Abstract

Abstract. The MACC reanalysis dust product is evaluated over Europe, northern Africa and the Middle East using the EARLINET-optimized CALIOP/CALIPSO pure dust satellite-based product LIVAS (2007–2012). MACC dust optical depth at 550 nm (DOD550) data are compared against LIVAS DOD532 observations. As only natural aerosol (dust and sea salt) profiles are available in MACC, here we focus on layers above 1 km a.s.l. to diminish the influence of sea salt particles that typically reside at low heights. So, MACC natural aerosol extinction coefficient profiles at 550 nm are compared against dust extinction coefficient profiles at 532 nm from LIVAS, assuming that the MACC natural aerosol profile data can be similar to the dust profile data, especially over pure continental regions. It is shown that the reanalysis data are capable of capturing the major dust hot spots in the area as the MACC DOD550 patterns are close to the LIVAS DOD532 patterns throughout the year. MACC overestimates DOD for regions with low dust loadings and underestimates DOD for regions with high dust loadings where DOD exceeds ∼ 0.3. The mean bias between the MACC and LIVAS DOD is 0.025 (∼ 25 %) over the whole domain. Both MACC and LIVAS capture the summer and spring high dust loadings, especially over northern Africa and the Middle East, and exhibit similar monthly structures despite the biases. In this study, dust extinction coefficient patterns are reported at four layers (layer 1: 1200–3000 m a.s.l., layer 2: 3000–4800 m a.s.l., layer 3: 4800–6600 m a.s.l. and layer 4: 6600–8400 m a.s.l.). The MACC and LIVAS extinction coefficient patterns are similar over areas characterized by high dust loadings for the first three layers. Within layer 4, MACC overestimates extinction coefficients consistently throughout the year over the whole domain. MACC overestimates extinction coefficients compared to LIVAS over regions away from the major dust sources while over regions close to the dust sources (the Sahara and Middle East) it underestimates strongly only for heights below ∼ 3–5 km a.s.l. depending on the period of the year. In general, it is shown that dust loadings appear over remote regions and at heights up to 9 km a.s.l. in MACC contrary to LIVAS. This could be due to the model performance and parameterizations of emissions and other processes, due to the assimilation of satellite aerosol measurements over dark surfaces only or due to a possible enhancement of aerosols by the MACC assimilation system.

Highlights

  • Eolian dust is mainly produced naturally by disintegration of soil aggregates over deserted, arid and semi-arid areas

  • The MACC–LIVAS dust optical depth (DOD) mean bias (MB) patterns shown in Fig. 2c are characterized by a general overestimation of DOD by MACC over continental Europe, over parts of Turkey and Iran, and over the sea (Atlantic Ocean, Mediterranean and Arabian Sea)

  • The MACC reanalysis dust product is evaluated over Europe, northern Africa and the Middle East (EUNM domain) using CALIOP/CALIPSO satellite observations for the period 2007–2012

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Summary

Introduction

Eolian dust is mainly produced naturally by disintegration of soil aggregates over deserted, arid and semi-arid areas. In dust-oriented studies, MACC forecasts have been used in conjunction with measurements from dropsondes and lidars onboard aircrafts, ships and satellites (Cloud-Aerosol Lidar with Orthogonal Polarization onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations – CALIOP/CALIPSO) to study the longrange transport of Saharan dust across the Atlantic within the framework of the Saharan Aerosol Long-range Transport and Aerosol–Cloud-interaction Experiment (SALTRACE) campaign in spring and summer 2013 (Chouza et al, 2016; Ansmann et al, 2017) In these studies forecast fields were used instead of analyses (MACC reanalysis data stop in 2012), focusing on the total aerosol optical depth (AOD) and extinction coefficients rather than on dust.

MACC reanalysis data
Spatial and temporal collocation of the datasets
Evaluation procedure
Annual dust optical depth patterns
Dust optical depth seasonal variability
Annual dust profiles
Seasonal biases between MACC and LIVAS dust profiles
Conclusions
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