  Many natural systems undergo critical transitions, i.e. sudden shifts from
one dynamical regime to another. In the climate system, the atmospheric
boundary layer can experience sudden transitions between fully turbulent states
and quiescent, quasi-laminar states. Such rapid transitions are observed in
Polar regions or at night when the atmospheric boundary layer is stably
stratified, and they have important consequences in the strength of mixing with
the higher levels of the atmosphere. To analyze the stable boundary layer, many
approaches rely on the identification of regimes that are commonly denoted as
weakly and very stable regimes. Detecting transitions between the regimes is
crucial for modeling purposes. In this work a combination of methods from
dynamical systems and statistical modeling is applied to study these regime
transitions and to develop an early-warning signal that can be applied to
non-stationary field data. The presented metric aims at detecting nearing
transitions by statistically quantifying the deviation from the dynamics
expected when the system is close to a stable equilibrium. An idealized
stochastic model of near-surface inversions is used to evaluate the potential
of the metric as an indicator of regime transitions. In this stochastic system,
small-scale perturbations can be amplified due to the nonlinearity, resulting
in transitions between two possible equilibria of the temperature inversion.
The simulations show such noise-induced regime transitions, successfully
identified by the indicator. The indicator is further applied to time series
data from nocturnal and Polar meteorological measurements.
