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We apply a Local Ensemble Transform Kalman Smoother (LETKS) in combination with the global aerosol climate model ECHAM-HAM to estimate aerosol emissions from POLDER-3/PARASOL observations for the year 2006. We assimilate Aerosol Optical Depth at 550mnm (AOD550), Ångström Exponent for 550nm and 865nm (AE550-865) and Single Scattering Albedo at 550nm (SSA550) in order to improve modeled aerosol mass, size and absorption simultaneously. The new global aerosol emissions increase to 1419 Tg·yr-1 (+28%) for dust, 1850 Tg·yr-1 (+75%) for sea salt, 215 Tg·yr-1 (+143%) for organic aerosol and 13.3 Tg·yr-1 (+75%) for black carbon, while the sulfur dioxide emissions increase to 198 Tg·yr-1 (+42%) and total deposition of sulfates to 293 Tg·yr-1 (+39%). Organic and black carbon emissions are much higher than their prior values from bottom up inventories with a stronger increase in biomass burning sources (+193% and +90%) than in anthropogenic sources (115% and 70%). The evaluation of the experiments with POLDER (assimilated) and AERONET as well as MODIS Dark Target (independent) observations shows a clear improvement compared to the ECHAM-HAM control run. Specifically based on AERONET the global mean error of AOD550 improves from -0.094 to - 0.006 while AAOD550 improves from -0.009 to -0.004 after the assimilation. A smaller improvement is observed also in AE550-865 mean absolute error (from 0.428 to 0.393), with a considerably higher improvement over isolated island sites over the ocean. The new dust emissions are closer to the ensemble median of AEROCOM I, AEROCOM III and CMIP5 as well as some of the previous assimilation studies. The new sea salt emissions get closer to the reported emissions from previous studies. Indications of a missing fraction of coarse dust and sea salt particles are discussed. The biomass burning changes (based on POLDER) can be used as alternative biomass burning scaling factors for the GFAS inventory distinctively estimated for organic carbon (2.93) and black carbon (1.90), instead of the recommended scaling of 3.4 (Kaiser et al. 2012). The estimated emissions are highly sensitive to the relative humidity due to aerosol water uptake, especially in the case of the sulfates. We found that ECHAM-HAM, like most of the GCMs that participated in AEROCOM and CMIP6, overestimated the relative humidity compared to ERA-5 and as a result the water uptake by aerosols, assuming the kappa values are not underestimated. If we use the ERA-5 relative humidity, sulfate emissions must be further increased, as modeled sulfate AOD is lowered. Specifically, over East Asia, the lower AOD can be attributed to the underestimated precipitation and the lack of simulated nitrates in the model. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols and their impact on cloud properties remain the largest uncertainty in the human forcing of the climate system. By increasing the concentration of cloud droplets (Nd), aerosols reduce droplet size and increase the reflectivity of clouds (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol (ß), the diversity of which explains much of the variation in radiative forcing in global climate models. This has made measuring ß a key target for developing observational constraints of the aerosol forcing. While the aerosol burden of the clean, pre-industrial atmosphere has been demonstrated as a key uncertainty for the aerosol forcing, here we show that the behaviour of clouds under these clean conditions is of equal importance for understanding the spread in radiative forcing estimates between models and observations. This means that the uncertainty in the aerosol impact on clouds is, counterintuitively, driven by situations with little aerosol. Discarding clean conditions produces a close agreement between different model and observational estimates of the cloud response to aerosol, but does not provide a strong constraint on the radiative forcing from aerosol-cloud interactions. This makes constraining aerosol behaviour in clean conditions an important goal for future observational studies. [ABSTRACT FROM AUTHOR]

Retrievals of atmospheric carbon dioxide (C[O.sub.2]) from space-borne measurements of backscattered near-infrared sunlight are hampered by aerosol and cirrus cloud scattering effects. We propose a retrieval approach that allows for the retrieval of a few effective aerosol parameters simultaneously with the C[O.sub.2] total column by parameterizing particle amount, height distribution, and microphysical properties. Two implementations of the proposed method covering different spectral bands are tested for an ensemble of simulated nadir observations for aerosol (and cirrus) loaded scenes over low- and mid-latitudinal land surfaces. The residual aerosol-induced C[O.sub.2] errors are mostly below 1% up to aerosol optical thickness 0.5. The proposed methods also perform convincing for scenes where cirrus clouds of optical thickness 0.1 overlay the aerosol. OCIS codes: 010.0280, 010.1280, 010.1310, 120.6200, 290.1090.

A framework to infer volume water fraction, soluble fraction and dry size distributions of fine mode aerosol from multi-angle, multi-spectral polarimetry retrievals of column-averaged ambient aerosol properties is presented. The method is applied to observations of the Research Scanning Polarimeter (RSP) obtained during two NASA aircraft campaigns, namely the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) and the Cloud, Aerosol, and Monsoon Processes-Philippines Experiment (CAMP2Ex). All aerosol retrievals are statistically evaluated using in situ data. Volume water fraction is inferred from the retrieved ambient real part of the refractive index, assuming a dry refractive index of 1.54 and by applying a volume mixing rule to obtain the effective ambient refractive index. The uncertainties in inferred volume water fraction resulting from this simplified model are discussed and estimated to be lower than 0.2 and decreasing with increasing volume water fraction. The daily mean retrieved volume water fractions correlate well with the in situ values with a mean absolute difference of 0.09. Polarimeter-retrieved ambient effective radius for daily data is shown to increase as a function of volume water fraction as expected. Furthermore, the effective variance of the size distributions also increases with increasing effective radius, which we show is consistent with an external mixture of soluble and insoluble aerosol. The relative variations of effective radius and variance over an observation period are then used to estimate the soluble fraction of the aerosol. Daily results of soluble fraction correlate well with in situ observed sulfate mass fraction with a correlation coefficient of 0.79. Subsequently, inferred water and soluble fractions are used to derive dry fine-mode size distributions from their ambient counterparts. While dry effective radii obtained in situ and from RSP show similar ranges, in situ values are generally substantially smaller during the ACTIVATE deployments, which may be due to biases in RSP retrievals or in the in situ observations, or both. Both RSP and in situ observations indicate the dominance of aerosol with low hygroscopicity during the ACTIVATE and CAMP2Ex campaigns. Furthermore, RSP indicates a high degree of external mixing of particles with low and high hygroscopicity. These retrievals of fine mode water volume fraction and soluble fraction may be used for the evaluation of water uptake in atmospheric models. Furthermore, the framework allows to estimate the variation in the concentration of fine-mode aerosol larger than a specific dry radius limit, which can be used as a proxy for the variation in cloud condensation nucleus concentrations. This framework may be applied to multi-angle, multi-spectral satellite data expected to be available in the near future. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service has recently produced a greenhouse gases reanalysis (version egg4) that covers almost two decades from 2003 to 2020 and will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gases concentrations on climate change, such as global and regional climate radiative forcing, assess intercontinental transport, and also serve as boundary conditions for regional simulations, among other applications and scientific studies. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as well as their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases. [ABSTRACT FROM AUTHOR]

We investigate the capabilities of different instrument concepts for the retrieval of aerosol properties over land. It was found that, if the surface reflection properties are unknown, only multiple-viewing-angle measurements of both intensity and polarization are able to provide the relevant aerosol parameters with sufficient accuracy for climate research. Furthermore, retrieval errors are only little affected when the number of viewing angles is increased at the cost of the number of spectral sampling points and vice versa. This indicates that there is a certain amount of freedom for the instrument design of dedicated aerosol instruments. The final choice on the trade-off between the spectral sampling and the number of viewing angles should be made taking other factors into account, such as instrument complexity and the ability to obtain global coverage. OCIS codes: 010.1100, 280.1100, 290.1090, 290.1350.

We present what we believe to be a novel approach to simulating the spectral fine structure ( OCIS codes: 010.0010, 040.1240, 070.4790, 290.5860, 300.6330.

Accurate radiative transfer calculations in cloudy atmospheres are generally time consuming, limiting their practical use in satellite remote sensing applications. We present a model to efficiently calculate the radiative transfer of polarized light in atmospheres that contain homogeneous cloud layers. This model combines the Gauss--Seidel method, which is efficient for inhomogeneous cloudless atmospheres, with the doubling method, which is efficient for homogeneous cloud layers. Additionally to reduce the computational effort for radiative transfer calculations in absorption bands, the cloud reflection and transmission matrices are interpolated over the absorption and scattering optical thicknesses within the cloud layer. We demonstrate that the proposed radiative transfer model in combination with this interpolation technique is efficient for the simulation of satellite measurements for inhomogeneous atmospheres containing one homogeneous cloud layer. For example, the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) measurements in the oxygen A band (758-773 nm) and the Hartley-Huggins ozone band (295-335 nm) with a spectral resolution of 0.4 nm can be simulated for these atmospheres within 1 min on a 2.8 GHz PC with an accuracy better than 0.1%. OCIS codes: 290.1090, 280.1310, 010.1310, 290.1350.

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and nonlinearity of radiative transfer near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on several neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the technique to uncertainty evaluation in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, and guide MAP algorithm development for current and future satellite systems such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. [ABSTRACT FROM AUTHOR]