Distinct bias structures for extratropical cyclones with strong or weak diabatic heating

Abstract. The development of extratropical cyclones (ETCs) is often significantly altered by diabatic processes, yet the representation of these processes in numerical weather prediction models has been shown to lead to significant forecast biases. To provide a systematic quantification of 12-hour ETC forecast errors, this study uses a cyclone-centred composite framework for North Atlantic wintertime (DJF) ETCs using the ERA5 reanalysis for the period 1979 to 2022. Cyclones are categorised into strong and weak diabatic heating at the time of their maximum intensification based on the domain-averaged 70th and 30th percentiles of vertically integrated diabatic heating.

While both groups exhibit a systematic underestimation of cyclone intensity, the error structures are markedly distinct. The weak heating group is characterised by an intensity underestimation near the cyclone core, whereas the strong heating group features a pronounced southwestward displacement bias together with a domain-wide intensity underestimation.

After removing the displacement bias, the strong heating group reveals an overestimation of low-level winds within the cold conveyor belt, sting jet, and dry intrusion regions, but a clear underestimation of moisture transport in the warm sector. These biases are accompanied by a pronounced overestimation of 850 hPa kinematic frontogenesis near the centre, likely associated with the wind field errors, and a substantial overestimation of total column liquid water along the bent-back warm front. This overestimated liquid water is likely related to the stronger frontogenesis, which induces an over-intensified secondary circulation. In contrast, cyclones in the weak heating group exhibit an underestimation of wind speed and moisture near the centre, consistent with the near centre intensity underestimation. Our findings highlight the impact of diabatic heating on structural cyclone forecast biases that can guide future model improvements.