Based on skill estimates from hindcasts made over the last couple of decades, recent studies have suggested that considerable success has been achieved in forecasting winter climate anomalies over the Euro-Atlantic area using current-generation dynamical forecast models. However, previous-generation models had shown that forecasts of winter climate anomalies in the 1960s and 1970s were less successful than forecasts of the 1980s and 1990s. Given that the more recent decades have been dominated by the North Atlantic Oscillation (NAO) in its positive phase, it is important to know whether the performance of current models would be similarly skilful when tested over periods of a predominantly negative NAO. To this end, a new ensemble of atmospheric seasonal hindcasts covering the period 1900-2009 has been created, providing a unique tool to explore many aspects of atmospheric seasonal climate prediction. In this study we focus on two of these: multi-decadal variability in predicting the winter NAO, and the potential value of the long seasonal hindcast datasets for the emerging science of probabilistic event attribution. The existence of relatively low skill levels during the period 1950s-1970s has been confirmed in the new dataset. The skill of the NAO forecasts is larger, however, in earlier and later periods. Whilst these inter-decadal differences in skill are, by themselves, only marginally statistically significant, the variations in skill strongly co-vary with statistics of the general circulation itself suggesting that such differences are indeed physically based. The mid-century period of low forecast skill coincides with a negative NAO phase but the relationship between the NAO phase/amplitude and forecast skill is more complex than linear. Finally, we show how seasonal forecast reliability can be of importance for increasing confidence in statements of causes of extreme weather and climate events, including effects of anthropogenic climate change.

The Atlantic Multidecadal Oscillation (AMO) significantly influences the climate of the surrounding continents and has previously been attributed to variations in the Atlantic Meridional Overturning Circulation. Recently, however, similar multidecadal variability was reported in climate models without ocean circulation variability. We analyze the relationship between turbulent heat fluxes and sea surface temperatures (SSTs) over the midlatitude North Atlantic in observations and coupled climate model simulations, both with and without ocean circulation variability. SST anomalies associated with the AMO are positively correlated with heat fluxes on decadal time scales in both observations and models with varying ocean circulation, whereas in models without ocean circulation variability the anomalies are negatively correlated when heat flux anomalies lead. These relationships are captured in a simple stochastic model and rely crucially on low‐frequency forcing of SST. The fully coupled models that better capture this signature more effectively reproduce the observed impact of the AMO on European summertime temperatures.

An index of the Kuroshio Extension front strength is produced using a maximum covariance analysis between sea-surface temperature (SST) and sea-surface height (SSH) gradient observations, and composites of the atmospheric state are presented during its positive and negative phases using reanalysis data (1992-2011).It is found that when the Kuroshio Extension is less (more) meandering, with a stronger (weaker) SST front, the atmospheric heat transport by transient eddies is increased in the western (eastern) Pacific region, consistent with an increase (decrease) in low-level baroclinicity. Analysis of the eddy-mean flow interaction shows that this zonal shift in heat transport forces anomalous barotropic flow in the Eastern Pacific, where blocking frequency is strongly influenced.The above relationships cannot be reconciled with the known response of the North Pacific storm track to remote forcing from the Tropical Pacific, nor can they be explained by the response of the ocean to atmospheric forcing via surface heat fluxes or winds. Rather, the zonal shift in the storm track highlighted here, and the associated changes in the large-scale circulation, are interpreted as a response to the interannual variability of the Kuroshio Extension front.

Seasonal hindcast experiments, using prescribed sea surface temperatures (SSTs), are analyzed for Northern Hemisphere winters from 1900 to 2010. Ensemble mean Pacific/North American index (PNA) skill varies dramatically, dropping toward zero during the mid‐twentieth century, with similar variability in North Atlantic Oscillation (NAO) hindcast skill. The PNA skill closely follows the correlation between the observed PNA index and tropical Pacific SST anomalies. During the mid‐century period the PNA and NAO hindcast errors are closely related. The drop in PNA predictability is due to mid‐century negative PNA events, which were not forced in a predictable manner by tropical Pacific SST anomalies. Overall, negative PNA events are less predictable and seem likely to arise more from internal atmospheric variability than positive PNA events. Our results suggest that seasonal forecasting systems assessed over the recent 30 year period may be less skillful in periods, such as the mid‐twentieth century, with relatively weak forcing from tropical Pacific SST anomalies.

Year‐to‐year variability in Northern European summer rainfall has profound societal and economic impacts; however, current seasonal forecast systems show no significant forecast skill. Here we show that skillful predictions are possible (r ~0.5, p < 0.001) using the latest high‐resolution Met Office near‐term prediction system over 1960–2017. The model predictions capture both low‐frequency changes (e.g., wet summers 2007–2012) and some of the large individual events (e.g., dry summer 1976). Skill is linked to predictable North Atlantic sea surface temperature variability changing the supply of water vapor into Northern Europe and so modulating convective rainfall. However, dynamical circulation variability is not well predicted in general—although some interannual skill is found. Due to the weak amplitude of the forced model signal (likely caused by missing or weak model responses), very large ensembles (>80 members) are required for skillful predictions. This work is promising for the development of European summer rainfall climate services.