On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
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 ensemblemean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a...
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ftunivtasmania:oai:eprints.utas.edu.au:30521 2023-05-15T18:18:32+02:00 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 application/pdf https://eprints.utas.edu.au/30521/ https://eprints.utas.edu.au/30521/1/133140%20-%20On%20the%20choice%20of%20ensemble%20mean%20for%20estimating%20the%20forced%20signal.pdf en eng Amer Meteorological Soc https://eprints.utas.edu.au/30521/1/133140%20-%20On%20the%20choice%20of%20ensemble%20mean%20for%20estimating%20the%20forced%20signal.pdf Frankcombe, LM, England, MH, Kajtar, JB, Mann, ME and Steinman, BA 2018 , 'On the choice of ensemble mean for estimating the forced signal in the presence of internal variability' , Journal of Climate, vol. 31 , pp. 5681-5693 , doi:10.1175/JCLI-D-17-0662.1 <http://dx.doi.org/10.1175/JCLI-D-17-0662.1>. climate variability CMIP5 Article PeerReviewed 2018 ftunivtasmania https://doi.org/10.1175/JCLI-D-17-0662.1 2021-09-13T22:19:49Z 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 ensemblemean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimateof the true forced signal even for models with very few ensemble members. In cases where only a singlemember is available for a given model, however, theSMEMfrom other models is in general out-performed bythe 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 ofwarming in the models causes their trajectories to diverge. We therefore apply the SMEM method to thosemodels with a sufficient number of ensemble members to estimate the change in the amplitude of internalvariability under a future forcing scenario. In line with previous results, we find that on average the surface airtemperature 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 levelpressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there areregional differences. Article in Journal/Newspaper Sea ice University of Tasmania: UTas ePrints Journal of Climate 31 14 5681 5693 |
institution |
Open Polar |
collection |
University of Tasmania: UTas ePrints |
op_collection_id |
ftunivtasmania |
language |
English |
topic |
climate variability CMIP5 |
spellingShingle |
climate variability CMIP5 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 |
topic_facet |
climate variability CMIP5 |
description |
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 ensemblemean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimateof the true forced signal even for models with very few ensemble members. In cases where only a singlemember is available for a given model, however, theSMEMfrom other models is in general out-performed bythe 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 ofwarming in the models causes their trajectories to diverge. We therefore apply the SMEM method to thosemodels with a sufficient number of ensemble members to estimate the change in the amplitude of internalvariability under a future forcing scenario. In line with previous results, we find that on average the surface airtemperature 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 levelpressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there areregional differences. |
format |
Article in Journal/Newspaper |
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 |
title |
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_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_sort |
on the choice of ensemble mean for estimating the forced signal in the presence of internal variability |
publisher |
Amer Meteorological Soc |
publishDate |
2018 |
url |
https://eprints.utas.edu.au/30521/ https://eprints.utas.edu.au/30521/1/133140%20-%20On%20the%20choice%20of%20ensemble%20mean%20for%20estimating%20the%20forced%20signal.pdf |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://eprints.utas.edu.au/30521/1/133140%20-%20On%20the%20choice%20of%20ensemble%20mean%20for%20estimating%20the%20forced%20signal.pdf Frankcombe, LM, England, MH, Kajtar, JB, Mann, ME and Steinman, BA 2018 , 'On the choice of ensemble mean for estimating the forced signal in the presence of internal variability' , Journal of Climate, vol. 31 , pp. 5681-5693 , doi:10.1175/JCLI-D-17-0662.1 <http://dx.doi.org/10.1175/JCLI-D-17-0662.1>. |
op_doi |
https://doi.org/10.1175/JCLI-D-17-0662.1 |
container_title |
Journal of Climate |
container_volume |
31 |
container_issue |
14 |
container_start_page |
5681 |
op_container_end_page |
5693 |
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1766195127666081792 |