the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Jul 2026
Fit-for-purpose assessment of satellite aerosol and cloud datasets for constraining and monitor aerosol–cloud interactions
Abstract. Aerosol-cloud interactions (ACI) remain one of the dominant sources of uncertainty in estimates of anthropogenic effective radiative forcing. Robust observational constraints on these processes require satellite datasets that are not only physically consistent but also demonstrably fit for the intended scientific purpose. Within the framework of the "Satellite observations to improve our understanding of aerosol–cloud interactions" (SATACI) project, we perform a comprehensive fit-for-purpose (F4P) assessment of existing satellite aerosol and cloud datasets used to (i) quantify aerosol indirect effects on liquid and mixed-phase clouds and (ii) support the feasibility study of a novel aerosol–cloud climate indicator. For each application, scientific and technical requirements are defined in terms of variables, spatial and temporal resolution, co-location capability, validation evidence, and the availability of uncertainty information. Candidate datasets from geostationary and polar-orbiting satellites are evaluated against these criteria, including datasets resulting from the ESA Climate Change Initiative and Copernicus Climate Change Service. Overall, the study highlights the importance of application-specific dataset evaluation and emphasises the complementary roles of geostationary and polar-orbiting satellite observations. While geostationary sensors provide new opportunities to investigate the temporal evolution of aerosol–cloud systems, polar-orbiting climate data records remain essential for long-term monitoring and climate indicator development. Together, these observations provide a robust basis for advancing satellite-based constraints on aerosol–cloud interactions.
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Status: open (until 09 Sep 2026)
- RC1: 'Comment on essd-2026-327', Anonymous Referee #1, 10 Aug 2026 reply
Data sets
MODIS/Terra Collection 6.1 Dark Target R. Levy et al. https://doi.org/10.5067/MODIS/MOD04_L2.061
Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations Copernicus Climate Change Service (C3S), Climate Data Store (CDS) https://doi.org/10.24381/cds.68653055
CLAAS-3: CM SAF CLoud property dAtAset using SEVIRI - Edition 3 J. F. Meirink et al. https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003
Model code and software
SEVIRI_ML D. Philipp et al. https://github.com/danielphilipp/seviri_ml
Optimal Retrieval of Aerosol and Cloud (ORAC) R. G. Grainger et al. https://github.com/ORAC-CC/orac
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- 1
Luffarelli et al. are interested in what it takes to quantify aerosol-cloud interactions from satellite, to constrain the effective radiative forcing and to evaluate climate models. They also add an interest in process studies of dust-cloud freezing mechanisms. This seemingly is a project report of a project “SATACI” turned into a journal submission.
The manuscript falls short of a scientific publication. There is a severe lack of substantial insights and reasoning.
A rather useful bit is the short summary of a “community survey” from which a list of five requirements is extracted: (1) aerosol-cloud co-location; (2) high temporal resolution; (3) meaningful aerosol proxy; (4) characterisation of cloud phase and regime; (5) robust uncertainty information. Only (2) and (5) and aspects of (4) are taken up later in the paper.
Section 3 is a lengthy list of seemingly arbitrarily selected satellite datasets for aerosols and clouds, it should go to an appendix or supplement or omitted.
Section 4 is the heart of the paper. It details what is necessary to quantify aci. Unfortunately, this section entirely falls short of what would be necessary. It seems the “requirements” are simply made up in an ad-hoc manner by the authors themselves. In the introduction, the paper correctly states that “Fitness criteria [have to be] defined separately for each application class”. For this, these “application classes” would need to be defined precisely, and the “fitness criteria” should be very precisely defined and motivated. The authors could rely on a meanwhile large body of literature e.g. for the Twomey effect (drop number response to aerosol perturbations), which, however, they do not even discuss (or at best only very superficially so).
There is also a section that describes data analysis for the role of dust in ice formation. This is a topic that is very different from the problem of aerosol-cloud-climate forcings. It is therefore very unclear how this should fit into the same manuscript, unless the introduction and motivation would be largely re-written.
The authors claim to have conducted a “fit for purpose assessment”. This seems a stark claim: they rather do a very superficial, straightforward check of criteria they previously set themselves without much motivation or evidence. A true “fit for purpose” demonstration could e.g. make use of modelling, demonstrating that parameters matching the satellite products allow to effectively retrieve the actual model parameterisation. An alternative could be ground-truth knowledge from aircraft data, and the authors could demonstrate their satellite data obtains the same result as aircraft for the same situation.
Below I also provide a number of specific comments.
L11-15 – These statements are common knowledge in the field. They should be replaced by an innovative conclusion
L17 – ACI are not feedbacks. If the authors wish to really investigate such impacts of ACI on climate feedbacks, they need to be more specific about what they mean
L20 – Why this outdated term “aerosol indirect effects” now?
L28 – The Angström exponent alone has not been used
L47 – Why “radiation budget particularly in the upper troposphere”? Isn’t the Earth radiation budget meant?
L58 - “indicators that can track aerosol–cloud interactions over long time scales” is not an understandable statement. What is meant by the “indicators”? What is meant by “tracking” ACI?
L60 – It is not understandable why the outdated term “aerosol direct effect” is introduced here
L63 – The sentence is not understandable. What is meant by “development of a climate indicator”? Is it “definition”? And then just a few words later, ERFaci and ERFari are defined as these indicators. The rest of the sentence is in that context not understandable.
L66 Why now only aci?
L100-104 – the five points are extraordinarily imbalanced. Point 1 is a very complex endeavour, while 2-4 is merely a list and ticking boxes.
L106 – I already note here that often, the reported uncertainties for satellite products are rather meaningless. An evaluation on whether the stated uncertainty matches the true uncertainty – e.g. via comparison to reference data from aircraft – is necessary.
L109 – Uncertainty is precisely defined as random error. Any known bias simply needs to be subtracted from the dataset.
L120 – The entire Section 3 is a lengthy description of specific retrieval products. It seems this is better just a table and reference list, or at best an appendix or supplementary material
L122 – The limitation to these data providers is rather puzzling. Many previous ACI studies relied on NASA data. However, later also MODIS data are introduced, contradicting this Europe-centered list.
L320 – From where again the antique term “indirect effect”?
L322 – “Leveraging long-term datasets” – at this point is is completely unclear to the reader why it is long-term data that would be the philosopher’s stone to solve the aci problem. Nor is it clear that ESA CCI and/or geostationary data are the key
L328 – It is not a priori clear that high temporal resolution is required, see e.g. Feingold et al. ACP 2025 for a recent discussion
L330 – “required aerosol proxies include AOD and FMAOD” is not evident. See e.g. Jia et al. Sci Adv 2026 for a recent discussion
L330 – The notion that CF is a required quantity implies aggregation of satellite retrievals at a scale coarser than the satellite resolution. Is this really what is meant? A discussion on scales would be useful.
L332 – From there it conclusion that 10 km is a required resolution for aerosol?
L334 – See above, the request for hourly data is not evident.
L335 – From where the 1 km resolution? From modelling studies it is known that 1 km is too coarse a resolution for many cloud problems. For shallow clouds, 50 m is a more appropriate resolution
L337 – The “one year” requirement is very unclear. The requirement, rather, is on the number of available data for a certain condition. If data are strictly filtered (e.g. for cloud type, aerosol type, region, season etc.; and of course variability in aerosol concentrations) one year is clearly insufficient.
L340 – The criterion of aerosol-cloud co-location is omitted yet – as correctly stated in L74 – crucial.
L344 – The published range is much larger, see e.g. Schmidt et al. (ACP 2015) for a list that was much larger even more than 10 years ago
L348 – “commonly considered” the authors should provide the references.
L378 – “good consistency” needs to be quantified
L383 – It is unclear what this study – dust – ice interactions – has to do with ACI. The former section was motivated by a better quantification of climate change forcing. Dust, in turn, is just an internal part of the climate system so a very different problem.
L397 – The most important metric to me seems dust number concentration at cirrus altitude. Point (4) fails to precisely say this.
L442 – At this point, I don’t understand what the authors might mean with “the indicator calculations”. What exactly is calculated, indicating what?
L443 – What is meant by “to parameterise aerosol indirect effects”? What exactly should be parameterised, cloud droplet activation?
L448 – What is “the climate indicator”?
L453 – “fitness for purpose”: the authors first need to define the purpose.
L476 – The authors posit “dataset stability” would be important. But at this point, the reader doesn’t know what the “indicator” should be, what its definition is, what its aim is, who would be interested.
L540 – I applaud this meaningful sentence. Indeed, the entire manuscript should have started by defining the “scientific application” precisely. Had it done so, a “purpose” could have been defined in a meaningful way, from which perhaps relevant fitness criteria could have been derived and later assessed.