Simulation of the Multi-Timescale Stratospheric Intrusion Processes in a Typical Cut-Off Low over Northeast Asia

This study used the FLEXPART-WRF trajectory model to perform forward and backward simulations of a cut-off low (COL) event over northeast Asia. The analysis reveals the detailed trajectories and sources of air masses within the COL. Their trajectories illustrate the multi-timescale deep intrusion processes in the upper troposphere and lower stratosphere (UTLS) caused by the COL. The processes of air intrusion from the lower stratosphere to the middle troposphere can be divided into three stages: a slow descent stage, a rapid intrusion stage and a relatively slow intrusion stage. A source analysis of targeted air masses at 300 hPa and 500 hPa shows that the ozone-rich air in the COL primarily originated from an extratropical cyclone over central Siberia and from the extratropical jet stream. The sources of air masses in different parts of the COL show some differences. These results can help explain the ozone distribution characteristics in the main body of a COL at 300 hPa and at 500 hPa that were revealed in a previous study.

Stratospheric intrusion depth and its effect on surface cyclogenesis: An idealized PV inversion experiment

Stratospheric intrusions of high potential vorticity (PV) air are well-known drivers of cyclonic development throughout the troposphere. PV anomalies have been well studied with respect to their effect on surface cyclogenesis. A gap however exists in the scientific literature describing the effect that stratospheric intrusion depth has on the amount of surface cyclogenetic forcing at the surface. Numerical experiments using PV inversion diagnostics reveal that stratospheric depth is crucial in the amount of cyclogenesis at the surface. In an idealised setting, shallow intrusions (above 300 hPa) resulted in a marginal effect on the surface, whilst growing stratospheric depth resulted in enhanced surface pressure anomalies and surface cyclogenetic forcing. The horizontal extent of the intrusion is shown to be more important in developing deeper surface cyclones than the vertical depth of the stratospheric intrusion. The size of vertical intrusion depths is however an important factor determining the surface relative vorticity, with larger intrusions resulting in stronger cyclonic circulations. Deeper stratospheric intrusions also result in intrusions reaching closer to the surface. The proximity of intrusions to the surface is a crucial factor favouring surface cyclogenetic forcing. This factor is however constrained by the height of the dynamical tropopause above the surface.

Tropospheric ozone based on S5P-BASCOE and extension to the past based on OMI and GOME-2 observation.

<p>Ozone in the troposphere has mainly two sources, the first one is stratospheric intrusion the second one is chemical reactions following the emissions of primary pollutions such as NOx and VOCS.</p><p>We combine TROPOMI total ozone columns with Microwave Limb Sounding ozone profiles assimilated to BASCOE to retrieve tropospheric ozone columns.</p><p>Based on a first analysis we observe a decrease of tropospheric ozone during April and May 2020. The lockdown as measure against the Corona pandemic also caused an economic shutdown, and thereby a reduction of primary pollutants mainly NOx. Within the cities centres the lack of NOx caused an increase in tropospheric ozone, due to non linear effects in the ozone NOx chemistry. Outside the cities however a decrease might be expected. Thereby the tropospheric ozone reduction in April May might be caused by the lockdown due to the COVID-19.</p><p>However the natural variabilty is high caused by metrological conditions. To redcue the influnece of indiviual metrological situation the timeseries is expanded to the past by using additional sensors like GOME-2 and OMI, combined with the BASCOE reanalysis data set BRAM. The tropospheric columns are haromized using the same time and latitude depended bias added as for harmonizing the total columns. Therby we generated a typical anual mean data set, where the exceptional year of 2020 can be compared to.</p>