the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Nov 2026
Cloud fraction driven thermal-infrared radiative effects of Arctic low-level clouds in marine cold air outbreaks
Abstract. Arctic marine cold air outbreaks (MCAOs) are important meridional air mass transformation events accompanied by spatially extensive cloud formation. Here, the top-of-atmosphere radiative effect of these MCAO clouds is analyzed using airborne thermal-infrared imaging observations conducted during the HALO-(AC)3 research campaign. Based on the capabilities of the high-resolution (∼ 10 m) imaging, a fine-scale cloud mask is derived. Furthermore, two cloud radiative effects (CREs), an all-sky CRE (including both cloudy and cloud-free areas) and a cloud-only CRE, are quantified. This way, the effects of cloud fraction and cloud properties are separated, and the drivers of the all-sky CRE are evaluated. It is found that the increase of the all-sky CRE downstream in an MCAO is primarily driven by cloud fraction, in particular for a strong MCAO event. In a next step, the approach applicability and its sensitivity to coarser spatial sampling is evaluated by scaling the airborne images down to lower resolutions of satellite observations provided by EarthCARE MSI (500 m) and Terra/Aqua MODIS (1000 m). This coarsening results in an underestimation of the CREs, with maximum values found in the strong MCAO case and at MODIS resolution with −1.9 W m−2 (all-sky CRE) and −2.9 W m−2 (cloud-only CRE). The relations between CREs and cloud fraction can still be identified for lower spatial resolutions but lack in robustness. This highlights the importance of high-resolution airborne thermal-infrared imaging in support of large-scale spaceborne cloud observations to determine CREs.
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Status: open (until 21 Dec 2026)
- RC1: 'Comment on egusphere-2026-5857', Anonymous Referee #1, 09 Nov 2026 reply
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RC2: 'Comment on egusphere-2026-5857', Anonymous Referee #2, 09 Nov 2026
reply
The authors use brightness temperature remote sensing measurements collected during an Arctic airborne campaign to generate cloud masks using a novel approach and compute longwave cloud radiative effects (CREs). The latter aspect is also mimicked for satellite imagers by degrading campaign measurements to coarser resolutions. The authors conclude that all-sky CREs are largely driven by cloud cover and that satellite-based estimates would be biased.
There are several minor and major concerns that the authors should address, as listed below. To proceed to the discussion section, the authors should address at least the major comments.
Major concerns
Cloud cover vs. cloud fraction – The authors use “cloud fraction” throughout the manuscript. It is my understanding that the term “fraction” is primarily used to express cloudy vs. clear proportions of a specific vertical layer (resulting in cloud fraction profiles as a function of height). In contrast, whenever referring to a vertically integrated metric (as is the case in this work), it is much more common to use the term “cloud cover”. I strongly recommend adhering to this standard and changing “fraction” to “cover” throughout.
Mimicking satellite perspective – The authors apply a new methodology to field campaign measurements and then apply it again to a coarsened version of that data, to mimic the satellite perspective. In my understanding, satellites employ a very different operational approach to retrieve cloudy vs. clear pixels and it is not clear if the operational algorithm also results in biased CREs. Throughout the paper, the authors should stress that the underestimation is based on a non-operational methodology and, therefore, merely hypothetical.
Clear-sky references – As the authors point out themselves, the clear-sky references may still contain clouds and that is problematic when estimating CREs. The authors should provide further analysis to justify their threshold. For example, can underlying SST be used to assess brightness temperature thresholds? Are there visible measurements that allow for the retrieval of cloud optical depth (COD)? If so, which COD values do brightness temperature thresholds correspond to, and is this correspondence a function of “time since sea ice”?
Minor comments
l.2 Is this really TOA or rather TOA-like? Please clarify.
ll. 19/20 It is not clear if “x/y” applies to all the following statements in form of (statement1/statement2). Please clarify.
l. 33 Perhaps better use “approximately” aligned.
l. 34 It is not clear which “process” is referred to here. In case the authors refer to the cloud streets, perhaps better use “organized convection” instead.
l. 36 There are other references that this list omits. I suggest including Abel et al. (2017) and Tornow et al. (2021).
l. 41 Similar to the above comment, the referencing could be improved. I suggest adding Tornow et al. (2023) and Juliano et al. (2026).
References
Abel, S.J., Boutle, I.A., Waite, K., Fox, S., Brown, P.R., Cotton, R., Lloyd, G., Choularton, T.W. and Bower, K.N., 2017. The role of precipitation in controlling the transition from stratocumulus to cumulus clouds in a Northern Hemisphere cold-air outbreak. Journal of the Atmospheric Sciences, 74(7), pp.2293-2314.
Juliano, T.W., Tornow, F., Fridlind, A.M., Ackerman, A.S., Elsaesser, G.S., Geerts, B., Lackner, C.P., Painemal, D., Silber, I., Ovchinnikov, M. and Svensson, G., 2026. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment model-observation intercomparison project (COMBLE-MIP)–Part 1: Model specification, observational constraints, and preliminary findings. Atmospheric Chemistry and Physics, 26(18), pp.13267-13302.
Tornow, F., Ackerman, A.S. and Fridlind, A.M., 2021. Preconditioning of overcast-to-broken cloud transitions by riming in marine cold air outbreaks. Atmospheric Chemistry and Physics, 21(15), pp.12049-12067.
Tornow, F., Ackerman, A.S., Fridlind, A.M., Tselioudis, G., Cairns, B., Painemal, D. and Elsaesser, G., 2023. On the impact of a dry intrusion driving cloud-regime transitions in a midlatitude cold-air outbreak. Journal of the Atmospheric Sciences, 80(12), pp.2881-2896.
Citation: https://doi.org/10.5194/egusphere-2026-5857-RC2
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The manuscript investigates the thermal-infrared cloud radiative effects (CREs) of Arctic low-level clouds during two marine cold air outbreak (MCAO) events observed during the HALO-(AC)³ campaign. Using high-resolution airborne thermal-infrared imaging observations, the authors develop a cloud classification method and distinguish between all-sky and cloud-only CREs to investigate the relative contributions of cloud fraction and cloud properties. This study provides a valuable extension by combining high-resolution TIR observations with pixel-wise CRE estimates and examining their sensitivity to spatial resolution. I recommend that the authors address the following major and minor comments before the manuscript can be considered for publication.
Major comments:
1. The discussion of cloud-street formation mechanisms, LES, and numerical model representation (lines 45–50) is somewhat disconnected from the primary focus of the manuscript. I suggest placing greater emphasis on how the spatial heterogeneity of MCAO clouds affects their thermal-infrared radiative effects.
2. Although the authors highlight the importance of high-resolution observations, the specific knowledge gap is not clearly established. In particular, the Introduction should explain what remains unknown about the relative contributions of cloud fraction and cloud radiative properties to CRE, and why existing observations are insusfficient to address this question.
3. The scientific motivation is not fully explained when the objectives are introduced: The connection between the two main objectives—identifying the drivers of CRE and evaluating the sensitivity of CRE estimates to spatial resolution—could be more clearly explained in the introduction. This would help readers understand how the resolution-degradation experiment contributes to the central scientific question rather than appearing as a separate analysis.
4. The definition of the cloud-free reference irradiance is important for the quantitative CRE estimates. The authors use the 95th percentile of irradiance from pixels classified as cloud-free for each MCAO case. Although Sect. 5.2 acknowledges that truly cloud-free pixels may be absent during the strong MCAO, the uncertainty associated with this reference choice is not quantitatively evaluated. I recognize that Appendix A includes a sensitivity analysis of the cloud-mask thresholds, which also indirectly affects the cloud-free reference. However, this does not isolate the sensitivity to the reference definition itself. I suggest conducting an additional sensitivity analysis in which the cloud mask is held fixed while alternative cloud-free reference definitions are tested, for example using different percentiles of the cloud-free irradiance distribution. This would help quantify the uncertainty in the absolute CRE estimates and the differences between the two MCAO cases.
5. The authors conclude that the downstream increase in all-sky CRE during MCAOs is primarily driven by increasing cloud fraction. This interpretation is supported by the observed downstream increase in cloud fraction (Fig. 5), the relatively stable cloud-only CRE compared with all-sky CRE (Fig. 6), and the strong correlation between all-sky CRE and cloud fraction (Fig. 7). While these results are consistent with the proposed interpretation, I am not fully convinced that they provide sufficient evidence to establish cloud fraction as the primary driver. Since all-sky CRE is defined as an area-weighted average of the radiative effects of different cloud classes, a strong relationship between all-sky CRE and cloud fraction is partly expected from its mathematical definition. Therefore, the high coefficients of determination shown in Fig. 7 do not necessarily provide independent evidence that cloud fraction is the dominant physical driver, nor do they quantify the relative contributions of cloud fraction and cloud radiative properties. Furthermore, changes in cloud-type composition may also contribute to the observed downstream evolution of all-sky CRE. I suggest that the authors provide a more explicit quantitative decomposition of the contributions from cloud fraction and cloud radiative properties, including the potential influence of changes in cloud-type composition. If such an analysis is beyond the scope of the study, I recommend moderating the conclusion and presenting the results as evidence of a strong association between increasing cloud fraction and downstream changes in all-sky CRE.
Minor comments
12: “lack in robustness”: “lack robustness” or “are less robust”
261: “pushbrooom images” typo
340: “this relation is three times larger”: “the slope of this relationship is approximately three times larger”
341: “with time since sea ice indicating that…. “ did you mean “time since crossing the sea ice edge”?