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On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
Citation
Frankcombe, LM and England, MH and Kajtar, JB and Mann, ME and Steinman, BA, On the choice of ensemble mean for estimating the forced signal in the presence of internal variability, Journal of Climate, 31 pp. 5681-5693. ISSN 0894-8755 (2018) [Refereed Article]
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Copyright Statement
Copyright 2018 American Meteorological Society
DOI: doi:10.1175/JCLI-D-17-0662.1
Abstract
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, theSMEMfrom 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.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | climate variability, CMIP5 |
| Research Division: | Earth Sciences |
| Research Group: | Atmospheric sciences |
| Research Field: | Atmospheric dynamics |
| Objective Division: | Environmental Policy, Climate Change and Natural Hazards |
| Objective Group: | Understanding climate change |
| Objective Field: | Global effects of climate change (excl. Australia, New Zealand, Antarctica and the South Pacific) (excl. social impacts) |
| UTAS Author: | Kajtar, JB (Dr Jules Kajtar) |
| ID Code: | 133140 |
| Year Published: | 2018 |
| Web of Science® Times Cited: | 38 |
| Deposited By: | Oceans and Cryosphere |
| Deposited On: | 2019-06-13 |
| Last Modified: | 2019-07-10 |
| Downloads: | 20 View Download Statistics |
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