EGU2020-3283
https://doi.org/10.5194/egusphere-egu2020-3283
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Intra-annual variations of spectrally resolved gravity wave activity and observations of turbulence in the UMLT region

René Sedlak1, Alexandra Zuhr1,2,3, Patrick Hannawald1,2, Carsten Schmidt2, Sabine Wüst2, and Michael Bittner1,2
René Sedlak et al.
  • 1Institute of Physics, University of Augsburg, Augsburg, Germany
  • 2German Aerospace Center (DLR), German Remote Sensing Data Center - Department Atmosphere, Weßling, Germany
  • 3now at: Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany

Multi-year temperature time series from OH-airglow infrared (IR) spectrometers deployed at different sites in Europe as part of the Network for the Detection of Mesospheric Change (NDMC) are used to estimate the gravity wave activity in the upper mesosphere / lower thermosphere (UMLT) region.

The seasonal course of gravity wave activity is found to be strongly dependent on the wave period. While there is almost no clear variability of gravity wave activity for periods lower than about 60 minutes, we find strong evidence for an increasing variation throughout the year for periods longer than ca. 60 min. A dominant semi-annual structure with maxima at the solstices is found up to a periodicity of about 200 minutes, where a gradual transition to an annual cycle with maximum activity during winter and minimum activity during summer is observed.

The energy and momentum carried by gravity waves is dissipated in terms of turbulent wave breaking. Using observations of airglow imagers with high spatial and temporal resolution which were operated at the same time as the abovementioned IR-spectrometers we performed an investigation of turbulent gravity wave dynamics. The estimations of the turbulent eddy diffusion coefficient and the energy dissipation rate from the image series of a turbulent wave front agree quite well with the few available values in literature. A machine learning approach for the systematic extraction of turbulent episodes from the very large data set is presented.

This work received funding from the Bavarian State Ministry of the Environment and Consumer Protection.

How to cite: Sedlak, R., Zuhr, A., Hannawald, P., Schmidt, C., Wüst, S., and Bittner, M.: Intra-annual variations of spectrally resolved gravity wave activity and observations of turbulence in the UMLT region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3283, https://doi.org/10.5194/egusphere-egu2020-3283, 2020.

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