The Copernicus Atmosphere Monitoring Service (CAMS) delivers a wide range of free and open products in relation to atmospheric composition at global and regional scales. The CAMS Regional Service produces daily forecasts, analyses, and reanalyses of air quality in Europe. This service relies on a distributed modelling production by 11 teams in 10 European countries: CHIMERE (France), DEHM (Denmark), EMEP (Norway), EURAD-IM (Germany), GEM-AQ (Poland), LOTOS-EUROS (the Netherlands), MATCH (Sweden), MINNI (Italy), MOCAGE (France), MONARCH (Spain), and SILAM (Finland). The project management and coordination of the service is conducted by a Centralised Regional Production Unit. Every day, each model produces 24 h analyses for the previous day and 97 h forecasts for 19 chemical species over a spatial domain at 0.1 x 0.1 degrees resolution (approximately 10 km x 10 km), with 420 points in latitude and 700 in longitude and 10 vertical levels. Six pollen species are also delivered for the surface forecasts. The 11 individual models are then combined into an ENSEMBLE median. In total, more than 82 billion data points are made available for public use on a daily basis.The design of the system follows clear technical requirements in terms of consistency in the model setup and forcing fields (meteorology, surface anthropogenic emission fluxes, and chemical boundary conditions). But it also benefits from a diversity in the description of atmospheric processes through the design of the 11 European chemistry-transport models (CTMs) involved.The present article aims to provide a comprehensive technical documentation, both for the setup and for the diversity of CTMs involved in the service. We also include an overview of the main output products, their public dissemination, and the related evaluation and quality control strategy.

These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 \(\mu\)m in diameter) and silt (2-63 \(\mu\)m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): C1999-SMA, and Journet et al. (2014): J2014-SMA. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. File details: C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction. C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction. J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction. J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction. There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the aerosol mass mineral fractions as obtained from the MONARCH model simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 \(\mu\)m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. File details: 20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. 20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. Legend for the minerals: quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). References: Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research Atmospheres, https://doi.org/10.1029/1999JD900416, 1999. FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974. FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995. FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012. Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014. Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.

Aerosol reanalyses are a well-established tool for monitoring aerosol trends, for validation and calibration of weather chemical models, as well as for the enhancement of strategies for environmental monitoring and hazard mitigation. By providing a consistent and complete data set over a sufficiently long period, they address the shortcomings of aerosol observational records in terms of temporal and spatial coverage and aerosol speciation. These shortcomings are particularly severe for dust aerosols. A 10-year dust aerosol regional reanalysis has been recently produced on the Barcelona Supercomputing Center HPC facilities at the high spatial resolution of 0.1 $$^{\circ }$$ . Here we present a brief description and an initial assessment of this data set. An innovative dust optical depth data set, derived from the MODIS Deep Blue products, has been ingested in the dust module of the MONARCH model by means of a LETKF with a four-dimensional extension. MONARCH ensemble has been generated by applying combined meteorology and emission perturbations. This has been achieved using for each ensemble member different meteorological fields as initial and boundary conditions, and different emission schemes, in addition to stochastic perturbations of emission parameters, which we show is beneficial for dust data assimilation. We prove the consistency of the assimilation procedure by analyzing the departures of the assimilated observations from the model simulations for a two-month period. Furthermore, we show a comparison with AERONET coarse optical depth retrievals during a period of 2012, which indicates that the reanalysis data set is highly accurate. While further analysis and validation of the whole data set are ongoing, here we provide a first evidence for the reanalysis to be a useful record of dust concentration and deposition extending the existing observational-based information intended for mineral dust monitoring.

The CALIOPE forecasting system, developed and operated by the Barcelona Supercomputing Center, has been providing daily forecasts of air quality for Europe and Spain since 2008. The system currently integrates an emission processing model (HERMESv3), a meteorological model (WRF-ARW), a chemical transport model (CMAQ), and a mineral dust model (BSC-DREAM8b) that accounts for the dust contribution from North Africa. Since its initial deployment, continuous updates have been implemented to the system, with improvements in emission source estimates and adjustments in the model configuration. Since 2020, and thanks to the co-development with the Catalan Regional Administration and the Barcelona Metropolitan Transport Authority, new approaches and methodologies have been implemented, including a new nested domain at higher spatial resolution (1 × 1 km2) over Catalonia, to further support decision-making. Additionally, beyond the mesoscale domains, the CALIOPE-Urban model has been incorporated into the modelling chain to simulate air quality at the urban street-scale in the city of Barcelona. In parallel to those improvements, the emission estimates have been improved through updates in emission activity data from the industrial and on-road traffic sectors. Lastly, a data-fusion method has been implemented to correct urban air quality fields and reduce biases affecting the forecasting system. In this contribution, we will present the most recent developments applied to the CALIOPE forecasting system and its application as an air quality management tool. We will also discuss future user-driven developments that take both scientific and technical aspects into consideration, including the migration of the mesoscale system to the on-line MONARCH model, a dissemination strategy aiming to promote the wider use of CALIOPE predictions through a new portal designed to improve the user experience, and the further integration of the system into decision-making processes.

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 http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98.

Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.

Desert dust is the most abundant aerosol by mass residing in the atmosphere. It plays a key role in the Earth’s system by influencing the radiation balance, by affecting cloud formation and cloud chemistry, and by acting as a fertilizer for the growth of phytoplankton and for soil through its deposition over the ocean and land. Due to the nature of its emission and transport, atmospheric dust concentrations are highly variable in space and time and, therefore, require a continuous monitoring by measurements. Dust observations are best exploited by being combined with model simulations for the production of analyses and reanalyses, i.e., complete and consistent four dimensional reconstructions of the atmosphere. Existing aerosol (and dust) reanalyses for the global domain have been produced by total aerosol constraint and at relatively coarse spatial resolution, while regional reanalyses exclude some of the regions containing the major sources of desert dust in Northern Africa and the Middle East. We present here a 10-year reanalysis data set of desert dust at a horizontal resolution of 0.1°, and which covers the domain of Northern Africa, the Middle East and Europe. The reanalysis has been produced by assimilating in the MONARCH chemical weather prediction system (Di Tomaso et al., 2017) satellite retrievals over dust source regions with specific dust observational constraint (Ginoux et al., 2012; Pu and Ginoux, 2016). Furthermore, we describe its evaluation in terms of data assimilation diagnostics and comparison against independent observations. Statistics of analysis departures from assimilated observations prove the consistency of the data assimilation system showing that the analysis is closer to the observations than the first-guess. Temporal mean of analysis increments show that the assimilation led to an overall reduction of dust with pattern of systematic corrections that vary with the seasons, and can be linked primarily to misrepresentation of source strength. Independent evaluation of the analysis with AERONET observations indicates that the reanalysis data set is highly accurate, and provides therefore a reliable historical record of atmospheric desert dust concentrations in a recent decade. References Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129. Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. (2012): Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50. Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448. Acknowledgements The authors 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 (435690462); PRACE (eDUST/eFRAGMENT1/eFRAGMENT2), RES (AECT-2020-3-0013/AECT-2019-3-0001/AECT-2020-1-0007) for awarding access to MareNostrum at BSC and for technical support.

A reanalysis-based study of desert dust in Northern Africa, the Middle East and Europe in a recent decade ENZA DI TOMASO1, JERÓNIMO ESCRIBANO1, PAUL GINOUX2, SARA BASART1, FRANCESCA MACCHIA1, FRANCESCA BARNABA3, MIGUEL CASTRILLO1, PAOLA FORMENTI4, ORIOL JORBA1, LUCIA MONA5, GILBERT MONTANÉ1, MICHAIL MYTILINAIOS5, VINCENZO OBISO1,6, NICK SCHUTGENS7, ATHANASIOS VOTSIS8, ERNEST WERNER9 AND CARLOS PÉREZ GARCÍAPANDO10,11 1Barcelona Supercomputing Center (BSC) 2NOAA Geophysical Fluid Dynamics Laboratory (GFDL) 3Consiglio Nazionale delle Ricerche-Istituto di Scienze dell’Atmosfera e del Clima (CNR-ISAC) 4LISA, UMR CNRS 7583, Université Paris-Est-Créteil, Université de Paris, Institut Pierre Simon Laplace (IPSL) 5Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA) 6NASA Goddard Institute for Space Studies 7Faculty of Science, Vrije Universiteit 8Finnish Meteorological Institute (FMI) 9State Meteorological Agency (AEMET) 10ICREA and Barcelona Supercomputing Center (BSC) 11ICREA, Catalan Institution for Research and Advanced Studies Presenting Author: enza.ditomaso@bsc.es

The generation of the ensemble forecast is a key step in the design of an ensemble-based data assimilation scheme as it bears a significant impact on the assimilation outcome. The ensemble of model states is used within the assimilation algorithm to derive a flow-dependent background error covariance which is used to express prior information uncertainty. The covariance matrix of the background errors is associated to both, the quality of the prior, and the relations between the elements of the control vector. This matrix drives the spread of the observational information through the control variables determining, in part, the quality of the analyses. Only a handful of studies have focused on investigating the generation of ensembles for aerosol data assimilation which might be compromising the optimal integration of model simulations and observations when it comes to the use of ensemble-based assimilation schemes.This work presents a series of design methodologies and approaches to create regional dust aerosol ensembles. We include in our experiments ensembles with and without perturbed meteorological boundary and initial conditions, spatially random source strength perturbations, perturbations of the size distribution at emission, and random perturbation of a linear combination of dust emission schemes. We compute analyses of dust optical depth by assimilating satellite dust optical depth retrievals and present our results qualitatively through the inspection of the prior correlation matrices structure, and quantitatively with a comparison against independent measurements of aerosol optical depth.This work is in the framework of the next upgrade of the operational forecast for the WMO Barcelona Dust Forecast Center (http://dust.aemet.es/) as well as of a contributing model to the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS, http://sds-was.aemet.es/ ), both services hosted by the Spanish Meteorological Agency (AEMET) and the Barcelona Supercomputing Center (BSC).

We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust–radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20 µm, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000 Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about −0.90 and −0.63 W m−2 and at the top of the atmosphere between −0.20 and −0.28 W m−2. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.

DustClim (Dust Storms Assessment for the development of user-oriented Climate Services in Northern Africa, Middle East and Europe) is a project of the European Research Area For Climate Services (ERA4CS). DustClim is aiming to provide reliable information on sand and dust storms for developing dust-related services for selected socio-economic sectors: air quality, aviation and solar energy. This contribution will describe the work done within the DustClim project towards the production of a dust reanalysis over the domain of Northern Africa, the Middle East and Europe at an unprecedented high spatial resolution (at 10km x 10km) using the state-of-art Multiscale Online Nonhydrostatic Atmosphere Chemistry model (MONARCH) and its data assimilation capability (Di Tomaso et al., 2017). An ensemble-based Kalman filter (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model simulations and satellite retrievals. Dust ensemble forecasts are used to estimate flow-dependent forecast uncertainty, which is used by the data assimilation scheme to optimally combine model prior information with satellite retrievals. Satellite observations from MODIS Deep Blue with specific observational constraint for dust (Ginoux et al., 2012; Pu and Ginoux, 2016; Sayer et al., 2014) are considered for assimilation over land surfaces, including source regions. MONARCH ensemble has been generated by applying multi-parameters, multi-physics, multi-meteorological initial and boundary conditions perturbations. Sensitive parameters of the assimilation configuration like the balance between observational and background uncertainty, or the spatial location of errors have been carefully calibrated. The dust reanalysis for the period 2011-2016 is being compared against independent dust-filtered observations from AERONET (AErosol RObotic NETwork) show the benefit of the assimilation of dust-related MODIS Deep Blue products over areas not easily covered by other observational datasets. Particularly relevant is the improvement of the model skills over the Sahara. References: Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129, doi:10.5194/gmd-10-1107-2017. Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50, doi:10.1029/2012rg000388 (2012). Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448. Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and Jeong, M.-J.: MODIS Collection 6 aerosol products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res.-Atmos., 119, 13965–13989, doi:10.1002/2014JD022453, 2014. Acknowledgement DustClim project 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 (Grant 690462). We acknowledge PRACE for awarding access to HPC resources through the eDUST and eFRAGMENT1 projects.

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An observing system simulation experiment (OSSE) is presented in the Sea of Marmara. A high-resolution ocean circulation model (FESOM) and an ensemble data assimilation tool (DART) are coupled. The OSSE methodology is used to assess the possible impact of a Ferry-Box network in the eastern Sea of Marmara. A reference experiment without assimilation is performed. Then, synthetic temperature and salinity observations are assimilated along the track of the ferries in the second experiment. The results suggest that a FerryBox network in the Sea of Marmara has potential to improve the forecasts significantly. The salinity and temperature errors get smaller in the upper layer of the water column. The impact of the assimilation is negligible in the lower layer due to the strong stratification. The circulation in the Sea of Marmara, particularly the Bosphorus outflow, helps to propagate the error reduction towards the western basin where no assimilation is performed. Overall, the proposed FerryBox network can be a good start to designing an optimal sustained marine observing network in the Sea of Marmara for assimilation purposes.

Paola Agostini (1), Giovanni Coppini (1), Sara Martinelli (1), Roberto Bonarelli (1), Rita Lecci (1), Nadia Pinardi (1,3), Sergio Cretì (1), Giuseppe Turrisi (1), Stefania Angela Ciliberti (1), Ivan Federico (1), Gianandrea Mannarini (1), Giorgia Verri (1), Eric Jansen (1), Letizia Lusito (1), Francesca Macchia (1), Fabio Montagna (1), Mauro Buonocore (1), Palmalisa Marra (2), Luca Tedesco (2), and Arturo Cavallo (2)

The present work aims at evaluating the scalability performance of a high-resolution global ocean biogeochemistry model (PELAGOS025) on massive parallel architectures and the benefits in terms of the time-to-solution reduction. PELAGOS025 is an on-line coupling between the Nucleus for the European Modelling of the Ocean (NEMO) physical ocean model and the Biogeochemical Flux Model (BFM) biogeochemical model. Both the models use a parallel domain decomposition along the horizontal dimension. The parallelisation is based on the message passing paradigm. The performance analysis has been done on two parallel architectures, an IBM BlueGene/Q at ALCF (Argonne Leadership Computing Facilities) and an IBM iDataPlex with Sandy Bridge processors at the CMCC (Euro Mediterranean Center on Climate Change). The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, the load unbalancing due to the memory structure of the BFM component and, for the BlueGene/Q, the absence of a hybrid parallelisation approach.