The Impacts of Atmospheric and Surface Parameters on Long-Term Variations in the Planetary Albedo

Bida Jian Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Jiming Li Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Guoyin Wang Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Yongli He Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Ying Han Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Min Zhang Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Jianping Huang Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Abstract

Planetary albedo (PA; shortwave broadband albedo) and its long-term variations, which are controlled in a complex way by various atmospheric and surface properties, play a key role in controlling the global and regional energy budget. This study investigates the contributions of different atmospheric and surface properties to the long-term variations of PA based on 13 years (2003–15) of albedo, cloud, and ice coverage datasets from the Clouds and the Earth’s Radiant Energy System (CERES) Single Scanner Footprint edition 4A product, vegetation product from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface albedo product from the Cloud, Albedo, and Radiation dataset, version 2 (CLARA-A2). According to the temporal correlation analysis, statistical results indicate that variations in PA are closely related to the variations of cloud properties (e.g., cloud fraction, ice water path, and liquid water path) and surface parameters (e.g., ice/snow percent coverage and normalized difference vegetation index), but their temporal relationships vary among the different regions. Generally, the stepwise multiple linear regression models can capture the observed PA anomalies for most regions. Based on the contribution calculation, cloud fraction dominates the variability of PA in the mid- and low latitudes while ice/snow percent coverage (or surface albedo) dominates the variability in the mid- and high latitudes. Changes in cloud liquid water path and ice water path are the secondary dominant factor over most regions, whereas change in vegetation cover is the least important factor over land. These results verify the effects of atmospheric and surface factors on planetary albedo changes and thus may be of benefit for improving the parameterization of the PA and determining the climate feedbacks.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0848.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiming Li, lijiming@lzu.edu.cn

Abstract

Planetary albedo (PA; shortwave broadband albedo) and its long-term variations, which are controlled in a complex way by various atmospheric and surface properties, play a key role in controlling the global and regional energy budget. This study investigates the contributions of different atmospheric and surface properties to the long-term variations of PA based on 13 years (2003–15) of albedo, cloud, and ice coverage datasets from the Clouds and the Earth’s Radiant Energy System (CERES) Single Scanner Footprint edition 4A product, vegetation product from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface albedo product from the Cloud, Albedo, and Radiation dataset, version 2 (CLARA-A2). According to the temporal correlation analysis, statistical results indicate that variations in PA are closely related to the variations of cloud properties (e.g., cloud fraction, ice water path, and liquid water path) and surface parameters (e.g., ice/snow percent coverage and normalized difference vegetation index), but their temporal relationships vary among the different regions. Generally, the stepwise multiple linear regression models can capture the observed PA anomalies for most regions. Based on the contribution calculation, cloud fraction dominates the variability of PA in the mid- and low latitudes while ice/snow percent coverage (or surface albedo) dominates the variability in the mid- and high latitudes. Changes in cloud liquid water path and ice water path are the secondary dominant factor over most regions, whereas change in vegetation cover is the least important factor over land. These results verify the effects of atmospheric and surface factors on planetary albedo changes and thus may be of benefit for improving the parameterization of the PA and determining the climate feedbacks.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0848.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiming Li, lijiming@lzu.edu.cn

Supplementary Materials

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