Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations

However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging.

It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Nino, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Nino-like response to volcanic eruptions and forced trends in the North Atlantic Oscillation.