Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion

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.

10 years of temperature and wind observation on a 45 m tower at Dome C, East Antarctic plateau

“…In terms of climate, the Antarctic continent is where some of the coldest in situ surface temperatures and largest surface wind speeds have been measured. The high Antarctic plateau has long been renowned for its frequent and extreme surface-based temperature inversions (Phillpot and Zillman, 1970;Zang et al, 2011), inspiring studies that (1) deepen understanding of polar boundary layer physics (van de Wiel et al, 2017;Baas et al, 2018;Abraham and Monahan, 2019;Kaiser et al, 2020) and (2) assess model simulation (Bazile et al, 2014;Couvreux et al, 2020;Vignon et al, 2017b;van der Linden et al, 2019) of the very stable atmospheric boundary layer. However, because both the environment itself and the logistics needed to access and work in such an environment are challenging, long continuous time series of meteorological observations in this region are sparse and mostly confined to nearsurface information.…”

External controls on the transition between stable boundary‐layer turbulence regimes

“…(2019), Abraham and Monahan (2019), and Kaiser et al . (2020). Factors that may determine this influence include surface roughness, surface thermal properties, local terrain, and the proximity to obstructions.…”

“…(2012) using a conceptual model that relates the sensible heat flux to the mean wind speed and radiative loss at the surface. Since then, different methods for finding the wind‐speed threshold between the weakly stable and very stable regimes have been proposed, based on hidden Markov model analysis (Monahan et al ., 2015; Abraham and Monahan, 2020), the sign of the vertical TKE gradient (Acevedo et al ., 2016), the ratio between horizontal and vertical velocity fluctuations (Mortarini et al ., 2019), and clustering analysis (Kaiser et al ., 2020). These studies come to different conclusions regarding whether the wind‐speed threshold between regimes agrees with the critical Richardson number concept from classical studies.…”

Partial tipping in a spatially heterogeneous world

“…Bistability and tipping between these two states has been explained using simple energy balance models [55,56]. These describe the near-surface inversion strength ∆T , defined roughly as the boundary layer's temperature minus the soil temperature, which evolves according to the net imbalance of the energy fluxes in the layer [56].…”