Exploring aerosol-cloud interactions over eastern China and its adjacent ocean using the WRF-SBM-MOSAIC model

Zhao, Jianqi; Ma, Xiaoyan; Quaas, Johannes; Jia, Hailing

doi:https://doi.org/10.5194/egusphere-2023-331

Preprints

https://doi.org/10.5194/egusphere-2023-331

Preprints

23 Mar 2023

| 23 Mar 2023

Exploring aerosol-cloud interactions over eastern China and its adjacent ocean using the WRF-SBM-MOSAIC model

Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia

Abstract. This study aims to explore aerosol-cloud interaction over eastern China (EC) and its adjacent ocean (ECO) in boreal winter by coupling of a spectral-bin cloud microphysics (SBM) and an online aerosol module (MOSAIC) in WRF-Chem, with the support of four-dimensional data assimilation. The evaluation shows that assimilation has an overall positive impact on the simulation, and the coupling system reproduces the satellite-retrieved cloud parameters while exhibiting significantly improved simulation ability compared to the original SBM scheme as well as the bulk microphysical and MOSAIC coupling system. Differences in aerosol composition and physical processes lead to clear discrepancies in the aerosol-cloud interactions of EC and ECO during the simulation period. In EC with the gradual increase of aerosol number concentration (N_aero), cloud droplet number concentration (N_d) first increases then decreases and fluctuates around 800 cm^-3, while N_d in ECO increases faster initially, but soon its activation is suppressed by aerosol hygroscopicity and high activation threshold of numerous small particles, and almost no additional cloud droplets are produced. In terms of rapid adjustments, more bursty atmospheric supersaturation and lack of subsequent water cause cloud liquid water content (CLWC) in EC to increase explosively with N_d when there are few cloud droplets, but only maintains a low increase rate with further increasing N_d. ECO exhibits a fast increase in CLWC with N_d at high proportion of naturally emitted large aerosol particles, but its CLWC increase gradually stagnates as N_d increases. For non-precipitating clouds with less water content, CLWC in EC increases slowly with N_d, but can maintain a stable trend. While ECO, which relies mainly on large scale water and temperature variations to reach supersaturation, the increase in N_d leads to a decrease in CLWC.

Received: 24 Feb 2023 – Discussion started: 23 Mar 2023

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Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia

Status: closed

RC1:
'Comment on egusphere-2023-331', Anonymous Referee #1, 29 Apr 2023
Review of “Exploring aerosol-cloud interactions over eastern China and its adjacent ocean using the WRF-SBM-MOSAIC model” by Zhao et al.
The study coupled WRF-Chem with SBM and used it to study aerosol-cloud interactions for a stratiform cloud case at 15-km resolution. There are a few serious problems as detailed below. Here is a high-level summary: (1) there is no clear motivation, particularly in terms of coupling MOSIAC with SBM since this was done and applied to many studies already, (2) the coupling that the authors did is not correct, (3) applying supersaturation-based activation in SBM to 15 km resolution is not appropriate. These are serious methodology problems, so I recommend a rejection of the study.
Detailed comments:
Abstract:
The advantage of WRF-Chem is to study aerosol effects from aerosol properties. It would allow us physically to study how Nd is affected by different aerosol properties. However, the study did not take this advantage and they only examine the relationship of cloud properties with Nd but did not even connect with aerosol properties. Aerosol effects starts from how Nd are changed through aerosol properties.

The sentence “more bursty atmospheric supersaturation and lack of subsequent water cause cloud liquid water content (CLWC) in EC to increase explosively with Nd”, the sentence is confusing. How would lack of water contribute to increase Nd?

Introduction,
The introduction is all about aerosol impacts on stratocumulus and warm clouds. Is this the cloud you study in this work? If so, you need to clarify this at the beginning of the introduction so that people would know your target since the mechanisms of aerosol effects are very different with different types of clouds.

Line 81-82, “WRF-Chem currently only provides the coupling of bulk microphysical schemes with an online aerosol module (Gao et al., 2016)”: This is totally wrong, what Gao et al. 2016 documented is the WRF-Chem coupled with spectral-bin microphysics (SBM). I noticed this is later stated. But here correction is needed.

Line 85-90, these statements were good for the motivation of Gao et al., (2016) because bin microphysics scheme was never coupled with chemistry/aerosol module before. However, it does not apply anymore now since Gao et al., (2016) already built the capability and it has been used in many studies such as Fan et al., 2020, https://doi.org/10.5194/acp-20-14163-2020, Zhang et al., 2021, https://doi.org/10.5194/acp-21-2363-2021.; Lin et al., 2021https://doi.org/10.1175/JAS-D-20-0106.1, , Lin et al. 2022https://doi.org/10.5194/acp-22-6749-2022). Thus I have a difficulty to understand the motivation of this work, which repeats Gao et al. (2016) without a justification.

Section 2,
I am very confused by the writing of this section. It starts from describing “This study is based on WRF-Chem v3.9, the full version of the SBM scheme coupled with the aerosol module (Khain et al.,2009)”, and provided detailed description of SBM, then saying SBM is not coupled with chemistry/aerosols instead of using prescribed CCN. First, in the introduction, Gao et al., 2016 which coupled with SBM with WRF-Chem is described. This writing is not only misleading but I’d ask what’s the logic and point you want to deliver? The authors are totally ignoring Gao et al., 2016 here and repeating what was done in Gao et al. 2016 but did not provide any justification why you are doing this.

Page 5-6, there is a significant problem I see in the coupling SBM with MOSAIC. MOSAIC predicted various aerosol composition and hygroscopicity over different size ranges and it is physically wrong to map the aerosols from MOSIAC into aerosol bins in SBM based on size only, since activation of aerosols in SBM is only based on simple composition (default sea salt) to get the critical supersaturations for each bin. This is the key but most tricky part for this coupling. That is why Gao et al. (2016) implemented an interface code to set up the 33 critical supersaturations bins so that aerosols in the interstitial size bins in MOSAIC are mapped (i.e., distributed) to the 33 preset aerosols size bins through the interface module, and the mapping is based on particle critical supersaturations calculated from MOSAIC-predicted aerosol properties (size and hygroscopicity) rather than dry size. This was clearly described in Gao et al., 2016 and provided clear explanation why it is done in this way, i.e., “Since MOSAIC-predicted aerosol compositions vary with bins and over each bin a lognormal size distribution is assumed, mapping of aerosols from MOSAIC into SBM bins based on Scrit is the easiest and most precise way to connect aerosols between the two models. That is, all aerosol types are represented on a single set of 33 CCN bins, but each aerosol type may distribute over different CCN sizes.” Besides this, I see the authors follow Gao et al. 2016 on all other coupling treatments.

They use 15 km resolution for this study. The advantage of using SBM is diminished at such coarse resolution. On the contrary, the original WRF-Chem with the bulk schemes is more appropriate since supersaturations cannot be resolved much so activation is parameterized with Abdul-Razzak and Ghan parameterization. However, the activation of aerosols in SBM is based on supersaturations (not parameterized as Abdul-Razzak and Ghan scheme) but supersaturation would be poorly simulated at 15 km resolution (only very limited supersaturation can be resolved at such a coarse resolution). Therefore, this makes the effort of coupling SBM with WRF-Chem and the use of such physics-complicated model meaningless

With such serious methodology problems, it does not make sense to review their model results so I stop there.
Citation: https://doi.org/10.5194/egusphere-2023-331-RC1
- AC1: 'Reply on RC1', Jianqi Zhao, 25 Jun 2023
  
  Please see the response in Supplement
  
  Citation: https://doi.org/10.5194/egusphere-2023-331-AC1
RC2:
'Comment on egusphere-2023-331', Anonymous Referee #2, 02 May 2023
This paper investigated the interactions of aerosols and clouds over eastern China (EC) and its adjacent ocean region (ECO) during wintertime based on WRF-Chem with the SBM scheme by coupling the online aerosol module. The results show that the cloud variables are simulated more precisely compared to the bulk model and the default SBM scheme. Besides, the use of the four-dimensional data assimilation is evaluated using multiple observations and shows a positive effect on the simulation results. Upon all these improved models and methods, the authors analyze the differences in aerosol-cloud interactions over EC and ECO owing to the distinct aerosol physical and chemical properties and the meteorological conditions and examine the variations of cloud droplet number concentration with the increase of aerosol number concentration. Moreover, the rapid adjustments for precipitation clouds and non-precipitation clouds are discussed with the variations of cloud liquid water content and cloud effective radius over EC and ECO. This seems like tremendous work exploring aerosol-cloud interactions.

Major comments:
One concern is about the evaluation of aerosol simulation. This study only evaluated the simulation of near-surface PM2.5, which is not sufficient considering that the aerosol effects on clouds act at a certain altitude. An evaluation of aerosol vertical profile or at least aerosol optical depth should be added.

The authors selected the liquid-only clouds from MODIS data based on some criteria, and the cloud top is detected by MODIS. To compare with MODIS, how to pick the liquid-only clouds from the model output? How is the cloud top of model output defined to evaluate COT, CLWP, CER, and N_d?

For the precipitation clouds investigated in this study, the simulated precipitation should be compared with observational data.

Do the samples in Figure 11-12 contain only liquid water? Could the samples contain the liquid part of the mixed clouds? How to exclude the influence of other types of clouds considering the mechanism of cloud formation and development varies with the cloud type?

As for the N_d variations in each N_aero interval in Figure 11, at the second stage, the authors stated that N_d in EC still increases swiftly with N_aero due to the relatively strong updraft and surface radiative cooling. Please explain why the N_d in ECO does not increase like N_d in EC.

Many studies using satellite data to explore aerosol-cloud interactions view AOD or aerosol index (AI) as an indicator of aerosol concentration due to the limit of observations. I wonder if the simulated AOD is improved after the assimilation. It would be great if the authors could plot the variations of the simulated CLWP with AOD in EC and ECO.

In section 3.4, when exploring aerosol-cloud interactions, meteorological fields may affect both aerosols and clouds, resulting in covariance between the two, such that changes in clouds cannot be attributed to aerosols. The authors need to further discuss the role of the meteorological field in this study and exclude its effects.

Minor comments:
Line 9: Delete “of”. “Coupling a spectral-bin cloud…” is suitable.

Line 22: Delete “, which”.

Line 22: It should be “large-scale”

Line 28: Aerosols show indirect effects as CCN and IN.

Line 29: “Remain” should be changed to “remains”.

Line 51: It should be “depends on” instead of “depends”.

Line 86: It should be “Benefiting from advances in computational science”.

Line 115: It should be “is” before “consistent”.

Line 130: “Thus greatly promoting” is more suitable. So is “optimizing” in Line 270.

Line 260: Replace “This” with “These”.

Line 355: Replace “comes” with “come” and delete “are” at the end of this line.

Line 370: Is there any direct evidence to prove that radiative cooling makes a large number of cloud droplets distributed near the surface?

Figure 11 shows the variation of Nd with aerosol and other related factors based on the statistics of the model grids with CF greater than 0 at each time. The method to calculate CF in this study is that CF equals 1 when the sum of cloud water and cloud ice mixing ratios is greater than 10^-6 kg·kg^-1, otherwise, the CF equals 0. Why does the calculation of CF use cloud ice mixing ratio since this study focuses on liquid clouds?

Figure 6-8: Have you done a significance test for the correlation coefficient?

How do the authors define the liquid cloud? I just wonder why the liquid clouds appear higher than 4 km in Figure 10b. Besides, should the role of sea salt acting as the ice nuclei be considered in the discussion?

What do the downdrafts in Figure 10d imply?

The manuscript mentioned that the near-surface areas around 29°N and 31°N (Fig. 10b) exhibit high atmospheric supersaturation due to the effect of topographic uplift (Fig. 10c). Why the cloud number concentration is rather low in the south of the topographic uplift?
Citation: https://doi.org/10.5194/egusphere-2023-331-RC2
- AC2: 'Reply on RC2', Jianqi Zhao, 25 Jun 2023
  
  Please see the response in Supplement
  
  Citation: https://doi.org/10.5194/egusphere-2023-331-AC2

Status: closed

RC1:
'Comment on egusphere-2023-331', Anonymous Referee #1, 29 Apr 2023
Review of “Exploring aerosol-cloud interactions over eastern China and its adjacent ocean using the WRF-SBM-MOSAIC model” by Zhao et al.
The study coupled WRF-Chem with SBM and used it to study aerosol-cloud interactions for a stratiform cloud case at 15-km resolution. There are a few serious problems as detailed below. Here is a high-level summary: (1) there is no clear motivation, particularly in terms of coupling MOSIAC with SBM since this was done and applied to many studies already, (2) the coupling that the authors did is not correct, (3) applying supersaturation-based activation in SBM to 15 km resolution is not appropriate. These are serious methodology problems, so I recommend a rejection of the study.
Detailed comments:
Abstract:
The advantage of WRF-Chem is to study aerosol effects from aerosol properties. It would allow us physically to study how Nd is affected by different aerosol properties. However, the study did not take this advantage and they only examine the relationship of cloud properties with Nd but did not even connect with aerosol properties. Aerosol effects starts from how Nd are changed through aerosol properties.

The sentence “more bursty atmospheric supersaturation and lack of subsequent water cause cloud liquid water content (CLWC) in EC to increase explosively with Nd”, the sentence is confusing. How would lack of water contribute to increase Nd?

Introduction,
The introduction is all about aerosol impacts on stratocumulus and warm clouds. Is this the cloud you study in this work? If so, you need to clarify this at the beginning of the introduction so that people would know your target since the mechanisms of aerosol effects are very different with different types of clouds.

Line 81-82, “WRF-Chem currently only provides the coupling of bulk microphysical schemes with an online aerosol module (Gao et al., 2016)”: This is totally wrong, what Gao et al. 2016 documented is the WRF-Chem coupled with spectral-bin microphysics (SBM). I noticed this is later stated. But here correction is needed.

Line 85-90, these statements were good for the motivation of Gao et al., (2016) because bin microphysics scheme was never coupled with chemistry/aerosol module before. However, it does not apply anymore now since Gao et al., (2016) already built the capability and it has been used in many studies such as Fan et al., 2020, https://doi.org/10.5194/acp-20-14163-2020, Zhang et al., 2021, https://doi.org/10.5194/acp-21-2363-2021.; Lin et al., 2021https://doi.org/10.1175/JAS-D-20-0106.1, , Lin et al. 2022https://doi.org/10.5194/acp-22-6749-2022). Thus I have a difficulty to understand the motivation of this work, which repeats Gao et al. (2016) without a justification.

Section 2,
I am very confused by the writing of this section. It starts from describing “This study is based on WRF-Chem v3.9, the full version of the SBM scheme coupled with the aerosol module (Khain et al.,2009)”, and provided detailed description of SBM, then saying SBM is not coupled with chemistry/aerosols instead of using prescribed CCN. First, in the introduction, Gao et al., 2016 which coupled with SBM with WRF-Chem is described. This writing is not only misleading but I’d ask what’s the logic and point you want to deliver? The authors are totally ignoring Gao et al., 2016 here and repeating what was done in Gao et al. 2016 but did not provide any justification why you are doing this.

Page 5-6, there is a significant problem I see in the coupling SBM with MOSAIC. MOSAIC predicted various aerosol composition and hygroscopicity over different size ranges and it is physically wrong to map the aerosols from MOSIAC into aerosol bins in SBM based on size only, since activation of aerosols in SBM is only based on simple composition (default sea salt) to get the critical supersaturations for each bin. This is the key but most tricky part for this coupling. That is why Gao et al. (2016) implemented an interface code to set up the 33 critical supersaturations bins so that aerosols in the interstitial size bins in MOSAIC are mapped (i.e., distributed) to the 33 preset aerosols size bins through the interface module, and the mapping is based on particle critical supersaturations calculated from MOSAIC-predicted aerosol properties (size and hygroscopicity) rather than dry size. This was clearly described in Gao et al., 2016 and provided clear explanation why it is done in this way, i.e., “Since MOSAIC-predicted aerosol compositions vary with bins and over each bin a lognormal size distribution is assumed, mapping of aerosols from MOSAIC into SBM bins based on Scrit is the easiest and most precise way to connect aerosols between the two models. That is, all aerosol types are represented on a single set of 33 CCN bins, but each aerosol type may distribute over different CCN sizes.” Besides this, I see the authors follow Gao et al. 2016 on all other coupling treatments.

They use 15 km resolution for this study. The advantage of using SBM is diminished at such coarse resolution. On the contrary, the original WRF-Chem with the bulk schemes is more appropriate since supersaturations cannot be resolved much so activation is parameterized with Abdul-Razzak and Ghan parameterization. However, the activation of aerosols in SBM is based on supersaturations (not parameterized as Abdul-Razzak and Ghan scheme) but supersaturation would be poorly simulated at 15 km resolution (only very limited supersaturation can be resolved at such a coarse resolution). Therefore, this makes the effort of coupling SBM with WRF-Chem and the use of such physics-complicated model meaningless

With such serious methodology problems, it does not make sense to review their model results so I stop there.
Citation: https://doi.org/10.5194/egusphere-2023-331-RC1
- AC1: 'Reply on RC1', Jianqi Zhao, 25 Jun 2023
  
  Please see the response in Supplement
  
  Citation: https://doi.org/10.5194/egusphere-2023-331-AC1
RC2:
'Comment on egusphere-2023-331', Anonymous Referee #2, 02 May 2023
This paper investigated the interactions of aerosols and clouds over eastern China (EC) and its adjacent ocean region (ECO) during wintertime based on WRF-Chem with the SBM scheme by coupling the online aerosol module. The results show that the cloud variables are simulated more precisely compared to the bulk model and the default SBM scheme. Besides, the use of the four-dimensional data assimilation is evaluated using multiple observations and shows a positive effect on the simulation results. Upon all these improved models and methods, the authors analyze the differences in aerosol-cloud interactions over EC and ECO owing to the distinct aerosol physical and chemical properties and the meteorological conditions and examine the variations of cloud droplet number concentration with the increase of aerosol number concentration. Moreover, the rapid adjustments for precipitation clouds and non-precipitation clouds are discussed with the variations of cloud liquid water content and cloud effective radius over EC and ECO. This seems like tremendous work exploring aerosol-cloud interactions.

Major comments:
One concern is about the evaluation of aerosol simulation. This study only evaluated the simulation of near-surface PM2.5, which is not sufficient considering that the aerosol effects on clouds act at a certain altitude. An evaluation of aerosol vertical profile or at least aerosol optical depth should be added.

The authors selected the liquid-only clouds from MODIS data based on some criteria, and the cloud top is detected by MODIS. To compare with MODIS, how to pick the liquid-only clouds from the model output? How is the cloud top of model output defined to evaluate COT, CLWP, CER, and N_d?

For the precipitation clouds investigated in this study, the simulated precipitation should be compared with observational data.

Do the samples in Figure 11-12 contain only liquid water? Could the samples contain the liquid part of the mixed clouds? How to exclude the influence of other types of clouds considering the mechanism of cloud formation and development varies with the cloud type?

As for the N_d variations in each N_aero interval in Figure 11, at the second stage, the authors stated that N_d in EC still increases swiftly with N_aero due to the relatively strong updraft and surface radiative cooling. Please explain why the N_d in ECO does not increase like N_d in EC.

Many studies using satellite data to explore aerosol-cloud interactions view AOD or aerosol index (AI) as an indicator of aerosol concentration due to the limit of observations. I wonder if the simulated AOD is improved after the assimilation. It would be great if the authors could plot the variations of the simulated CLWP with AOD in EC and ECO.

In section 3.4, when exploring aerosol-cloud interactions, meteorological fields may affect both aerosols and clouds, resulting in covariance between the two, such that changes in clouds cannot be attributed to aerosols. The authors need to further discuss the role of the meteorological field in this study and exclude its effects.

Minor comments:
Line 9: Delete “of”. “Coupling a spectral-bin cloud…” is suitable.

Line 22: Delete “, which”.

Line 22: It should be “large-scale”

Line 28: Aerosols show indirect effects as CCN and IN.

Line 29: “Remain” should be changed to “remains”.

Line 51: It should be “depends on” instead of “depends”.

Line 86: It should be “Benefiting from advances in computational science”.

Line 115: It should be “is” before “consistent”.

Line 130: “Thus greatly promoting” is more suitable. So is “optimizing” in Line 270.

Line 260: Replace “This” with “These”.

Line 355: Replace “comes” with “come” and delete “are” at the end of this line.

Line 370: Is there any direct evidence to prove that radiative cooling makes a large number of cloud droplets distributed near the surface?

Figure 11 shows the variation of Nd with aerosol and other related factors based on the statistics of the model grids with CF greater than 0 at each time. The method to calculate CF in this study is that CF equals 1 when the sum of cloud water and cloud ice mixing ratios is greater than 10^-6 kg·kg^-1, otherwise, the CF equals 0. Why does the calculation of CF use cloud ice mixing ratio since this study focuses on liquid clouds?

Figure 6-8: Have you done a significance test for the correlation coefficient?

How do the authors define the liquid cloud? I just wonder why the liquid clouds appear higher than 4 km in Figure 10b. Besides, should the role of sea salt acting as the ice nuclei be considered in the discussion?

What do the downdrafts in Figure 10d imply?

The manuscript mentioned that the near-surface areas around 29°N and 31°N (Fig. 10b) exhibit high atmospheric supersaturation due to the effect of topographic uplift (Fig. 10c). Why the cloud number concentration is rather low in the south of the topographic uplift?
Citation: https://doi.org/10.5194/egusphere-2023-331-RC2
- AC2: 'Reply on RC2', Jianqi Zhao, 25 Jun 2023
  
  Please see the response in Supplement
  
  Citation: https://doi.org/10.5194/egusphere-2023-331-AC2

Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia

Supplement

https://doi.org/10.5194/egusphere-2023-331-supplement

Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia

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Short summary

We improve the ability of WRF-Chem model to simulate aerosol-cloud physical and chemical processes by coupling a spectral-bin cloud microphysics scheme and online aerosol module, and consequently explore the aerosol-cloud interactions over eastern China and its adjacent ocean in boreal winter. Our study highlights the differences in aerosol-cloud interactions between land and ocean, precipitation clouds and non-precipitation clouds, and differentiates and quantifies their underlying mechanisms.


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