Abstract
As an important parameter of the Earth’s energy budget, the planetary albedo of Earth varies with the dynamics of atmospheric and surface variables. In this study, we investigated the spatiotemporal characteristics and driving factors of the global planetary albedo using the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) dataset and the Geodetector method. The results revealed that the planetary albedo can be decomposed into atmospheric and surface contributions, and the planetary albedo in the middle and low latitudes was predominantly affected by the atmospheric contribution. The global planetary albedo and the atmospheric and surface contributions exhibited decreasing trends of − 0.0020, − 0.0015, and − 0.0004/decade from 2001 to 2018, respectively, which were closely related to the variations of atmospheric and surface variables. The cloud fraction was the driving factor of the atmospheric contribution in the middle and low latitudes, and its influence was further enhanced by the aerosol optical thickness (AOT), ice water path (IWP), and liquid water path (LWP). The snow/ice coverage and normalized difference vegetation index (NDVI) were the driving factors of the surface contribution in the snow/ice-covered and vegetated areas, respectively. The interaction relationships between the surface variables were mainly bi-enhanced and nonlinearly enhanced. These results provide useful information about the driving factors of the planetary albedo and are benefit for improving the parametrization of the planetary albedo.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The CERES EBAF Edition 4.1 dataset can be downloaded from https://asdc.larc.nasa.gov/project/CERES/CERES_EBAF_Edition4.1, the CERS SSF dataset can be downloaded from https://asdc.larc.nasa.gov/project/CERES/CER_SSF_Aqua-FM3-MODIS_Edition4A, the MERRA-2 data can be downloaded from https://disc.gsfc.nasa.gov/datasets?project=MERRA-2, and the MODIS NDVI data can be downloaded from https://lpdaac.usgs.gov/products/mod13c2v006.
Code availability
Code used in this study is available upon request.
References
Abe M, Takata K, Kawamiya M, Watanabe S (2017) Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM. J Gerontol Ser A Biol Med Sci 122(17):9245–9261. https://doi.org/10.1002/2017JD026957
Barkstrom B (1984) The Earth radiation budget experiment (ERBE). Bull Am Meteor Soc 65(11):1170–1185. https://doi.org/10.1175/1520-0477(1984)065%3c1170:TERBE%3e2.0.CO;2
Bender FAM, Rodhe H, Charlson RJ, Ekman AML, Loeb N (2006) 22 views of the global albedo—comparison between 20 GCMs and two satellites. Tellus A 58(3):320–330. https://doi.org/10.1111/j.1600-0870.2006.00181.x
Budyko MI (1969) The effect of solar radiation variations on the climate of the Earth. Tellus 21(5):611–619. https://doi.org/10.1111/j.2153-3490.1969.tb00466.x
Donohoe A, Battisti DS (2011) Atmospheric and Surface Contributions to Planetary Albedo. J Clim 24(16):4402–4418. https://doi.org/10.1175/2011JCLI3946.1
Fu Q, Liou KN (1993) Parameterization of the radiative properties of cirrus clouds.J Atmos Sci 50:2008–2025. https://doi.org/10.1175/1520-0469(1993)050%3c2008:POTRPO%3e2.0.CO;2
Gelaro R, McCarty W, Suarez MJ, Todling R, Molod A, Takacs L, Randles CA et al (2017) The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J Clim 30(14):5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1
He M, Yongxiang Hu, Chen N, Wang D, Huang J, Stamnes K (2019) High cloud coverage over melted areas dominates the impact of clouds on the albedo feedback in the Arctic. Sci Rep 9(1):9529. https://doi.org/10.1038/s41598-019-44155-w
Hilboll A, Richter A, Burrows JP (2013) Long-term changes of tropospheric NO2 over megacities derived from multiple satellite instruments. Atmos Chem Phys 13(8):4145–4169. https://doi.org/10.5194/acp-13-4145-2013
Jian B, Li J, Wang G, He Y, Han Y, Zhang M, Huang J (2018) The Impacts of atmospheric and surface parameters on long-term variations in the planetary albedo. J Clim 31(21):8705–8718. https://doi.org/10.1175/JCLI-D-17-0848.1
Jian B, Li J, zhao, Yuxin, Yongli He, Jing Wang, Jianping Huang. (2020) Evalution of the CMIP6 planetary albedo climatology using satellite obsvervations. Clim Dyn 54:5145–5161. https://doi.org/10.1007/s00382-020005277-4
Kaskaoutis D, Shailesh Kharol PR, Sinha R, Singh KB, Waseem Md, Sharma M (2011) Contrasting Aerosol Trends Over South Asia During the Last Decade Based on MODIS Observations. Atmospheric Measurement Techniques and Discussions 4:5275–5323. https://doi.org/10.5194/amtd-4-5275-2011
Kato S, Loeb NG, Minnis P, Francis JA, Charlock TP, Rutan DA, Clothiaux EE, Sun-Mack S (2006) Seasonal and interannual variations of top-of-atmosphere irradiance and cloud cover over polar regions derived from the CERES data set. Geophys Res Lett 33(19):L19804. https://doi.org/10.1029/2006GL026685
Kato S, Loeb NG, Rutan DA, Rose FG (2015) Clouds and the Earth’s Radiant Energy System (CERES) Data Products for Climate Research. J Meteorol Soc Jpn 93(6):597–612. https://doi.org/10.2151/jmsj.2015-048
Kato, Seiji, Rose, Fred G., Rutan, David A., Thorsen, Tyler J., Loeb, Norman G., Doelling, David R., Huang, Xianglei, Smith, William L., Su, Wenying, Ham, Seung Hee. 2018. “Surface Irradiances of Edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product.” J Clim , JCLI-D-17–0523.1. https://doi.org/10.1175/JCLI-D-17-0523.1
Li X, Qu Y (2018) Evaluation of Vegetation Responses to Climatic Factors and Global Vegetation Trends using GLASS LAI from 1982 to 2010. Can J Remote Sens 44(4):357–372. https://doi.org/10.1080/07038992.2018.1526064
Loeb NG, Doelling DR, Wang H, Wenying Su, Nguyen C, Corbett JG, Liang L, Mitrescu C, Rose FG, Kato S (2018) Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product. J Clim 31(2):895–918. https://doi.org/10.1175/JCLI-D-17-0208.1
Loeb NG, Wielicki BA, Rose FG, Doelling DR (2007) Variability in global top-of-atmosphere shortwave radiation between 2000 and 2005. Geophys Res Lett 34(3):L03704. https://doi.org/10.1029/2006GL028196
Loranty MM, Berner LT, Goetz SJ, Jin Y, Randerson JT (2014) Vegetation controls on northern high latitude snow-albedo feedback: observations and CMIP5 model simulations. Glob Change Biol 20(2):594–606. https://doi.org/10.1111/gcb.12391
Mann HB (1945) Non-parametric tests against trend. Econometrica 13(3):245–259
Qu X, Hall A (2005) Surface contribution to planetary albedo variability in cryosphere regions. J Clim 18(24):5239–5252. https://doi.org/10.1175/JCLI3555.1
Seinfeld JH, Bretherton C, Carslaw KS, Coe H, DeMott PJ, Dunlea EJ, Feingold G et al (2016) Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system. Proc Natl Acad Sci USA 113(21):5781–5790. https://doi.org/10.1073/pnas.1514043113
Sledd A, L’Ecuyer T (2019) How much do clouds mask the impacts of arctic sea ice and snow cover variations? different perspectives from observations and reanalyses. Atmosphere 10(1):12. https://doi.org/10.3390/atmos10010012
Stephens GL, O’Brien D, Webster PJ, Pilewski P, Kato S, Li J-L (2015) The albedo of Earth. Rev Geophys 53(1):141–163. https://doi.org/10.1002/2014RG000449
Theil, Henri. 1992. “A Rank-Invariant Method of Linear and Polynomial Regression Analysis.” In Henri Theil’s Contributions to Economics and Econometrics: Econometric Theory and Methodology, edited by Baldev Raj and Johan Koerts, 345–81. Dordrecht: Springer Netherlands.
Urbain M, Clerbaux N, Ipe A, Tornow F, Hollmann R, Baudrez E, Blazquez AV, Moreels J (2017) The CM SAF TOA radiation aata record using MVIRI and SEVIRI. Remote Sensing 9(5):466. https://doi.org/10.3390/rs9050466
Wang J-F, Zhang T-L, Bo-Jie Fu (2016) A measure of spatial stratified heterogeneity. Ecol Ind 67:250–256. https://doi.org/10.1016/j.ecolind.2016.02.052
Wielicki B, A., Barkstrom, Bruce, R., Harrison, Edwin, F., and Lee. (1996) Clouds and the Earth’s Radiant Energy System (CERES): An earth observing system experiment. Bull Amer Meteor Soc 77(5):853–868. https://doi.org/10.1175/1520-0477(1996)077%3c0853:catere%3e2.0.co;2
Wielicki BA, Wong T, Loeb N, Minnis P, Priestley K, Kandel R (2005) Changes in Earth’s Albedo Measured by Satellite. Science 308(5723):825. https://doi.org/10.1126/science.1106484
Zhang R, Wang H, Qiang Fu, Rasch PJ, Wang X (2019) Unraveling driving forces explaining significant reduction in satellite-inferred Arctic surface albedo since the 1980s. Proc Natl Acad Sci USA 116(48):23947–23953. https://doi.org/10.1073/pnas.1915258116
Funding
This work was financially supported by the National Key Research and Development Program of China under the grant number 2020YFA0714102, the National Natural Science Foundation of China under the grant numbers 41971287 and 41601349, and the Fundamental Research Funds for the Central Universities under the grant number 2412019FZ003.
Author information
Authors and Affiliations
Contributions
Mingzhu Lv: methodology, data curation, formal analysis, and writing original draft. Yan Song: validation and investigation. Xijia Li: methodology and investigation. Mengsi Wang: software and visualization. Ying Qu: conceptualization, writing review and editing, and funding acquisition.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Lv, M., Song, Y., Li, X. et al. Spatiotemporal characteristics and driving factors of global planetary albedo: an analysis using the Geodetector method. Theor Appl Climatol 147, 737–752 (2022). https://doi.org/10.1007/s00704-021-03858-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-021-03858-9