Conference on Intelligent Data Understanding 2010 (CIDU)

CLAIRE MONTELEONI, GAVIN SCHMIDT, AND SHAILESH SAROHA

Climate models are complex mathematical models designed by meteorologists, geophysicists,
and climate scientists to simulate and predict climate. Given temperature predictions
from the top 20 climate models worldwide, and over 100 years of historical temperature data, we
track the changing sequence of which model currently predicts best. We use an algorithm due to
Monteleoni and Jaakkola that models the sequence of observations using a hierarchical learner,
based on a set of generalized Hidden Markov Models (HMM), where the identity of the current
best climate model is the hidden variable. The transition probabilities between climate models
are learned online, simultaneous to tracking the temperature predictions. On historical data, our
online learning algorithm’s average prediction loss nearly matches that of the best performing
climate model in hindsight. Moreover its performance surpasses that of the average model prediction,
which was the current state-of-the-art in climate science, the median prediction, and least
squares linear regression. We also experimented on climate model predictions through the year
2098. Simulating labels with the predictions of any one climate model, we found significantly improved
performance using our online learning algorithm with respect to the other climate models,
and techniques.