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

• 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: Amer Meteorological Soc 2018
• Subjects: climate variability; CMIP5; Sea ice
• Online Access: 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

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.