Understanding the uncertainty cascaded in climate change projections for agricultural decision making
Climate projections have confirmed the need to adapt to a changing climate, but have been less beneficial in guiding how to effectively adapt. The reason is the uncertainty cascade, from assumptions about future emissions of greenhouse gases to what that means for the climate to real decisions on a local scale. Each of the steps in the process contains uncertainty and these uncertainties from various levels of the assessment accumulate. This cascade of uncertainty should be critically analyzed to inform decision makers about the certain range of future changes. Most widely used approaches like Bayesian and Monte Carlo gives specific values of parameters and their confidence, yet for agricultural decision making the range of possible changes itself is required as such to understand impact at every point of these ranges. This paper addresses these issues and examines the uncertainties in climate projections at a local scale.