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

Wills, Robert C. J. and Battisti, David S. and Armour, Kyle C. and Schneider, Tapio and Deser, Clara (2020) Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations. Journal of Climate, 33 (20). pp. 8693-8719. ISSN 0894-8755. https://resolver.caltech.edu/CaltechAUTHORS:20200729-145256584

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Abstract

Ensembles 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 ten 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1175/JCLI-D-19-0855.1DOIArticle
https://doi.org/10.1175/JCLI-D-19-0855.s1DOISupplemental Material
https://github.com/rcjwills/lfcaRelated ItemCode
ORCID:
AuthorORCID
Battisti, David S.0000-0003-4871-1293
Armour, Kyle C.0000-0002-6833-5179
Schneider, Tapio0000-0001-5687-2287
Deser, Clara0000-0002-5517-9103
Additional Information:© 2020 American Meteorological Society. Manuscript received 25 November 2019, in final form 16 July 2020. R.C.J.W. and D.S.B. acknowledge support from the National Science Foundation (Grant AGS-1929775) and the Tamaki Foundation. R.C.J.W. and K.C.A. acknowledge support from the National Science Foundation (Grant AGS-1752796). R.C.J.W. is also supported by the University of Washington eScience Institute. T.S. is supported by Eric and Wendy Schmidt by recommendation of the Schmidt Futures program and by the Earthrise Alliance. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based on work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement no. 1852977. We thank Dennis Hartmann, Cristian Proistosescu, Flavio Lehner, Elizabeth Maroon, Mingfang Ting, and David Bonan for valuable input on this work. The code for S/NP filtering is available at github.com/rcjwills/forced806 patterns. The code for LFCA is available at github.com/rcjwills/lfca.
Funders:
Funding AgencyGrant Number
NSFAGS-1929775
Tamaki FoundationUNSPECIFIED
NSFAGS-1752796
University of WashingtonUNSPECIFIED
Eric and Wendy SchmidtUNSPECIFIED
Schmidt Futures ProgramUNSPECIFIED
Earthrise AllianceUNSPECIFIED
National Center for Atmospheric Research (NCAR)UNSPECIFIED
NSFAGS-1852977
Subject Keywords:Climate change; Climate variability; Pattern detection; Statistical techniques; Climate models; Ensembles
Issue or Number:20
Record Number:CaltechAUTHORS:20200729-145256584
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200729-145256584
Official Citation:Wills, R. C. J., D. S. Battisti, K. C. Armour, T. Schneider, and C. Deser, 2020: Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations. J. Climate, 33, 8693–8719, https://doi.org/10.1175/JCLI-D-19-0855.1
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104645
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Jul 2020 22:34
Last Modified:18 Sep 2020 18:00

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