If weather is predictable for only two weeks in advance, how can climate be predictable at lead times of months to a year or two (i.e., on seasonal-to-interannual time scales)? The definition of climate provides an answer to this question and also shows the path toward prediction: "Climate" refers to the statistics of the atmosphere. The atmosphere interacts strongly with the surface through the interchange of fluxes of heat, momentum, and water. The climatic state of the atmosphere therefore depends strongly on the state of the surface, which can be characterized by its temperature, reflectivity, and surface moisture. Because of the interaction of the atmosphere with the surface, the surface conditions will generally change, in turn causing the atmospheric statistics to change in response. The evolution of the climate is therefore dependent on the boundary conditions at the surface with which the atmosphere interacts.
Among the more important statistics of the atmosphere are the averaged temperature and the averaged precipitation (the average must be taken over several time scales for weather systemsusually a month or more). In general, we want to predict monthly averaged temperature and precipitation. We also want to predict the variance of these quantities in order to have some indication of changes in the number of extreme events during the averaging periods and of how much reliance should be placed on predictions of the averages. Since the statistics of the atmosphere depend on the boundary conditions, the key to predicting these statistics is predicting the boundary conditions.
There is a long history of trying to predict the climate, just as there was a long history of weather prediction before the advent of numerical weather prediction. There are traditional methods of forecasting: by divination, by perceived patterns (e.g., a perception borne of experience that, in a given region, a warm summer follows a cold winter), by precursors (the appearance of wooly bear caterpillars are followed by cold winters), and by other traditional means (e.g., The Farmer's Almanac).
This century has seen the development of statistical forecasting techniques, both univariate (e.g., predicting the rain in terms of the past history of rainfall) and multivariate (predicting the rain in terms of other quantities that seem to correlate with the rain, such as temperature and pressure). These methods are still in widespread use, but, when directly compared with numerical model predictions (described below), they generally have lower skill at shorter prediction lead times. For reasons described below, the numerical methods are limited by the availability of