Learning About Climate Sensitivity from Disparate Information Sources

In this tutorial, I use Uninet, a Bayesian belief net program, to illustrate learning about climate sensitivity from disparate information sources.

I focus on three key concepts:

• Negative learning—how more information can make us less certain
• Deconfliction—how we can deal with conflicting observational signals
• Obsolescence—how seemingly obsolete systems can increase our understanding

The tutorial uses real values from joint research with NASA Langley Research Center.