Explore the vast range of titles available.

The underwater Hunga Tonga-Hunga Ha-apai volcano erupted in the early hours of 15th January 2022, and injected volcanic gases and aerosols to over 50 km altitude. Here we synthesise satellite, ground-based, in situ and radiosonde observations of the eruption to investigate the strength of the stratospheric aerosol and water vapour perturbations in the initial weeks after the eruption and we quantify the net radiative impact across the two species using offline radiative transfer modelling. We find that the Hunga Tonga-Hunga Ha-apai eruption produced the largest global perturbation of stratospheric aerosols since the Pinatubo eruption in 1991 and the largest perturbation of stratospheric water vapour observed in the satellite era. Immediately after the eruption, water vapour radiative cooling dominated the local stratospheric heating/cooling rates, while at the top-of-the-atmosphere and surface, volcanic aerosol cooling dominated the radiative forcing. However, after two weeks, due to dispersion/dilution, water vapour heating started to dominate the top-of-the-atmosphere radiative forcing, leading to a net warming of the climate system.

We present a new multispectral approach for observing lowermost tropospheric ozone from space by synergism of atmospheric radiances in the thermal infrared (TIR) observed by IASI (Infrared Atmospheric Sounding Interferometer) and earth reflectances in the ultraviolet (UV) measured by GOME-2 (Global Ozone Monitoring Experiment-2). Both instruments are onboard the series of MetOp satellites (in orbit since 2006 and expected until 2022) and their scanning capabilities offer global coverage every day, with a relatively fine ground pixel resolution (12 km-diameter pixels spaced by 25 km for IASI at nadir). Our technique uses altitude-dependent Tikhonov–Phillips-type constraints, which optimize sensitivity to lower tropospheric ozone. It integrates the VLIDORT (Vector Linearized Discrete Ordinate Radiative Transfer) and KOPRA (Karlsruhe Optimized and Precise Radiative transfer Algorithm) radiative transfer codes for simulating UV reflectance and TIR radiance, respectively. We have used our method to analyse real observations over Europe during an ozone pollution episode in the summer of 2009. The results show that the multispectral synergism of IASI (TIR) and GOME-2 (UV) enables the observation of the spatial distribution of ozone plumes in the lowermost troposphere (LMT, from the surface up to 3 km a.s.l., above sea level), in good agreement with the CHIMERE regional chemistry-transport model. In this case study, when high ozone concentrations extend vertically above 3 km a.s.l., they are similarly observed over land by both the multispectral and IASI retrievals. On the other hand, ozone plumes located below 3 km a.s.l. are only clearly depicted by the multispectral retrieval (both over land and over ocean). This is achieved by a clear enhancement of sensitivity to ozone in the lowest atmospheric layers. The multispectral sensitivity in the LMT peaks at 2 to 2.5 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km a.s.l. at lowest (above the LMT). The degrees of freedom for the multispectral retrieval increase by 0.1 (40% in relative terms) with respect to IASI only retrievals for the LMT. Validations with ozonesondes (over Europe during summer 2009) show that our synergetic approach for combining IASI (TIR) and GOME-2 (UV) measurements retrieves lowermost tropospheric ozone with a mean bias of 1% and a precision of 16%, when smoothing by the retrieval vertical sensitivity (1% mean bias and 21% precision for direct comparisons).

Monitoring upper-tropospheric–lower-stratospheric (UTLS) secondary sulfate aerosols and their chemical and microphysical properties from satellite nadir observations is crucial to better understand their formation and evolution processes and then to estimate their impact on UTLS chemistry, and on regional and global radiative balance. Here we present a study aimed at the evaluation of the sensitivity of thermal infrared (TIR) satellite nadir observations to the chemical composition and the size distribution of idealised UTLS sulfate aerosol layers. The extinction properties of sulfuric acid/water droplets, for different sulfuric acid mixing ratios and temperatures, are systematically analysed. The extinction coefficients are derived by means of a Mie code, using refractive indices taken from the GEISA (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database and log-normal size distributions with different effective radii and number concentrations. IASI (Infrared Atmospheric Sounding Interferometer) pseudo-observations are generated using forward radiative transfer calculations performed with the 4A (Automatized Atmospheric Absorption Atlas) radiative transfer model, to estimate the impact of the extinction of idealised aerosol layers, at typical UTLS conditions, on the brightness temperature spectra observed by this satellite instrument. We found a marked and typical spectral signature of these aerosol layers between 700 and 1200 cm−1, due to the absorption bands of the sulfate and bisulfate ions and the undissociated sulfuric acid, with the main absorption peaks at 1170 and 905 cm−1. The dependence of the aerosol spectral signature to the sulfuric acid mixing ratio, and effective number concentration and radius, as well as the role of interfering parameters like the ozone, sulfur dioxide, carbon dioxide and ash absorption, and temperature and water vapour profile uncertainties, are analysed and critically discussed. The information content (degrees of freedom and retrieval uncertainties) of synthetic satellite observations is estimated for different instrumental configurations. High spectral resolution (IASI-like pseudo-observations) and broadband spectral features (Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and InfraRed Imager (SEVIRI)-like pseudo-observations) approaches are proposed and discussed.

Besides their strong contribution to weather forecast improvement through data assimilation, thermal infrared sounders onboard polar-orbiting platforms are now playing a key role for monitoring atmospheric composition changes. The Infrared Atmospheric Sounding Interferometer (IASI) instrument developed by the French space agency (CNES) and launched by EUMETSAT onboard the Metop satellite series is providing essential inputs for weather forecasting and pollution/climate monitoring owing to its smart combination of large horizontal swath, good spectral resolution and high radiometric performance. EUMETSAT is currently preparing the next polar-orbiting program (EPS-SG) with the Metop-SG satellite series that should be launched around 2020. In this framework, CNES is studying the concept of a new instrument, the IASI-New Generation (IASI-NG), characterized by an improvement of both spectral and radiometric characteristics as compared to IASI, with three objectives: (i) continuity of the IASI/Metop series; (ii) improvement of vertical resolution; and (iii) improvement of the accuracy and detection threshold for atmospheric and surface components. In this paper, we show that an improvement of spectral resolution and radiometric noise fulfil these objectives by leading to (i) a better vertical coverage in the lower part of the troposphere, thanks to the increase in spectral resolution; and (ii) an increase in the accuracy of the retrieval of several thermodynamic, climate and chemistry variables, thanks to the improved signal-to-noise ratio as well as less interference between the signatures of the absorbing species in the measured radiances. The detection limit of several atmospheric species is also improved. We conclude that IASI-NG has the potential to strongly benefit the numerical weather prediction, chemistry and climate communities now connected through the European GMES/Copernicus initiative.

As a consequence of extreme heat and drought, record-breaking wildfires developed and ravaged south-eastern Australia during the fire season 2019–2020. The fire strength reached its paroxysmal phase at the turn of the year 2019–2020. During this phase, pyrocumulonimbus clouds (pyroCb) developed and injected biomass burning aerosols and gases into the upper troposphere and lower stratosphere (UTLS). The UTLS aerosol layer was massively perturbed by these fires, with aerosol extinction increased by a factor of 3 in the visible spectral range in the Southern Hemisphere, with respect to a background atmosphere, and stratospheric aerosol optical depth reaching values as large as 0.015 in February 2020. Using the best available description of this event by observations, we estimate the radiative forcing (RF) of such perturbations of the Southern Hemispheric aerosol layer. We use offline radiative transfer modelling driven by observed information of the aerosol extinction perturbation and its spectral variability obtained from limb satellite measurements. Based on hypotheses on the absorptivity and the angular scattering properties of the aerosol layer, the regional (at three latitude bands in the Southern Hemisphere) clear-sky TOA (top-of-atmosphere) RF is found varying from small positive values to relatively large negative values (up to −2.0 W m−2), and the regional clear-sky surface RF is found to be consistently negative and reaching large values (up to −4.5 W m−2). We argue that clear-sky positive values are unlikely for this event, if the ageing/mixing of the biomass burning plume is mirrored by the evolution of its optical properties. Our best estimate for the area-weighted global-equivalent clear-sky RF is -0.35±0.21 (TOA RF) and -0.94±0.26 W m−2 (surface RF), thus the strongest documented for a fire event and of comparable magnitude with the strongest volcanic eruptions of the post-Pinatubo era. The surplus of RF at the surface, with respect to TOA, is due to absorption within the plume that has contributed to the generation of ascending smoke vortices in the stratosphere. Highly reflective underlying surfaces, like clouds, can nevertheless swap negative to positive TOA RF, with global average RF as high as +1.0 W m−2 assuming highly absorbing particles.

We use a combination of spaceborne instruments to study the unprecedented stratospheric plume after the Tonga eruption of 15 January 2022. The aerosol plume was initially formed of two clouds at 30 and 28 km, mostly composed of submicron-sized sulfate particles, without ash, which is washed out within the first day following the eruption. The large amount of injected water vapour led to a fast conversion of SO2 to sulfate aerosols and induced a descent of the plume to 24–26 km over the first 3 weeks by radiative cooling. Whereas SO2 returned to background levels by the end of January, volcanic sulfates and water still persisted after 6 months, mainly confined between 35∘ S and 20∘ N until June due to the zonal symmetry of the summer stratospheric circulation at 22–26 km. Sulfate particles, undergoing hygroscopic growth and coagulation, sediment and gradually separate from the moisture anomaly entrained in the ascending branch Brewer–Dobson circulation. Sulfate aerosol optical depths derived from the IASI (Infrared Atmospheric Sounding Interferometer) infrared sounder show that during the first 2 months, the aerosol plume was not simply diluted and dispersed passively but rather organized in concentrated patches. Space-borne lidar winds suggest that those structures, generated by shear-induced instabilities, are associated with vorticity anomalies that may have enhanced the duration and impact of the plume.

We use an offline radiative transfer model driven by IAGOS aircraft observations to estimate the tropospheric ozone radiative forcing (RF) at decadal time scale (three time intervals with a common starting period in 1994–2004 and ending periods: 2011–2016, 2017–2019 and 2020–2023), over 11 selected Northern Hemispheric regions. We found a positive trend in the tropospheric ozone column (TOC) for the three time intervals, even if decadal trends are progressively reduced from 2011–2016 (ΔTOC +3.6 ± 2.0 DU, +14.8 ± 11.5 %, 2.5 ± 1.4 DU per decade) to 2017–2019 (ΔTOC +3.9 ± 3.9 DU, +17.5 ± 22.1 %, 2.0 ± 2.0 DU per decade) to 2020–2023 (ΔTOC +3.5 ± 2.8 DU, +13.8 ± 12.1 %, 1.5 ± 1.2 DU per decade). The progressively reduced average TOC decadal trends in 2020–2023 and 2017–2019, with respect to 2011–2016, originate from stagnation in ΔTOC in more recent time intervals (especially in the COVID and post-COVID crisis periods) and decreases of trends of the more radiative efficient upper tropospheric ozone. These average trend reductions are accompanied with reductions of the tropospheric ozone RF, from 2011–2016 (41.6 ± 24.0 mW m−2 per decade) to 2017–2019 (27.6 ± 36.7 mW m−2 per decade) to 2020–2023 (14.6 ± 25.6 mW m−2 per decade). The total tropospheric ozone RF sensitivity varies between 18.4 ± 7.4 mW m−2 DU−1, in 2011–2016, and 11.1 ± 10.4 mW m−2 DU−1, in 2020–2023. Our decadal RF estimates vary from being ∼ 25 %–90 % larger (2011–2016 and 2017–2019 ending periods) to ∼ 30 % smaller (2020–2023 ending period) than the most recent global average RF estimates with online modelling. Our study underlines the importance of the evolution of ozone vertical profiles for the tropospheric ozone RF.

In this paper, a new neural network (NN) algorithm to retrieve the tropospheric ozone column from Ozone Monitoring Instrument (OMI) Level 1b data is presented. Such an algorithm further develops previous studies in order to improve the following: (i) the geographical coverage of the NN, by extending its training set to ozonesonde data from midlatitudes, tropics and poles; (ii) the definition of the output product, by using tropopause pressure information from reanalysis data; and (iii) the retrieval accuracy, by using ancillary data (NCEP tropopause pressure and temperature profile, monthly mean tropospheric ozone column from a satellite climatology) to better constrain the tropospheric ozone retrievals from OMI radiances. The results indicate that the algorithm is able to retrieve the tropospheric ozone column with a root mean square error (RMSE) of about 5–6 DU in all the latitude bands. The design of the new NN algorithm is extensively discussed, validation results against independent ozone soundings and chemistry/transport model (CTM) simulations are shown, and other characteristics of the algorithm – i.e., its capability to detect non-climatological tropospheric ozone situations and its sensitivity to the tropopause pressure – are discussed.

Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis. In this work we have developed a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal InfraRed (TIR) spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases. The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD) algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: \"ashy\" and \"non-ashy\" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part. The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km−2 has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement.

Monitoring tropospheric ozone from space is of critical importance in order to gain more thorough knowledge on phenomena affecting air quality and the greenhouse effect. Deriving information on tropospheric ozone from UV/VIS nadir satellite spectrometers is difficult owing to the weak sensitivity of the measured radiance spectra to variations of ozone in the troposphere. Here we propose an alternative method of analysis to retrieve tropospheric ozone columns from Ozone Monitoring Instrument radiances by means of a neural network algorithm. An extended set of ozone sonde measurements at northern mid-latitudes for the years 2004–2008 has been considered as the training and test data set. The design of the algorithm is extensively discussed. Our retrievals are compared to both tropospheric ozone residuals and optimal estimation retrievals over a similar independent test data set. Results show that our algorithm has comparable accuracy with respect to both correlative methods and its performance is slightly better over a subset containing only European ozone sonde stations. Possible sources of errors are analyzed. Finally, the capabilities of our algorithm to derive information on boundary layer ozone are studied and the results critically discussed.