Abstract
AbstractTo study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k‐means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro‐Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub‐seasonal and interannual time‐scales. We find that sub‐seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual time‐scales 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 response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual time‐scales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO‐index where the signal strength in the observations is underestimated by a factor of 2 in the model. 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.
Highlights
Atmospheric circulation regimes, or weather regimes, provide a way to study the low-frequency variability in the atmosphere (Hannachi et al, 2017)
For the interannual variability we discuss whether there is any predictable signal for the regime occurrence rates and compare this to the signal identified for an North Atlantic Oscillation (NAO)-index
While for the NAO-index we see an underestimation of the signal in the model compared to observations, in line with the signal-to-noise paradox, this is not the case for the signal in the occurrence rates of the two zonal regimes NAO+ and SB−, which are the regimes with interannual predictability
Summary
Atmospheric circulation regimes, or weather regimes, provide a way to study the low-frequency variability in the atmosphere (Hannachi et al, 2017). This paradox relates to the observation that most models are better at predicting the real world NAO than the NAO of their own ensemble members (Eade et al, 2014; Scaife and Smith, 2018), while the variance of single ensemble members and of the observations is comparable. We start by discussing the problem setting of using clustering methods to identify circulation regimes in model ensembles exhibiting a wide spread in their regime representation, and show a motivational example for the regularisation method proposed to handle this spread and identify a more robust signal.
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