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| Titel |
A global empirical system for probabilistic seasonal climate prediction |
| VerfasserIn |
J. M. Eden, G. J. Oldenborgh, E. Hawkins, E. B. Suckling |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 12 ; Nr. 8, no. 12 (2015-12-11), S.3947-3973 |
| Datensatznummer |
250116705
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| Publikation (Nr.) |
 copernicus.org/gmd-8-3947-2015.pdf |
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| Zusammenfassung |
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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