the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: development and technical paper 17 Feb 2021
Submitted as: development and technical paper | 17 Feb 2021
Integrating CVMix into GOTM (v6.0): A consistent framework for testing, comparing, and applying ocean mixing schemes
- 1Fluid Dynamics and Solid Mechanics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
- 2Bolding & Bruggeman ApS., 5466 Asperup, Denmark
- 3Plymouth Marine Laboratory, Prospect Place, the Hoe, Plymouth PL1 3DH, UK
- 4Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
- 5Department of Bioscience, Aarhus University, 8600 Silkeborg, Denmark
- 1Fluid Dynamics and Solid Mechanics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
- 2Bolding & Bruggeman ApS., 5466 Asperup, Denmark
- 3Plymouth Marine Laboratory, Prospect Place, the Hoe, Plymouth PL1 3DH, UK
- 4Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
- 5Department of Bioscience, Aarhus University, 8600 Silkeborg, Denmark
Abstract. The General Ocean Turbulence Model (GOTM) is a one-dimensional water column model including a set of state-of-the-art turbulence closure models, and has widely been used in various applications in the ocean modeling community. Here we extend GOTM to include a set of newly developed ocean surface vertical mixing parameterizations of Langmuir turbulence via coupling with the Community Vertical Mixing Project (CVMix). A Stokes drift module is also implemented in GOTM to provide the necessary ocean surface waves information to the Langmuir turbulence parameterizations, as well as to facilitate future development and evaluation of new Langmuir turbulence parameterizations. In addition, a streamlined workflow with Python and Jupyter Notebook is also described, enabled by the newly developed and more flexible configuration capability of GOTM. The newly implemented Langmuir turbulence parameterizations are evaluated against theoretical scalings and available observations in four test cases, including an idealized wind-driven entrainment case and three realistic cases at ocean station Papa, the northern North Sea and the central Gotland Sea, and compared with the existing General Length Scale scheme in GOTM. The results are consistent with previous studies. This development extends the capability of GOTM towards including the effects of ocean surface waves and provides useful toolsets for the ocean modeling community to further study the effects of Langmuir turbulence in a broader scope.
Qing Li et al.
Status: closed
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RC1: 'Comment on gmd-2020-437', Anonymous Referee #1, 02 Apr 2021
I found this paper to be well-written, easy-to-read, and extremely informative. The background sections explain what experiments are being run and why, and the experiments themselves are quite thorough. Incorporating CVMix in GOTM is a novel approach to modeling vertical mixing that will hopefully be a boon to the rest of the community. The paper provides enough details for others to repeat the experiments on their own, and I agree with how the authors interpret the results.
The only small quibble I have is with the sentence beginning on line 184 (in section 3.1): "KPP-CVMix predicts an enhanced turbulent viscosity than GLS-C01A..."; "predicts an enhanced turbulent viscosity than" is a klunky phrasing. I'd recommend "predicts more enhanced turbulent viscosity", but there are probably other ways to clean up that sentence as well.
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RC2: 'Comment on gmd-2020-437', Anonymous Referee #2, 22 May 2021
The authors implemented three different KPP-based vertical mixing schemes in the GOTM, among two of which include LT-enhanced vertical mixing or entrainment, and then systematically assessed their performances by comparing to observations of three oceanic scenarios, yielding consistency with previous studies and improvements in the LT-parameterized schemes, particularly in significant mixed layer deepening events during winter. This work provides both framework and example of implementing LT-effects into ocean models, which benefits the ocean modeling community. In general, the paper is well written and supports its conclusions as well as findings except for some minor places, which may need to be further clarified.
- In section 3, table 1, the flags for Ekman depth limit and MO length limit are both false while there exist strong heating events (re-stratification in FLEX) and significant mixed layer deepening (Gotland Basin). Can you clarify why these two depth limits are not considered here?
- In your Fig.5, panel IV, why the temperature bias in the well-mixed upper layer is much greater than the salinity bias in the same layer? It seems the salinity prediction is generally better than temperature. Can you briefly explain why?
- Section 3.2, Page 12, do you have any discussion on wind-wave misalignment effects on LT intensity and thus their effects on turbulent mixing?
- Page 16, L335-336, Can you be more specific about why a second-order turbulence closure scheme will be more helpful?
- Page 19, L371-375, small Langmuir number is also occasionally observed in the spring or summer, can you explain why LT effects are most pronounced in the winter? Is it easier for LT to erode halocline than thermocline?
- AC1: 'Comment on gmd-2020-437', Qing Li, 04 Jun 2021
Status: closed
-
RC1: 'Comment on gmd-2020-437', Anonymous Referee #1, 02 Apr 2021
I found this paper to be well-written, easy-to-read, and extremely informative. The background sections explain what experiments are being run and why, and the experiments themselves are quite thorough. Incorporating CVMix in GOTM is a novel approach to modeling vertical mixing that will hopefully be a boon to the rest of the community. The paper provides enough details for others to repeat the experiments on their own, and I agree with how the authors interpret the results.
The only small quibble I have is with the sentence beginning on line 184 (in section 3.1): "KPP-CVMix predicts an enhanced turbulent viscosity than GLS-C01A..."; "predicts an enhanced turbulent viscosity than" is a klunky phrasing. I'd recommend "predicts more enhanced turbulent viscosity", but there are probably other ways to clean up that sentence as well.
-
RC2: 'Comment on gmd-2020-437', Anonymous Referee #2, 22 May 2021
The authors implemented three different KPP-based vertical mixing schemes in the GOTM, among two of which include LT-enhanced vertical mixing or entrainment, and then systematically assessed their performances by comparing to observations of three oceanic scenarios, yielding consistency with previous studies and improvements in the LT-parameterized schemes, particularly in significant mixed layer deepening events during winter. This work provides both framework and example of implementing LT-effects into ocean models, which benefits the ocean modeling community. In general, the paper is well written and supports its conclusions as well as findings except for some minor places, which may need to be further clarified.
- In section 3, table 1, the flags for Ekman depth limit and MO length limit are both false while there exist strong heating events (re-stratification in FLEX) and significant mixed layer deepening (Gotland Basin). Can you clarify why these two depth limits are not considered here?
- In your Fig.5, panel IV, why the temperature bias in the well-mixed upper layer is much greater than the salinity bias in the same layer? It seems the salinity prediction is generally better than temperature. Can you briefly explain why?
- Section 3.2, Page 12, do you have any discussion on wind-wave misalignment effects on LT intensity and thus their effects on turbulent mixing?
- Page 16, L335-336, Can you be more specific about why a second-order turbulence closure scheme will be more helpful?
- Page 19, L371-375, small Langmuir number is also occasionally observed in the spring or summer, can you explain why LT effects are most pronounced in the winter? Is it easier for LT to erode halocline than thermocline?
- AC1: 'Comment on gmd-2020-437', Qing Li, 04 Jun 2021
Qing Li et al.
Model code and software
General Ocean Turbulence Model (Version v6.0.0) Karsten Bolding, Jorn Bruggeman, Hans Burchard, and Lars Umlauf https://doi.org/10.5281/zenodo.4541583
Python tools for working with GOTM in Jupyter Notebook Qing Li https://doi.org/10.5281/zenodo.4314038
Jupyter notebooks and data for "Integrating CVMix into GOTM (v6.0): A consistent framework for testing, comparing, and applying ocean mixing schemes" Qing Li https://doi.org/10.5281/zenodo.4314036
Qing Li et al.
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