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Roxana C. Wajsowicz

Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) and the National Aeronautics and Space Administration (NASA) Seasonal-to-Interannual Prediction Project (NSIPP) system. There are two types of climate prediction problem ( Lorenz 1975 ). In the boundary value problem, the task is to assess the change in climate due to some change in external forcing, for example, anthropogenic changes. In the initial value problem considered here, compare with ENSO forecasting, the task is to

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

Ocean dipole mode. We have used a long integration obtained from a coupled general circulation model (CGCM), and the results have been compared with observations and reanalysis. The ability of the coupled model to reproduce the main features of the climate of the Indian Ocean region, such as, for example, the IODM, has been shown in Gualdi et al. (2003a) . Here, we analyze the characteristics of the simulated ISM, focusing on the feedbacks with the tropical Indian Ocean. A new statistical technique

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

environmental errors (such as ionospheric or water vapor corrections to travel time). Along-track data are filtered with a low-pass filter and binned every 0.25° and every 1/36 of a year. Operationally, TPJ anomalies are defined as sea level deviations from a 9-yr (1993–2001) mean sea level, which removes the geoid error. Next, the along-track data is gridded spatially using an optimal interpolation technique that takes into account grid location and propagation speed in the sea level signal. The 1993

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Gabriel A. Vecchi and Matthew J. Harrison

-scale subseasonal and interannual variability of subsurface temperature. In the next section we describe the model used in these OSSEs. Section 3 describes the technique used to perform the OSSEs: particular interest is placed on developing a parameterization of subdaily temperature variability ( section 3c ). Section 4 explores the ability of the evaluated Indian Ocean observing system to capture interannual and subseasonal variability. Finally, Section 5 offers a summary and discussion of the results. 2

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Karumuri Ashok, Hisashi Nakamura, and Toshio Yamagata

characteristics of storm tracks over the SH Indo–Pacific sector remain qualitatively the same throughout the period ( Nakamura and Shimpo 2004 ). The partial correlation technique (e.g., Spiegel 1988 ) is employed to distinguish the impacts of the individual tropical phenomena (see the appendix for details). 3. Results a. Climatological features and interannual variability Figure 1 shows the climatological distributions of 300-hPa Z e , 850-hPa poleward eddy heat flux ( ), and zonal wind speeds at the

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Bohua Huang and J. Shukla

SST anomalies ( Fig. 5e ). These are consistent with the larger intraensemble SST fluctuations there in the fall season of 1997 described in the last section. Moreover, the forced patterns are largely similar to what we derived from the leading EOF modes of the SST and HC anomalies using the ensemble mean data. Therefore, unlike the tropical Atlantic Ocean, where the regional and random air–sea signals are relatively strong and more sophisticated statistical techniques are required to extract the

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