On the reliability of global seasonal forecasts: sensitivity to ensemble size, hindcast length and region definition

One of the key quality aspects in a probabilistic prediction is its reliability. However, this property is difficult to estimate in the case of seasonal forecasts due to the limited size of most of the hindcasts that are available nowadays. To shed light on this issue, this work presents a detailed analysis of how the ensemble size, the hindcast length and the number of points pooled together within a particular region affect the resulting reliability estimates. To do so, we build on 42 land reference regions recently defined for the IPCC-AR6 and assess the reliability of global seasonal forecasts of temperature and precipitation from the European Center for Medium Weather Forecasts SEAS5 prediction system, which is compared against its predecessor, System4. Our results indicate that whereas longer hindcasts and larger ensembles lead to increased reliability estimates, the number of points that are pooled together within a homogeneous climate region is much less relevant.This research has been partially supported by the AfriCultuReS (“Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing”) and FOCUS-Africa projects, which received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreements No. 77465 and 869575, respectively.Peer ReviewedPostprint (published version

The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections

The enhanced warming trend and precipitation decline in the Mediterranean region make it a climate change hotspot. We compare projections of multiple Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) historical and future scenario simulations to quantify the impacts of the already changing climate in the region. In particular, we investigate changes in temperature and precipitation during the 21st century following scenarios RCP2.6, RCP4.5 and RCP8.5 for CMIP5 and SSP1-2.6, SSP2-4.5 and SSP5-8.5 from CMIP6, as well as for the HighResMIP high-resolution experiments. A model weighting scheme is applied to obtain constrained estimates of projected changes, which accounts for historical model performance and inter-independence in the multi-model ensembles, using an observational ensemble as reference. Results indicate a robust and significant warming over the Mediterranean region during the 21st century over all seasons, ensembles and experiments. The temperature changes vary between CMIPs, CMIP6 being the ensemble that projects a stronger warming. The Mediterranean amplified warming with respect to the global mean is mainly found during summer. The projected Mediterranean warming during the summer season can span from 1.83 to 8.49 ∘C in CMIP6 and 1.22 to 6.63 ∘C in CMIP5 considering three different scenarios and the 50 % of inter-model spread by the end of the century. Contrarily to temperature projections, precipitation changes show greater uncertainties and spatial heterogeneity. However, a robust and significant precipitation decline is projected over large parts of the region during summer by the end of the century and for the high emission scenario (−49 % to −16 % in CMIP6 and −47 % to −22 % in CMIP5). While there is less disagreement in projected precipitation than in temperature between CMIP5 and CMIP6, the latter shows larger precipitation declines in some regions. Results obtained from the model weighting scheme indicate larger warming trends in CMIP5 and a weaker warming trend in CMIP6, thereby reducing the difference between the multi-model ensemble means from 1.32 ∘C before weighting to 0.68 ∘C after weighting.The work in this paper was partly supported by the European Commission H2020 project EUCP (grant no. 776613).Peer ReviewedPostprint (published version

Role of the Atlantic Multidecadal Variability in modulating the climate response to a Pinatubo-like volcanic eruption

The modulation by the Atlantic multidecadal variability (AMV) of the dynamical climate response to a Pinatubo-like eruption is investigated for the boreal winter season based on a suite of large ensemble experiments using the CNRM-CM5 Coupled Global Circulation Model. The volcanic eruption induces a strong reduction and retraction of the Hadley cell during 2 years following the eruption and independently of the phase of the AMV. The mean extratropical westerly circulation simultaneously weakens throughout the entire atmospheric column, except at polar Northern latitudes where the zonal circulation is slightly strengthened. Yet, there are no significant changes in the modes of variability of the surface atmospheric circulation, such as the North Atlantic Oscillation (NAO), in the first and the second winters after the eruption. Significant modifications over the North Atlantic sector are only found during the third winter. Using clustering techniques, we decompose the atmospheric circulation into weather regimes and provide evidence for inhibition of the occurrence of negative NAO-type circulation in response to volcanic forcing. This forced signal is amplified in cold AMV conditions and is related to sea ice/atmosphere feedbacks in the Arctic and to tropical-extratropical teleconnections. Finally, we demonstrate that large ensembles of simulations are required to make volcanic fingerprints emerge from climate noise at mid-latitudes. Using small size ensemble could easily lead to misleading conclusions especially those related to the extratropical dynamics, and specifically the NAO.This research was carried out within the pro- jects: (i) MORDICUS funded by the French Agence Nationale de la Recherche (ANR-13-SENV-0002-02); (ii) SPECS funded by the European Commission’s Seventh Framework Research Programme under the grant agreement 308378; (iii) VOLCADEC funded by the Spanish program MINECO/FEDER (ref. CGL2015-70177-R). We thank Javier Garcia-Serrano for its comments about the NAO precursors, Omar Bellprat for its suggestions concerning the statistical analysis and François Massonnet for its recommendations in terms of graphical presentation. CC is grateful to Marie-Pierre Moine, Laure Coquart and Isabelle Dast for technical help to run the model. Computer resources have been provided by Cerfacs. We thank the two anonymous referees for their useful comments and suggestions to improve this manuscript.Peer ReviewedPostprint (author's final draft

Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information

Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skillful climate information. This barrier is addressed through the development of an R package. Climate Services Toolbox (CSTools) is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi-annual scales. The package contains process-based, state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the modular design of the toolbox in individual functions, the users can develop their own post-processing chain of functions, as shown in the use cases presented in this paper, including the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model, and the post-processing of temperature and precipitation data to be used as input in impact models.This research has been supported by the Horizon 2020 (S2S4E; grant no. 776787), EUCP (grant no. 776613), ERA4CS (grant no. 690462), and the Ministerio de Ciencia e Innovación (grant no. FPI PRE2019-088646).Peer Reviewed"Article signat per 19 autors/es: Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté , Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie"Postprint (published version

Quality Management Framework for Climate Datasets

Data from a variety of research programmes are increasingly used by policy makers, researchers, and private sectors to make data-driven decisions related to climate change and variability. Climate services are emerging as the link to narrow the gap between climate science and downstream users. The Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO) offers an umbrella for the development of climate services and has identified the quality assessment, along with its use in user guidance, as a key aspect of the service provision. This offers an extra stimulus for discussing what type of quality information to focus on and how to present it to downstream users. Quality has become an important keyword for those working on data in both the private and public sectors and significant resources are now devoted to quality management of processes and products. Quality management guarantees reliability and usability of the product served, it is a key element to build trust between consumers and suppliers. Untrustworthy data could lead to a negative economic impact at best and a safety hazard at worst. In a progressive commitment to establish this relation of trust, as well as providing sufficient guidance for users, the Copernicus Climate Change Service (C3S) has made significant investments in the development of an Evaluation and Quality Control (EQC) function. This function offers a homogeneous user-driven service for the quality of the C3S Climate Data Store (CDS). Here we focus on the EQC component targeting the assessment of the CDS datasets, which include satellite and in-situ observations, reanalysis, climate projections, and seasonal forecasts. The EQC function is characterised by a two-tier review system designed to guarantee the quality of the dataset information. While the need of assessing the quality of climate data is well recognised, the methodologies, the metrics, the evaluation framework, and how to present all this information to the users have never been developed before in an operational service, encompassing all the main climate dataset categories. Building the underlying technical solutions poses unprecedented challenges and makes the C3S EQC approach unique. This paper describes the development and the implementation of the operational EQC function providing an overarching quality management service for the whole CDS data.This study is based on work carried out in the C3S_512 contract funded by Copernicus Programme and operated by ECMWF on behalf of the European Commission (Service Contract number: ECMWF/COPERNICUS720187C3S_512_BSC). We would like to acknowledge the work of colleagues from several European institutions, the data providers and C3S, who contributed to the development of the EQC framework as well as to the QAR production. We would also like to acknowledge the focus group users, who took time to review and provide valuable feedback on the QARs, QATs, minimum requirements and the CDS quality assessment tab. The authors are grateful to the anonymous reviewers for their constructive comments that have helped for the improvement of this paper.Peer Reviewed"Article signat per 23 autors/es: Carlo Lacagnina , Francisco Doblas-Reyes, Gilles Larnicol, Carlo Buontempo, André Obregón, Montserrat Costa-Surós, Daniel San-Martín, Pierre-Antoine Bretonnière, Suraj D. Polade, Vanya Romanova, Davide Putero, Federico Serva, Alba Llabrés-Brustenga, Antonio Pérez, Davide Cavaliere, Olivier Membrive, Christian Steger, Núria Pérez-Zanón, Paolo Cristofanelli, Fabio Madonna, Marco Rosoldi, Aku Riihelä, Markel García Díez"Postprint (published version

Model output statistics (MOS) applied to Copernicus Atmospheric Monitoring Service (CAMS) O3 forecasts: trade-offs between continuous and categorical skill scores

Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with model output statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate the extent to which AQ forecasts can be improved using a variety of MOS methods, including moving average, quantile mapping, Kalman filter, analogs and gradient boosting machine methods, and consider as well the persistence method as a reference. We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a comprehensive set of continuous and categorical metrics at various timescales, along different lead times and using different meteorological input datasets. Our results show that O3 forecasts can be substantially improved using such MOS corrections and that improvements go well beyond the correction of the systematic bias. Depending on the timescale and lead time, root mean square errors decreased from 20 %–40 % to 10 %–30 %, while Pearson correlation coefficients increased from 0.7–0.8 to 0.8–0.9. Although the improvement typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. The MOS methods relying on meteorological data were found to provide relatively similar performance with two different meteorological inputs. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with the lowest errors and highest correlations. However, they are not necessarily the best in predicting the peak O3 episodes, for which simpler MOS methods can achieve better results. Although the complex impact of MOS methods on the distribution of and variability in raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.This research has been funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433, as well as the MITIGATE project (PID2020-116324RA-I00/AEI/10.13039/501100011033) from the Agencia Estatal de Investigacion (AEI). We also acknowledge support by the AXA Research Fund and Red Temática ACTRIS España (CGL2017-90884-REDT), the BSC-CNS “Centro de Excelencia Severo Ochoa 2015-2019” program (SEV-2015-0493), PRACE, and RES for awarding us access to the MareNostrum supercomputer in the Barcelona Supercomputing Center as well as H2020 ACTRIS IMP (no. 871115). We also acknowledge support from the VITALISE project (PID2019-108086RA-I00) funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula

In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time. We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from −10 % to −40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from −40 % in summer to +60 % in winter in the most urbanized areas, and from −10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting; this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets. Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.We acknowledge the RES (AECT-2022-1- 0008, AECT-2022-2-0003) for awarding us access to the MareNostrum supercomputer in the Barcelona Supercomputing Center, and we also acknowledge the support from the Red Temática ACTRIS España (CGL2017-90884-REDT) and the H2020 ACTRIS IMP (no. 871115). SC acknowledges support from BELSPO through BRAIN-BE 2.0 project LEGO-BEL-AQ (contract B2/191/P1/LEGO-BEL-AQ). This research has received fund- ing from the Ramon y Cajal grant (RYC2021-034511-I, MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR), the MITIGATE (PID2020- 16324RA695 I00/AEI/10.13039/501100011033) and VITALISE (PID2019-108086RA-I00 MCIN/AEI/10.13039/501100011033) projects from the Agencia Estatal de Investigación (AEI), the European Union’s Horizon 2020 research and innovation program under grant agreement no. 870301 (AQ-WATCH H2020 project), and the AXA Research Fund.Peer ReviewedPostprint (published version

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

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 column-integrated 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 in a rotated grid, and the temporal resolution is 3 h. The reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic 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 variables (dust mass concentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust optical depth and load). 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 Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and 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), the 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 Thematic Real-time Environmental Distributed Data Services (THREDDS) at BSC and is freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 10 June 2022).This research has been supported by the DustClim project, which is part of ERA4CS, an ERA-NET programme co-funded by the European Union’s Horizon 2020 research and innovation programme (grant no. 690462); the European Research Council (FRAGMENT (grant no. 773051)); grant no. RYC-2015- 18690 funded by MCIN/AEI/10.13039/501100011033 and ESF Investing in your future; grant no. CGL2017-88911-R funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe; the AXA Research Fund (AXA Chair on Sand and Dust Storms); the European Commission, Horizon 2020 Framework Programme (grant no. 792103 (SOLWARIS)); and ATMO-ACCESS (Access to Atmospheric Research Facilities) funded in the frame of the programme H2020-EU.1.4.1.2 (grant no. 101008004, 1 April 2021–31 March 2025). Jerónimo Escribano and Martina Klose have received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements H2020-MSCACOFUND-2016-754433 and H2020-MSCA-IF-2017-789630, respectively. Martina Klose received further support through the Helmholtz Association’s Initiative and Networking Fund (grant no. VH-NG-1533). This work has been partially funded by the contribution agreement between AEMET and BSC to carry out development and improvement activities of the products and services supplied by the World Meteorological Organization (WMO) Barcelona Dust Regional Center (i.e. the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Regional Center for Northern Africa, the Middle East and Europe).Peer ReviewedArticle signat per 24 autors/es: Enza Di Tomaso (1) , Jerónimo Escribano (1) , Sara Basart (1) , Paul Ginoux (2) , Francesca Macchia (1) , Francesca Barnaba (3) , Francesco Benincasa (1), Pierre-Antoine Bretonnière (1), Arnau Buñuel (1), Miguel Castrillo (1), Emilio Cuevas (4) , Paola Formenti (5) , María Gonçalves (1,6), Oriol Jorba (1), Martina Klose (1,7), Lucia Mona (8), Gilbert Montané Pinto (1) , Michail Mytilinaios (8), Vincenzo Obiso (1,a), Miriam Olid (1), Nick Schutgens (9) , Athanasios Votsis (10,11), Ernest Werner (12), and Carlos Pérez García-Pando (1,13) // (1) Barcelona Supercomputing Center (BSC), Barcelona, Spain; (2) NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA; (3) Consiglio Nazionale delle Ricerche–Istituto di Scienze dell’Atmosfera e del Clima (CNR–ISAC), Rome, Italy; (4) Izaña Atmospheric Research Center (IARC), Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain; (5) Université Paris Cité and Univ Paris-Est Créteil, CNRS, LISA, 75013 Paris, France; (6) Department of Project and Construction Engineering, Universitat Politècnica de Catalunya – BarcelonaTech (UPC), Terrassa, Spain; (7) Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; (8) Consiglio Nazionale delle Ricerche–Istituto di Metodologie per l’Analisi Ambientale (CNR–IMAA), Tito Scalo (PZ), Italy; (9) Department of Earth Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands; (10) Section of Governance and Technology for Sustainability (BMS-CSTM), University of Twente, Enschede, the Netherlands; (11) Weather and Climate Change Impact Research, Finnish Meteorological Institute (FMI), Helsinki, Finland; (12) Agencia Estatal de Meteorología (AEMET), Barcelona, Spain; (13) ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain anow at: NASA Goddard Institute for Space Studies (GISS), New York, New York, USAObjectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.3 - Millorar l’educació, la conscienciació i la capacitat humana i institucional en relació amb la mitigació del canvi climàtic, l’adaptació a aquest, la reducció dels efectes i l’alerta primerencaObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaPostprint (published version

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

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.We acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union’s Horizon 2020 research and innovation programme (Grant n. 690462)