Cloud-Type-dependent Mixed-Phase Cloud Climatology from CloudSat/CALIPSO Measurements

Yang, Kang; Wang, Zhien; Deng, Min

doi:10.5194/egusphere-2026-2435

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https://doi.org/10.5194/egusphere-2026-2435

Preprints

05 May 2026

| 05 May 2026

Cloud-Type-dependent Mixed-Phase Cloud Climatology from CloudSat/CALIPSO Measurements

Kang Yang, Zhien Wang, and Min Deng

Abstract. Mixed-phase clouds play a critical role in Earth’s radiation budget but remain a major source of uncertainty in climate models. Existing satellite climatologies mostly describe mixed-phase clouds in aggregate, without separating cloud types that differ in dynamics, vertical structure, spatial distribution, and microphysical properties. Here we use CloudSat/CALIPSO observations to develop a global, cloud-type-dependent climatology of mixed-phase cloud and examine its spatial, vertical, seasonal, and regional variations. Identified mixed-phase clouds have a global mean occurrence of 18.7 %. Stratus plus stratocumulus (St+Sc) dominates high-latitude mixed-phase occurrence, with local values exceeding 40 % over the Southern Ocean and the Greenland-Iceland-Norwegian seas, whereas altocumulus (Ac) and nimbostratus plus deep convection (Ns+DC) contribute most strongly in midlatitude storm-track regions and convectively active tropical regions. At a given cloud-top temperature, the dominant cloud phase differs substantially among cloud types and regions, indicating that cloud-top temperature alone does not uniquely determine mixed-phase occurrence or phase partitioning. Seasonal and surface contrasts are especially strong for St+Sc: in the NH 45–75° N band, monthly mean occurrence over open ocean increases from about 2–6 % in summer to 24–25 % in winter, whereas in the SH 45–75° S band St+Sc over open ocean reaches its annual minimum in austral summer but over sea ice reaches its minimum in austral winter. These results demonstrate the importance of cloud type for characterizing mixed-phase cloud climatology and provide observational constraints for evaluating the representation of mixed-phase clouds in climate models.

Received: 28 Apr 2026 – Discussion started: 05 May 2026

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Kang Yang, Zhien Wang, and Min Deng

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-2435', Anonymous Referee #1, 14 Jun 2026
This manuscript uses the 2B-CLDCLASS-LIDAR product (based on both CALIOP/CALIPSO and CPR/CloudSat) to classify mixed-phase cloud properties by “cloud types” defined by the satellite product during the period 2006-2010. Some of their key findings are: 1) in the tropics, Ac and Ns + DC clouds shift with the location of the storm tracks and ITCZ whereas 2) mid-latitude and polar Sc + St clouds are affected by atmosphere-ocean coupling, and 3) Cu cloud types are driven primarily by orographic convection. There is great value in classifying clouds by “cloud type” and the authors have carried out and presented a detailed analysis. The authors have also done a great job explaining most of the features they present and are quite careful to address uncertainty in their analysis as well. Mixed-phase clouds are challenging to observe and represent in models of all scales, and few analyses focus on “cloud type” classifications of mixed-phase clouds, especially on a global scale. This study is thus very valuable to the cloud and climate community. I only have a few suggestions regarding the manuscript: 1) though detailed, some of the written descriptions explaining the features of the plots are redundant and could be shortened to be more concise, 2) their explanation of features mostly focus on meteorological influences and do not say much about the role of aerosols, 3) the implications of their work can be strengthened. Overall, I recommend relatively minor revisions.

Specific comments:
It may be worth clarifying what “cloud type” means in the manuscript. It clearly follows the 2B-CLDCLASS-LIDAR classification, what worth explicitly defining that in the manuscript, and also how that contrasts with other definitions in the literature, e.g. “cloud regimes” defined by Cho et al. 2021 (https://journals.ametsoc.org/view/journals/apme/60/7/JAMC-D-20-0247.1.xml) using the MODIS dataset but also “weather states” defined by Tselioudis et al. 2021 (https://journals.ametsoc.org/view/journals/clim/34/17/JCLI-D-21-0076.1.xml). Are the cloud types in distinct updraft (e.g. omega500) categories?

Lines 38-46: This information is outdated. Many CMIP6 models tend to overestimate supercooled liquid in mixed-phase clouds. See e.g. Tan et al. (2025) https://www.nature.com/articles/s41612-025-00948-7

Lines 70-72: Another application of satellite observations to study mixed-phase clouds is to study their phase heterogeneity which is of great interest for accurately modelling their radiation and longevity (e.g. Coopman & Tan (2023) satellite mixed-phase cloud heterogeneity characterization (DARDAR) based grid cells classified as “stratiform” using MODIS cloud regimes https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL104977 )

Line 155: What months does the dataset start and end during the years 2006-2010?

It was stated that MODIS daytime LWP values were used in the analysis. But what if MODIS and 2B-CLDCLASS-LIDAR do not agree on the cloud phase? How do the authors determine which cloud phase is correct or do they reject that data point and exclude it from the analysis?

Figure 1: The “potential” mixed-phase cloud definition based simply on temperature range does not seem useful to show

Passage describing Figure 8: this is somewhat redundant with previous passages. Can this be condensed to be more concise especially since the word length is somewhat long (main text well exceed 700 lines)?

Methods and uncertainty: What is the penetration depth of both lidar and radar? Can the authors provide statistics on what percentage of clouds were multilayer and how retrievals were treated in their analysis? Were any quality control flags considered and filtered out in their analysis?

Line 315: I do not consider IWP a cloud “microphysical” property. It’s a macrophysical property.

Figure 3: Why do Cu clouds consistency have similar LWP as Ns + DC clouds (a2, b2, c2)? I would expect Cu clouds to have much lower LWP than those of Ns + DC clouds.

332-333: Could the higher IWP in the NH compared to SH be due to the presence of INPs? See e.g. the satellite study of Tan et al. 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JD021333) on temperature vs aerosol (INP) effects. “Surface-type” and “aerosol loading” on line 335 are mentioned, but that can also be connected to not only aerosol loading but also aerosol type (whether they act as CCN or INPs).

Lines 450-453: This summary only addresses meteorological effects on cloud type formation but not aerosol effects. Please also consider aerosol effects as well.

Figure 4: I found this plot confusing not only because of how the double x-axes should be interpreted in relation to each other but also because I am unsure why only As + Ac and St + Sc were arbitrarily selected for the “fraction” axis and the other categories ignored or not grouped together.

In Figures 4, 6, 8, and 9 I recommend replacing “T” with “CTT” so that the reader is clear that this is cloud-top temperature and not the mean cloud layer temperature at the resolution of the satellite product.

Line 577-579: Are these MCAO clouds?

In my opinion, Figure 7 does not add much more novel/interesting information to the analysis/manuscript and recommend moving it to the Supporting Information.

Figure 9 caption: It would be clearer and more concise to write that the caption here is the same as that for Figure 8 except for 45S-75S.

Line 724-730: The implications of the results are discussed here, though the information it draw on is perhaps outdated about the cloud phase bias in global climate models, but also does not address novel ways that the dataset can be used. One example is a new method that was developed to diagnose how changing cloud types/regimes/weather states influence cloud feedback that can be applied to evaluate climate models as well (Zelinka et al. 2023 https://link.springer.com/article/10.1007/s00382-022-06488-7 and Tan et al. 2024 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JD040540)

Can the authors comment on future satellite missions such as VIIRS/EarthCARE in the context of extending their record or what the newer generation of satellites can reveal in terms of cloud type-based analyses of mixed-phase cloud properties?
Citation: https://doi.org/10.5194/egusphere-2026-2435-RC1
- AC1: 'Reply on RC1', Kang Yang, 11 Aug 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2435/egusphere-2026-2435-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2026-2435-AC1
RC2:
'Comment on egusphere-2026-2435', Anonymous Referee #2, 29 Jun 2026
In this paper, a global cloud-type-dependent climatology of mixed-phase clouds is developed using the CloudSat/CALIPSO observations, and the spatial, vertical, seasonal, and regional variations of these clouds is analyzed.
The analysis is useful in understanding the global distribution of mixed-phase clouds, but there are several major technique flaws in the paper. The paper might be accepted after addressing the following comments:
In this paper, mixed-phase clouds are grouped into five cloud-type categories: As, Ac, St+Sc, Cu, and Ns+DC, while cirrus clouds are excluded in this study. However, there are a lot of multiple layer clouds in the atmosphere, e. g., a cirrus cloud layer overlapping a cumulus cloud layer. So how are these multiple layer clouds handled in this paper?

The MODIS cloud phase is used in this paper according to Line 137. However, MODIS provides the cloud top phase instead of the total cloud-layer phase, and a lot of mixed-layer convective clouds are identified as ice clouds due to their ice top (for example, Wang et al., doi: 10.1109/TGRS.2023.3317532). How is this issue handled in this paper?

The uncertainty induced by cloud-type classification algorithm should be evaluated or at least discussed. In classic land observations, the cloud type is partially determined based on their spatial distribution pattern, but the CloudSat 2B-CLDCLASS-LIDAR product used in this study (Line 111) does not use the 2-dimensional spatial information of clouds, so the CloudSat 2B-CLDCLASS-LIDAR cloud type product is likely to be different from ground observations and passive satellites.
Citation: https://doi.org/10.5194/egusphere-2026-2435-RC2
- AC2: 'Reply on RC2', Kang Yang, 11 Aug 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2435/egusphere-2026-2435-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2026-2435-AC2

Kang Yang, Zhien Wang, and Min Deng

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Short summary

Mixed-phase clouds, which contain both ice crystals and supercooled liquid droplets, strongly influence Earth’s radiation budget but remain difficult to represent in climate models. Using global satellite observations, we show that they vary greatly by cloud type, with distinct spatial distributions, vertical structures, and seasonal and surface contrasts. These results provide new observational constraints for improving model representation of mixed-phase clouds.


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