Skilful seasonal prediction of Korean winter temperature.

Korean winter temperature exhibits significant year‐to‐year variability, an extreme example of which was the severe cold winter of 2012/2013. Such events can have significant societal and financial impacts. We investigate the seasonal forecast skill for Korean winter temperature using Met Office initialised climate prediction systems. We find significant skill using two independent hindcasts covering the last decades. Observed Korean winter temperature variability appears to be driven by large‐scale dynamical circulation anomalies with centers over the northwest Pacific High (PH) and the Siberian High (SH). While the model has a good simulation of these observed teleconnections, we find that skilfully predicted PH variability dominates the model skill. This is due to the relatively strong PH teleconnection to highly predictable tropical Pacific and Indian Ocean variability. Whereas, observed SH variability is currently much less predictable in seasonal forecast systems. Furthermore, we find that the modelled SH exhibits lower predictability than the observed SH, suggesting a model "signal‐to‐noise" problem over extratropical East Asia which is similar to that reported over northern Europe. We explore one possible pathway, the representation of stratosphere–troposphere coupling, by examining the model simulation of stratospheric sudden warming (SSWs) events. While the SSW frequency and the spatial pattern of surface circulation response appears well simulated, the amplitude of the model response over SH appears anomalously weak. Nevertheless, we conclude that current seasonal forecasts of Korean winter temperature are skilful and have the potential to provide useful climate services. Seasonal prediction of Korean winter temperature and associated circulation. (a) Time series of DJF temperature over Korea in observations (black) and ensemble mean hindcasts from GloSea5 (red) and DePreSys3 (blue). Maps show correlations between the temperature over Korea and mean sea level pressure (MSLP) using observations (b) and ensemble members (c). In (d) we investigate the relationship between observed Korean temperature and the model ensemble mean MSLP predictions. Stippling identifies where correlations are significant at the 90% level according to a two‐tailed t test. The green box shows Korea and black boxes show SH and PH. [ABSTRACT FROM AUTHOR]