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Article
Peer-Review Record

Aerosol, Clouds and Radiation Interactions in the NCEP Unified Forecast Systems

Meteorology 2025, 4(2), 14; https://doi.org/10.3390/meteorology4020014
by Anning Cheng * and Fanglin Yang
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
Meteorology 2025, 4(2), 14; https://doi.org/10.3390/meteorology4020014
Submission received: 5 November 2024 / Revised: 3 February 2025 / Accepted: 15 May 2025 / Published: 23 May 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 

Review of “Aerosol, clouds and radiation interactions in the NCEP unified forecast systems” by Chen and Yang.

 

In this study, the authors aimed to understand the aerosol-cloud-radiation interactions using a state-of-the-art operational numerical weather prediction model. Two sets of model forecasts were conducted and compared with each other, with the first one without aerosol activation on CCN and IN while the second one contains aerosol-cloud interactions. The difference in simulated cloud properties and radiative fluxes between two model experiments were analyzed globally in GFS.v17.p8 model to understand the representation of aerosol-cloud interactions. In addition, single column model simulations of CCPP-SCM were also performed over a few selected cloud regimes to obtain further detailed process-level understanding of aerosol-cloud interactions. Although the idea of this study is interesting, I found the analysis summarized in the manuscript is not sufficient or solid. There are also a number of obvious errors in figures, and the manuscript lacks some vital information such as the details of model setup. These issues make the manuscript hard to follow. Therefore, I recommend rejection on the current version of manuscript. Please find my two biggest concerns below.

 

EXP CTL: it is unclear how CCN/IN are treated in the default experiment that does not use the CCN/IN activation from aerosols. Are CCN/IN prescribed in this experiment? If so, what is the number concentration of CCN/IN in the simulation and how is it treated in the model? Without this information, it is hard to interpret the results between CTL and ACI. Please clarify.

 

There is a lack of information about the CCPP-SCM model. For example, what physics and aerosol parameterizations are used in this model? What is the relation with CCPP-SCM and GFS.v17.v8? Is it an entirely different model compared to the GFS.v17.p8? If it is a different model, the model setup and combination of physics parameterizations can be quite different. The model behavior difference between simulations with and without aerosol cloud interaction in CCPP-SCM can then differ substantially from the model performance of GFS.v17.p8. Therefore, it is confusing to me how the results from CCPP-SCM could provide additional insights in aerosol-cloud interactions over different cloud regimes and environment scenarios in addition to the global analysis based on GFS.v17.p8. The results of aerosol-cloud interactions in section 3.1 and later sections may be irrelevant.

 

Please find my detailed comments below.

 

L153: “runs performed every three days throughout the summer season” is confusing. Does it mean the forecasts were initialized every three days? If so, why not initialize the forecast every day? Please justify.

 

Figure 1 and L178: In the figure caption, it says the results shown are from day 5 of the forecasts. How long was each forecast run and why did you select to analyze day-5 results? Are the results consistent throughout the forecasts? For example, will you have the same conclusion if using the day-3 results in your analysis?

 

Figure 1c: The color range from 50-200 W/m2 is hard to distinguish. Please consider using different colors.

 

L113: The full name of CCPP should be given here instead of L123.

 

L144: “Subscripts” needs to capitalize.

 

L204-206: The greater OLR is also possibly due to the reduced cloud fraction rather than the changed ice cloud properties.

 

Figure 2: What is the unit of cloud liquid and ice water content? Are rain and snow water mass included in cloud liquid and ice?

 

L227: What purpose does “this purpose” refer to? Does it refer to understanding aerosol-cloud interaction or reducing uncertainty in numerical models?

 

L238: Should be Figure 3d as it refers to the relations between AOD and effective radius.

Figure 5: Figure caption is the same as Figure 4.

 

L308-309: As aforementioned, it is difficult to understand how CCPP-SCM simulation would help to better investigate the impact of aerosol-cloud interactions on radiative fluxes in GFS.v17.p8 model without a clear justification on the relation between these two models.

 

L309: Different from other classic field campaigns such as ASTEX and TWPICE, the justification for the choice of typical point that represents Indian monsoon is weak. Please add more justification on the reason of selecting this location and time period for CCPP-SCM simulation analysis.

 

L315 and L319: The EXP CTL used pre-industrial aerosol and EXP ACI used climatological aerosol data? If this is the case, the aerosol background is so different. How can we understand the aerosol-cloud interaction?

 

L349-351: The model setup of EXP ACI and EXP CTL for stratocumulus cloud case study seem quite different from GFS.v17.p8 in section 3.3. I suggest remove this section as the discussion provided is not helpful to understand the aerosol-cloud interaction in GFS.v17.p8.

 

Figure 8a: What is the x axis, and where are LES and CTL data? Why are they both zero?

 

Figure 9: It is not cited and has the same issue as Figure 8a.

 

L397-398: It may be helpful to add observed TOA radiative fluxes to the comparison.

 

Figure 10: CIN is not discussed.

 

Figure 11: What does each row of the panel represent?

 

Section 3.5: Same as the issue for the study of aerosol-cloud interaction impact on Indian monsoon, it is unclear why this location is selected, which makes it hard to follow the analysis. Is this location representative for the deep convection invigoration or did previous observational research have demonstrated the occurrence of deep convection invigoration by aerosols over this region in the literature?

 

L456: Why did the authors only analyze the profiles between hours 4 and 5.5? Is it because of the large difference in precipitation between CTL and ACI? The precipitation in CTL is substantially underestimated compared to observations. The large difference is probably because of the issue in CTL simulation, instead of the aerosol impact on convection invigoration. Without a detailed description of model setup, it is hard to follow.

Author Response

Although the idea of this study is interesting, I found the analysis summarized in the manuscript is not sufficient or solid. There are also a number of obvious errors in figures, and the manuscript lacks some vital information such as the details of model setup. These issues make the manuscript hard to follow. Therefore, I recommend rejection on the current version of manuscript. Please find my two biggest concerns below.

We greatly appreciate the reviewer’s constructive feedback, except for the rejection comment. The manuscript has been significantly improved by incorporating more critical information about the model setup, detailing the CCN/IN calculation, and correcting errors such as a misplaced title and mislabeled figures. We hope the revised version satisfies the reviewer’s expectations.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript titled “Aerosol, Clouds and Radiation Interactions in the NCEP Unified Forecast Systems” by Anning Cheng et.al evaluated the interactions between aerosol, cloud and radiation in GFSv17p8 via the control experiments and the sensitivity experiments. The main parameters including aerosol optical depth, the liquid water, cloud droplet number concentration are involved in the radiation fluxes, India monsoon, stratocumulus clouds, deep convection invigoration and cloud system life cycle. The results are also compared with the satellite observations and other models. Considering the comprehensive discussion on the interaction of aerosol, clouds and radiation, I appreciate the originality of the work presented in the current version, it is recommended the paper is accepted after minor revisions.

 

1.         In Figure 2, cloud liquid water and cloud ice are discussed, and further explanations are required to add.

2.         The variation trends of the AOD and TOASW, OLR, Re and CNDC are showed in Figure 3, and AOD= 0.1 can serve as a dividing point, and positive linear correlation or uncertainty can be obtained. How to interpret the correlation between AOD and CNDC under the large deviation?

Comments on the Quality of English Language

well-written

 

Author Response

The manuscript titled “Aerosol, Clouds and Radiation Interactions in the NCEP Unified Forecast Systems” by Anning Cheng et.al evaluated the interactions between aerosol, cloud and radiation in GFSv17p8 via the control experiments and the sensitivity experiments. The main parameters including aerosol optical depth, the liquid water, cloud droplet number concentration are involved in the radiation fluxes, India monsoon, stratocumulus clouds, deep convection invigoration and cloud system life cycle. The results are also compared with the satellite observations and other models. Considering the comprehensive discussion on the interaction of aerosol, clouds and radiation, I appreciate the originality of the work presented in the current version, it is recommended the paper is accepted after minor revisions.

We appreciate the reviewer’s overall positive and constructive comments!

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Review

Aerosol, Clouds and Radiation Interactions in the NCEP 2 Unified Forecast Systems

 

This work aimed to assess aerosol, cloud and radiation interactions which significantly impact net radiation both at the top and bottom of the atmosphere. By effecting radiation balance aerosols may change vertical structure of atmosphere, lead to redistribution of energy, changes in convective potential energy, and atmospheric stability, effecting precipitations. All listed above prove the importance of this research.

 

This study is well structured. First there is a very detailed and good written Introduction, then section 2 outlines the experimental design, Section 3 presents the results and Section 4 provides a summary and conclusions which are consisted with the evidence in the main text.

 

This study would benefit from adding some climatic information about period of experiments. Since experiments span the period from June 1, 2020, to September 1, 2020 authors should describe which weather patterns usually characterized this part of summer in the assessed regions, were there any untypical episodes, etc.

I think that this study addressed an important topic. Research based on the observational and modelling data, presented in understandable, accessible scientific language. Reference list covers a lot of recent researches. Analysis of the results is deep and full within the framework of this study.

I would recommend to accept after minor revisions:

Lines 106-107 In this study, we first investigate aerosol, cloud, and radiation interactions in the UFS model and compare these interactions with available satellite observations - From here it is not clear which satellite data authors mean? Just data obtained from (Quaas et al. 2005)?

Line 338 formation of more, smaller - as far as I know, “more smaller” not used in English, only smaller supposed to be used

There is no citation Figure 9. Also, there is note in the margin at the line 365

Line 391 between 8 LST and 20 LST, - LST should be described earlier in the text

Line 421 which results in more, smaller ice crystals -  “more smaller” not used in English, only smaller supposed to be used

Line 151 Both experiments span the period from June 1, 2020, to September 1, 2020. Why these period had been chosen? Is it data availability only or something else?

Author Response

I think that this study addressed an important topic. Research based on the observational and modelling data, presented in understandable, accessible scientific language. Reference list covers a lot of recent researches. Analysis of the results is deep and full within the framework of this study.

 We appreciate the reviewer’s overall positive and constructive comments!

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

the manuscript has been sufficiently improved and can be published.

best wishes,

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