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Annalisa Cherchi, Silvio Gualdi, Swadhin Behera, Jing Jia Luo, Sebastien Masson, Toshio Yamagata, and Antonio Navarra

in the ocean model. b. Description of the datasets used for comparison The results of the coupled model simulation have been compared with analysis and observed data. The SST fields are the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST1.1: full details are provided by Rayner et al. 2003 ). The Climatic Research Unit (CRU) TS 2.0 dataset ( Mitchell et al. 2004 ) contains global land precipitation on a regular grid (0.5° × 0.5°) for the period 1901–2002. Wind fields are taken from the

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Joaquim Ballabrera-Poy, Eric Hackert, Raghu Murtugudde, and Antonio J. Busalacchi

locations of the moorings through the analysis of the error structure obtained from a reduced-space Kalman filter. Second, we identify the most redundant moorings of the proposed array to find out if the proposed array may be simplified. Because a single array cannot encompass all relevant spatial and temporal scales, the focus of this study will be on determining the optimal mooring sites that best observe the large-scale, interseasonal-to-interannual variability in the IO. The experiments used in this

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Rui-Jin Hu and J. Stuart Godfrey

eastward in longitude starting from zero over land; only the average of adjacent grid points in y is plotted. The result (shading in Fig. 12a ) may be thought of as the net downward entrainment, west of a given point, through 15°C. Strong zonal negative gradients of this net downwelling imply strong upward entrainment. Using this fact, inspection of Fig. 12a shows that the bulk of the upwelling and entrainment occurs within a few grid points of the western boundary between 0° and 8°N (consistent

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Tomoki Tozuka, Jing-Jia Luo, Sebastien Masson, and Toshio Yamagata

variability in the tropical Indian Ocean. The content is organized as follows. A brief description of the CGCM along with its validity is given in the next section. In section 3 , two modes of decadal variability in the tropical Indian Ocean are presented. In particular, a detailed discussion on the real nature of the decadal IOD is given there. The final section summarizes the main results. 2. Model a. Model description The model data used in this study are obtained from an atmosphere–ocean–land CGCM

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R. J. Murray, Nathaniel L. Bindoff, and C. J. C. Reason

band of deep mixed layers that develop as a result of wintertime convection in the Sub-Antarctic Zone (SAZ) on the northern side of the Sub-Antarctic Front (SAF) in the South Indian Ocean ( McCartney 1977 , 1982 ). Maximum densities are attained at the eastern end of the band, where convection reaches a depth of 300–500 m. McCartney (1982) recognized the mode as a pycnostad or potential vorticity (PV) minimum in zonal hydrographic sections taken in the interior of the subtropical gyre using data

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Clémentde Boyer Montégut, Jérôme Vialard, S. S. C. Shenoi, D. Shankar, Fabien Durand, Christian Ethé, and Gurvan Madec

) ( Shetye 1986 ; Molinari et al. 1986 ; McCreary and Kundu 1989 ; McCreary et al. 1993 ) and the heat budget of the upper ocean using data ( Düing and Leetmaa 1980 ; Rao et al. 1989 ; Rao and Sivakumar 2000 ; Shenoi et al. 2002 , hereinafter SSS02 ) and numerical models ( Fischer 2000 ; Prasad 2004 ). Shenoi et al. (2005b) examined the heat budget of the near-surface layers of the NIO using model output and found that the model reproduced the surface heat content correctly, except during the

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Gabriel A. Vecchi and Matthew J. Harrison

various locations in the Indian basin (e.g., Premkumar et al. 2000 ; Bhat et al. 2001 ; Masumoto et al. 2005a ), and efforts are under way to coordinate a basinwide, multisensor observing system (e.g., Meyers et al. 2000 ; Masumoto et al. 2005b ; CLIVAR–GOOS Indian Ocean Panel 2006 ). To help in the process of the observing system development, various groups have undertaken observing system simulation experiments (OSSEs) using diverse methods and models (e.g., Schiller et al. 2004 ; Oke and

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Qian Song, Gabriel A. Vecchi, and Anthony J. Rosati

description The model used in this study is the GFDL CM2.1 ocean–atmosphere–land–ice global CGCM. Details of the model formulation are documented in Gnanadesikan et al. (2006 , ocean model), GFDL Global Atmospheric Model Development Team (2004 , atmosphere and land model), Delworth et al. (2006 , coupled model), Wittenberg et al. (2006 , ENSO), and Stouffer et al. (2006 , climate sensitivity). Here, only a brief description of the coupled model is provided. The ocean component of the coupled model is

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H. Annamalai, H. Okajima, and M. Watanabe

list of studies that related observed SST anomalies in the tropical Pacific with anomalies in other ocean basins. The tropical Indian Ocean, the focus here, witnesses a basin-wide warming in boreal winter that peaks in the following spring after the mature phase of El Niño ( Fig. 1a ). Are these warm anomalies a passive response to El Niño or do they influence the PNA pattern? We are interested in answering this question by performing systematic experiments with two AGCMs. Many past studies, using

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Hae-Kyung Lee Drbohlav, Silvio Gualdi, and Antonio Navarra

( Black et al. 2003 ), and the Indian summer monsoon region ( Terray et al. 2003 ). In particular, the presence of the IODM during El Niño years may reduce the influence of an El Niño on the Indian summer rainfall ( Ashok et al. 2004 ). In addition, Saji and Yamagata (2003) suggested that the impact of the IODM reaches several remote regions away from the Indian Ocean. They found a strong correlation between the IODM, warm land surface temperatures, and reduced rainfall over Europe, northeast Asia

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