globalchange  > 气候减缓与适应
DOI: 10.1175/JCLI-D-17-0662.1
Scopus记录号: 2-s2.0-85049749619
论文题名:
On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
作者: Frankcombe L.M.; England M.H.; Kajtar J.B.; Mann M.E.; Steinman B.A.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期:14
起始页码: 5681
结束页码: 5693
语种: 英语
英文关键词: Climate variability ; Climate variability ; Decadal variability ; Interdecadal variability ; Model evaluation/performance ; Multidecadal variability
Scopus关键词: Climate change ; Sea ice ; Sea level ; Climate variability ; Decadal variability ; Inter-decadal variability ; Model evaluation/performance ; Multidecadal variability ; Climate models ; climate modeling ; climate variation ; decadal variation ; ensemble forecasting ; error analysis ; model test ; model validation ; Northern Hemisphere ; Southern Hemisphere ; weather forecasting
英文摘要: 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. © 2018 American Meteorological Society.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111470
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作者单位: Australian Research Council Centre of Excellence for Climate System Science, University of New South Wales, Sydney, NSW, Australia; Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom; Department of Meteorology, The Pennsylvania State University, University Park, PA, United States; Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, United States; Department of Earth and Environmental Sciences and Large Lakes Observatory, University of Minnesota Duluth, Duluth, MN, United States

Recommended Citation:
Frankcombe L.M.,England M.H.,Kajtar J.B.,et al. On the choice of ensemble mean for estimating the forced signal in the presence of internal variability[J]. Journal of Climate,2018-01-01,31(14)
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