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A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.

Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1∘ × 0.1∘) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff (36.7 Gt) and CO2bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389, last access: 15 May 2023; Urraca Valle et al., 2023).

Air pollution in the central zone of Chile is not only a public health concern but also threatens water resources and climate, in connection with the transport and deposition of black carbon (BC) from urban centers onto the glaciers of the Andes. Chemistry-transport simulations reveal a seasonal dichotomy in the flux and latitudinal pattern of BC deposition on glaciers of the central Chilean Andes. The average deposition flux of BC on glaciers between 30 and 37∘ S is 4 times larger in winter, affecting mostly low-elevation glaciers, whereas the smaller summertime flux affects glaciers evenly, irrespective of their elevation. The contribution of emissions from the city of Santiago is dominant in summertime with more than 50 % along the Andes but minor in wintertime with less than 20 % even close to the capital city. Transport at larger scales and more local sources likely account for the remaining flux. The superimposition of synoptic-scale circulation and local mountain-valley circulation along the Andes drives the differences between summertime and wintertime deposition fluxes and generates a greater meteorological export potential during summer months. Future emissions and climate projections suggest that under the RCP8.5 scenario the gap between summertime and wintertime BC export and deposition flux could decrease, thereby pointing to summertime emission control gaining relevance. The chemistry-transport modeling approach for BC deposition on the Andes sheds light on the importance of the often disregarded summertime emissions on the radiative balance of its glaciers, particularly in the vicinity of Santiago.

This description paper presents a detailed and consistent estimate and analysis of exhaust pollutant emissions generated by Chile's road transport activity for the period 1990–2020. The complete database for the period 1990–2020 is available at the following DOI: https://doi.org/10.17632/z69m8xm843.2 (Osses et al., 2021). Emissions are provided at a high spatial resolution (0.01∘ × 0.01∘) over continental Chile from 18.5 to 53.2∘ S, including local pollutants (CO; volatile organic compounds, VOCs; NOx; PM2.5), black carbon (BC) and greenhouse gases (CO2, CH4). The methodology considers 70 vehicle types, based on 10 vehicle categories, subdivided into 2 fuel types and 7 emission standards. Vehicle activity was calculated based on official databases of vehicle records and vehicle flow counts. Fuel consumption was calculated based on vehicle activity and contrasted with fuel sales to calibrate the initial dataset. Emission factors come mainly from the Computer programme to calculate emissions from road transport version 5 (COPERT 5), adapted to local conditions in the 15 political regions of Chile, based on emission standards and fuel quality. While vehicle fleet grew 5-fold between 1990 and 2020, CO2 emissions have followed this trend at a lower rate, and emissions of air local pollutants have decreased due to stricter abatement technologies, better fuel quality and enforcement of emission standards. In other words, there has been decoupling between fleet growth and emissions' rate of change. Results were contrasted with global datasets (EDGAR, CAMS, CEDS), showing similarities in CO2 estimations and striking differences in PM, BC and CO; in the case of NOx and CH4 there is coincidence only until 2008. In all cases of divergent results, global datasets estimate higher emissions.

The multidisciplinary project Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) is dedicated to the development and implementation of an air quality analysis and forecasting system to assess pollution impacts on human health and economy. In this context, a comprehensive emission inventory for South America was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGAR v4.3.2 historical data), enriching it with data derived from locally available emission inventories for Argentina, Chile, and Colombia. This work presents the results of the first joint effort of South American researchers and European colleagues to generate regional maps of emissions, together with a methodological approach to continue incorporating information into future versions of the dataset. This version of the PAPILA dataset includes CO, NOx, NMVOCs, NH3, and SO2 annual emissions from anthropogenic sources for the period 2014–2016, with a spatial resolution of 0.1∘ × 0.1∘ over a domain that covers 32–120∘ W and 34∘ N–58∘ S. The PAPILA dataset is presented as netCDF4 files and is available in an open-access data repository under a CC-BY 4 license: https://doi.org/10.17632/btf2mz4fhf.3 (Castesana et al., 2021). A comparative assessment of PAPILA–CAMS datasets was carried out for (i) the South American region, (ii) the countries with local data (Argentina, Colombia, and Chile), and (iii) downscaled emission maps for urban domains with different environmental and anthropogenic factors. Relevant differences were found at both country and urban levels for all the compounds analyzed. Among them, we found that when comparing PAPILA total emissions versus CAMS datasets at the national level, higher levels of NOx and considerably lower levels of the other species were obtained for Argentina, higher levels of SO2 and lower levels of CO and NOx for Colombia, and considerably higher levels of CO, NMVOCs, and SO2 for Chile. These discrepancies are mainly related to the representativeness of local practices in the local emission estimates, to the improvements made in the spatial distribution of the locally estimated emissions, or to both. Both datasets were evaluated against surface concentrations of CO and NOx by using them as input data to the WRF-Chem model for one of the analyzed domains, the metropolitan area of Buenos Aires, for summer and winter of 2015. PAPILA-based modeling results had a smaller bias for CO and NOx concentrations in winter while CAMS-based results for the same period tended to deliver an underestimation of these concentrations. Both inventories exhibited similar performances for CO in summer, while the PAPILA simulation outperformed CAMS for NOx concentrations. These results highlight the importance of refining global inventories with local data to obtain accurate results with high-resolution air quality models.

This study presents the first high-resolution national inventory of anthropogenic emissions for Chile (Inventario Nacional de Emisiones Antropogénicas, INEMA). Emissions for the vehicular, industrial, energy, mining and residential sectors are estimated for the period 2015–2017 and spatially distributed onto a high-resolution grid (approximately 1 km×1 km). The pollutants included are CO2, NOx, SO2, CO, VOCs (volatile organic compounds), NH3 and particulate matter (PM10 and PM2.5) for all sectors. CH4 and black carbon are included for transport and residential sources, while arsenic, benzene, mercury, lead, toluene, and polychlorinated dibenzo-p-dioxins and furan (PCDD/F) are estimated for energy, mining and industrial sources. New activity data and emissions factors are compiled to estimate emissions, which are subsequently spatially distributed using census data and Chile's road network information. The estimated annual average total national emissions of PM10 and PM2.5 during the study period are 191 and 173kt a−1 (kilotons per year), respectively. The residential sector is responsible for over 90 % of these emissions. This sector also emits 81 % and 87 % of total CO and VOC, respectively. On the other hand, the energy and industry sectors contribute significantly to NH3, SO2 and CO2 emissions, while the transport sector dominates NOx and CO2 emissions, and the mining sector dominates SO2 emissions. In general, emissions of anthropogenic air pollutants and CO2 in northern Chile are dominated by mining activities as well as thermoelectric power plants, while in central Chile the dominant sources are transport and residential emissions. The latter also mostly dominates emissions in southern Chile, which has a much colder climate. Preliminary analysis revealed the dominant role of the emission factors in the final emission uncertainty. Nevertheless, uncertainty in activity data also contributes as suggested by the difference in CO2 emissions between INEMA and EDGAR (Emission Database for Global Atmospheric Research). A comparison between these two inventories also revealed considerable differences for all pollutants in terms of magnitude and sectoral contribution, especially for the residential sector. EDGAR presents larger emissions for most of the pollutants except for CH4 and PM2.5. The differences between both inventories can partly be explained by the use of different emission factors, in particular for the residential sector, where emission factors incorporate information on firewood and local operation conditions. Although both inventories use similar emission factors, differences in CO2 emissions between both inventories indicate biases in the quantification of the activity. This inventory (available at https://doi.org/10.5281/zenodo.4784286, Alamos et al., 2021) will support the design of policies that seek to mitigate climate change and improve air quality by providing policymakers, stakeholders and scientists with qualified scientific spatially explicit emission information.

Central Chile faces atmospheric pollution issues all year long as a result of elevated concentrations of fine particulate matter during the cold months and tropospheric ozone during the warm season. In addition to public health issues, environmental problems regarding vegetation growth and water supply, as well as meteorological feedback, are at stake. Sharp spatial gradients in regional emissions, along with a complex geographical situation, make for variable and heterogeneous dynamics in the localization and long-range transport of pollutants, with seasonal differences. Based on chemistry–transport modeling with Weather Research Forecasting (WRF)–CHIMERE, this work studies the following for one winter period and one summer period: (i) the contribution of emissions from the city of Santiago to air pollution in central Chile, and (ii) the reciprocal contribution of regional pollutants transported into the Santiago basin. The underlying 3-dimensional advection patterns are investigated. We find that, on average for the winter period, 5 to 10 µg m−3 of fine particulate matter in Santiago come from regional transport, corresponding to between 13 % and 15 % of average concentrations. In turn, emissions from Santiago contribute between 5 % and 10 % of fine particulate matter pollution as far as 500 km to the north and 500 km to the south. Wintertime transport occurs mostly close to the surface. In summertime, exported precursors from Santiago, in combination with mountain–valley circulation dynamics, are found to account for most of the ozone formation in the adjacent Andes cordillera and to create a persistent plume of ozone of more than 50 ppb (parts per billion), extending along 80 km horizontally and 1.5 km vertically, and located slightly north of Santiago, several hundred meters above the ground. This work constitutes the first description of the mechanism underlying the latter phenomenon. Emissions of precursors from the capital city also affect daily maxima of surface ozone hundreds of kilometers away. In parallel, cutting emissions of precursors in the Santiago basin results in an increase in surface ozone mixing ratios in its western area.

In wintertime, high concentrations of atmospheric fine particulate matter (PM2.5) are commonly observed in the metropolitan area of Santiago, Chile. Hourly peaks can be very strong, up to 10 times above average levels, but have barely been studied so far. Based on atmospheric composition measurements and chemistry-transport modeling (WRF-CHIMERE), the chemical signature of sporadic skyrocketing wintertime PM2.5 peaks is analyzed. This signature and the timing of such extreme events trace their origin back to massive barbecue cooking by Santiago's inhabitants during international soccer games. The peaks end up evacuated outside Santiago after a few hours but trigger emergency plans for the next day. Decontamination plans in Santiago focus on decreasing emissions from traffic, industry, and residential heating. Thanks to the air quality network of Santiago, this study shows that cultural habits such as barbecue cooking also need to be taken into account. For short-term forecast and emergency management, cultural events such as soccer games seem a good proxy to prognose possible PM2.5 peak events. Not only can this result have an informative value for the Chilean authorities but also a similar methodology could be reproduced for other cases throughout the world in order to estimate the burden on air quality of cultural habits.

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.

Mineral dust is the major continental contributor to the global atmospheric aerosol burden with important effects on the climate system. Regionally, a large fraction of the emitted dust is produced in northern Africa; however, the total emission flux from there is still highly uncertain. In order to reduce these uncertainties, emission estimates through top-down approaches (i.e. usually models constrained by observations) have been successfully developed and implemented. Such studies usually rely on a single observational dataset and propagate the possible observational errors of this dataset onto the emission estimates. In this study, aerosol optical depth (AOD) products from five different satellites are assimilated one by one in a source inversion system to estimate dust emission fluxes over northern Africa and the Arabian Peninsula. We estimate mineral dust emissions for the year 2006 and discuss the impact of the assimilated dataset on the analysis. We find a relatively large dispersion in flux estimates among the five experiments, which can likely be attributed to differences in the assimilated observation datasets and their associated error statistics.

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.

Natural and anthropogenic emissions of primary aerosols and sulphur dioxide (SO2) are estimated for the year 2010 by assimilating daily total and fine mode aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument into a global aerosol model of intermediate complexity. The system adjusts monthly emission fluxes over a set of predefined regions tiling the globe. The resulting aerosol emissions improve the model performance, as measured from usual skill scores, both against the assimilated observations and a set of independent ground-based measurements. The estimated emission fluxes are 67 Tg S yr−1 for SO2, 12 Tg yr−1 for black carbon (BC), 87 Tg yr−1 for particulate organic matter (POM), 17 000 Tg yr−1 for sea salt (SS, estimated at 80 % relative humidity) and 1206 Tg yr−1 for desert dust (DD). They represent a difference of +53, +73, +72, +1 and −8%, respectively, with respect to the first guess (FG) values. Constant errors throughout the regions and the year were assigned to the a priori emissions. The analysis errors are reduced with respect to the a priori ones for all species and throughout the year, they vary between 3 and 18% for SO2, 1 and 130% for biomass burning, 21 and 90 % for fossil fuel, 1 and 200% for DD and 1 and 5% for SS. The maximum errors on the global-yearly scale for the estimated fluxes (considering temporal error dependence) are 3% for SO2, 14% for BC, 11% for POM, 14% for DD and 2% for SS. These values represent a decrease as compared to the global-yearly errors from the FG of 7% for SO2, 40% for BC, 55% for POM, 81% for DD and 300% for SS. The largest error reduction, both monthly and yearly, is observed for SS and the smallest one for SO2. The sensitivity and robustness of the inversion system to the choice of the first guess emission inventory is investigated by using different combinations of inventories for industrial, fossil fuel and biomass burning sources. The initial difference in the emissions between the various set-ups is reduced after the inversion. Furthermore, at the global scale, the inversion is sensitive to the choice of the BB (biomass burning) inventory and not so much to the industrial and fossil fuel inventory. At the regional scale, however, the choice of the industrial and fossil fuel inventory can make a difference. The estimated baseline emission fluxes for SO2, BC and POM are within the estimated uncertainties of the four experiments. The resulting emissions were compared against projected emissions for the year 2010 for SO2, BC and POM. The new estimate presents larger emissions than the projections for all three species, with larger differences for SO2 than POM and BC. These projected SO2 emissions are outside the uncertainties of the estimated emission inventories.

Simulated multi-model "diversity" in aerosol direct radiative forcing estimates is often perceived as a measure of aerosol uncertainty. However, current models used for aerosol radiative forcing calculations vary considerably in model components relevant for forcing calculations and the associated "host-model uncertainties" are generally convoluted with the actual aerosol uncertainty. In this AeroCom Prescribed intercomparison study we systematically isolate and quantify host model uncertainties on aerosol forcing experiments through prescription of identical aerosol radiative properties in twelve participating models. Even with prescribed aerosol radiative properties, simulated clear-sky and all-sky aerosol radiative forcings show significant diversity. For a purely scattering case with globally constant optical depth of 0.2, the global-mean all-sky top-of-atmosphere radiative forcing is −4.47 Wm−2 and the inter-model standard deviation is 0.55 Wm−2, corresponding to a relative standard deviation of 12%. For a case with partially absorbing aerosol with an aerosol optical depth of 0.2 and single scattering albedo of 0.8, the forcing changes to 1.04 Wm−2, and the standard deviation increases to 1.01 W−2, corresponding to a significant relative standard deviation of 97%. However, the top-of-atmosphere forcing variability owing to absorption (subtracting the scattering case from the case with scattering and absorption) is low, with absolute (relative) standard deviations of 0.45 Wm−2 (8%) clear-sky and 0.62 Wm−2 (11%) all-sky. Scaling the forcing standard deviation for a purely scattering case to match the sulfate radiative forcing in the AeroCom Direct Effect experiment demonstrates that host model uncertainties could explain about 36% of the overall sulfate forcing diversity of 0.11 Wm−2 in the AeroCom Direct Radiative Effect experiment. Host model errors in aerosol radiative forcing are largest in regions of uncertain host model components, such as stratocumulus cloud decks or areas with poorly constrained surface albedos, such as sea ice. Our results demonstrate that host model uncertainties are an important component of aerosol forcing uncertainty that require further attention.

This study estimates the emission fluxes of a range of aerosol species and one aerosol precursor at the global scale. These fluxes are estimated by assimilating daily total and fine mode aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) into a global aerosol model of intermediate complexity. Monthly emissions are fitted homogenously for each species over a set of predefined regions. The performance of the assimilation is evaluated by comparing the AOD after assimilation against the MODIS observations and against independent observations. The system is effective in forcing the model towards the observations, for both total and fine mode AOD. Significant improvements for the root mean square error and correlation coefficient against both the assimilated and independent datasets are observed as well as a significant decrease in the mean bias against the assimilated observations. These improvements are larger over land than over ocean. The impact of the assimilation of fine mode AOD over ocean demonstrates potential for further improvement by including fine mode AOD observations over continents. The Angström exponent is also improved in African, European and dusty stations. The estimated emission flux for black carbon is 15 Tg yr−1, 119 Tg yr−1 for particulate organic matter, 17 Pg yr−1 for sea salt, 83 TgS yr−1 for SO2 and 1383 Tg yr−1 for desert dust. They represent a difference of +45 %, +40 %, +26 %, +13 % and −39 % respectively, with respect to the a priori values. The initial errors attributed to the emission fluxes are reduced for all estimated species.

We describe and evaluate the NMMB/BSC-Dust, a new dust aerosol cycle model embedded online within the NCEP Non-hydrostatic Multiscale Model (NMMB). NMMB is a further evolution of the operational Non-hydrostatic Mesoscale Model (WRF-NMM), which together with other upgrades has been extended from meso to global scales. Its unified non-hydrostatic dynamical core is prepared for regional and global simulation domains. The new NMMB/BSC-Dust is intended to provide short to medium-range weather and dust forecasts from regional to global scales and represents a first step towards the development of a unified chemical-weather model. This paper describes the parameterizations used in the model to simulate the dust cycle including sources, transport, deposition and interaction with radiation. We evaluate monthly and annual means of the global configuration of the model against the AEROCOM dust benchmark dataset for year 2000 including surface concentration, deposition and aerosol optical depth (AOD), and we evaluate the daily AOD variability in a regional domain at high resolution covering Northern Africa, Middle East and Europe against AERONET AOD for year 2006. The NMMB/BSC-Dust provides a good description of the horizontal distribution and temporal variability of the dust. Daily AOD correlations at the regional scale are around 0.6–0.7 on average without dust data assimilation. At the global scale the model lies within the top range of AEROCOM dust models in terms of performance statistics for surface concentration, deposition and AOD. This paper discusses the current strengths and limitations of the modeling system and points towards future improvements.

This study presents the results of a broad intercomparison of a total of 15 global aerosol models within the AeroCom project. Each model is compared to observations related to desert dust aerosols, their direct radiative effect, and their impact on the biogeochemical cycle, i.e., aerosol optical depth (AOD) and dust deposition. Additional comparisons to Angström exponent (AE), coarse mode AOD and dust surface concentrations are included to extend the assessment of model performance and to identify common biases present in models. These data comprise a benchmark dataset that is proposed for model inspection and future dust model development. There are large differences among the global models that simulate the dust cycle and its impact on climate. In general, models simulate the climatology of vertically integrated parameters (AOD and AE) within a factor of two whereas the total deposition and surface concentration are reproduced within a factor of 10. In addition, smaller mean normalized bias and root mean square errors are obtained for the climatology of AOD and AE than for total deposition and surface concentration. Characteristics of the datasets used and their uncertainties may influence these differences. Large uncertainties still exist with respect to the deposition fluxes in the southern oceans. Further measurements and model studies are necessary to assess the general model performance to reproduce dust deposition in ocean regions sensible to iron contributions. Models overestimate the wet deposition in regions dominated by dry deposition. They generally simulate more realistic surface concentration at stations downwind of the main sources than at remote ones. Most models simulate the gradient in AOD and AE between the different dusty regions. However the seasonality and magnitude of both variables is better simulated at African stations than Middle East ones. The models simulate the offshore transport of West Africa throughout the year but they overestimate the AOD and they transport too fine particles. The models also reproduce the dust transport across the Atlantic in the summer in terms of both AOD and AE but not so well in winter-spring nor the southward displacement of the dust cloud that is responsible of the dust transport into South America. Based on the dependency of AOD on aerosol burden and size distribution we use model bias with respect to AOD and AE to infer the bias of the dust emissions in Africa and the Middle East. According to this analysis we suggest that a range of possible emissions for North Africa is 400 to 2200 Tg yr−1 and in the Middle East 26 to 526 Tg yr−1.

We have developed a simplified aerosol model together with its tangent linear and adjoint versions for the ultimate aim of optimizing global aerosol and aerosol precursor emission using variational data assimilation. The model was derived from the general circulation model LMDz; it groups together the 24 aerosol species simulated in LMDz into 4 species, namely gaseous precursors, fine mode aerosols, coarse mode desert dust and coarse mode sea salt. The emissions have been kept as in the original model. Modifications, however, were introduced in the computation of aerosol optical depth and in the processes of sedimentation, dry and wet deposition and sulphur chemistry to ensure consistency with the new set of species and their composition. The simplified model successfully manages to reproduce the main features of the aerosol distribution in LMDz. The largest differences in aerosol load are observed for fine mode aerosols and gaseous precursors. Differences between the original and simplified models are mainly associated to the new deposition and sedimentation velocities consistent with the definition of species in the simplified model and the simplification of the sulphur chemistry. Furthermore, simulated aerosol optical depth remains within the variability of monthly AERONET observations for all aerosol types and all sites throughout most of the year. Largest differences are observed over sites with strong desert dust influence. In terms of the daily aerosol variability, the model is less able to reproduce the observed variability from the AERONET data with larger discrepancies in stations affected by industrial aerosols. The simplified model however, closely follows the daily simulation from LMDz. Sensitivity analyses with the tangent linear version show that the simplified sulphur chemistry is the dominant process responsible for the strong non-linearity of the model.

Black carbon transport from the Santiago Metropolitan Area, Chile, up to the adjacent Andes Cordillera and its glaciers is of major concern. Its deposition accelerates the melting of the snowpack, which could lead to stress on water supply in addition to climate feedback. A proposed pathway for this transport is the channelling through the network of canyons that connect the urban basin to the elevated summits, as suggested by modelling studies, although no observations have validated this hypothesis so far. In this work, atmospheric measurements from a dedicated field campaign conducted in winter 2015, under severe urban pollution conditions, in Santiago and the Maipo canyon, southeast of Santiago, are analysed. Wind (speed and direction) and particulate matter concentrations measured at the surface and along vertical profiles, demonstrate intrusions of thick layers (up to 600 m above ground) of urban black carbon deep into the canyon on several occasions. Transport of PM down-valley occurs mostly through shallow layers at the surface except in connection with deep valley intrusions, when a secondary layer in altitude with return flow (down-valley) at night is observed. The transported particulate matter is mostly from the vicinity of the entrance to the canyon and uncorrelated to concentrations observed in downtown Santiago. Reanalyses data show that for 10% of the wintertime days, deep intrusions into the Maipo canyon are prevented by easterly winds advecting air pollutants away from the Andes. Also, in 23% of the cases, intrusions proceed towards a secondary north-eastward branch of the Maipo canyon, leaving 67% of the cases with favourable conditions for deep penetrations into the main Maipo canyon. Reanalyses show that the wind directions associated to the 33% anomalous cases are related to thick cloud cover and/or the development of coastal lows., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The chemical composition of snow provides insights on atmospheric transport of anthropogenic contaminants at different spatial scales. In this study, we assess how human activities influence the concentration of elements in the Andean mountain snow along a latitudinal transect throughout Chile. The concentration of seven elements (Al, Cu, Fe, Li, Mg, Mn and Zn) was associated to gaseous and particulate contaminants emitted at different spatial scales. Our results indicate carbon monoxide (CO) averaged at 20 km and nitrogen oxide (NOx) at 40 km as the main indicators of the chemical elements analyzed. CO was found to be a significant predictor of most element concentrations while concentrations of Cu, Mn, Mg and Zn were positively associated to emissions of NOx. Emission of 2.5 μm and 10 μm particulate matter averaged at different spatial scales was positively associated to concentration of Li. Finally, the concentration of Zn was positively associated to volatile organic compounds (VOC) averaged at 40 km around sampling sites. The association between air contaminants and chemical composition of snow suggests that regions with intensive anthropogenic pollution face reduced quality of freshwater originated from glacier and snow melting., (© 2021. The Author(s).)

Background: Several countries have documented the relationship between long-term exposure to air pollutants and epidemiological indicators of the COVID-19 pandemic, such as incidence and mortality. This study aims to explore the association between air pollutants, such as PM 2.5 and PM 10 , and the incidence and mortality rates of COVID-19 during 2020., Methods: The incidence and mortality rates were estimated using the COVID-19 cases and deaths from the Chilean Ministry of Science, and the population size was obtained from the Chilean Institute of Statistics. A chemistry transport model was used to estimate the annual mean surface concentration of PM 2.5 and PM 10 in a period before the current pandemic. Negative binomial regressions were used to associate the epidemiological information with pollutant concentrations while considering demographic and social confounders., Results: For each microgram per cubic meter, the incidence rate increased by 1.3% regarding PM 2.5 and 0.9% regarding PM 10 . There was no statistically significant relationship between the COVID-19 mortality rate and PM 2.5 or PM 10 ., Conclusions: The adjusted regression models showed that the COVID-19 incidence rate was significantly associated with chronic exposure to PM 2.5 and PM 10 , even after adjusting for other variables.

Ice and snow in the Central Andes contain significant amounts of light-absorbing particles such as black carbon. The consequent accelerated melting of the cryosphere is not only a threat from a climate perspective but also for water resources and snow-dependent species and activities, worsened by the mega-drought affecting the region since the last decade. Given its proximity to the Andes, emissions from the Metropolitan Area of Santiago, Chile, are believed to be among the main contributors to deposition on glaciers. However, no evidence backs such an assertion, especially given the usually subsident and stable conditions in wintertime, when the snowpack is at its maximum extent. Based on high-resolution chemistry-transport modeling with WRF-CHIMERE, the present work shows that, for the month of July 2015, up to 40% of black carbon dry deposition on snow or ice covered areas in the Central Andes downwind from the Metropolitan area can be attributed to emissions from Santiago. Through the analysis of aerosol tracers we determine (i) that the areas of the Metropolitan Area where emissions matter most when it comes to export towards glaciers are located in Eastern Santiago near the foothills of the Andes, (ii) the crucial role of the network of Andean valleys that channels pollutants up to remote locations near glaciers, following gentle slopes. A direct corollary is that severe urban pollution, and deposition of impurities on the Andes, are anti-correlated phenomena. Finally, a two-variable meteorological index is developed that accounts for the dynamics of aerosol export towards the Andes, based on the zonal wind speed over the urban area, and the vertical diffusion coefficient in the valleys close to ice and snow covered terrain. Numerous large urban areas are found along the Andes so that the processes studied here can shed light on similar investigations for other glaciers-dependent Andean regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Vertical profiles of black carbon (BC) and other light-absorbing impurities were measured in seasonal snow and permanent snowfields in the Chilean Andes during Austral winters 2015 and 2016, at 22 sites between latitudes 18°S and 41°S. The samples were analyzed for spectrally-resolved visible light absorption. For surface snow, the average mass mixing ratio of BC was 15 ng/g in northern Chile (18-33°S), 28 ng/g near Santiago (a major city near latitude 33°S, where urban pollution plays a significant role), and 13 ng/g in southern Chile (33-41°S). The regional average vertically-integrated loading of BC was 207 µg/m 2 in the north, 780 µg/m 2 near Santiago, and 2500 µg/m 2 in the south, where the snow season was longer and the snow was deeper. For samples collected at locations where there had been no new snowfall for a week or more, the BC concentration in surface snow was high (~10-100 ng/g) and the sub-surface snow was comparatively clean, indicating the dominance of dry deposition of BC. Mean albedo reductions due to light-absorbing impurities were 0.0150, 0.0160, and 0.0077 for snow grain radii of 100 µm for northern Chile, the region near Santiago, and southern Chile; respective mean radiative forcings for the winter months were 2.8, 1.4, and 0.6 W/m 2 . In northern Chile, our measurements indicate that light-absorption by impurities in snow was dominated by dust rather than BC.