Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions

Bartolomé García, Irene; Sourdeval, Odran; Spang, Reinhold; Krämer, Martina

doi:https://doi.org/10.5194/egusphere-2023-754

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https://doi.org/10.5194/egusphere-2023-754

Preprints

22 May 2023

| 22 May 2023

Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions

Irene Bartolomé García, Odran Sourdeval, Reinhold Spang, and Martina Krämer

Abstract. The cloud particle size distribution (PSD) is a key parameter for the retrieval of micro-physical and optical properties from remote sensing instruments, which in turn are necessary for determining the radiative effect of clouds. Current representations of PSDs for ice clouds rely on parameterizations that were largely based on in situ measurements where the distribution of small ice crystals were uncertain. This makes current parameterizations deficient to simulate remote sensing observations sensitive to small ice, such as from lidar and thermal infrared instruments. In this study we fit the in situ PSDs of ice crystals from the JULIA (JÜLich In situ Aircraft data set) database, which consists of 11 campaigns covering the tropics, mid-latitudes and the Arctic, consistently processes and considered more robust in their measurements of small ice. For the fitting, we implement an established approach to PSD parameterizations, which consists in finding an adequate set of parameters for a modified gamma function after normalization of both PSD axes. These parameters are constrained to match in-situ mea surements when predicting microphysical properties from the PSDs, via a cost function minimization method. We selected the ice water content and the ice crystal number concentration, which are currently key parameters for modern satellite retrievals and model microphysics schemes. We found that a bimodal parameterization yields better results than a monomodal one. The bimodal parameterization has a lower spread for almost all ice crystal sizes over the entire range of analyzed temperatures and fits better the observations, especially for particles between 20 and about 110 μm at temperatures between −60 and −20 °C. For this temperature range, the root mean square error for the retrieved N_ice is reduced from 0.36 to 0.20. This demonstrates a clear advantage to considering the bimodality of PSDs, e.g. for satellite retrievals.

Received: 17 Apr 2023 – Discussion started: 22 May 2023

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06 Feb 2024

Technical note: Bimodal parameterizations of in situ ice cloud particle size distributions

Irene Bartolomé García, Odran Sourdeval, Reinhold Spang, and Martina Krämer

Atmos. Chem. Phys., 24, 1699–1716, https://doi.org/10.5194/acp-24-1699-2024,https://doi.org/10.5194/acp-24-1699-2024, 2024

Short summary

Irene Bartolomé García, Odran Sourdeval, Reinhold Spang, and Martina Krämer

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-754', Anonymous Referee #1, 03 Jun 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-754-RC1
- AC1: 'Reply on RC1', Irene Bartolome Garcia, 28 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-754-AC1
RC2:
'Second review of Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions', Anonymous Referee #2, 19 Jun 2023

Review of Tecnical note: Bimodal Parameterizations of in-situ Ice Clouds Particle Size Distributions

Authors: Irene Bartolomé Garcia et al.

The authors are proposing a new technic for the parameterization of ice particle size distributions with gamma normalized size distributions as in Delanoë et al 2014. But, they are using two normalized distributions, one for Diameters smaller than 50µm and one for Diameters larger than 50µm, instead of one for all spectrum of size of measured ice crystals. They are comparing their retrieved ice PSD with the ones of retrieved with the former methods i.e Delanoë et al., (2014 and 2005) and applied to their dataset. Globally, overall their dataset (Figure 4 and 6) the new method seems to be more accurate to retrieve small ice crystals concentration. They motivate their study, on the fact that concentrations of small ice crystals are too often neglected or not considered, impacting accuracy of retrieval methods for clouds properties. The main reason being the measurment uncertainty of small ice crystals.

This is not the first study that offers a parameterization of ice PSD with two modes (two gamma distributions cf. Field et al., 2007). However, this is the first in my knowledge that includes ice particles since 3 µm.

Major Comments:

Bimodality:

Are you assuming that all ice PSD in your ice clouds are bimodal ?

Line 53: You are introducing frequencies of bimodality in the discussion, would it be consistent to divide the distribution in two modes if there is only one mode ?

Hu et al 2022 have developped a methode to estimate the number of modes in ice PSD. They, showed that at coldest temperature (-50°C to -40°C) ice PSD are 60% of the time one mode; except for IWC> 1.5 g.m3.

Why there should be bimodality ?

In the introduction you are linking the shape of the ice PSD and the growth process. Then, it is shortly discussed in section 4.3. You are assuming that it is the difference of newly formed ice particle against sedimenting sizes. I encourage you to improve the discussion on this topic. Because, if the evolution of the size of the hydrometeors is linked to the growth rate: vapor diffusion, aggregation and riming. Then, If there is more than one growth process (without counting secondary ice production) there should be more than one mode in ice PSD !?

Then, you choose a cutting diameter of 50 µm, do you mean that the division of the growth processes such growth by vapor diffusion against growth by aggregation (or sedimentation) is here. Can you give a reference or an argument, assumpltion maybe, for this cutting diameter ? If I observe one column of few hundreds of micron wasn’t it a monocrystal of few microns in its past ?

You are citing Field et al., (2007) that also proposed a bimodal normalized parameterization, but as function of optical maximum length and effective radius. However, they did not use concentrations of small ice under 100 microns. What would be the impact by taking the concentration from 3µm (this would be maybe to consider for a second part publication).

Melting diameter and mass-size relations:

To retrieve the metling diameter you are using a mass-dimension relationship used in Krämer et al., (2016). In this later study, it is justified for temperature less than -38°C (235.15K) in cirrus cloud and based on former studies. Is it consistent to use it for T> 235K, knowing that few studies with direct measurment of IWC have shown an impact of the temperature on the m-D coefficients in ice clouds.

I would like to see IWC retrieved with this m-D and original ice PSD, compared with the measured IWC available in your dataset; and also as function of temperature. Why not use, your own retrieved m-D from the dataset you are using and see the impact on the Nice. And also with Brown and Francis as in the original version (see first review comment).

Figure 4 and 5, I would consider plotting the error in percent regarding original concentrations instead of pure concentration, with a recall of your measurment uncertainties especially for smaller size. Small crystals and large ones do not have the same order of concentrations ; this is important.

Figure 5 only, AS your study is questioning the retrieval of small ice concentration, I would summarize it, for small and large ice particles i.e. below and above the cutting diameter, instead of showing it as function of size bins.

Line 243 : I do not think that IWC and ice PSD can be dissociated. Can you be more clear on your description of the error of IWC, dimension you are using instead of log, , rate of underestimation and overestimation. It does not talk for someone who is not a specialist.

“IWC is sensitive to large particles” : it is more complicated than that. Where do you define large ice particles hundred of microns, millimeter … The spectrum of all ice cristals goes from few microns to centimeter in some case. Then, C, S and X band radars would be enougth to retrieve IWC in cloud. For a fact, W band and Ka band do a better job i.e Delanoë et al., (2005 & 2014) which are less sensitive to very large ice crystals.

Remarks on the conclusion ;

The methods of Delanoë et al ., is developped for all ice clouds, while I understand that the dataset used in this study is mainly made with sampling in cirrus clouds (except for ACRIDION campaign). What about the temperature below -20°C ? Can we generalize your conclusions to all ice clouds and to all range of temperature ? If yes Why ?

Maybe you can recall the definition of cirrus clouds you are using, does it agree with the one in Heymsfield et al., (2017) and the AMS glossary for example ?

I suggest these references to help the discussion :

Korolev, A., Heckman, I., Wolde, M., Ackerman, A.S., Fridlind, A.M., Ladino, L.A., Lawson, R.P., Milbrandt, J., Williams, E., 2020. A new look at the environmental conditions favorable to secondary ice production. Atmospheric Chemistry and Physics 20, 1391–1429. https://doi.org/10.5194/acp-20-1391-2020

Heymsfield, A.J., Schmitt, C., Bansemer, A., 2013. Ice Cloud Particle Size Distributions and Pressure-Dependent Terminal Velocities from In Situ Observations at Temperatures from 0° to −86°C. J. Atmos. Sci. 70, 4123–4154. https://doi.org/10.1175/JAS-D-12-0124.1

Schmitt, C.G., Heymsfield, A.J., 2010. The Dimensional Characteristics of Ice Crystal Aggregates from Fractal Geometry. Journal of the Atmospheric Sciences 67, 1605–1616. https://doi.org/10.1175/2009JAS3187.1

Heymsfield, A.J., Krämer, M., Luebke, A., Brown, P., Cziczo, D.J., Franklin, C., Lawson, P., Lohmann, U., McFarquhar, G., Ulanowski, Z., Tricht, K.V., 2017. Cirrus Clouds. Meteorological Monographs 58, 2.1-2.26. https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1

Citation: https://doi.org/10.5194/egusphere-2023-754-RC2
- AC2: 'Reply on RC2', Irene Bartolome Garcia, 28 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-754-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-754', Anonymous Referee #1, 03 Jun 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-754-RC1
- AC1: 'Reply on RC1', Irene Bartolome Garcia, 28 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-754-AC1
RC2:
'Second review of Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions', Anonymous Referee #2, 19 Jun 2023

Review of Tecnical note: Bimodal Parameterizations of in-situ Ice Clouds Particle Size Distributions

Authors: Irene Bartolomé Garcia et al.

The authors are proposing a new technic for the parameterization of ice particle size distributions with gamma normalized size distributions as in Delanoë et al 2014. But, they are using two normalized distributions, one for Diameters smaller than 50µm and one for Diameters larger than 50µm, instead of one for all spectrum of size of measured ice crystals. They are comparing their retrieved ice PSD with the ones of retrieved with the former methods i.e Delanoë et al., (2014 and 2005) and applied to their dataset. Globally, overall their dataset (Figure 4 and 6) the new method seems to be more accurate to retrieve small ice crystals concentration. They motivate their study, on the fact that concentrations of small ice crystals are too often neglected or not considered, impacting accuracy of retrieval methods for clouds properties. The main reason being the measurment uncertainty of small ice crystals.

This is not the first study that offers a parameterization of ice PSD with two modes (two gamma distributions cf. Field et al., 2007). However, this is the first in my knowledge that includes ice particles since 3 µm.

Major Comments:

Bimodality:

Are you assuming that all ice PSD in your ice clouds are bimodal ?

Line 53: You are introducing frequencies of bimodality in the discussion, would it be consistent to divide the distribution in two modes if there is only one mode ?

Hu et al 2022 have developped a methode to estimate the number of modes in ice PSD. They, showed that at coldest temperature (-50°C to -40°C) ice PSD are 60% of the time one mode; except for IWC> 1.5 g.m3.

Why there should be bimodality ?

In the introduction you are linking the shape of the ice PSD and the growth process. Then, it is shortly discussed in section 4.3. You are assuming that it is the difference of newly formed ice particle against sedimenting sizes. I encourage you to improve the discussion on this topic. Because, if the evolution of the size of the hydrometeors is linked to the growth rate: vapor diffusion, aggregation and riming. Then, If there is more than one growth process (without counting secondary ice production) there should be more than one mode in ice PSD !?

Then, you choose a cutting diameter of 50 µm, do you mean that the division of the growth processes such growth by vapor diffusion against growth by aggregation (or sedimentation) is here. Can you give a reference or an argument, assumpltion maybe, for this cutting diameter ? If I observe one column of few hundreds of micron wasn’t it a monocrystal of few microns in its past ?

You are citing Field et al., (2007) that also proposed a bimodal normalized parameterization, but as function of optical maximum length and effective radius. However, they did not use concentrations of small ice under 100 microns. What would be the impact by taking the concentration from 3µm (this would be maybe to consider for a second part publication).

Melting diameter and mass-size relations:

To retrieve the metling diameter you are using a mass-dimension relationship used in Krämer et al., (2016). In this later study, it is justified for temperature less than -38°C (235.15K) in cirrus cloud and based on former studies. Is it consistent to use it for T> 235K, knowing that few studies with direct measurment of IWC have shown an impact of the temperature on the m-D coefficients in ice clouds.

I would like to see IWC retrieved with this m-D and original ice PSD, compared with the measured IWC available in your dataset; and also as function of temperature. Why not use, your own retrieved m-D from the dataset you are using and see the impact on the Nice. And also with Brown and Francis as in the original version (see first review comment).

Figure 4 and 5, I would consider plotting the error in percent regarding original concentrations instead of pure concentration, with a recall of your measurment uncertainties especially for smaller size. Small crystals and large ones do not have the same order of concentrations ; this is important.

Figure 5 only, AS your study is questioning the retrieval of small ice concentration, I would summarize it, for small and large ice particles i.e. below and above the cutting diameter, instead of showing it as function of size bins.

Line 243 : I do not think that IWC and ice PSD can be dissociated. Can you be more clear on your description of the error of IWC, dimension you are using instead of log, , rate of underestimation and overestimation. It does not talk for someone who is not a specialist.

“IWC is sensitive to large particles” : it is more complicated than that. Where do you define large ice particles hundred of microns, millimeter … The spectrum of all ice cristals goes from few microns to centimeter in some case. Then, C, S and X band radars would be enougth to retrieve IWC in cloud. For a fact, W band and Ka band do a better job i.e Delanoë et al., (2005 & 2014) which are less sensitive to very large ice crystals.

Remarks on the conclusion ;

The methods of Delanoë et al ., is developped for all ice clouds, while I understand that the dataset used in this study is mainly made with sampling in cirrus clouds (except for ACRIDION campaign). What about the temperature below -20°C ? Can we generalize your conclusions to all ice clouds and to all range of temperature ? If yes Why ?

Maybe you can recall the definition of cirrus clouds you are using, does it agree with the one in Heymsfield et al., (2017) and the AMS glossary for example ?

I suggest these references to help the discussion :

Korolev, A., Heckman, I., Wolde, M., Ackerman, A.S., Fridlind, A.M., Ladino, L.A., Lawson, R.P., Milbrandt, J., Williams, E., 2020. A new look at the environmental conditions favorable to secondary ice production. Atmospheric Chemistry and Physics 20, 1391–1429. https://doi.org/10.5194/acp-20-1391-2020

Heymsfield, A.J., Schmitt, C., Bansemer, A., 2013. Ice Cloud Particle Size Distributions and Pressure-Dependent Terminal Velocities from In Situ Observations at Temperatures from 0° to −86°C. J. Atmos. Sci. 70, 4123–4154. https://doi.org/10.1175/JAS-D-12-0124.1

Schmitt, C.G., Heymsfield, A.J., 2010. The Dimensional Characteristics of Ice Crystal Aggregates from Fractal Geometry. Journal of the Atmospheric Sciences 67, 1605–1616. https://doi.org/10.1175/2009JAS3187.1

Heymsfield, A.J., Krämer, M., Luebke, A., Brown, P., Cziczo, D.J., Franklin, C., Lawson, P., Lohmann, U., McFarquhar, G., Ulanowski, Z., Tricht, K.V., 2017. Cirrus Clouds. Meteorological Monographs 58, 2.1-2.26. https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1

Citation: https://doi.org/10.5194/egusphere-2023-754-RC2
- AC2: 'Reply on RC2', Irene Bartolome Garcia, 28 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-754-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Irene Bartolome Garcia on behalf of the Authors (28 Sep 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (19 Oct 2023) by Barbara Ervens

RR by Andrew Heymsfield (28 Oct 2023)

RR by Anonymous Referee #2 (03 Nov 2023)

Suggestions for revision or reasons for rejection

Section 4.2 and 4.3: I found it difficult to understand what is exactly Julia 1M. I understand it is one mode distribution fitting, but what makes it different regards to D05 or D14 ? Can you clarify please.

section 4.3
lines 230 : this is not well said. If in cirrus (your definition?) the aggregation processe play no role. Hence, there would not need to use two distribution (or two modes) to fit measured PSD in your dataset. But, later you say that bimodality start to play an important role for T> -60°C. I wonder about the cause of the physic processes that lead to two distributions, if it is not vapor diffusion on one side and aggregation on the other side (riming being impossible if no supercooled water) ?

lines244 : Indeed, there can be many orders of magnitude between the concentration of small and large ice hydrometeors. Do large errors for large hydrometeors are less important ? Maybe it needs more explanation.

Notes about Figure 5, all parameterisations are not accurate for large hydrometeors. however, surprisingly your new parameterisation that is supposed to improve the representation of small ice crystals show more benefits in the modeling of large hdyrometeors.This is well highlighted looking median error for D>50µm in all range of temperatures. While, median error for D<50 are similar between D05 and Julia 2M. For the case of small ice crystals, bimodal parameterisations start to pay for the warmer temperature i.e. -30 TO -20°C.
Also, for temperature ranges warmer than -50°C, the Julia2M improves the representation of ice crystals from 50(20 you said) to 100microns (the modes of large hydrometeors !?)...
So adding a mode of small ice crystals do not benefit only small ice crystals but also larger !!!
This is really interesting when taking into account the measurement uncertainties that are commonly admited by the community (Baumgardner 2017) for small and large ice crystals : 100% for D<100µm and 50% for D>100µm.
Moreover, these former parameterizations do not use concentrations for D<50µm.
Clearly, there is a need to use more than one distributions to model the concentrations of hydrometeors from pristine ice to aggregates.

lines 285 : " it adjusts better to the bimodal shape of the PSDs " I would add when "it occurs".

Hide

ED: Publish subject to minor revisions (review by editor) (07 Nov 2023) by Barbara Ervens

AR by Irene Bartolome Garcia on behalf of the Authors (30 Nov 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (06 Dec 2023) by Barbara Ervens

Dear Authors,
many thanks for addressing the remaining referee comments. I am happy to accept your paper for publication in ACP.
Prior to uploading your files for paper production, please fix the minor/technical issues as listed below.
Sincerely,
Barbara Ervens
=========================
Line numbers refer to the manuscript version without annotations.
l. 63: analyzes

l. 71: Please define IWC here (unless I missed it before)

l. 86: consisted of

Table1: State at least in the caption what ‘ranges’ refer to (e.g., ‘...of ice particle diameters’) so that the table is more self-explanatory

l. 128, 131, 202, 202 (and possibly other instances): ‘warm temperature’ is scientifically not fully correct:
temperature denotes a value – which can be high or low; warm/cold describes an intensive property of matter (e.g. gases, ice, water). Thus, high temperature leads to warming.

l. 131: can you specify the temperature range for which it is valid?

l. 135: should it be ‘using’ and ‘fitting’ to logically continue the list of steps starting with ‘computing’ ?

l. 140: Is it relevant that Dm is in units of meters? This equation is generally valid, independently of units, provided consistent units, e.g. mass in kg and density in kg/m^3.
I understand that the coefficients alpha, beta might have been derived using SI units (kg, m, ...) but this could be generally stated around Eq.-6.

l. 160 ff: The equations should be numbered with separate numbers, i.e. 7, 8, 9. Make sure to refer to them accordingly in the text (e.g. l. 207)

l. 175/6: The new sentence does not read well. May be better something like: In the temperature range just below 235 K, the clouds may originate as mixed-phase clouds ascending from lower altitudes, undergoing complete glaciation at ≥ 235 K.

l. 181: Either ‘in a temperature range of’ or ‘at temperatures’

Table 2: Please improve the table caption so that the table is more self-explanatory

l. 191: Either ‘another indicator of cirri that have... ‘(https://www.merriam-webster.com/dictionary/cirrus) or ‘another indicator of cirrus clouds...’

l. 206: define (remove ‘d’)

l. 229: each of them

Figure 3, caption: What do you mean by ‘left triangle’? The symbol for the new parameterization (JULIA 1 M) looks to me like a circle.

l. 233: ...do not play any role

l. 235/6: replace ‘and’ by ‘whereas’ to avoid ambiguity:
due to depositional growth WHEREAS sedimentation and aggregation are less significant.

l. 238/9: The structure of the new sentence does not seem right (verb missing?). Maybe better:
Initially, the few heterogeneously nucleated ice crystals may grow to larger sizes, followed by ...

l. 254: Better: From Fig. 4a and 4c... (to avoid confusion as you refer to Fig 2 in the previous sentence.. which doesn’t even have a, b, c...).

l. 256, 260: panel b, d should be Fig 4b, 4d etc (see previous comment)

l. 271: small and large modes (add ‘s’)

l. 281: ‘subset’ cannot be used as a verb. Better:
Binning this data into 10-K temperature intervals between -90C and -60 C...

l. 284/5: Please clarify: (i) what do you mean by ‘temperature ranges’ (also Fig 5 caption)? Temperature intervals (or ‘bins’)?
(ii) intervals of 10 degree C and 10 K are the same. What exactly did you compare here?

l. 290: do you mean indeed ‘when’ or rather ‘if’ (implying that it is not always the case)?

Figure 4, caption: Is there a word missing at the end? Over the complete size RANGE?

l. 301/2: The new sentence does not read well. Please improve. My suggestion (check whether reflects the intended meaning!):
Considering a second mode improves the PSD prediction of both small and large ice crystals despite the large measurement uncertainties associated with the latter.

Hide

AR by Irene Bartolome Garcia on behalf of the Authors (13 Dec 2023) Author's response Manuscript

Journal article(s) based on this preprint

06 Feb 2024

Technical note: Bimodal parameterizations of in situ ice cloud particle size distributions

Irene Bartolomé García, Odran Sourdeval, Reinhold Spang, and Martina Krämer

Atmos. Chem. Phys., 24, 1699–1716, https://doi.org/10.5194/acp-24-1699-2024,https://doi.org/10.5194/acp-24-1699-2024, 2024

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Irene Bartolomé García, Odran Sourdeval, Reinhold Spang, and Martina Krämer

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How many ice crystals of each size are in a cloud is a key parameter for the retrieval of cloud properties. The distribution of ice crystals is obtained from in situ measurements and used to create parameterizations that can be used when analyzing the remote sensing data. Current parameterizations are based in datasets that do not include realible measurements of small crystals, but in our study we use a dataset that includes very small ice crystals to improve these parameterizations.


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Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions

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