Abstract

Abstract. We derived two observation-based global monthly mean dust aerosol optical depth (DAOD) climatological datasets from 2007 to 2019 with a 2∘ (latitude) × 5∘ (longitude) spatial resolution, one based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the other on Moderate Resolution Imaging Spectroradiometer (MODIS) observations. In addition, the CALIOP climatological dataset also includes dust vertical extinction profiles. Dust is distinguished from non-dust aerosols based on particle shape information (e.g., lidar depolarization ratio) for CALIOP and on dust size and absorption information (e.g., fine-mode fraction, Ångström exponent, and single-scattering albedo) for MODIS, respectively. The two datasets compare reasonably well with the results reported in previous studies and the collocated Aerosol Robotic Network (AERONET) coarse-mode AOD. Based on these two datasets, we carried out a comprehensive comparative study of the spatial and temporal climatology of dust. On a multi-year average basis, the global (60∘ S–60∘ N) annual mean DAOD is 0.032 and 0.067 according to CALIOP and MODIS retrievals, respectively. In most dust-active regions, CALIOP DAOD generally correlates well (correlation coefficient R>0.6) with the MODIS DAOD, although the CALIOP value is significantly smaller. The CALIOP DAOD is 18 %, 34 %, 54 %, and 31 % smaller than MODIS DAOD over the Sahara, the tropical Atlantic Ocean, the Caribbean Sea, and the Arabian Sea, respectively. Applying a regional specific lidar ratio (LR) of 58 sr instead of the 44 sr used in the CALIOP operational retrieval reduces the difference from 18 % to 8 % over the Sahara and from 34 % to 12 % over the tropical Atlantic Ocean. However, over eastern Asia and the northwestern Pacific Ocean (NWP), the two datasets show weak correlation. Despite these discrepancies, CALIOP and MODIS show similar seasonal and interannual variations in regional DAOD. For dust aerosol over the NWP, both CALIOP and MODIS show a declining trend of DAOD at a rate of about 2 % yr−1. This decreasing trend is consistent with the observed declining trend of DAOD in the southern Gobi Desert at a rate of 3 % yr−1 and 5 % yr−1 according to CALIOP and MODIS, respectively. The decreasing trend of DAOD in the southern Gobi Desert is in turn found to be significantly correlated with increasing vegetation and decreasing surface wind speed in the area.

Highlights

  • Mineral dust, referred to as dust for short, is one of the most abundant type of atmospheric aerosol in terms of dry mass (Textor et al, 2006; Yu et al, 2012; Kok et al, 2017)

  • Moderate Resolution Imaging Spectroradiometer (MODIS)-based monthly mean dust aerosol optical depth (DAOD) retrievals are larger than Aerosol Robotic Network (AERONET) coarse-mode AOD (COD) measurements, while CALIOPbased DAOD retrievals are smaller than AERONET COD, which seems to suggest that the true DAODs fall between the MODIS and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) DAOD products

  • The DAOD climatology datasets derived from the CALIOP and MODIS observations, as described in Sect. 3, have two major sources of uncertainty: 1. The first is the uncertainty associated with the total aerosol optical depth (TAOD) retrieval

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Summary

Introduction

Mineral dust, referred to as dust for short, is one of the most abundant type of atmospheric aerosol in terms of dry mass (Textor et al, 2006; Yu et al, 2012; Kok et al, 2017). Yu et al, 2019), Moderate Resolution Imaging Spectroradiometer (MODIS) (Ginoux et al, 2010; Remer et al, 2005; Yu et al, 2009), multi-angular and polarimetric POLarization of Directionality of the Earth’s Reflectances/Polarization and Anisotropy of Reflectances for Atmospheric science coupled with Observations from a Lidar (POLDER/PARASOL) measurements (Chen et al, 2018) and the Infrared Atmospheric Sounding Interferometer (IASI) (Klüser et al, 2011; Clarisse et al, 2019) On one hand, these passive sensors provide global or quasi-global coverage of column integrated properties of aerosol with satisfactory temporal resolution.

CALIOP dust detection and AOD partition
MODIS dust detection and AOD partition
Comparison with previous studies and uncertainty analysis
Comparison with previous studies
Uncertainty analysis
Global dust climatology
Comparison between CALIOP and MODIS DAOD climatology
C K RMSE
Comparison between CALIOP and MODIS DAOD seasonality
DAOD interannual variation from CALIOP and MODIS observations
Findings
Summary and conclusion
Full Text
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