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Yukiko Imada, Hiroaki Tatebe, Masayoshi Ishii, Yoshimitsu Chikamoto, Masato Mori, Miki Arai, Masahiro Watanabe, and Masahide Kimoto

/discharge paradigm (e.g., Neelin et al. 1998 ), for which the characteristic time scale is several seasons and is thus much longer than in the atmosphere. In addition, nonlinear dynamics and stochastic atmospheric variability are also important factors that influence the irregularity of ENSO. Thus, a dynamical seasonal forecasting system with a comprehensive atmosphere–ocean coupled general circulation model (AOGCM) with an ensemble approach is now commonly used by most of the major meteorological centers

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Bohua Huang and J. Shukla

reflects consistent propagation of thermocline disturbances in both the ECMWF and reconstructed forcing cases, first as equatorial waves, then as coastal Kelvin waves after reaching the eastern coast. 6. Summary We have examined a set of the monthly mean surface wind stress and winds in the lower troposphere for 1986–92, derived from a simulation of an atmospheric general circulation model forced with observed sea surface temperature. It is found that the AGCM surface stress fields have considerable

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Femke C. Vossepoel, Anthony T. Weaver, Jérôme Vialard, and Pascale Delecluse

1. Introduction Oceanic wind stress forcing largely determines the variability and thermal structure of the equatorial oceans (e.g., Cane 1979 ; DeWitt and Leetmaa 1978 ). As a consequence, errors in wind stress forcing cause errors in the model representation of the equatorial thermodynamics, such as the structure of the thermocline (e.g., Hackert et al. 2001 ; Menkes et al. 1998 ). Different wind products are available for the equatorial oceans, and each give a different dynamical

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C. Maes, G. Madec, and P. Delecluse

reported by Latif et al. (1994) and the development of theoretical work and numerical models reviewed by McCreary and Anderson (1991) . Nevertheless, oceanic general circulation models (OGCMs), which have the ambition to simulate an oceanic circulation comparable with observations, still have limited success in producing ENSO-like oscillations when coupled to an atmospheric GCM. In a comparison of 17 coupled ocean–atmosphere GCMs, Neelin et al. (1992) concluded that the lack of robustness in

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Ming Ji and Thomas M. Smith

1 l-yr Pacific Ocean simulations using an ocean general circulation model are compared with corresponding ocean analyses and with in sire observations from moorings and island tide gauges. The ocean simulationswere forced by combining the climatological wind stress of Hellerman and Rosenstein with wind stress anomaliesobtained from (a) The Florida State University surface wind analysis and (b) a two-member ensemble from anatmospheric model simulation. The ocean analyses were obtained by

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Shu-Chih Yang, Eugenia Kalnay, Ming Cai, and Michele M. Rienecker

dominant modes of variability of the coupled system. Since the upper-ocean circulation is mostly a wind-driven process, the changes in sea surface temperature (SST) anomalies result from coupled atmosphere–ocean processes. Most of the current methods for generating ensemble perturbations for coupled models intend to perturb the wind to assess the uncertainties in SST fields. For example, in a tier 2 system, the atmospheric ensemble is generated under the influence of only a single SST field ( Bengtsson

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Chaojiao Sun, Michele M. Rienecker, Anthony Rosati, Matthew Harrison, Andrew Wittenberg, Christian L. Keppenne, Jossy P. Jacob, and Robin M. Kovach

/Experimental Prediction (CDEP) program. The consortium was focused toward improving ocean data assimilation methods and their implementation in support of forecasts with coupled general circulation models. The consortium activities were coordinated across four themes: ocean data assimilation product intercomparisons, development of observational data streams; model sensitivity experiments, and validation of assimilation products in forecast experiments. The ultimate goal was to accelerate progress in improving

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Bohua Huang and Edwin K. Schneider

-2002.Rebert, J. P., J. R. Donguy, G. Eldin, and E. Wyrtki, 1985: Relations between sea level, thermocline depth, heat content, and dynamic height in the tropical Pacific Ocean. J..Geophys. Res., 90, 11 719-11 725.Reynolds, R. W., 1988: A real-time global sea surface temperature analysis. J. Climate, 1, 75-86.Rosati, A., and K. Miyakoda, 1988: A general circulation model for upper ocean simulation. J. Phys. Oceanogr., 18, 1601-1626.Schlesinger, M. E., and W. L. Gates, 1980: The January and July

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A. Rosati, K. Miyakoda, and R. Gudgel

) that fluctuations in upper-ocean heat content are both systematic and significant in the evolution of ENSO. To quantize this character, the heat content is calculated by where 248-m depth is taken to include the thermocline level around the equatorial Pacific, and it corresponds to the 11th level from the surface in the ocean model, ρ = 1.02 g cm −3 and c p = 4.187 J g −1 K −1 . Figure 1 shows Hovmöller diagrams of heat content along the equator for temperature nudging at the right

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A. T. Weaver, J. Vialard, and D. L. T. Anderson

) ocean general circulation model (OGCM) of the Laboratoire d'Océanographie Dynamique et de Climatologie (LODYC; Madec et al. 1998 ). One of the main motivations for developing the system is to produce ocean analyses for seasonal climate forecasting. In the present study, the 3DVAR and 4DVAR systems are applied to produce a reanalysis of the tropical Pacific Ocean over the period 1993–98 using in situ temperature observations. Here, and in the companion paper by Vialard et al. (2003 , hereafter

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