Empirical Bifurcation Analysis of Atmospheric Stable Boundary Layer Regime Occupation

Abstract:

Turbulent collapse and recovery are both observed to occur abruptly in the atmospheric stable boundary layer (SBL). The understanding and predictability of turbulent recovery remains limited, reducing numerical weather prediction accuracy. Previous studies have shown that regime occupation is the result of the net effect of highly variable processes, from turbulent to synoptic scales making stochastic methods a compelling approach. Idealized single column stable boundary layer models have shown that under some circumstances regimes can be related to the stable branches of model equilibria, and an additional unstable equilibrium is predicted. This work seeks observational evidence of this bistability using a data driven stochastic method. The drift and diffusion coefficients of the stochastic differential equation of an input time series are approximated from their averaged time tendencies. These approximated coefficients are fit using Gaussian Process Regression. Probabilistic estimates of the system's equilibrium points are then found and used to create a bifurcation diagram without making any prior assumptions on the dynamical form of the system. This entirely data driven bifurcation diagram is then compared to modelled predictions. The analysis is repeated on several meteorological towers around the world to assess the influence of the local meteorological setting and surface conditions. This work provides empirical insights into the nature of regime transitions and the extent to which the SBL displays hysteresis.