Forecast Errors Attributed to Synoptic Features

It is often argued that numerical weather prediction models remain deficient in forecasting specific weather features and that such deficiencies contribute significantly to overall forecast errors. To clarify these claims, we quantify how cyclones, fronts, upper tropospheric jets, moisture transport axes (MTAs), and cold-air outbreaks (CAOs) contribute to short-term (12-h) forecast errors and biases in the ERA5 reanalysis dataset from 1979 to 2022. Employing a feature-based attribution method, we evaluate errors globally, focusing particularly on temperature, moisture, and wind fields, and examine regional and seasonal variations during winter (DJF) and summer (JJA). The presence of weather features is generally associated with increased forecast errors (RMSEs) compared to feature-free conditions. RMSEs are especially pronounced for moisture fields in conjunction with fronts and MTAs, where errors in total column water vapor can be twice as large. Cyclone-related errors are more pronounced in the low-level wind field. During CAOs, on the other hand, errors are reduced. In terms of systematic biases, wind speeds and moisture are underestimated along western boundary currents, together with insufficient moisture transport along MTAs. Wintertime temperature biases over the Northern Hemisphere oceans have stronger associations with fronts and MTAs than those over the Southern Hemisphere oceans. A persistence analysis confirms that for some features and specific variables, forecasts yield less added value relative to non-feature conditions. Cyclones are the most notable example, where forecasts provide less added value in most cases. In contrast, jets and CAOs are features where forecasts consistently add more added value. The identified feature-based error diagnostics can aid targeted efforts to improve numerical weather prediction systems.