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Suryachandra A. Rao, Sebastien Masson, Jing-Jia Luo, Swadhin K. Behera, and Toshio Yamagata

capability in simulating intraseasonal disturbances and the termination of IOD events are presented in section 3 . Section 4 discusses and summarizes the results of the present study. 2. Model and data analysis methods The CGCM used here is SINTEX-F1 [SINTEX-Frontier Research Center for Global Change (FRCGC) model, version 1.0], a modified version of a CGCM developed under the framework of the European Union’s Scale Interaction Experiment project. The model is based on versions of the European

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Lisan Yu, Xiangze Jin, and Robert A. Weller

the European Centre for Medium-Range Weather Forecasts (ECMWF; Gibson et al. 1997 ) and NCEP models are both underestimated. They also found that the University of Wisconsin—Madison (UWM)/COADS climatology ( da Silva et al. 1994 ) is physically less representative if the fluxes are constrained in a way that the time mean globally integrated air–sea heat flux is zero. The accuracy of the heat flux estimates impacts the extent and scope of the use of the flux products in climate research studies

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

1. Introduction The Indian Ocean dipole (IOD) is an air–sea coupled phenomenon associated with a positive sea surface temperature anomaly (SSTA) to the west and a negative SSTA to the east ( Saji et al. 1999 ; Webster et al. 1999 ). It has turned out that the IOD has a large impact on the climate of both the surrounding and remote regions such as east Asia, Europe, and South America ( Guan and Yamagata 2003 ; Saji and Yamagata 2003 ; Yamagata et al. 2004 ). Thus, understanding, as well as

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

40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40), realized from 1958 to 2002 (for more details see the Web site ). Global distribution of ocean temperature is taken from an ocean analysis for the period 1948–99 ( Masina et al. 2004 ). All observations and reanalysis datasets refer to the 1958–2002 period for consistency with the ERA-40 time record length. The Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP

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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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J. C. Hermes, C. J. C. Reason, and J. R. E. Lutjeharms

1986–88 monthly climatology ( Barnier et al. 1995 ) of the European Centre for Medium-Range Weather Forecasts (ECMWF) model interpolated to 3-day values. The surface salinity is restored on a 50-day time scale to the Levitus et al. (1994) climatology. The model run was integrated for a further 12 yr after the 30-yr spinup, and the model volume and temperature fluxes were calculated from the surface to the model ocean floor, as in Reason et al. (2003) . It is important to note that since the

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Tommy G. Jensen

, salinity, and mixed layer depth was forced by monthly climatological winds from the European Centre for Medium-Range Weather Forecasts and relaxed to surface temperature ( Levitus and Boyer 1994 ) and salinity ( Levitus et al. 1994 ). The model was used to investigate the Arabian Sea and Bay of Bengal water exchanges and the associated cross-equatorial flows. However, it became apparent that this exchange was part of a much larger basinwide circulation with transport of low- and high-salinity water

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

dynamics has been demonstrated in Xie et al. (2002) . b. AGCMs For identical forcings, the reproducibility of the results from different AGCMs enhances the confidence in the major conclusions arrived at. This is important since systematic biases in AGCMs can distort the underlying sensitivity to forcing, and therefore parallel analyses using ensemble simulations of different AGCMs are clearly needed ( Kumar and Hoerling 1998a ). 1) ECHAM5 We use ECHAM5, the latest Hamburg version of the European

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

boundary condition is given using the weekly European Remote Sensing Satellites-1 and -2 ( ERS-1–2 ) wind stress interpolated daily with a cubic spline method. The insolation, longwave radiation and turbulent heat fluxes (and the evaporation) are computed from the semiempirical or bulk formulae ( Timmermann et al. 2005 ), which relate the fluxes to the SST (computed by the model) and to meteorological parameters (10-m wind speed, surface air temperature and relative humidity, cloudiness). The daily

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Karumuri Ashok, Hisashi Nakamura, and Toshio Yamagata

variability in the SH jet streams and storm tracks in winter. As shown in Fig. 2a , interannual variability in the upper-tropospheric westerlies, measured as the local standard deviation of 300-hPa zonal wind ( U 300 ), is particularly strong over the midlatitude South Pacific and Atlantic Oceans. The distribution is overall consistent with the interannual variability in 200-hPa winter zonal wind obtained by Hurrell et al. (1998) , based on global analyses by the European Centre for the Medium

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