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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 12
Geosci. Model Dev., 8, 3947–3973, 2015
https://doi.org/10.5194/gmd-8-3947-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 3947–3973, 2015
https://doi.org/10.5194/gmd-8-3947-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

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. Eden1, G. J. van Oldenborgh1, E. Hawkins2, and E. B. Suckling2 J. M. Eden et al.
  • 1Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
  • 2NCAS-Climate, Department of Meteorology, University of Reading, Reading, UK

Abstract. Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction.

Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

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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.
Our paper reports on a simple regression-based system for producing probabilistic forecasts of...
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