资源环境科技发展态势分析平台

Abstract. Ozone in the Arctic stratosphere is subject to large interannual variability, driven by both chemical ozone depletion and dynamical variability. Anomalies in Arctic stratospheric ozone become particularly important in spring, when returning sunlight allows them to alter stratospheric temperatures via shortwave heating, thus modifying atmospheric dynamics. At the same time, the stratospheric circulation undergoes a transition in spring with the Stratospheric Final Warming (FSW), which marks the end of winter. A causal link between stratospheric ozone anomalies and FSWs is plausible and might increase the predictability of stratospheric and tropospheric responses on sub-seasonal to seasonal timescales. However, it remains to be fully understood how ozone influences the timing and evolution of the springtime vortex breakdown. Here, we contrast results from chemistry climate models with and without interactive ozone chemistry to quantify the impact of ozone anomalies on the timing of the FSW and its effects on surface climate. We find that ozone feedbacks increase the variability in the timing of the FSW, especially in the lower stratosphere. In ozone-deficient springs, a persistent strong polar vortex and a delayed FSW in the lower stratosphere are partly due to lacking heating by ozone in that region. High ozone anomalies, on the other hand, result in additional shortwave heating in the lower stratosphere, where the FSW therefore occurs earlier. We further show that FSWs in high ozone springs are predominantly followed by a negative phase of the Arctic Oscillation (AO) with positive sea level pressure anomalies over the Arctic and cold anomalies over Eurasia and Europe. These conditions are to a significant extent (at least 50 %) driven by ozone. In contrast, FSWs in low ozone springs are not associated with a discernible surface climate response. These results highlight the importance of ozone-circulation coupling in the climate system and the potential value of interactive ozone chemistry for sub-seasonal to seasonal predictability.