Abstract

AbstractCharacteristic 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.

Highlights

  • The ability of global climate models to accurately represent the key aspects of climate variability enhances confidence in projections of future climate change

  • Sensitivity tests indicate that shifting the center for Northern Annular Mode (NAM) in winter to 15 February does not lead to significant changes of the timescales, though peaks in timescales can occur at different times of the year as shown in Gerber et al [2008a]

  • Northern and Southern Annular Mode timescales for 15 different Coupled Model Intercomparison Project Phase 5 (CMIP5) models were investigated by looking at the power spectra and autocorrelation function at 850 hPa

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Summary

Introduction

The ability of global climate models to accurately represent the key aspects of climate variability enhances confidence in projections of future climate change. A first-order autoregressive process (AR1) is fit, which parameterizes possible stochastic variability [Keeley et al, 2009; Feldstein, 2000; Hasselmann, 1976]. These methods differ from previous studies, such as Baldwin et al [2003] and Gerber et al [2008b], who fit an exponential to the autocorrelation function, and thereby assume a purely stochastic description of annular mode variability across all timescales.

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