Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/5805
AuthorsBellucci, A.* 
Gualdi, S.* 
Navarra, A.* 
TitleThe double-ITCZ syndrome in coupled general circulation models: the role of large-scale vertical circulation regimes
Issue Date2009
DOI10.1175/2009JCLI3002.1
URIhttp://hdl.handle.net/2122/5805
Keywordsdouble ITCZ
climate models
Subject Classification01. Atmosphere::01.01. Atmosphere::01.01.02. Climate 
AbstractThe double-intertropical convergence zone (DI) systematic error, affecting state-of-the-art coupled general circulation models (CGCM) is examined in the multi-model Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) ensemble of simulations of the twentieth-century climate. Aim of this study is to quantify the DI error on precipitation in the tropical Pacific, with a specific focus on the relationship between the DI error and the representation of large-scale vertical circulation regimes in climate models. The DI rainfall signal is analysed using a regime sorting approach for the vertical circulation regimes. Through the use of this compositing technique, precipitation events are regime-sorted based on the large scale vertical motions, as represented by the mid-tropospheric lagrangian pressure tendency omega500 dynamical proxy. This methodology allows the partition of the precipitation signal into deep and shallow convective components. Following the regime-sorting diagnosis, the total DI bias is split into an error affecting the magnitude of precipitation associated with individual convective events and an error affecting the frequency of occurrence of single convective regimes. It is shown that, despite the existing large intra-model differences, CGCMs can be ultimately grouped into a few homegenous clusters, each featuring a well defined rainfall-vertical circulation relationship in the DI region. Three major behavioural clusters are identified within the AR4 models ensemble: two unimodal distributions, featuring maximum precipitation under subsidence and deep convection regimes, respectively, and one bimodal distribution, displaying both components. Extending this analysis to both coupled and uncoupled (atmosphere-only) AR4 simulations reveals that the DI bias in CGCMs is mainly due to the overly frequent occurrence of deep convection regimes, whereas the error on rainfall magnitude associated with individual convective events is overall consistent with errors already present in the corresponding atmosphere stand-alone simulations. A critical parameter controlling the strength of the DI systematic error is identified in the model-dependent sea surface temperature (SST) threshold leading to the onset of deep convection (THR), combined with the average SST in the south-eastern Pacific.
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