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Zewdu T. Segele, Michael B. Richman, Lance M. Leslie, and Peter J. Lamb

; Charney and Shukla 1981 ; Xue and Shukla 1993 ; Clark and Arritt 1995 ; Clark et al. 2001 ). The surface boundary focus of the present Ethiopian study is SST. However, El Niño–Southern Oscillation (ENSO)-related “predictability barrier” in Northern Hemisphere spring (e.g., Goswami and Shukla 1991 ; Webster and Yang 1992 ; Webster et al. 1998 ) can pose a major challenge to providing seasonal rainfall forecasts two or more months in advance in the tropics ( Goddard et al. 2001 ; Korecha and

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Liew Juneng, Fredolin T. Tangang, Hongwen Kang, Woo-Jin Lee, and Yap Kok Seng

must be recomputed to derive a new empirical relationship between the predictands and predictors. Another significant advancement in seasonal climate forecasts over the past few decades was the use of compositing multiple GCM forecast techniques to obtain the multimodel ensemble (MME) forecast ( Krishnamurti et al. 1999 ; Palmer and Shukla 2000 ). The MME technique provides an effective way to handle any uncertainties among the GCMs. Combining the MME and downscaling have proven to have further

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Timothy DelSole and Jagadish Shukla

: Strategies for assessing skill and significance of screening regression models with emphasis on Monte Carlo techniques. J. Climate Appl. Meteor. , 23 , 1454 – 1458 . Lawley , N. D. , 1956 : Tests of significance for the latent roots of covariance and correlation matrices. Biometrika , 43 , 128 – 136 . Michaelson , J. , 1987 : Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor. , 26 , 1589 – 1600 . Montgomery , R. B. , 1940 : Report on the work of G. T

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Arlan Dirkson, William J. Merryfield, and Adam H. Monahan

, which can then be used to forecast not only SIP but any function of the SIC distribution. The first of these improvements is the application of a suitable parametric probability distribution for fitting SIC ensemble forecasts. The second is the introduction of a novel calibration method based on the well-known quantile mapping technique that explicitly accounts for the observed trends in SIC. In section 2 , we briefly describe the model and hindcast experiments used to test this methodology, as

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Ying Zhang, Semu Moges, and Paul Block

, traditional approaches typically include aspects of subjective delineation. Here we evaluate various regionalization methods for objective delineation and define a number of approaches for optimally selecting an appropriate number of clusters. These techniques are applied to seasonal precipitation in Ethiopia for illustration; however, transferability to other variables and regions is possible. Precipitation in Ethiopia is tied to many important sectors, defining lives, livelihoods, and major parts of the

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Mohammad Zaved Kaiser Khan, Rajeshwar Mehrotra, Ashish Sharma, and A. Sankarasubramanian

multimodel forecasts has been pursued using various techniques ranging from simple pooling of the ensembles to optimizing weights to maximize the skill of multimodel forecasts ( Rajagopalan et al. 2002 ; Robertson et al. 2004 ) or statistically estimating the weights conditioned on the dominant predictor conditions ( Devineni and Sankarasubramanian 2010a ). In addition, various statistical techniques such as simple regression ( Krishnamurti et al. 1999 ), dynamic pairwise weighting based on logistic

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Timothy DelSole, Liwei Jia, and Michael K. Tippett

, L21711, doi:10.1029/2009GL040896. Yun , W. T. , L. Stefanova , and T. N. Krishnamurti , 2003 : Improvement of the multimodel superensemble technique for seasonal forecasts . J. Climate , 16 , 3834 – 3840 . Zwiers , F. W. , 1987 : A potential predictability study conducted with an atmospheric general circulation model . Mon. Wea. Rev. , 115 , 2957 – 2974 .

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Kyong-Hwan Seo, Wanqiu Wang, Jon Gottschalck, Qin Zhang, Jae-Kyung E. Schemm, Wayne R. Higgins, and Arun Kumar

GFS upgrades improve the MJO forecast. These estimates of the ability of MJO forecasting are especially important because the MJO temporal scale bridges the gap between synoptic weather forecasting and seasonal climate forecasting, and the information on the MJO-related weather and climate can benefit global regions with a high population density. MJO prediction beyond lead times that the dynamical forecast models provide is routinely extended through statistical prediction techniques. First

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Timothy DelSole and Arindam Banerjee

compute the eigenvectors using the Green’s function technique of DelSole and Tippett (2015) . The resulting eigenfunctions are orthogonal with respect to an area-weighted norm and normalized to unit-area-weighted norm. The leading Laplacian eigenvectors in the North Pacific are shown in Fig. 1 . The first eigenfunction, not shown, is merely a constant and corresponds to the mean over the North Pacific basin. The second and third eigenfunctions measure the east–west and north–south gradients across

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Terry C. K. Lee, Francis W. Zwiers, Xuebin Zhang, and Min Tsao

forecast large-scale climate change on decadal time scales. Given their potential applications, many of which would involve some type of hedging, it is desirable that any decadal-scale forecast should be probabilistic rather than deterministic. One approach for obtaining such forecasts would be to produce large ensembles of forced climate simulations that would then be interpreted in a probabilistic manner, either directly or after some type of postprocessing to adjust for model biases. Unfortunately

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