Large model biases in the Pacific centre of the Northern Annular Mode due to exaggerated variability of the Aleutian Low

Abstract

AbstractThe Northern Annular Mode (NAM) is traditionally defined as the leading empirical orthogonal function (EOF) of mean sea‐level pressure (MSLP) anomalies during winter. Previous studies have shown that the Pacific centre‐of‐action of the NAM is typically more amplified in models than in reanalysis. Here, we analyse the NAM in hindcasts from nine seasonal prediction models over 1993/1994–2016/2017. In all the models, the Pacific centre‐of‐action is much larger than in reanalysis over that period, during which the NAM and the North Atlantic Oscillation (NAO) are almost indistinguishable. As a result, the NAM in the models is correlated with Aleutian Low variability around four times more strongly than in reanalysis. We show that this discrepancy can be explained primarily by the amplitude of Aleutian Low variability, which is on average 17% higher in models than in reanalysis, with a secondary effect from a stronger correlation between the Aleutian Low and NAO. When the NAM is computed using zonally averaged MSLP, the Aleutian Low amplitude does not influence the pattern directly. Instead, the amplitude of the Pacific centre‐of‐action is governed primarily by the correlation between the Aleutian Low and NAO, reducing the apparent Pacific biases in models. While the two methods yield almost identical results in reanalysis, the large Aleutian Low biases result in differences when applied to model data. Modifying the MSLP statistically to alter the Aleutian Low amplitude reveals that the spatial pattern of the traditionally defined NAM is highly sensitive to Aleutian Low variability, even without modifying the correlation between the Aleutian Low and NAO. Hence, the NAM in models may not be as biased as the traditional method would suggest. We therefore conclude that the traditional EOF method is unsuitable for defining the NAM in the presence of highly amplified Aleutian Low variability, and encourage the use of the zonal‐mean method.