What potential for improving sub‐seasonal predictions of the winter NAO?

• Published in: Atmospheric Science Letters
• Main Authors: Chris Kent, Adam A. Scaife, Nick Dunstone
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: ensembles; geophysical sphere; scale; atmosphere; tools and methods; seasonal; Meteorology. Climatology; QC851-999; North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.1002/asl.1146; https://doaj.org/article/13531292bf6547eab4b4e1e2e78d05bf

Abstract The North Atlantic Oscillation (NAO) is the leading mode of variability across the Atlantic sector and is a key metric of extratropical forecast performance. Skilful predictions of the NAO are possible at medium‐range (1–2 weeks) and seasonal time scales. However, in a leading dynamical prediction system, we find that sub‐seasonal predictions (1 month NAO with a lead time of 20–30 days) are not statistically significant and represent a gap in forecast skill. In this study, we have investigated the potential for improving predictions using a large ensemble of dynamical hindcasts. First, we find that monthly predictions of the NAO are only weakly related to forecast errors at the medium‐range. This implies that improving medium‐range forecast performance is unlikely to drive significant improvements at longer lead times. Second, the Madden‐Julian Oscillation (MJO) is the leading mode of sub‐seasonal variability in the Tropics and projects onto the NAO with a lag of 10–15 days, but its teleconnection is only partially represented in current forecast systems. We, therefore, assess whether improved MJO‐NAO teleconnections are likely to lead to improved monthly NAO predictions. We find that even perfect MJO forecasts and teleconnections lead to only small improvements in NAO prediction skills. This work indicates that monthly timescales may represent a predictability gap for the NAO and hence the Euro‐Atlantic winter climate in which genuine skill improvements are difficult to achieve. Potential progress in this area could stem from currently unknown sources of skill and large initialised climate ensembles will be a vital tool for investigating these.