the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Apr 2024
iPyCLES v1.0: A New Isotope-Enabled Large-Eddy Simulator for Mixed-Phase Clouds
Abstract. Recent field campaigns and advances in observational techniques have yielded a wealth of observations of stable water isotopes in the atmosphere, but the heirarchy of isotope-enabled models is not well-placed to leverage these observation for improving constraints on parameterized physics in global models. Here, we introduce the isotope-enabled Python Cloud Large-Eddy Simulation model (iPyCLES) for mixed-phase clouds. Isotopic tracers are implemented in a parallel passive water cycle and experience all processes and phase changes that affect the model's prognostic total water variable. Isotopic fractionation occurs during cloud and precipitation processes as well as surface evaporation, with facilities for applying external forcing. In addition to isotopic tracers, we extend the two-moment warm cloud microphysics scheme to enable prognostic simulation of cloud liquid water and ice while eliminating dependence on saturation adjustment. Relative to a one-moment mixed-phase scheme with saturation adjustment, the new microphysical scheme yields substantial benefits in simulating phase partitioning and isotopic exchange in mixed-phase regions. The LES model is based on an energetically-consistent implementation of the anelastic equations and employs high-order weighted, essentially non-oscillatory numerics, and is therefore theoretically suitable for simulations spanning the gray zone of the convective spectrum. In this initial evaluation, we present the results of test cases for non-precipitating subtropical shallow cumulus, precipitating subtropical shallow cumulus, and precipitating Arctic mixed-phase stratocumulus clouds. The iPyCLES simulations agree well with available observations and previous model simulations in all three cases, with distinct signatures among the cases that highlight the added potential of isotopic tracers. The benefits of the revised microphysics scheme are especially evident in the Arctic mixed-phase cloud test case, with vapor-liquid-ice exchange within the cloud producing a conspicuous peak in deuterium excess near the top of the cloud. As an idealized testbed, the iPyCLES model can bridge gaps between cloud chamber experiments, real-world observations, and global and regional models, allowing information provided by water isotopes to be translated more effectively into observational constraints for cloud and boundary layer parameterizations.
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Status: final response (author comments only)
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CC1: 'Comment on egusphere-2024-828', lixian Zhang, 10 Apr 2024
Impressive effort! Currently seeking collaboration for code migration within the national supercomputing framework in Shenzhen.
Citation: https://doi.org/10.5194/egusphere-2024-828-CC1 -
RC1: 'Comment on egusphere-2024-828', Anonymous Referee #1, 07 May 2024
This manuscript describes the implementation of a water isotopic tracer modeling capability plus a prognostic mixed phase microphysics scheme to an existing atmospheric large eddy simulation code, PyCLES, and presents results from three established intercomparison cases to assess the model performance. Given the potential of isotope based analyses for deepening understanding of the processes controlling the distribution of clouds and precipitation, the addition of another isotope enabled LES model is attractive. However, there are some key gaps in the formulation and evaluation of iPyCLES that need to be addressed.
Greatest concerns:
I will begin with my greatest technical concern. The authors do not address the proper handling of PyCLES' specific entropy prognostic thermodynamic variable with the addition of the SB06 mixed phase microphysics scheme. In particular, they do not discuss how they deal with phase changes among water vapor, cloud ice, and cloud liquid occurring at non-equilibrium conditions (i.e., not subject to saturation adjustment). As Pressel et al 2015 discuss the nuances of employing a prognostic entropy equation in LES extensively and in particular as relates to phase partitioning, these authors should have been aware of the theoretical challenges in adding more complex microphysical schemes to PyCLES. However, this issue was not even alluded to, let alone addressed. The authors must explain how they adapt the thermodynamics of iPyCLES for compatibility with more general mixed-phase microphysics schemes. Given the temperature dependence of the fractionation processes, it would be reassuring to know that temperature is being diagnosed appropriately within the host atmospheric model.
In terms of presentation of the overall modeling framework, the authors are commendably thorough in documenting and referencing the equations and closures used for the isotopic module. The current approach appears to closely follow Blossey et al 2010 as regards the coupling to cloud microphysics. It would be extremely useful for the authors to provide a conceptual overview of their approach, noting important similarities and differences with prior approaches and highlighting any significant innovations. Additionally, discussion of the most significant uncertainties within the isotopic tracer module is warranted.
In terms of model validation/evaluation, the authors make some efforts towards evaluating the results of their three case studies (notably, these case studies were implemented in the original PyCLES code and are not new to iPyCLES). However, the isotopic tracer predictions themselves are simply presented and discussed. In other words, there's no "ground truth" because none of the chosen test cases are accompanied by isotopic observations. There is also an absence of systematic sensitivity studies that could help to establish the internal consistency of the model and check for implementation bugs. In particular, the spikes around cloud top in the ISDAC test case are never satisfactorily explained; it's not even clear whether their occurrence is truly erroneous. Given the complexity of the isotopic coupling, there really needs to be more evidence presented that it has been correctly implemented.
The comparison of the Kaul15 and SB06 schemes is interesting, but perhaps overstated, in that Kaul15 is not really intended as a high-fidelity mixed phase microphysics schemes in terms of capturing individual microphysical process rates. Also, the Kaul15 scheme could have been compared to the SBWarm/SB06 scheme for the RICO and BOMEX cases (including comparison of isotopes), but instead the authors chose to add a comparison to MicroHH (without any isotopic information).
Minor comments:
At the beginning of section 3, RICO and BOMEX are incorrectly referred to as stratocumulus cloud cases.
Figure 13: Kail --> Kaul
Figure 16 and associated discussion: should use "peak" not "peek"
Citation: https://doi.org/10.5194/egusphere-2024-828-RC1 - AC1: 'Author Reply', Jonathon Wright, 10 Jul 2024
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RC2: 'Comment on egusphere-2024-828', Franziska Aemisegger, 21 May 2024
Review of Hu et al.: iPyCLES v1.0: A new isotope-enabled large-eddy simulator for mixed-phase clouds
This is a well-written, and very interesting paper introducing a new isotope-enabled LES system to study cloud formation processes and their impact on the climate. The newly introduced isotope module is well described, and some first idealised test cases are presented. I missed a description or discussion of the potential to use the LES in more realistic meteorology setups and related to this the use of isotope observations available from recent campaigns such as EUREC4A for a more process-oriented evaluation of the modelling system. Also I am not entirely sure, why the authors present the BOMEX case, which I find a bit lengthy to read, but which (to me) brings not much interesting material, while the RICO and Arctic cases are super interesting. To some extent the summary and discussion section helps in this respect but I would have hoped for more guidance earlier in the text. Maybe help the reader understand why the BOMEX case is presented at all already in the introduction.
I recommend accepting this valuable contribution after following minor revisions:
- L.5: new sentence for the external forcings? I don’t understand why they are mentioned together with isotopic fractionation, there’s no direct link, right? And in a new sentence on external forcings: as I understood the available “facilities for external forcings” are idealised and don’t allow for realistic meteorology setups, this should be discussed shortly at the end of the paper.
- L.17: “conspicuous peak of deuterium excess at the top of clouds” mention in which phase(s) you find this peak.
- L. 44: It would help me if you could guide the reader a bit more for the following two paragraphs by starting with a sentence that explicitly states that you plan to use LES to address Morrison challenge 1 and isotopes to address challenge 2 and that you will expand on these two tools in the following two paragraphs.
- L. 72: “… a degree of dependence that depends on the extent of differential fractionation among the isotopes”: I know what you mean, but I think this is not clear for non-isotope specialists. Please reformulate.
- L. 91: a recent publication on isotope effects and what we can learn from them on the dynamics and microphysics of tropical mixed phase clouds is de Vries et al. 2022 in addition to Bolot et al. 2013.
de Vries, A. J., Aemisegger, F., Pfahl, S., and Wernli, H.: Stable water isotope signals in tropical ice clouds in the West African monsoon simulated with a regional convection-permitting model, Atmos. Chem. Phys., 22, 8863–8895, https://doi.org/10.5194/acp-22-8863-2022, 2022. - L. 101: Maybe Eckstein et al. 2018 is not the best reference for high-resolution simulations of cloud and boundary layer processes over short time scales. Since you also use shallow cumulus cases: Villiger et al. 2023 who performed COSMOiso simulations at 1 km horizontal grid spacing in the western tropical North Atlantic would be closer to what you mean here.
Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 1: A process-oriented evaluation of COSMOiso simulations with EUREC4A observationsLeonie Villiger, Marina Dütsch, Sandrine Bony, Marie Lothon, Stephan Pfahl, Heini Wernli, Pierre-Etienne Brilouet, Patrick Chazette, Pierre Coutris, Julien Delanoë, Cyrille Flamant, Alfons Schwarzenboeck, Martin Werner, and Franziska AemiseggerAtmos. Chem. Phys., 23, 14643–14672, https://doi.org/10.5194/acp-23-14643-2023, 2023. - L. 105-108: Mentioning recent field campaigns with isotope observations, there’s a key one that is missing here with observations of shallow cumulus clouds on 2 aircrafts, 4 ships and a land station. That’s the EUREC4A campaign on elucidating the role of clouds-circulation coupling for climate. With a general description of the campaign here:
Stevens, B., Bony, S., Farrell, D., Ament, F., Blyth, A., Fairall, C., Karstensen, J., Quinn, P. K., Speich, S., Acquistapace, C., Aemisegger, F., Albright, A. L., Bellenger, H., Bodenschatz, E., Caesar, K.-A., Chewitt-Lucas, R., de Boer, G., Delanoë, J., Denby, L., Ewald, F., Fildier, B., Forde, M., George, G., Gross, S., Hagen, M., Hausold, A., Heywood, K. J., Hirsch, L., Jacob, M., Jansen, F., Kinne, S., Klocke, D., Kölling, T., Konow, H., Lothon, M., Mohr, W., Naumann, A. K., Nuijens, L., Olivier, L., Pincus, R., Pöhlker, M., Reverdin, G., Roberts, G., Schnitt, S., Schulz, H., Siebesma, A. P., Stephan, C. C., Sullivan, P., Touzé-Peiffer, L., Vial, J., Vogel, R., Zuidema, P., et al. EUREC4A, Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, 2021.
And the isotope-observations paper here:
Bailey, A., Aemisegger, F., Villiger, L., Los, S. A., Reverdin, G., Quiñones Meléndez, E., Acquistapace, C., Baranowski, D. B., Böck, T., Bony, S., Bordsdorff, T., Coffman, D., de Szoeke, S. P., Diekmann, C. J., Dütsch, M., Ertl, B., Galewsky, J., Henze, D., Makuch, P., Noone, D., Quinn, P. K., Rösch, M., Schneider, A., Schneider, M., Speich, S., Stevens, B., and Thompson, E. J.: Isotopic measurements in water vapor, precipitation, and seawater during EUREC4A, Earth Syst. Sci. Data, 15, 465–495, https://doi.org/10.5194/essd-15-465-2023, 2023.
- L. 139: “...moisture and heat...” why heat?
- L. 147: “tightly intervowen with model thermodynamics” and dynamics?
- Eq. 1: not all terms are described.
- L. 180: “smaller than the freezing point...”
- L. 197: decribed
- L. 264: “Isotopes in rain and snow sediment according to the fall velocities computed for rain and snow in the microphysics scheme”. Reformulate, it could be misunderstood, there’s no fractionation in sedimentation, and Heavy and light water sediments with the same fall velocities.
- L. 280 ff: d a variable and should be in italics, while 0 is the oxygen atom and should be in normal font.
- L. 282: I think the deuterium excess could be explained in a better way by mentioning that it is a tracer for non-equilibrium fractionation. At L. 280 it is not so clear what “mismatch” is exactly meant. The global meteoric water line is not introduced properly, so referring to it, while explaining the dexcess might not be too helpful for non-isotope readers.
- L. 283-284: a higher HDO concentration relative to H218O compared to equilibrium fractionation but not in absolute terms higher.
- Section 2.4.2 is very elegantly and clearly written! A pleasure to read!
- L. 317: to what extent is this consistent with the Stewart 1978 approach? Pfahl et al. 2012 raise this very interesting and relevant question, which to my knowledge has never been addressed so far in the literature. An idealised modelling system such as iPyCLES could clarify this very elegantly.
- L. 329: The diffusivites by Cappa et al. 2003 have been discussed quite critically in the literature, see Pfahl and Wernli, 2009. Maybe at least mention that there is some disagreement among experts about the best choice for the diffusivities.
- L. 348: To me it was not clear at L. 290 that this was for SB06, so here I am a bit surprised and confused to what is done in which scheme.
- Eq. 33: interesting: in several recent publications (Aemisegger et al. 2021 and Villiger et al. 2022, Weather and Climate Dynamics), the influence of extratropical intrusions into the tropics have been highlighted, which are associated with enhanced slantwise subsidence from the extratropics. Could such a flow regime be represented with the simple idealised setup chosen in iPyCLES?
- L. 386: replace “conditionsis” by “conditions”.
- L. 390: I think the choice of the campaigns to evaluate the model is a bit surprising given that the EUREC4A effort would provide similar conditions as BOMEX and RICO but including isotope observations. But probably, this choice was due to the time frame of the project.
- L. 409: strictly speeking BOMEX happened in the tropics, isn’t it more of a “trade wind cumulus test case”?
- L445: I would expect a decrease of dexcess with height due to temperature decrease in a simple Rayleigh experiment (see e.g. Fig. A1 in Thurnherr et al. 2021 WCD, https://wcd.copernicus.org/articles/2/331/2021/wcd-2-331-2021.pdf)
We see a slight decrease in vapour dexcess from ATR-aircraft observations from EUREC4A in Barbados (see Fig. 13h in Villiger et al. 2023) - Analyses related to Fig. 8: how different are cloudy vs. non-cloudy profiles vs. dry-warm (subsidence dominated) profiles? Villiger et al. 2023 proposed a simple way of evaluating isotope signals from numerical models in their representation of shallow cumulus clouds. Are the differences between cloudy and non-cloudy profiles similar as in their study? I very much recomment the authors to do this analysis because it allows them to relate their simulations to observations from EUREC4A in an elegant and simple way.
- L. 500-510: this is a very interesting paragraph. How comes that the evaporation of hydrometeors is maximum in the cloud layer. I struggle a bit with understanding that. Is the cloud layer undersaturated? Mainly in downdrafts? Or Is this evaporation from anvil precipitation from above falling into subsaturated layers? Can this analysis be done for cloudy and non-cloudy profiles?
- L. 507: yes agreed, this is also what we see in Villiger et al. 2023.
- L 516: surprising that rain is slightly more enriched than cloud liquid water given that the rain most likely originates from above and cloud water might be of more local origin (?), so is this again a signature of rain evaporation?
- Fig. 8 and on the previous point: I would find a disequilibrium analysis quite useful here, because it is relatively cumbersome to compare the delta vapour to the delta liquid and rain water content to know which phase is enriched compared to the other (see de Vries et al. 2022 ACP, https://acp.copernicus.org/articles/22/8863/2022/acp-22-8863-2022.html)
- L. 655: these measurements are available from EUREC4A.
- L. 662: “of of” remove one “of”
- To me the Arctic mixed phase case is very much focused on the two microphysical schemes and their comparison. I find that the isotope aspects come a bit more as a byproduct and I find the dexcess peak at cloud top very suspicious.
- 725-727: yes, I totally agree on the need for combined use of observations and high-resolution model simulations to be able to fully use the potential of isotopes as tracers. And I really think iPyCLES makes a significant contribution to this. But: then can you give more details about how you would recommend to run the system in realistic meteorology setups to make the simulations comparable to observations?
Citation: https://doi.org/10.5194/egusphere-2024-828-RC2
Data sets
Data for iPyCLES v1.0: A New Isotope-Enabled Large-Eddy Simulator for Mixed-Phase Clouds Zizhan Hu and Jonathon S. Wright https://zenodo.org/doi/10.5281/zenodo.10911096
Model code and software
isotope-enabled Python Cloud Large Eddy Simulation model code Zizhan Hu https://github.com/huzizhan/ipycles/tree/isotopetracer
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