Articles | Volume 8, issue 12
Geosci. Model Dev., 8, 3947–3973, 2015
https://doi.org/10.5194/gmd-8-3947-2015
Geosci. Model Dev., 8, 3947–3973, 2015
https://doi.org/10.5194/gmd-8-3947-2015
Methods for assessment of models
11 Dec 2015
Methods for assessment of models | 11 Dec 2015

A global empirical system for probabilistic seasonal climate prediction

J. M. Eden et al.

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Cited articles

Alexander, M. A., Bladé, I., Newman, M., Lanzante, J. R., Lau, N.-C., and Scott, J. D.: The atmospheric bridge: The influence of ENSO teleconnections on air-sea interaction over the global oceans, J. Climate, 15, 2205–2231, 2002.
Balmaseda, M. and Anderson, D.: Impact of initialization strategies and observations on seasonal forecast skill, Geophys. Res. Lett., 36, L01701, https://doi.org/10.1029/2008GL035561, 2009.
Brands, S., Manzanas, R., Gutiérrez, J. M., and Cohen, J.: Seasonal predictability of wintertime precipitation in Europe using the snow advance index, J. Climate, 25, 4023–4028, 2012.
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Our paper reports on a simple regression-based system for producing probabilistic forecasts of seasonal climate. We discuss the physical motivation behind the statistical relationships underpinning our empirical model and provide a validation of hindcasts produced for the last half century. The generation of probabilistic forecasts on a global scale along with the use of the long-term trend as a source of skill constitutes a novel approach to empirical forecasting of seasonal climate.