Dave Stainforth

Two Approaches to Quantifying Uncertainty in Global Temperature Changes

A Bayesian statistical model developed to produce probabilistic projections of regional climate change using observations and ensembles of general circulation models (GCMs) is applied to evaluate the probability distribution of global mean temperature change under different forcing scenarios. The results are compared to probabilistic projections obtained using optimal fingerprinting techniques that constrain GCM projections by observations. It is found that, due to the different assumptions underlying these statistical approaches, the predicted distributions differ significantly in particular in their uncertainty ranges. Results presented herein demonstrate that probabilistic projections of future climate are strongly dependent on the assumptions of the underlying methodologies.

Distributed computing for public-interest climate modeling research

The rapid increase in the speed and capacity of commonly available PCs is providing an opportunity to use distributed computing to tackle major modeling tasks such as climate simulation. The CLIMATEPREDICTION.COM project has developed the software necessary to carry out such a project in the public domain. The paper describes the development of the demonstration release software, along with the computational challenges such as data mining, visualization, and distributed database management.