Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations
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...
Published in: | Journal of Climate |
---|---|
Other Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://doi.org/10.1175/JCLI-D-19-0855.1 |
id |
ftncar:oai:drupal-site.org:articles_23824 |
---|---|
record_format |
openpolar |
spelling |
ftncar:oai:drupal-site.org:articles_23824 2024-04-28T08:30:13+00:00 Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations Wills, Robert C. J. (author) Battisti, David S. (author) Armour, Kyle C. (author) Schneider, Tapio (author) Deser, Clara (author) 2020-10-15 https://doi.org/10.1175/JCLI-D-19-0855.1 en eng Journal of Climate--0894-8755--1520-0442 articles:23824 ark:/85065/d7sb491d doi:10.1175/JCLI-D-19-0855.1 Copyright 2020 American Meteorological Society (AMS). article Text 2020 ftncar https://doi.org/10.1175/JCLI-D-19-0855.1 2024-04-04T17:33:50Z 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 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. 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. 1852977 Article in Journal/Newspaper North Atlantic North Atlantic oscillation OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Journal of Climate 33 20 8693 8719 |
institution |
Open Polar |
collection |
OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
op_collection_id |
ftncar |
language |
English |
description |
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 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. 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. 1852977 |
author2 |
Wills, Robert C. J. (author) Battisti, David S. (author) Armour, Kyle C. (author) Schneider, Tapio (author) Deser, Clara (author) |
format |
Article in Journal/Newspaper |
title |
Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations |
spellingShingle |
Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations |
title_short |
Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations |
title_full |
Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations |
title_fullStr |
Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations |
title_full_unstemmed |
Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations |
title_sort |
pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations |
publishDate |
2020 |
url |
https://doi.org/10.1175/JCLI-D-19-0855.1 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
Journal of Climate--0894-8755--1520-0442 articles:23824 ark:/85065/d7sb491d doi:10.1175/JCLI-D-19-0855.1 |
op_rights |
Copyright 2020 American Meteorological Society (AMS). |
op_doi |
https://doi.org/10.1175/JCLI-D-19-0855.1 |
container_title |
Journal of Climate |
container_volume |
33 |
container_issue |
20 |
container_start_page |
8693 |
op_container_end_page |
8719 |
_version_ |
1797588166203408384 |