ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set

Monitoring and describing the spatiotemporal variability in dust aerosols is crucial for understanding their multiple effects, related feedbacks, and impacts within the Earth system. This study describes the development of the ModIs Dust AeroSol (MIDAS) data set. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at a global scale and fine spatial resolution (0.1∘ × 0.1∘) over a 15-year period (2003–2017). This new data set combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1; Level 2), along with DOD-to-AOD ratios provided by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis to derive DOD on the MODIS native grid. The uncertainties of the MODIS AOD and MERRA-2 dust fraction, with respect to the AEronet RObotic NETwork (AERONET) and LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS), respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the dust belt in the tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere, where dust sources are smaller. MIDAS, MERRA-2, and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with colocated global DOD averages of 0.033, 0.031, and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89) and only shows a small positive bias (0.004 or 2.7 %). Among the major dust areas of the planet, the highest R values (>0.9) are found at sites of North Africa, the Middle East, and Asia. MIDAS expands, complements, and upgrades the existing observational capabilities of dust aerosols, and it is suitable for dust climatological studies, model evaluation, and data assimilation.
Antonis Gkikas acknowledges support from the European Union's Horizon 2020 Research and Innovation programme under the Marie Skłodowska-Curie Actions (grant no. 749461; DUST-GLASS). We would like to thank the principal investigators maintaining the AERONET sites used in the present work. We thank the NASA CALIPSO team and NASA/LaRC/ASDC for making the CALIPSO products available, which have been used to build the LIVAS products, and ESA, who funded the LIVAS project (contract no. 4000104106/11/NL/FF/fk). We are grateful to the AERIS/ICARE Data and Services Center for providing access to the CALIPSO data and their computational center (http://www.icare.univ-lille1.fr/, last access: 8 August 2019). Vassilis Amiridis acknowledges support from the European Research Council (grant no. 725698; D-TECT). Eleni Marinou was funded by a DLR VO-Ryoung investigator group and the Deutscher Akademischer Austauschdienst (grant no. 57370121). Carlos Pérez García-Pando acknowledges support from the European Research Council (grant no. 773051; FRAGMENT), the AXA Research Fund, and the Spanish Ministry of Science, Innovation and Universities (grant nos. RYC-2015-18690 and CGL2017-88911-R). The authors acknowledge support from the DustClim project as part of ERA4CS, an ERA-NET project initiated by JPI Climate and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR), with cofunding by the European Union (grant no. 690462). PRACE (Partnership for Advanced Computing in Europe) is acknowledged for awarding access to the MareNostrum Supercomputer in the Barcelona Supercomputing Center. We acknowledge support of this work by the PANhellenic infrastructure for Atmospheric Composition and climatE chAnge (PANACEA) project (grant no. MIS 5021516), which is implemented under the Horizon 2020 Action of “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme Competitiveness, Entrepreneurship, and Innovation (NSRF 2014–2020) and co
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