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Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact the climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from ground or aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) as well as PM2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and with airborne aerosol sensors. In this study, we address the main challenges associated with this approach: (1) the degree to which a limited number of samples is representative for the integral smoke plume and (2) the performance of the lightweight aerosol sensors. While aerosol measurements can be made continuously in a UAS set-up thanks to the lightweight analysers, the representativeness of our Tedlar bag filling approach was tested during prescribed burning experiments in the Kruger National Park, South Africa. We compared fire-averaged EFs from UAS-sampled bags for savanna fires with integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R2 ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors with high-fidelity equipment to empirically determine specific calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, although the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multi-wavelength aethalometer (AE33) (linear R2 of 0.95 at λ=880 nm) and the wavelength corrected multi-angle absorption photometer (MAAP, R2 of 0.83 measuring at λ=637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2±5.1 m2 g−1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS set-up can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood.

Aerosol observations always suffer from limited sampling, be they in-situ or remote sensing measurements. This introduces representation errors when using these observations to derive regional estimates, or when using them to evaluate models. In the latter case, one can collocate the model data with the observations to alleviate the problem. The downside is that only part of the model data (the collocated part) can be compared to observations).We present a technique to homogenize observations (i.e. "remove" their limited sampling). In essence, this is possible because aerosol exhibits spatial and temporal correlations. In practice, we use models to provide this information. Here the technique is applied to satellite data.First we use synthetic observations to show that remaining representation errors due to this homogenization technique are below 10%. Next, we show that after applying this homogenization technique, estimates of regional AOD (or AAOD) from 14 (or 5) different observational datasets are far better in agreement than without homogenziation. Lastly, we present evidence that remaining differences in homogenzied (A)AOD in these daatsets is dominated by retrieval error.We also discuss the evaluation of AEROCOM models with these homogenized data. In particular, we highlight existing biases in modelled AAOD.Although we have not tried it yet, the technique should also be applicable to in-situ data.

The current generation of satellite sensors (such as AISI, CrIS, MOPITT, OMI, and POLDER) has a spatial resolution that is still too coarse for urban scale air quality monitoring or is not sensitive enough to the ground level. The Dutch S5p TROPOMI instrument has advanced significantly in studying reactive gases with daily coverage. With the upcoming launches of 3MI on MetOp-SG and SPEXone on NASA's PACE mission, remote sensing of aerosols will soon take a huge step forward. The infrastructure for air quality modeling will need to advance appropriately to get the most significant benefits from these technology expenditures. In order to assimilate satellite data from atmospheric chemistry missions on reactive trace gases and aerosols, this effort will ultimately integrate the regional model LOTOS-EUROS fitted with a Local Ensemble Transform Kalman Smoother (LETKS) data assimilation module. The data assimilation algorithm will be put into practice with the ability to optimize surface emissions. All simulations are performed in the European domain to be used as inputs for future high-resolution simulations of urban dynamics in the Netherlands. Finally, following the main objective mentioned above, we develop OSSEs (Observing System Simulation Experiments) to benefit future satellite missions, understanding the opportunities and challenges these observations will bring.

Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from ground- or aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as PM2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and airborne aerosol sensors. In this study, we address the main uncertainties associated with this approach (1) the degree to which taking a limited number of samples is representative for the integral smoke plume and including (2) the reliability of the lightweight aerosol sensors. This was done for prescribed burning experiments in the Kruger national park, South Africa where we compared fire-averaged EF from UAS-sampled bags for savanna fires to integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R2 ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors to high-fidelity equipment to empirically determine specific calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, albeit that the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multi-wavelength aethalometer (AE33) (linear R2 of 0.95 at λ = 880 nm) and the wavelength corrected Multi-Angle Absorption Photometer (MAAP, R2 0.83 measuring at λ = 637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2 ± 5.1 m2 g-1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS setup can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood.

We present a top-down approach for aerosol emission estimation from SPEXone polarimetric retrievals related to the aerosol amount, size, and absorption using a fixed-lag ensemble Kalman smoother (LETKS) in combination with the ECHAM-HAM model. We assess the system by performing Observing System Simulation Experiments (OSSEs), in order to evaluate the ability of the future multi-angle polarimeter instrument, SPEXone, as well as a satellite with near perfect global coverage. In our OSSEs, the Nature Run (NAT) is a simulation by the global climate aerosol model ECHAM-HAM with altered aerosol emissions. The Control (CTL) and the data assimilation (DAS) experiments are composed of an ensemble of ECHAM-HAM simulations, where the default aerosol emissions are perturbed with factors taken from a Gaussian distribution. Synthetic observations, specifically Aerosol Optical Depth at 550 nm (AOD550), Angstrom Exponent from 550 nm to 865 nm (AE550-865) and Single Scattering Albedo at 550 nm (SSA550) are assimilated in order to estimate the aerosol emission fluxes of desert dust (DU), sea salt (SS), organic carbon (OC), black carbon (BC) and sulphate (SO4), along with the emission fluxes of two SO4 precursor gases (SO2, DMS). The synthetic observations are sampled from the NAT according to two satellite observing systems, with different spatial coverages. The first is the sensor SPEXone, a hyperspectral multi-angle polarimeter with a narrow swath (~100 km), that will be a part of the NASA PACE mission. The second is an idealized sensor that can retrieve observations over the whole globe even under cloudy conditions. The prior emission global relative Mean Absolute Error (MAE) before the assimilation ranges from 33 % to 117 %. Depending on the species, the assimilated observations sampled using the idealized sensor, reduce this error to equal to or lower than 5 %. Despite its limited coverage, the SPEXone sampling bares similar results, with somewhat larger errors for DU and SS (both having a MAE equal to 11 %). Further, experiments show that doubling the measurement error, increases the global relative MAE to 22 % for DU and SS. The emission estimation of the other species is not affected as much by these changes. In addition, the role of biased meteorology on emission estimation was quantified by using two different datasets (ERA-5 and ERA-interim) to nudge the U and V wind components of the model. The results reveal that when the wind of DAS uses a different reanalysis dataset than the NAT the estimated SS emissions are negatively affected the most, while the estimated emissions of DU, OC, BC and SO2 are negatively affected to a smaller extent. If the DAS uses dust or sea salt emission parametrisations that are very different from the NAT, posterior emissions can still be successfully estimated but this experiment revealed that the source location is important for the estimation of dust emissions. This work suggests that the upcoming SPEXone sensor will provide observations related to aerosol amount, size and absorption with sufficient coverage and accuracy, in order to estimate aerosol emissions.

More than 1 Tg smoke aerosol was emitted into the atmosphere by the exceptional 2019-2020 southeastern Australian wildfires. Triggered by the extreme fire heat, several deep pyroconvective events carried the smoke directly into the stratosphere. Once there, smoke aerosol remained airborne considerably longer than in lower atmospheric layers. The thick plumes traveled eastward, thereby being distributed across the high and mid-latitudes in the Southern Hemisphere, enhancing the atmospheric opacity. Due to the increased atmospheric lifetime of the smoke plume, its radiative effect increased compared to smoke that remains in lower altitudes. Global models describing aerosol-climate impacts lack adequate descriptions of the emission height of aerosols from intense wildfires. Here, we demonstrate, by a combination of aerosol-climate modeling and lidar observations, the importance of the representation of those high-altitude fire smoke layers for estimating the atmospheric energy budget. Through observation-based input into the simulations, the Australian wildfire emissions by pyroconvection are explicitly prescribed to the lower stratosphere in different scenarios. Based on our simulations, the 2019-2020 Australian fires caused a significant top-of-atmosphere (TOA) hemispheric instantaneous direct radiative forcing signal that reached a magnitude comparable to the radiative forcing induced by anthropogenic absorbing aerosol. Up to +0.50Wm(-2) instantaneous direct radiative forcing was modeled at TOA, averaged for the Southern Hemisphere (+0.25Wm (-2) globally) from January to March 2020 under all-sky conditions. At the surface, on the other hand, an instantaneous solar radiative forcing of up to 0.81Wm(-2) was found for clear-sky conditions, with the respective estimates depending on the model configuration and subject to the model uncertainties in the smoke optical properties. Since extreme wildfires are expected to occur more frequently in the rapidly changing climate, our findings suggest that high-altitude wildfire plumes must be adequately considered in climate projections in order to obtain reasonable estimates of atmospheric energy budget changes., Atmospheric Chemistry and Physics, 22 (15), ISSN:1680-7375, ISSN:1680-7367

One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in-situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide columnintegrated aerosol measurements, but observationally-constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean sea and parts of Central Asia, and the Atlantic and Indian Oceans between 2007 and 2016. The horizontal resolution is 0.1° latitude × 0.1° longitude, and the temporal resolution is 3 hours. The reanalysis was produced using Local Ensemble Transform Kalman Filter (LETKF) data assimilation in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper air (dust mass concentrations and extinction coefficient), surface (dust deposition and solar irradiance fields, among them) and total column (e.g., dust optical depth and load) variables. Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 μm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first-guess, which proves the consistency of the data assimilation method. Independent evaluation using AERONET dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12, r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g., poor representation of small-scale emission processes), presence of aerosols other than dust in the observations used in the evaluation, and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via a Thematic Real-time Environmental Distributed Data Service (THREDDS) at BSC and freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98.

Tropospheric aerosol radiative forcing has persisted for many years as one of the major causes of uncertainty in global climate model simulations. To sample the range of plausible aerosol and atmospheric states and perform robust statistical analyses of the radiative forcing, it is important to account for the combined effects of many sources of model uncertainty, which is rarely done due to the high computational cost. This paper describes the designs of two ensembles of the Met Office Hadley Centre Global Environment Model-U.K. Chemistry and Aerosol global climate model and provides the first analyses of the uncertainties in aerosol radiative forcing and their causes. The first ensemble was designed to comprehensively sample uncertainty in the aerosol state, while the other samples additional uncertainties in the physical model related to clouds, humidity, and radiation, thereby allowing an analysis of uncertainty in the aerosol effective radiative forcing. Each ensemble consists of around 200 simulations of the preindustrial and present-day atmospheres. The uncertainty in aerosol radiative forcing in our ensembles is comparable to the range of estimates from multimodel intercomparison projects. The mean aerosol effective radiative forcing is −1.45 W/m2 (credible interval of −2.07 to −0.81 W/m2), which encompasses but is more negative than the −1.17 W/m2 in the 2013 Atmospheric Chemistry and Climate Model Intercomparison Project and −0.90 W/m2 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The ensembles can be used to reduce aerosol radiative forcing uncertainty by challenging them with multiple measurements as well as to isolate potential causes of multimodel differences.

Desert dust is the most abundant aerosol by mass residing in the atmosphere. It plays a key role in the Earth’s system by influencing the radiation balance, by affecting cloud formation and cloud chemistry, and by acting as a fertilizer for the growth of phytoplankton and for soil through its deposition over the ocean and land.Due to the nature of its emission and transport, atmospheric dust concentrations are highly variable in space and time and, therefore, require a continuous monitoring by measurements. Dust observations are best exploited by being combined with model simulations for the production of analyses and reanalyses, i.e., complete and consistent four dimensional reconstructions of the atmosphere. Existing aerosol (and dust) reanalyses for the global domain have been produced by total aerosol constraint and at relatively coarse spatial resolution, while regional reanalyses exclude some of the regions containing the major sources of desert dust in Northern Africa and the Middle East.We present here a 10-year reanalysis data set of desert dust at a horizontal resolution of 0.1°, and which covers the domain of Northern Africa, the Middle East and Europe. The reanalysis has been produced by assimilating in the MONARCH chemical weather prediction system (Di Tomaso et al., 2017) satellite retrievals over dust source regions with specific dust observational constraint (Ginoux et al., 2012; Pu and Ginoux, 2016).Furthermore, we describe its evaluation in terms of data assimilation diagnostics and comparison against independent observations. Statistics of analysis departures from assimilated observations prove the consistency of the data assimilation system showing that the analysis is closer to the observations than the first-guess. Temporal mean of analysis increments show that the assimilation led to an overall reduction of dust with pattern of systematic corrections that vary with the seasons, and can be linked primarily to misrepresentation of source strength.Independent evaluation of the analysis with AERONET observations indicates that the reanalysis data set is highly accurate, and provides therefore a reliable historical record of atmospheric desert dust concentrations in a recent decade.ReferencesDi Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129.Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. (2012): Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50.Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448.Acknowledgements The authors acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (435690462); PRACE (eDUST/eFRAGMENT1/eFRAGMENT2), RES (AECT-2020-3-0013/AECT-2019-3-0001/AECT-2020-1-0007) for awarding access to MareNostrum at BSC and for technical support.

Absorbing aerosol has the capacity to warm the climate, but the amount of warming is highly uncertain. AAOD (Absorptive Aeorosl Optical Depth) is an optical measure of the abundance of this absorbing aerosol, comprising mineral dust, black and brown carbon and can be retrieved from satellite measurements providing an almost global view on absorbing aerosol.In this study we evaluate AEROCOM models with satellite observations of AAOD and SSA (Single Scattering Albedo) and interpret the discrepancies. Over source regions, diversity in model AAOD is mostly due to emissions even though models employ different assumptions regarding the imaginary refractive index. On the one hand this suggests emissions to be a major error source, on the other hand it suggests that the AEROCOM ensemble as a whole may have a bias with regards to MAC (Mass Absorption Coefficient). We show that in the models AAOD scales almost linearly with emissions (either black carbon or dust) and this allows the use of observations as a constraint. In contrast, model diversity in AOD is shown to depend in almost equal measure on emissions, lifetimes and MECs (Mass Extinction Coefficient). We also analyse mineral dust and black carbon lifetimes by considering the contrast in AAOD over source regions and over outflow regions, and again provide observations constraints.While the older Phase II models generally underestimate AAOD, Phase III models tend to straddle the observations, with some models over-estimating and other models underestimating AAOD. Emissions seem to be the driving factor in this difference. The amount of diversity is larger in the Phase III than Phase II models.This study was conducted using four satellite datasets of AAOD and SSA. These datasets were extensively evaluated with AERONET. Dearth of observations prevents global assesment of the satellite retrievals. However, we show that model evaluation is relatively independent of the chosen dataset, even though we identify significant biases between the datasets.

A top-down approach for aerosol emission estimation from polarimetric retrievals of aerosol amount, size, and absorption is employed . The method uses a fixed-lag ensemble Kalman smoother (LETKF-Smoother) under the framework of Observing System Simulation Experiments (OSSEs), in order to evaluate the observational capabilities of a satellite with near perfect global coverage as well as of the future multi-angle polarimeter instrument, SPEXone. ECHAM-HAM is used for the nature runs (NATs), the control (CTL) and the data assimilation (DAS) experiments. The ensemble is composed by 32 simulations where the default aerosol emissions for all species are perturbed with factors taken from a Gaussian distribution. Synthetic observations, specifically Aerosol Optical Depth at 550nm (AOD550), Angstrom Exponent 550nm to 865nm (AE550-865) and Single Scattering Albedo at 550nm (SSA550) are assimilated in order to estimate the aerosol emission fluxes of desert dust (DU), sea salt (SS), organic carbon (OC), black carbon (BC) and sulphates (SO4), along with the emission fluxes of two SO4 precursor gases (SO2, DMS). The synthetic observations are sampled from the NATs according to two satellite observing systems, with different spatial coverage capabilities. The first, is an idealized sensor that retrieves observations over the whole globe in 2days even under cloudy conditions, hence is named PERFECT. The second, is the sensor SPEXone, a hyperspectral multi-angle polarimeter with a narrow swath (100km), that will be a part of the NASA PACE mission. The assimilated observations sampled using the PERFECT sensor, estimate the emission of all aerosol species with a global relative Mean Absolute Error (MAE) equal or lower than 5% (except SO4). Despite its limited coverage, the SPEXone sampling bares similar results, although MAE is a bit larger for Dust and Sea Salt. Further, experiments show that doubling the measurement error on the assimilated observations, increases additionally the global relative MAE by less than 10%. In addition, the role of biased meteorology on emission estimation was quantified by using two different datasets (ERA5 and ERAi) to nudge the U and V wind components of the model. The results reveal that when the wind of NAT and DAS are nudged to different datasets the global relative MAE of SS grows by 24%, while the estimated emissions of DU, OC, BC and SO2 are negatively affected to a smaller extent (~10%). The upcoming SPEXone sensor will provide observations related to aerosol amount size and absorption, with sufficient coverage and accuracy, in order to estimate aerosol emission accurately.

Biomass burning (BB) injects aerosols into the atmosphere and can thereby affect the earth climate and human health. Yet the modeling of BB aerosols exhibits significant bias. Here we present a comprehensive evaluation of AeroCom model simulations with satellite observations to understand such uncertainties. A total of 59 model runs using 17 models from three AeroCcom Phase III experiments (i.e., Biomass Burning emissions, CTRL2016, and CTRL2019 experiment) and 14 satellite products are involved. AOD (aerosol optical depth) at 550 nm wavelength during the fire season over three typical fire regions (Amazon, South Hemisphere Africa, and Boreal North America, or AMAZ, SHAF, and BONA) is the focus of our study, although we also consider AE and SSA from POLDER.The 14 satellite products are shown to have quite substantial differences from AERONET observation. But we show that such differences have little impact on the model evaluation which is mainly affected by modeling bias. Through the comparison with POLDER observation, we found the modeled AOD are biased by -93% ~ 174% with most models showing significant underestimations even for the most recent modeling experiment (i.e., CTRL19). SHAF is among the three regions with the strongest underestimation in general. By scaling up the input emissions, such negative bias would be significantly reduced, which, however, has little impact on the day-to-day correlation between models and observations.On top of the satellite-based model evaluation, we interpret the model diversity from the aspect of aerosol emissions, lifetime, and MEC (mass extinction coefficient), which are further linked with specific parameters in models. These three parameters cause similar levels of AOD diversity, which is quite different from the modeled aerosols during non-fire season when the contribution of lifetime is predominant. During the fire season, diversity caused by lifetime is strongly affected by local deposition; as a matter of fact, models tend to do quite poorly in simulating precipitation strength. Modeled MECs show significant correlations with aerosol wet-growth (which is known to be challenging to models) and AE (Angstrom Exponent) for some involved models. Comparisons with POLDER observed AE suggests some models tend to underestimate AE and thus MEC, which might be responsible for the overall AOD underestimation in certain models. Additionally, we show that model AOD biases correlate with satellite observed formaldehyde columns, suggesting SOA formation may be insufficiently captured by models.

Global measurements of absorbing aerosol optical depth (AAOD) are scarce and mostly provided by the ground network AERONET (AErosol RObotic NETwork). In recent years, several satellite products of AAOD have been developed. This study's primary aim is to establish the usefulness of these datasets for AEROCOM (Aerosol Comparisons between Observations and Models) model evaluation with a focus on the years 2006, 2008 and 2010. The satellite products are super-observations consisting of 1∘×1∘×30 min aggregated retrievals. This study consists of two papers, the current one that deals with the assessment of satellite observations and a second paper (Schutgens et al., 2021) that deals with the evaluation of models using those satellite data. In particular, the current paper details an evaluation with AERONET observations from the sparse AERONET network as well as a global intercomparison of satellite datasets, with a focus on how minimum AOD (aerosol optical depth) thresholds and temporal averaging may improve agreement between satellite observations. All satellite datasets are shown to have reasonable skill for AAOD (three out of four datasets show correlations with AERONET in excess of 0.6) but less skill for SSA (single-scattering albedo; only one out of four datasets shows correlations with AERONET in excess of 0.6). In comparison, satellite AOD shows correlations from 0.72 to 0.88 against the same AERONET dataset. However, we show that performance vs. AERONET and inter-satellite agreements for SSA improve significantly at higher AOD. Temporal averaging also improves agreements between satellite datasets. Nevertheless multi-annual averages still show systematic differences, even at high AOD. In particular, we show that two POLDER (Polarization and Directionality of the Earth's Reflectances) products appear to have a systematic SSA difference over land of ∼0.04, independent of AOD. Identifying the cause of this bias offers the possibility of substantially improving current datasets. We also provide evidence that suggests that evaluation with AERONET observations leads to an underestimate of true biases in satellite SSA. In the second part of this study we show that, notwithstanding these biases in satellite AAOD and SSA, the datasets allow meaningful evaluation of AEROCOM models.

Global measurements of absorptive aerosol optical depth (AAOD) are scarce and mostly provided by the ground network AERONET (AErosol RObotic NETwork). In recent years, several satellite products of AAOD have appeared. This study's primary aim is to establish the usefulness of these datasets for AEROCOM (AEROsol Comparisons between Observations and Models) model evaluation with a focus on the years 2006, 2008 and 2010. The satellite products are super-observations consisting of 1° × 1° × 30min aggregated retrievals. This study consist of two parts: 1) an assessment of satellite datasets; 2) their application to the evaluation of AEROCOM models. The current paper describes the first part and details an evaluation with AERONET observations from the sparse AERONET network as well as a global intercomparison of satellite datasets, with a focus on how minimum AOD (Aerosol Optical Depth) thresholds and temporal averaging may improve agreement. All satellite datasets are shown to have reasonable skill for AAOD (3 out of 4 datasets show correlations with AERONET in excess of 0.6) but less skill for SSA (Single Scattering Albedo; only 1 out of 4 datasets shows correlations with AERONET in excess of 0.6). In comparison, satellite AOD shows correlations from 0.72 to 0.88 against the same AERONET dataset. We do show that performance vs. AERONET and satellite agreements for SSA significantly improve at higher AOD. Temporal averaging also improves agreements between satellite datasets. Nevertheless multi-annual averages still show systematic differences, even at high AOD. In particular, we show that two POLDER products appear to have a systematic SSA difference over land of about 0.04, independent of AOD. Identifying the cause of this bias offers the possibility of substantially improving current datasets. We also provide evidence that suggests that evaluation with AERONET observations leads to an underestimate of true biases in satellite SSA. In the second part of this study we show that, notwithstanding these biases in satellite AAOD and SSA, the datasets allow meaningful evaluation of AEROCOM models.

A data assimilation system for aerosol, based on an ensemble Kalman filter, has been developed for the ECHAM – Hamburg Aerosol Model (ECHAM-HAM) global aerosol model and applied to POLarization and Directionality of the Earth's Reflectances (POLDER)-derived observations of optical properties. The advantages of this assimilation system is that the ECHAM-HAM aerosol modal scheme carries both aerosol particle numbers and mass which are both used in the data assimilation system as state vectors, while POLDER retrievals in addition to aerosol optical depth (AOD) and the Ångström exponent (AE) also provide information related to aerosol absorption like aerosol absorption optical depth (AAOD) and single scattering albedo (SSA). The developed scheme can simultaneously assimilate combinations of multiple variables (e.g., AOD, AE, SSA) to optimally estimate mass mixing ratio and number mixing ratio of different aerosol species. We investigate the added value of assimilating AE, AAOD and SSA, in addition to the commonly used AOD, by conducting multiple experiments where different combinations of retrieved properties are assimilated. Results are evaluated with (independent) POLDER, Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target, MODIS Deep Blue and Aerosol Robotic Network (AERONET) observations. The experiment where POLDER AOD, AE and SSA are assimilated shows systematic improvement in mean error, mean absolute error and correlation for AOD, AE, AAOD and SSA compared to the experiment where only AOD is assimilated. The same experiment reduces the global ME against AERONET from 0.072 to 0.001 for AOD, from 0.273 to 0.009 for AE and from −0.012 to 0.002 for AAOD. Additionally, sensitivity experiments reveal the benefits of assimilating AE over AOD at a second wavelength or SSA over AAOD, possibly due to a simpler observation covariance matrix in the present data assimilation framework. We conclude that the currently available AE and SSA do positively impact data assimilation.

The uncertainty in present-day anthropogenic forcing is dominated by uncertainty in the strength of the contribution from aerosol. Much of the uncertainty in the direct aerosol forcing can be attributed to uncertainty in the anthropogenic fraction of aerosol in the present-day atmosphere, due to a lack of historical observations. Here, we present a robust relationship between total present-day aerosol optical depth and the anthropogenic contribution across three multimodel ensembles and a large single-model perturbed parameter ensemble. Using observations of aerosol optical depth, we determine a reduced likely range of the anthropogenic component and hence a reduced uncertainty in the direct forcing of aerosol.

Biomass burning on the African continent emits large amounts of CO2, CO, and aerosols. Our aim is to use measurements of the stable carbon isotope 13C in organic carbon, CO and CO2 in biomass burning smoke to estimate the contribution of C3 plants (trees and bushes) and C4 plants (mainly Savannah grass), which have very distinct 13C/12C ratios. This is possible, if 13C/12C ratios are not significantly altered by the combustion process. This assumption is investigated in a series of laboratory experiments, where C3 and C4 plants (corn and willow wood), or C3-C4 plant mixtures are burned. The laboratory results are used to interpret the results of pilot studies of smoke sampled in African savannah fires. First results from the laboratory studies indicate that organic carbon (OC) from combustion of willow or corn shows 13C/12C ratios comparable to the burned plant material. For combustion of willow (C3), the 13C/12C ratios in OC tend to be slightly higher than in the wood fuel, depending on combustion conditions. For combustion of corn 13C/12C ratios of OC tend to be slightly lower than in the fuel. For mixtures of willow and corn the relationship between 13C/12C ratios in the emitted organic carbon and the fuel mixture is slightly non-linear: For a 50-50% oak wood and corn mixture the 13C/12C ratio in OC is closer to that of corn than that of willow. First results from pilot field studies indicate that a larger fraction of OC comes from trees and bushes, although mainly Savannah grass is burned in the investigated fires.

In contrast to most aerosol species, black carbon and dust absorb visual light and may heat the atmosphere. However, their overall effect is highly uncertain. In this study we explore the use of novel satellite AAOD (Absorptive Aerosol Optical Depth) measurements in evaluating global (AEROCOM) models. Two POLDER retrieval products, and one product each from OMI and a CALIOP/MODIS combination are intercompared and evaluated with AERONET ("truth") data. While all products have skill in measuring AAOD, there are substantial biases amongst the products. In particular, we note a bias between the two POLDER products of 0.04 in SSA (Single Scattering Albedo), independent of AOD (Aerosol Optical Depth). Identification of the cause of this bias would allow a substantial improvement in AAOD observations. However, we show that even with such biases, consistent evaluation of global models with satellite products is possible.In particular we show that there can be substantial under- and over-estimates of AAOD, depending on model. Furthermore, in recent years, models have diverged amongst themselves. This can be traced to different emission inventories, and we show that satellite AAOD may be used to provide constraints on these emissions. At the same time, models still differ in their particle properties, and we show that this can, to some extent, be evaluated with observations as well.In addition, we will introduce a similar study for an ensemble of 14 satellite products of AOD. This larger ensemble allows us to study AOD diversity between the products in detail. In particular, we show that this diversity is a pretty good predictor of AOD uncertainty (versus "truth" data) in multi-year averages. This provides us with uncertainty estimates even in the absence of truth data, which allows many exciting applications (to be discussed).These studies are the fruit of collaboration between the AEROCOM (AEROsol Comparisons between Observations and Models, https:// aerocom.met.no) and AEROSAT (International Satellite Aerosol Science Network, https://aero-sat.org) communities.

A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets. The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species. This work was funded by the SEV-2011- 00067 grant of the Severo Ochoa Program awarded by the Spanish Government, the CGL-2013-46736-R grant of the Spanish Ministry of Economy and Competitiveness, and the ACTRIS Research Infrastructure Project of the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 654169. The authors thank all the Principal Investigators and their staff for establishing and maintaining the AERONET sites, NRL/University of North Dakota for the MODIS AOD L3 product, and the MODIS and OMI mission scientists and associated NASA personnel for the production of the AOD, AAI, and AE data used in this investigation. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (RES-AECT-2015-1-0007). They also thank Francesco Benincasa for his technical support. Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund, as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness. Comments from two anonymous reviewers are gratefully acknowledged.

Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using a four-dimensional local ensemble transform Kalman filter (4-D LETKF). A parallel assimilation experiment using bias-corrected aerosol optical thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations (and whether to include vertical information) on the model performances. Additionally, an experiment simultaneously assimilating both CALIOP and MODIS observations is conducted. The assimilation experiments are successfully performed for 1 month, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP-observed aerosol vertical extinction coefficients and the AOT observations from MODIS and the AErosol RObotic NETwork (AERONET). Our results reveal that both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations, and the joint CALIOP and MODIS assimilation can further improve the simulated aerosol vertical distribution. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation, and the inclusion of the CALIOP observations has an insignificant impact on the AOT analysis. This is probably due to the nadir-viewing CALIOP having much sparser coverage than MODIS. The assimilation efficiencies of CALIOP decrease with increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation.

Remote sensing observations from the AERONET and GAW networks are intermittent in time and have a limited field-of-view. A global high-resolution simulation (GEOS5 Nature Run) is used to conduct an Observing System Simulation Experiment (OSSE) for AERONET and GAW observations of AOT and AAOT and estimate the spatio-temporal representativity of individual sites for larger areas (from 0.5° to 4° in size). G5NR and the OSSE are evaluated and shown to have sufficient skill, although daily AAOT variability is significantly underestimated while the frequency of AAOT observations is over-estimated (both resulting in an under-estimation of temporal representativity errors in AAOT). Yearly representation errors are provided for a host of scenarios: varying grid-box size, temporal collocation protocols, and site altitudes are explored. Monthly representation errors are shown to correlate strongly throughout the year, with a pronounced annual cycle. The collocation protocol for AEROCOM model evaluation (using daily data) is shown to be sub-optimal and the use of hourly data advocated instead. A previous subjective ranking of site spatial representativity (Kinne et al., 2013) is analysed and a new objective ranking proposed. Several sites are shown to have yearly representation errors in excess of 40 %. Lastly, a recent suggestion by Wang et al. (2018) that AERONET observations of AAOT suffer a positive representation bias of 30 % globally is analysed and evidence is provided that this bias is likely an overestimate (the current paper finds 4 %) due methodological choices.

We present the inversions (back-calculations or optimizations) of dust emissions for a severe winter dust event over East Asia in November 2016. The inversion system based on a fixed-lag ensemble Kalman smoother is newly implemented in the Weather Research and Forecasting model and is coupled with Chemistry (WRF-Chem). The assimilated observations are the hourly aerosol optical depths (AODs) from the next-generation geostationary satellite Himawari-8. The posterior total dust emissions (2.59 Tg) for this event are 3.8 times higher than the priori total dust emissions (0.68 Tg) during 25&ndash, 27 November 2016. The net result is that the simulated aerosol horizontal and vertical distributions are both in better agreement with the assimilated Himawari-8 observations and independent observations from the ground-based AErosol RObotic NETwork (AERONET), the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The developed emission inversion approach, combined with the geostationary satellite observations, can be very helpful for properly estimating the Asian dust emissions.

The next-generation geostationary satellite Himawari-8 has a much higher observation frequency of the aerosol field than polar-orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one-monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari-8-retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non-hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM-SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four-dimensional LETKF (4D-LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari-8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)- and AErosol RObotic NETwork (AERONET)-retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root-mean-square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari-8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D-LETKF experiments are generally not so different, but the computational load of the 4D-LETKF is much lighter than that of the LETKF.

We present the one month results of global aerosol optical properties for April 2006, using the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-hydrostatic ICosahedral Atmospheric Model (NICAM), by assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) with Local Ensemble Transform Kalman Filter (LETKF). The simulated AOD, Angstrom Exponent (AE) and single scattering albedo (SSA) are validated by independent Aerosol Robotic Network (AERONET) observations over the global sites. The data assimilation has the strongest positive effect on the AOD simulation and slight positive influences on the AE and SSA simulations. For the time-averaged globally spatial distribution, the data assimilation increases the model skill score (S) of AOD, AE, and SSA from 0.55, 0.92, and 0.75 to 0.79, 0.94, and 0.80, respectively. Over the North Africa (NAF) and Middle East region where the aerosol composition is simple (mainly dust), the simulated AODs are best improved by the data assimilation, indicating the assimilation correctly modifies the wrong dust burdens caused by the uncertainties of the dust emission parameterization. Assimilation also improves the simulation of the temporal variations of the aerosol optical properties over the AERONET sites, with improved S at 60 (62%), 45 (55%) and 11 (50%) of 97, 82 and 22 sites for AOD, AE and SSA. By analyzing AOD and AE at five selected sites with best S improvement, this study further indicates that the assimilation can reproduce short duration events and ratios between fine and coarse aerosols more accurately.

We introduce and evaluate the aerosol simulations with the global aerosol-climate model ECHAM6.3-HAM2.3, which is the aerosol component of the fully coupled aerosol-chemistry-climate model ECHAM-HAMMOZ. Both the host atmospheric climate model ECHAM6.3 and the aerosol model HAM2.3 were updated from previous versions. The updated version of the HAM aerosol model contains improved parameterizations of aerosol processes such as cloud activation, as well as updated emission fields for anthropogenic aerosol species and modifications in the online computation of sea salt and mineral dust aerosol emissions. Aerosol results from nudged and free running simulations for the 10-year period 2003 to 2012 are compared to various measurements of aerosol properties. While there are regional deviations between model and observations, the model performs well overall in terms of aerosol optical thickness, but may underestimate coarse mode aerosol concentrations to some extent, so that the modeled particles are smaller than indicated by the observations. Sulfate aerosol measurements in the US and Europe are reproduced well by the model, while carbonaceous aerosol species are biased low. Both mineral dust and sea salt aerosol concentrations are improved compared to previous versions of ECHAM-HAM. The evaluation of the simulated aerosol distributions serves as a basis for the suitability of the model for simulating aerosol-climate interactions in a changing climate.

The spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument provides valuable information on the vertical distribution of global aerosol and is often used to evaluate vertical aerosol distributions in general circulation models (GCMs). Here we show, however, that the detection limit of the CALIOP retrievals mean background aerosol is not detected, leading to substantially skewed statistics that moreover differ significantly by product. In the CALIOP Level 2 product this missing low-backscatter aerosol results in the retrieved aerosol distribution significantly overrepresenting aerosol backscatter and extinction in the middle and upper troposphere if taken to be representative of the undetected aerosol. The CALIOP Level 3 product assumes no aerosol where none is detected, which then leads to an underestimation in the aerosol extinction profile in the upper troposphere. Using the ECHAM-HAM GCM, we estimate that the mean fraction of aerosol undetected by CALIOP daytime (nighttime) retrievals is 41% (44%) globally.

We present a validation study of Collection 5 MODIS level 2 Aqua and Terra AOT (aerosol optical thickness) and AE (Ångström exponent) over ocean by comparison to coastal and island AERONET (AErosol RObotic NETwork) sites for the years 2003–2009. We show that MODIS (MODerate-resolution Imaging Spectroradiometer) AOT exhibits significant biases due to wind speed and cloudiness of the observed scene, while MODIS AE, although overall unbiased, exhibits less spatial contrast on global scales than the AERONET observations. The same behaviour can be seen when MODIS AOT is compared against Maritime Aerosol Network (MAN) data, suggesting that the spatial coverage of our datasets does not preclude global conclusions. Thus, we develop empirical correction formulae for MODIS AOT and AE that significantly improve agreement of MODIS and AERONET observations. We show these correction formulae to be robust. Finally, we study random errors in the corrected MODIS AOT and AE and show that they mainly depend on AOT itself, although small contributions are present due to wind speed and cloud fraction in AOT random errors and due to AE and cloud fraction in AE random errors. Our analysis yields significantly higher random AOT errors than the official MODIS error estimate (0.03 + 0.05 τ), while random AE errors are smaller than might be expected. This new dataset of bias-corrected MODIS AOT and AE over ocean is intended for aerosol model validation and assimilation studies, but also has consequences as a stand-alone observational product. For instance, the corrected dataset suggests that much less fine mode aerosol is transported across the Pacific and Atlantic oceans.

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only.