ABSTRACT
The latest generation of NASA Earth Observing System (EOS) satellites has brought a new dimension to monitoring the living part of the Earth system – the biosphere. EOS data can now measure weekly global productivity of plants and ocean chlorophyll and related biophysical factors, such as changes to land cover and to the rate of snowmelt. However, a greater economic benefit would be realized by forecasting biospheric conditions (Clark et al., 2001). Such predictive ability would provide an advanced decision-making tool to be used in the mitigation of natural hazards or in the exploitation of economically advantageous trends. Imagine if it were possible to accurately predict shortfalls or bumper crops, epidemics of vector-borne diseases such as malaria andWest Nile virus, or wildfire danger as much as 3 to 6months in advance. Such a predictive tool would allow improved preparation and logistical efficiencies. Forecasting provides decision-makers with insight into the future status of ecosystems and allows for the evaluation of the status quo as well as alternatives or preparatory actions that could be taken in anticipation of future conditions. Whether preparing for the summer fire season or for spring floods, knowledge of the magnitude and direction of future conditions can save time, money, and valuable resources. Space and groundbased observations have significantly improved the ability tomonitor natural resources and to identify potential changes, but these observations can describe current conditions only. This information is useful, but many resource managers often need to make decisions months in advance for the coming season. Recent advances in climate forecasting have elicited strong interest in a variety of economic sectors: agriculture (Cane et al., 1994), health (Thomson et al., 2005) and water resources (Wood et al., 2001). The climate forecasting capabilities of coupled ocean-atmosphere global circulation models (GCMs) have steadily improved over the past decade (Zebiak, 2003).
