The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures

Abstract

The succession of European surface weather patterns has limited predictability because disturbances quickly transfer to the large-scale flow. Some aggregated statistics, however, such as the average temperature exceeding a threshold, can have extended predictability when adequate spatial scales, temporal scales and thresholds are chosen. This study benchmarks how the forecast skill horizon of probabilistic 2-m temperature forecasts from the subseasonal forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) evolves with varying scales and thresholds. We apply temporal aggregation by rolling-window averaging and spatial aggregation by hierarchical clustering. We verify 20 years of re-forecasts against the E-OBS dataset and find that European predictability extends at maximum into the fourth week. Simple aggregation and standard statistical post-processing extend the forecast skill horizon with two and three skilful days on average, respectively. The intuitive notion that higher levels of aggregation capture large-scale and low-frequency variability and can therefore tap into extended predictability holds in many cases. However, we show that the effect can be saturated and that there exist regional optimums beyond which extra aggregation reduces the forecast skill horizon. We expect such windows of predictability to result from specific physical mechanisms that only modulate and extend predictability locally. To optimize subseasonal forecasts for Europe, aggregation should thus be limited in certain cases.