the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Oct 2024
Building a comprehensive library of observed Lagrangian trajectories for testing modeled cloud evolution, aerosol-cloud interactions, and marine cloud brightening
Abstract. As marine low clouds’ evolution is sensitive to the current state of the atmosphere and varying meteorological forcing, it is crucial to ascertain how cloud responses differ across a spectrum of those conditions. In this study, we introduce an innovative approach to encompass a wide array of conditions prevalent in low marine cloud regions by creating a comprehensive library of observed environmental conditions. Using reanalysis and satellite data, over 2200 Lagrangian trajectories are generated within the stratocumulus deck region of the Northeast Pacific during summer 2018–2021. By using 8 important cloud-controlling factors (CCFs), we employ Principal Component Analysis (PCA) to reduce the dimensionality of data. This technique demonstrates that two principal components capture 43 % of the variability among CCFs. Notably, PCA facilitates the selection of a reduced number of trajectories (e.g., 54) that represent a diverse array of the observed CCF, aerosol, and cloud variability and co-variability. These trajectories can then be used for process model studies, e.g., with Large-Eddy Simulations (LES), to evaluate the efficacy of Marine Cloud Brightening. Two distinct cases are selected to initiate two-day-long, high-resolution, large-domain LES experiments. The results highlight the ability of our LES to simulate observed conditions. Although perturbed aerosols delay cloud breakup and enhance cloud radiative effect, the strength of such effects is sensitive to “precipitation-aerosol feedback”. The first case is precipitating and shows the potential for “precipitation-driven” cloud breakup due to positive precipitation-aerosol feedback. The second case is non-precipitating with classic cloud breakup of “deepening-warming” type, highlighting the impact of entrainment.
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Notice on discussion status
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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Preprint
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Supplement
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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.
- Preprint
(6799 KB) - Metadata XML
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Supplement
(3797 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-3232', Anonymous Referee #1, 23 Nov 2024
Review of "Building a comprehensive library of observed Lagrangian trajectories for testing modeled cloud evolution, aerosol-cloud interactions, and marine cloud brightening" by Erfani et al.
Summary:
This paper presents an approach to construct a library of Lagrangian trajectories and meteorological factors using reanalysis and satellite data, aiming to represent a comprehensive range of environmental conditions typical of low marine cloud regions for process modeling studies. PCA analysis was performed to identify representative trajectories that capture the variability and co-variability of observed cloud-controlling factors, aerosols, and cloud fields. The authors subsequently selected two cases to initiate LES simulations based purely on satellite and reanalysis data, and demonstrate that their LES model is capable of simulating observed conditions. The paper concludes that the strength of the aerosol-induced cloud radiative effect is sensitive to "precipitation-aerosol feedback".
I enjoyed reading this paper, which is well-written and well-organized. I believe it will make a valuable contribution to ACP. I have only a few minor comments, which are detailed below.Major comments:
The accurate tracking of trajectories is a fundamental aspect of this study and the authors' upcoming work. While some details on the trajectory tracking method are provided in Section 2.2, it would be beneficial to further discuss any potential limitations or assumptions associated with the trajectory code used in this study. Are there any known biases or uncertainties in the trajectory data that readers should be aware of? Additionally, given that HYSPLIT is one of the most widely used tools for trajectory tracking, I was wondering why the authors chose not to use this model for generating the trajectories in their study. By the way, recent study have compared trajectory models that rely on wind speed for tracking (e.g., HYSPLIT) with actual cloud movements, and have found notable discrepancies between the two (Larson et al., 2022). Therefore, It would strengthen the manuscript if the authors could include a more detailed discussion of the uncertainties associated with their chosen trajectory tracking method.
Reference:
Larson K M, Shand L, Staid A, et al. An optical flow approach to tracking ship track behavior using GOES-R satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 6272-6282.Specific Comments:
L69: Change "altered." to "altered" (remove the period).
L204: Since trajectory generation is central to this paper, it would be helpful to include a brief explanation of the principle behind the trajectory generation code used. Additionally, how does the trajectory generation code used here differ from the ‘uw-trajectory’ Python package mentioned later in the manuscript?
L212: When calculating the grid mean at a particular spatial scale, why not set the grid size to match the LES domain size? This would allow for a more direct comparison between the LES results and the observational statistics.
L214: The authors should clarify why only the first 48 hours of the trajectories were used for analysis.
L221: Some readers may benefit from a reference or further explanation of how the threshold for excluding trajectories with significant ice content was determined.
L379: Shouldn’t this be "decreased cloudiness" instead of "increased cloudiness"?
L633: It might be interesting to include some analysis or hypotheses about why the cell size increases for the Na×9 case.
L871: The ‘Conclusions’ section should serve as a summary of the main findings. However, it seems to be summarized already in Section 5. Consider renaming this section to ‘Discussion’ and relocating it before the ‘Summary’ section.Citation: https://doi.org/10.5194/egusphere-2024-3232-RC1 - AC1: 'Reply on RC2', Ehsan Erfani, 01 Apr 2025
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RC2: 'Comment on egusphere-2024-3232', Anonymous Referee #2, 19 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3232/egusphere-2024-3232-RC2-supplement.pdf
- AC1: 'Reply on RC2', Ehsan Erfani, 01 Apr 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-3232', Anonymous Referee #1, 23 Nov 2024
Review of "Building a comprehensive library of observed Lagrangian trajectories for testing modeled cloud evolution, aerosol-cloud interactions, and marine cloud brightening" by Erfani et al.
Summary:
This paper presents an approach to construct a library of Lagrangian trajectories and meteorological factors using reanalysis and satellite data, aiming to represent a comprehensive range of environmental conditions typical of low marine cloud regions for process modeling studies. PCA analysis was performed to identify representative trajectories that capture the variability and co-variability of observed cloud-controlling factors, aerosols, and cloud fields. The authors subsequently selected two cases to initiate LES simulations based purely on satellite and reanalysis data, and demonstrate that their LES model is capable of simulating observed conditions. The paper concludes that the strength of the aerosol-induced cloud radiative effect is sensitive to "precipitation-aerosol feedback".
I enjoyed reading this paper, which is well-written and well-organized. I believe it will make a valuable contribution to ACP. I have only a few minor comments, which are detailed below.Major comments:
The accurate tracking of trajectories is a fundamental aspect of this study and the authors' upcoming work. While some details on the trajectory tracking method are provided in Section 2.2, it would be beneficial to further discuss any potential limitations or assumptions associated with the trajectory code used in this study. Are there any known biases or uncertainties in the trajectory data that readers should be aware of? Additionally, given that HYSPLIT is one of the most widely used tools for trajectory tracking, I was wondering why the authors chose not to use this model for generating the trajectories in their study. By the way, recent study have compared trajectory models that rely on wind speed for tracking (e.g., HYSPLIT) with actual cloud movements, and have found notable discrepancies between the two (Larson et al., 2022). Therefore, It would strengthen the manuscript if the authors could include a more detailed discussion of the uncertainties associated with their chosen trajectory tracking method.
Reference:
Larson K M, Shand L, Staid A, et al. An optical flow approach to tracking ship track behavior using GOES-R satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 6272-6282.Specific Comments:
L69: Change "altered." to "altered" (remove the period).
L204: Since trajectory generation is central to this paper, it would be helpful to include a brief explanation of the principle behind the trajectory generation code used. Additionally, how does the trajectory generation code used here differ from the ‘uw-trajectory’ Python package mentioned later in the manuscript?
L212: When calculating the grid mean at a particular spatial scale, why not set the grid size to match the LES domain size? This would allow for a more direct comparison between the LES results and the observational statistics.
L214: The authors should clarify why only the first 48 hours of the trajectories were used for analysis.
L221: Some readers may benefit from a reference or further explanation of how the threshold for excluding trajectories with significant ice content was determined.
L379: Shouldn’t this be "decreased cloudiness" instead of "increased cloudiness"?
L633: It might be interesting to include some analysis or hypotheses about why the cell size increases for the Na×9 case.
L871: The ‘Conclusions’ section should serve as a summary of the main findings. However, it seems to be summarized already in Section 5. Consider renaming this section to ‘Discussion’ and relocating it before the ‘Summary’ section.Citation: https://doi.org/10.5194/egusphere-2024-3232-RC1 - AC1: 'Reply on RC2', Ehsan Erfani, 01 Apr 2025
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RC2: 'Comment on egusphere-2024-3232', Anonymous Referee #2, 19 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3232/egusphere-2024-3232-RC2-supplement.pdf
- AC1: 'Reply on RC2', Ehsan Erfani, 01 Apr 2025
Peer review completion
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Ehsan Erfani
Robert Wood
Peter Blossey
Sarah J. Doherty
Ryan Eastman
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6799 KB) - Metadata XML
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Supplement
(3797 KB) - BibTeX
- EndNote
- Final revised paper