click on Cryosphere towards the top of the page. The list that appears shows the available datasets - your new calculation for 1994 (user variable), and the original whole dataset. Uncheck the box for your average, and check the one next to Monthly Snow and Ice Amount (so we can define a new variable from the entire dataset), and click Next.
b. Repeat the steps in 3 above, this time setting the ending on the name to '_avg04' (or similar), and making the dates from 15 Jan 2004 to 15 Jan 2005. Again save the map, then retrieve and paste the data into a second worksheet in the same Excel file as the first dataset. Be sure all your maps and worksheets are clearly labeled so you can tell them apart.
5. Find the difference between the 1994 and 2004 averages.
a. Click on the blue Compare Two tab at the upper left. This lets you compare any 2 datasets or variables.
b. Define what we will compare: When we subtract our averages, the server will subtract the value in each square of the map from the corresponding value per square in the other map, and make a new map of the results. Specifically, it will do (Variable 1 - Variable 2), and if we do (2004 data - 1994 data), locations that increase in snow and ice cover over this period will be positive and places that decrease will be negative, as would make sense. For this reason, we usually subtract (Later - Earlier) to compare data over time. To set this up, click on Variable 1 at left. In the list that appears, check off the 2004 data (may be already done) and click Next to set this choice. Then click on Variable 2 at left, check off the 1994 data, and click Next again. In the Constraints page that appears, check to be sure the 2 variables are correctly listed at the top of the page (your '_avg04' and '_avg94' designations can be found in the middle of the descriptions).
c. Verify the 'Select output' line is 'Difference plot'.
d. If we didn't change it, we'd only get a blue-to-white color pattern in the map (the default for snow and ice maps), which would make it hard to see details. So, in the choices at the bottom of the page, make 'Palette' be 'temperature rainbow'.
e. The default values for snow and ice cover are 0 to 100, as we have seen in the average maps. But our subtractions will likely have negative values, and won't have very many values above 40, since a change of plus or minus 40 percentage points would be a lot. So in the space for 'Color fill levels' type in (without the single quotes, no spaces, watch the negative signs!): '( 100)(-40,40,2)(100)'. This creates 2-unit divisions for values between -40 and 40, and single classes for values outside that range between -100 and 100.
f. When the settings are correct, click Next to get the map. Save it as usual, and then close that window.
g. As before, get the table of values for this calculation - now called 'Table of difference values (text)' - and save it as a third worksheet in your Excel file for this exercise. |
Questions:
1. Take a qualitative look at the results to see if they seem correct: Set up the 3 maps so they can be viewed on top of each other (could be in a photo editor, or PowerPoint or GIS), and compare them - can you see increases or decreases moving from 1994 to 2004, and are these changes correctly shown in the differences map? If so, good. If not, the next step will help verify the situation.
2. Take a quantitative look at the results: Examine the 3 numerical datasets to be sure the computer did what we expected, that is, if you subtract a 2004 value from the corresponding one (same location) in 1994, do you get the value in the difference table (there might be rounding differences that you can ignore)? If not, something might not have been set correctly in the procedure, and you may need to try it again. Notice, by the way, we haven't verified that the yearly averages are correct - which would involve getting the 12 monthly datasets and checking them ourselves. A working scientist might do this to make sure his or her work is valid.
3. Now that we think the data seems OK, what does it show?
a. In what areas has the snow and ice cover increased and where has it decreased?
b. Does this pattern agree with what you have read regarding climate change?
c. Try to explain the causes for areas of significant gain or loss in snow and ice cover, especially 'anomalous' ones that you haven't read about.
d. Ten years of snow and ice data is about all that is available from this source. What level of confidence would you have in conclusions resulting from this dataset? |
Extensions:
This exercise compares averages for 1994 and 2004, but different years would have different averages, so other years in the datasets could also be compared. Of course, as the years get closer together, change might be harder to see and conclusions about the change would be more short-term. Note this exercise used close to a calendar year (e.g., Jan 1994 to Jan 1995), but it also could use a seasonal year, e.g., centering on northern hemisphere winter such as July 1994 to July 1995.
Once students understand from this exercise how the LAS can do calculations, they can do calculations with other datasets to look for changes. |
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Lesson plan contributed by Martin F. Schmidt, Jr., Owings Mills, MD
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Click here for Teachers Notes |
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