Reconstructing albedo from mean cloud properties

Wojciechowska, Izabela; Gryspeerdt, Edward

doi:10.5194/acp-26-4571-2026

Articles | Volume 26, issue 7

https://doi.org/10.5194/acp-26-4571-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-26-4571-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 7

Research article

|

07 Apr 2026

Research article |

| 07 Apr 2026

Reconstructing albedo from mean cloud properties

Izabela Wojciechowska and Edward Gryspeerdt

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Final revised paper (published on 07 Apr 2026)
Preprint (discussion started on 20 Oct 2025)

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-4784', Anonymous Referee #1, 13 Nov 2025

Please see the attached PDF for the review.

Citation: https://doi.org/10.5194/egusphere-2025-4784-RC1
- AC1: 'Reply on RC1', Izabela Wojciechowska, 13 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4784/egusphere-2025-4784-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4784-AC1
CC1:
'Comment on egusphere-2025-4784', Jesse Loveridge, 14 Nov 2025

Comment on egusphere-2025-4784
The subject of how mean albedo relates to mean cloud properties has been the focus of decades of investigative effort, but these works and their insight are not represented or cited in this article. I have listed a few references below as a starting point (e.g., Cahalan et al., 1994; Oreopoulos & Davies; 1998; Rossow et al. 2002), including some about how the issue of cloud heterogeneity is represented in the radiative transfer schemes of climate models. I recommend the authors read them and other associated works (far more relevant works exist than I have listed) and revise this article to place their results within the context of our existing knowledge about radiative transfer through cloudy atmospheres.
References
Barker, H. W. and Räisänen, P.: Radiative sensitivities for cloud structural properties that are unresolved by conventional GCMs, Quarterly Journal of the Royal Meteorological Society, 131, 3103–3122, https://doi.org/10.1256/qj.04.174, 2005.
Barker, H. W., Wiellicki, B. A., and Parker, L.: A Parameterization for Computing Grid-Averaged Solar Fluxes for Inhomogeneous Marine Boundary Layer Clouds. Part II: Validation Using Satellite Data, Journal of the Atmospheric Sciences, 53, 2304–2316, https://doi.org/10.1175/1520-0469(1996)053%253C2304:APFCGA%253E2.0.CO;2, 1996.
Boutle, I. A., Abel, S. J., Hill, P. G., and Morcrette, C. J.: Spatial variability of liquid cloud and rain: observations and microphysical effects, Quarterly Journal of the Royal Meteorological Society, 140, 583–594, https://doi.org/10.1002/qj.2140, 2014.
Cahalan, R. F., Ridgway, W., Wiscombe, W. J., Bell, T. L., and Snider, J. B.: The Albedo of Fractal Stratocumulus Clouds, Journal of the Atmospheric Sciences, 51, 2434–2455, https://doi.org/10.1175/1520-0469(1994)051%253C2434:TAOFSC%253E2.0.CO;2, 1994.
Hill, P. G., Morcrette, C. J., and Boutle, I. A.: A regime-dependent parametrization of subgrid-scale cloud water content variability, Quarterly Journal of the Royal Meteorological Society, 141, 1975–1986, https://doi.org/10.1002/qj.2506, 2015.
Kawai, H. and Teixeira, J.: Probability Density Functions of Liquid Water Path and Total Water Content of Marine Boundary Layer Clouds: Implications for Cloud Parameterization, Journal of Climate, 25, 2162–2177, https://doi.org/10.1175/JCLI-D-11-00117.1, 2012.
Oreopoulos, L. and Cahalan, R. F.: Cloud Inhomogeneity from MODIS, Journal of Climate, 18, 5110–5124, https://doi.org/10.1175/JCLI3591.1, 2005.
Oreopoulos, L. and Davies, R.: Plane Parallel Albedo Biases from Satellite Observations. Part I: Dependence on Resolution and Other Factors, Journal of Climate, 11, 919–932, https://doi.org/10.1175/1520-0442(1998)011%253C0919:PPABFS%253E2.0.CO;2, 1998.
Rossow, W. B., Delo, C., and Cairns, B.: Implications of the Observed Mesoscale Variations of Clouds for the Earth’s Radiation Budget, Journal of Climate, 15, 557–585, https://doi.org/10.1175/1520-0442(2002)015%253C0557:IOTOMV%253E2.0.CO;2, 2002.
Zhang, F., Liang, X.-Z., Li, J., and Zeng, Q.: Dominant roles of subgrid-scale cloud structures in model diversity of cloud radiative effects, Journal of Geophysical Research: Atmospheres, 118, 7733–7749, https://doi.org/10.1002/jgrd.50604, 2013.

Citation: https://doi.org/10.5194/egusphere-2025-4784-CC1
- AC4: 'Reply on CC1', Izabela Wojciechowska, 13 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4784/egusphere-2025-4784-AC4-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4784-AC4
RC2:
'Additional comment from Ref#1 on egusphere-2025-4784', Anonymous Referee #1, 14 Nov 2025

After thinking about the approach used in the paper a little more I (Referee #1) have a further, potentially more serious, concern about the bias calculations for the albedo estimated from the Cf-Nd-LWP binned approach. It particularly applies for the estimate from the 1x1 degree resolution binned estimates, but it would be worth looking into for the global estimates too. I’m afraid that this may affect the conclusions of the paper and require some additional analysis and re-writing (although likely only for the 1x1 degree resolution part I think).
For the 1x1 deg correction is there a chance that each bin in the CF-LWP-Nd space is only filled once so that the mean over time for each bin contains only one value. Then when matching the daily MODIS datapoints to a value from the look-up-table, the value selected will not be the time-average of several points, but simply the same value again? This might then lead to the very small errors observed (assuming that the kernel method is a good match to the CERES observation). So this then becomes a test of the kernel method rather than testing the utility of using a time averaged look-up-table estimate. I.e., it would not be a good test of how well the look-up-table approach would work for new datapoints given just the values of CF, Nd and LWP (without the extra information that presumably goes into the kernel calculation).
For each 1x1 datapoint in the daily MODIS record for 2003–2021 (19 years) there are around 19*365 = 6935 data points. But there are a total of 50*40*30=60,000 bins in the CF-Nd-LWP space used. This might make it likely that some (all?) of the bins are only used once – although some bins are likely more populated than others.
Therefore, you should examine how many datapoints are being used to calculate the average albedo in each bin for the 1x1 degree binned look-up-tables and come up with a statistical measure for how many datapoints you would need in a given bin for the bias estimate to be useful (i.e., a useful measure of how good the look-up-table approach would be for estimating the albedo of datapoints that weren’t used in the look-up-table (using only CF, Nd and LWP). It would be good to do this for the other estimates too (e.g., the global mean binned look-up-table, 5x5 degree, etc.).
Perhaps a better approach would be to build the look-up-table using only part of the MODIS/CERES record and then to calculate the biases vs CERES using the other part of the MODIS/CERES record. This would ensure that the same data is not used for the kernel calculation and the bias testing.

Citation: https://doi.org/10.5194/egusphere-2025-4784-RC2
- AC2: 'Reply on RC2', Izabela Wojciechowska, 13 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4784/egusphere-2025-4784-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4784-AC2
RC3:
'Comment on egusphere-2025-4784', Anonymous Referee #2, 21 Nov 2025

Please find the review attached.

Citation: https://doi.org/10.5194/egusphere-2025-4784-RC3
- AC3: 'Reply on RC3', Izabela Wojciechowska, 13 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4784/egusphere-2025-4784-AC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4784-AC3

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

AR by Izabela Wojciechowska on behalf of the Authors (13 Feb 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (01 Mar 2026) by Tom Goren

AR by Izabela Wojciechowska on behalf of the Authors (05 Mar 2026)

Short summary

Marine clouds play a major role in cooling the Earth by reflecting sunlight, but predicting how bright they appear is not straightforward. We used satellite data from 2003 to 2021 and examined whether the brightness of marine clouds can be explained by their main properties. We found that the relationship varies across the globe, and that regional differences need to be considered to better understand cloud impact on climate.