ALBERT

Description: Biomass burning emissions are a major source of trace gases and aerosols. Wildfires being highly variable in time and space, calculating emissions requires a numerical tool able to estimate fluxes at the kilometer scale and with an hourly time step. Here, the APIFLAME model version 2.0 is presented. It is structured to be modular in terms of input databases and processing methods. The main evolution compared to version 1.0 is the possibility of merging burned area and fire radiative power (FRP) satellite observations to modulate the temporal variations of fire emissions and to integrate small fires that may not be detected in the burned area product. Accounting for possible missed detection due to small fire results in an increase in burned area ranging from ∼5 % in Africa and Australia to ∼30 % in North America on average over the 2013–2017 time period based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) Collection 6 fire products. An illustration for the case of southwestern Europe during the summer of 2016, marked by large wildfires in Portugal, is presented. Emissions calculated using different possible configurations of APIFLAME show a dispersion of 80 % on average over the domain during the largest wildfires (8–14 August 2016), which can be considered as an estimate of uncertainty of emissions. The main sources of uncertainty studied, by order of importance, are the emission factors, the calculation of the burned area, and the vegetation attribution. The aerosol (PM10) and carbon monoxide (CO) concentrations simulated with the CHIMERE regional chemistry transport model (CTM) are consistent with observations (good timing for the beginning and end of the events, ±1 d for the timing of the peak values) but tend to be overestimated compared to observations at surface stations. On the contrary, vertically integrated concentrations tend to be underestimated compared to satellite observations of total column CO by the Infrared Atmospheric Sounding Interferometer (IASI) instrument and aerosol optical depth (AOD) by MODIS. This underestimate is lower close to the fire region (5 %–40 % for AOD depending on the configuration and 8 %–18 % for total CO) but rapidly increases downwind. For all comparisons, better agreement is achieved when emissions are injected higher into the free troposphere using a vertical profile as estimated from observations of aerosol plume height by the Multi-angle Imaging SpectroRadiometer (MISR) satellite instrument (injection up to 4 km). Comparisons of aerosol layer heights to observations by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) show that some parts of the plume may still be transported at too low an altitude. The comparisons of the different CTM simulations to observations point to uncertainties not only on emissions (total mass and daily variability) but also on the simulation of their transport with the CTM and mixing with other sources. Considering the uncertainty of the emission injection profile and of the modeling of the transport of these dense plumes, it is difficult to fully validate emissions through comparisons between model simulations and atmospheric observations.

Description: Space-based measurements of the outgoing longwave radiation (OLR) are essential for the study of Earth’s climate system. While the CERES instrument provides accurate measurements of this quantity, its measurements are not spectrally resolved. Here we present a high-resolution OLR product (sampled at 0.25 cm−1), derived from measurements of the IASI satellite sounder. The applied methodology relies on precalculated angular distribution models (ADMs). These are usually calculated for tens to hundreds of different scene types (characterized by surface and atmosphere parameters). To guarantee accurate results in the range 645–2300 cm−1 covered by IASI, we constructed ADMs for over 140 000 scenes. These were selected from one year of CAMS reanalysis data. A dissimilarity-based selection algorithm was applied to choose scenes as different from each other as possible, thereby maximizing the performance on real data, while keeping the number of scenes manageable. A comparison of the IASI OLR integrated over the 645–2300 cm−1 range was performed with the longwave broadband OLR products from CERES and the AIRS instrument. The latter are systematically higher due to the contribution of the far infrared to the total IR spectral range, but as expected exhibit generally high spatial correlations with the IASI OLR, except for some areas in the tropical region. We also compared the IASI OLR against the spectrally resolved OLR derived from AIRS. A good agreement was found above 1200 cm−1 while AIRS OLR appeared to be systematically higher in the atmospheric window region, likely related to differences in overpass time or to the use of a different cloud detection algorithm.

Description: Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis.

Description: The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI’s large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications. In 2019, EUMETSAT released a reprocessed IASI-A 2007–2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0.02 K at 700 cm−1 to 0.1 K at 2200 cm−1. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5–10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014. These results suggest that it is not recommended to use the IASI inhomogeneous temperature products for trend analysis, both for temperature and trace gas trends.

Description: The Paris megacity experiences frequent particulate matter (i.e.PM2.5, particulate matter with a diameter less than 2.5 µm) pollution episodes in spring (March–April). At this time of the year, large numbers of the particles consist of ammonium sulfate and nitrate which are formed from ammonia (NH3) released during fertilizer spreading practices and transported from the surrounding areas to Paris. There is still limited knowledge of the emission sources around Paris, their magnitude, and their seasonality. Using space-borne NH3 observation records of 10 years (2008–2017) and 5 years (2013–2017) provided by the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) instrument, regional patterns of NH3 variabilities (seasonal and interannual) are derived. Observations reveal identical high seasonal variability with three major NH3 hotspots found from March to August. The high interannual variability is discussed with respect to atmospheric total precipitation and temperature. A detailed analysis of the seasonal cycle is performed using both IASI and CrIS instrument data, together with outputs from the CHIMERE atmospheric model. For 2014 and 2015, the CHIMERE model shows coefficients of determination of 0.58 and 0.18 when compared to IASI and CrIS, respectively. With respect to spatial variability, the CHIMERE monthly NH3 concentrations in spring show a slight underrepresentation over Belgium and the United Kingdom and an overrepresentation in agricultural areas in the French Brittany–Pays de la Loire and Plateau du Jura region, as well as in northern Switzerland. In addition, PM2.5 concentrations derived from the CHIMERE model have been evaluated against surface measurements from the Airparif network over Paris, with which agreement was found (r2 = 0.56) with however an underestimation during spring pollution events. Using HYSPLIT cluster analysis of back trajectories, we show that NH3 total columns measured in spring over Paris are enhanced when air masses originate from the north-east (e.g. the Netherlands and Belgium), highlighting the importance of long-range transport in the NH3 budget over Paris. Variability in NH3 in the north-east region is likely to impact NH3 concentrations in the Parisian region since the cross-correlation function is above 0.3 (at lag = 0 and 1 d). Finally, we quantify the key meteorological parameters driving the specific conditions important for the formation of PM2.5 from NH3 in the Île-de-France region in spring. Data-driven results based on surface PM2.5 measurements from the Airparif network and IASI NH3 measurements show that a combination of the factors such as a low boundary layer of ∼500 m, a relatively low temperature of 5 ∘C, a high relative humidity of 70 %, and wind from the north-east contributes to a positive PM2.5 and NH3 correlation.

Description: Despite a number of studies on biomass burning (BB) emissions in the atmosphere, observation of the associated aerosols and pollutants requires continuous efforts. Brazil, and more broadly Latin America, is one of the most important seasonal sources of BB, particularly in the Amazon region. Uncertainty about aerosol loading in the source regions is a limiting factor in terms of understanding the role of aerosols in climate modelling. In the present work, we investigated the Amazon BB episode that occurred during August 2019 and made the international headlines, especially when the smoke plumes plunged distant cities such as São Paulo into darkness. Here, we used satellite and ground-based observations at different locations to investigate the long-range transport of aerosol plumes generated by the Amazon fires during the study period. The monitoring of BB activity was carried out using fire related pixel count from the moderate resolution imaging spectroradiometer (MODIS) onboard the Aqua and Terra platforms, while the distribution of carbon monoxide (CO) concentrations and total columns were obtained from the infrared atmospheric sounding interferometer (IASI) onboard the METOP-A and METOP-B satellites. In addition, AERONET sun-photometers as well as the MODIS instrument made aerosol optical depth (AOD) measurements over the study region. Our datasets are consistent with each other and highlight AOD and CO variations and long-range transport of the fire plume from the source regions in the Amazon basin. We used the Lagrangian transport model FLEXPART (FLEXible PARTicle) to simulate backward dispersion, which showed good agreement with satellite and ground measurements observed over the study area. The increase in Rossby wave activity during the 2019 austral winter the Southern Hemisphere may have contributed to increasing the efficiency of large-scale transport of aerosol plumes generated by the Amazon fires during the study period.

Description: Agriculture is the main source of ammonia (NH3) in France, an important gaseous precursor of atmospheric particulate matter (PM). National and global emission inventories are known to have difficulty representing the large spatial and temporal variability inherent to atmospheric NH3. In this study, we compare NH3 emissions in France during spring 2011 from one reference inventory, the TNO inventory, and two alternative inventories that account in different manners for both the spatial and temporal variabilities of the emissions: (i) the NH3SAT satellite-derived inventory based on IASI NH3 columns and (ii) the CADASTRE-CIT inventory that combines NH3 emissions due to nitrogen fertilization calculated with the mechanistic model VOLT'AIR on the database of the CADASTRE_NH3 framework and other source emissions from the CITEPA. The total spring budgets, from March to May 2011, at the national level are higher when calculated with both alternative inventories than with the reference, the difference being more marked with CADASTRE-CIT. NH3SAT and CADASTRE-CIT inventories both yield to large NH3 spring emissions due to fertilization on soils with high pH in the northeastern part of France (65 and 135 kt NH3, respectively, vs. 48 kt NH3 for TNO-GEN), while soil properties are not accounted for by the TNO-GEN methodology. For the other parts of France, the differences are smaller. The timing of fertilization and associated ammonia emissions is closely related to the nitrogen requirements and hence the phenological stage of the crops, and therefore to the crop year's specific weather conditions. Maximum emissions are observed in March for 2011 for some regions for both alternative inventories, while April is the period with maximum emissions for the reference inventory regardless of the region or the year. Comparing the inventories at finer temporal resolutions, typically at daily scale, large differences are found. The convergence of alternative, independent and complementary methods on the spatiotemporal representation of the spring NH3 emissions, particularly over areas where the contribution of mineral fertilizer spreading to the spring budget is strong, encourages further developments in both prospective complementary directions, as this will help improve national NH3 emission inventories.

Description: Ammonia (NH3) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.89 %/yr from 2002 to 2018 in the FTIR columns, 8.88 ± 5.08 %/yr from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤25 km and ≤20 min, with r=0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r=0.72 and r=0.75, respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r=0.39. The GEOS-Chem model, run at 2∘ × 2.5∘ resolution, was compared to FTIR and IASI to assess model performance and investigate the correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35 to 53∘ N and 93.75 to 63.75∘ W) resulted in r=0.57 and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2=0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2=0.13, indicating that a finer spatial resolution is needed for modeling NH3.