Interpreting the nature of Northern and Southern Annular Mode variability in CMIP5 Models. Issue 21 (13th November 2015)

• Record Type: Journal Article
• Authors: Schenzinger, V.; Osprey, S. M.
• Is Part Of: Journal of geophysical research. Volume 120:Issue 21(2015:Nov.)
• Page Start: 11, 203
• Page End: 11, 214
• Publication Date: 2015-11-13
• Subjects: annular mode -- power spectra -- stochastic; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2014JD022989
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Characteristic timescales for the Northern Annular Mode (NAM) and Southern Annular Mode (SAM) variability are diagnosed in historical simulations submitted to the Coupled Model Intercomparison Project Phase 5 (CMIP5) and are compared to the European Centre for Medium‐Range Weather Forecasts ERA‐Interim data. These timescales are calculated from geopotential height anomaly spectra using a recently developed method, where spectra are divided into low‐frequency (Lorentzian) and high‐frequency (exponential) parts to account for stochastic and chaotic behaviors, respectively. As found for reanalysis data, model spectra at high frequencies are consistent with low‐order chaotic behavior, in contrast to an AR1 process at low frequencies. This places the characterization of the annular mode timescales in a more dynamical rather than purely stochastic context. The characteristic high‐frequency timescales for the NAM and SAM derived from the model spectra at high frequencies are ∼5 days, independent of season, which is consistent with the timescales of ERA‐Interim. In the low‐frequency domain, however, models are slightly biased toward too long timescales, but within the error bars, a finding which is consistent with previous studies of CMIP3 models. For the SAM, low‐frequency timescales in November, December, January, and February are overestimated in the models compared to ERA‐Interim. In some models, the overestimation in the SAM austral summer timescale is partly due to interannual variability, which can inflate these timescales by up to ∼40% in the models but only accounts for about 5% in the ERA‐Interim reanalysis. Key Points: Annular modes in CMIP5 models are found to be best described having two timescales On short timescales annular modes are consistent with a chaotic process Interannual variability comprises up to 40% of signal in SAM in some models