Why Build Computer Models?

Computer models are essential for studying complex systems, where many variables are operating simultaneously. Most biology experiments are done by holding all other factors constant while studying one variable. But the controls, constraints, and behavior of complex systems cannot be studied that way (Waring and Running 1998). Running recounts a previous experience in which one of his models predicted a system-level behavior that he later confirmed through direct measurements.

"In 1974, we had never measured the fluxes of water in really big trees like redwoods or Douglas firs," he recalls. "We created a computer model and entered all we knew then about the transpiration rates and water balance of those trees." (Water balance refers to the amount of water stored within a tree after adding the amount of water taken up through the roots and subtracting the amount evaporated from the leaves during transpiration.)

"The models showed us that those trees could not have the same transpiration rates per unit leaf area as small trees, or they would die by mid-summer. The model suggested that large trees had transpiration rates almost 10 times lower than small trees. This had never been measured before. When we went to the field years later and made the measurements, we found that the model was correct. This is the first example in my career where a model taught me something about the ecosystem that I didn’t previously know." (Running et al. 1975)

Running’s current global model allows his team to visualize changes in the biosphere at resolutions no real data set can provide. At time scales ranging from minutes to months, and at spatial scales ranging from acres to the entire Earth, Running and his colleagues can toggle certain variables and then run a scenario over and over, watching and learning as their virtual creation plays out possibilities.

In a matter of seconds to minutes, Running’s team uses the model to simulate interactions that in reality would take them years to measure. For instance, they can use the model to examine what causes changes in the beginning date and length of growing seasons (Waring and Running 1998). We know that growing seasons vary from one year to the next. Sometimes the differences are subtle, sometimes extreme. There are climate anomalies such as El Niño and La Niña that can also dramatically affect a region's growing season. Witness some of the record high temperatures recorded in North America during the 1997-98 El Niño, followed by the most severe drought ever in the mid-Atlantic United States during the 1998-99 La Niña.

Using current satellite sensors, Running's team measures many of the same variables that they model, but the satellite data are integrated into 14-day "composites" (meaning that one image represents the average values measured over a 14-day period). But suppose something interesting happens in the middle of those 14 days? Without models, Running's team could miss important details.
 

This is a simple diagram of the model used by Steve Running to predict the rate at which large douglas fir trees exchange water with the air. The model incorporated exchange of water from the soil to the roots, storage of water in the root system and tree trunk, and release of water from the needles to the atmosphere.

Additionally, Running points out that models allow his team to compute variables that cannot be directly measured. "We can only directly measure photosynthesis for an individual plant," he explains. "But models allow us to compute that variable on a global scale."

Models provide the only means for comprehensively examining the terrestrial biosphere. It is difficult to measure one variable at one scale of time and space (such as the photosynthetic activity of a single plant), and then integrate that measurement with other measurements at other scales of time and space (such as changes in temperature and precipitation). Models allow scientists to integrate multiple measurements across varying scales of time and space into a single tool for visualizing the system and predicting future changes.

"A mature, well-tested model can provide a prediction capability that data alone cannot provide," Running states. "There is no way you can look at data and extrapolate into the future. The only way we can imagine what the terrestrial biosphere may be doing 10 or 100 years from now is through modeling."

How to build better climate models
Modeling Earth's Land Biosphere

Modeled soil temperature (animation) for a single day of 1984. Blues are cold temperatures (below freezing) and reds are warm, while white indicates zero degrees celsius. Because plants stop photosynthesizing when the ground is frozen, soil temperature is an important factor in determining net primary productivity. (Image by Robert Simmon, based on data provided by the University of Montana Numerical Terradynamic Simulation Group)