Modeling the GABLS4 Strongly‐Stable Boundary Layer With a GCM Turbulence Parameterization: Parametric Sensitivity or Intrinsic Limits?. (19th March 2021)

• Record Type: Journal Article
• Authors: Audouin, O.; Roehrig, R.; Couvreux, F.; Williamson, D.
• Is Part Of: Journal of advances in modeling earth systems. Volume 13:Number 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-19
• Subjects: Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2020MS002269
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: The representation of stable boundary layers (SBLs) still challenges turbulence parameterizations implemented in current weather or climate models. The present work assesses whether these model deficiencies reflect calibration choices or intrinsic limits in currently‐used turbulence parameterization formulations and implementations. This question is addressed for the CNRM atmospheric model ARPEGE‐Climat 6.3 in a single‐column model/large‐eddy simulation (SCM/LES) comparison framework, using the history matching with iterative refocusing statistical approach. The GABLS4 case, which samples a nocturnal strong SBL observed at Dome C, Antarctic Plateau, is used. The standard calibration of the ARPEGE‐Climat 6.3 turbulence parameterization leads to a too deep SBL, a too high low‐level jet and misses the nocturnal wind rotation. This behavior is found for low and high vertical resolution model configurations. The statistical tool then proves that these model deficiencies reflect a poor parameterization calibration rather than intrinsic limits of the parameterization formulation itself. In particular, the role of two lower bounds that were heuristically introduced during the parameterization implementation to increase mixing in the free troposphere and to avoid runaway cooling in snow‐ or ice‐covered region is emphasized. The statistical tool identifies the space of the parameterization free parameters compatible with the LES reference, accounting for the various sources of uncertainty. This space is non‐empty, thus proving that the ARPEGE‐Climat 6.3 turbulence parameterization contains the required physics to capture the GABLS4 SBL. The SCM framework is also used to validate the statistical framework and a few guidelines for its use in parameterization development and calibration are discussed. Plain Language Summary: During the night or in snow‐ or ice‐covered region, a stable atmospheric boundary layer (SBL) often develops. Their representation still challenges turbulence parameterizations implemented in numerical weather or climate models. The present work assesses whether the ARPEGE‐Climat atmospheric model deficiencies reflect calibration choices or intrinsic limits in its turbulence parameterization using a statistical approach from the Uncertainty Quantification community. A single‐column version of the model is evaluated on the GABLS4 case, a nocturnal strong SBL observed at Dome C, Antarctic plateau, and compared to high‐resolution simulations. The standard calibration of the ARPEGE‐Climat 6.3 turbulence parameterization leads to a too deep SBL and an incorrect wind pattern and so for different vertical resolutions. The statistical tool proves that these model deficiencies are rectified with proper calibration of the turbulence parameterization. In particular, it is shown that two lower bounds, introduced to increase turbulent mixing, are key to capture the GABLS4 SBL. Finally, the potential and relevance of the Uncertainty Quantification approach applied to the single‐column model/large‐eddy simulation comparison framework for the calibration of climate models are highlighted and few guidelines for its use are proposed. Key Points: The ARPEGE‐Climat 6.3 performance in modeling a stable boundary layer is assessed in a single‐column model/large‐eddy simulation comparison The use of history matching with iterative refocusing indicates turbulence parameterization deficiencies due to poor calibration Validation of the statistical framework and guidelines for its use in parameterization development and calibration are discussed