Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (2010–2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes. The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust).

Radiative forcing provides an important basis for understanding and predicting global climate changes, but its quantification has historically been done independently for different forcing agents, has involved observations to varying degrees, and studies have not always included a detailed analysis of uncertainties. The Copernicus Atmosphere Monitoring Service reanalysis is an optimal combination of modelling and observations of atmospheric composition. It provides a unique opportunity to rely on observations to quantify the monthly and spatially resolved global distributions of radiative forcing consistently for six of the largest forcing agents: carbon dioxide, methane, tropospheric ozone, stratospheric ozone, aerosol–radiation interactions, and aerosol–cloud interactions. These radiative-forcing estimates account for adjustments in stratospheric temperatures but do not account for rapid adjustments in the troposphere. On a global average and over the period 2003–2017, stratospherically adjusted radiative forcing of carbon dioxide has averaged +1.89 W m⁻² (5 %–95 % confidence interval: 1.50 to 2.29 W m⁻²) relative to 1750 and increased at a rate of 18 % per decade. The corresponding values for methane are +0.46 (0.36 to 0.56) W m⁻² and 4 % per decade but with a clear acceleration since 2007. Ozone radiative-forcing averages +0.32 (0 to 0.64) W m⁻², almost entirely contributed by tropospheric ozone since stratospheric ozone radiative forcing is only +0.003 W m⁻². Aerosol radiative-forcing averages −1.25 (−1.98 to −0.52) W m⁻², with aerosol–radiation interactions contributing −0.56 W m⁻² and aerosol–cloud interactions contributing −0.69 W m⁻² to the global average. Both have been relatively stable since 2003. Taking the six forcing agents together, there is no indication of a sustained slowdown or acceleration in the rate of increase in anthropogenic radiative forcing over the period. These ongoing radiative-forcing estimates will monitor the impact on the Earth's energy budget of the dramatic emission reductions towards net-zero that are needed to limit surface temperature warming to the Paris Agreement temperature targets. Indeed, such impacts should be clearly manifested in radiative forcing before being clear in the temperature record. In addition, this radiative-forcing dataset can provide the input distributions needed by researchers involved in monitoring of climate change, detection and attribution, interannual to decadal prediction, and integrated assessment modelling. The data generated by this work are available at https://doi.org/10.24380/ads.1hj3y896 (Bellouin et al., 2020b).

We develop a method to derive aerosol properties over land surfaces using combined spectral and angular information, such as available from ESA Sentinel-3 mission, to be launched in 2015. A method of estimating aerosol optical depth (AOD) using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3. The method aims to improve the estimation of AOD, and to explore the estimation of fine mode fraction (FMF) and single scattering albedo (SSA) over land surfaces by inversion of a coupled surface/atmosphere radiative transfer model. The surface model includes a general physical model of angular and spectral surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values to the physical model. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground-based sun photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.

Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (2010–2013) algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components and their mixing ratios. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data qualitatively by visible analysis of monthly mean AOD maps and quantitatively by comparing global daily gridded satellite data against daily average AERONET sun photometer observations for the different versions of each algorithm. The analysis allowed an assessment of sensitivities of all algorithms which helped define the best algorithm version for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR.

A method has been developed to estimate Aerosol Optical Depth (AOD), Fine Mode Fraction (FMF) and Single Scattering Albedo (SSA) over land surfaces using simulated Sentinel-3 data. The method uses inversion of a coupled surface/atmosphere radiative transfer model, and includes a general physical model of angular surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values for a number of view angles and wavelengths with those provided by the physical model. A method of estimating AOD using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3 and the additional aerosol properties. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground based sun-photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.

Radiative forcing provides an important basis for understanding and predicting global climate changes, but its quantification has historically been done independently for different forcing agents, involved observations to varying degrees, and studies have not always included a detailed analysis of uncertainties. The Copernicus Atmosphere Monitoring Service reanalysis is an optimal combination of modelling and observations of atmospheric composition. It provides a unique opportunity to rely on observations to quantify the monthly- and spatially-resolved global distributions of radiative forcing consistently for six of the largest forcing agents: carbon dioxide, methane, tropospheric ozone, stratospheric ozone, aerosol-radiation interactions, and aerosol-cloud interactions. These radiative forcing estimates account for adjustments in stratospheric temperatures, but do not account for rapid adjustments in the troposphere. On a global average and over the period 2003–2016, stratospherically adjusted radiative forcing of carbon dioxide has averaged +1.84 W m⁻² (5–95% confidence interval: 1.46 to 2.22 W m⁻²) relative to 1750 and increased at a rate of 17 % per decade. The corresponding values for methane are +0.45 (0.35 to 0.55) W m⁻² and 3 % per decade, but with a clear acceleration since 2007. Ozone radiative forcing averages +0.32 (0 to 0.64) W m⁻² and aerosol radiative forcing averages −1.37 (−2.17 to −0.57) W m⁻². Both have been relatively stable since 2003. Taking the six forcing agents together, there no indication of a slowdown or acceleration in the rate of increase in anthropogenic radiative forcing over the period. These ongoing radiative forcing estimates will monitor the impact on the Earth’s energy budget of the dramatic emission reductions towards net-zero that are needed to limit surface temperature warming to the Paris Agreement temperature targets. Indeed, such impacts should be clearly manifested in radiative forcing before being clear in the temperature record. In addition, this radiative forcing dataset can provide the input distributions needed by researchers involved in monitoring of climate change, detection and attribution, interannual to decadal prediction, and integrated assessment modelling. The data generated by this work are available at https://doi.org/10.24380/ads.1hj3y896 (Bellouin et al., 2020).