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Understanding Turbulence in the Evening Boundary Layer: Factors Influencing Stability

This paper explores the dynamics of the evening boundary layer, focusing on the transition between stable and turbulent regimes. It addresses critical wind speeds necessary to sustain turbulence, the role of surface energy balance, and the implications of temperature gradients. Utilizing direct numerical simulations and local similarity closures, we reveal how shear capacity influences transport capacity against flux demand. We also discuss the relevance of reliable parameterizations in forecasting and the broader climatological implications of our findings.

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Understanding Turbulence in the Evening Boundary Layer: Factors Influencing Stability

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  1. Next The evening boundary layer: turbulence or no turbulence? Bas van de Wiel, Ivo van Hooijdonk & Judith Donda in collaboration with: Fred Bosveld, Peter Baas, Arnold Moene, Harm Jonker, Jielun Sun, Herman Clercx, e.a.

  2. A fruitful symbiosis ~ 1 paper / year

  3. Scope The very stable boundary layer: ‘cold’ How much wind needed to keep turbulence going? The weakly stable boundary layer: ‘warm’

  4. Need for reliable process parameterizations

  5. The collapse of turbulence:Practical track -----Formal track

  6. Concept: collapse driven by SEB Idealistic case: Fresh snow (low heat capacity & conductance) Surface Energy Balance: Here: Qn denoted as H0 Regime 1 H=H0 Regime 2 H<H0

  7. Non-linear diffusion K decreases with increasing dT/dz

  8. Surface flux defines velocity scale

  9. “Weather lab”: ensembles • Clear nights: similar radiative forcing • “Wind strength decides on regime” • Classes 40m wind: • -0.5-1.0 m/s • -1.0-1.5 m/s • -2.0-... etc.

  10. Temperature inversion:T(z)-T(1.0 m) Can we predict critical wind ?

  11. Turbulence ‘hockey stick’ 10m 20m 40m 80m Can we predict critical wind ?

  12. Predicting minimum wind speed

  13. -for given wind a flux maximum is found vice versa -for given demand (Qn-G) characteristic speed is found: Umin

  14. After some calculations…… Correction for soil heat Radiative Loss parameters The minimum wind speed for sustainable turbulence

  15. Height-independent regime-classification

  16. Height-independent regime-classification

  17. Comparison with classical parameters

  18. Stability indicators Scaling based on fluxes: Scaling based on gradients: Combined scaling: Or formally: “Shear Capacity” Also combines knowledge flow AND boundary condition

  19. Problem with classical stability parameters

  20. Comparison with classical parameters

  21. Formal track • Step 1: Direct Numerical Simulation of regime transition

  22. Formal track • Step 2: Analogy with local similarity closure

  23. Formal track • Step 3: analysis, prediction • -local scaling, gradient form • -model independent scaling: • -derivation from TKE-equation

  24. prediction ‘normalized hockey stick’ Step 4: validation laminar turbulent

  25. Conclusion • Shear Capacity (U/Umin) compares transport capacity flow to flux demand at surface • Prediction idealized configurations & observed reality Details: Van Hooijdonk et al. (2014; J.A.S. Submitted) Donda et al. (submission June 2014)

  26. Outlook • Parameterisation Forecast models • DNS/LES/RANS simulations • Other climatologies: Fluxnet – data

  27. Thank you for your attention

  28. What’s the use?? Physically preferable In practice (Louis 1979) -Non-physical curve aimed to enhance mixing in the very stable regime only -But..... -it causes too much mixing in the well-behaved, weakly stable case as well....!

  29. regime based enhanced mixing Very stable Continuous turbulent

  30. Extra: relation to tke budget Note: SC=shear capacity ~ U/Umin

  31. Strong wind: steady balance In the following: blue/green points

  32. Weak wind: no balance initially In the following: red points

  33. Maximum found in observations Here: z=40 m

  34. Tendency for regeneration

  35. Sensibelewarmteflux

  36. Instortenturbulentietgvsterkekoeling

  37. Concept

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