3 types of Uncertainty in Forecasts - using Global Warming as an example.
First, there is what Schmidt calls initial condition uncertainty-the short-term factors that compete with the greenhouse signal and impact the way we experience the climate. The greenhouse effect is a long-term phenomenon, and it may be obscured by all types of events on a day-to-day or year-to-year basis.
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Another type of uncertainty, however-what Schmidt calls scenario uncertainty- increases with time. This concerns the level of CO, and other greenhouse gases in the atmosphere. At near time horizons, atmospheric composition is quite predictable. The level of industrial activity is fairly constant, but CO, circulates quickly into the atmosphere and remains there for a long time. (Its chemical half-life has been estimated at about thirty years)
Last, there is the structural uncertainty in the models. This is the type of uncertainty that both climate scientists and their critics are rightly most worried about, because it is the most challenging to quantify. It concerns how well we understand the dynamics of the climate system and how well we can represent them mathematically. Structural uncertainty might increase slightly over time, and errors can be self-reinforcing in a model of a dynamic system like the climate.
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