Incorporating Uncertainty into a Regression Neural Network Enables
Identification of Decadal State-Dependent Predictability
Abstract
Predictable internal climate variability on decadal timescales (2-10
years) is associated with large-scale oceanic processes, however these
predictable signals may be masked by the noisy climate system. One
approach to overcoming this problem is investigating state-dependent
predictability - how differences in prediction skill depend on the
initial state of the system. We present a machine learning approach to
identify state-dependent predictability on decadal timescales in the
Community Earth System Model version 2 by incorporating uncertainty
estimates into a regression neural network. We leverage the network’s
prediction of uncertainty to examine state dependent predictability in
sea surface temperatures by focusing on predictions with the lowest
uncertainty outputs. In particular, we study two regions of the global
ocean - the North Atlantic and North Pacific - and find that skillful
initial states identified by the neural network correspond to particular
phases of Atlantic multi-decadal variability and the interdecadal
Pacific oscillation.