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Bhaskar Jha and Arun Kumar

climate predictions; for example, if the influence of SSTs is dominantly on the first moment of the PDF, then the spread of the PDF can be assumed to be a constant in the formulation of the seasonal forecasts. A comparative assessment of the influence of SSTs on μ and σ is the focus of the present paper. Efforts to compare the influence of SSTs on the first and the second moment of the PDF of selected atmospheric variables have been previously made. For different variables—for example, rainfall

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D. San-Martín, R. Manzanas, S. Brands, S. Herrera, and J. M. Gutiérrez

configurations have a stochastic component and were chosen to avoid the main shortcoming of weather typing techniques, which is the reduction of the variance ( Enke and Spegat 1997 ). Moreover, M2c can simulate predictand values beyond the observed range. A sensitivity experiment to determine the optimum k to be used (keeping a balance between forecast error and predicted variability) was performed, yielding the best results for k ≃ 100. The third family is based on GLMs. These models are an extension of

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J. M. Gutiérrez, D. San-Martín, S. Brands, R. Manzanas, and S. Herrera

-scale variables of interest (predictands). Different SD techniques have been proposed to infer these relationships from data under the so-called perfect prog approach ( Maraun et al. 2010 ). In this case, reanalysis outputs for a representative period of the past (typically 30 yr) are used as predictors while simultaneous historical observations at the local scale are used as predictands for model training. Once the optimal model configuration has been found using these (quasi) observed data ( Brands et al

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Laurie Trenary and Timothy DelSole

dominant role were found to be problematic in other areas, such as inconsistencies between modeled and observed upper-ocean heat content variability ( Zhang et al. 2013 ). Despite unresolved questions about mechanisms, most studies agree that AMO variability is predictable on multiyear time scales ( Griffies and Bryan 1997a ; Boer 2004 ; Pohlmann et al. 2004 ; Collins et al. 2006 ; DelSole et al. 2011 , 2013 ). Actual forecast skill for the observed North Atlantic SST is on the order of 3–5 years

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Savin S. Chand and Kevin J. E. Walsh

statistical cyclone modeling. Vecchi et al. (2011) , for example, developed a statistical–dynamical hurricane forecasting system for seasonal North Atlantic hurricane activity. Chan et al. (1998 , 2001 ) and Liu and Chan (2003) , for example, used the projection pursuit regression technique of Friedman and Stueltzle (1981) to develop seasonal forecasting schemes for the western North Pacific and the South China Sea. Elsner and Schmertmann (1993) considered a different approach to predict seasonal

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Adam H. Monahan

al. 2012 ; van der Kamp et al. 2012 ), short-term wind forecasts (e.g., De Rooy and Kok 2004 ; Howard and Clark 2007 ; Salameh et al. 2009 ; Sloughter et al. 2010 ; Thorarinsdottir and Gneiting 2010 ), and future climate changes (e.g., Pryor et al. 2005 ; Merryfield et al. 2009 ; Goubanova et al. 2011 ). This study has addressed some basic questions regarding the predictability of monthly time-scale surface winds from the large-scale flow with a particular focus on winds measured at

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Anthony G. Barnston and Michael K. Tippett

by model, season, lead time, and location. The presence of biases creates an opportunity for statistical models to detect and correct them, resulting in improved final forecast quality. Such methods can be used to modify the positions and/or amplitudes of large-scale patterns and also to refine the details of anomaly patterns for local downscaling. Here, we apply statistical corrections to the models in the North American Multimodel Ensemble (NMME) and focus on the correction of biases in the

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James Doss-Gollin, Ángel G. Muñoz, Simon J. Mason, and Max Pastén

. Forecasts and model output statistics A wide variety of methods, generically known as model output statistics (MOS) ( Glahn and Lowry 1972 ), have been proposed to correct for different types of bias in model output. In this work, we analyze how well the rainfall events could have been predicted, using both the raw subseasonal forecasts and MOS-adjusted subseasonal forecasts. We use four types of MOS techniques: homoscedastic extended logistic regression (XLR), heteroscedastic extended logistic

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Rodrigo J. Bombardi, Laurie Trenary, Kathy Pegion, Benjamin Cash, Timothy DelSole, and James L. Kinter III

integrations (~30 years) and relatively few ensemble members (five members). They found that summer precipitation over equatorial South America is predictable, while poor forecast skill is found over subtropical and extratropical regions. The high predictability of precipitation over the equator is because the intertropical convergence zone (ITCZ) is dominated by sea surface temperature forcing ( Taschetto and Wainer 2008 ). The SACZ, however, is less predictable than the ITCZ. In addition, the oceanic

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Junho Yang, Mikyoung Jun, Courtney Schumacher, and R. Saravanan

convection by assuming a physically motivated predictive relationship between the resolved atmospheric state and rainfall amounts. Our statistical analysis attempts to mimic the behavior of such a convection parameterization, in that we identify a statistical predictive relationship between the atmospheric state and the subsequent occurrence/amount of different types of rainfall. However, our goal is not to forecast individual rainfall events as in a weather model, but rather to simulate the slowly

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