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Climate Variability

In this activity students will simulate climate variability and come to understand that long-term climate averages are the result of significant annual climate variability. Students will be able to express the fact that random climate variability makes detecting climate change more difficult.


Atmospheric scientists investigating the possibility that human influences are changing the earth's climate confront a significant problem: how do we actually detect climate change? We know that weather can be highly variable on a daily, weekly, or even yearly basis, but climate, which is based on longer time scales, can be variable as well. If the last 30 years were generally warmer worldwide than the previous 30 years, would this be solid evidence that the climate is changing in a particular direction? Or could this only be a long-term, normal statistical fluctuation in climate? This is a critical and surprisingly difficult question for atmospheric scientists to answer. While computer models may predict climate change, citizens are unlikely to support significant social, economic, and/or technological changes to slow the rate of change unless they are sure that the climate is truly changing, not just experiencing random variability.

To begin answering these questions, it is important to understand what constitutes normal climate variability versus actual climate change. You can think of climate variability as the way climatic variables (such as temperature and precipitation) depart from some average state, either above or below the average value. For example, the average maximum temperature in July in Boulder, CO may be 87�F (averaged over the last 30 years), but each year, July's daily average maximum temperature will be less than or greater than this long-term average value. Similarly, for a given year (for example, 1989 as shown in the graphic), Boulder's mean maximum temperature for the month of July might be 90�F, but the maximum temperature on any given day within that month will depart from the monthly average value. Although daily weather data depart from the climatic mean, we consider the climate to be stable if the long-term average does not significantly change.

Climate change can be defined as a trend in one or more climatic variables characterized by a fairly smooth continuous increase or decrease of the average value during the period of record. As we look at 30-year average values, however, we also detect variability. For example, the 30-year average July temperature from 1971 to 2000 is lower by approximately 1�F than that of 1941 to 1970.

Does this mean that global climate change has started? Usually when we read about global climate change, we think of warming. In this case, we observed a slight cooling. Can we reasonably expect 1991 to 2020 averages for Boulder in July to be different still? Will they be warmer or cooler? It is very important to keep in mind that this is temperature data for one location only. If we had picked different years or even months to use as examples, we would likely see even different results. For example, during the same time period the global average yearly temperature has warmed, but at this location, for the month of July, the average temperature has cooled. This seemingly contradictory example illustrates the effect of your sample over time and space in determining climate trends.

Climatologists also grapple with the occurrence of "extreme" events. These are specific climate events that depart from the average in some significant way. For example, days that exceed 100�F in Boulder may be considered "extreme." While it's possible that any given summer day in Boulder might be 100+�F, under conditions of climatic warming, we would expect the frequency of such extreme days to increase. In other words, the probability that a given summer day would exceed 100�F would be higher under climatic warming than a stable climate.

Climatologists are concerned with more than temperature changes. Changes in precipitation are also of critical importance. Precipitation patterns that deviate significantly from the average can result in droughts or floods. The Midwest floods of 1993 are a recent and devastating example of an extreme event.

The average July precipitation pattern for the thirty-year period from 1961 to 1990 for the United States was very different from the average July precipitation recorded in 1993 as shown on the map below.

Both climatic averages and the probability of climate extremes are, by definition, statistical measurements based on probabilities, not certainties. This makes the absolute detection of climate trends difficult to predict and very difficult to measure, except by looking at long-term historical data. Without waiting decades to decide whether climate change is "real" and whether we should respond, we are left to "play the odds." In this activity, students will do just that, using a deck of cards to simulate climate variability.

Learning Goals

  1. Students will understand that long-term climate averages are the result of significant annual climate variability.

  2. Students will be able to express the fact that random climate variability makes detecting climate change more difficult.

Alignment to National Standards

National Science Education Standards

Earth and Space Science, Energy in the Earth System, Grades 9 to 12, pg. 189, Item #4: "Global climate is determined by energy transfers from the sun at and near the earth's surface. This energy transfer is influenced by dynamic processes such as cloud cover and the earth's rotation, and static conditions such as the position of mountain ranges and oceans."

Benchmarks for Science Literacy, Project 2061, AAAS


Grade Level/Time



  1. Shuffle a deck of cards.

  2. Use black cards to designate cooler average global temperatures for one year and red cards to designate warmer average global temperatures.

  3. Display 30 cards, one at a time. This will represent global average temperatures for 30 years. Look at the pattern.

  4. Make a graph of cooler and warmer years.

  5. Gather the cards. Remove four black cards from the deck. Recall that the black cards represent cooler than average years. By removing them, we are simulating the influence of global warming.

  6. Shuffle the deck and repeat steps three and four.

  7. Repeat this several more times, each time taking out four more black cards.

Observations and Questions

  1. Make a graph of cooler and warmer years for the first 30-year period.

  2. Make a graph of cooler and warmer years for the second 30-year period.

  3. How many cards do you have to take out to make a noticeable change in a 30-year period?

  4. Relate the activity to actual weather data in the past 30 years.


You can make the activity more sophisticated by assigning a value (amount of temperature increase or decrease) to each card. For example, assign the following values to the heart or diamond (red) cards:

Ace =

0.1�F temperature rise

Two =

0.2�F rise

Etc. through 10; 10 =

1.0�F rise

Jack =

1.5�F rise

Queen =

2.0�F rise

King =

2.5�F rise

Compute the average global temperature change for the 30-year period. Repeat this several times, taking out four black cards each time.

Assessment Ideas

For your formative assessment, circulate around the room and ask students to explain, in climatology terms, what they are finding and why.

A summative assessment should relate to understanding the difficulties involved in detecting directional changes in climate. Have students imagine that they're climatologists, asked by a citizens' group to explain why they shouldn't expect all summers and winters to be warmer from now on if global warming is real. They may do this in writing or as an oral assessment.

You may want to ask students to make transparencies of their graphed data so they can share and discuss their results with other groups to look for trends, patterns, and differences among their graphs.

You can also ask students to discuss the simulation's limits – how the activity is NOT like the pattern of climate variation. (While each card pick is truly random, climate patterns can be longer-term and non-random. A sequence of cool or warm years may be caused by global-scale forces and occur regularly, even in the absence of long-term climate change.)

Modifications for Alternative Learners

This will be quite challenging to students who have difficulty with abstractions. While the activity may be clear, the meaning may be difficult to follow. For these students (and perhaps all students), a constant reminder and connection to the climate data is in order. For example, move around the room and ask students to explain what they've found so far in terms of climate, not cards.

When you're finished with the activity, click on Back to Activities List at the top of the page to return to the activity menu.