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Data for the manuscript: "Aerosol emissions estimation with POLDER" atAtmospheric Chemistry and Physics. Thefiles include the yearly ensemble mean of all experiments (CTL_ECHAM, DAS_ECHAM, CTL_ERA5, DAS_ERA5, RES_LOW) for most variables produced by the model. In addition the Aerosol Optical Depth at 550nm (AOD550) and at 865nm (AOD865) along with the Absorption Aerosol Optical Depth at 550nm (ABS550) are provided in 3-hourly temporal resolution for all experiments and for POLDER.

著者人数: 41名, 形態: カラー図版あり, Number of authors: 41, Physical characteristics: Original contains color illustrations, Accepted: 2020-01-20, 資料番号: PA2010002000

The Directional Polarization Camera (DPC) onboard the Chinese Gaofen-5 satellite, launched in May 2018, has similar specifications as the POLDER-3 instrument. The SRON Remote Sensing of Trace gas and Aerosol Products (RemoTAP) full retrieval algorithm is applied to DPC measurements to retrieve aerosol properties including the total Aerosol Optical Depth (AOD), the fine/coarse mode AOD and the SSA (Single Scattering Albedo). Measurements of the global ground-based AERONET network between December 2019 and April 2020 have been used for the validation of the DPC retrievals. According to the average Fine Mode Fraction (FMF) of the selected AERONET stations, the stations are divided into urban stations (FMF ≥ 0.5) and dust stations (FMF < 0.5). For the total AOD validation, DPC retrievals show better performance over urban stations than over dust stations, with average biases of 0.055 and 0.106, and RMSEs of 0.151 and 0.228, respectively. Regarding the fine mode AOD, the retrieval also performs better over urban stations. Compared with the total AOD validation, both the relatively lower bias (0.021 and 0.065) and the higher Gfrac (Fraction of Good retrieval, 63.8% and 47.3%, respectively) further indicate that DPC performs better when fine mode aerosols dominate. For the land SSA validation, most of our SSA retrievals (~71%) show differences with AERONET SSA retrievals lower than 0.05. Case studies over fire spots and dust over northern China demonstrate the encouraging application potential of DPC aerosol products. The difference between fine and coarse AOD can provide more aerosol source information compared with the total AOD alone. Since the SSA retrievals are particularly sensitive to absorbing fine particles, they can be easily used in the tracking of biomass burning aerosol.

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO$_{2}$) and methane (CH$_{4}$), denoted XCO$_{2}$ and XCH$_{4}$, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO$_{2}$) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5°x5°data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO$_{2}$ or XCH$_{4}$, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO$_{2}$ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1$^{σ}$): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1$^{σ}$): 0.66 ppm (0.70 ppm). The corresponding values for the XCH$_{4}$ products are singleobservation random error (1$^{σ}$): 17.4 ppb (monthly: 8.7 ppb); global bias: -2.0 ppb (-2.9 ppb); and spatiotemporal bias (1$^{σ}$): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO$_{2}$ and XCH$_{4}$ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations.

The Twomey effect describes the radiative forcing associated with a change in cloud albedo due to an increase in anthropogenic aerosol emissions. It is driven by the perturbation in cloud droplet number concentration (ΔNd, ant) in liquid-water clouds and is currently understood to exert a cooling effect on climate. The Twomey effect is the key driver in the effective radiative forcing due to aerosol–cloud interactions, but rapid adjustments also contribute. These adjustments are essentially the responses of cloud fraction and liquid water path to ΔNd, ant and thus scale approximately with it. While the fundamental physics of the influence of added aerosol particles on the droplet concentration (Nd) is well described by established theory at the particle scale (micrometres), how this relationship is expressed at the large-scale (hundreds of kilometres) perturbation, ΔNd, ant, remains uncertain. The discrepancy between process understanding at particle scale and insufficient quantification at the climate-relevant large scale is caused by co-variability of aerosol particles and updraught velocity and by droplet sink processes. These operate at scales on the order of tens of metres at which only localised observations are available and at which no approach yet exists to quantify the anthropogenic perturbation. Different atmospheric models suggest diverse magnitudes of the Twomey effect even when applying the same anthropogenic aerosol emission perturbation. Thus, observational data are needed to quantify and constrain the Twomey effect. At the global scale, this means satellite data. There are four key uncertainties in determining ΔNd, ant, namely the quantification of (i) the cloud-active aerosol – the cloud condensation nuclei (CCN) concentrations at or above cloud base, (ii) Nd, (iii) the statistical approach for inferring the sensitivity of Nd to aerosol particles from the satellite data and (iv) uncertainty in the anthropogenic perturbation to CCN concentrations, which is not easily accessible from observational data. This review discusses deficiencies of current approaches for the different aspects of the problem and proposes several ways forward: in terms of CCN, retrievals of optical quantities such as aerosol optical depth suffer from a lack of vertical resolution, size and hygroscopicity information, non-direct relation to the concentration of aerosols, difficulty to quantify it within or below clouds, and the problem of insufficient sensitivity at low concentrations, in addition to retrieval errors. A future path forward can include utilising co-located polarimeter and lidar instruments, ideally including high-spectral-resolution lidar capability at two wavelengths to maximise vertically resolved size distribution information content. In terms of Nd, a key problem is the lack of operational retrievals of this quantity and the inaccuracy of the retrieval especially in broken-cloud regimes. As for the Nd-to-CCN sensitivity, key issues are the updraught distributions and the role of Nd sink processes, for which empirical assessments for specific cloud regimes are currently the best solutions. These considerations point to the conclusion that past studies using existing approaches have likely underestimated the true sensitivity and, thus, the radiative forcing due to the Twomey effect.

International audience; Polarimetry is one of the most promising types of remote sensing for improved characterization of atmospheric aerosol. Indeed, aerosol particles constitute a highly variable atmospheric component characterized by a large number of parameters describing particle sizes, morphologies (including shape and internal structure), absorption and scattering properties, amounts, horizontal and vertical distribution, etc. Reliable monitoring of all these parameters is very challenging, and therefore the aerosol effects on climate and environment are considered to be among the most uncertain factors in climate and environmental research. In this regard, observations that provide both the angular distribution of the scattered atmospheric radiation as well as its polarization state at multiple wavelengths covering the UV-SWIR spectral range carry substantial implicit information on the atmospheric composition. Therefore, high expectations in improving aerosol characterization are associated with detailed passive photopolarimetric observations. 475 The critical need to use space-borne polarimetry for global accurate monitoring of detailed aerosol properties was first articulated in the late 1980s and early 1990s. By now, several orbital instruments have already provided polarization observations from space, and a number of advanced missions are scheduled for launch in the coming years by international and national space agencies. The first and most extensive record of polarimetric imagery was provided by POLDER-I, POLDER-II, and POLDER/PARASOL multi-angle multi-spectral polarization sensors. Polarimetric observations with the POLDER-like design intended for collecting extensive multi-angular multi-spectral measurements will be provided by several instruments, such as the MAI/TG-2, CAPI/TanSat, and DPC/GF-5 sensors recently launched by the Chinese Space Agency. Instruments such as the 3MI/MetOp-SG, MAIA, SpexOne and HARP2 on PACE, POSP, SMAC, PCF, DPC-Lidar, ScanPol and MSIP/Aerosol-UA, MAP/Copernicus CO2 Monitoring, etc. are planned to be launched by different space agencies in the coming decade. The concepts of these future instruments, their technical designs, and the accompanying algorithm development have been tested intensively and analyzed using diverse airborne prototypes. Certain polarimetric capabilities have also been implemented in such satellite sensors as GOME-2/MetOp and SGLI/GCOM-C. A number of aerosol retrieval products have been developed based on the available measurements and successfully used for different scientific applications. However, the completeness and accuracy of aerosol data operationally derived from polarimetry do not yet appear to have reached the accuracy levels implied by theoretical sensitivity studies that analyzed the potential information content of satellite po-larimetry. As a result, the dataset provided by MODIS is still most frequently used by the scientific community , yet this sensor has neither polarimetric nor multi-angular capabilities. Admittedly polarimetric multi-angular observations are highly complex and have extra sensitivities to aerosol particle morphology, vertical variability of aerosol properties, polarization of surface reflectance, etc. As such, they necessitate state-of-the-art forward modeling based on first-principles physics which remains rare, and conventional retrieval approaches based on look-up tables turn out to be unsuitable to fully exploit the information implicit in the measurements. Several new-generation retrieval approaches have recently been proposed to address these challenges. These methods use improved forward modeling of atmospheric (polarized) radiances and implement a search in the continuous space of solutions using rigorous statistically optimized inversions. Such techniques provide more accurate retrievals of the main aerosol parameters such as aerosol optical thickness and yield additional parameters such as aerosol absorption. However, the operational implementation of advanced retrieval approaches generally requires a significant extra effort, and the forward-modeling part of such retrievals still needs to be substantially improved. Ground-based passive polarimetric measurements have also been evolving over the past decade. Although polarimetry helps improve aerosol characterization, especially of the fine aerosol mode, the operators of major observational networks such as AERONET remain reluctant to include polarimetric measurements as part of routine retrievals owing to their high complexity and notable increase in effort required to acquire and interpret polarization data. In addition to remote-sensing observations, polarimetric characteristics of aerosol scattering have been measured in situ as well as in the laboratory using polar nephelometers. Such measurements constitute direct observations of single scattering with no contributions from multiple scattering effects and therefore provide unique data for the validation of aerosol optical models and retrieval concepts. This article overviews the above-mentioned polarimetric observations, their history and expected developments , and the state of resulting aerosol products. It also discusses the main achievements and challenges in the exploitation of polarimetry for the improved characterization of atmospheric aerosols.

International audience

The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined globally and for selected latitude bands from satellite retrievals of column-average dry-air mole fractions of CO2, i.e., XCO2, for the years 2003 to 2016. The global XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0±0.24ppm/year; R: 0.87). This new and independent data set confirms record large growth rates around 3 ppm/year in 2015 and 2016, which are attributed to the 2015/2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil fuel burning related emissions in explaining the variance of the atmospheric CO2 growth rate.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2016-12-30, 資料番号: PA1710008000

International audience; We investigated differences in the five currently-available datasets of column-integrated CO2 concentrations (XCO2) retrieved from spectral soundings collected by Greenhouse gases Observing SATellite (GOSAT) and assessed their impact on regional CO2 flux estimates. We did so by estimating the fluxes from each of the five XCO2 datasets combined with surface-based CO2 data, using a single inversion system. The five XCO2 datasets are available in raw and bias-corrected versions, and we found that the bias corrections diminish the range of the five coincident values by ~30% on average. The departures of the five individual inversion results (annual-mean regional fluxes based on XCO2-surface combined data) from the surface-data-only results were close to one another in some terrestrial regions where spatial coverage by each XCO2 dataset was similar. The mean of the five annual global land uptakes was 1.7 ± 0.3 GtC yr-1, and they were all smaller than the value estimated from the surface-based data alone.

To assess the impact of atmospheric aerosols on health, climate, and air traffic, aerosol properties must be measured with fine spatial and temporal sampling. This can be achieved by actively involving citizens and the technology they own to form an atmospheric measurement network. We establish this new measurement strategy by developing and deploying iSPEX, a low-cost, mass-producible optical add-on for smartphones with a corresponding app. The aerosol optical thickness (AOT) maps derived from iSPEX spectropolarimetric measurements of the daytime cloud-free sky by thousands of citizen scientists throughout the Netherlands are in good agreement with the spatial AOT structure derived from satellite imagery and temporal AOT variations derived from ground-based precision photometry. These maps show structures at scales of kilometers that are typical for urban air pollution, indicating the potential of iSPEX to provide information about aerosol properties at locations and at times that are not covered by current monitoring efforts.

The man-made emissions of the greenhouse gases carbon dioxide (CO2) and methane (CH4) are considered the main drivers of anthropogenically induced climate change. Major uncertainties persist when it comes to quantifying regional scale surface fluxes of these gases or predicting the evolution of the relevant source/sink processes in a changing climate. Remote sensing of the atmospheric greenhouse gas concentrations from space-borne and ground-based platforms offers the opportunity to significantly advance our knowledge on spatial and temporal scales that are suitable for process attribution and mitigation actions. Overall, the most promising remote-sensing strategy exploits the rotational-vibrational absorption of CO2 and CH4 in sunlight penetrating the Earth’s atmosphere. Typically, satellite sounders such as GOSAT (Greenhouse Gases Observing Satellite), OCO-2 (Orbiting Carbon Observatory), and S5P (Sentinel-5 precursor) as well as the ground-based spectrometers of the TCCON (Total Carbon Column Observing Network) cover various CO2, CH4, and O2 absorption bands in the near and shortwave infrared spectral range between 0.75 micron (13400cm−1) and 2.5 micron (4000cm−1). Accuracy of the inferred gas concentrations is contingent on the accuracy of the adopted spectroscopic parameters and spectroscopic models available in these spectral regions. Here, I will report on recent achievements and challenges within our greenhouse-gas remote-sensing activities mainly focusing on the GOSAT observational record. Since its launch in early 2009, the Fourier Transform Spectrometer onboard GOSAT delivers solar absorption spectra with good spectral resolution and high signal-to-noise. It has been shown that the CO2 and CH4 retrievals from these observations can achieve an accuracy on the order of fractions of a percent which makes them suitable for tracking regional scale source/sink processes and their response to climate events. In order to achieve the required accuracy, it is crucial to develop highly accurate radiative-transfer algorithms and to validate the satellite soundings by ground-based observations. I will illustrate some cases where the excellent quality of the absorption spectra collected by GOSAT reveals spectroscopic deficiencies and inconsistencies among the various absorption bands covered. As such, lessons learned from GOSAT can be used as a feedback to the spectroscopy community. Beyond GOSAT, future satellite missions such as S5P or the planned S5 (Sentinel-5, launch ∼2020) will cover spectral ranges which have not yet been spectroscopically optimized for remote-sensing purposes. In that case, simulations and studies based on ground-based observations show that spectroscopic uncertainties constitute a dominant contribution to the error budget of the retrieved gas concentrations. Therefore, further improvements of spectroscopic parameters and line-shape models is of paramount interest for remote sensing of greenhouse gases.

This report is the second in a series of companion papers describing the effects of atmospheric light scattering in observations of atmospheric carbon dioxide (CO2) by the Greenhouse gases Observing SATellite (GOSAT), in orbit since 23 January 2009. Here we summarize the retrievals from six previously published algorithms; retrieving column-averaged dry air mole fractions of CO2 (XCO2) during 22 months of operation of GOSAT from June 2009. First, we compare data products from each algorithm with ground-based remote sensing observations by Total Carbon Column Observing Network (TCCON). Our GOSAT-TCCON coincidence criteria select satellite observations within a 5° radius of 11 TCCON sites. We have compared the GOSAT-TCCON XCO2 regression slope, standard deviation, correlation and determination coefficients, and global and station-to-station biases. The best agreements with TCCON measurements were detected for NIES 02.xx and RemoTeC. Next, the impact of atmospheric light scattering on XCO2 retrievals was estimated for each data product using scan by scan retrievals of light path modification with the photon path length probability density function (PPDF) method. After a cloud pre-filtering test, approximately 25% of GOSAT soundings processed by NIES 02.xx, ACOS B2.9, and UoL-FP: 3G and 35% processed by RemoTeC were found to be contaminated by atmospheric light scattering. This study suggests that NIES 02.xx and ACOS B2.9 algorithms tend to overestimate aerosol amounts over bright surfaces, resulting in an underestimation of XCO2 for GOSAT observations. Cross-comparison between algorithms shows that ACOS B2.9 agrees best with NIES 02.xx and UoL-FP: 3G while RemoTeC XCO2 retrievals are in a best agreement with NIES PPDF-D.

International audience

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO$_{2}$) and methane (CH$_{4}$), denoted XCO$_{2}$ and XCH$_{4}$, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO$_{2}$) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5°x5°data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO$_{2}$ or XCH$_{4}$, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO$_{2}$ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1$^{σ}$): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1$^{σ}$): 0.66 ppm (0.70 ppm). The corresponding values for the XCH$_{4}$ products are singleobservation random error (1$^{σ}$): 17.4 ppb (monthly: 8.7 ppb); global bias: -2.0 ppb (-2.9 ppb); and spatiotemporal bias (1$^{σ}$): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO$_{2}$ and XCH$_{4}$ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations.