Signal, noise and skill in sub-seasonal forecasts: the role of tropical teleconnections and stratosphere-troposphere coupling

A set of relaxation experiments with the European Centre for Medium-range Weather Forecasts (ECMWF) model is used to explore the influence of tropical and stratospheric teleconnections on forecast skill, variability of forecast ensemble mean (EM) and ensemble spread (ES) in the wintertime Northern Hemisphere at sub-seasonal timescales. The influence is diagnosed by comparing the relaxation experiments, which relax the temperature and wind fields in specific regions to observed values, with the free running (control) experiment. During weeks 3–6 the tropical relaxation increases the forecast skill for sea level pressure (SLP) mostly south of 50°N but also over the North Atlantic, Northern Europe and eastern Canada. Skill improvements occur via both stratospheric and tropospheric pathways. The stratospheric relaxation improves the skill mostly in high latitudes, over Europe, and North Atlantic. Skill improvements are smaller for surface temperature and total precipitation, suggesting a smaller role of the teleconnections in their predictability.

The increases in skill are generally associated with increased variability of EM, considered to represent the predictable signal, and reduced ES representing noise. However, this does not happen in all areas where the skill is increased. In high latitudes, where the stratospheric impacts are strongest, the EM variability does not increase in the stratospheric relaxation experiments consistently with increases in skill, implying that EM does not reflect well the predictable signal. We suggest that the ensemble size available in the experiments (11 members) is not always enough to make it possible to fully extract signal from noise, and that larger ensembles (20–50 members or even more depending on area and variable) would be beneficial for studies of sub-seasonal predictability associated with the teleconnections in mid- and high latitudes, including windows for forecast opportunities.