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Hyeong-Seog Kim, Chang-Hoi Ho, Joo-Hong Kim, and Pao-Shin Chu

tracks is rather unreliable in climate models ( Camargo et al. 2006 ), which will very likely be improved in the next-generation climate forecast systems. The objective of this study is to develop a novel seasonal prediction technique that aims at producing a probabilistic map of seasonal TC occurrences for the entire WNP basin. To accomplish this objective, a track-pattern-based model is devised based on a finite number of representative patterns of WNP TC tracks. The possibility of track

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Dimitrios Giannakis and Andrew J. Majda

coarse-grained long-range forecasting introduced in Part I in the perfect-model setting. The analysis is further developed in section 3 to provide strategies for assessing errors in imperfect models. As an instructive application of these techniques, in section 4 we study the predictive skill and model error in Markov models of regime transitions in the 1.5-layer ocean model. We conclude in section 5 . 2. Predictability in the perfect model a. Partitioning observation space for long

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Robert Vautard, Guy Plaut, Risheng Wang, and Gilbert Brunet

the anomaly contours (compare with Fig. 8a ). Figure 9 shows that the probabilistic version of our two-step model performs reasonably well in forecasting this typical El Niño winter. 5. Summary and conclusions The overall aim of this article was to propose several statistical models, based on space–time principal components (ST PCs), for the seasonal prediction of surface air temperatures (SATs) over North America. The models are validated using a cross-validation technique and are compared to

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Faez Bakalian, Harold Ritchie, Keith Thompson, and William Merryfield

respective eigenvector. This technique has been shown to be effective at revealing patterns in the overall variability of ocean and atmosphere variables on regional to global scales ( Bretherton et al. 1992 ). However, PCA was not designed to investigate correlation structures “between” fields but rather the correlation structure “within” a single field. Contrary to its original design, a substantial number of authors have applied PCA to address issues of coupling and correlation between fields, also

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Erin E. Thomas, Daniel J. Vimont, Matthew Newman, Cécile Penland, and Cristian Martínez-Villalobos

anomalies using LIM have comparable skill to multimodel ensemble mean forecasts made using the full nonlinear coupled ocean–atmosphere models of the North American Multimodel Ensemble (NMME; Kirtman et al. 2014 ). Both forecast techniques have spatial and temporal variations of skill that are similar both to each other and to the potential skill estimated from the forecast signal-to-noise ratios within a perfect linear inverse model framework. This suggests that the deterministic evolution of ENSO

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P. Hutchinson

influences on the Der season rainfall. Itdoes not investigate the causative factors in the relationships. Nevertheless, and despite the deficiencies ofthe data, a possible forecasting technique has been indicated.However, there are limitations to this technique. Itprovides a probability forecast, and the rainfall varianceaccounted for is not high. Therefore, the farmer, who,in general, aims to minimize risk rather than maximizeproduction, would not be able to place enough confidence in the forecast to

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Sailes Sengupta and James S. Boyle

more information than can be easily assimilated by a forecaster. The CPC technique provides a consensus summary that is usually the type of information needed. There are some indications that the SSTs are predictable for a month or season in advance, and if the atmospheric models are then driven by these SSTs, a climate prediction can be made. A CPC analysis of an ensemble of such atmospheric predictions would be an efficient way of producing a robust climate forecast. Figure 8 is the same as Fig

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Gareth Berry and Michael J. Reeder

forecast depends on both the synoptic environment and the model formulation. Here, a more sophisticated objective method of deriving the ITCZ position is presented; this method uses lower-tropospheric convergence, which can be computed from standard dynamic fields from numerical weather prediction (NWP) or climate model output. The technique presented is fully automated and objective allowing large amounts of data (e.g., those produced for climate model intercomparison projects) to be easily processed

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Kwang-Y. Kim and Gerald R. North

instead of a few POPs (e.g., Penland and Magorian 1993 ). In the present technique, temporal evolution of spatial patterns is extracted from the PC time series of EOFs. Unlike POPs, which have unique decay ( e -folding) times and oscillation periods attached to them, temporal evolution of each EOF pattern is given in terms of temporal EOFs of the corresponding PC time series. In essence, temporal EOFs show how the matching spatial pattern evolves in time. The resolution of temporal oscillation

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M. Neil Ward and Antonio Navarra

important to assess the extent to which the ensemble members cluster together. This is commonly referred to as reproducibility and is used as an indication of potential seasonal forecast skill from SST forcing (e.g., Stern and Miyakoda 1995 ; Rowell et al. 1995 ). Following on from the work of Madden (1976) and Zwiers (1987) , techniques have been developed to assess reproducibility for a given location ( Stern and Miyakoda 1995 ; Rowell et al. 1995 ). Again, as for simulation skill, assessment of

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