Small-scale variability of stratospheric ozone during the sudden stratospheric warming 2018/2019 observed at Ny-Ålesund, Svalbard

Abstract

Abstract. Middle atmospheric ozone, water vapour and zonal and meridional wind profiles have been measured with the two ground-based microwave radiometers GROMOS-C and MIAWARA-C. The instruments have been located at the Arctic research base AWIPEV at Ny-Ålesund, Svalbard (79∘ N, 12∘ E), since September 2015. GROMOS-C measures ozone spectra in the four cardinal directions with an elevation angle of 22∘. This means that the probed air masses at an altitude of 3 hPa (37 km) have a horizontal distance of 92 km to Ny-Ålesund. We retrieve four separate ozone profiles along the lines of sight and calculate daily mean horizontal ozone gradients which allow us to investigate the small-scale spatial variability of ozone above Ny-Ålesund. We present the evolution of the ozone gradients at Ny-Ålesund during winter 2018/2019, when a major sudden stratospheric warming (SSW) took place with the central date at 2 January, and link it to the planetary wave activity. We further analyse the SSW and discuss our ozone and water vapour measurements in a global context. At 3 hPa we find a distinct seasonal variation of the ozone gradients. The strong polar vortex during October and March results in a decreasing ozone volume mixing ratio towards the pole. In November the amplitudes of the planetary waves grow until they break in the end of December and an SSW takes place. From November until February ozone increases towards higher latitudes and the magnitude of the ozone gradients is smaller than in October and March. We attribute this to the planetary wave activity of wave numbers 1 and 2 which enabled meridional transport. The MERRA-2 reanalysis and the SD-WACCM model are able to capture the small-scale ozone variability and its seasonal changes.

Comparative study between ground-based observations and NAVGEM-HA analysis data in the mesosphere and lower thermosphere region

“…Validation and assessment of potential biases between the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures and ground-based lidar measurements can be found in Xu et al (2006) and Dawkins et al (2018). A crosscomparison of the MLS and SABER temperatures is presented in Schwartz et al (2008). A detailed description of how the data assimilation in NAVGEM-HA treats the temperature biases between both satellites is given in Eckermann et al (2018).…”

The Diurnal Variation in Stratospheric Ozone from MACC Reanalysis, ERA-Interim, WACCM, and Earth Observation Data: Characteristics and Intercomparison

“…GROMOS-C permanently sounds in East, West, North, and South direction with a horizontal range of about 150 km around the station. The intercomparison of the strengths and directions of horizontal ozone gradients measured by GROMOS-C and coincidently simulated by models showed that SD-WACCM and MERRA-2 can capture the observed small-scale variability [55]. Finally, it can be stated that both kinds of diurnal ozone cycle are interesting for research: a smooth zonal mean diurnal ozone cycle, as well as a temporally intermittent, regional variable diurnal ozone cycle.…”

“…For ground stations, the small-scale variability is the most serious problem, e.g., ozone laminae can drift through the sounding volume, enhancing σ m,1 and generating a bias of the derived daily ozone cycle. The four beam-radiometer GROMOS-C [55] led to a progress in the study of small-scale variability of middle-atmospheric ozone. GROMOS-C permanently sounds in East, West, North, and South direction with a horizontal range of about 150 km around the station.…”

Atmospheric tomography using the Nordic Meteor Radar Cluster and Chilean Observation Network De Meteor Radars: network details and 3D-Var retrieval

“…Optimal estimation is a technique often applied for atmospheric remote sounding and is described in detail in Rodgers (2000). The optimal estimation technique has become a standard tool in radiometry to retrieve atmospheric quantities such as wind, temperature or trace gas concentration (Livesey et al, 2006;Schwartz et al, 2008;Stähli et al, 2013;Hagen et al, 2018;Schranz et al, 2020;Navas-Guzmán et al, 2016) The optimal estimation technique makes use of Bayes' theorem and presents a general view to all solutions of an inverse problem (Rodgers, 2000). The Bayesian approach relates the posteriori probability density function (PDF) for a given measurement using prior knowledge of the PDF of the state x and observations y before a measurement is made.…”