Effect of stratospheric temperature perturbation on the onset of Ural blocking events in winter

Abstract The impact of stratospheric temperature uncertainty on ural blocking (UB) onset is investigated by utilizing the Open Integrated Forecasting System model. The results reveal that the stratospheric optimal initial temperature perturbation obtained by the conditional nonlinear optimal perturbation (CNOP) approach could obviously suppress UB onset, leading to the weakening or even collapse of the UB. This perturbation that contains multiple‐scale signals is mainly located in the upper stratosphere and UB upstream sector. Further diagnosis indicates that the stratospheric optimal temperature perturbation develops and causes anomalous Eliassen–Palm (E‐P) flux propagating downward into the troposphere. This planetary wave activity further contributes to tropospheric energy divergence. Then, an enhanced meridional temperature gradient and zonal winds appear in the Ural and its adjacent sectors. Moreover, the Ural sector is dominated by the increased meridional potential vorticity gradient. Eventually, UB is weakened during the onset phase. Then, the impact of the optimal perturbation in different scales on UB onset is explored. Numerical results show that optimal perturbations at different scales calculated via the CNOP method could clearly suppress the UB onset. It is noted that these optimal perturbations have similar effects on the UB. Further diagnosis shows that optimal initial perturbations with different spatial distributions tend to evolve specific perturbations dominated by planetary‐scale perturbations. The above results are the key factors explaining that optimal perturbations at different scales have analogous effects on UB onset. These results emphasize the notable role of stratospheric optimal initial temperature perturbations in UB onset, which deepens our understanding in the stratosphere–troposphere coupling. Moreover, our results contribute to the implementation of stratospheric targeted observations to improve UB predictions.