Details

Autor(en) / Beteiligte

• Hall, Richard J.
• Wei, Hua‐Liang
• Hanna, Edward

Titel

Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: A new approach to North Atlantic seasonal forecasting

Ist Teil von

• Quarterly journal of the Royal Meteorological Society, 2019-07, Vol.145 (723), p.2568-2585

Ort / Verlag

Chichester, UK: John Wiley & Sons, Ltd

Erscheinungsjahr

2019

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Seasonal forecasts of winter North Atlantic atmospheric variability have until recently shown little skill. Here we present a new technique for developing both linear and nonlinear statistical forecasts of the winter North Atlantic Oscillation (NAO) based on complex systems modelling, which has been widely used in a range of fields, but generally not in climate research. Our polynomial NARMAX models demonstrate considerable skill in out‐of‐sample forecasts and their performance is superior to that of linear models, albeit with small sample sizes. Predictors can be readily identified and this has the potential to inform the next generation of dynamical models, and models allow for the incorporation of nonlinearities in interactions between predictors and atmospheric variability. In general there is more skill in forecasts developed over a shorter training period from 1980 compared with an equivalent forecast using training data from 1956. This latter point may relate to decreased inherent predictability in the period 1955–1980, a wider range of available predictors since 1980 and/or reduced data quality in the earlier period, and is consistent with previously identified decadal variability of the NAO. A number of predictors such as sea‐level pressure over the Barents Sea, and a clear tropical signal, are commonly selected by both linear and polynomial NARMAX models. Tropical signals are modulated by higher‐latitude boundary conditions. Both approaches can be extended to developing probabilistic forecasts and to other seasons and indices of atmospheric variability such as the East Atlantic pattern and jet stream metrics. A new approach to seasonal forecasting based on complex systems modelling is presented. The focus is on North Atlantic winter atmospheric circulation, specifically the NAO. Polynomial models show greater skill than linear versions and out‐of‐sample forecasts show promising skill, closely matching the observed time series. Potential nonlinear interactions between predictors are identified.