Case study on the influence of synoptic-scale processes on the paired H₂O-O₃ distribution in the UTLS across a North Atlantic jet stream

Abstract

Abstract. During a research flight of the Wave-driven ISentropic Exchange (WISE) campaign, which was conducted over the eastern North Atlantic on 1 October 2017, the composition of the Upper Troposphere and Lower Stratosphere (UTLS) across the North Atlantic jet stream was observed by airborne, range-resolved Differential Absorption Lidar (DIAL) profiles. We investigate how the high variability in the paired H2O and O3 distribution along the two-dimensional lidar cross section is affected by synoptic-scale weather systems, as revealed by the Lagrangian history of the observed air masses. To this aim, the lidar observations are combined with 10-day backward trajectories along which meteorological parameters and derived turbulence diagnostics are traced. The transport and mixing characteristics are then projected to the vertical cross sections of the lidar measurements and to the H2O–O3 phase space to explore linkages with the evolution of synoptic scale weather systems and their interaction. Tropical, midlatitude and arctic weather systems in the region of the jet stream and the related transport and mixing explain the complex H2O and O3 distribution to a large extent: O3-rich stratospheric air from the high Arctic interacts with midlatitude air from the North Pacific in a northward deflected jet stream associated with an anticyclone over the US and forms a filament extending into the tropopause fold beneath the jet stream. In the troposphere, lifting related to convection in the innertropical convergence zone (ITCZ) and two tropical cyclones continuously injected H2O into dry descending air from the tropical Atlantic and Pacific forming filamentary H2O structures. One tropical cyclone that transitioned into a midlatitude cyclone lifted moist boundary layer air explaining the highest tropospheric H2O values. During the two days before the observations the air with mixed tropospheric and stratospheric characteristics experienced frequent turbulence along the North Atlantic jet stream indicating a strong influence of turbulence on the formation of the Extratropical Transition Layer (ExTL). This investigation highlights the complexity of stirring and mixing processes and their close connection to interacting tropospheric weather systems from the tropics to polar regions, which strongly influenced the observed fine-scale H2O and O3 distributions. The identified non-local character of mixing should be kept in mind when interpreting mixing lines in tracer–tracer phase space diagrams.

Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes

“…In addition, the maximum bias occurs in summer when cross-tropopause mixing is strongest (Hoor et al, 2002), and, finally, the bias is reduced in stratospheric background humidity at highest altitudes, which are not influenced by mixing processes at the extratropical tropopause. Schäfler et al (2022) investigate the Lagrangian history of the observed air for the presented WISE case study on 1 October 2017 and find that the ExTL air experienced strong turbulent mixing in the jet stream during 48 h before the observation. They also find that the mixed air (in which we identified the increased bias) shows highly variable origins and transport pathways related to tropospheric weather systems which may be indicative of the relevance of different mixing processes.…”

“…In the extratropical UTLS, the distribution of water vapour is driven by transport and mixing processes related to baroclinic waves and associated synoptic-and meso-scale weather systems, which are interacting with chemical processes (e.g. Gettelman et al, 2011;Schäfler et al, 2022). The increased static stability above the tropopause (Birner et al, 2002) impedes water vapour from being vertically transported.…”

“…The complexity of transport and mixing processes is mirrored in the high water vapour variability in the extratropical UTLS on synoptic and seasonal timescales (e.g. Pan et al, 2000;Randel and Wu, 2010;Zahn et al, 2014;Dyroff et al, 2015;Bland et al, 2021;Schäfler et al, 2022).…”