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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Apr 2026
Research article |
| 07 Apr 2026
| 07 Apr 2026
Reconstructing albedo from mean cloud properties
Izabela Wojciechowska
CORRESPONDING AUTHOR
Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmiescie 30, 00-927 Warsaw, Poland
Edward Gryspeerdt
Department of Physics, Imperial College London, London, SW7 2BX, UK
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Roger Teoh, Ulrich Schumann, Edward Gryspeerdt, Marc Shapiro, Jarlath Molloy, George Koudis, Christiane Voigt, and Marc E. J. Stettler
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Hailing Jia, Johannes Quaas, Edward Gryspeerdt, Christoph Böhm, and Odran Sourdeval
Atmos. Chem. Phys., 22, 7353–7372, https://doi.org/10.5194/acp-22-7353-2022, https://doi.org/10.5194/acp-22-7353-2022, 2022
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Rebecca J. Murray-Watson and Edward Gryspeerdt
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Edward Gryspeerdt, Tom Goren, and Tristan W. P. Smith
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Cloud responses to aerosol are time-sensitive, but this development is rarely observed. This study uses isolated aerosol perturbations from ships to measure this development and shows that macrophysical (width, cloud fraction, detectability) and microphysical (droplet number) properties of ship tracks vary strongly with time since emission, background cloud and meteorological state. This temporal development should be considered when constraining aerosol–cloud interactions with observations.
Cited articles
Barker, H. W. and Räisänen, P.: Radiative sensitivities for cloud structural properties that are unresolved by conventional GCMs, Q. J. R. Meteorol. Soc., 131, 3103–3122, https://doi.org/10.1256/qj.04.174, 2005.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020.
Bender, F. A.-M., Charlson, R. J., Ekman, A. M. L., and Leahy, L. V: Quantification of Monthly Mean Regional-Scale Albedo of Marine Stratiform Clouds in Satellite Observations and GCMs, J. Appl. Meteorol. Climatol., 50, 2139–2148, https://doi.org/10.1175/JAMC-D-11-049.1, 2011.
Bretherton, C. S., McCoy, I. L., Mohrmann, J., Wood, R., Ghate, V., Gettelman, A., Bardeen, C. G., Albrecht, B. A., and Zuidema, P.: Cloud, Aerosol, and Boundary Layer Structure across the Northeast Pacific Stratocumulus–Cumulus Transition as Observed during CSET, Mon. Weather Rev., 147, 2083–2103, https://doi.org/10.1175/MWR-D-18-0281.1, 2019.
Budyko, M. I.: The effect of solar radiation variations on the climate of the Earth, Tellus, 21, 611–619, https://doi.org/10.1111/j.2153-3490.1969.tb00466.x, 1969.
Cahalan, R. F., Ridgway, W., Wiscombe, W. J., Bell, T. L., and Snider, J. B.: The Albedo of Fractal Stratocumulus Clouds, J. Atmos. Sci., 51, 2434–2455, https://doi.org/10.1175/1520-0469(1994)051<2434:TAOFSC>2.0.CO;2, 1994.
Ceppi, P., McCoy, D. T., and Hartmann, D. L.: Observational evidence for a negative shortwave cloud feedback in middle to high latitudes, Geophys. Res. Lett., 43, 1331–1339, https://doi.org/10.1002/2015GL067499, 2016.
Cess, R. D.: Climate Change: An Appraisal of Atmospheric Feedback Mechanisms Employing Zonal Climatology, J. Atmos. Sci., 33, 1831–1843, https://doi.org/10.1175/1520-0469(1976)033<1831:CCAAOA>2.0.CO;2, 1976.
Choudhury, G. and Goren, T.: Thin Clouds Control the Cloud Radiative Effect Along the Sc-Cu Transition, J. Geophys. Res.-Atmos., 129, e2023JD040406, https://doi.org/10.1029/2023JD040406, 2024.
Duran, B. M., Wall, C. J., Lutsko, N. J., Michibata, T., Ma, P.-L., Qin, Y., Duffy, M. L., Medeiros, B., and Debolskiy, M.: A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models, Atmos. Chem. Phys., 25, 2123–2146, https://doi.org/10.5194/acp-25-2123-2025, 2025.
Engström, A., Bender, F. A.-M., and Karlsson, J.: Improved Representation of Marine Stratocumulus Cloud Shortwave Radiative Properties in the CMIP5 Climate Models, J. Clim., 27, 6175–6188, 2014.
Engström, A., Bender, F. A.-M., Charlson, R. J., and Wood, R.: Geographically coherent patterns of albedo enhancement and suppression associated with aerosol sources and sinks, Tellus B Chem. Phys. Meteorol., https://doi.org/10.3402/tellusb.v67.26442, 2015.
Feingold, G., Balsells, J., Glassmeier, F., Yamaguchi, T., Kazil, J., and McComiskey, A.: Analysis of albedo versus cloud fraction relationships in liquid water clouds using heuristic models and large eddy simulation, J. Geophys. Res.-Atmos., 122, 7086–7102, https://doi.org/10.1002/2017JD026467, 2017.
Goren, T., Sourdeval, O., Kretzschmar, J., and Quaas, J.: Spatial Aggregation of Satellite Observations Leads to an Overestimation of the Radiative Forcing due to Aerosol-Cloud Interactions, Geophys. Res. Lett., 50, e2023GL105282, https://doi.org/10.1029/2023GL105282, 2023.
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, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., Unglaub, C., Gettelman, A., and Christensen, M.: Constraining the aerosol influence on cloud liquid water path, Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp-19-5331-2019, 2019.
Gryspeerdt, E., Mülmenstädt, J., Gettelman, A., Malavelle, F. F., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., Wang, M., and Zhang, K.: Surprising similarities in model and observational aerosol radiative forcing estimates, Atmos. Chem. Phys., 20, 613–623, https://doi.org/10.5194/acp-20-613-2020, 2020.
Gryspeerdt, E., McCoy, D. T., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J., Sorooshian, A., and Ziemba, L.: The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data, Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, 2022a.
Gryspeerdt, E., McCoy, D., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J., Sorooshian, A., and Ziemba, L.: Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/864a46cc65054008857ee5bb772a2a2b, 2022b.
Hansen, J., Lacis, A., Rind, D., Russell, G., Stone, P., Fung, I., Ruedy, R., and Lerner, J.: Climate Sensitivity: Analysis of Feedback Mechanisms, in: Climate Processes and Climate Sensitivity, 130–163, https://doi.org/10.1029/GM029p0130, 1984.
Hao, D., Wen, J., Xiao, Q., Wu, S., Lin, X., Dou, B., You, D., and Tang, Y.: Simulation and Analysis of the Topographic Effects on Snow-Free Albedo over Rugged Terrain, Remote Sens., 10, https://doi.org/10.3390/rs10020278, 2018.
Hao, D., Wen, J., Xiao, Q., Lin, X., You, D., Tang, Y., Liu, Q., and Zhang, S.: Sensitivity of Coarse-Scale Snow-Free Land Surface Shortwave Albedo to Topography, J. Geophys. Res.-Atmos., 124, 9028–9045, https://doi.org/10.1029/2019JD030660, 2019.
Hartmann, D. L. and Short, D. A.: On the Use of Earth Radiation Budget Statistics for Studies of Clouds and Climate, J. Atmos. Sci., 37, 1233–1250, https://doi.org/10.1175/1520-0469(1980)037<1233:OTUOER>2.0.CO;2, 1980.
Herman, B. M. and Browning, S. R.: The Effect of Aerosols on the Earth-Atmosphere Albedo, J. Atmos. Sci., 32, 1430–1445, https://doi.org/10.1175/1520-0469(1975)032<1430:TEOAOT>2.0.CO;2, 1975.
Loeb, N. G., Wielicki, B. A., Rose, F. G., and Doelling, D. R.: Variability in global top-of-atmosphere shortwave radiation between 2000 and 2005, Geophys. Res. Lett., 34, https://doi.org/10.1029/2006GL028196, 2007.
Loeb, N. G., Ham, S.-H., Allan, R. P., Thorsen, T. J., Meyssignac, B., Kato, S., Johnson, G. C., and Lyman, J. M.: Observational Assessment of Changes in Earth's Energy Imbalance Since 2000, Surv. Geophys., 45, 1757–1783, https://doi.org/10.1007/s10712-024-09838-8, 2024.
McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., and Bender, F. A.-M.: The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback, Geophys. Res. Lett., 50, e2022GL101042, https://doi.org/10.1029/2022GL101042, 2023.
Miao, X., Guo, W., Qiu, B., Lu, S., Zhang, Y., Xue, Y., and Sun, S.: Accounting for Topographic Effects on Snow Cover Fraction and Surface Albedo Simulations Over the Tibetan Plateau in Winter, J. Adv. Model. Earth Syst., 14, e2022MS003035, https://doi.org/10.1029/2022MS003035, 2022.
Mueller, R., Trentmann, J., Träger-Chatterjee, C., Posselt, R., and Stöckli, R.: The Role of the Effective Cloud Albedo for Climate Monitoring and Analysis, Remote Sens., 3, 2305–2320, https://doi.org/10.3390/rs3112305, 2011.
NASA/LARC/SD/ASDC: CERES Time-Interpolated TOA Fluxes, Clouds and Aerosols Daily Aqua Edition4A, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/AQUA/CERES/SSF1DEGDAY_L3.004A, 2015a.
NASA/LARC/SD/ASDC: CERES Time-Interpolated TOA Fluxes, Clouds and Aerosols Daily Terra Edition4A, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/TERRA/CERES/SSF1DEGDAY_L3.004, 2015b.
Nkemdirim, L. C.: A Note on the Albedo of Surfaces, J. Appl. Meteorol. Climatol., 11, 867–874, https://doi.org/10.1175/1520-0450(1972)011<0867:ANOTAO>2.0.CO;2, 1972.
North, G. R., Cahalan, R. F., and Coakley Jr., J. A.: Energy balance climate models, Rev. Geophys., 19, 91–121, https://doi.org/10.1029/RG019i001p00091, 1981.
Oreopoulos, L. and Davies, R.: Plane Parallel Albedo Biases from Satellite Observations. Part I: Dependence on Resolution and Other Factors, J. Clim., 11, 919–932, https://doi.org/10.1175/1520-0442(1998)011<0919:PPABFS>2.0.CO;2, 1998.
Platnick, S. and Twomey, S.: Remote sensing the susceptibility of cloud albedo to changes in drop concentration, Atmos. Res., 34, 85–98, https://doi.org/10.1016/0169-8095(94)90082-5, 1994.
Platnick, S., King, M., and Hubanks, P.: MODIS Atmosphere L3 Daily Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD08_D3.061, 2017a.
Platnick, S., King, M., and Hubanks, P.: MODIS Atmosphere L3 Daily Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MYD08_D3.061, 2017b.
Quaas, J., Boucher, O., Bellouin, N., and Kinne, S.: Satellite-based estimate of the direct and indirect aerosol climate forcing, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD008962, 2008.
Rosenfeld, D., Kaufman, Y. J., and Koren, I.: Switching cloud cover and dynamical regimes from open to closed Benard cells in response to the suppression of precipitation by aerosols, Atmos. Chem. Phys., 6, 2503–2511, https://doi.org/10.5194/acp-6-2503-2006, 2006.
Rossow, W. B., Delo, C., and Cairns, B.: Implications of the Observed Mesoscale Variations of Clouds for the Earth's Radiation Budget, J. Clim., 15, 557–585, https://doi.org/10.1175/1520-0442(2002)015<0557:IOTOMV>2.0.CO;2, 2002.
Sailor, D. J.: Simulated Urban Climate Response to Modifications in Surface Albedo and Vegetative Cover, J. Appl. Meteorol. Climatol., 34, 1694–1704, https://doi.org/10.1175/1520-0450-34.7.1694, 1995.
Stephens, G. L.: Cloud Feedbacks in the Climate System: A Critical Review, J. Clim., 18, 237–273, https://doi.org/10.1175/JCLI-3243.1, 2005.
Stephens, G. L., O'Brien, D., Webster, P. J., Pilewski, P., Kato, S., and Li, J.: The albedo of Earth, Rev. Geophys., 53, 141–163, https://doi.org/10.1002/2014RG000449, 2015.
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, Bull. Am. Meteorol. Soc., 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1, 2009.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974.
Vonder Haar, T. H. and Suomi, V. E.: Measurements of the Earth's Radiation Budget from Satellites During a Five-Year Period. Part I: Extended Time and Space Means, J. Atmos. Sci., 28, 305–314, https://doi.org/10.1175/1520-0469(1971)028<0305:MOTERB>2.0.CO;2, 1971.
Wall, C. J., Norris, J. R., Possner, A., McCoy, D. T., McCoy, I. L., and Lutsko, N. J.: Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean, Proc. Natl. Acad. Sci. USA, 119, 1–9, 2022.
Wielicki, B. A., Wong, T., Loeb, N., Minnis, P., Priestley, K., and Kandel, R.: Changes in Earth's Albedo Measured by Satellite, Science, 308, 825, https://doi.org/10.1126/science.1106484, 2005.
Wood, R. and Bretherton, C. S.: On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability, J. Clim., 19, 6425–6432, https://doi.org/10.1175/JCLI3988.1, 2006.
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth, J. Clim., 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1, 2012.
Zhang, J. and Feingold, G.: Distinct regional meteorological influences on low-cloud albedo susceptibility over global marine stratocumulus regions, Atmos. Chem. Phys., 23, 1073–1090, https://doi.org/10.5194/acp-23-1073-2023, 2023.
Zhang, Y., Jin, Z., and Sikand, M.: The Top-of-Atmosphere, Surface and Atmospheric Cloud Radiative Kernels Based on ISCCP-H Datasets: Method and Evaluation, J. Geophys. Res.-Atmos., 126, e2021JD035053, https://doi.org/10.1029/2021JD035053, 2021.
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.
Marine clouds play a major role in cooling the Earth by reflecting sunlight, but predicting how...
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