Influence of Solar Variability on the North Atlantic/European Sector.

AGU Fall Meeting Abstracts (2016)

Recent Change—Atmosphere

Chapter in North Sea Region Climate Change Assessment, Springer Nature (2016) 55-84

Authors:

Martin Stendel, Else van den Besselaar, Abdel Hannachi, Elizabeth C Kent, Christiana Lefebvre, Frederik Schenk, Gerard van der Schrier, Tim Woollings

Global temperature response to the major volcanic eruptions in multiple reanalysis data sets

Atmospheric Chemistry and Physics Copernicus Publications 15:23 (2015) 13507-13518

Authors:

M Fujiwara, T Hibino, SK Mehta, L Gray, D Mitchell, J Anstey

Global and European climate impacts of a slowdown of the AMOC in a high resolution GCM

Climate Dynamics Springer Nature 45:11-12 (2015) 3299-3316

Authors:

LC Jackson, R Kahana, T Graham, MA Ringer, T Woollings, JV Mecking, RA Wood

Interpreting the nature of Northern and Southern Annular Mode variability in CMIP5 Models

Journal of Geophysical Research: Atmospheres Wiley 120:21 (2015) 11203-11214

Authors:

Verena Schenzinger, Scott Osprey

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.