the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Nov 2023
Subgrid-scale variability of cloud ice in the ICON-AES-1.3.00
Sabine Doktorowski
Jan Kretzschmar
Johannes Quaas
Marc Salzmann
Odran Sourdeval
Abstract. This paper presents a stochastical approach for the aggregation process rate in the ICON-GCM, which takes subgrid-scale variability into account. This method creates a stochastic parameterisation of the process rate by choosing a new specific cloud ice mass at random from a uniform distribution function. This distribution, which is consistent with the model's cloud cover scheme, is evaluated in terms of cloud ice mass variance with a combined satellite retrieval product (DARDAR) from the satellite cloud radar CloudSat and cloud lidar CALIPSO. For a realistic comparison with the simulated cloud ice, an estimate of precipitating and convective cloud ice is removed from the observational data set. The global patterns of simulated and observed cloud ice mixing ratio variance are in a good agreement, despite some regional differences. Due to this stochastical approach the yearly mean of cloud ice shows an overall decrease. As a result of the non-linear nature of the aggregation process, the yearly mean of the process rates increases when taking subgrid-scale variability into account. An increased process rate leads to a stronger transformation of cloud ice into snow and therefore, to a cloud ice loss. The yearly averaged global mean aggregation rate is more than 20 % higher at selected pressure levels due to the stochastical approach. A strong interaction of aggregation and accretion, however, lowers the effect of cloud ice loss due to a higher aggregation rate. The presented new stochastical method lowers the bias of the aggregation rate.
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Sabine Doktorowski et al.
Status: open (until 04 Jan 2024)
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RC1: 'Comment on gmd-2022-34', Anonymous Referee #1, 06 Dec 2023
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Summary
This article describes a new method for representing the effect of subgrid variability of cloud ice in the calculation of ice aggregation in the ICON-AES general circulation model. This stochastic method is based on using a random sample from a distribution of ice water content as input to the ice aggregation equation. The distribution of ice water content that is sampled is consistent with the ICON-AES cloud scheme.
The authors compare the ICON-AES mean cloud ice water content and the cloud scheme cloud ice water content variance to DARDAR satellite observations (with appropriate sampling to remove convective cloud and precipitating cloud from DARDAR). They argue that the consistency between the two demonstrates that it is reasonable to use the cloud scheme ice condensate variance as in their method for accounting for subgrid variability. They then demonstrate that including the effect of subgrid variability of cloud ice in the aggregation rate reduces the mean ice water content in ICON-AES, due to an increase in aggregation rate, which is partially offset by a decrease in the accretion rate in the model.
General Comments
This article is well structured and clear, and the language is fluent and precise. While there is a significant body of literature detailing methods for and consequences of accounting for subgrid variability of cloud water content in atmospheric model radiation calculations and warm rain processes, to my knowledge there are no previous studies on accounting for subgrid variability of cloud ice in microphysical process calculations. The methods are clearly described, and the results support the interpretation and conclusions.
My main concern with the article is that I think this is quite a minor advance in modelling science – in the authors’ own words, this produces “no important change in radiation”. The title of the article suggests a broad analysis of subgrid scale variability of cloud ice in the model, but the study is limited to a single process. I think this paper would be more signicant if it considered other processes. Do any of the other ice microphysics processes in the model have a nonlinear dependence on ice water content? If so, can this method be extended to those processes? What about the model radiative transfer calculations? Do they account for subgrid-scale variability of cloud ice and if so, do they use the same variability as used for ice aggregation here?
Specific Comments
- It would be good to include analytically derived corrections (e.g., Morrison and Gettelman, 2008; Larson and Griffin, 2012; Boutle et al, 2014) in the discussion of how the effects of subgrid variability of cloud water content on microphysical process rates have been represented in previous studies. Although most of this literature focuses on warm rain microphysics rather than ice, I think it is relevant to the discussion. Is it possible to derive an analytical correction to the ice aggregation rate?
- Can you explain why you only apply a representation of the effects of subgrid variability of ice water to the aggregation calculation? Do other ice microphysical processes in ICON-AES depend nonlinearly on ice water content?
- Does the ICON-AES radiation scheme include the effect of subgrid variability of ice water content on radiative fluxes and heating rates? If so, what does it use for the subgrid variability? Assuming the radiation scheme does not already use ice cloud water content variability that is consistent with the cloud scheme, what difference would this make?
- I am not convinced that the comparison between DARDAR and the model is particularly useful. I think it would be more useful to compare aggregation rates calculated using the cloud scheme subgrid ice variance, aggregation rates calculated using the “true” variance (i.e., values derived from DARDAR) and aggregation rates calculated using only the mean value (i.e., a similar analysis to that done for figure 8). This could be an additional plot and, in my opinion, would better demonstrate the utility of the cloud scheme subgrid ice cloud variance.
- I’m not sure how fair it is to compare the variance along a 1D line through a cloud (i.e., what DARDAR sees) with that in a 3D gridbox (ICON-AES). For example, Hill et al (2015) estimated that the standard deviation of water content in a 2D cloud would be approximately 1.3 times larger than that in a 1D cross-section through the cloud. Can you comment on how this might affect your DARDAR – model comparison?
- I’m not entirely convinced that the way that the DARDAR data is sampled (I.e., removing convective and precipitating ice) achieves the aim of making it more consistent with the model. It would be interesting to see how much difference removing convective and precipitating ice makes to the variance of ice water content calculated from DARDAR. It would also be interesting to try some alternative comparisons between the two. For example, precipitating ice is removed from DARDAR based on a surface precipitation flag. What difference would it make if you removed ICON points with nonzero surface precipitation from the comparison?
- For Figure. 2 and 3 a third column showing the difference between the two would be really useful. If necessary, you could plot this at lower resolution to reduce noise.
- You state that there is “No important change in radiation”. Is this also true for precipitation rates? If this doesn’t lead to any important changes then is it worth implementing in the model?
- I think it may better to have figure 8 and the discussion of the change in aggregation rate before figure 5, to demonstrate that the stochastic method does a good job or reproducing the unbiased aggregation rate before showing the effect of the stochastic method in the model.
- The paper is quite concise already but could be made more so by removing table 1, which in my opinion does not add a great deal.
Technical Corrections
L5: “For a realistic comparison … removed from the observational data set.” I think this is more technical detail than is needed for an abstract and suggest removing this sentence.
L6: “The global patterns of … despite some regional differences”. This is a bit vague, and I would add some more specific details e.g., quantify the % difference between the model and observations.
L39: “Instead of taking a grid-box mean … with a randomly chosen cloud ice mass”. I think it might be clearer to rewrite this as: “… with a cloud ice mass randomly chosen from the distribution of cloud ice mass assumed in the cloud scheme”.
L57: Change “with an instantaneously output” to “with instantaneous diagnostics output”
L86: Change “which allows biases” to “which introduces biases”.
L171: “see the supplement material” should be “see the supplementary material”. However, I was not able to find any supplementary material to view anyway.
L177: typo “at the same lebvels”.
L179: Change “Therefore it is usable for” to “Therefore it is suitable for use in”.
L188: typo: “averaged aggregattion rate”
References
Morrison, H. and Gettelman, A., 2008. A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests. Journal of Climate, 21(15), pp.3642-3659.
Larson, V.E. and Griffin, B.M., 2013. Analytic upscaling of a local microphysics scheme. Part I: Derivation. Quarterly Journal of the Royal Meteorological Society, 139(670), pp.46-57.
Boutle, I.A., Abel, S.J., Hill, P.G. and Morcrette, C.J., 2014. Spatial variability of liquid cloud and rain: Observations and microphysical effects. Quarterly Journal of the Royal Meteorological Society, 140(679), pp.583-594.
Hill, P.G., Morcrette, C.J. and Boutle, I.A., 2015. A regime‐dependent parametrization of subgrid‐scale cloud water content variability. Quarterly Journal of the Royal Meteorological Society, 141(691), pp.1975-1986.
Citation: https://doi.org/10.5194/gmd-2022-34-RC1
Sabine Doktorowski et al.
Sabine Doktorowski et al.
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