On the choice of ensemble mean for estimating the forced signal in the presence of internal variability

This is the final version of the article. Available from American Meteorological Society via the DOI in this record. In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variabil...

Full description

Bibliographic Details
Published in:Journal of Climate
Main Authors: Frankcombe, LM, England, MH, Kajtar, JB, Mann, ME, Steinman, BA
Format: Article in Journal/Newspaper
Language:English
Published: American Meteorological Society 2018
Subjects:
Online Access:http://hdl.handle.net/10871/33880
https://doi.org/10.1175/JCLI-D-17-0662.1
_version_ 1828676541386588160
author Frankcombe, LM
England, MH
Kajtar, JB
Mann, ME
Steinman, BA
author_facet Frankcombe, LM
England, MH
Kajtar, JB
Mann, ME
Steinman, BA
author_sort Frankcombe, LM
collection University of Exeter: Open Research Exeter (ORE)
container_issue 14
container_start_page 5681
container_title Journal of Climate
container_volume 31
description This is the final version of the article. Available from American Meteorological Society via the DOI in this record. In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences. This work was supported by the Australian Research Council (ARC) through grants to L. M. F. (DE170100367) and to M. H. E. through the ARC Centre of Excellence in Climate System Science (CE110001028). J. B. K. is supported by the Natural Environment Research Council (Grant NE/N005783/1). B. A. S. was supported by the U.S. National Science Foundation (EAR-1447048).
format Article in Journal/Newspaper
genre Sea ice
genre_facet Sea ice
id ftunivexeter:oai:ore.exeter.ac.uk:10871/33880
institution Open Polar
language English
op_collection_id ftunivexeter
op_container_end_page 5693
op_doi https://doi.org/10.1175/JCLI-D-17-0662.1
op_relation http://hdl.handle.net/10871/33880
Journal of Climate
op_rights © 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
2018-12-22
Under embargo until 22 December 2018 in compliance with publisher policy
publishDate 2018
publisher American Meteorological Society
record_format openpolar
spelling ftunivexeter:oai:ore.exeter.ac.uk:10871/33880 2025-04-06T15:06:09+00:00 On the choice of ensemble mean for estimating the forced signal in the presence of internal variability Frankcombe, LM England, MH Kajtar, JB Mann, ME Steinman, BA 2018 http://hdl.handle.net/10871/33880 https://doi.org/10.1175/JCLI-D-17-0662.1 en eng American Meteorological Society http://hdl.handle.net/10871/33880 Journal of Climate © 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). 2018-12-22 Under embargo until 22 December 2018 in compliance with publisher policy Climate variability Model evaluation/performance Decadal variability Interdecadal variability Multidecadal variability Article 2018 ftunivexeter https://doi.org/10.1175/JCLI-D-17-0662.1 2025-03-11T01:39:57Z This is the final version of the article. Available from American Meteorological Society via the DOI in this record. In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences. This work was supported by the Australian Research Council (ARC) through grants to L. M. F. (DE170100367) and to M. H. E. through the ARC Centre of Excellence in Climate System Science (CE110001028). J. B. K. is supported by the Natural Environment Research Council (Grant NE/N005783/1). B. A. S. was supported by the U.S. National Science Foundation (EAR-1447048). Article in Journal/Newspaper Sea ice University of Exeter: Open Research Exeter (ORE) Journal of Climate 31 14 5681 5693
spellingShingle Climate variability
Model evaluation/performance
Decadal variability
Interdecadal variability
Multidecadal variability
Frankcombe, LM
England, MH
Kajtar, JB
Mann, ME
Steinman, BA
On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
title On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
title_full On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
title_fullStr On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
title_full_unstemmed On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
title_short On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
title_sort on the choice of ensemble mean for estimating the forced signal in the presence of internal variability
topic Climate variability
Model evaluation/performance
Decadal variability
Interdecadal variability
Multidecadal variability
topic_facet Climate variability
Model evaluation/performance
Decadal variability
Interdecadal variability
Multidecadal variability
url http://hdl.handle.net/10871/33880
https://doi.org/10.1175/JCLI-D-17-0662.1