Modifying the Abdul-Razzak &amp; Ghan aerosol activation parameterization (version ARG2000) impacts simulated cloud radiative effects (shown in the UK Met Office Unified Model, version 13.0)

Ghosh, Pratapaditya; Evans, Katherine J.; Grosvenor, Daniel P.; Kang, Hyun-Gyu; Mahajan, Salil; Xu, Min; Zhang, Wei; Gordon, Hamish

doi:https://doi.org/10.5194/egusphere-2024-2423

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https://doi.org/10.5194/egusphere-2024-2423

Preprints

17 Oct 2024

| 17 Oct 2024

Modifying the Abdul-Razzak & Ghan aerosol activation parameterization (version ARG2000) impacts simulated cloud radiative effects (shown in the UK Met Office Unified Model, version 13.0)

Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon

Abstract. The representation of aerosol activation is a key source of uncertainty in global composition-climate model simulations of aerosol-cloud interactions. The Abdul-Razzak and Ghan (ARG) activation parameterization is used in several global and regional models that employ modal aerosol microphysics schemes. In this study, we investigate the ability of the ARG parameterization to reproduce simulations with a cloud parcel model, and find its performance is sensitive to the geometric standard deviations (widths) of the lognormal aerosol modes. We recommend adjustments to three constant parameters in the ARG equations, which improve the performance of the parameterization for small mode widths and its ability to simulate activation in polluted conditions. For the accumulation mode width of 1.4 used in the Met Office Unified Model (UM), our modifications decrease the mean bias in the activated fraction of aerosols compared to a cloud parcel model from -6.6 % to +1.2 %. We implemented our improvements in the UM and compared simulated global cloud droplet concentrations with satellite observations. The simulated cloud radiative effect changes by -1.43 Wm^-2 and aerosol indirect radiative forcing over the industrial period changes by -0.10 Wm^-2.

Received: 31 Jul 2024 – Discussion started: 17 Oct 2024

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Journal article(s) based on this preprint

11 Aug 2025

Assessing modifications to the Abdul-Razzak and Ghan aerosol activation parameterization (version ARG2000) to improve simulated aerosol–cloud radiative effects in the UK Met Office Unified Model (UM version 13.0)

Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon

Geosci. Model Dev., 18, 4899–4913, https://doi.org/10.5194/gmd-18-4899-2025,https://doi.org/10.5194/gmd-18-4899-2025, 2025

Short summary

Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-2423', Anonymous Referee #1, 28 Nov 2024
This work presents an update to the Abdul-Razzak and Ghan (ARG) parametrization for aerosol activation. ARG is a well-established and widely used parametrization in the regional and global modelling community, so this is a very useful development with a large application potential.
The paper is clearly written and the method is documented in detail, facilitating the implementation by other modelling groups. The improved parametrization is then tested and evaluated in a state-of-the-art global aerosol-climate model, demonstrating a generally improved model performance for CDNC against satellite data.
The paper is definitely worth of publication in GMD. Just a few questions and remarks, which the authors may consider before publication:
The global model simulations performed for this study cover only 1 year (2014), so it is not possible to quantify the variability of the results. Could you briefly comment on that, also based on your previous experience with the model?

Sect. 3.2: it would be good to complement the evaluation with the RMSE in addition to the NMB, as the latter is affected by the compensation of over/underestimation.

Would it be reasonable to also look at other variables, like liquid water path, cloud cover and precipitation? Do you expect changes in these variables with the improved ARG parametrization?

Minor suggestions
Line 10: you could also provide the changes of CRE and aerosol forcing in relative terms.

Line 40: in the conditions used, can you be more specific?

Line 52: maybe add that targeting the accumulation mode is justified since this is the most relevant mode for activation?

Line 63: citing the original Köhler theory would be appropriate in this context.

Line 90: could you add a reference to the Latin hypercube sampling method?

Table 2: in the list of parameters, Aitken should appear before accumulation.

Table 3: I would sort the columns in a way that the old and new values of a given parameter appear next to each other, otherwise it is quite hard to compare them.

Figure 1 caption: please use the SI units for pressure (Pa) instead of atm throughout the paper.

Figs. S8 and S9: the captions refer to "annual averages" and "January 2014", but the plot show DJF and JJA, respectively (the main text too). Please check.

Line 253: you could mention the IPCC-AR6 ranges and the recent Bellouin et al. (2020, doi:10.1029/2019RG000660) results here, to support the statement that your RFs are reasonable.

Line 269: different accumulation mode -> different aerosol accumulation mode.
Citation: https://doi.org/10.5194/egusphere-2024-2423-RC1
- AC1: 'Reply on RC1', Pratapaditya Ghosh, 21 Mar 2025
  
  Please see the attached file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2423-AC1
RC2:
'Comment on egusphere-2024-2423', Anonymous Referee #2, 29 Jan 2025

Review of “Modifying the Abdul-Razzak & Ghan aerosol activation parameterization (version ARG2000) impacts simulated cloud radiative effects (shown in the UK Met Office Unified Model, version 13.0)” by Ghosh et al. (2024)
This paper used a parcel model to explore the parameter space and optimize the performance of the widely used Abdul-Razzak & Ghan aerosol activation scheme (ARG2000). The research question is well-defined and the comparison with other schemes and parcel model results is comprehensive. This paper addresses an important topic in aerosol-cloud interaction. However, the evaluation within the global model context could benefit from additional rigor. I recommend publication in GMD after addressing the comments below.
General Comment on Nd Comparison: While the comparison between model output Nd and MODIS Nd is interesting, it may be more complex due to the various differences between the model and satellite data. These differences make it challenging to determine whether the bias in the model with the old scheme is ‘underestimated’ or ‘overestimated,’ and consequently, whether the reported ‘improvements’ or ‘degradations’ are fully reliable. I believe the comparison using the MODIS simulator results might be more appropriate than using the model’s Nd output.
L210: It would be helpful to provide more details about how the model’s Nd is calculated. Specifically, how is the liquid cloud-top defined in the model, and what methodology is used to derive Nd from this definition?
L215: The MODIS Nd estimates from Grosvenor et al. (2018) involve several thresholds to sample clouds. I would recommend clarifying whether similar thresholds are applied to your model results. If not, it would be valuable to discuss how the sampling differences due to post-processing thresholds and simulator assumptions may impact the comparison presented in Figure 3. Addressing these differences will enhance the clarity and reliability of the evaluation.
Section 3.4: The conclusion drawn in this section is somewhat unclear. It would be helpful to either explicitly state the implications of the findings or, if they are not central to the paper’s argument, consider removing or rewording this part. This would enhance the clarity and focus of the discussion.
L85: To improve clarity, it would be helpful to show the formula of non-dimensional variables η and ζ. This would allow readers to better understand the discussion around kinetic limitations and how these variables related to Nd and w.
L130: Could you clarify the nudging relaxation time scale?
L174: ‘AAF’ should be changed into ‘TAF’.
L202: the modification to the constant ‘p’ does not fix the underlying problem but alleviates it. It would be insightful to discuss why the improvement appears to be limited and consider other possible explanations or avenues for further improvement.
L241: ‘Figure S12’ should be changed into ‘Figure 4’.

Citation: https://doi.org/10.5194/egusphere-2024-2423-RC2
- AC2: 'Reply on RC2', Pratapaditya Ghosh, 21 Mar 2025
  
  Please see the attached file
  
  Citation: https://doi.org/10.5194/egusphere-2024-2423-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-2423', Anonymous Referee #1, 28 Nov 2024
This work presents an update to the Abdul-Razzak and Ghan (ARG) parametrization for aerosol activation. ARG is a well-established and widely used parametrization in the regional and global modelling community, so this is a very useful development with a large application potential.
The paper is clearly written and the method is documented in detail, facilitating the implementation by other modelling groups. The improved parametrization is then tested and evaluated in a state-of-the-art global aerosol-climate model, demonstrating a generally improved model performance for CDNC against satellite data.
The paper is definitely worth of publication in GMD. Just a few questions and remarks, which the authors may consider before publication:
The global model simulations performed for this study cover only 1 year (2014), so it is not possible to quantify the variability of the results. Could you briefly comment on that, also based on your previous experience with the model?

Sect. 3.2: it would be good to complement the evaluation with the RMSE in addition to the NMB, as the latter is affected by the compensation of over/underestimation.

Would it be reasonable to also look at other variables, like liquid water path, cloud cover and precipitation? Do you expect changes in these variables with the improved ARG parametrization?

Minor suggestions
Line 10: you could also provide the changes of CRE and aerosol forcing in relative terms.

Line 40: in the conditions used, can you be more specific?

Line 52: maybe add that targeting the accumulation mode is justified since this is the most relevant mode for activation?

Line 63: citing the original Köhler theory would be appropriate in this context.

Line 90: could you add a reference to the Latin hypercube sampling method?

Table 2: in the list of parameters, Aitken should appear before accumulation.

Table 3: I would sort the columns in a way that the old and new values of a given parameter appear next to each other, otherwise it is quite hard to compare them.

Figure 1 caption: please use the SI units for pressure (Pa) instead of atm throughout the paper.

Figs. S8 and S9: the captions refer to "annual averages" and "January 2014", but the plot show DJF and JJA, respectively (the main text too). Please check.

Line 253: you could mention the IPCC-AR6 ranges and the recent Bellouin et al. (2020, doi:10.1029/2019RG000660) results here, to support the statement that your RFs are reasonable.

Line 269: different accumulation mode -> different aerosol accumulation mode.
Citation: https://doi.org/10.5194/egusphere-2024-2423-RC1
- AC1: 'Reply on RC1', Pratapaditya Ghosh, 21 Mar 2025
  
  Please see the attached file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2423-AC1
RC2:
'Comment on egusphere-2024-2423', Anonymous Referee #2, 29 Jan 2025

Review of “Modifying the Abdul-Razzak & Ghan aerosol activation parameterization (version ARG2000) impacts simulated cloud radiative effects (shown in the UK Met Office Unified Model, version 13.0)” by Ghosh et al. (2024)
This paper used a parcel model to explore the parameter space and optimize the performance of the widely used Abdul-Razzak & Ghan aerosol activation scheme (ARG2000). The research question is well-defined and the comparison with other schemes and parcel model results is comprehensive. This paper addresses an important topic in aerosol-cloud interaction. However, the evaluation within the global model context could benefit from additional rigor. I recommend publication in GMD after addressing the comments below.
General Comment on Nd Comparison: While the comparison between model output Nd and MODIS Nd is interesting, it may be more complex due to the various differences between the model and satellite data. These differences make it challenging to determine whether the bias in the model with the old scheme is ‘underestimated’ or ‘overestimated,’ and consequently, whether the reported ‘improvements’ or ‘degradations’ are fully reliable. I believe the comparison using the MODIS simulator results might be more appropriate than using the model’s Nd output.
L210: It would be helpful to provide more details about how the model’s Nd is calculated. Specifically, how is the liquid cloud-top defined in the model, and what methodology is used to derive Nd from this definition?
L215: The MODIS Nd estimates from Grosvenor et al. (2018) involve several thresholds to sample clouds. I would recommend clarifying whether similar thresholds are applied to your model results. If not, it would be valuable to discuss how the sampling differences due to post-processing thresholds and simulator assumptions may impact the comparison presented in Figure 3. Addressing these differences will enhance the clarity and reliability of the evaluation.
Section 3.4: The conclusion drawn in this section is somewhat unclear. It would be helpful to either explicitly state the implications of the findings or, if they are not central to the paper’s argument, consider removing or rewording this part. This would enhance the clarity and focus of the discussion.
L85: To improve clarity, it would be helpful to show the formula of non-dimensional variables η and ζ. This would allow readers to better understand the discussion around kinetic limitations and how these variables related to Nd and w.
L130: Could you clarify the nudging relaxation time scale?
L174: ‘AAF’ should be changed into ‘TAF’.
L202: the modification to the constant ‘p’ does not fix the underlying problem but alleviates it. It would be insightful to discuss why the improvement appears to be limited and consider other possible explanations or avenues for further improvement.
L241: ‘Figure S12’ should be changed into ‘Figure 4’.

Citation: https://doi.org/10.5194/egusphere-2024-2423-RC2
- AC2: 'Reply on RC2', Pratapaditya Ghosh, 21 Mar 2025
  
  Please see the attached file
  
  Citation: https://doi.org/10.5194/egusphere-2024-2423-AC2

Peer review completion

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

AR by Pratapaditya Ghosh on behalf of the Authors (21 Mar 2025) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (08 Apr 2025) by Graham Mann

This manuscript began peer review in October 2024, and 2 reviewers submitted comments (RC1 28 Nov 2024 and RC2 29 Jan 2025), each rating the manuscript at Good or Excellent in all 4 categories, both submitting a grading of Excellent for “Scientific reproducibility”.

Both reviewers gave a good number of minor revisions, with Reviewer 2 having some more significant general comments, and finding “reconsider after Major Revisions”. By contrast Reviewer 1’s grading was “publish with minor revisions”.

I have read through Reviewer 2’s comments, and have seen their major reservation is based around three main comments, referred to here as R2-M1, R2-M2, R2-M3 (main comments from reviewer 2).

Reviewer 2’s main concern (R2-M1) relates to how comparisons of the model predicted cloud droplet number concentrations with the MODIS retrieved cloud droplet number concentration are interpreted.

The authors have made substantial efforts to address this main issue (see specifics of comments below, and author replies), and clarify that their methods does align with the majority of the Grosvenor et al. (2018) MODIS CDN methodological specifics (but acknowledge not all of them).

They have revised the section 3.2 substantially (see track-changes manuscript), and also added a new Figure S8, to provide extra test for the difference when excluding gridboxes with less than 25% cloud fraction, and the revised section 3.2 text openly discusses these differences. (Note also there is typo in the authors reply they state “Figure S11” but they clearly meant Figure S8, and the indexing is clear in the Supplementary Information (SI).)

From this consideration, I give my interpretation here objectively and transparently, to a Topical Editor decision that I believe the revisions to the manuscript do sufficiently address each of Reviewer 2’s main comments, such that although Reviewer 2 has indicated they would be willing to review the revised manuscript, that won’t be necessary in this case.

I further note my Topical Editor research expertise aligns closely to the topic here, re: aerosol-cloud interactions, and their treatment within global models, and I therefore have felt qualified to adjudicate on the author replies to reviewer comments, and the corresponding manuscript revisions.

I give some further explanation in the paragraphs below, to evidence my decision that the additions to the manuscript, including the extra Figure in the SI, do achieve the change R2 requests. The “major” level adjudication of R2’s review is noted, but given the substantial changes the authors have made, and that the R2 main comments are relatively minor, I am recommending publication in GMD once a further set of minor comments have been addressed (see below).

In addition to the methodological clarification, and sensitivity analysis from the Figure S8, the revised manuscript now also quantifies a “representativeness error” and has quantified RMSE and NMAEF comparison metrics for how the alternate-derived model CDN compares to the MODIS CDNC.

The authors have also addressed reviewer 2’s other main comments (R2-M2 & R2-M3). For R2-M3 equations for the calculation of the two key parameters in equation 1 have been added (for Smax within the ARG parameterisation). The revised text there now also has a more complete explainer for the other variables in equation 1.
For Reviewer 1’s minor comments these have been replied to, and addressed satisfactorily, with appropriate minor additions to the manuscript, e.g. to cite CMIP6 as the motivation for selecting only a single reference-year, and reply re: point about alternate model-obs skill/bias measures/metrics.

I have read through the added text, and seen that some minor revisions are needed within the text added for section 3.2, with also a few remaining minor colloquialisms such as “our results”, “our parameterization” etc. changed to more formalised language.

However, once these minor revisions have been addressed, (see remaining minor revisions) at end of this TE review, the manuscript can then proceed to publication in Geoscientific Model Development.

R2-M1a: “While the comparison between model output Nd and MODIS Nd is interesting, it may be more complex due to the various differences between the model and satellite data. These differences make it challenging to determine whether the bias in the model with the old scheme is ‘underestimated’ or ‘overestimated,’ and consequently, whether the reported ‘improvements’ or ‘degradations’ are fully reliable. I believe the comparison using the MODIS simulator results might be more appropriate than using the model’s Nd output. “

with also follow-on points R2-M1b and R2-M1c:

“General Comment on Nd Comparison: While the comparison between model output Nd and MODIS Nd is interesting, it may be more complex due to the various differences between the model and satellite data. These differences make it challenging to determine whether the bias in the model with the old scheme is ‘underestimated’ or ‘overestimated,’ and consequently, whether the reported ‘improvements’ or ‘degradations’ are fully reliable. I believe the comparison using the MODIS simulator results might be more appropriate than using the model’s Nd output.”

“L210: It would be helpful to provide more details about how the model’s Nd is calculated. Specifically, how is the liquid cloud-top defined in the model, and what methodology is used to derive Nd from this definition?”
R2-M2: “Section 3.4: The conclusion drawn in this section is somewhat unclear. It would be helpful to either explicitly state the implications of the findings or, if they are not central to the paper’s argument, consider removing or rewording this part. This would enhance the clarity and focus of the discussion.”
R2-M3: “L85: To improve clarity, it would be helpful to show the formula of non-dimensional variables η and ζ. This would allow readers to better understand the discussion around kinetic limitations and how these variables related to Nd and w.
Remaining minor revisions (n.b. line numbers are those in the Track-Changes MS)

1) Manuscript title, lines 1-4. (title needs further amending)

The title was revised following the initial Topical Editor review, primarily to ensure adherence to the GMD policy, for then having an indicator “ARG2000” to denote a specific version of the activation parameterization, with also the version number for the host UK Met Office Unified model (v13.0).

However, further slight amendment is needed, as although technically not grammatically incorrect, the statement made there does not actually summarise what the manuscript finds.

The manuscript type is model evaluation paper, and it’s this specifics of the evaluation that needs to be stated within the title there.

Specifically, please change “Modifying the….” Instead to “Assessing modifications to the…”.
The grammar of the statement also is not great, and please change “impacts simulated cloud radiative effects” instead to “to improve simulated aerosol-cloud radiative effects”.

The words “shown” and “the” should also be deleted from the parentheses to keep the title concise.

2) Section 3.2, lines 238

This equation has been added following Reviewer 2’s comments (see general comments above), but the formalism here for the summation indices needs to be improved. With this being a specialist model development journal the formalism ought to be more precise, so please use sub-script i rather than i in parentheses for each term.

3) Section 3.2, lines 238-241

Further to comment 2, the variable names here “cf” and “Pr” should be presented with just 1 main symbol to denote them, with subscripts for indicating any secondary specifications from the primary quantity denoted.

There are 2 instances here where that’s not the case, the “cf” for “cloud fraction” and the “Pr” for probability. In both cases there is a recognised roman letter that tends to be used within equations,
(capital F for cloud fraction, and capital P for probability), and then the secondary aspects should only be communicated via sub-scripts.

Since the cloud fraction is clarified to be liquid cloud fraction, I suggest sub-script lc for liquid cloud, with a comma then to the sub-script I for the summation (i.e. F subscript “cl,I”}. Although Pr(i) represents a probability for photon transfer, suggest to denote simply as P subscript I to focus the main attention on the other variables, these being the primary quantities to consider.

Please also amend the text on lines 240-241 accordingly.

4) Section 3.2, line 267-268

Be more specific here, re: “in the cloudiest regions which most affect the global average”
The North Pacific has the region of persistent marine stratocumulus off the coast of the Pacific Northwestern US, and I guess that will be one of the “poleward of 30N cloudiest regions” you mean there, right? Are there similarly 1 (or possibly 2) other regions of persistent low cloud that could be stated there (e.g. within the North Atlantic?)

5) Section 3.2, lines 279-280

The “We conduct a sensitivity study by filtering out…” needs to be re-worded – since this is 1 model run, the “study” is too big a word. I guess you mean “sensitivity experiment” here, right?. With this sentivity added from one of Reviewer 2’s comments (RC2-MC1), it’s important to mention this is to assess the temporal sampling. Suggest to re-word as “To assess temporary sampling in the monthly means, we carried out a sensitivity experiment, filtering out…. ”.
6) Section 3.2, line 285-286

Further to comment 5, with RC2’s comments, re-word “While our investigation remains approximate..” to be clear this is an alternate methodology: “Whist this alternate methodology is approximate, it provides an indication of the sensitivity to the temporal sampling, and the results indicate only a modest change (12.5%), not changing the results substantially.”

7) Section 3.2, line 289: “The spatial pattern of the bias in Figure 3 are consistent with…”
“The spatial pattern of the CDN biases (Figure 3), are consistent with…..”

8) Section 3.2, line 290: “the biases likely originate in aerosol modelling unrelated to the activation”,
The word “likely” is too strong an association here, and this is not evidenced in the manuscript.
The biases could also potentially originate in the cloud processes, I’d argue with equal probability. Please change to “… the biases are quite likely unrelated to the activation parameterization, i.e. some more general bias (e.g. in either aerosol or cloud)”

9) Section 3.2, lines 292-293: “it is still helpful to understand the impact of our improved functions”

As mentioned in the general comments, please change all instances of “our results”, “our method” etc. (too colloquial), needs to be informal/neutral language in peer-reviewed journal article.

The wording “improved functions” also pre-judges the outcome of the results, and although the revised title states this is the aim, this should also be moderated to remain “the new parameterisation” or “the revised parameter settings” or parameter ranges etc.

10) Section 3.2: line 322, “direct radiative forcing”  “aerosol-radiation interaction radiative effect” and “cloud radiative forcing”  “aerosol-cloud interaction radiative effect” (or “effective radiative forcing” if appropriate).
The preceding sentence to this (on lines 320-321) is clear in referring to aerosol radiative forcings, and there also specifies with the IPCC report terminology “effective radiative forcing”. This follow-on sentence needs also to use the approved terminology (direct  “aerosol-radiation interaction”, and indirect or cloud radiative effect  “aerosol-cloud interaction” radiative effect.
And also the instances of “direct” and “indirect” on line 324.

11) Section 3.2, line 350 to 364, Further to the above please change the several instances of “our” in this paragraph to the impersonal “the” (line 350, line 356, line 358, line 362). Change also “Like several other recent studies (for example Christensen et al., 2023), our results underline the importance of ...” to “Several other studies (e.g. Christensen et al., 2023) have highlighted the importance of…”

For the “Our proposed changes are extremely simple”, suggest simply to delete that, beginning the paragraph “In all modal aerosol microphysics models we are aware of”.

However that sentence needs to be clarified to climate model aerosol schemes. There are certainly modal aerosol microphysics schemes in regional air quality models that are generalisable to variable mode widths (e.g. see Figure 4 of Whitby, E. et al., 2002, and see also Whitby E. et al., 1991; Seigneur et al., 1986).

12) Caption to Figure S8: Re-word the start of the penultimate sentence, currently worded as “To prepare this figure from that diagnostic, for each month we find…”. The text explaining the method seems slightly oblique also. Suggest to replace this last sentence with “For this Figure, the model CDN is diagnosed with different temporal sampling, omitting gridboxes with less than 25% liquid cloud fraction (see section 3.2).”

References
---------------

Seigneur, C., Hudischewskyja, A. B., Seinfeld, J. H., Whitby, K. T., Whitby, E. R. ,
Brock, J.R. and Barnes, H. M. (1986)
Simulation of Aerosol Dynamics: A Comparative Review of Mathematical Models,
Aer. Sci. Technol., vol. 5(2), 205-222, http://dx.doi.org/10.1080/02786828608959088

Whitby, E.R., Stratmann, F., Wilck, M. (2002)
Merging and remapping modes in modal aerosol dynamics models: a “Dynamic Mode Manager”
Aerosol Science, 33, 623–645, https://doi.org/10.1016/S0021-8502(01)00197-5

Whitby, E. R., McMurry, P. H. Shankar, U. and Binkowski, F. S. (1991)
Modal aerosol dynamics modelling, US EPA report, EPA/600/3-91/020, 282 pages.
https://www.researchgate.net/publication/236373518_Modal_Aerosol_Dynamics_Modeling

Whitby, E. R. and McMurry (1997)
Modal aerosol dynamics modelling,
Aer. Sci. Technol., 27(6), 673-688, https://doi.org/10.1080/02786829708965504

Seigneur, C., A. B. Hudischewskyj, J. H. Seinfeld, K. T. Whitby, E. R. Whitby, J. R. Brock & H. M. Barnes
Simulation of aerosol dynamics: A comparative review of mathematical models,
Aer. Sci. Technol., 5(2), 205-222, http://dx.doi.org/10.1080/02786828608959088

Hide

AR by Pratapaditya Ghosh on behalf of the Authors (14 Apr 2025) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (24 Apr 2025) by Graham Mann

AR by Pratapaditya Ghosh on behalf of the Authors (02 May 2025) Author's response Manuscript

Post-review adjustments

AA: Author's adjustment | EA: Editor approval

AA by Pratapaditya Ghosh on behalf of the Authors (04 Aug 2025) Author's adjustment Manuscript

EA: Adjustments approved (06 Aug 2025) by Graham Mann

Journal article(s) based on this preprint

11 Aug 2025

Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon

Geosci. Model Dev., 18, 4899–4913, https://doi.org/10.5194/gmd-18-4899-2025,https://doi.org/10.5194/gmd-18-4899-2025, 2025

Short summary

Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon

Supplement

https://doi.org/10.5194/egusphere-2024-2423-supplement

Data sets

Improving the Abdul-Razzak & Ghan (2000) aerosol activation parameterization impacts simulated cloud radiative effects (shown in the Unified Model, version 13.0) Pratapaditya Ghosh, Katherine Evans, Daniel Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon https://doi.org/10.5281/zenodo.13112444

Model code and software

Interactive computing environment

Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon

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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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The most popular algorithm for calculating cloud droplet number concentrations in climate models is sensitive to parameters that control simulated aerosol particle number concentrations at different sizes. We recommend small modifications to functions in the algorithm to improve its performance. Implementing our changes in the UK Met Office climate model reduced average bias in simulated global droplet number concentrations, leading to more reflected solar radiation and a net cooling effect.


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