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The atmospheric composition forecasting system used to produce the Copernicus Atmosphere Monitoring Service (CAMS) forecasts of global aerosol and trace gas distributions, the Integrated Forecasting System (IFS-COMPO), undergoes periodic upgrades. In this study we describe the development of the future operational cycle 49R1 and focus on the implementation of the thermodynamical model EQSAM4Clim version 12, which represents gas–aerosol partitioning processes for the nitric acid–nitrate and ammonia–ammonium couples and computes diagnostic aerosol, cloud, and precipitation pH values at the global scale. This information on aerosol acidity influences the simulated tropospheric chemistry processes associated with aqueous-phase chemistry and wet deposition. The other updates of cycle 49R1 concern wet deposition, sea-salt aerosol emissions, dust optics, and size distribution used for the calculation of sulfate aerosol optics. The implementation of EQSAM4Clim significantly improves the partitioning of reactive nitrogen compounds, decreasing surface concentrations of both nitrate and ammonium in the particulate phase, which reduces PM2.5 biases for Europe, the US, and China, especially during summertime. For aerosol optical depth there is generally a decrease in the simulated wintertime biases and for some regions an increase in the summertime bias. Improvements in the simulated Ångström exponent are noted for almost all regions, resulting in generally good agreement with observations. The diagnostic aerosol and precipitation pH calculated by EQSAM4Clim have been compared to ground observations and published simulation results. For precipitation pH, the annual mean values show relatively good agreement with the regional observational datasets, while for aerosol pH the simulated values over continents are quite close to those simulated by ISORROPIA II. The use of aerosol acidity has a relatively smaller impact on the aqueous-phase production of sulfate compared to the changes in gas-to-particle partitioning induced by the use of EQSAM4Clim.

Atmospheric mineral dust aerosol constitutes a threat to aircraft engines from deterioration of internal components. Here we fulfil an overdue need to quantify engine dust ingestion at airports worldwide. The vertical distribution of dust is of key importance since ascent/descent rates and engine power both vary with altitude and affect dust ingestion. We use representative jet engine power profile information combined with vertically and seasonally varying dust concentrations to calculate the “dust dose” ingested by an engine over a single ascent or descent. Using the Copernicus Atmosphere Monitoring Service (CAMS) model reanalysis, we calculate climatological and seasonal dust dose at 10 airports for 2003–2019. Dust doses are mostly largest in Northern Hemisphere summer for descent, with the largest at Delhi in June–August (JJA; 6.6 g) followed by Niamey in March–May (MAM; 4.7 g) and Dubai in JJA (4.3 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb, and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP, though large variations in dose magnitude are found, with CAMS producing lower doses by a factor of 1.9 to 2.8, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44 % and 41 % respectively. We suggest that a likely low bias of dust concentration in the CAMS reanalysis should be considered by aviation stakeholders when estimating dust-induced engine wear.

The Equilibrium Simplified Aerosol Model for Climate version 12 (EQSAM4Clim-v12) has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. EQSAM4Clim is based on an analytical concept that is not only sufficiently fast for chemical weather prediction applications but also free of numerical noise, which also makes it attractive for air quality forecasting. EQSAM4Clim allows the calculation of aerosol composition based on the gas–liquid–solid and the reduced gas–liquid partitioning with the associated water uptake for both cases and can therefore provide important information about the acidity of the aerosols. Here we provide a comprehensive description of the recent changes made to the aerosol acidity parameterization (referred to as a version 12) which builds on the original EQSAM4Clim. We evaluate the pH improvements using a detailed box model and compare them against previous model calculations and both ground-based and aircraft observations from the USA and China, covering different seasons and scenarios. We show that, in most cases, the simulated pH is within reasonable agreement with the reference results of the Extended Aerosol Inorganics Model (E-AIM) and of satisfactory accuracy.

The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of aerosol optical depth (AOD) trends and attribution over multi-decadal timescales. Notable multi-year aerosol reanalyses currently available include NAAPS-RA from the US Naval Research Laboratory, the NASA MERRA-2, JRAero from the Japan Meteorological Agency (JMA), and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, as well as data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where divergence and challenges are prominent. Regions with high pollution and often mixed fine-mode and coarse-mode aerosol environments, such as South Asia, East Asia, Southeast Asia, and the Maritime Continent, pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions and remote oceans, exhibit large relative differences in speciated AODs and fine-mode versus coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis consensus (MRC, i.e., ensemble mean) approach was developed similarly to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.

Organic aerosols are predominantly emitted from biomass burning and biofuel use. The fraction of these aerosols that strongly absorbs ultraviolet and short visible light is referred to as brown carbon (BrC). The life cycle and the optical properties of BrC are still highly uncertain, thus contributing to the uncertainty of the total aerosol radiative effect. This study presents the implementation of BrC aerosols in the Tropospheric Aerosols for ClimaTe In CNRM (TACTIC) aerosol scheme of the atmospheric component of the Centre National de Recherches Météorologiques (CNRM) climate model. This implementation has been achieved using a BrC parameterization based on the optical properties of Saleh et al. (2014). Several simulations have been carried out with the CNRM global climate model, over the period of 2000–2014, to analyze the BrC radiative and climatic effects. Model evaluation has been carried out by comparing numerical results of single-scattering albedo (SSA), aerosol optical depth (AOD), and absorption aerosol optical depth (AAOD) to data provided by Aerosol Robotic Network (AERONET) stations, at the local scale, and by different satellite products, at the global scale. The implementation of BrC and its bleaching parameterization has resulted in an improvement of the estimation of the total SSA and AAOD at 350 and 440 nm. This improvement is observed at both the local scale, for several locations of AERONET stations, and the regional scale, over regions of Africa (AFR) and South America (AME), where large quantities of biomass burning aerosols are emitted. The annual global BrC effective radiative forcing (all-sky conditions) has been calculated in terms of both aerosol–radiation interactions (ERFari, 0.029 ± 0.006 W m−2) and aerosol–cloud interactions (ERFaci, −0.024 ± 0.066 W m−2). This study shows, on an annual average, positive values of ERFari of 0.292 ± 0.034 and 0.085 ± 0.032 W m−2 over the AFR and AME regions, respectively, which is in accordance with the BrC radiative effect calculated in previous studies. This work also reveals that the inclusion of BrC in the TACTIC aerosol scheme causes a statistically significant low-level cloud fraction increase over the southeastern Atlantic Ocean during the burning season partially caused by a vertical velocity decrease at 700 hPa (semi-direct aerosol effect). Lastly, this study also highlights that the low-level cloud fraction changes, associated with more absorbing biomass burning aerosols, contribute to an increase in both solar heating rate and air temperature at 700 hPa over this region.

In this paper, we report on the first implementation of atmospheric chemistry and aerosol as part of the European Centre for Medium-Range Weather Forecasts (ECMWF) OpenIFS model. OpenIFS is a portable version of ECMWF's global numerical weather prediction model. Modules and input data for model cycle CY43R3, which have been developed as part of the Copernicus Atmosphere Monitoring Service (CAMS), have been ported to OpenIFS with the modified CB05 tropospheric chemistry scheme, the bulk bin tropospheric aerosol module, and the option to use Belgian Assimilation System for Chemical ObsErvations (BASCOE)-based stratospheric ozone chemistry. We give an overview of the model, and describe the datasets used for emissions and dry deposition, which are similar to those used in the model configuration applied to create the CAMS reanalysis. We evaluate two reference model configurations with and without the stratospheric chemistry extension against standard observational datasets for tropospheric ozone, surface carbon monoxide (CO), tropospheric nitrogen dioxide (NO2), and aerosol optical depth. The results give basic confidence in the model implementation and configuration. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.

Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by −93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.

This article describes the Integrated Forecasting System aerosol scheme (IFS-AER) used operationally in the IFS cycle 47R1, which was operated by the European Centre for Medium Range Weather Forecasts (ECMWF) in the framework of the Copernicus Atmospheric Monitoring Services (CAMS). It represents an update of the Rémy et al. (2019) article, which described cycle 45R1 of IFS-AER in detail. Here, we detail only the parameterisations of sources and sinks that have been updated since cycle 45R1, as well as recent changes in the configuration used operationally within CAMS. Compared to cycle 45R1, a greater integration of aerosol and chemistry has been achieved. Primary aerosol sources have been updated, with the implementation of new dust and sea salt aerosol emission schemes. New dry and wet deposition parameterisations have also been implemented. Sulfate production rates are now provided by the global chemistry component of IFS. This paper aims to describe most of the updates that have been implemented since cycle 45R1, not just the ones that are used operationally in cycle 47R1; components that are not used operationally will be clearly flagged. Cycle 47R1 of IFS-AER has been evaluated against a wide range of surface and total column observations. The final simulated products, such as particulate matter (PM) and aerosol optical depth (AOD), generally show a significant improvement in skill scores compared to results obtained with cycle 45R1. Similarly, the simulated surface concentration of sulfate, organic matter and sea salt aerosol are improved by cycle 47R1 compared to cycle 45R1. Some biases persist, such as the surface concentrations of nitrate and organic matter being simulated too high. The new wet and dry deposition schemes that have been implemented into cycle 47R1 have a mostly positive impact on simulated AOD, PM and speciated aerosol surface concentration.

Aerosol forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System aerosol module (IFS-AER) for the years 2016–2019 (cycles 41r1–46r1) are compared to vertical profiles of particle backscatter from the Deutscher Wetterdienst (DWD) ceilometer network. The system has been developed in the Copernicus Atmosphere Monitoring Service (CAMS) and its precursors. The focus of this article is to evaluate the realism of the vertical aerosol distribution from 0.4 to 8 km above ground, coded in the shape, bias and temporal variation of the profiles. The common physical quantity, the attenuated backscatter β∗(z), is directly measured and calculated from the model mass mixing ratios of the different particle types using the model's inherent aerosol microphysical properties. Pearson correlation coefficients of daily average simulated and observed vertical profiles between r=0.6–0.8 in summer and 0.7–0.95 in winter indicate that most of the vertical structure is captured. It is governed by larger β∗(z) in the mixing layer and comparably well captured with the successive model versions. The aerosol load tends to be biased high near the surface, underestimated in the mixing layer and realistic at small background values in the undisturbed free troposphere. A seasonal cycle of the bias below 1 km height indicates that aerosol sources and/or lifetimes are overestimated in summer and pollution episodes are not fully resolved in winter. Long-range transport of Saharan dust or fire smoke is captured and timely, only the dispersion to smaller scales is not resolved in detail. Over Germany, β∗(z) values from Saharan dust and sea salt are considerably overestimated. Differences between model and ceilometer profiles are investigated using observed in situ mass concentrations of organic matter (OM), black carbon, SO4, NO3, NH4 and proxies for mineral dust and sea salt near the surface. Accordingly, SO4 and OM sources as well as gas-to-particle partitioning of the NO3–NH4 system are too strong. The top of the mixing layer on average appears too smooth and several hundred meters too low in the model. Finally, a discussion is included of the considerable uncertainties in the observations as well as the conversion from modeled to observed physical quantities and from necessary adaptions of varying resolutions and definitions.

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

An aerosol climatology to represent aerosols in the radiation schemes of global atmospheric models was recently developed. We derived the climatology from a reanalysis of atmospheric composition produced by the Copernicus Atmosphere Monitoring Service (CAMS). As an example of an application in a global atmospheric model, we discuss the technical aspects of the implementation in the European Centre for Medium Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) and the impact of the new climatology on the medium-range weather forecasts and 1-year simulations. The new aerosol climatology was derived by combining a set of model simulations with constrained meteorological conditions and an atmospheric composition reanalysis for the period 2003–2013 produced by the IFS. The aerosol fields of the reanalysis are constrained by assimilating the aerosol optical thickness (AOT) retrievals product by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. In a further step, we used modelled aerosol fields to correct the aerosol speciation and the vertical profiles of the aerosol reanalysis fields. The new climatology provides the monthly-mean mass mixing ratio of five aerosol species constrained by assimilated MODIS AOT. Using the new climatology in the ECMWF-IFS leads to changes in the direct aerosol radiative effect compared to the climatology previously implemented, which have a small but non-impact on the forecast skill of large-scale weather patterns in the medium-range. However, details of the regional distribution of aerosol radiative forcing can have a large local impact. This is the case for the area of the Arabian Peninsula and the northern Indian Ocean. Here changes in the radiative forcing of the mineral dust significantly improve the summer monsoon circulation.

This article describes the IFS-AER aerosol module used operationally in the Integrated Forecasting System (IFS) cycle 45R1, operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) in the framework of the Copernicus Atmospheric Monitoring Services (CAMS). We describe the different parameterizations for aerosol sources, sinks, and its chemical production in IFS-AER, as well as how the aerosols are integrated in the larger atmospheric composition forecasting system. The focus is on the entire 45R1 code base, including some components that are not used operationally, in which case this will be clearly specified. This paper is an update to the Morcrette et al. (2009) article that described aerosol forecasts at the ECMWF using cycle 32R2 of the IFS. Between cycles 32R2 and 45R1, a number of source and sink processes have been reviewed and/or added, notably increasing the complexity of IFS-AER. A greater integration with the tropospheric chemistry scheme of the IFS has been achieved for the sulfur cycle and for nitrate production. Two new species, nitrate and ammonium, have also been included in the forecasting system. Global budgets and aerosol optical depth (AOD) fields are shown, as is an evaluation of the simulated particulate matter (PM) and AOD against observations, showing an increase in skill from cycle 40R2, used in the CAMS interim ReAnalysis (CAMSiRA), to cycle 45R1.

Asian dust is a seasonal meteorological phenomenon which affects east Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three 1-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to the aerosol load over China. Data used were the Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target and the Deep Blue aerosol optical depth (AOD). In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative of the availability of independent AOD data from two established ground-based networks (AERONET, Aerosol Robotic Network, and CARSNET, China Aerosol Remote Sensing Network), which could be used to evaluate experiments. Particulate matter (PM) data from the China Environmental Protection Agency were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert dust events and to improve the short-range forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better than the unconstrained experiment, generally showing smaller normalized mean bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts into the forecast up to 48 h. The performance of the global model in terms of particulate matter does not show the same degree of skill as the performance in terms of optical depth. Despite this, the global model is able to capture some regional pollution patterns. This indicates that the global model analyses may be used as boundary conditions for regional air quality models at higher resolution, enhancing their performance in situations in which part of the pollution may have originated from large-scale mechanisms. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a role in delivering improved monitoring of Asian dust optical depth.

Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas–aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol–climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term requirements is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of bin and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.

The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

The Global Fire Assimilation System (GFAS) assimilates fire radiative power (FRP) observations from satellite-based sensors to produce daily estimates of biomass burning emissions. It has been extended to include information about injection heights derived from fire observations and meteorological information from the operational weather forecasts of ECMWF. Injection heights are provided by two distinct methods: the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) parameterisation and the one-dimensional plume rise model (PRM). A global database of daily biomass burning emissions and injection heights at 0.1° resolution has been produced for 2003–2015 and is continuously extended in near-real time with the operational GFAS service of the Copernicus Atmospheric Monitoring Service (CAMS). In this study, the two injection height data sets were compared with the new MPHP2 (MISR Plume Height Project 2) satellite-based plume height retrievals. The IS4FIRES parameterisation showed a better overall agreement than the observations, while the PRM was better at capturing the variability of injection heights. The performance of both parameterisations is also dependent on the type of vegetation. Furthermore, the use of biomass burning emission heights from GFAS in atmospheric composition forecasts was assessed in two case studies: the South AMerican Biomass Burning Analysis (SAMBBA) campaign which took place in September 2012 in Brazil, and a series of large fire events in the western USA in August 2013. For these case studies, forecasts of biomass burning aerosol species by the Composition Integrated Forecasting System (C-IFS) of CAMS were found to better reproduce the observed vertical distribution when using PRM injection heights from GFAS compared to aerosols emissions being prescribed at the surface. The globally available GFAS injection heights introduced and evaluated in this study provide a comprehensive data set for future fire and atmospheric composition modelling studies.

Aerosols affect the atmosphere through direct interaction with short-wave and long-wave radiation and the microphysical properties of clouds. In this paper we report in detail on several mechanisms by which the short-term impact of dust on surface radiative fluxes can affect the dust loading of the atmosphere via modification of boundary-layer meteorology. This in turn affects the aerosol radiative forcing itself. Examples of these feedbacks between dust and boundary layer meteorology were observed during a series of dust storms in the Sahara and the eastern Mediterranean in April 2012. These case studies have been analysed using the Monitoring Atmospheric Composition and Climate – Interim Implementation (MACC-II) system. The radiative fluxes in the short-wave and long-wave spectra were both significantly affected by the prognostic aerosol–radiation interaction, which in turn impacted the meteorological simulation. Reduced incoming solar radiation below the aerosol layers led to a decrease in maximum surface temperatures and to a more stable thermal stratification of the lower atmosphere. This in turn forced weaker surface wind speeds and eventually smaller dust emissions. Moreover, we also observed a secondary impact of the aerosol radiative forcing, whereby horizontal gradients of surface temperature were increased at the edge of the dust plume, which led to local increases of surface wind speeds due to the thermal wind effect. The differentiated impact of the aerosol layer on surface pressure also contributed to the increase in surface wind speed and dust production in the same area. Enhanced long-wave radiative fluxes by the dust mass were associated with opposite processes. Less stable thermal stratification at night, brought mainly by higher minimum temperatures at the surface, caused stronger surface winds. At the edge of the dust storm, weaker horizontal temperature and pressure gradients forced lower winds and reduced dust production. Regarding dust emissions, short-wave radiative forcing had a larger impact than long-wave radiative forcing, corroborating several previous studies. For surface temperature, short-wave and long-wave contribution were close in intensity. These feedbacks were amplified when using data assimilation to build the aerosol analysis of the MACC-II global system. This led to an improvement in the short-term forecasts of thermal radiative fluxes and surface temperatures.

The Copernicus Atmosphere Monitoring Service (CAMS) provides daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as O3, CO, NO2, HCHO and SO2; aerosol species; and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecasting System (IFS) for numerical weather prediction (NWP) and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. The CAMS system receives regular upgrades, following the upgrades of IFS. The last upgrade, Cy48R1, operational since 27 June 2023, was major with a large number of code changes, both for IFS-COMPO and for NWP. The main IFS-COMPO innovations include the introduction of full stratospheric chemistry; a major update of the emissions; a major update of the aerosol model, including the representation of secondary organic aerosol; several updates of the dust life cycle and optics; updates to the inorganic chemistry in the troposphere; and the assimilation of Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) and TROPOspheric Monitoring Instrument (TROPOMI) CO. The CAMS Cy48R1 upgrade was validated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft, and balloon and satellite observations. In this paper we present the validation results for Cy48R1 by comparing them with the skill of the previous operational system (Cy47R3), with the independent observations as reference, for the period October 2022 to June 2023, during which daily forecasts from both cycles are available. Major improvements in skill are found for the ozone profile in the lower–middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations between 10 and 200 hPa, with larger deviations between 1 and 10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3, NO2 and SO2. The CO column assimilation is now anchored by the Infrared Atmospheric Sounding Interferometer (IASI) instead of the Measurements Of Pollution in The Troposphere (MOPITT) instrument, which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. The results of the 47 comparisons are summarised in a scorecard, which shows that 83 % of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17 % indicate a (slight) degradation. This demonstrates the overall success of this upgrade.

In the realm of sensor networks, the substantial rise in multimedia data production, covering audio, video, and acoustic measurements, has expanded the scale of big data. Multimedia Sensor Networks (MSN) excel in managing diverse sensor outputs, representations, and encoding across domains. Existing models for event detection in sensor networks fall short in handling the sheer volume and speed of these measurements from a Big Data perspective. This research work introduces “Saltus,” a model that aligns multimedia data from sensor networks to a standardized feature space. Saltus employs a machine learning-centric architecture to enhance data analysis possibilities. Crucially, the model integrates federated learning to address the evolving landscape of sensor networks. This approach optimizes the collaborative learning capabilities by allowing distributed nodes to train machine learning models locally, preserving data privacy. Saltus emerges as a solution that not only streamlines multimedia data processing but also establishes a more secure and privacy-preserving analytics framework in large-scale sensor networks. The model signifies a step forward in integrating multimedia data into an easily analyzable format, leveraging the advantages of federated learning in big data analytics.

Phishing websites, mimicking legitimate counterparts, pose significant threats by stealing user information through deceptive Uniform Resource Locators (URLs). Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effective approach utilizing residual pipelining for phishing URL detection. Our method extracts common URL features and sentiments, employing a residual pipeline comprising convolutional and inverted residual blocks. These resultant features are then fed into a Multi-Layer Perceptron (MLP) for classification. We evaluate the efficacy of our approach against traditional algorithms using a Kaggle dataset. Our results demonstrate superior accuracy, precision, F1 Score, and recall, showcasing its effectiveness in mitigating phishing threats. Utilizing a residual pipeline made up of convolutional and inverted residual blocks, we start our method by identifying similar URL features and sentiments. We also use domain age research to figure out how long URLs have been around. Additionally, the lexical study of URL structure makes our method more useful, resulting in impressive accuracy. With an accuracy of 98.29%, this research highlights the importance of innovative techniques in combating evolving cyber threats. Future research directions could focus on enhancing the model’s robustness against adversarial attacks and integrating real-time monitoring for proactive defense strategies.

An extensive worldwide network known as the Internet of Things (IoT) links different electronic devices and facilitates easy communication and group work. This interdependency is especially apparent in Low Power and Lossy Networks (LLNs), where resource-constrained devices adhere to specified protocols for effective communication. Such systems frequently use Routing Protocol for LLNs (RPL). Nevertheless, due to its basic simplicity, there are numerous ways to exploit it, thereby compromising network security. It is also difficult to carry out complex computational operations on LLNs due to their resource constraints. A highly developed system called the Trust-Based Intrusion Detection System for RPL (TIDSRPL) is presented in this research study. Complex trust computations are offloaded to the root node by TIDSRPL, which assesses node trust based on network behavior. Reduce the possibility of resource depletion with this strategic transfer that preserves energy, storage, and computational resources at the node level. Comparative analysis with the default RPL Objective Function (OF), MRHOF-RPL, demonstrates TIDSRPL’s superior efficacy in detecting and isolating malicious nodes engaged in Sinkhole, Selective forwarding, and Sybil attacks. Notably, TIDSRPL exhibits a 20-35% reduction in average packet loss ratio and attains 33-45% greater energy efficiency compared to MRHOF-RPL, reinforcing its robustness in securing LLN operations.

Skin lesion classification is a pivotal process in dermatology, enabling the early detection and precise diagnosis of skin diseases, leading to improved patient outcomes. Deep learning has shown great potential for this task by leveraging its ability to learn complex patterns from images. However, diagnostic accuracy is compromised by exclusive reliance on single-modality images. This research work proposes an innovative framework that unifies a Vision Transformer model with transfer learning, channel attention mechanism, and ROI for the accurate detection of skin conditions, including skin cancer. The proposed approach blends computer vision and machine-learning techniques, leveraging a comprehensive dataset comprised of macroscopic dermoscopic images, appended with patient metadata. Compared with conventional techniques, the proposed methodology exhibits significant improvements in various parameters, including sensitivity, specificity, and precision. Moreover, it demonstrates outstanding performance in real-world datasets, reinforcing its potential for clinical implementation. With a remarkable accuracy of 99%, the method outperforms existing approaches. Overall, this investigation underscores the transformative impact of deep learning and multimodal data analysis in the dermoscopic domain, projecting substantial headway into the field of skin lesion analytic diagnosis.

Phishing is an electronic fraud through which an attacker can access user credentials. Phishing websites are the ones that mimic legitimate websites. Fraudsters can replace them within hours to evade their detection. The effects of phishing attacks exhibit the need for anti-phishing mechanisms. Several approaches were there to recognize the phishing websites, the white list approach, blacklist approach, machine learning, and heuristic-based approach. Earlier studies have shown that classifiers may be subject to evasion attacks although this point has only been explored on a small scale. As a result, the study covers evasion attacks and their detection within the context of website classifiers, which is rarely explored. In response to the inadequacies, the proposed technique includes extracting information from URLs and classifying webpages using various machine learning methods. The methodology involves crafting adversarial samples targeting classification features, with a focus on maintaining the functionality and appearance of phishing websites. The appearance is evaluated using image distortion metrics named mean squared error. Then a resemblance approach is utilized for the aim of detecting assaults that happened as a result of evasion attacks. This research introduces a novel defense mechanism against evasion attacks, marking a significant contribution to the field.

Global monitoring of aerosols is required to analyse the impacts of aerosols on air quality and to understand their role in modulating the climate variability. The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecasts and reanalyses of aerosols using the ECMWF Integrated Forecasting System (IFS), constrained by the assimilation of MODIS and Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). Given the potential end of lifetime of MODIS AOD, implementing new AOD observations in the CAMS operational suite is a priority to ensure the continuity of the CAMS forecast performances. The objective of this work is to test the assimilation of the NOAA VIIRS AOD product from S-NPP and NOAA20 satellites in the IFS model. Simulation experiments assimilating VIIRS on top or in place of MODIS were carried out from June 2021 to November 2021 to evaluate the impacts on the AOD analysis. For maritime aerosol background, the assimilation of VIIRS and the use of VIIRS from NOAA20 as an anchor reduce the analysis AOD values compared to MODIS-based experiments, in which the analysis values were too high due to the positive bias of MODIS/Terra over ocean. Over land, the assimilation of VIIRS induces a large increase in the analysis over biomass burning regions where VIIRS shows larger AOD than MODIS due to differences in the aerosol models and cloud filtering between MODIS and VIIRS retrieval algorithms. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, the Arabian Peninsula and a few places in Asia in the July–August period. The assimilation of VIIRS leads to an overall reduction of the bias in AOD analysis evaluated against AERONET measurements, with the largest bias reduction over Europe and desert and maritime sites.

The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is explained by instrument and retrieval algorithm similarities. VIIRS AOD is frequently lower over the ocean background and higher over biomass burning and dust source land regions compared to MODIS. VIIRS shows larger spatial coverage over land and resolves finer spatial structures such as the transport of Australian biomass burning smoke over the Pacific, which can be explained by the use of a heavy aerosol detection test in the retrieval algorithm. Our results confirm the positive offset over ocean (i) between Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration degradation of Terra/MODIS in the Dark Target algorithm and (ii) between SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the rest of the products, which is mainly related to differences in spatial representativity at the IFS grid spatial resolution due to the stringent cloud filtering applied to the SLSTR radiances. Finally, the geometry characteristics of the instrument, which drive the range of scattering angles sampled by the instrument, can explain a large part of the differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A.

Ammonium nitrate is a major aerosol constituent over many land regions and contributes to air pollution episodes, ecosystem destruction, regional haze, and aerosol-induced climate forcing. Many climate models that represent ammonium nitrate assume that the ammonium–sulfate–nitrate chemistry reaches thermodynamic equilibrium instantaneously without considering kinetic limitations on condensation rates. The Met Office's Unified Model (UM) is employed to investigate the sensitivity of ammonium nitrate concentrations to the nitric acid uptake coefficient (γ) in a newly developed nitrate scheme in which first-order condensation theory is utilised to limit the rate at which thermodynamic equilibrium is attained. Two values of γ representing fast (γ=0.193) and slow (γ=0.001) uptake rates are tested in 20-year global UM integrations. The global burden of nitrate associated with ammonium in the “fast” simulation (0.11 Tg[N]) is twice as great as in the “slow” simulation (0.05 Tg[N]), while the top-of-the-atmosphere radiative impact of representing nitrate is −0.19 W m−2 in the fast simulation and −0.07 W m−2 in the slow simulation. In general, the fast simulation exhibits better spatial correlation with observed nitrate concentrations, while the slow simulation better resolves the magnitude of concentrations. Local near-surface nitrate concentrations are found to be highly correlated with seasonal ammonia emissions, suggesting that ammonia is the predominant limiting factor controlling nitrate prevalence. This study highlights the high sensitivity of ammonium nitrate concentrations to nitric acid uptake rates and provides a novel mechanism for reducing nitrate concentration biases in climate model simulations. The new UM nitrate scheme represents a step change in aerosol modelling capability in the UK across weather and climate timescales.

Aerosol-induced absorption of shortwave radiation can modify the climate through local atmospheric heating, which affects lapse rates, precipitation, and cloud formation. Presently, the total amount of aerosol absorption is poorly constrained, and the main absorbing aerosol species (black carbon (BC), organic aerosols (OA), and mineral dust) are diversely quantified in global climate models. As part of the third phase of the Aerosol Comparisons between Observations and Models (AeroCom) intercomparison initiative (AeroCom phase III), we here document the distribution and magnitude of aerosol absorption in current global aerosol models and quantify the sources of intermodel spread, highlighting the difficulties of attributing absorption to different species. In total, 15 models have provided total present-day absorption at 550 nm (using year 2010 emissions), 11 of which have provided absorption per absorbing species. The multi-model global annual mean total absorption aerosol optical depth (AAOD) is 0.0054 (0.0020 to 0.0098; 550 nm), with the range given as the minimum and maximum model values. This is 28 % higher compared to the 0.0042 (0.0021 to 0.0076) multi-model mean in AeroCom phase II (using year 2000 emissions), but the difference is within 1 standard deviation, which, in this study, is 0.0023 (0.0019 in Phase II). Of the summed component AAOD, 60 % (range 36 %–84 %) is estimated to be due to BC, 31 % (12 %–49 %) is due to dust, and 11 % (0 %–24 %) is due to OA; however, the components are not independent in terms of their absorbing efficiency. In models with internal mixtures of absorbing aerosols, a major challenge is the lack of a common and simple method to attribute absorption to the different absorbing species. Therefore, when possible, the models with internally mixed aerosols in the present study have performed simulations using the same method for estimating absorption due to BC, OA, and dust, namely by removing it and comparing runs with and without the absorbing species. We discuss the challenges of attributing absorption to different species; we compare burden, refractive indices, and density; and we contrast models with internal mixing to models with external mixing. The model mean BC mass absorption coefficient (MAC) value is 10.1 (3.1 to 17.7) m2 g−1 (550 nm), and the model mean BC AAOD is 0.0030 (0.0007 to 0.0077). The difference in lifetime (and burden) in the models explains as much of the BC AAOD spread as the difference in BC MAC values. The difference in the spectral dependency between the models is striking. Several models have an absorption Ångstrøm exponent (AAE) close to 1, which likely is too low given current knowledge of spectral aerosol optical properties. Most models do not account for brown carbon and underestimate the spectral dependency for OA.

The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

A unique 4-week ship cruise from Guadeloupe to Cabo Verde in April–May 2013 see part 1, Rittmeister et al. (2017) is used for an in-depth comparison of dust profiles observed with a polarization/Raman lidar aboard the German research vessel Meteor over the remote tropical Atlantic and respective dust forecasts of a regional (SKIRON) and two global atmospheric (dust) transport models (NMMB/BSC-Dust, MACC/CAMS). New options of model–observation comparisons are presented. We analyze how well the modeled fine dust (submicrometer particles) and coarse dust contributions to light extinction and mass concentration match respective lidar observations, and to what extent models, adjusted to aerosol optical thickness observations, are able to reproduce the observed layering and mixing of dust and non-dust (mostly marine) aerosol components over the remote tropical Atlantic. Based on the coherent set of dust profiles at well-defined distances from Africa (without any disturbance by anthropogenic aerosol sources over the ocean), we investigate how accurately the models handle dust removal at distances of 1500 km to more than 5000 km west of the Saharan dust source regions. It was found that (a) dust predictions are of acceptable quality for the first several days after dust emission up to 2000 km west of the African continent, (b) the removal of dust from the atmosphere is too strong for large transport paths in the global models, and (c) the simulated fine-to-coarse dust ratio (in terms of mass concentration and light extinction) is too high in the models compared to the observations. This deviation occurs initially close to the dust sources and then increases with distance from Africa and thus points to an overestimation of fine dust emission in the models.

A new global reanalysis data set of atmospheric composition (AC) for the period 2003–2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.

Lidar observations of smoke aerosols have been analysed from six flights of the Facility for Airborne Atmospheric Measurements BAe-146 research aircraft over Brazil during the biomass burning season (September 2012). A large aerosol optical depth (AOD) was observed, typically ranging 0.4–0.9, along with a typical aerosol extinction coefficient of 100–400 Mm−1. The data highlight the persistent and widespread nature of the Amazonian haze, which had a consistent vertical structure, observed over a large distance ( ∼ 2200 km) during a period of 14 days. Aerosols were found near the surface; but the larger aerosol load was typically found in elevated layers that extended from 1–1.5 to 4–6 km. The measurements have been compared to model predictions with the Met Office Unified Model (MetUM) and the ECMWF-MACC model. The MetUM generally reproduced the vertical structure of the Amazonian haze observed with the lidar. The ECMWF-MACC model was also able to reproduce the general features of smoke plumes albeit with a small overestimation of the AOD. The models did not always capture localised features such as (i) smoke plumes originating from individual fires, and (ii) aerosols in the vicinity of clouds. In both these circumstances, peak extinction coefficients of the order of 1000–1500 Mm−1 and AODs as large as 1–1.8 were encountered, but these features were either underestimated or not captured in the model predictions. Smoke injection heights derived from the Global Fire Assimilation System (GFAS) for the region are compatible with the general height of the aerosol layers.

The El Niño–Southern Oscillation (ENSO) not only affects meteorological fields but also has a large impact on atmospheric composition. Atmospheric composition fields from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis are used to identify the ENSO signal in tropospheric ozone, carbon monoxide, nitrogen oxide and smoke aerosols, concentrating on the months October to December. During El Niño years, all of these fields have increased concentrations over maritime South East Asia in October. The MACC Composition Integrated Forecasting System (C-IFS) model is used to quantify the relative magnitude of dynamically induced and emission-driven changes in the atmospheric composition fields. While changes in tropospheric ozone are a combination of dynamically induced and emission-driven changes, the changes in carbon monoxide, nitrogen oxides and smoke aerosols are almost entirely emission-driven in the MACC model. The ozone changes continue into December, i.e. after the end of the Indonesian fire season while changes in the other fields are confined to the fire season.

We use the global circulation model ECHAM6 extended by the aerosol module HAM2 to simulate global patterns in wildfire emission heights. Prescribed plume heights in ECHAM6 are replaced by an implementation of a simple, semi-empirical plume height parametrization. In a first step, the global performance of the plume height parametrization is evaluated for plumes reported in the Multiangle Imaging Spectroradiometer (MISR) Plume Height Project (MPHP) data set. Our results show that the parametrization simulates a largely reasonable global distribution of plume heights. While the modeled global mean plume height (1411 ± 646 m) is in good agreement with the observed mean (1382 ± 702 m), the upper and lower tails of the plume height distribution tend to be slightly underrepresented. Furthermore, we compare plume heights simulated by the simple parametrization to a more complex, analytical plume model. Major differences in global plume height distributions are found for the lowest 1.5 km, but reasonable agreement is observed for higher plumes. In a second step, fire radiative power (FRP) as reported in the global fire assimilation system (GFAS) is used to simulate plume heights for observed fires globally for the period 2005–2011. The global fraction of simulated daytime plumes injecting emissions into the free troposphere (FT) ranges from 3.7 ± 0.7 to 5.2 ± 1.0 %. This range is comparable to results from observational studies, but it is much lower than results for prescribed plume heights in the ECHAM6-HAM2 standard setup. Nevertheless, occasionally deep emission injections exceeding 5–7 km in height are simulated for intense fires and favorable meteorological conditions. The application of a prescribed diurnal cycle in FRP turns out to be of minor importance. For a hypothetical doubling in FRP, moderate changes in plume heights of 100–400 m are simulated. These small changes indicate that a potential future increase in fire intensity will only slightly impact the emission heights on a global scale.

Fires are important emitters of aerosol and trace gases and as such need to be taken into account in any atmospheric composition modelling enterprise. One method to estimate these emissions is to convert fire radiative power (FRP) analysis into dry matter burnt and emissions of smoke constituents using land-cover-dependent conversion factors. Inventories like the Global Fire Assimilation System (GFAS) follow this approach by calculating daily global smoke emissions from FRP observed by the MODIS instruments onboard the Terra and Aqua satellites. Observations with different overpass times systematically sample fires at different stages in the strong diurnal fire cycle. For some time periods, observations are available from only one instrument, which leads to a bias in the observed average FRP. We develop a method to correct this bias in daily FRP observations from any low Earth orbit (LEO) satellite, so that the budget of daily smoke emissions remains independent of the number of satellites from which FRP observations are taken into account. This ensures the possibility of running, for example, GFAS in case of failure of one of the MODIS instruments. It also enables the extension GFAS to 2000–2002 and the inclusion of FRP observations from upcoming satellite missions. The correction combines linear and non-linear regressions and uses an adaptive regionalization algorithm. It decreases the bias in daily average FRP from Terra and Aqua by more than 95%, and RMSE by 75% for Aqua and 55% for Terra. The correction algorithm is applied to Terra observations from 25 February 2000 to 31 December 2002, when Aqua observations were not available. The database of fire emissions GFASv1.0 is extended correspondingly.

Marek's disease virus (MDV) vaccines were the first vaccines that protected against cancer. The avirulent turkey herpesvirus (HVT) was widely employed and protected billions of chickens from a deadly MDV infection. It is also among the most common vaccine vectors providing protection against a plethora of pathogens. HVT establishes latency in T-cells, allowing the vaccine virus to persist in the host for life. Intriguingly, the HVT genome contains telomeric repeat arrays (TMRs) at both ends; however, their role in the HVT life cycle remains elusive. We have previously shown that similar TMRs in the MDV genome facilitate its integration into host telomeres, which ensures efficient maintenance of the virus genome during latency and tumorigenesis. In this study, we investigated the role of the TMRs in HVT genome integration, latency, and reactivation in vitro and in vivo. Additionally, we examined HVT infection of feather follicles. We generated an HVT mutant lacking both TMRs (vΔTMR) that efficiently replicated in cell culture. We could demonstrate that wild type HVT integrates at the ends of chromosomes containing the telomeres in T-cells, while integration was severely impaired in the absence of the TMRs. To assess the role of TMRs in vivo, we infected one-day-old chickens with HVT or vΔTMR. vΔTMR loads were significantly reduced in the blood and hardly any virus was transported to the feather follicle epithelium where the virus is commonly shed. Strikingly, latency in the spleen and reactivation of the virus were severely impaired in the absence of the TMRs, indicating that the TMRs are crucial for the establishment of latency and reactivation of HVT. Our findings revealed that the TMRs facilitate integration of the HVT genome into host chromosomes, which ensures efficient persistence in the host, reactivation, and transport of the virus to the skin., Competing Interests: Zoltán Penzes is employed by and received a salary from Ceva Santé Animale. All other authors declare that they have no conflict of interest., (Copyright: © 2024 Denesvre et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Childhood diseases correspond to major public health issues. A large number of studies using different approaches provide evidence regarding effects of environmental exposures, encompassed in the exposome, on children's health. We aimed to summarize the overall level of evidence (LoE) from all streams of evidence regarding exposome effects on child health. For 88 selected chemical and urban factors, we retrieved the conclusions of agency reports or literature reviews published between 2015 and 2021 regarding effects on child health, including cardiovascular, metabolic, neurodevelopmental, respiratory and other health outcomes. Adapted versions of PRISMA flowchart and AMSTAR-2 tool were used to select and assess the quality of the systematic reviews retrieved from PubMed and SCOPUS databases. For each factor-outcome pair, conclusions in three streams of evidence (epidemiological, toxicological and mechanistic, the latter corresponding to in vitro and in silico approaches) were translated into stream-specific LoEs and then combined into an overall LoE ranging from "very unlikely" to "very likely". The 88 environmental factors were implied in 611 factor-outcome pairs. Forty-four pairs (7%), corresponding to 16 factors, had a very likely overall LoE (≥80%); 127 pairs (21%), corresponding to 49 factors, had a likely or more overall LoE (≥60%). For 81 pairs (13%), no evidence was available in agency reports or published reviews, while for 275 pairs (45%), corresponding to 68 factors, the overall LoE was very unlikely (<20%). Exposure factors with the greatest number of associated health outcomes with a high overall LoE were HCB, PCBs, temperature (8 outcomes), PFOA (7 outcomes), PFOS, cotinine (6 outcomes), arsenic, lead (5 outcomes), bisphenols A and S, PFNA and PM 2.5 (4 outcomes), DDT, DDE and DDD, PFHxA, PFDA, green space, UV radiation (3 outcomes). We developed an approach to extract and summarize the existing evidence about effects of environmental factors on health. The plausibility database built for children's health can be used to identify research gaps, conduct quantitative risk assessment studies. It could be expanded to consider a larger fraction of the exposome and other age groups and should be updated on a regular basis., (Copyright © 2023 The Authors. Published by Elsevier GmbH.. All rights reserved.)

Absorbing aerosols emitted from biomass burning (BB) greatly affect the radiation balance, cloudiness, and circulation over tropical regions. Assessments of these impacts rely heavily on the modeled aerosol absorption from poorly constrained global models and thus exhibit large uncertainties. By combining the AeroCom model ensemble with satellite and in situ observations, we provide constraints on the aerosol absorption optical depth (AAOD) over the Amazon and Africa. Our approach enables identification of error contributions from emission, lifetime, and MAC (mass absorption coefficient) per model, with MAC and emission dominating the AAOD errors over Amazon and Africa, respectively. In addition to primary emissions, our analysis suggests substantial formation of secondary organic aerosols over the Amazon but not over Africa. Furthermore, we find that differences in direct aerosol radiative effects between models decrease by threefold over the BB source and outflow regions after correcting the identified errors. This highlights the potential to greatly reduce the uncertainty in the most uncertain radiative forcing agent.

pUL51 is a minor tegument protein important for viral assembly and cell-to-cell spread (CCS) but dispensable for replication in cell culture of all Herpesviruses for which its role has been investigated. Here, we show that pUL51 is essential for the growth of Marek's disease virus, an oncogenic alphaherpesvirus of chickens that is strictly cell-associated in cell culture. MDV pUL51 localized to the Golgi apparatus of infected primary skin fibroblasts, as described for other Herpesviruses. However, the protein was also observed at the surface of lipid droplets in infected chicken keratinocytes, hinting at a possible role of this compartment for viral assembly in the unique cell type involved in MDV shedding in vivo. Deletion of the C-terminal half of pUL51 or fusion of GFP to either the N- or C-terminus were sufficient to disable the protein's essential function(s). However, a virus with a TAP domain fused at the C-terminus of pUL51 was capable of replication in cell culture, albeit with viral spread reduced by 35% and no localization to lipid droplets. In vivo , we observed that although the replication of this virus was moderately impacted, its pathogenesis was strongly impaired. This study describes for the first time the essential role of pUL51 in the biology of a herpesvirus, its association to lipid droplets in a relevant cell type, and its unsuspected role in the pathogenesis of a herpesvirus in its natural host. IMPORTANCE Viruses usually spread from cell to cell through two mechanisms: cell-released virus and/or cell-to-cell spread (CCS). The molecular determinants of CCS and their importance in the biology of viruses during infection of their natural host are unclear. Marek's disease virus (MDV) is a deadly and highly contagious herpesvirus of chickens that produces no cell-free particles in vitro , and therefore, spreads only through CCS in cell culture. Here, we show that viral protein pUL51, an important factor for CCS of Herpesviruses, is essential for MDV growth in vitro . We demonstrate that the fusion of a large tag at the C-terminus of the protein is sufficient to moderately impair viral replication in vivo and almost completely abolish pathogenesis while only slightly reducing viral growth in vitro . This study thus uncovers a role for pUL51 associated with virulence, linked to its C-terminal half, and possibly independent of its essential functions in CCS., Competing Interests: The authors declare no conflict of interest.

Aims: Degenerative mitral valve dystrophy (MVD) leading to mitral valve prolapse is the most frequent form of MV disease, and there is currently no pharmacological treatment available. The limited understanding of the pathophysiological mechanisms leading to MVD limits our ability to identify therapeutic targets. This study aimed to reveal the main pathophysiological pathways involved in MVD via the multimodality imaging and transcriptomic analysis of the new and unique knock-in (KI) rat model for the FilaminA-P637Q (FlnA-P637Q) mutation associated-MVD., Methods and Results: Wild-type (WT) and KI rats were evaluated morphologically, functionally, and histologically between 3-week-old and 3-to-6-month-old based on Doppler echocardiography, 3D micro-computed tomography (microCT), and standard histology. RNA-sequencing and Assay for Transposase-Accessible Chromatin (ATAC-seq) were performed on 3-week-old WT and KI mitral valves and valvular cells, respectively, to highlight the main signalling pathways associated with MVD. Echocardiographic exploration confirmed MV elongation (2.0 ± 0.1 mm vs. 1.8 ± 0.1, P = 0.001), as well as MV thickening and prolapse in KI animals compared to WT at 3 weeks. 3D MV volume quantified by microCT was significantly increased in KI animals (+58% vs. WT, P = 0.02). Histological analyses revealed a myxomatous remodelling in KI MV characterized by proteoglycans accumulation. A persistent phenotype was observed in adult KI rats. Signalling pathways related to extracellular matrix homeostasis, response to molecular stress, epithelial cell migration, endothelial to mesenchymal transition, chemotaxis and immune cell migration, were identified based on RNA-seq analysis. ATAC-seq analysis points to the critical role of transforming growth factor-β and inflammation in the disease., Conclusion: The KI FlnA-P637Q rat model mimics human myxomatous MVD, offering a unique opportunity to decipher pathophysiological mechanisms related to this disease. Extracellular matrix organization, epithelial cell migration, response to mechanical stress, and a central contribution of immune cells are highlighted as the main signalling pathways leading to myxomatous MVD. Our findings pave the road to decipher underlying molecular mechanisms and the specific role of distinct cell populations in this context., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Spondylarthritis (SpA) development in HLA-B27/human β2-microglobulin transgenic rat (B27-rat) is correlated with altered conventional dendritic cell (cDC) function that promotes an inflammatory pattern of CD4+T cells, including a biased expansion of pro-inflammatory Th 17 population and imbalance of regulatory T cells cytokine profile. Transcriptomic analysis revealed that cDCs from B27-rats under express IL-27, an anti-inflammatory cytokine which induces the differentiation of IL-10 + regulatory T cells and inhibits Th 17 cells., Methods: Here, we first investigated whether in vitro addition of exogenous IL-27 could reverse the inflammatory pattern observed in CD4 + T cells. Next, we performed preclinical assay using IL-27 to investigate whether in vivo treatment could prevent SpA development in B27-rats., Results: in vitro addition of IL-27 to cocultures of cDCs and CD4 + T cell subsets from B27-rats reduced IL-17 and enhanced IL-10 production by T cells. Likewise, IL-27 inhibited the production of IL-17 by CD4 + T cells from SpA patients. Interestingly, in vivo treatment with recombinant IL-27 starting before SpA onset, inhibited SpA development in B27-rats through the suppression of IL-17/TNF producing CD4 + T cells., Discussion: Overall, our results reveal a potent inhibitory effect of IL-27 and highlight this cytokine as a promising new therapeutic target in SpA, especially for SpA patients non responders to currently approved biotherapies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jouhault, Cherqaoui, Jobart-Malfait, Glatigny, Lauraine, Hulot, Morelle, Hagege, Ermoza, El Marjou, Izac, Saintpierre, Letourneur, Rémy, Anegon, Boissier, Chiocchia, Breban and Araujo.)

Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emissions. Using the relationship from an ensemble of aerosol models and satellite observations, we show that the bias in aerosol modelling results primarily from incorrect lifetimes and underestimated mass extinction coefficients. In turn, these biases seem to be related to incorrect precipitation and underestimated particle sizes. We further show that boosting BB emissions to correct AOD biases over the source region causes an overestimation of AOD in the outflow from Africa by 48%, leading to a double warming effect compared with when biases are simultaneously addressed for both aforementioned factors. Such deviations are particularly concerning in a warming future with increasing emissions from fires., (© 2022. The Author(s).)