Modelling microscale atmospheric turbulence in the surface layer
The PhD candidate will investigate the role of apparently random, small-scale, natural fluctuations of atmospheric processes to trigger stably stratified boundary layer regime transitions.
Morning fog formed in a shallow stable boundary layer on a boreal forest site in Telemark, Norway
Physical processes at the earth-atmosphere interface shape the atmosphere, hydrosphere and biosphere. Turbulent fluxes transport heat, moisture and momentum between the land surface and the atmosphere. During night and winter, stable boundary layers (SBL) frequently develop. Classical approaches to turbulence parameterisation fail to reproduce dissipation in SBL or complex terrain contexts, in part due to poor representation of multi-scale processes, causing errors in weather and climate models. One major research challenge is to develop accurate representations of distinct SBL regimes and transitions between them.
The PhD candidate will investigate the role of apparently random, small-scale, natural fluctuations of atmospheric processes to trigger SBL regime transitions. An existing single-column numerical model of the SBL will be extended to include stochastic perturbations of the wind field and the cloud cover. Based on this computational tool, the PhD student will investigate noise-induced SBL regime transitions.
A combination of statistical modelling and dynamical systems analysis will be used to develop statistical tools to detect rapid regime transitions in non-stationary contexts. The developed indicator of regime transitions will be applied to results from the stochastic numerical simulations as well as to meteorological measurements from e.g. eddy-covariance and drone mounted sensors.
With the ability to detect regime transitions, the turbulence parameterisation can be adapted, avoiding the assumption of turbulence stationarity. The stochastic single-column model will be a first step towards including random flow perturbations in numerical models.
- MSc in atmospheric science, physics, applied mathematics or a related field, preferably with experience in statistical and computational methods.
- Candidates with documented experience in scientific programming will be prioritized.
Call 1: Project start autumn 2021
This project is in call 1, starting autumn 2021. Read about how to apply