the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Mar 2023
Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.0
Abstract. Marine stratocumulus clouds play an important role in the planet’s radiation budget by reflecting the incident solar radiation. Some studies have shown that the uncertainty in temperature projections in global warming simulations is mainly caused by the representation of marine low clouds in global climate models. Using the Super Droplet Method (SDM), an advanced and highly accurate particle-based numerical simulation method for cloud microphysics, the characteristics and morphology of the simulated clouds are closer to those of natural clouds. We explore separately how small a grid length is necessary for accurate simulations of stratocumulus using the SDM and a double-moment scheme called SN14 which is a traditional and simpler cloud microphysics scheme and how many Super-droplet number per grid is required for an accurate simulation using SDM. This result can be used as a reference for future related research, saving computational resources while ensuring the accuracy of the simulation. The results of both schemes are compared with the results of model intercomparison project (MIP) results showing a good agreement. The difference of results of SDM and SN14 could be explained by the numerical diffusion and different performance of aerosol particles. The former is a numerical calculation error that is present in the simulation of SN14 but not in the SDM. SDM can simulate the motion and microphysical processes of aerosol particles more accurately, so it explicitly calculates the process of aerosol removal, and this would make the cloud holes larger and longer lasting. The results of this comparison also suggest that cloud-aerosol interactions could be critical to understanding the behavior and morphology of marine stratocumulus. We hope that our findings on the mechanisms of cloud-aerosol interactions will provide new insights for future studies and help us understand stratocumulus clouds.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-133', Anonymous Referee #1, 14 Apr 2023
The paper studies the convergence of a Lagrangian particle-based microphysics scheme (SDM) with the vertical and horizontal resolution, as well as with the number of computational particles. It also compares the the Lagrangian scheme with a 2-moment bulk scheme (SN14). The tests are done using a well known Stratocumulus LES setup DYCOMS-II. The paper is well written and clearly discusses the findings.
I only have some minor questions and suggestions:
- Two ideas are proposed for explaining the differences between the SDM and the SN14 schemes: the numerical diffusion, and the droplet sedimentation. I was wondering if the differences could also be caused by the differences in the representation of collisions (stochastic SDM vs deterministic SM14)? Are the precipitation formation and evaporation rates and locations similar between the two schemes? Do the ql and qr co-vary in a similar way between the two schemes? I'm guessing the precipitation is more "continuous" when simulated by SN14? I realise that the precipitation rates reported in this study are very small, but I was wondering if they could still be affecting the simulations?
- Figure 3 vs 11. I didn't fully understand from the discussion why the buoyancy production and w variance are so different in the cloud layer between the SDM and SN14 runs? Is it only related to the differences in CC? Can the SDM method match those profiles in simulations with larger ql? Similarly, the integrated tke still looks different for SN14 and SDM without sedimentation on Figure 15? Could the authors comment on why that is the case?
- Figure 13 - Why is the pressure so different between SDM and SN14?
- dz ~ 2.5 m is a very fine resolution for LES, and yet the simulations do not converge. I was wondering what recommendations the authors have related to that issue. What should be done in cases where due to computational limitations the simulations cannot be run at such high resolutions? Would using stretched grids help? Would using higher order advection schemes help? Any other suggestions?
Technical comments:
- Table 1 - Seems like most of the dts are the same. Would it improve the presentation to only show the different ones? For example in the last column just say DT_cnd = DT_coa = DT_adv and then just print one number in the column?
- Figure 3 and 11 - Would it be possible to also include a qr plot with the axis limits set to showcase the SDM and SN14 results?
- Caption of Fig 13 - Should be ql and not qt?
Citation: https://doi.org/10.5194/egusphere-2023-133-RC1 -
AC2: 'Reply on RC1', Chongzhi Yin, 03 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-133/egusphere-2023-133-AC2-supplement.pdf
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AC2: 'Reply on RC1', Chongzhi Yin, 03 Oct 2023
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RC2: 'Comment on egusphere-2023-133', Anonymous Referee #2, 06 May 2023
This manuscript discusses the convergence characteristics of the stratocumulus cloud simulations (DYCOMS-II-RF02 case) using a Lagrangian (SDM) and an Eulerian bulk (SN14) microphysics scheme. The authors evaluated grid and super-droplet (SD) number convergence and compared the simulation results from the bulk and super-droplet schemes. The article thoroughly investigated this topic and is appropriate for publication in GMD. I have the following minor comments to consider.
The super-droplet simulations show convergence at around 16 SDs/grid for this case. It’s a small SD number. But I wonder if this could apply only to this case where precipitation formation is extremely low. This low super-droplet number per grid box may not be sufficient for cases with significant precipitation formation. It may affect the precipitation formation rate and the spatial structure of the rain and cloud water fields. Similarly, for a polluted case with GCCN, a sufficient number of super-droplets might be needed to appropriately sample the aerosol size spectrum and capture the effect of GCCN on precipitation initiation. I recommend the authors clarify this point at appropriate places in the manuscript or present a convergence test for a precipitating case.
335-340: This argument about a higher droplet concentration for lower SD numbers could be improved. A higher droplet concentration for lower SD numbers may result from a higher multiplicity of SDs and associated statistical fluctuations in the activation process (not a longer phase relation timescale). A lower SD case will have more fluctuations in the phase relaxation timescale, with some grids having extremely short timescales and some with cloud-free conditions. Thus, a higher probability of large positive supersaturation excursions.
Could some of the differences in the cloud field between the SDM and bulk runs be due to the spurious in-cloud activation and the Twomey scheme in the bulk run compared to an explicit activation scheme in SDM?
Citation: https://doi.org/10.5194/egusphere-2023-133-RC2 -
AC1: 'Reply on RC2', Chongzhi Yin, 03 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-133/egusphere-2023-133-AC1-supplement.pdf
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AC1: 'Reply on RC2', Chongzhi Yin, 03 Oct 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-133', Anonymous Referee #1, 14 Apr 2023
The paper studies the convergence of a Lagrangian particle-based microphysics scheme (SDM) with the vertical and horizontal resolution, as well as with the number of computational particles. It also compares the the Lagrangian scheme with a 2-moment bulk scheme (SN14). The tests are done using a well known Stratocumulus LES setup DYCOMS-II. The paper is well written and clearly discusses the findings.
I only have some minor questions and suggestions:
- Two ideas are proposed for explaining the differences between the SDM and the SN14 schemes: the numerical diffusion, and the droplet sedimentation. I was wondering if the differences could also be caused by the differences in the representation of collisions (stochastic SDM vs deterministic SM14)? Are the precipitation formation and evaporation rates and locations similar between the two schemes? Do the ql and qr co-vary in a similar way between the two schemes? I'm guessing the precipitation is more "continuous" when simulated by SN14? I realise that the precipitation rates reported in this study are very small, but I was wondering if they could still be affecting the simulations?
- Figure 3 vs 11. I didn't fully understand from the discussion why the buoyancy production and w variance are so different in the cloud layer between the SDM and SN14 runs? Is it only related to the differences in CC? Can the SDM method match those profiles in simulations with larger ql? Similarly, the integrated tke still looks different for SN14 and SDM without sedimentation on Figure 15? Could the authors comment on why that is the case?
- Figure 13 - Why is the pressure so different between SDM and SN14?
- dz ~ 2.5 m is a very fine resolution for LES, and yet the simulations do not converge. I was wondering what recommendations the authors have related to that issue. What should be done in cases where due to computational limitations the simulations cannot be run at such high resolutions? Would using stretched grids help? Would using higher order advection schemes help? Any other suggestions?
Technical comments:
- Table 1 - Seems like most of the dts are the same. Would it improve the presentation to only show the different ones? For example in the last column just say DT_cnd = DT_coa = DT_adv and then just print one number in the column?
- Figure 3 and 11 - Would it be possible to also include a qr plot with the axis limits set to showcase the SDM and SN14 results?
- Caption of Fig 13 - Should be ql and not qt?
Citation: https://doi.org/10.5194/egusphere-2023-133-RC1 -
AC2: 'Reply on RC1', Chongzhi Yin, 03 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-133/egusphere-2023-133-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Chongzhi Yin, 03 Oct 2023
-
RC2: 'Comment on egusphere-2023-133', Anonymous Referee #2, 06 May 2023
This manuscript discusses the convergence characteristics of the stratocumulus cloud simulations (DYCOMS-II-RF02 case) using a Lagrangian (SDM) and an Eulerian bulk (SN14) microphysics scheme. The authors evaluated grid and super-droplet (SD) number convergence and compared the simulation results from the bulk and super-droplet schemes. The article thoroughly investigated this topic and is appropriate for publication in GMD. I have the following minor comments to consider.
The super-droplet simulations show convergence at around 16 SDs/grid for this case. It’s a small SD number. But I wonder if this could apply only to this case where precipitation formation is extremely low. This low super-droplet number per grid box may not be sufficient for cases with significant precipitation formation. It may affect the precipitation formation rate and the spatial structure of the rain and cloud water fields. Similarly, for a polluted case with GCCN, a sufficient number of super-droplets might be needed to appropriately sample the aerosol size spectrum and capture the effect of GCCN on precipitation initiation. I recommend the authors clarify this point at appropriate places in the manuscript or present a convergence test for a precipitating case.
335-340: This argument about a higher droplet concentration for lower SD numbers could be improved. A higher droplet concentration for lower SD numbers may result from a higher multiplicity of SDs and associated statistical fluctuations in the activation process (not a longer phase relation timescale). A lower SD case will have more fluctuations in the phase relaxation timescale, with some grids having extremely short timescales and some with cloud-free conditions. Thus, a higher probability of large positive supersaturation excursions.
Could some of the differences in the cloud field between the SDM and bulk runs be due to the spurious in-cloud activation and the Twomey scheme in the bulk run compared to an explicit activation scheme in SDM?
Citation: https://doi.org/10.5194/egusphere-2023-133-RC2 -
AC1: 'Reply on RC2', Chongzhi Yin, 03 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-133/egusphere-2023-133-AC1-supplement.pdf
-
AC1: 'Reply on RC2', Chongzhi Yin, 03 Oct 2023
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Chongzhi Yin
Shin-ichiro Shima
Lulin Xue
Chunsong Lu
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3384 KB) - Metadata XML