Detecting regime transitions of the nocturnal and Polar near-surface temperature inversion

TL;DR

This paper develops a statistical and dynamical systems-based method to detect and predict regime transitions in the nocturnal and Polar near-surface temperature inversion, which are critical for understanding boundary layer dynamics.

Contribution

It introduces a novel early-warning metric for regime transitions in stable boundary layers, validated through idealized stochastic models and real meteorological data.

Findings

Successfully identified noise-induced regime transitions in simulations

Effectively applied the indicator to real field data

Enhanced understanding of boundary layer regime dynamics

Abstract

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.