🦋 Chaos and Predictability

Why weather forecasts fail beyond two weeks, why climate projections don't — and what deterministic chaos teaches us about the limits of prediction in the atmosphere and ocean. Notes, schedule, and interactive notebooks for the course.

Syllabus

Syllabus coming soon. Course description, learning objectives, grading, and readings will be posted here.

Schedule

Schedule coming soon. Weekly topics and readings will be listed here as the semester is finalized.

Notebooks

Deterministic Chaos

Chaos, Predictability & Ensemble Forecasting on Lorenz 63

An interactive walkthrough of the Lorenz (1963) system: the strange attractor, sensitive dependence on initial conditions (the butterfly effect), the Lyapunov exponent, and why ensemble forecasting is the correct operational response. Connects the toy model's numbers to real atmospheric predictability limits and distinguishes predictability of the first kind (weather) from the second kind (climate).

dX/dt = σ(Y−X), dY/dt = X(ρ−Z)−Y, dZ/dt = XY−βZ
  • The strange attractor and the route to chaos
  • Sensitive dependence on initial conditions & the Lyapunov exponent
  • Ensemble forecasting and the predictability horizon
  • Predictability of the 1st kind (weather) vs. 2nd kind (climate)
  • Guided questions for in-class discussion