Title: Tracking Climate Models: Advances in Climate Informatics"
Abstract:
Climate models are complex mathematical models designed by
meteorologists, geophysicists, and climate scientists, and run as
computer simulations, to predict climate. There is currently high
variance among the predictions of 20 global climate models that
inform the Intergovernmental Panel on Climate Change (IPCC). Given
temperature predictions from 20 IPCC global climate models, 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 (HMMs), 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 global mean temperature data, our algorithm's average
prediction loss nearly matches that of the best performing climate
model in hindsight. Moreover its performance surpasses that of the
average over climate model predictions, which is the default
practice 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 algorithm with respect to the other
climate models, and techniques. Drilling down on Africa, Europe, and
North America, on historical data, at both annual and monthly
time-scales, and in future simulations, our algorithm typically
outperforms both the best climate model per geographical region, and
linear regression, and consistently outperforms the average climate
model prediction, the benchmark.
This talk is based on joint work with Gavin Schmidt (NASA GISS and
Columbia Earth Institute), Shailesh Saroha, and Eva Asplund (Computer Science,
Columbia).