Meteorological, dust and saltation data used in González-Flórez et al., 2023. Data are based on measurements taken during an intensive dust field campaign conducted in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The campaign took place in September 2019 in a small ephemeral lake, locally named "L'Bour", located in the Lower Drâa Valley in Morocco. The description of the data is provided below:

- t.nc: time series of temperature measured with four aspirated shield temperature sensors (Campbell Scientific 43502 fan-aspirated shield with 43347 RTD Temperature probe) placed at heights of 1m, 2m, 4m and 8m.

- t005.nc time series of temperature measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height.

- rh005.nc: time series relative humidity measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height.

- wspd.nc: time series of wind speed measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m.

- sdir.nc: time series of wind direction measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m.

- radout.nc: time series of outgoing long wave radiation measured with a four-component net radiometer (Campbell Scientific NR01-L radiometer) placed at 1.5m height.

- p015.nc: times series barometric pressure measured with a barometer (Campbell Scientific CS106) at around 1.5m height.

- u_star_law.nc: time series friction velocity calculated through the law of the wall method.

- z0_law.nc: time series of roughness length calculated through the law of the wall method.

- zeta_law.nc: time series of dimensionless height, zref/L, where zref is the reference height (zref=2m) and L is the Obukhov length calculated through the law of the wall method.

- psd_lower_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~1.8m height.

- psd_upper_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~3.5m height and corrected for systematic bias based on an intercomparison between the two Fidas at the end of the campaign.

- diff_flux_nb_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number diffusive flux calculated using the flux-gradient method.

- q_15avg.nc: time series of 15-min average saltation flux calculated based on measurements with optical gate devices at heights of 0.05m, 0.15m and 0.3m as part of the Standalone AeoliaN Transport Real-time Instrument (SANTRI, Desert Research Institute).

- geometric_diameters_integrated_size_bins.csv: containing the minimum, maximum and mean logarithmic optical diameter of the integrated size bins.

- optical_diameters_integrated_size_bins: containing the minimum, maximum and mean logarithmic geometric diameter of the integrated size bins.

SANTRI data were processed by Martina Klose (martina.klose@kit.edu) and the rest by Cristina González Flórez (cristina.gonzalez@bsc.es). Please, cite González-Flórez et al. (2023, ACP) if you use these data. If the data become the key main component of a paper then co-authorship may be offered. Contact Carlos Pérez García-Pando (carlos.perez@bsc.es) if more details are needed.

This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051, FRAGMENT)., Peer reviewed

Meteorological and dust data used in Dupont et al. 2024. Data are based on measurements recorded above two erosive surfaces from two very different desert regions, one at a high-latitude location in Iceland and the other at a low-latitude location in Jordan. The Iceland campaign was co-organized by two projects: FRAGMENT (FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe) and HiLDA (Iceland as a model for high-latitude dust sources – a combined experimental and
modeling approach for characterization of dust emission and transport processes); and the Jordan campaign (J-WADI: Jordan Wind erosion And Dust Investigation) was co-organized by the FRAGMENT and Helmholtz Young Investigator Group ``A big unknown in the climate impact of atmospheric aerosol: Mineral soil dust'' projects., Dust emission fluxes during wind soil erosion are usually estimated using a dust concentration vertical gradient, by assuming a constant dust flux layer between the surface and the dust measurement levels. Here, we investigate the existence of this layer during erosion events recorded in Iceland and Jordan. Size-resolved dust fluxes were estimated at three levels between 2 and 4 m using the eddy-covariance method. Dust fluxes were found mainly constant only between the two upper levels in Iceland, the lower dust flux being often stronger and richer in coarse particles, while dust fluxes in Jordan were nearly constant across all levels. The wind dynamics could not explain the absence of a constant dust flux layer in Iceland. We show that the presence of stationary dust source patches in Iceland, related to surface humidity, created a non-uniform dust layer near the surface, named dust roughness sublayer (DRSL), where individual plumes behind each patch interact but do not fully mix. The lowest dust measurement level was probably located within this sublayer while the upper ones were located above, such that there the emitted dust became spatially well-mixed. This explains near the surface in Iceland, the more intermittent dust concentration, its low correlation with the dust concentrations above, and the richer dust flux in coarse particles due to their lower deposition contribution. Our findings highlight the importance of estimating dust fluxes above a dust blending height whose characteristics depend on the dust source patchiness caused by surface humidity or the presence of sparse non-erosive elements., We acknowledge the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant FRAGMENT (Grant agreement no 773051), and the AXA Research Fund through the AXA Chair on Sand and Dust Storms at BSC for financial support of the field campaigns in Iceland and Jordan. S. Dupont acknowledge the financial support of the Department Agroecosystem of INRAE. K. Kandler and K. Schepanski are funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—416816480; 417012665. For the Icelandic campaign, we thank the staff from the ranger station at Dreki as well as the wardens of the Dreki campsite and the Dreki mountain rescue service for their valuable support and advice. We also thank Vilhjalmur Vernharðsson and his crew from Fjalladýrð for their permanent logistic help. Without all of them, the measurement campaign would not have been successfully feasible. For the Jordanian campaign, we acknowledge efforts by the staff at Wadi Rum Protected Area, Aqaba Special Economic Zone Authority, and Directorate of Environmental Monitoring and Assessment at Ministry of Environment. We also thank the Helmholtz Association's Initiative and Networking Fund (Grant agreement no VH-NG-1533) for financial support of the field campaigns in Iceland and Jordan., Peer reviewed

[Data Access]
The data processed in version 1.5.1 was a result of research undertaken in two separate projects. The processing and creation of new aerosol optical property products was done within the FOCI project, and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The processed data is designed to be complementary to the data provided in version 1.5 of GHOST.

The data is separated out per network, per temporal resolution, per component, and is saved as netCDF4 files, per year and month. There is additionally one synthetic network entitled "GHOST", which aggregates data across all networks. The dataset is compressed as .zip files per network. Beneath each network, collections of files per temporal resolution, per component, are compressed as tar.xz files.

Each network .zip file can be decompressed via the following syntax:
unzip [network].zip

Component tar.xz files can be decompressed via the following syntax:
tar -xf [component].tar.xz, [How to Use]
Inside the GHOST dataset are a plethora of variables, thus it can difficult to fully exploit the extent of the available information. For this reason a companion publication has been written, detailing every aspect of the GHOST dataset: https://doi.org/10.5194/essd-2023-397

If you have any other doubts of queries regarding the dataset, please email: dene.bowdalo@bsc.es, [How to Cite]
If you plan to use this work please kindly cite both this dataset and the describing publication:


Bowdalo, D.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.10637449, 2024.

Bowdalo, D., Basart, S., Guevara, M., Jorba, O., Pérez García-Pando, C., Jaimes Palomera, M., Rivera Hernandez, O., Puchalski, M., Gay, D., Klausen, J., Moreno, S., Netcheva, S., and Tarasova, O.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, 2024., [GHOST]
Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, ~10 billion measurements from 1970-2025, of ~600 different components, from ~40 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties.

The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided., We gratefully acknowledge all data providers for the substantial work done in establishing and maintaining the measuring stations that provide the data contained in this dataset. We would also like to warmly thank all data providers who met with GHOST authors through this work, and for all support given, from helping resolve data rights issues, to giving suggestions for improvements.

We acknowledge the computing resources of MareNostrum, and the technical support provided by the Barcelona Supercomputing Center (AECT-2020-1-0007, AECT-2021-1-0027, AECT-2022-1-0008, and AECT-2022-3-0013). We also acknowledge the Red Temática ACTRIS España (CGL2017-90884-REDT), and the H2020 project ACTRIS IMP (\#871115).

The processing and creation of new aerosol optical property products was funded by EU HORIZON EUROPE under grant agreement no. 101056783 (FOCI project), and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative.

The research leading to the creation of this dataset has also received funding from the grant RTI2018-099894-BI00 funded by MCIN/AEI/ 10.13039/501100011033 (BROWNING), the EU H2020 Framework Programme under grant agreement No. GA 821205 (FORCES), the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant grant agreement No. 773051 (FRAGMENT), the AXA Research Fund (AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center), and the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550)., Peer reviewed

Iceland is a significant high-latitude dust source area. Airborne Icelandic dust influences the climate system by interacting with radiation, clouds, and biogeochemical cycles; it also affects snow and ice albedo and air quality. These impacts are sensitive to the dust’s mineralogical, chemical, and physical properties. However, comprehensive measurements and analyses of Icelandic dust particles remain limited. This study examines dust samples collected during a field campaign in the Dyngjusandur desert (August–September 2021) using active and passive aerosol sampling. Over 190 000 individual particles, ranging from 0.1 to 120 µm, were analyzed for their chemical and physical properties using computer-controlled scanning electron microscopy/energy-dispersive X-ray spectroscopy (ccSEM/EDX). Results show heterogeneity in particle size, shape, and composition. The most abundant particle type was medium-Al mixed particles, likely glass-like, comprising 35 %–92 % of the aerosol volume. Sulfate particles, suggesting volcanic contributions, were detected in some samples. Iron (Fe)- and titanium (Ti)-rich particles made up 3.3 % and 6 % of the aerosol volume, respectively, mainly in the size fraction < 1 µm. The median aspect ratio ranged from 1.37 to 1.53, increasing with particle size. Our findings highlight key differences in Icelandic dust compared to Saharan dust, including higher iron and titanium content and a lack of potassium in Icelandic dust. Additionally, Icelandic dust shows a size-dependent increase in aspect ratio, unlike Saharan dust, which remains constant. These observations can improve model simulations that account for the effect of high-latitude dust in the Earth system., Peer reviewed

This dataset comprises several NetCDF files containing processed meteorological, dust, and saltation data from the J-WADI field campaign used in Meyer et al. (2025). Data were collected from multiple instruments and processed into several products, including corrected and uncorrected mass concentrations from several instruments for selected time steps, optical to geometric diameter conversions, scanning electron microscopy (SEM) measurements, and dust mass concentrations from combining size distributions from several instruments., SANTRI data were processed by Martina Klose (martina.klose@kit.edu), SEM data by Konrad Kandler (kandler@geo.tu-darmstadt.de), optical to geometric conversion by by Jeronimo Escribano (jeronimo.escribano@bsc.es), and the rest by Hannah Meyer (hannah.meyer@kit.edu)., J-WADI was funded through the Helmholtz Association's Initiative and Networking Fund (grant no. VH-NG-1533) and the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant FRAGMENT (grant no. 773051)., Peer reviewed