Probabilistic climate forecasts and inductive problems

Abstract

The development of ensemble-based ‘probabilistic’ climate forecasts is often seen as a promising avenue for climate scientists. Ensemble-based methods allow scientists to produce more informative, nuanced forecasts of climate variables by reflecting uncertainty from various sources, such as similarity to observation and model uncertainty. However, these developments present challenges as well as opportunities, particularly surrounding issues of experimental design and interpretation of forecast results. This paper discusses different approaches and attempts to set out what climate prediction .net and other large ensemble, complex model experiments might contribute to this research programme.