Towards a Robust Detection of Interannual Ensemble Forecast Signals over the North Atlantic and Europe using Atmospheric Circulation Regimes

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Swinda Klaasje Jantine Falkena, Jana de Wiljes, Antje Weisheimer, Theodore G Shepherd


In order to study the non-stationary dynamics of atmospheric circulation regimes, the use of model ensembles is often necessary. However, the regime representation within models exhibits substantial variability, making it difficult to detect robust signals. To this end we employ a regularised k-means clustering algorithm to prevent overfitting. The approach allows for the identification of six robust regimes and helps filter out noise in the transition probabilities and frequency of occurrence of the regimes. This leads to more pronounced regime dynamics, compared to results without regularisation, for the wintertime Euro-Atlantic sector. We find that sub-seasonal variability in the regime occurrence rates is mainly explained by the seasonal cycle of the mean climatology. On interannual timescales, relations between the occurrence rates of the regimes and the El Niño Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed circulation response to ENSO compared to the common use of four regimes. In particular, whilst with four regimes El Niño has been identified with a more frequent negative phase of the North Atlantic Oscillation (NAO), with six regimes it is also associated with more frequent occurrence of a regime constituting a negative geopotential height anomaly over the Norwegian Sea and Scandinavia. Predictable interannual signals in occurrence rate are found for the two zonal flow regimes, namely the just-described Scandinavian regime and the positive phase of the NAO. The signal strength for these regimes is comparable for observations and model, in contrast to the NAO-index where the signal strength in the model is underestimated by a factor of two compared to observations. Our regime analysis suggests that this signal-to-noise problem for the NAO-index is primarily related to those atmospheric flow patterns associated with the negative NAO-index, as we find poor predictability for the corresponding NAO- regime.



Applied Mathematics, Atmospheric Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics


interannual variability, atmospheric circulation regimes, ensemble data, regularised $k$-means clustering, interannual variabilitysignal-to-noise, ensemble data, regularised k-means clustering, signal-to-noise


Published: 2021-05-27 11:02

Last Updated: 2021-05-27 18:02


CC BY Attribution 4.0 International

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