the range of uncertainty? Or how can we remove the rapid oscillations and
variations that come from meteorology to concentrate
on the long term variations that we need in climate science? So far, climate
modelers have been so focused on making the best predictions possible that
they have not devoted as much effort to quantifying the uncertainty. But for
practical decision-making, the uncertainty is as important as the prediction. If, for
example, a utility is laying water pipe that will be used for 50 years, they need to
know not only the coldest wintertime temperatures that are expected, but a range
of the coldest temperatures with their probabilities, and the possibility that
extreme temperatures may become more likely in a new climate regime. The
mathematical and statistical tools required to understand this kind of uncertainty
don’t yet exist.
These kinds of mathematical tools and insights are needed to understandHESs of all types. Contrary to the central lesson of complex adaptive systems –
that understanding how the components interact is key to predicting how the total
system will behave – the environment and human activity are almost always
studied in isolation. For example, demographic trends are used to predict how
much farmland people will demand, while a separate study might look at how
human migration patterns are affected by landslides. But as people move into an
area they need more farmland, so they farm steeper, less suitable land and as a
consequence make the land more susceptible to landslides – and in turn, when
landslides destroy farmland, people are forced to migrate away. Understanding
the system as a whole requires integrating these two types of studies. In fact,
population is a primary driver of every environmental challenge that threatens
sustainability: generation of greenhouse gases, other pollutants and toxic waste;
depletion of resources, including water, oil, fisheries, topsoil; resource wars and
civil conflicts; malnutrition and world hunger; lack of resources for education and
health care, especially in poor countries; best farmland converted to urban and
suburban sprawl; garbage disposal and the need to find more landfill space;
species extinction. But, the classic mathematically-based topic of population
science does not begin to address the true complexity of factors affecting and
affected by population.