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Bred vectors and forecast errors in the NASA coupled general ...

Bred vectors and forecast errors in the NASA coupled general ...

Abstract The

Abstract The breeding method has been implemented in the NASA Global Modeling and Assimilation Office (GMAO) coupled general circulation model (CGCM) in its operational configuration where ocean data assimilation is used to initialize the coupled forecasts. Bred vectors (BVs), designed to capture the dominant growing errors in the atmosphere-ocean coupled system, are applied as initial ensemble perturbations. We investigate the potential improvement for ensemble prediction by comparing BVs with the oceanic growing errors, estimated by the one-month forecast error from the nonperturbed forecast. Our results show that one-month forecast errors and BVs from the NASA CGCM share very similar features: BVs are clearly related to forecast errors in both SST and equatorial subsurface temperature, particularly when the BV growth rate is large. Both the forecast errors and the BVs in the subsurface are dominated by large-scale structures near the thermocline. Our results suggest that the forecast errors are dominated by dynamically evolving structures related to the variations of the background anomalous state, and that their shapes can be captured by BVs, especially during the strong 1997-1998 El Niño. Hindcast experiments starting from January 1997 with one pair of BVs achieve a significant improvement compared to the control (unperturbed) hindcast by capturing many important features of this event, including the westerly wind burst in early 1997. 2

1. Introduction During the last decade, the ability to simulate and predict El Niño-Southern Oscillation (ENSO) phenomena has greatly increased because of (1) the establishment of new types of observing systems contributing to a better understanding of the dominant physical process and improved initial conditions, and (2) the improvement of coupled ocean-atmosphere modeling (Hayes et al. 1991; Wallace et al. 1998; McPhaden et al., 1998, Saha et al. 2006). However, there are still limitations in advancing ENSO forecast skill to several seasons ahead (Latif et al. 1993, Chang et al. 1996; Fedorov et al. 2003; Chen et al. 2004, and the special volume of El Niño from JGR, 1998). These limitations arise from (1) errors in the initial conditions, (2) systematic errors/biases in the oceanatmosphere coupled models, (3) problems with the initialization process, e.g. model shocks in the initial coupling, and (4) the physical processes in coupled oceanatmosphere models used to represent ENSO, including feedbacks from variabilities of different time scales (e.g. Madden-Julian Oscillation, Slingo et al. 1999). Ensemble forecasting and initialization using ocean data assimilation are, at the present, the most straightforward solutions for improving ENSO prediction (Stockdale et al. 1998; Kirtman et al. 2003; Chen et al. 1998; Ji et al. 1997; Rosati et al., 1997; Saha et al. 2006). Ensemble forecasting systems are designed to use a set of initial perturbations to represent the uncertainties and sensitivities of the state. The ensemble mean from a well-designed ensemble forecast system is expected to improve the control forecast (without perturbations) assuming the true state can be encompassed within a set of 3

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