Atmospheric processing of iron in dust and combustion aerosols is simulated using an
intermediate-complexity soluble iron mechanism designed for Earth system
models. The solubilization mechanism ...includes both a dependence on aerosol
water pH and in-cloud oxalic acid. The simulations of size-resolved total,
soluble and fractional iron solubility indicate that this mechanism captures
many but not all of the features seen from cruise observations of labile
iron. The primary objective was to determine the extent to which our
solubility scheme could adequately match observations of fractional iron
solubility. We define a semi-quantitative metric as the model mean at points
with observations divided by the observational mean (MMO). The model is in
reasonable agreement with observations of fractional iron solubility with an
MMO of 0.86. Several sensitivity studies are performed to ascertain the
degree of complexity needed to match observations; including the oxalic acid
enhancement is necessary, while different parameterizations for calculating
model oxalate concentrations are less important. The percent change in
soluble iron deposition between the reference case (REF) and the simulation
with acidic processing alone is 63.8 %, which is consistent with previous
studies. Upon deposition to global oceans, global mean combustion iron
solubility to total fractional iron solubility is 8.2 %; however, the
contribution of fractional iron solubility from combustion sources to ocean
basins below 15∘ S is approximately 50 %. We conclude that, in
many remote ocean regions, sources of iron from combustion and dust aerosols
are equally important. Our estimates of changes in deposition of soluble iron
to the ocean since preindustrial climate conditions suggest roughly a doubling due to a combination of
higher dust and combustion iron emissions along with more efficient
atmospheric processing.
Since the first International Cooperative for Aerosol Prediction (ICAP) multi‐model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An ...update of the current ICAP status is provided, along with an evaluation of the performance of ICAP‐MME over 2012–2017, with a focus on June 2016–May 2017. Evaluated with ground‐based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate‐resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP‐MME AOD consensus remains the overall top‐scoring and most consistent performer among all models in terms of root‐mean‐square error (RMSE), bias and correlation for total, fine‐ and coarse‐mode AODs as well as dust AOD; this is similar to the first ICAP‐MME study. Further, over the years, the performance of ICAP‐MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP‐MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP‐MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012–2017 suggests a general tendency for model improvements in fine‐mode AOD, especially over Asia. No significant improvement in coarse‐mode AOD is found overall for this time period.
International Cooperative for Aerosol Predictions (ICAP) model 550 nm total AOD RMSE of the 72 h forecast versus corresponding mean AODs for AERONET sites listed in Table 2. Large black dots are ICAP multi‐model ensemble consensus means. Individual models are in small coloured dots. Validation of fine‐ and coarse‐modal AODs and dust AOD is also available in Figure 2.
The large uncertainty in the mineral dust direct radiative effect (DRE) hinders projections of future climate change due to anthropogenic activity. Resolving modeled dust mineral speciation allows ...for spatially and temporally varying refractive indices consistent with dust aerosol composition. Here, for the first time, we quantify the range in dust DRE at the top of the atmosphere (TOA) due to current uncertainties in the surface soil mineralogical content using a dust mineral-resolving climate model. We propagate observed uncertainties in soil mineral abundances from two soil mineralogy atlases along with the optical properties of each mineral into the DRE and compare the resultant range with other sources of uncertainty across six climate models. The shortwave DRE responds region-specifically to the dust burden depending on the mineral speciation and underlying shortwave surface albedo: positively when the regionally averaged annual surface albedo is larger than 0.28 and negatively otherwise. Among all minerals examined, the shortwave TOA DRE and single scattering albedo at the 0.44–0.63 µm band are most sensitive to the fractional contribution of iron oxides to the total dust composition. The global net (shortwave plus longwave) TOA DRE is estimated to be within −0.23 to +0.35 W/sq. m. Approximately 97 % of this range relates to uncertainty in the soil abundance of iron oxides. Representing iron oxide with solely hematite optical properties leads to an overestimation of shortwave DRE by +0.10 W/sq. m at the TOA, as goethite is not as absorbing as hematite in the shortwave spectrum range. Our study highlights the importance of iron oxides to the shortwave DRE: they have a disproportionally large impact on climate considering their small atmospheric mineral mass fractional burden (∼2 %). An improved description of iron oxides, such as those planned in the Earth Surface Mineral Dust Source Investigation (EMIT), is thus essential for more accurate estimates of the dust DRE.
We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will ...allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine
learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21 February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes 5 weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for non-methane volatile organic compounds (NMVOCs), −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction
is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOCs), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other
pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.
In late March 2018, a large part of the Eastern Mediterranean experienced an extraordinary episode of African dust, one of the most intense in recent years, here referred to as the “Minoan Red” ...event. The episode mainly affected the Greek island of Crete, where the highest aerosol concentrations over the past 15 yeas were recorded, although impacts were also felt well beyond this core area. Our study fills a gap in dust research by assessing the multi-sectoral impacts of sand and dust storms and their socioeconomic implications. Specifically, we provide a multi-sectoral impact assessment of Crete during the occurrence of this exceptional African dust event. During the day of the occurrence of the maximum dust concentration in Crete, i.e. March 22nd, 2018, we identified impacts on meteorological conditions, agriculture, transport, energy, society (including closing of schools and cancellation of social events), and emergency response systems. As a result, the event led to a 3-fold increase in daily emergency responses compare to previous days associated with urban emergencies and wildfires, a 3.5-fold increase in hospital visits and admissions for Chronic Obstructive Pulmonary Disease (COPD) exacerbations and dyspnoea, a reduction of visibility causing aircraft traffic disruptions (eleven cancellations and seven delays), and a reduction of solar energy production. We estimate the cost of direct and indirect effects of the dust episode, considering the most affected socio-economic sectors (e.g. civil protection, aviation, health and solar energy production), to be between 3.4 and 3.8 million EUR for Crete. Since such desert dust transport episodes are natural, meteorology-driven and thus to a large extent unavoidable, we argue that the efficiency of actions to mitigate dust impacts depends on the accuracy of operational dust forecasting and the implementation of relevant early warning systems for social awareness.
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•Impacts of an extraordinary episode of African dust transport are investigated.•Several types of impacts are identified in ecosystems, economy, society and health.•Increase (3 times) of emergencies responses and hospital admissions.•Reduction of visibility caused aircraft traffic disruptions in Crete.•Reduction of solar energy production is estimated on ~10 MW.
Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost ...the bias, they are often coupled with model output statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate the extent to which AQ forecasts can be improved using a variety of MOS methods, including moving average, quantile mapping, Kalman filter, analogs and gradient boosting machine methods, and consider as well the persistence method as a reference. We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a comprehensive set of continuous and categorical metrics at various timescales, along different lead times and using different meteorological input datasets. Our results show that O3 forecasts can be substantially improved using such MOS corrections and that improvements go well beyond the correction of the systematic bias. Depending on the timescale and lead time, root mean square errors decreased from 20 %–40 % to 10 %–30 %, while Pearson correlation coefficients increased from 0.7–0.8 to 0.8–0.9. Although the improvement typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. The MOS methods relying on meteorological data were found to provide relatively similar performance with two different meteorological inputs. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with the lowest errors and highest correlations. However, they are not necessarily the best in predicting the peak O3 episodes, for which simpler MOS methods can achieve better results. Although the complex impact of MOS methods on the distribution of and variability in raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.
Monitoring and describing the spatiotemporal variability in dust aerosols is crucial for understanding their multiple effects, related feedbacks, and impacts within the Earth system. This study ...describes the development of the ModIs Dust AeroSol (MIDAS) data set. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at a global scale and fine spatial resolution (0.1∘ × 0.1∘) over a 15-year period (2003–2017). This new data set combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1; Level 2), along with DOD-to-AOD ratios provided by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis to derive DOD on the MODIS native grid. The uncertainties of the MODIS AOD and MERRA-2 dust fraction, with respect to the AEronet RObotic NETwork (AERONET) and LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS), respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the dust belt in the
tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere, where dust sources are smaller. MIDAS, MERRA-2, and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with colocated global DOD averages of 0.033, 0.031, and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89) and only shows a small positive bias (0.004 or 2.7 %). Among the major dust areas of the planet, the highest R values (>0.9) are found at sites of North Africa, the Middle East, and Asia. MIDAS expands, complements, and upgrades the existing observational capabilities of dust aerosols, and it is suitable for dust climatological studies, model evaluation, and data assimilation.
It is well established that in Europe, high O
concentrations are most pronounced in southern/Mediterranean countries due to the more favourable climatological conditions for its formation. However, ...the contribution of the different sources of precursors to O
formation within each country relative to the imported (regional and hemispheric) O
is poorly quantified. This lack of quantitative knowledge prevents local authorities from effectively designing plans that reduce the exceedances of the O
target value set by the European air quality directive. O
source attribution is a challenge because the concentration at each location and time results not only from local biogenic and anthropogenic precursors, but also from the transport of O
and precursors from neighbouring regions, O
regional and hemispheric transport and stratospheric O
injections. The main goal of this study is to provide a first quantitative estimation of the contribution of the main anthropogenic activity sectors to peak O
events in Spain relative to the contribution of imported (regional and hemispheric) O
. We also assess the potential of our source apportionment method to improve O
modelling. Our study applies and thoroughly evaluates a countrywide O
source apportionment method implemented in the CALIOPE air quality forecast system for Spain at high resolution (4 × 4 km
) over a 10-day period characterized by typical summer conditions in the Iberian Peninsula (IP). The method tags both O
and its gas precursor emissions from source sectors within one simulation, and each tagged species is subject to the typical physico-chemical processes (advection, vertical mixing, deposition, emission and chemistry) as the actual conditions remain unperturbed. We quantify the individual contributions of the largest NO
local sources to high O
concentrations compared with the contribution of imported O
. We show, for the first time, that imported O
is the largest input to the ground-level O
concentration in the IP, accounting for 46 %-68 % of the daily mean O
concentration during exceedances of the European target value. The hourly imported O
increases during typical northwestern advections (70 %-90 %, 60-80 μg m
), and decreases during typical stagnant conditions (30 %-40 %, 30-60 μg m
) due to the local NO titration. During stagnant conditions, the local anthropogenic precursors control the O
peaks in areas downwind of the main urban and industrial regions (up to 40 % in hourly peaks). We also show that ground-level O
concentrations are strongly affected by vertical mixing of O
-rich layers present in the free troposphere, which result from local/regional layering and accumulation, and continental/hemispheric transport. Indeed, vertical mixing largely explains the presence of imported O
at ground level in the IP. Our results demonstrate the need for detailed quantification of the local and remote contributions to high O
concentrations for local O
management, and show O
source apportionment to be an essential analysis prior to the design of O
mitigation plans in any non-attainment area. Achieving the European O
objectives in southern Europe requires not only ad hoc local actions but also decided national and European-wide strategies.
Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly ...trade wind blowing from the Sahara into the Gulf of Guinea.Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels.Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January-May. Incidence was used as a proxy for immunological state.
Regional variations of dust mineral composition are fundamental to climate impacts but generally neglected in climate models. A challenge for models is that atlases of soil composition are derived ...from measurements following wet sieving, which destroys the aggregates potentially emitted from the soil. Aggregates are crucial to simulating the observed size distribution of emitted soil particles. We use an extension of brittle fragmentation theory in a global dust model to account for these aggregates. Our method reproduces the size‐resolved dust concentration along with the approximately size‐invariant fractional abundance of elements like Fe and Al in the decade‐long aerosol record from the Izaña Observatory, off the coast of West Africa. By distinguishing between Fe in structural and free forms, we can attribute improved model behavior to aggregation of Fe and Al‐rich clay particles. We also demonstrate the importance of size‐resolved measurements along with elemental composition analysis to constrain models.
Key Points
Brittle fragmentation theory is extended to represent emitted soil aggregates that are otherwise removed during soil characterization
We reproduce the dust concentration and the nearly size‐invariant fractional abundance of Fe off the coast of West Africa
By distinguishing structural and free forms of elemental Fe model improvement can be attributed to emission of soil aggregates
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