Towards stochastic modeling of turbulence in the stable atmospheric boundary layer

• Applicant: Professor Dr.-Ing. Rupert Klein, since 4/2021
• Subject Area: Atmospheric Science
• Term: from 2015 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 310574835

• Final Report Year: 2022

Final Report Abstract

Limited computer resources lead to a simplified representation of unresolved small-scale processes in weather and climate models, through parameterisation schemes. Among the parameterised processes, turbulent fluxes exert a critical impact on the exchange of heat, water and carbon between the land and the atmosphere. Turbulence theory was, however, developed for homogeneous and flat terrain, with stationary conditions. The theory fails in unsteady flow contexts or with heterogeneous landscapes, but no alternative, viable theory is available. This is not only a source of error in forecasts or climate scenarios, but also a source of model uncertainty which should be characterised and considered when using weather and climate models. The model uncertainty is greatest in cold environments or at nighttime, where the atmospheric boundary layer is stably stratified. Turbulence then coexists with non-turbulent motions from the grey zone between the largest turbulent eddies and smallest mesoscale motions, traditionally specified to be 2km horizontal scale. These non-turbulent motions can include density currents, wave-like motions or two-dimensional modes and represent a non-stationary forcing of turbulence. In this project, measurements of turbulent quantities were analysed in great detail to classify flow regimes occurring in cold or nocturnal environments, to quantify the variability of turbulent fluxes and to understand the physical origin of the variability. A data-driven approach was developed and used to derive a stochastic parameterisation of turbulent fluxes, thereby representing the model uncertainty arising from the incomplete representation of our unsteady atmosphere. The most surprising and promising result is that the uncertainty was found to scale with physical quantities which are available in weather and climate models. The stochastic parameterisation thereby extends conventional models and enables the representation of the resulting unsteady, intermittent fluxes. It could help overcome some of the limitations of weather and climate models to represent mixing in the stable boundary layer.

Publications

• A Clustering Method to Characterize Intermittent Bursts of Turbulence and Interaction with Submesomotions in the Stable Boundary Layer. Journal of the Atmospheric Sciences, 72(4), 1504-1517.
  Vercauteren, Nikki & Klein, Rupert
  
• Investigation of interactions between scales of motion in the stable boundary layer. Quarterly Journal of the Royal Meteorological Society, 142(699), 2424-2433.
  Vercauteren, Nikki; Mahrt, Larry & Klein, Rupert
  
• Statistical-mechanical approach to study the hydrodynamic stability of the stably stratified atmospheric boundary layer. Physical Review Fluids, 2(8).
  Nevo, G.; Vercauteren, N.; Kaiser, A.; Dubrulle, B. & Faranda, D.
  
• Scale interactions and anisotropy in stable boundary layers. Quarterly Journal of the Royal Meteorological Society, 145(722), 1799-1813.
  Vercauteren, Nikki; Boyko, Vyacheslav; Faranda, Davide & Stiperski, Ivana
  
• Statistical Investigation of Flow Structures in Different Regimes of the Stable Boundary Layer. Boundary-Layer Meteorology, 173(2), 143-164.
  Vercauteren, Nikki; Boyko, Vyacheslav; Kaiser, Amandine & Belušić, Danijel
  
• Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion. Journal of the Atmospheric Sciences, 77(8), 2921-2940.
  Kaiser, Amandine; Faranda, Davide; Krumscheid, Sebastian; Belušić, Danijel & Vercauteren, Nikki
  
• Multiscale Shear Forcing of Turbulence in the Nocturnal Boundary Layer: A Statistical Analysis. Boundary-Layer Meteorology, 179(1), 43-72.
  Boyko, Vyacheslav & Vercauteren, Nikki
  
• Boundary-Layer Processes Hindering Contemporary Numerical Weather Prediction Models. Boundary-Layer Meteorology, 186(1), 43-68.
  Calaf, Marc; Vercauteren, Nikki; Katul, Gabriel G.; Giometto, Marco G.; Morrison, Travis J.; Margairaz, Fabien; Boyko, Vyacheslav & Pardyjak, Eric R.
  
• Statistical Learning of Nonlinear Stochastic Differential Equations from Nonstationary Time Series using Variational Clustering. Multiscale Modeling & Simulation, 20(4), 1251-1283.
  Boyko, Vyacheslav; Krumscheid, Sebastian & Vercauteren, Nikki
  
• A stochastic stability equation for unsteady turbulence in the stable boundary layer. Quarterly Journal of the Royal Meteorological Society, 149(755), 2125-2145.
  Boyko, Vyacheslav & Vercauteren, Nikki
  
• Sources of anisotropy in the Reynolds stress tensor in the stable boundary layer. Quarterly Journal of the Royal Meteorological Society, 149(750), 277-299.
  Gucci, Federica; Giovannini, Lorenzo; Stiperski, Ivana; Zardi, Dino & Vercauteren, Nikki