Modeling the GABLS4 Strongly-Stable Boundary Layer With a GCM Turbulence Parameterization: Parametric Sensitivity or Intrinsic Limits?
Modeling the GABLS4 Strongly-Stable Boundary Layer With a GCM Turbulence Parameterization: Parametric Sensitivity or Intrinsic Limits?
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DOI：
10.1029/2020ms002269
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发表时间：
2021-03-01
影响因子：
6.8
通讯作者：
Williamson, D.
中科院分区：
文献类型：
--
作者：
Audouin, O.;Roehrig, R.;Williamson, D.
稳定边界层（SBL）的代表性仍然是当前天气或气候模式中湍流参数化的挑战。目前的工作评估这些模型的不足之处是否反映了校准的选择或内在的限制，在目前使用的湍流参数化配方和实施。在单柱模式/大涡模拟（SCM/LES）比较框架下，使用历史匹配与迭代重聚焦统计方法，针对CNRM大气模式ARPEGE-Climat 6.3解决了这个问题。GABLS 4的情况下，在南极高原的圆顶C观测到的夜间强SBL的样本，被使用。ARPEGE-Climat 6.3湍流参数化的标准校准导致太深的SBL，太高的低空急流，错过了夜间风旋转。这种行为被发现为低和高垂直分辨率模式配置。然后，统计工具证明，这些模型的缺陷反映了一个穷人的参数化校准，而不是参数化制定本身的内在限制。特别是，两个下限的参数化实施过程中，以增加自由对流层中的混合，以避免失控的冷却在雪或冰覆盖的区域被强调的作用。统计工具识别与LES参考兼容的参数化自由参数的空间，考虑各种不确定性来源。这个空间是非空的，因此证明ARPEGE-Climat 6.3湍流参数化包含捕获GABLS 4 SBL所需的物理学。SCM框架还用于验证统计框架，并讨论了其在参数化开发和校准中使用的一些指导方针。简单语言总结在夜间或冰雪覆盖的地区，经常会形成稳定的大气边界层（SBL）。他们的代表性仍然挑战湍流参数化实施数值天气或气候模式。目前的工作评估是否ARPEGE-Climat大气模型的不足之处反映了校准的选择或内在的限制，在其湍流参数化使用的统计方法从不确定性量化社区。GABLS 4的情况下，夜间观察到的强SBL在圆顶C，南极高原，和高分辨率模拟的单柱版本的模型进行了评估。ARPEGE-Climat 6.3湍流参数化的标准校准导致SBI太深，以及不正确的风型，因此对于不同的垂直分辨率。统计工具证明，这些模型的不足之处是纠正与适当的湍流参数化校准。特别是，它示出了两个下限，引入增加湍流混合，是捕捉GABLS 4 SRI的关键。最后，不确定性量化方法应用于单柱模式/大涡模拟气候模式校准比较框架的潜力和相关性被强调，并提出了一些使用指南。
The representation of stable boundary layers (SBLs) still challenges turbulence parameterization 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 parameterization 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 SBI, 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 SRI,. 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.