28 Oct 2021

28 Oct 2021

Review status: this preprint is currently under review for the journal ESSD.

The MONARCH high-resolution reanalysis of desert dust aerosol over Northern Africa, the Middle East and Europe (2007–2016)

Enza Di Tomaso1, Jerónimo Escribano1, Sara Basart1, Paul Ginoux2, Francesca Macchia1, Francesca Barnaba3, Francesco Benincasa1, Pierre-Antoine Bretonnière1, Arnau Buñuel1, Miguel Castrillo1, Emilio Cuevas4, Paola Formenti5, María Gonçalves1,6, Oriol Jorba1, Martina Klose1,7, Lucia Mona8, Gilbert Montané1, Michail Mytilinaios8, Vincenzo Obiso1,9, Miriam Olid1, Nick Schutgens10, Athanasios Votsis11,12, Ernest Werner13, and Carlos Pérez García-Pando1,14 Enza Di Tomaso et al.
  • 1Barcelona Supercomputing Center (BSC), Barcelona, Spain
  • 2Geophysical Fluid Dynamics Laboratory (GFDL), Princeton, NJ, USA
  • 3Consiglio Nazionale delle Ricerche-Istituto di Scienze dell’Atmosfera e del Clima (CNR-ISAC), Italy
  • 4Izaña Atmospheric Research Center, AEMET, Santa Cruz de Tenerife, Spain
  • 5LISA, UMR CNRS 7583, Université Paris-Est-Créteil, Université de Paris, Institut Pierre-Simon Laplace (IPSL), Créteil, France
  • 6Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Department of Project and Construction Engineering, Terrassa, Spain
  • 7Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-TRO), Department Troposphere Research, Karlsruhe, Germany
  • 8Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Italy
  • 9NASA Goddard Institute for Space Studies (GISS), New York, NY, USA
  • 10Department of Earth Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
  • 11University of Twente, Department of Governance and Technology for Sustainability (BMS-CSTM), the Netherlands
  • 12Finnish Meteorological Institute (FMI), Weather and Climate Change Impact Research, Finland
  • 13State Meteorological Agency (AEMET), Spain
  • 14ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain

Abstract. One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in-situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide columnintegrated aerosol measurements, but observationally-constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean sea and parts of Central Asia, and the Atlantic and Indian Oceans between 2007 and 2016. The horizontal resolution is 0.1° latitude × 0.1° longitude, and the temporal resolution is 3 hours. The reanalysis was produced using Local Ensemble Transform Kalman Filter (LETKF) data assimilation in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper air (dust mass concentrations and extinction coefficient), surface (dust deposition and solar irradiance fields, among them) and total column (e.g., dust optical depth and load) variables. Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 μm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first-guess, which proves the consistency of the data assimilation method. Independent evaluation using AERONET dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12, r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g., poor representation of small-scale emission processes), presence of aerosols other than dust in the observations used in the evaluation, and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via a Thematic Real-time Environmental Distributed Data Service (THREDDS) at BSC and freely available at

Enza Di Tomaso et al.

Status: open (until 23 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-358', Anonymous Referee #1, 26 Nov 2021 reply

Enza Di Tomaso et al.

Data sets

MONARCH high-resolution reanalysis data set of desert dust aerosol over Northern Africa, the Middle East and Europe Di Tomaso, E., Escribano, J., Basart, S., Macchia, F., Benincasa, F., Bretonnière, P.-A., Buñuel, A., Castrillo, M., Gonçalves, M., Jorba, O., Klose, M., Montané, G., Olid, M., Pérez García-Pando, C.

Enza Di Tomaso et al.


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Short summary
MONARCH reanalysis of desert dust aerosols extends the existing observational-based information for mineral dust monitoring by providing 3-hourly upper air, surface and total column key geophysical variables of the dust cycle over Northern Africa, the Middle East and Europe, at a 0.1° horizontal resolution, from 2007 to 2016. This work provides evidence of the high accuracy of this data set and its suitability for air quality/health and climate service applications.