Regime Transitions in Near-Surface Temperature Inversions: A Conceptual Model
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Abstract A conceptual model is used in combination with observational analysis to understand regime transitions of near-surface temperature inversions at night as well as in Arctic conditions. The model combines a surface energy budget with a bulk parameterization for turbulent heat transport. Energy fluxes or feedbacks due to soil and radiative heat transfer are accounted for by a “lumped parameter closure,” which represents the “coupling strength” of the system. Observations from Cabauw, Netherlands, and Dome C, Antarctica, are analyzed. As expected, inversions are weak for strong winds, whereas large inversions are found under weak-wind conditions. However, a sharp transition is found between those regimes, as it occurs within a narrow wind range. This results in a typical S-shaped dependency. The conceptual model explains why this characteristic must be a robust feature. Differences between the Cabauw and Dome C cases are explained from differences in coupling strength (being weaker in the Antarctic). For comparison, a realistic column model is run. As findings are similar to the simple model and the observational analysis, it suggests generality of the results. Theoretical analysis reveals that, in the transition zone near the critical wind speed, the response time of the system to perturbations becomes large. As resilience to perturbations becomes weaker, it may explain why, within this wind regime, an increase of scatter is found. Finally, the so-called heat flux duality paradox is analyzed. It is explained why numerical simulations with prescribed surface fluxes show a dynamical response different from more realistic surface-coupled systems.
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This process invokes a transition to a weakly stable boundary layer as described by Van de Wiel et al. (2017).
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Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion
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Spatial Dependence of Stably Stratified Nocturnal Boundary-Layer Regimes in Complex Terrain
Abraham, C., & Monahan, A. H. (2020) · Boundary-Layer Meteorology · doi:10.1007/s10546-020-00532-x · Cited by 17 · see citing works
Abraham, C., & Monahan, A. H. (2019) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-18-0261.1 · Cited by 16 · see citing works
External controls on the transition between stable boundary‐layer turbulence regimes
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Parameters for the Collapse of Turbulence in the Stratified Plane Couette Flow
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Dutch fog: On the observed spatio‐temporal variability of fog in the Netherlands
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Self-citationNear-Surface Temperature Inversion Growth Rate during the Onset of the Stable Boundary Layer
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Self-citationMiller, N. M., Juárez, M. D. L. T., & Tamppari, L. K. (2018) · Geophysical Research Letters · doi:10.1029/2018gl080542 · Cited by 14 · see citing works
Abraham, C., Holdsworth, A. M., & Monahan, A. H. (2019) · Nonlinear processes in geophysics · doi:10.5194/npg-26-401-2019 · Cited by 14 · see citing works
Day, J. J., Svensson, G., Casati, B., Uttal, T., Khalsa, S. S., Bazile, É., Akish, E., Azouz, N., Ferrighi, L., Frank, H. P., et al. (2024) · Geoscientific model development · doi:10.5194/gmd-17-5511-2024 · Cited by 16 · see citing works
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Lapo, K., Freundorfer, A., Fritz, A., Schneider, J., Olesch, J., Babel, W., & Thomas, C. K. (2022) · Earth system science data · doi:10.5194/essd-14-885-2022 · Cited by 13 · see citing works
Small-Scale Horizontal Variability of Mean and Turbulent Quantities in the Nocturnal Boundary Layer
Guerra, V. D. S., Acevedo, O. C., Medeiros, L. E., Oliveira, P. E. S., & dos Santos, D. M. (2018) · Boundary-Layer Meteorology · doi:10.1007/s10546-018-0381-3 · Cited by 13 · see citing works
An Idealized Description for the Diurnal Cycle of the Dry Atmospheric Boundary Layer
van Hooft, A., Baas, P., van Tiggelen, M., Ansorge, C., & van de Wiel, B. J. H. (2019) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-19-0023.1 · Cited by 13 · see citing works
Self-citationStable Boundary Layer Regimes in Single-Column Models
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Pohorsky, R., Baccarini, A., Brett, N., Barret, B., Bekki, S., Pappaccogli, G., Dieudonné, E., Temime-Roussel, B., D'Anna, B., Cesler-Maloney, M., et al. (2025) · Atmospheric Chemistry and Physics · doi:10.5194/acp-25-3687-2025 · Cited by 12 · see citing works
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Simulating the Unsteady Stable Boundary Layer With a Stochastic Stability Equation
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Ice fog observed at cirrus temperatures at Dome C, Antarctic Plateau
Vignon, É., Raillard, L., Genthon, C., Guasta, M. D., Heymsfield, A. J., Madeleine, J., & Berne, A. (2022) · Atmospheric chemistry and physics · doi:10.5194/acp-22-12857-2022 · Cited by 11 · see citing works
Self-citationCoupled-decoupled turbulence structures of stable boundary layer during heavy haze pollution events
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Spatial Variability of Nocturnal Stability Regimes in an Operational Weather Prediction Model
Kähnert, M., Sodemann, H., Remes, T. M., Fortelius, C., Bazile, É., & Esau, I. (2022) · Boundary-Layer Meteorology · doi:10.1007/s10546-022-00762-1 · Cited by 10 · see citing works
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Representation of Arctic Winter Atmospheric Boundary Layer Stability Over Sea Ice in CMIP6 Models
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Flux–Profile Relationships in the Stable Boundary Layer—A Critical Discussion
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Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion
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Capturing the Variability of the Nocturnal Boundary Layer through Localized Perturbation Modeling
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- Authors
- Van de Wiel, B. J. H., Vignon, E., Baas, P., van Hooijdonk, I. G. S., van der Linden, S. J. A., Antoon van Hooft, J., Bosveld, F. C., de Roode, S. R., Moene, A. F., & Genthon, C.
- Affiliations
- Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands
- CNRS/Université Grenoble Alpes, Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble, France
- Delft University of Technology (NL)
- Fluid Dynamics Laboratory, and J.M. Burgerscentrum, Eindhoven University of Technology, Eindhoven, Netherlands
- Royal Netherlands Meteorological Institute (NL)
- Wageningen University & Research (NL)
- Centre National de la Recherche Scientifique (FR)
- Venue
- Journal of the Atmospheric Sciences
- Publisher
- American Meteorological Society
- Published
- 2017-03-20
- Volume
- 74
- Issue
- 4
- Pages
- 1057-1073
- ISSN
- 0022-4928 (ISSN-L)
- 1520-0469
- OpenAlex
- W2572079327
- Cited by
- 90
- References
- 72
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 12 · 2023: 7 · 2022: 10 · 2021: 15 · 2020: 7 · 2019: 18 · 2018: 5 · 2017: 6
- FWCI
- 5.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Atmospheric Sciences (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- TU/e Research Portal (submittedVersion)
- TU/e Research Portal (submittedVersion, other-oa)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- European Research Council (648666)
- NWO Dutch National Science Foundation (82301010)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Engineering › Computational Mechanics › Fluid Dynamics and Turbulent Flows
- Keywords
- tipping points, surface energy balance, turbulent heat transfer, bulk parameterization, coupling strength, wind speed dependence, Arctic boundary layer, Cabauw, Dome C, system resilience, column model
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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