Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations

Author:

Wills Robert C. J.1,Battisti David S.1,Armour Kyle C.1,Schneider Tapio2,Deser Clara3

Affiliation:

1. University of Washington, Seattle, Washington

2. California Institute of Technology, Pasadena, California

3. National Center for Atmospheric Research, Boulder, Colorado

Abstract

AbstractEnsembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. 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 Niño, 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 Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.

Funder

National Science Foundation

Tamaki Foundation

Schmidt Futures

Earthrise Alliance

Publisher

American Meteorological Society

Subject

Atmospheric Science

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