• Atmosphere-ocean climate dynamics
• Neural network models, machine learning, computational intelligence
• Seasonal and intraseasonal climate prediction

Our goal is to understand climate variability, that subtle, nonlinear interplay between atmosphere, ocean and land. To accomplish this goal, we are deeply involved in the development and application of neural networks and other methods from the field of machine learning, a branch of computational intelligence. On the pragmatic side, we aim to develop models for climate prediction at the seasonal and intraseasonal time scales.

A prominent example of climate variability is the famous El Niño phenomenon, an irregular fluctuation of the climate system which produces anomalous warming in the eastern equatorial Pacific (and usually warmer winters in Canada). El Niño can now be forecasted with reasonable accuracy 3-15 months in advance. Forecast techniques range from coupled global atmosphere-ocean general circulation models run on supercomputers to elegant statistical techniques on PCs.

Our group has pioneered the use of neural network models for analyzing climate data and for El Niño prediction. We have developed neural network models for nonlinear principal component analysis, nonlinear canonical correlation analysis, and nonlinear singular spectrum analysis (our codes are freely downloadable and have users from over 60 countries). We have identified nonlinear atmospheric teleconnection patterns in the extra-tropical Northern Hemisphere associated with the El Niño and with the Arctic Oscillation.

Neural network models can also be combined with dynamical models to improve the parametrization in dynamical models, or even to form hybrid models -- e.g. a neural network atmospheric model has been coupled to a dynamical ocean model, yielding a hybrid coupled model of the tropical Pacific. With general circulation models having spatial resolution too coarse to reveal climate variaibility at local scales, neural
network and kernel methods are being used to downscale the model output to finer spatial scales.

My graduate-level book "Machine Learning Methods in Environmental Sciences -- Neural Networks and Kernels" is scheduled to be published by Cambridge Univ. Press in June, 2009 (click here to see the preface and table of contents).

Our climate forecasts are updated monthly on our web site: http://www.ocgy.ubc.ca/projects/clim.pred/.

 

Selected Publications