the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jan 2025
Signatures of aerosol-cloud interactions in GiOcean: A coupled global reanalysis with two-moment cloud microphysics
Abstract. Aerosols in the atmosphere affect top of atmosphere radiation through direct interactions with radiation and by affecting cloud properties. Through aerosol-cloud interactions (ACI), and ensuing adjustments, anthropogenic aerosols have led to cooling during the industrial era. However, there is substantial uncertainty in our global models regarding the cooling driven by ACI. In part, global models are subject to substantial disagreement in terms of cloud properties, thermodynamic state, hydrological cycle, and general circulation. Reanalysis provides a useful avenue for exploring the impact of ACI on clouds and radiation because its atmosphere is nudged to observations of these quantities, but until now reanalyses have not included two-moment microphysics coupled to aerosols. Here, we explore the impact of ACI on clouds in the GiOcean reanalysis- the first to incorporate aerosol-cloud adjustments. We develop souce-sink models of ACI in GiOcean and contrast these to satellite observations and allow attribution of changes in cloud droplet number (Nd) and liquid water path (LWP) to aerosol and meteorology.
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Status: open (until 27 Feb 2025)
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RC1: 'Comment on egusphere-2024-4108', Anonymous Referee #1, 18 Jan 2025
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In this manuscript, the authors produce a purportedly new dataset whose novel contribution is the addition of two-moment cloud microphysics to couple aerosols to classical reanalysis data. This type of research is quite valuable in that it adds another approach for example to cross-check and validate other data (e.g., data from GCMs). This manuscript is full of promise, but unfortunately it falls short.
The dataset is not available yet; the methodology to reproduce it isn’t really clear; no code is offered to reproduce anything; the exact contribution of GiOcean in the context of other modeling details is unclear; the “one-way coupled” nature of GiOcean isn’t really defined; and the comparison to satellite data shows that GiOcean is quite far off.
I hope the authors find my comments below constructive. I will be happy to review this manuscript again, and I am looking forward to it being ready/suitable for publication.
Overall, this manuscript is difficult to read and disappointing. Potential avenues for improvement include:
- The manuscript feels rushed and several issues could be improved (in terms of writing, quality of presentation, precision of definitions, etc.)
- Depending on how tedious it is to redo the reanalysis (i.e., reproduce GiOcean), I’d very strongly encourage the authors to “tune” the processes that you assess to be “too strong” (your words), including precipitation suppression (L 341, 358, 372, 420), dependencies on sources (L 327, 234, 400, 420; how does this relate to activation btw?), dependencies on sinks (L 337, 234, 400, 420)
- Relatedly, could you provide correlations plots (a la Figures 5 and 6) of AOD vs Nd and Nd vs LWP? That is, make AOD the x-axis and Nd the y-axis in one and in the other make Nd the x-axis and LWP the y-axis.
- How are these processes (droplet activation, droplet/aerosol removal, and precipitation suppression) represented in the microphysics scheme in this study?
More comments:
L 1: not to be too pedantic, but aerosols affect the atmosphere radiation everywhere in the column they exist, and they in fact almost never exist in “top of the atmosphere” (that layer of often thought to be empty) — you probably forgot to add “balance” between radiation and through.
L 2: “Adjustments” are part of aerosol–cloud interaction (as you correctly define them on L14). Please rephrase to clarify what you mean here.
L 3: remove “our”
L 18: in the sentence just before this, you defined ACI as both Twomey and adjustments, but not you’re saying ACI *and* adjustments as if they were two separate things.
L 22: Maybe cite a few of these “numerous researchers” here?
L 35: This sentence can be deleted (it’s readily implied by the one before it)
L 33–52: this entire paragraph is pretty awkward and a little haphazard. For example, the word “therefore” appears multiple times (almost every other sentence). And some assertions are pretty questionable. I would simplify and just say, very basically and succinctly, what you want to say (which is likely something about how a two-moment scheme gets you some info about ACI in GCMs)
L 93: you never really get around defining what you mean by “one-way coupled” — please define and be explicit somewhere.
L 93: also, could you explain the “time lag” part? What’s its impact? Can it be made shorter?
Section 2.1: After reading this multiple times, I am still confused about the setup. You’re describing one thing after another, without really actually making connections between paragraphs (and sometimes even sentences).
L 109: you say GiOcean is a dataset, but it sounds more like a model if it simulates somethings?
Section 2.1: I read this section a few times and I am still unsure how this whole thing works and more impotently what *new* thing you added to this the whole setup? You say earlier the microphysics part is the new part; was there microphysics in before? Did you invent the whole workflow from scratch? It’s just not clear to me what you did and how you did it, and what’s new about it. Please carefully explain the details.
L 138: I’d prefer you keep a present tense (especially that you do in fact use mostly present tense throughout)
Section 3.1 and Figures 1 and 2: Consider adding difference plots between GiOcean reanalysis and satellite observations (i.e., take difference between 2nd and 1st column into a 3rd column for Figure 1)
Figures 1 and 2: I would probably encourage you to use the same scaling (you used linear in Figure 1ab, but you used logarithmic in Figure 2a)
L 192: “enhance this disagreement” — do you mean exacerbate it or ameliorate it?
L 250: you say you develop a steady-state model (you also say that in the abstract) but I actually don’t think you do? Or am I missing something?
Section 3.3: I am not entirely sure what these “models” are and how they were used in this context?? Maybe “models” is the wrong word to use in this context? I am confused! Maybe you mean “look-up tables” as you sometimes refer to these relationships later? Either way, please state precisely what you mean and how you went about producing the corresponding results.
Section 3.2: I think “explained variance” should be defined clearly before it is used in the text
Data availability: Is it appropriate to ask for the underlying code/processing to be shared too? It’d be good if the authors think it is shareable.
Data availability: Because the dataset isn’t available yet, it is hard to recommend this manuscript for publication.
Citation: https://doi.org/10.5194/egusphere-2024-4108-RC1 -
AC1: 'Reply on RC1', Ci Song, 27 Jan 2025
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Clarifications to the reviewer #1 comments on “Signatures of aerosol-cloud interactions in GiOcean: A coupled global reanalysis with two-moment cloud microphysics” by Ci Song et al.
Thank you for your time reviewing our manuscript. We realize that this is only review number 1 of several, but we wanted to quickly jump in and provide some context and update links to help the rest of the reviewers. This focuses on the availability of data and code. We provide those below. Our apologies for not having these in the initial submission due to some delays on posting the full data and documentation for the model.
Reviewer comment: The dataset is not available yet;
Response: Thank you for pointing this out. When we submitted the paper, the dataset wasn’t publicly available yet, but we had access to it and completed all the analyses presented in the manuscript. It was definitely an awkward timeline. The good news is that the dataset is now publicly available, so anyone interested can access it. We believe this will help make the work more transparent and reproducible. It can now be accessed at:
https://portal.nccs.nasa.gov/datashare/gmao/geos-s2s-3/GiOCEAN_e1/.
The collections used in our analysis are:- aci_tavg_1dy_glo_L360x181_p27
- aci_tavg_1dy_glo_L360x181_sfc
When we are in possession of all reviews we will update the data availability statement.
Reviewer comment: The methodology to reproduce it isn’t really clear;
Response: We apologize for any lack of clarity in the methodology section. Reproducibility is incredibly important, and we realize now that we could have been more detailed in explaining the steps we took in the revised version:
The GiOcean reanalysis is based on the NASA GEOS Subseasonal to Seasonal (GEOS-S2S) forecast system, detailed in Molod et al. (2020). The forecast integrates three data assimilation systems (DASs) for the atmosphere, aerosol, and ocean. These systems assimilate a vast array of observational data to calculate six-hourly “increments” that adjust meteorological, oceanic, and aerosol states, forcing the model to align with observations. Unlike typical reanalyses, which focus solely on meteorological states, GiOcean incorporates data from all three domains, providing a more comprehensive representation.
When we are in possession of all reviews we will update the methodology section to include this.
Reviewer comment: No code is offered to reproduce anything;
Response: Thank you for pointing this out, and we’re sorry for not properly clarifying this earlier. Reproducing GiOcean requires the following resources referenced in the manuscript:
- GEOS-ESM codebase: Available at https://github.com/GEOS-ESM.
- MERRA-2 dataset: and available at https://gmao.gsfc.nasa.gov/reanalysis/merra-2/.
- Observational constraints: Detailed in Gelaro et al. (2017), Randles et al. (2017), and Molod et al. (2020).
When we are in possession of all the reviews we will make sure to bring this point up in the ‘Code and Data Availability’ section of the paper to ensure everything is clear. It should be noted that reproducing this work requires a high-performance computing environment due to the computational intensity of processing over six million observations every six hours.
Reviewer comment: the “one-way coupled” nature of GiOcean isn’t really defined;
Response: Thank you for pointing this out, and we are sorry we didn’t clarify this in the manuscript. we will make sure to include a clear explanation of the “one-way coupled” nature of GiOcean in the revised version:
GiOcean employs weak or “one-way” coupling, meaning meteorological fields are "replayed" using the MERRA-2 reanalysis. The term "replayed" refers to the process of feeding pre-existing, time-evolving MERRA-2 into the base model (GEOS) at each simulation step, rather than generating meteorological fields dynamically within GEOS itself. In this approach, the atmospheric analysis increments used for model correction are derived from MERRA-2 but adjusted for differences in model physics. This approach stabilizes the reanalysis by avoiding a full meteorological DAS, though it limits feedback between the ocean and atmosphere. The aerosol and ocean DASs, however, remain fully active.
When we are in possession of all reviews we will update the methodology section to include this.
Reviewer comment: the exact contribution of GiOcean in the context of other modeling details is unclear
Response: Thank you for your feedback. We believe the contributions of GiOcean stands out in several key ways and will make sure the contributions are clear in the revised manuscript:
- Unlike typical modeling studies (e.g., CMIP archives), which do not assimilate observations, GiOcean integrates data across atmosphere, ocean, and aerosol systems.
- Unlike traditional reanalyses, which use simplified physics and focus on a single domain, GiOcean includes ocean, atmosphere and aerosol. This is also the first reanalysis to include aerosol-cloud interactions, enhancing our understanding of their impact on climate.
We will include this in the results section when we are in possession of all the reviews.
Reviewer comment: the comparison to satellite data shows that GiOcean is quite far off.
Response: GiOcean, based on the GEOS-S2S system, closely aligns with observations of temperature, water vapor, winds, precipitation, ocean salinity, and aerosol optical depth, as detailed in Molod et al. (2020). Furthermore, its cloud microphysics, central to this study, is well-validated in Barahona et al. (2014) and Tan and Barahona(2022), which demonstrate robust representation of cloud optical and microphysical properties.
We think this refers to discrepancies in cloud droplet number concentration (Nd) compared to MODIS retrievals. This is accurate. However, there are some limitations of this data set that prevent it being used by the GiOcean development team to either tune the model or to be assimilated into the reanalsysis:
- Nd retrievals have large uncertainties (Grosvenor et al., 2018).
- Model-sampling is challenging. Unlike quantities like reflectivities, the way to incorporate this into the GiOcean framework is unclear.
We will emphasize these points in the revised manuscript and make it clear that what we are presenting here is predicated on the Nd observational skill.
Reviewer comment: The manuscript feels rushed and several issues could be improved (in terms of writing, quality of presentation, precision of definitions, etc.)
Response: We appreciate your observations regarding the writing, presentation, and precision of definitions. Once we have all the reviewer comments, we will carefully review the manuscript and work on improving these aspects the reviewer mentioned to ensure it meets a higher standard of clarity and quality.
Reviewer comment: Depending on how tedious it is to redo the reanalysis (i.e., reproduce GiOcean), I’d very strongly encourage the authors to “tune” the processes that you assess to be “too strong” (your words), including precipitation suppression (L 341, 358, 372, 420), dependencies on sources (L 327, 234, 400, 420; how does this relate to activation btw?), dependencies on sinks (L 337, 234, 400, 420)
Response: We understand the importance of process tuning to better align models with observations. However, we have deliberately chosen not to tune processes in this case, as the observational data itself has significant uncertainties that don’t make it a reliable tuning or assimilation product (see above). Tuning the model to match observations with such variability could risk overfitting and misrepresenting the underlying physical processes.
Reviewer comment: Relatedly, could you provide correlations plots (a la Figures 5 and 6) of AOD vs Nd and Nd vs LWP? That is, make AOD the x-axis and Nd the y-axis in one and in the other make Nd the x-axis and LWP the y-axis.
Response: Of course! We’d be happy to provide correlation plots to support the analysis. We will include them in the revised manuscript to help clarify the relationships and trends discussed. Thank you for the suggestion!
Reviewer comment: How are these processes (droplet activation, droplet/aerosol removal, and precipitation suppression) represented in the microphysics scheme in this study?
Response: The microphysics scheme follows Barahona et al., 2014. We will include more details about the physical processes represented in the scheme in the revised manuscript, rather than relying solely on the citation. This should make the description clearer and more informative.
References
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Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Journal of Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017b.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D.,Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current
State of Knowledge and Perspectives, Reviews of Geophysics, 56, 409–453, https://doi.org/10.1029/2017rg000593, 2018.
Molod, A., Hackert, E., Vikhliaev, Y., Zhao, B., Barahona, D., Vernieres, G., Borovikov, A., Kovach, R. M., Marshak, J., Schubert, S., Li, Z., Lim, Y.-K., Andrews, L. C., Cullather, R., Koster, R., Achuthavarier, D., Carton, J., Coy, L., Friere, J. L. M., Longo, K. M., Nakada, K., and Pawson, S.: GEOS-S2S Version 2: The GMAO High Resolution Coupled Model and Assimilation System for Seasonal Prediction, J. Geophys. Res. Atmos., 125, 2020a.
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair,J., Shinozuka, Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation, Journal of Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017.
Tan, I. and Barahona, D.: The impacts of immersion ice nucleation parameterizations on Arctic mixed-phase stratiform cloud properties andthe Arctic radiation budget in GEOS-5, Journal of Climate, 35, 4049–4070, https://doi.org/10.1175/JCLI-D-21-0368.1, 2022
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