2013
DOI: 10.1175/jcli-d-12-00860.1
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Impact of Surface Forcing on Southern Hemisphere Atmospheric Blocking in the Australia–New Zealand Sector

Abstract: Characteristics of atmospheric blocking in the Southern Hemisphere (SH) are explored in atmospheric general circulation model (AGCM) simulations with the Community Atmosphere Model, version 3, with a particular focus on the Australia-New Zealand sector. Preferred locations of blocking in SH observations and the associated seasonal cycle are well represented in the AGCM simulations, but the observed magnitude of blocking is underestimated throughout the year, particularly in late winter and spring. This is rela… Show more

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Cited by 16 publications
(13 citation statements)
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References 69 publications
(118 reference statements)
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“…Palmer et al (2008) have demonstrated that the frequency of winter blocking is systematically underestimated in the Northern Hemisphere (NH) by a selection of these climate models. Similar detailed studies of the behavior of climate models in relation to blocking in the Southern Hemisphere (SH) have been undertaken recently (e.g., Ummenhofer et al 2013;Marshall et al 2013).…”
Section: Introductionmentioning
confidence: 88%
“…Palmer et al (2008) have demonstrated that the frequency of winter blocking is systematically underestimated in the Northern Hemisphere (NH) by a selection of these climate models. Similar detailed studies of the behavior of climate models in relation to blocking in the Southern Hemisphere (SH) have been undertaken recently (e.g., Ummenhofer et al 2013;Marshall et al 2013).…”
Section: Introductionmentioning
confidence: 88%
“…In the Australian region, Ummenhofer et al (2013) found that, while an atmospheric GCM (with no coupled ocean) could reproduce the preferred location and seasonal cycle of blocking, it also underestimated the magnitude, especially in late winter and spring. Grose et al (2012) showed that dynamical downscaling of CMIP3 models only partly improved the representation.…”
Section: Blocking and Other Longitudinally-varying Featuresmentioning
confidence: 96%
“…CAM3 uses a spectral dynamical core, 26 vertical levels, and was run at a T85 horizontal resolution (∼1.4° latitude/longitude). Other model‐specific aspects relevant to this study are described in relation to the model's simulation of the hydrological cycle [ Hack et al , ], tropical Pacific variability [ Zelle et al , ], and specifically the climate of the Australian region [ Taschetto et al , ; Ummenhofer et al , ]. The following AGCM simulations were conducted: Control simulation: A 120 year run with a repeating monthly climatology of global SST [ Hurrell et al , ] (based on the 1951–2010 period); Hindcast simulation: A simulation forced by global observed SST [ Smith et al , ] for the period 1951–2010; as such, the SST forcing contains the seasonal cycle, interannual variability, and the long‐term trend; Observed 2010/2011 SST experiment: Observed SST during the period January 2010 to March 2011 is used to force the AGCM; 57 ensemble members of 15 months duration each, initialized on 1 January from different years of the control simulation.…”
Section: Data Sets and Model Experimentsmentioning
confidence: 99%