Northern Hemisphere extratropical cyclone biases in ECMWF subseasonal forecasts

• Main Authors: Büeler, Dominik, id_orcid:0 000-0002-9904-6281, Sprenger, Michael, Wernli, Heini, id_orcid:0 000-0001-9674-4837
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer 2024
• Subjects: cyclone intensity; cyclone frequency; European Centre for Medium-Range Weather Forecasts; extratropical cyclones; Northern Hemisphere; storm track; subseasonal forecast bias; subseasonal predictability; North Atlantic
• Online Access: https://hdl.handle.net/20.500.11850/658256; https://doi.org/10.3929/ethz-b-000658256

Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large-scale flow. Biases in cyclone frequency and characteristics in medium-range to subseasonal numerical weather prediction might therefore hinder exploitation of potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 20 years (2000-2020) of subseasonal ensemble reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. The reforecasts reproduce the climatology of cyclone frequency and life-cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis, size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest regional deficit of cyclones of more than 30% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones reach too-high intensities during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime might partly but not exclusively explain this intensity bias. While the cyclone bias patterns often appear in lead-time weeks 1 and 2, their magnitudes typically grow further at subseasonal lead times, in some cases up to weeks 5 and 6. Most of the dynamical sources of these biases thus likely appear in the early medium range, but sources on longer timescales probably contribute to the further increase of biases with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large-scale flow and in surface weather in subseasonal weather forecasts. Given the considerable biases during summer, when subseasonal predictions of precipitation and surface temperature will become increasingly important, this ...