Tracking Climate Models

Abstract. 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 ...

• Type: Journal
• Year: 2010
• Where: CIDU
• Authors: Claire Monteleoni, Gavin Schmidt, Shailesh Saroha
• Keywords: CIDU 2010, Climate Models, Machine Learning, Model, Predictions