2018
DOI: 10.1007/s00382-018-4404-z
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Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe

Abstract: There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. Th… Show more

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Cited by 51 publications
(44 citation statements)
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References 83 publications
(83 reference statements)
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“…The second main result is the predictability of summer temperature in the Mediterranean region obtained without de-trending the observed and forecast data. Significant skill (anomaly correlation) is also shown by Mishra et al (2018) in their skill assessment of EUROSIP for the forecasts of summer temperature with one-month lead time for areas including the CS4 and CS6 domains of this study for three models. The skill for CS6 is also supported by their results with a probabilistic skill score and for the multi-model.…”
Section: Discussionsupporting
confidence: 51%
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“…The second main result is the predictability of summer temperature in the Mediterranean region obtained without de-trending the observed and forecast data. Significant skill (anomaly correlation) is also shown by Mishra et al (2018) in their skill assessment of EUROSIP for the forecasts of summer temperature with one-month lead time for areas including the CS4 and CS6 domains of this study for three models. The skill for CS6 is also supported by their results with a probabilistic skill score and for the multi-model.…”
Section: Discussionsupporting
confidence: 51%
“…In their assessment of the skill of EUROSIP, Mishra et al (2018) select a common available period of 1992-2012. As we have seen that sample length and ensemble size are critical, we take these two parameters as integral parts of a seasonal prediction system and we propose to take advantage of the full hindcast information and evaluate each model on the longest series available and pool the ensembles together, each bringing all its members.…”
mentioning
confidence: 99%
“…For the European winter season, factors such as stratospheric processes, soil moisture, sea-ice extension, and North Atlantic Oscillation (NAO) phase have been identified as sources of predictability [7][8][9]. Specifically, for the winter season evidence indicates a stratospheric variability that significantly impacts seasonal temperature anomalies [10].…”
Section: Introductionmentioning
confidence: 99%
“…However, a poorer predictability characterizes the mid-latitudes compared to tropical latitudes. In fact, the latter are affected by a larger chaotic component which limits the skill/quality of the current seasonal forecast systems [9].…”
Section: Introductionmentioning
confidence: 99%
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