Detecting failure of climate predictions

Nature Climate Change
By: , and 



The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1, 2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

Publication type Article
Publication Subtype Journal Article
Title Detecting failure of climate predictions
Series title Nature Climate Change
DOI 10.1038/nclimate3041
Volume 6
Year Published 2016
Language English
Publisher Nature
Contributing office(s) Patuxent Wildlife Research Center
Description 4 p.
First page 861
Last page 864
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