Combining Physical and Statistical Models in Projected Global Warming

Explore a Stanford seminar on combining physical and statistical models to reduce uncertainty in global warming projections. Delve into Patrick Brown's research, which reveals strong statistical relationships between models' simulations of Earth's energy budget and future warming predictions. Discover how models that best match recent observations tend to project more significant future warming. Learn about the implications of integrating physical models with observational data, suggesting higher warming expectations with narrower uncertainty ranges. Gain insights into climate modeling, Earth's energy budget, emergent properties of complex systems, and climate-society interactions. Understand the seminar's context within the EE380: Computer Systems Colloquium series, covering topics from integrated circuits to programming languages.

Introduction
Title
Political Implications
Response Uncertainty
Basic Question
Ice Age Cycles
Physical Global Climate Models
Global Climate Model Resolution
Uncertainty
Emergence
Cross validation
Results
Physical Mechanisms