How potentially predictable is northern European winter climate a season ahead?

• Published in: International Journal of Climatology
• Main Authors: Folland, C.K., Scaife, A.A., Lindesay, Janette, Stephenson, D.B.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: John Wiley & Sons Inc
• Subjects: Keywords: EL Nino; North Atlantic oscillations; QBO; Sea surface temperature (SST); Seasonal forecasting; Atmospheric pressure; Atmospheric temperature; Benchmarking; Climate change; Regression analysis; Upper atmosphere; Volcanoes; Forecasting; air temperature; cl El Nino; North Atlantic Oscillation; Sea surface temperature; Stratosphere; Two-stage linear regression; North Atlantic; North Atlantic oscillation
• Online Access: http://hdl.handle.net/1885/62625; https://doi.org/10.1002/joc.2314; https://openresearch-repository.anu.edu.au/bitstream/1885/62625/5/11_Folland_-_How_potentially_predictable.pdf.jpg; https://openresearch-repository.anu.edu.au/bitstream/1885/62625/7/01_Folland_How_potentially_predictable_is_2011.pdf.jpg

We estimate the potential predictability of European winter temperature using factors based on physical studies of their influences on European winter climate. These influences include sea surface temperature patterns in different oceans, major tropical volcanoes, the quasi-biennial oscillation in the tropical stratosphere, and anthropogenic climate change. We first assess the predictive skill for winter mean temperature in northern Europe by evaluating statistical hindcasts made using multiple regression models of temperature for Europe for winter and the January-February season. We follow this up by extending the methodology to all of Europe on a 5° × 5° grid and include rainfall for completeness. These results can form the basis of practical prediction methods. However, our main aim is to develop ideas to act as a benchmark for improving the performance of dynamical climate models. Because we consider only potential predictability, many of the predictors have estimated values coincident with the winter season being forecast. However, in each case, these values are predictable on average with considerable skill in advance of the winter season. A key conclusion is that to reproduce the results of this paper, dynamical forecasting models will require a fully resolved stratosphere.