Recently announced at the GLOBE Learning Expedition was the upcoming worldwide GLOBE Student Research Campaign on Climate Change, 2011-2013. This campaign will enhance climate change literacy, understanding and involvement in research for more than a million students around the globe. The GLOBE Program Office is encouraging students to contact the GPO with research ideas in areas such as water, oceans, energy, biomes, human health, food and climate. Please send your Climate Change Campaign research ideas to ClimateChangeCampaign@globe.gov.

With the upcoming GLOBE Student Research Campaign on Climate Change in mind, I thought it might be interesting to check for temperature trends in the data from GLOBE schools. (A preliminary version of the yearly-averaged GLOBE student data is included at the end of this blog.)

GLOBE was founded in 1995. By 1996, some schools were already recording temperature data regularly. This provides us with up to 12 years of data from some schools.

Figure 1 shows an example of a long record of monthly mean temperature.

Figure 1. Monthly average temperatures from 4. Zakladni Skola in Jicin, the Czech Republic. The straight line through the data in a “best fit” linear trend determined by least-squares regression.

Figure 1 shows strong seasonal changes, with monthly average temperatures ranging from below freezing to around 20 degrees Celsius. While there is a long-term trend, the large departures from the trend line indicate that the estimate of warming rate is rather uncertain.

I decided to re-compute the trends by taking yearly averages. If a month was missing, I assigned a mean temperature equal to the average of the data from the two surrounding months (Fortunately, such gaps occurred in the spring or autumn, when filling in the data like this makes some sense). If too many months were missing, I didn’t include the year in the averages. Figure 2 shows the yearly-averaged data for 4. Zakladni Skola.

Note that the “best-fit” line in Figure 2 still shows a warming - but a different value. This is the result of the uncertainty in the linear trend, from a purely statistical point of view. This is not surprising - even the yearly averages don’t fit on a straight line. In fact the warmest year is 2000, near the beginning of the record.

Figure 2. Average annual temperatures for the data in Figure 1. Note that the “best-fit” line still shows a warming, but a larger value.

We can reduce the uncertainty by adding more data. So I include data from five other schools in Europe in Figure 3.

Figure 3. Temperature trends for six schools in Europe, selected so that no two schools are in the same country. Represented are Belgium, Estonia, Finland, Germany, and Hungary, as well as the Czech Republic.

In Figure 3, the best-fit trend lines for all six schools show warming. Note that the most rapid warming rates are at the farthest-north latitudes. Figure 3 gives us some confidence that Europe has been warming for the last decade, but there are year-to-year changes that are much larger than the 10-year trend. These short-term changes tell us there is a lot of uncertainty in the trend lines, but the fact that there are six lines instead of one gives us a little more confidence that the result might be “real” for the roughly 10 years data were collected.

For comparison, we take three sites in the United States, selected for having a continuous data record (Figure 4). In this case, two out of the three sites actually show cooling! This is quite different from Europe. However, as in the case of Europe, the year-to-year changes are greater than the long-term trend.

Figure 4. As for Figure 2, but for three schools in the United States.

Such differences could be real. The maps of temperature changes in Figure 5 show that the trends over 30 and 100 years show a lot of variation. For both time periods, the figure shows that Europe is getting warmer. Both periods also show more warming at higher northern latitudes. Results for the United States are mixed. Between 1905 and 2005, temperatures were warming over the northwest United States but cooling over the southeast United States. However, temperatures were warming over most of the United States between 1979 and 2005, with the possible exception of part of Maine (northeast corner of the United States).

Figure 5. Linear trend of annual temperature for 1905-2005 (top) and 1979-2005 (bottom). Areas in gray don’t have enough data to get a good trend. The data were produced by the National Climate Data Center (NCDC) from Smith and Reynolds (2005, J. Climate, 2021-2036). This figure and an excellent commentary on recent climate change are found at www.ncdc.noaa.gov/oa/climate/globalwarming.html.

In this blog, I have avoided using statistics to estimate the uncertainty in the trends, but I think you can see two things. First, even with all this carefully-collected data, there is uncertainty in the local trends; but the uncertainty can be reduced by including more data in the same region. And second, the trends can be quite different in different parts of the world.

To close, I include two more plots. The first is a version of the well-known curve that shows Earth’s average temperature warming with time. I plotted the curve from data from the Climate Research Unit (CRU) of the Hadley Centre in the United Kingdom

Figure 6. Annual average temperature, averaged over the globe. From the UK Hadley Centre (www.cru.uea.ac.uk/cru/data/temperature/hadcrut3gl.txt).

Figure 7. Data from Figure 6, with linear trend based on data from 1996-2007, on the same scale as for Figure 2 and 3.

The second plot is based on data since 1996 and plotted on the same scale as for the GLOBE schools. Notice how tiny the change is! This is, of course, because some parts of the Earth were cooling or warming less rapidly. But there is much more information included in that curve - and hence a lot more statistical certainty. Also, the scientists who worked on the data worked very hard to remove the effects of changing thermometers or station location, beginning and ending of observations, and many other things that can cause artificial trends. (By the way, a plot of the averages of the nine GLOBE sites produces a very slight warming with time of 0.0018 degrees Celsius per year - with the temperature peak in the year 2000 really standing out).

Clearly, this simple-looking curve took a lot of careful work to produce!

GLOBE STUDENT DATA

Below are the data used for Figures 1-4. For details in processing see the text.

xxxx - missing data (see below)

Documentation of the data

Summary of Sites

GLOBE school locations

1. Hartland, Maine, USA
2. Utajarvi, Finland
3. Tahlequah, Oklahoma, USA
4. Karcaq, Hungary
5. Eupen, Belgium
6. Waynesboro, Pennsylvania, USA
7. Hamburg, Germany
8. Jicin, Czech Republic
9. Tartumaa, Estonia
10. Average of Temperatures 1-9

Yearly averaging

Missing months are “filled in” by averaging the surrounding months. This was done when one month was missing or two months was missing (very rare). Fortunately, the missing data tended to occur in the spring or autumn, when the missing temperatures would be expected to be between the temperatures of the neighboring months. The average was then computed by summing up the data for all 12 months and then dividing by 12.

Average of all nine sites

The average is found by summing up the temperatures in columns 1 through 9 and then dividing by 9. If a temperature is missing (as in the first row, 1996), an average is not computed. Why do you think we did it this way? Two out of the three sites (3 - Tahlequah and 6 - Waynesboro) are the two warmest of the nine, and the third (8 - Jicin) is in the middle of the temperature range. If we used the average of those three points, it would make the average temperature in 1996 too warm.

NOTE: The data here are reported to two decimal places, while some of the data used for the graphs has three or four decimal places, so results might vary slightly from the results shown here.

What can be done to “improve” the dataset? We will be calculating averages for other schools with long temperature records and adding them.

This blog was written just before departing for the GLOBE Learning Expedition meeting in South Africa. I’ll be posting some additional blogs about the meeting in the coming weeks. In the meantime, after you read this blog, check out the GLOBE home page for student blogs and photos!

The weather report always tells you the wind direction and speed reported by a weather station near you. Sometimes you hear about the strong winds in the “jet stream” that exists several kilometers above the ground.

Did you ever wonder how strong the winds are in a thunderstorm? The up and down winds, I mean. You can make a rough guess on how strong the updraft in a thunderstorm is, if you have hail.

On the night of 4 June 2008, we had hail, so I decided to see how big it was. There are two ways to do this. You can go out and collect the hail, and measure it before it melts (which I have done), or you can take a picture of the hail – with a ruler or something to compare the hail to, and measure the size of the hailstones from a photograph.

Figure 1. Picture of hail on our back porch, 1830 Local Daylight Time, 4 June 2008. Typical size is one centimeter in diameter. Since the slate surface was warm some of the hail that fell earlier may have melted some. Location: north part of Boulder, Colorado, USA.

Figure 2. As in Figure 1, but hail on the grass. Typical size is 1 centimeter in diameter. The grass was cool enough so that the hail wasn’t melting as much as in the first picture.

In both pictures, the larger hailstones are typically about a centimeter in diameter, with a few that even larger. I don’t think there was much melting after the hailstones hit the ground, because I was taking the pictures as the hail was falling.

How can hail size tell you how strong the updraft is? The updraft has to be strong enough to hold the hail while it is growing. In other words, the hail continues to grow until its downward speed (which goes up with size and weight) is greater than the upward speed of the air.

Hail fall speed is determined by a balance between two forces: the downward pull of gravity and the drag force (air resistance) on the hailstone created by the air. As the hailstone falls faster, the air resistance gets bigger. Gravity of course stays the same. When the drag force is equal to the force of gravity, the hailstone reaches a constant downward speed, called its terminal velocity or terminal fall speed. The updraft has to be this strong to keep the hail from falling.

So we use the terminal fall speed to estimate the updraft speed. The hail will fall to the ground when the updraft weakens slightly, or when the hailstorm travels out of the updraft horizontally.

People have estimated the terminal fall speed of hail using equations, and they have measured it. I actually saw scientists measuring the fall speed of artificial hailstones (same shape and density as hailstones, but not ice) by dropping them down a stairwell that extended vertically about seven stories. Assuming a story is about 3.7 meters, that’s about 26 meters. Sometimes scientists measure the fall speeds of hail in nature. They can photograph them falling with a high-speed camera using strobe lights that flash on at regular intervals. Or they can measure hail vertical speed with a Doppler radar pointing straight up. It is more likely that the “natural” hailstones reached their terminal fall speeds than those in the stairwell.

Knight and Knight (2001) argue that the terminal fall speed is related to:

1. Air density (hail falls faster through thinner air)
2. Hailstone density (less dense hailstones fall more slowly)
3. Drag coefficient (the effectiveness of the air in slowing down the hailstones)

The shape of the hailstone is also important, but Knight and Knight assume the hailstones are spherical to keep the problem simple.

The graph shows how hail terminal velocity (or fall speed) is related to hail diameter.

Figure 3. Hail fall speed (and hence updraft needed) as a function of hail diameter. Red curves are from Knight and Knight (2001); Black points read off figure in http://www.jdkoontz.com/articles/hail.pdf.

For our one-centimeter hailstone, the graph shows a range of values, based on assumptions on air density at the height the hail is forming (taken by Knight and Knight as somewhere around 5.5 kilometer above sea level, where the air pressure is about 500 millibars or hectoPascals, temperature 253.16 K), drag coefficient, and the ice density in the hailstones. I picked up the hailstones, and they appeared to be solid ice rather than soft, so the ice density was probably about 0.9 grams per cubic centimeter. This suggests the updraft speed was between 13 and 18 meters per second, or between 29 miles per hour and 40 miles per hour.

According to the U.S. National Severe Storms Laboratory website, a one-centimeter hailstone falls at about nine meters per second – meaning that the updraft has to be that strong. This means the air had to be moving upward at 32 kilometers per hour or 20 miles per hour. This is more consistent with the less-dense hail.

So – to be safe, I would say the updraft overhead was between 9 meters per second and 18 meters per second. There are too many factors that we don’t really know to get much more accurate than that. This is between 32 and 65 kilometers an hour, or between 20 and 40 miles per hour.

The Encyclopedia of Climate and Weather (New York, Oxford University Press, Stephen Schneider) quotes a 47 meter per second fall speed (or necessary updraft) for a 14.4 centimeter hailstone, translates to a little over 100 miles per hour!

So – next time you have a hailstorm, measure the diameter of some hailstones to find out roughly how strong the updraft was! But if the hail is large, either photograph it from a safe place or wait until the large hail has stopped. If you don’t have a camera, collect some hail stones, put them in a plastic bag, and put them in a freezer until you have time to measure them.

Related blog: “More about Hail,” (No 19, 1 November 2006).

Reference:

Knight, Charles, and Nancy Knight, 2001: Hailstorms. In Severe Convective Storms, C. A. Doswell III, Ed., Meteorological Monographs, volume 28, No. 50. Published by the American Meteorological Society

According to the most recent report by the Intergovernmental Panel on Climate Change, land use change has a relatively minor impact on the recent rise in global average temperature.

Yet, as stressed in an earlier set of blogs on Iowa Dew Points and an apparent increase in stormy activity regionally, land use seems to be quite important at local and even regional scales.

Why the difference?

According to an article by Raddatz in a recent issue of Agricultural and Forest Meteorology, about 3.6% of Earth’s surface is covered in crops, and about 6.6% is in pasture. Figures 1 and 2 show what these percentages look like. Even if all the temperature trends associated with changes in land cover were all in the same direction, it would require large changes indeed to show up significantly in the average global temperature for a whole year.

Figure 1. Fraction of Earth’s surface covered with crops, rounded up to 4%.

Figure 2. Fraction of Earth’s surface covered with pasture, rounded up to 7%.

Further, crops grow actively only part of the year. So, for example, winter wheat or corn will lead to cooler maximum temperatures during their growing season. (Recall this is because more of the sun’s energy hitting the surface is going into evapotranspiration and less into heating). During the rest of the year, the stubble or plowed ground would have a different effect from surrounding green vegetation. In fact, the dormant fields could be warmer than grasslands if the grasses are green. Thus it is not surprising that converting natural land cover to crops or pasture does not always have the same effect on temperature change. In some areas, there is a cooling effect (e.g., if more moisture is being evaporated or transpired, or if more sunlight is reflected), while in other areas, there is a warming effect (e.g., more sunlight absorbed, less evaporation or transpiration). And finally, as pointed out in the Iowa Dew Points blogs, regional changes in land cover could have an indirect effect, like shifting the wind patterns. This can in some cases decrease the local influence on temperature. Figure 3 illustrates the “mixed” effect of crops.

Figure 3. Possible scenario showing effects of crops on the global average temperature, with the “red” representing a net warming effect over the whole year, and the “blue” representing a net cooling effect. This is only to illustrate that the net effect of crops will be mixed, rather than saying what the effect will be.

As noted in earlier blogs, cities also affect the temperatures, but they occupy a tiny fraction of Earth’s surface.

This does not mean that land use isn’t important. We live on land, which occupies only 30% of the globe. If we change our percentages of Earth’s surface to percentages of Earth’s land surface, they become bigger – 3.6% of Earth’s surface becomes 12% of Earth’s land surface, and 6.6% of Earth’s surface becomes 22% of Earth’s land surface! Furthermore, we humans aren’t evenly distributed: there are vast parts of Earth that are uninhabited. Human influence on land cover is where humans are. And, of course, those seasonal effects on temperature are important to us if they are happening where we live.

Figure 4. Fraction (12/100) of land surface on Earth covered with crops.

Figure 5. Fraction (22/100) of land surface on Earth covered with pasture.

Climate researchers know this. You have mainly heard about the predicted average annual temperature because it is the easiest “measure” that sums up all the details in one convenient number. This is particularly helpful in sending the message to governments, businesses, and individuals that Earth is getting warmer. Once you think about how this will affect how you live, one temperature is clearly not enough. You want to know how the temperature varies seasonally and where you live.

Also, climate models cover both a large area (the whole Earth) and a long time (decades), so they are expensive to run and require the biggest computers. And, they have to account for the various things that affect climate from the outside – the variability in the sun, the variation in greenhouse gases and particles, etc. By comparison, weather models are run for at most around 10 days or so. To make the runs doable, climate models work on points too far apart to really represent smaller-scale atmospheric motions (”weather”), smaller-scale regional effects (such as vegetation changes), and even terrain.

Some climate scientists have looked at regional climate changes by running weather-type (regional) models to describe what’s happening at the boundaries of a smaller area – such as the United States. (If the model only covers the United States, it still has to account for what the wind brings from the outside!) These models have taught us something. However, there is a worry that the climate models supplying the boundary information are themselves faulty because the effects of “weather” could affect the details of the local wind, temperature, etc.

So, in spite of the enormous costs, climate scientists are just now starting to run climate models on computers or clusters of computers working together that are powerful enough that global climate models can represent these regional changes better. The first such computer system was the Japanese Earth Simulator. It enabled model runs with grid points spaced at one-tenth the distance of most climate models. As more and more people start focusing on regional climate, and more computers with the capability of the Earth Simulator become available, the issues of our effect on Earth’s surface will be studied more intensely.

And our discussion didn’t even consider more long-term effects, such as the effect of land use on changes in greenhouse gas concentrations! Nor have we considered the effects of forests.

Reference: Raddatz, R.L., 2006. Evidence for the influence of agriculture on weather and climate through the transformation and management of vegetation: Illustrated by examples from the Canadian Prairies. Agricultural and Forest Meteorology, 142, 186-202.

At a recent meeting, someone commented to me that the “global-warming folks” must be wrong, since we haven’t had a strong hurricane season since 2005, and weren’t they saying that a warmer climate means more hurricanes?

Since we had work to do, I let the comment go, but decided later it would be a good subject for a blog. Particularly since the “official” hurricane season starts on 1 June in the United States.

In 2005, a couple of papers (see references with asterisks, below) came out that implied that there could be more strong tropical cyclones in a warmer climate. (”Tropical cyclone” is the more general term for such storms; “hurricanes” are tropical cyclones that affect North and Central America and the Eastern Pacific north of the Equator.) These papers were well-timed, because 2005 was a devastating North Atlantic hurricane season, with four – Emily, Katrina, Rita, and Wilma, reaching Category 5 on the Saffir-Simpson scale (sustained winds of 155 miles per hour (135 knots or 249 kilometers per hour – henceforth km/hr). Katrina was the most devastating hurricane in memory, with a death toll (well over 1000) exceeded only by the “1900 storm” that destroyed Galveston, Texas and killed between 6000 and 12,000 people. Hurricane Wilma had the lowest central pressure (882 millibars) of any recorded Atlantic hurricane, with sustained winds of 175 miles per hour or 292 km/hr. (The strongest tropical cyclone was Typhoon Tip, whose central pressure dipped to 870 millibars with sustained winds of 190 mph (305 km/hour) on 12 October 1979).

Figure 1. Number of named tropical storms (blue) and named hurricanes (red) by year. From the U.S. National Climatic Data Center.

Finally, 2005 was the year they ran out of names and had to start using Greek letters to name hurricanes, with Zeta, the 26th and last storm, occurring between 30 December 2005 and 6 January 2006. (For the North Atlantic list, names starting with Q, U, X, Y, and Z are left out; the remaining hurricanes were named for the first five letters of the Greek alphabet).

The arguments used for strong hurricanes in a warming climate related to the warming of the sea-surface temperature. Basically, a hurricane is like a heat engine, getting its energy primarily from water vapor evaporating from the warm sea surface, and cooling off at cloud top, around 15 kilometers above the surface.

Figure 2. Globally-averaged sea-surface temperature anomaly (sea surface temperature minus mean for 1961-1990). Data from Climate Research Unit, Hadley Centre, UK. (http://www.cru.uea.ac.uk/cru/data/temperature/)

Although there is variation from region to region, the global average of carefully-compiled sea surface temperatures (Figure 2) does indicate a warming. The warming is due to more greenhouse gases in the atmosphere. These gases trap heat in Earth’s lower atmosphere, land surface, and ocean.

However, there are changes superimposed on this long-term trend. In the North Atlantic, these changes can take several decades. The relatively few strong hurricanes during the 1970s and 1980s follow more strong hurricanes in the 1950s and 1960s so this “natural variability” is important as well. One familiar example of natural variability is El Nino, which spreads warm surface waters eastward across the Equatorial Pacific Ocean and affects wind and weather patterns over much of the earth. As noted in previous blogs, aerosols and solar variability can also affect temperature changes on earth, but the effect of the sun is probably fairly minor over the last several decades.

Other things being equal, warmer sea surface temperatures would mean stronger hurricanes. However, other things are not equal. Certain wind patterns favor hurricane development, while other wind patterns do not. For example:

• Converging winds (more air flowing horizontally into an area than leaving) favor hurricanes. Hurricanes are storms with air near the surface spiraling in to the center, until it reaches the eye wall, where it spirals upward and slightly outward. Such motions are favored in regions where the air is slowly moving upward. This happens where winds converge into an area.

• Little wind change (called wind shear) with height favors hurricanes. If the wind changes enough with height, it can disrupt the air circulation in a developing tropical storm, keeping it from developing into a hurricane.

• Wind patterns are much harder to predict in climate models. For example, researchers have found that fewer hurricanes occur during El Nino years. This is because El Nino warms the eastern equatorial Pacific, and this leads to wind shear over the Atlantic basin. But it is not clear how the warming climate will affect the occurrence of El Ninos. If there are more in the future – this effect could offset that of the generally warming sea surface temperatures. Indeed, a new paper by Knutson and colleagues has just pointed out such a possibility. However, it is interesting to note their caution and list of caveats (mostly that the input to their modeling studies is based on global climate models that are still not adequate at regional scales).

What about 2008? On 22 May, the U.S. Climate Prediction Center issued a “2008 Hurricane Outlook” that called for a “90% probability of a near-normal or above-normal hurricane season” in the United States, with the above-normal season more likely (65% chance). Among the factors considered was La Nina (the “cold” phase of El Nino).

As for the rest of the world, the northern hemisphere has already experienced one of the most deadly tropical cyclones in recent history, Cyclone Nargis, which devastated parts of Myanmar and killed tens of thousands of people.

For the longer-term future, the warmer oceans should lead to stronger tropical cyclones – when the wind conditions favor their formation and growth. The real question is how often the favorable wind conditions will happen.

References

*Emanuel, K. 2005: Increasing destructiveness of tropical cyclones over the last 30 years. Nature, 436, 686-688.

Knutson, T.R., et al., 2008: Simulated reduction in Atlantic hurricane frequency under twenty-first century warming conditions. Nature Geoscience, doi:10.1038/ngeo202.

*Webster, P.J., G.J. Holland, J.A. Curry, and H.-R. Chang, 2005: Changes in Tropical Cyclone Number, Duration, and Intensity in a warming environment. Science, 309, 1844-1846.

Hurricane Statistics from
NCDC: Climate of 2005: Atlantic Hurricane Season Summary.
http://www.ncdc.noaa.gov/oa/climate/research/2005/hurricanes05.htlm

Acknowledgments: I wish to acknowledge Caspar Ammann of NCAR for checking this blog and pointing out the Knutson reference.

I’m going to interrupt blogging about surprising liquid puddles and soil temperature to talk about the Second Pole-to-Pole Videoconference, which took place yesterday (8 April 2008). Several scientists participated, as did five schools: in Ushuaia, Argentina, the Escuela Provincial No. 38 Julio Argentina Roca; and in Alaska, the Randy Smith Middle School (Fairbanks), Moosewood Farm Home School (Fairbanks), Wasilla High School (Wasilla), and Innoko River School (Shageluk). The Web Conference was hosted by the GLOBE Seasons and Biomes Earth System Science team, at the University of Alaska at Fairbanks.

Figure 1. Locations of the schools in Alaska. Courtesy Dr. Elena Sparrow

Flgure 2. Location of Ushuaia, which is near the southern tip of South America. Part of Antarctica appears on the southern part of the map.

The focus was on climate change, in particular:

1. The most important seasonal indicators (things that change with season)
2. Whether they are being impacted by climate change (if so, how?)
3. How students could study these indicators to see if they are impacted by climate change.

As was the case last year, the students had an opportunity to ask questions of the students at the other schools as well as the scientists, but the conversation was more structured. We organized the conversations into three rounds. In Round 1, the Alaskan and Argentinean students were to ask each other about signs of seasonal change or share their own observations. In Round 2, the focus was on how to narrow questions down enough so that students could investigate them. And in Round 3, we were supposed to discuss the ways the investigations could be done.

The questions in Round 1 were wide-ranging. Why do leaves change color? Why is the soil frozen when the air is warm? Does the melting of permafrost cause damage to buildings and trees? Are glaciers disappearing? Do scientists use Native knowledge in their research? How does climate change affect plants and animals?

We learned that soil below ground warms and cools with the seasons more slowly than the air, and – the farther you go down, the less the temperature changes (this is also discussed in the previous few blogs). We also learned that the changes in the lower layers of the soil took place after the changes higher up (in scientific terms, the changes in the lower layers lags the changes in the upper layers). So a student was able to guess that late summer is the best time to test for permafrost, rather than the height of summer, when the sun angle is the highest.

We discovered that scientists are using Native knowledge in their research in many parts of the world, including not only Alaska and Canada, but also in Australia. We learned that magpies are coming farther north to Shageluk, and there are more pine grosbeaks than there used to be, although a student in Fairbanks didn’t notice any changes. We also learned that tree line is moving up in the mountains near Ushuaia.

In Round 2, questions focused on some fascinating things to investigate, including changes in the snowboarding season (of interest to students in both hemispheres), changes in temperature and precipitation, and succession of species after wildland fires. In fact, the students at Shageluk are already investigating the succession of species of some land recovering from a forest fire (see pictures at the Shageluk web site). The discussion of temperatures taught us the difference between maritime (Ushuaia and Wasilla) and continental (Fairbanks and Shageluk) climates: Ushuaia rarely gets below freezing, but Fairbanks has temperatures as low as -40 (same in Fahrenheit and Celsius), although such cold temperatures aren’t as common and persistent as they used to be). The discussion of snowboarding led to suggestions of investigating how long ski areas remain open, interviewing someone at a ski area about what conditions are good for snowboarding, thinking about what makes snow last (amount of precipitation, timing of precipitation, temperature). Two intriguing observations were that there were both more cumulus clouds in Ushuaia than there used to be, and more heavy rains.

With so many ideas generated in Round 2, some investigations were already outlined in some detail by the time we got to Round 3 – especially related to snowboarding. But snowboarding ideas continued to come up. A ski area had closed in Ushuaia, because its elevation was too low in the warming climate; and students in both hemispheres thought snowboarding might be an interesting thing to investigate together. Since the seasons are opposite, the study could be continuous.

Some new ideas also emerged about items to investigate. How about looking at when people take off or put on snow tires? Is that a good indicator of climate change? What about using frost tubes to monitor freezing and thawing in the soil in Ushuaia as well as Alaska? And how would frost-tube measurements relate to air temperature or the times that lakes and rivers freeze? And one could investigate the long-term seasonal geographic changes in diseases (mosquito-borne diseases, corn diseases).

It was pointed out to us that using a simple variable like temperature could yield some fascinating results beyond averages and simple trends. Is there a trend in how many days that the temperature stays above freezing? How about for the number of days when temperatures stay below freezing? How does this relate to precipitation? Clouds?

Also, we were reminded that not all changes we see are due to climate change – we humans are changing our environments in many other ways, such as destroying wilderness areas. And that trends we see in a few years can be quite different from the long-term trend. (That is, one cold winter doesn’t mean that it is getting colder on the long term.)

Through this rich mix of ideas for research topics and data to look at, the students continuously asked about each others’ lives. One of the most fascinating exchanges took place toward the end of the videoconference, when a student from Alaska asked the students in Ushuaia what kind animals they had and what kind of wildlife they ate. The Ushuaia students listed foxes, llamas, beaver, rabbit, birds, and penguins as the animals they had; and said that they ate rabbits, fish, and some beavers (but mostly tourists ate beavers). The beavers were apparently introduced to the region in 1946, and there are no natural enemies, so people are being encouraged to eat them.

A student from Shugaluk closed the discussion section of the conference by putting things in perspective. Yes, skate boarding and dog mushing are interesting, but for the Native peoples of the far North, their very way of life is being threatened. Earlier, a student in Ushuaia said that a glacier that was supplying water to the city was melting and would be gone in a few decades, leading to a shortage of drinking water. As one of the scientists said earlier, like the canary in the coal mine that warned of dangerous gases in a mine– the people in the Polar regions are the first to see the real danger in climate change. We need to remember this as we begin to take steps to try to slow down climate change and its impacts.

NOTES IN CLOSING:

There will be a web chat and web forum April 10-11. The purpose is to help students develop research ideas and projects, and interact with scientists. Links to the chat and forum can be found on the Pole-to-Pole Videoconference page of GLOBE Web site.

Three PowerPoint presentations describe the science and people of Ushuaia. They are also available on the Videoconference page at the above link.

Finally, I recall promising a student from Fairbanks that we would return to the topic of leaves changing color. Since we didn’t follow up on this question, I thought I would include a discussion here. The leaves change color because the chlorophyll, which gives the leaves their green color, disappears in the fall, so that other chemicals in the leaves give them their color. The chlorophyll, of course, is involved in photosynthesis, which provides plants the energy to grow. Different types of trees change different colors. For example, some maple trees turn bright red, while aspen trees turn yellow in the autumn. The weather actually affects how bright the colors are in the fall. In long term, the climate also affects the trees that can stay healthy in a given place. Thus the mix of trees, and hence the colors could change over many decades.

More information is available about leaf color under the Seasons and Phenology Learning Activities, Activity P5 “Investigating Leaf Pigments” in the Earth as a System Chapter of the GLOBE Teachers’ Guide.

The seasons and Biomes project is an effort to engage students in Earth system science studies as a way of learning science. It is a timely project for this fourth International Polar Year with many and intense collaborative research efforts on the physical, biological, and social components and their interactions. Changes in the Polar Regions affect the rest of the world and vice versa, since we are all connected in the earth system. I encourage students to conduct their own inquiry whether collectively as a class or in small groups, or individually. Students can use the many already-established GLOBE measurements in the areas of atmosphere/weather. soils/land cover/biology, hydrology, and plant phenology in their local areas (You can access the protocols by clicking on “For Teachers” on the menu bar at the top of the GLOBE homepage.) Soon there will be new measurement protocols such as fresh-water ice freeze-up and break-up protocols and a frost-tube protocol that will be posted on the GLOBE web site. Students can conduct a study on things that interest them as part of the upcoming GLOBE Student Research campaign.