08 Jun 2022
08 Jun 2022
Status: this preprint is currently under review for the journal ACP.

Size-Resolved Dust Direct Radiative Effect Efficiency Derived from Satellite Observations

Qianqian Song1,2, Zhibo Zhang1,2, Hongbin Yu3, Jasper F. Kok4, Claudia Di Biagio5, Samuel Albani6, Jianyu Zheng1,2, and Jiachen Ding7 Qianqian Song et al.
  • 1Physics Department, UMBC, Baltimore, Maryland, USA
  • 2Goddard Earth Sciences Technology and Research II, UMBC, Baltimore, Maryland, USA
  • 3Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 4Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA
  • 5Université de Paris Cité and Université Paris Est Creteil, CNRS, LISA, F−75013 Paris, France
  • 6Department of Environmental and Earth Sciences, University of Milano–Bicocca, Milan, Italy
  • 7Department of Atmospheric Sciences, Texas A&M University, College Station, Texas, USA

Abstract. The role of mineral dust aerosol in global radiative energy budget is often quantified by the dust direct radiative effect (DRE). The dust DRE strongly depends on dust aerosol optical depth (DAOD), therefore, DRE efficiency (DREE=DRE/DAOD) is widely compared across different studies to eliminate difference due to the various dust load. Nevertheless, DREE is still influenced by the uncertainties associated with dust particle size distribution (PSD) and optical properties. In this study, we derive a global clear-sky size-resolved DREE dataset in both shortwave (SW) and longwave (LW) at top of the atmosphere (TOA) and surface based on satellite observations (i.e., satellite-retrieved dust extinction spatial and vertical distributions). In the DREE dataset, dust geometric diameter from 0.1µm to 100 µm is divided into 10 bins and the corresponding monthly mean DREE (with respect to DAOD at 532 nm) for each size bin is derived by using the Rapid Radiative Transfer Model (RRTM). Three sets of state-of-the-art dust refractive indices (RI) and two sets of dust shape models (sphere vs. spheroid) are adopted to investigate the sensitivity of dust DREE to dust absorption and shape. As a result, the size-resolved dust DREE dataset contains globally distributed monthly mean dust DREE at TOA and surface for each of 10 size bins with 5° (longitude) × 2° (latitude) resolution as well as for each dust RI and shape combination. The size-resolved dust DREE dataset can be used to readily calculate global dust DRE for any DAOD and dust PSD, including the uncertainty in the DRE induced by dust microphysical properties (e.g., dust PSD, RI and shape). By calculating dust DRE based on DAOD climatology retrieved from different satellite sensors and based on different dust PSD, we find that uncertainty in the spatial pattern of DAOD induces more than 10 % of the uncertainty in SW dust DRE at TOA. The observation-based dust PSD induces around 15 %~20 % uncertainty in dust DRE at TOA and in the atmosphere. The sensitivity assessments of dust DRE to dust RI and shape further suggest that dust non-sphericity induces a negligible effect on dust DRE estimations, while dust RI turns out to be the most important factor in determining dust DRE, particularly in SW.

Qianqian Song et al.

Status: open (until 20 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Qianqian Song et al.

Data sets

Size-Resolved Dust Direct Radiative Effect Efficiency Derived from Satellites Observations Qianqian Song, Zhibo Zhang, Hongbin Yu, Jasper F. Kok, Claudia Di Biagio, Samuel Albani, Jianyu Zheng, Jiachen Ding

Model code and software

Size-Resolved Dust Direct Radiative Effect Efficiency Derived from Satellites Observations Qianqian Song, Zhibo Zhang, Hongbin Yu, Jasper F. Kok, Claudia Di Biagio, Samuel Albani, Jianyu Zheng, Jiachen Ding

Qianqian Song et al.


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Short summary
This study developed a dataset that enables us to efficiently calculate dust direct radiative effect (i.e., cooling or warming our planet, hereafter DRE) for any given dust size distribution in addition to three sets of dust mineral components and two dust shapes. We demonstrate and validate the method of using this dataset to calculate dust DRE. Moreover, by using this dataset, we found that dust mineral composition is the most important factor in determining dust DRE than dust size and shape.