The blog about carbon dioxide (CO2) produced by our bodies during respiration created so much discussion that I decided to work harder to put the numbers into context.

Last time, we calculated an average adult human breathes out between 0.7 and 0.9 kg of carbon dioxide each day. This is based on lots of assumptions, with people of all ages and nationalities counted as processing 0.5 liters of air, 16 times an hour, for 24 hours.

Let’s compare this rough estimate to some other numbers.

The amount of carbon dioxide given off by an automobile in a mile (1.6 kilometers)

I’ve heard a number quoted for this one, but thought it would be good to estimate to find out how close I was, and then I will convert the number to metric units. We start from some facts.

• Density of gasoline is 0.71-0.77 grams per cubic centimeter (that’s 0.71-0.77 kg per liter)
• Gasoline is 85% carbon by mass

So there is approximately 0.74 times 0.85 = 0.63 kg carbon per liter.

This converts to 0.63 kg C x 3.79 liter/gallon or 2.39 kg C per gallon (C=Carbon).

If our car drives 20 miles on one gallon of gas (this is clearly not a very efficient car!), the car burns 2.39 kg per gallon x 1 gallon per 20 miles, or 0.12 kg of carbon per mile.

This is equivalent to 0.12 x 44 divided by 12 = 0.44 kg per mile, or 0.96 pounds (~1) pound of carbon dioxide per mile. Or, in metric units, 0.28 kg per kilometer.

And, driving this car for two miles (3.2 km) produces 0.88 kg carbon dioxide – as much as we produce by breathing all day! (What if the car could travel twice as far per gallon?)

Carbon dioxide released by going from Point A to Point B.

I’m going to suppose someone wants to travel two miles or 3.2 kilometers. That’s a distance many of us would be willing to walk (and about the distance between where I live and where I work).

That means:

If we walk three miles per hour, it would take us 40 minutes to reach Point B walking 3 miles an hour.

If we ride a bicycle at 8 miles (12.8 kilometers) per hour on average, it would take 15 minutes to get to Point B.
The Web is full of charts listing the number of Calories (kCal, abbreviated kCal) used in different types of exercise. I’ll select the following values. For a 155-pound (70 kg) person:

• Walking at 3 miles per hour (4.8 km/hr) burns 250 kCal
• Riding a bicycle at 8 miles per hour (12.8 kilometers per hour) burns 280 kCal

Which means the number kCal burned going from Point A to Point B is:

• 167 kCal walking for 40 minutes compared to 56 kCal for 40 minutes at rest
• 70 kCal riding a bicycle for 15 minutes compared to 21 kCal for 15 minutes at rest

The “at rest” numbers are based on the previous blog, where we used energy production to estimate carbon dioxide output. We assumed a human produced 2000 kCal of energy (equal to the amount eaten) and found that roughly equivalent to 0.7 kilograms of carbon dioxide a day. (0.9 kg a day could be used as well. We used 0.7 simply because that was the number associated with the 2000 kCal.

The carbon dioxide we produce by going two miles on foot or on a bicycle is then, if we count the total:

• 0.7 kg CO2 per 2000 kCal times 167 kCal: 0.058 kg CO2 walking
• 0.7 kg CO2 per 2000 kCal times 70 kCal: 0.025 kg CO2 biking

But the “extra cost” of traveling the distance should be the difference between the “exercising” number and the “at rest” number, namely:

• 0.7 kg per 2000 kCal times (167-56) kCal = 0.039 kg of extra CO2 walking
• 0.7 kg per 2000 kCal times (70-21) kCal = 0.017 kg of extra CO2 riding a bike

Thus: traveling the 2 miles (3.2 kilometers) produces this amount of CO2 above what was produced by respiration at rest:

Traveling 2 miles (3.2 kilometers)

By car: 0.88 kg CO2
Walking: 0.039 kg CO2
Riding a bike: 0.017 kg CO2

While the numbers aren’t exact, the large factor – 20 or more, is probably close. Walking or riding a bicycle does reduce the production of CO2 relative to driving. And – these modes of transportation provide healthful exercise as well! If we have to drive, putting more people in the car reduces the impact of driving. And, driving a car that uses half as much gasoline per unit distance would also help.

This blog was inspired by activities at the 2008 GLOBE Learning Expedition (GLE) in South Africa. As part of their field activities, the students visited the Global Atmosphere Watch station (GAWS) at Cape Point, where carbon dioxide and several other trace gases are measured from the top of a 30-m tower. The carbon dioxide record goes back to 1978, showing a rise comparable to that seen in the Northern Hemisphere.

Standing for much of two days with groups of students at the base of the weather tower at the GAWS site at Cape Point, I found myself wondering how much we were contributing to the carbon dioxide in the atmosphere. I returned home, resolving to estimate how much carbon dioxide an average human gives off in a given day simply by breathing.

Figure 1a. 30-meter tall Global Atmosphere Watch Station (GAWS) tower from a distance. It is located almost at the southern tip of Africa.

Figure 1b Close-up of GAWS tower. The air is pumped in from the top of the tower into the laboratory building, when it is analyzed for the fraction of carbon dioxide and other trace gases.

I will estimate this in two ways. First, based on how many Calories a “typical” human consumes. And secondly, based on how much carbon dioxide is released with each breath.

Based on how much we eat

I start with some rather gross assumptions:

1. The average human eats 2000 Calories (kiloCalories) of food a day
2. 100% of this food is processed, with all the carbon returning to the atmosphere
3. All of the food eaten is in the form of sugars with carbon:hydrogen:oxygen ratios of 1:2:1.

And some information:
Atomic weight of carbon: 12
Atomic weight of hydrogen: 1
Atomic weight of oxygen: 16
Molecular weight of carbon dioxide (2 x 16 + 12 = 44)

This means that:
By mass, the sugars are 40% carbon
By mass, carbon dioxide is 27% carbon

Sugar provides 4 kiloCalories of energy per gram, meaning that our human eats 500 grams of sugar each day. 40% of this or 200 grams is carbon. Assuming all this carbon is released as part of carbon dioxide, our human releases 733 grams of carbon dioxide (200 grams x 44/12).

So, let’s just call our estimate 700 grams of carbon dioxide a day, recognizing that the number is an approximate one.

There are a number of reasons this is probably an overestimate. Our human wouldn’t eat all sugar. He/she would eat some fat as well, which has 9 kiloCalories per gram. We are assuming our human to be in steady state – so that net uptake by the body would be zero. But our human would release carbon in other forms (feces, dried skin, shed hair, etc.) So there would be some solid waste as well as gas – but over long term, there would be some carbon dioxide released from that.

Based on carbon dioxide released through breathing (respiration)

Let’s try another way to estimate the amount of carbon dioxide our human releases. But this time we focus on breathing. Again, some facts:

A human adult breathes 15 times a minute, on average (Reference 1). While I am writing this, my respiration rate is 16 breaths per minute, so this number seems reasonable. And, just for fun, I’ll use my respiration rate.

Each breath exchanges 500 cubic centimeters of air (Reference 2)

Assuming an air density of 1 kilogram per cubic meter, we can find out how many kilograms of air are exchanged for each breath:

500 cm x cm x cm x 0.01 m/cm x 0.01 m/cm x 0.01 m/cm
= 0.0005 cubic meters

0.0005 cubic meters x 1 kilogram per cubic meter
= 0.0005 kilograms of air per breath.

We now use this to estimate the kilograms of air processed each day, which is

0.0005 kilograms per breath x 16 breaths per minute x 1440 minutes per day
= 11.52 kilograms per day “processed” by breathing

To find out how much carbon dioxide is put into the atmosphere, we compare the amount of carbon dioxide (0.038% by volume) inhaled to the amount (4.6-5.9% by volume exhaled, Reference 3.), from the same web site. But first we need to allow that “by volume” means (using carbon dioxide as an example)

0.038 carbon dioxide molecules per 100 air molecules, or
3.8 carbon dioxide molecules per 10000 air molecules.

From above, we know that the molecular weight for carbon dioxide is about 44. The molecular weight for moist air is about 28, which means that the air we inhale contains about

3.8 x 44 divided by 28 x 10000 = or 0.0006 grams carbon dioxide per gram of air

The number “.0006″ is really a fraction – which I am labeling in grams per gram. It could just as easily be pound per pound.

Similarly, the fractional amount of carbon dioxide exhaled, by mass is, assuming 5% by volume:

0.05 x 44 divided by 28 x 100 or 0.0786

So the net fractional change in carbon dioxide for each breath is

0.0786 – 0.0006 or 0.0.078

Now we convert this to a mass by multiplying the fraction times the mass per breath, namely:

11.52 kilograms of air exchanged each day x 0.078 fractional increase in carbon dioxide,

= 0.9 kilograms of carbon dioxide for each day per human.

Again, we made assumptions to make things simple. Our human wasn’t exercising. Our human was an adult. And our human was exchanging a typical amount of air. Recognizing that the number is a crude estimate, I will again round the number to one significant figure, so that we have 0.9 kilograms of carbon dioxide released each day per human.

Isn’t it exciting that we came up with roughly the same answer! For comparison, Wickipedia (http://en.wikipedia.org/wiki/Breathing) quotes an estimate of 900 grams of carbon dioxide a day by the United States Department of Agriculture (USDA).

Here are some questions to think about:

The respiration rate I used was for an average adult. When I measured my respiration, I was sitting, so I’m thinking this is for an average adult at rest. How would these numbers be changed for someone who was exercising? Children breathe faster (Reference 3) but have smaller lungs. How would each of these factors affect the result? Finally, if you wanted a more accurate number, how would you change the calculations?

Comparison to carbon dioxide uptake by plants

How does that compare to some other things?

Prairie near Mandan, ND during the growing season (24 Apr – 26 October) 1996-1999, (reference 4)
1.85 grams CO2 per square meter taken from the atmosphere on average
(Meaning that 380 square meters of land would cancel out the effect of our human) – but remember – this in only during the growing season!

A generic tree (reference 5)

This tree (I’m assuming this is a big one) is said to take up 21.8 kilograms of carbon dioxide a year. For a year, our human produces about 365 x 0.7 kilograms a year, or 255 kilograms. So we’d need 10 of these threes to cancel the carbon dioxide we exhale. This site unfortunately does not quote a source.

Pine forest in Finland (Reference 6)

During the period of measurement, this forest took up
2.4 grams carbon dioxide per square meter per day during July/August, and
1.7 grams carbon dioxide per square meter per day during September

In “human units”, taking 0.7 kg/day, this means we’d need
290 square meters to offset our exhaled carbon dioxide in July and August, and 410 square meters to offset our exhaled carbon dioxide in September.

So – we are part of the carbon cycle, too! At Cape Point, we were breathing out carbon dioxide, but the atmosphere sampled was 30 meters above us – so we probably did not affect the measurements there. But I hear stories from scientists who are measuring carbon dioxide uptake about how they avoid contaminating their measurements. Some of the things they do – push their cars when they get close to the instruments instead of driving them, and leaving their dogs inside the car instead of letting them wander around the site. For more about the carbon cycle, visit the carbon cycle pages on the GLOBE web site.

References

1. p. 151, Berkow, , R., et al., 1997: The Merck Manuel for Medical Information: Home Edition. Merck & Co, publishers, 1509 pp.

2. p. 44, Kapit, W., et al., 1987: The Physiology Coloring Book. HarperCollins. 154 pp.

3. The five percent was decided on based on several references. The Argonne National Laboratory “Ask a Scientist” (http://www.newton.dep.anl.gov/askasci/zoo00/zoo00065.htm) lists 5.3 per cent by volume for “alveolar air” in response to a question about how much CO2 is exhaled. This is slightly lower than the range of values for arterial blood gases derived from p. 907, Taylor, C., C. Lillis, and P. LeMone, 1989: Fundamentals of Nursing. J. B. Lippincott Company, Philadelphia. 1356 pp. On the other hand, http://en.wikipedia.org/wiki/Breath writes exhaled air has 4-5% carbon dioxide by volume, with the BBC listing 4%.

4. Frank, A.B., and A. Dugas, 2001: Carbon dioxide fluxes over a northern, semiarid. mixed-grass prairie. Agricultural and Forest Meteorology. 108, 317-326,

5. http://www.coloradotrees.org/benefits.htm#10

6. U. Rannik et al, 2002 fluxes of carbon dioxide and water vapour over Scots pine forest and clearing. Agricultural and Forest Meteorology, 111, 187-202

Acknowledgments. I talked about this blog a great deal with colleagues. I am indebted to Jimy Dudhia and Greg Holland for contributing useful ideas and information. Also, our sincere thanks to the staff at the Cape Point GAWS station for sharing their facility with the students at the GLE.

I asked a climate scientist at NCAR, Caspar Ammann, to review the previous blog, and he brought up some interesting points that I thought I would talk about a little bit further. I am hoping this will inspire some of you to play with the data a little bit, in order to get a better “feel” for what makes the trends at the GLOBE sites “uncertain.”

The effect of extreme values on the trend line

Let’s start with the Jicin, Czech Republic, annual average temperatures. But this time, we will include 1996:

Figure 1. For GLOBE data at 4. Zakladni Skola in Jicin, the Czech Republic, change of trend from leaving out the first point.

In the figure the red points are those used for Fig. 2 of the previous blog. You see the trend, 0.04 degrees Celsius per year. If we add the point from 1996, the trend more than doubles – to 0.1 degrees Celsius per year – 1 degree Celsius per decade.

But 1996 might be a cold year. Remember – weather and climate vary from days to weeks to years to decades.

2001 was a cold year, too, relative to the surrounding points. What if we left 2001 out? How much would you expect 2001 to affect the trend? Note in Figure 2 that there is almost no effect. This is because 2001 is close to the middle of the data record. This makes sense: if you drew a straight line through the points by eye, you would be influenced more by the points at the beginning and end of the time series.

Figure 2. For same dataset, but ignoring the cold point in 2001.

Now, you might think that you should get rid of both years. Maybe they are not representative of the long term trend. Something happened in the Jicin area to make it a really cold year in 1996, and a really warm year in 2001. So, you plot the data without either of the two points.

Figure 3. Same data as for Figures 1 and 2, but minus the averages for 1996 and 2001.

Now we are back to the trend in the first graph – 0.04 degrees Celsius per year!

Someone might look at Figure 3, and say that the average temperatures are just going up and down with time, like the seasons. And, that the trend is just because you didn’t have two (or three, or four) complete oscillations! You couldn’t really say this person isn’t right without having temperature measurements from before 1996.

Obviously, the actual value of the temperature trend depends on how you look at the data! You might try this exercise for other stations in the data provided in the last blog.

Let’s try this exercise for the global points in Figure 7 of the last blog:

Table: Global Annual Average Temperature minus the 1961-1990 Mean. Source, Climate Research Unit, Hadley Centre, UK.

(The alert reader will notice that Figure 7 in the previous blog is slightly different now – I had accidentally included data for 2008, which is incomplete.)

Figure 4. For the most recent 12 years of the Hadley Climate Research Unit data, the effect of ignoring “extreme” points in the time series.

Note from Figure 4 that the slope varies depending on the data selected, but that the trends remain positive.

Reducing the influence of extreme points by smoothing

Recall last time that I took out the seasons because I thought they might affect the trend. Climate scientists average in time to get rid of the effect of large year-to-year changes like the ones in 1996 and 2001.

To show the effect of smoothing the data for Jicin, I will do a “three-point running mean average.” This means that I will average the first three temperatures and the first three years. That is, I will average

7.7500
8.8500
9.2300 to get 8.61

And I will average the years, too

1996
1997
1998 to get 1997

Then I will average the next three temperatures for 1997, 1998, and 1999 to get the temperature for 1998, and so on. Let’s say how this smoothing affects the data

Figure 5. For Jicin data, change in trend from smoothing the data.

And you might want to try four-point averages or five-point averages. The fact that the trend is positive, no matter what we do, gives us a little more confidence that there is a warming trend. Just as adding more stations would. But no matter how good the data in Figure 4, this is a trend only for one place – and only for 12 years.

Defining the average temperature

Unlike trends, which are affected by where the numbers are in time, the year doesn’t matter when you take an average. The larger the number of points, the less difference an odd year makes. Let’s do the averages for Jicin, starting with 2 years, then three years, then four years, and so on, for the complete record, to see how each new year affects the average temperature. The results are in Figure 6

Figure 6. For the Jicin data set, the average as a function of the number of points. To take the average, we start by averaging 1996 and 1997 (two points), then 1996, 1997, and 1998 (3 points), and so on.

As you can see, even the large changes at the end don’t really show up much in the average. And I think you can also see that, the more points in the average, the less one difference one more point will make.

Climatologists have chosen to take their average over 30 years. Thus the HAD.CRUT3 curve in Figure 7 of the last blog is relative to a thirty-year average – from 1961 to 1990.

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.

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.